[ { "path": ".gitignore", "content": "# mac file\n.DS_Store\n\n# Compiled Object files\n*.slo\n*.lo\n*.o\n*.obj\n\n# Precompiled Headers\n*.gch\n*.pch\n\n# Compiled Dynamic libraries\n*.so\n*.dylib\n*.dll\n\n# Fortran module files\n*.mod\n*.smod\n\n# Compiled Static libraries\n*.lai\n*.la\n*.a\n*.lib\n\n# Executables\n*.exe\n*.out\n*.app\n\n\n" }, { "path": "README.md", "content": "# NNS Benchmark: Evaluating Approximate Nearest Neighbor Search Algorithms in High Dimensional Euclidean Space\n\nNearest neighbor search (NNS) is a fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Hundreds of algorithms have been proposed to attack the problem from different perspectives, yet there is no **open** and **comprehensive** comparison. By \"comprehensive\", we mean using state-of-the-art algorithms proposed from different research areas, and evaluating them on a diverse range of datasets.\n\nTo aid researchers and practitioners working on or whose work depends on the problem, we setup this benchmark for Nearest Neighbor Search (NNS) based on the Euclidean distance on High Dimensional Data. The benefit is twofold:\n\n- For researchers, it allows one to **easily** compare their new algorithms with state-of-the-art ones and on a diverse set of datasets. The latter is especially important to gain a **complete** picture of the performance of an algorithm.\n- For practitioners, it allows one to **easily** understand the performances of different algorithms and their **tradeoffs**. This helps them to choose the best implementation meeting their own goals and constraints.\n\nWe also would like to leverage the entire community to collaboratively build and maintain this benchmark. For example, submitting new algorithms, useful datasets, or offering suggestions or improvements.\n\n## Scope\n\nWe limit the scope of this benchmark by imposing the following constraints. \n\n- **Representative and competitive approximate NNS algorithms**. It has been well recognized that the exact NNS algorithms often cannot even outperform even the simple linear scan algorithm when the dimensionality is high. We therefore only consider the approximate solutions. In this benchmark, we consider the state-of-the-art algorithms in several research areas and those from practitioners.\n\n- **No hardware specific optimizations**. Not all the implementations we obtained or implemented have the same level of sophistication in utilizing the hardware specific features to speed up the query processing. To perform a (relatively) fair comparison and gain insight into the real strength and weakness of algorithms, we modified several implementations so that, as far as we are aware, no implementation of algorithm in this benchmark uses multiple threads, multiple CPUs, SIMD instructions, hardware pre-fetching, or GPUs.\n\n- **Dense vectors**. We mainly focus on the case where the input data vectors are dense, i.e., non-zero in most of the dimensions.\n\n- **Support the Euclidian distance**. The Euclidean distance is one of the most widely used measures on high-dimensional datasets. It is also supported by most of the NNS algorithms.\n\n- **Exact kNN as the ground truth**. In some existing works, each data point has a label (typically in classification or clustering applications) and the labels are regarded as the ground truth when evaluating the recall of approximate NN algorithms. In our benchmark, we use the exact kNN points as the ground truth as this works for all datasets and majority of the applications.\n\nCurrently, we evaluate **15 representative NNS algorithms** on **20 datasets** where details are reported in our [Nearest Neighbor Search (NNS) Experimental Evaluation Paper](https://arxiv.org/abs/1610.02455)[1].\n\n## Algorithms Evaluted \n\nThanks to the original authors, all the algorithms considered in this benchmark have their sources publicly available. Algorithms are implemented in C++ unless otherwise specified. We carefully go through all the implementations and make necessary modifications to for fair comparisons. For instance, we re-implement the search process of some algorithms in C++. We also disable the multi-threads, SIMD instructions, fast-math, and hardware prefetching technique.\n\nBelow are brief introuductions of the algorithms evaluated in the benchmark as well as our revisions, which are grouped into three/four categories.\n\n### 1. Locality Sensitive Hashing (LSH)-Based Methods\n\n- **QALSH** Query-Aware LSH ([2], PVLDB’15). \n[Originial source code](http://ss.sysu.edu.cn/~fjl/qalsh/qalsh_1.1.2.tar.gz)\n\n- **SRS** ([3], PVLDB’14).\n[Originial source code](https://github.com/DBWangGroupUNSW/SRS). \nNote that both external memory and in-memory versions of **SRS** are available.\n\n### 2. Space Partitioning-based Methods\n\n#### 2.1. Encoding-Based Space Partitioning Methods\n\n- **SGH** Scalable Graph Hashing ([4], IJCAI’15). \n[Originial source code](http://cs.nju.edu.cn/lwj). \n * Source codes (index construction and search) are implemented by MATLAB. \n * In our implementation, we use the hierarchical clustering trees in **FLANN** (C++) to index the resulting binary vectors to support more efficient search.\n\n- **AGH** Anchor Graph Hashing ([5], ICML’11). \n[Originial source code](http://www.ee.columbia.edu/ln/dvmm/downloads)\n * Source codes (index construction and search) are implemented by MATLAB. \n * In our implementation, we use the hierarchical clustering trees in **FLANN** (C++) to index the resulting binary vectors to support more efficient search.\n\n\n- **NSH** Neighbor-Sensitive Hashing ([6], PVLDB’15). \n[Originial source code](https://github.com/pyongjoo/nsh)\n * Source codes (index construction and search) are implemented by MATLAB. \n * In our implementation, we use the hierarchical clustering trees in **FLANN** (C++) to index the resulting binary vectors to support more efficient search.\n\n- **SH** Selective Hashing ([7], KDD’15). \n[Originial source code](http://www.comp.nus.edu.sg/~dsh/download.html).\n Note that we have confirmed with the authors that the source code released implements the algorithm in their KDD'15 paper, not that in their previous SIGMOD'14 paper. \n\n- **OPQ** Optimal Product Quantization ([8], TPAMI’14). \n[Originial source code](http://research.microsoft.com/en-us/um/people/kahe). \nNote that, in our impementation, we use the [inverted multi-indexing technique](http://arbabenko.github.io/MultiIndex/index.html) [17] to perform non-exhaustive search for **OPQ**.\n\n- **NAPP** Neighborhood APProximation index ([9], PVLDB’15). \n[Originial source code](https://github.com/searchivarius/nmslib)\n * We disable the SIMD, multi-threading, and `-Ofast` compiler option.\n\n#### 2.2. Tree-Based Space Partitioning Methods\n\n- **FLANN** ([10], TPAMI’14). \n[Originial source code](http://www.cs.ubc.ca/research/flann)\n * We disable the multi-threading techniques in **FLANN**.\n\n- **Annoy** ([11]). \n[Originial source code](https://github.com/spotify/annoy)\n * Source codes are implemented by C++ (core algorithms) and Python (for binding). We re-implemente the Python binding part with C++. \n * We disable `-ffast-math` compiler option in **Annoy**.\n\n- **VP-Tree** Vantage-Point tree ([12], NIPS’13). \n[Originial source code](https://github.com/searchivarius/nmslib)\n * We disable the SIMD, multi-threading, and `-Ofast` compiler option.\n\n### 3. Neighborhood-Based Methods\n\n- **SW** Small World Graph ([13], IS'14). \n[Originial source code](https://github.com/searchivarius/nmslib) \n * We disable the SIMD, multi-threading, and `-Ofast` compiler option.\n\n- **HNSW** Hierarchical Navigable Small World ([20], arXiv'16). \n[Originial source code](https://github.com/searchivarius/nmslib) \n * We disable the SIMD, multi-threading, and `-Ofast` compiler option.\n\n- **RCT** Rank Cover Tree ([14], TPAMI'15).\n Originial source code is obtained from authors by email, who kindly allow us to release them through this benchmark. \n\n- **KGraph** ([15] [16], WWW'11). \n [Originial source code](https://github.com/aaalgo/kgraph)\n * We disable SIMD and multi-threading techniques in **KGraph**. Note the besides the compiler flag, we also comment out the SIMD related code in `metric.cpp`\n * We reduce the index size of KGraph by discarding the distances of the edges, which is not used in the search process anyway. \n\n- **DPG** Diversified Proximity Graph ([1]). \n[Originial source code](https://github.com/DBWangGroupUNSW/nns_benchmark/tree/master/algorithms/DPG)\n We engineered a new algorithm, DPG, that constructs an alternative neighborhood graph index, yet uses the same search algorithm as the KGraph. DPG is more robust than KGraph across different datasets. For more details about the algorithm and its analysis, please refer to our [NNS Experimental Evaluation Paper](https://arxiv.org/abs/1610.02455)[1]. \n \n- **NSG** Navigating Spreading-out Graph ([21], VLDB 2019). \n[Originial source code](https://github.com/ZJULearning/nsg)\n * NSG is a newly graph-based algorithm. We disable the SIMD instructions and the hardware prefetching technique. \n\n\n## Datasets Used \n\nCurrently, we use\n\n- **18 real datasets** that are used by existing work and cover a wide range of application and data types, including image, audio, video, text, and deep-learning data. \n- **2 sythetic datasets**: Rand (for Random) and Gauss (for mixed Gaussian). \n\nTable 1 summarizes the characteristics of the datasets including the number of data points (*n*), dimensionality (*d*), Relative Contrast (*RC* [18]), local intrinsic dimensionality (*LID* [19]), and data type, where RC and LID are used to describe the hardness of the datasets.\n\n| **Name** | **n X 10^3** | **d** | **RC** | **LID** | **Type**|\n| ------ | ------ | ------ | ------ | ------ | -------- |\n| Nus* | 269 | 500 | 1.67 | 24.5 | Image |\n| Gist* | 983 | 960 | 1.94 | 18.9 | Image |\n| Rand* | 1,000 | 100 | 3.05 | 58.7 | Synthetic |\n| Glove* | 1,192 | 100 | 1.82 | 20.0 | Text | \n| Cifa | 50 | 512 | 1.97 | 9.0 | Image |\n| Audio | 53 | 192 | 2.97 | 5.6 | Audio |\n| Mnist | 69 | 784 | 2.38 | 6.5 | Image |\n| Sun | 79 | 512 | 1.94 | 9.9 | Image |\n| Enron | 95 | 1,369 | 6.39 | 11.7 | Text |\n| Trevi | 100 | 4,096 | 2.95 | 9.2 | Image |\n| Notre | 333 | 128 | 3.22 | 9.0 | Image |\n| Yout | 346 | 1,770 | 2.29 | 12.6 | Video |\n| Msong | 922 | 420 | 3.81 | 9.5 | Audio |\n| Sift | 994 | 128 | 3.50 | 9.3 | Image |\n| Deep | 1,000 | 128 | 1.96 | 12.1 | Image |\n| Ben | 1,098 | 128 | 1.96 | 8.3 | Image |\n| Imag | 2,340 | 150 | 2.54 | 11.6 | Image |\n| Gauss | 2,000 | 512 | 3.36 | 19.6 | Synthetic |\n| UQ-V | 3,038 | 256 | 8.39 | 7.2 | Video |\n| BANN | 10,000 | 128 | 2.60 | 10.3 | Image |\n\n**Table 1: Dataset Summary**\n\nWe mark the first four datasets in Table 1 with asterisks to indicate that they are **hard datasets** compared with\nothers.\n\nBelow, we give more descriptions of these datasets.\n\n- [**Sift**](http://corpus-texmex.irisa.fr) consists of 1 million 128-d SIFT vectors.\n\n- [**Nusw**](http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm) includes around $2.7$ million web images,\neach as a 500-dimensional bag-of-words vector.\n\n- [**Msong**](http://www.ifs.tuwien.ac.at/mir/msd/download.html) is a collection of audio features and metadata for a million contemporary popular music tracks with $420$ dimensions.\n\n- [**Gist**]() is is an image dataset which contains about 1 million data points with 960 dimensions.\n\n- [**Deep**](https://yadi.sk/d/I_yaFVqchJmoc) contains deep neural codes of natural images obtained from the activations of a convolutional neural network, which contains about 1 million data points with 256 dimensions.\n\n- [**Bann**](http://corpus-texmex.irisa.fr/) is used to evaluate the scalability of the algorithms, where 1M, 2M, 4M, 6M, 8M, and 10M data points are sampled from 128-dimensional SIFT descriptors extracted from natural images.\n\n- **Guass** is generated by randomly choosing $1000$ cluster centers with in space $[0,10]^512$, and each cluster follows the a Gaussian distribution with deviation 1 on each dimension.\n\n- **Random** contains $1$M randomly chosen points in a unit hypersphere with dimensionality 100.\n\n- [**Audio**](http://www.cs.princeton.edu/cass/audio.tar.gz) has about 0.05 million 192-d audio feature vectors extracted by Marsyas library from DARPA TIMIT audio speed dataset.\n\n- [**Cifar**](http://www.cs.toronto.edu/~kriz/cifar.html) is a labeled subset of *TinyImage* dataset, which consists of 60000 32 X color images in 10 classes, with each image represented by a 512-d GIST feature vector.\n\n- [**Enron**](http://www.cs.cmu.edu/~enron) origins from a collection of emails. Yifang et. al. extract bi-grams and form feature vectors of 1369 dimensions. \n\n- [**Glove**](http://nlp.stanford.edu/projects/glove) contains 1.2 million 100-d word feature vectors extracted from Tweets.\n\n- [**Imag**](http://cloudcv.org/objdetect) is introduced and employed by [The ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](http://www.image-net.org/challenges/LSVRC/), which contains about 2.4 million data points with 150-d dense SIFT features.\n\n- [**Mnist**](http://yann.lecun.com/exdb/mnist) consists of 70k images of hand-written digits, each as a 784-d vector concatenating all pixels. we randomly sample 1k as the queries and use the remaining as the data base.\n \n- [**Sun**](http://groups.csail.mit.edu/vision/SUN) contains about 0.08 million 512-d GIST features of images.\n\n- [**Notre**](http://phototour.cs.washington.edu/datasets) contains about 0.3 million 128-d features of a set of Flickr images and a reconstruction.\n\n- [**UKbench**](http://vis.uky.edu/~stewe/ukbench) contains about 1 million 128-d features of images.\n\n- [**Trevi**](http://phototour.cs.washington.edu/patches/default.htm) consists of 0.4 million X 1024 bitmap(.bmp) images, each containing a `16 X 16` array of image patch. Each patch is sampled as `64 X 64` grayscale, with a canonical scale and orientation. Therefore, Trevi patch dataset consists of around 100,000 4096-d vectors.\n\n- [**UQ_V**](http://staff.itee.uq.edu.au/shenht/UQ_VIDEO/) is a video dataset. A\n local feature based on some keyframes are extracted which include 256\n dimensions.\n \n- [**Yout**](http://www.cs.tau.ac.il/~wolf/ytfaces/index.html) contains 3,425 videos of 1,595 different people. All the videos were downloaded from YouTube. An average of 2.15 videos are available for each subject. The shortest clip duration is 48 frames, the longest clip is 6,070 frames, and the average length of a video clip is 181.3 frames.\n\n## PERFORMANCE EVALUATION and ANALYSES \n\nPlease refer to our [NNS Experimental Evaluation paper](https://arxiv.org/abs/1610.02455)[1]. \n\n## License\n\nOur own code is released under the [Apache License Version 2.0](http://www.apache.org/licenses/). Copyright is owned by DBWang Group (University of New South Wales, Australia) and Database group at QCIS, UTS (Centre for Quantum Computation & Intelligent Systems, The University of Technology Sydney, Australia). \n\nBelow are the license information for the included implementations: \n\n1. KGraph: BSD license. Users are still encouraged to download the binary distributions from [its home site](https://github.com/aaalgo/kgraph) so building issues can be avoided.\n\n2. NMSLib and Annoy: [Apache License Version 2.0](http://www.apache.org/licenses/).\n\n3. AGH: License information below\n\n```\nTerms of Use\n--\nCopyright (c) 2009-2011 by\n--\nDVMM Laboratory\nDepartment of Electrical Engineering\nColumbia University\nRm 1312 S.W. Mudd, 500 West 120th Street\nNew York, NY 10027\nUSA\n--\nIf it is your intention to use this code for non-commercial purposes, such as in academic research, this code is free.\n--\nIf you use this code in your research, please acknowledge the authors, and cite our related publication:\n--\n\nWei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang, \"Hashing with Graphs,\" International Conference on Machine Learning (ICML), Bellevue, Washington, USA, 2011\n```\n\n4. SRS: GPL License.\n\n5. FLANN: BSD License. \n\n6. RCT: The Authors grant us the permission to release source code by email.\n\n7. Algorithms whose license information are not mentioned: NSH, OPQ, QALSH, SGH, and SH. \n\n## Recall vs Speedup\nHere, we present the Recall with respect to Speedup of all the algorithms on four datasets, the k is set to be 20.\n\n![image](https://github.com/MingjieL/ANN/blob/master/sift_Recall_speedup.jpg \"Sift\")\n **Sift**\n![image](https://github.com/MingjieL/ANN/blob/master/gist_Recall_speedup.jpg \"Gist\")\n **Gist**\n![image](https://github.com/MingjieL/ANN/blob/master/glove_Recall_speedup.jpg \"Glove\")\n **Glove**\n![image](https://github.com/MingjieL/ANN/blob/master/millionSong_Recall_speedup.jpg \"MillionSong\")\n **MillionSong**\n\n\n \n## REFERENCES \n\n[1] W. Li, Y. Zhang, Y. Sun, W. Wang, W. Zhang, X. Lin, *Nearest Neighbor Search on High Dimensional Data — Experiments, Analyses, and Improvement (v1.0)*. CoRR, vol. abs/1610.02455, 2016. [Online version](https://arxiv.org/abs/1610.02455)\n\n[2] Q. Huang, J. Feng, Y. Zhang, Q. Fang, and W. Ng. *Query-aware locality-sensitive hashing for approximate nearest neighbor search*. PVLDB, 9(1):1–12, 2015.\n\n[3] Y. Sun, W. Wang, J. Qin, Y. Zhang, and X. Lin. *SRS: solving c-approximate nearest neighbor queries in high\ndimensional euclidean space with a tiny index*. PVLDB,8(1):1–12, 2014\n\n[4] Q. Jiang and W. Li. *Scalable graph hashing with feature transformation*. In IJCAI, pages 2248–2254, 2015.\n\n[5] W. Liu, J. Wang, S. Kumar, and S. Chang. *Hashing with graphs*. In ICML, pages 1–8, 2011.\n\n[6] Y. Park, M. J. Cafarella, and B. Mozafari. *Neighbor-sensitive hashing*. In PVLDB, 9(3):144–155, 2015.\n\n[7] J. Gao, H. V. Jagadish, B. C. Ooi, and S. Wang. *Selective hashing: Closing the gap between radius search and k-nn search*. In SIGKDD, 2015.\n\n[8] T. Ge, K. He, Q. Ke, and J. Sun. *Optimized product quantization*. IEEE Trans. Pattern Anal. Mach. Intell., 36(4):744–755, 2014.\n\n[9] B. Naidan, L. Boytsov, and E. Nyberg. *Permutation search methods are efficient, yet faster search is possible*. PVLDB,\n8(12):1618–1629, 2015.\n\n[10] M. Muja and D. G. Lowe. *Scalable nearest neighbor algorithms for high dimensional data*. IEEE Trans. Pattern\nAnal. Mach. Intell., 36(11):2227–2240, 2014.\n\n[11] E. Bernhardsson. [*Annoy at github*](https://github.com/spotify/annoy).\n\n[12] L. Boytsov and B. Naidan. *Learning to prune in metric and non-metric spaces*. In NIPS, 2013.\n\n[13] Y. Malkov, A. Ponomarenko, A. Logvinov, and V. Krylov. *Approximate nearest neighbor algorithm based on navigable small world graphs*. Inf. Syst., 45:61–68, 2014\n\n[14] M. E. Houle and M. Nett. *Rank-based similarity search: Reducing the dimensional dependence*. IEEE TPAMI, 37(1):136–150, 2015.\n\n[15] W. Dong. [*Kgraph website*](http://www.kgraph.org).\n\n[16] W. Dong, M. Charikar, and K. Li. *Efficient k-nearest neighbor graph construction for generic similarity measures*. In WWW, 2011.\n\n[17] A. Babenko and V. S. Lempitsky. *The inverted multi-index*. In CVPR, pages 3069–3076, 2012.\n\n[18] J. He, S. Kumar, and S. Chang. *On the difficulty of nearest neighbor search*. In ICML, 2012.\n\n[19] L. Amsaleg, O. Chelly, T. Furon, S. Girard, M. E. Houle, K. Kawarabayashi, and M. Nett. *Estimating local intrinsic dimensionality*. In SIGKDD, 2015.\n\n[20] Yu. A. Malkov and D. A. Yashunin *Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs*, In arXiv, 2016\t\n\n[21] C. Fu, C. Wang, and D. Cai. *Fast approximate nearest neighbor search with navigating spreading-out graphs*. In VLDB, pages 461- 474, 2019.\n\n\n\n\n\n\n" }, { "path": "algorithms/AGH/README.md", "content": "# AGH - Anchor-based Graph Hashing\n\nThis is a modified version of AGH. The original version can be downloaded from [here](http://www.ee.columbia.edu/ln/dvmm/downloads/WeiGraphConstructCode2011/dlform.htm), which is implemented with MATLAB. \n\nWe use the original code to generate the hashcodes of the data points and transformation parameters. We rewrite the searching part in C++ to transform the query points to binary vectors and use the hierarchical clustering trees in FLANN (C++) to index the data binary vectors to support more efficient search.\n\n## Compile (AGH/src)\n\nTo compile AGH, go to AGH/src and run \n\n```\nmake\n```\n\nto get the executable file.\n\n## Index (AGH/preprocess)\n\nTo build the index of AGH, enter into preprocess folder and run \n```\nmain.m\n```\nin MATLAB to get the transformed data and transformation parameters.\nAlternatively, one may run \n```\nmatlab -nodisplay -nojvm -nosplash -nodesktop -r \"try, run('main.m'), end, exit(0)\"\n``` \n\nNote that main.m will build the index for 1-AGH and 2-AGH. One could choose to generate only one of them.\n\n## Search (AGH/script)\n\ngo to AGH/script and run \n```\nsearcher.sh \n```\nto obtain the searching performance of AGH. We use c value (e.g. the number of the visited points) to achieve the trade-off between search speed and search quality (recall).\n\n\n" }, { "path": "algorithms/AGH/preprocess/OneLayerAGH_Test.m", "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction Y = OneLayerAGH_Test(X, Anchor, W, s, sigma)\r\n%\r\n% One-Layer Anchor Graph Hashing Test \r\n% Written by Wei Liu (wliu@ee.columbia.edu)\r\n% X = nXdim input data \r\n% Anchor = mXdim anchor points (m<0); %% logical format\r\nclear Z;\r\n" }, { "path": "algorithms/AGH/preprocess/OneLayerAGH_Train.m", "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction [Y, W, sigma] = OneLayerAGH_Train(X, Anchor, r, s, sigma)\r\n%\r\n% One-Layer Anchor Graph Hashing Training \r\n% Written by Wei Liu (wliu@ee.columbia.edu)\r\n% X = nXdim input data \r\n% Anchor = mXdim anchor points (m<0 & eigenvalue<1-1e-3);\r\neigenvalue = eigenvalue(ind);\r\nW = W(:,ind);\r\nW = diag(lamda.^-0.5)*W(:,1:r)*diag(eigenvalue(1:r).^-0.5);\r\nclear ind;\r\nclear eigenvalue;\r\nclear lambda;\r\n\r\n\r\n%% get binary codes\r\nY = (Z*W>0); %% logical format\r\nclear Z;\r\n" }, { "path": "algorithms/AGH/preprocess/TwoLayerAGH_Test.m", "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction Y = TwoLayerAGH_Test(X, Anchor, W, Thres, s, sigma)\r\n%\r\n% Two-Layer Anchor Graph Hashing Test \r\n% Written by Wei Liu (wliu@ee.columbia.edu)\r\n% X = nXdim input data \r\n% Anchor = mXdim anchor points (m<0);\r\n neg = setdiff([1:n],pos);\r\n Y1(pos,k) = Y1(pos,k)-Thres(1,k);\r\n Y1(neg,k) = -Y1(neg,k)+Thres(2,k);\r\nend\r\nY = [Y,Y1];\r\n\r\nY = (Y>0); %% logical format\r\n" }, { "path": "algorithms/AGH/preprocess/TwoLayerAGH_Train.m", "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction [Y, W, Thres, sigma] = TwoLayerAGH_Train(X, Anchor, r, s, sigma)\r\n%\r\n% Two-Layer Anchor Graph Hashing Training \r\n% Written by Wei Liu (wliu@ee.columbia.edu)\r\n% X = nXdim input data \r\n% Anchor = mXdim anchor points (m<0 & eigenvalue<1-1e-3);\r\neigenvalue = eigenvalue(ind);\r\nW = W(:,ind);\r\nW = diag(lamda.^-0.5)*W(:,1:r/2)*diag(eigenvalue(1:r/2).^-0.5)*sqrt(n);\r\nclear ind;\r\nclear eigenvalue;\r\nclear lambda;\r\n\r\n\r\n%% get binary codes\r\nY = Z*W;\r\nY1 = Y;\r\nThres = zeros(2,r/2);\r\nfor k = 1:r/2\r\n pos = find(Y(:,k)>0);\r\n neg = setdiff([1:n],pos);\r\n n1 = length(pos);\r\n n2 = n-n1;\r\n get = Y(:,k);\r\n get(neg) = -1*get(neg);\r\n right = full(get'*Z);\r\n clear get;\r\n right = right./lamda;\r\n left = sum(Z(pos,:));\r\n s1 = sum(left.*right,2);\r\n left1 = left./lamda;\r\n s2 = n1-sum(left.*left1,2);\r\n s3 = sum(Y(pos,k),1);\r\n clear left\r\n clear left1;\r\n clear right;\r\n \r\n Thres(1,k) = (2*s3+n2*(s3-s1)/s2)/n;\r\n Thres(2,k) = (n1*(s3-s1)/s2-2*s3)/n;\r\n Y1(pos,k) = Y1(pos,k)-Thres(1,k);\r\n Y1(neg,k) = -Y1(neg,k)+Thres(2,k);\r\n clear pos;\r\n clear neg;\r\nend\r\nclear Z;\r\nY = [Y,Y1];\r\nclear Y1;\r\n\r\nY = (Y>0); %% logical format\r\n" }, { "path": "algorithms/AGH/preprocess/compactbit.m", "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction cb = compactbit(b)\r\n%\r\n% Written by Rob Fergus\r\n% b = bits array\r\n% cb = compacted string of bits (using words of 'word' bits)\r\n%\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n\r\n[nSamples nbits] = size(b);\r\nnwords = ceil(nbits/8);\r\ncb = zeros([nSamples nwords], 'uint8');\r\n\r\nfor j = 1:nbits\r\n w = ceil(j/8);\r\n cb(:,w) = bitset(cb(:,w), mod(j-1,8)+1, b(:,j));\r\nend" }, { "path": "algorithms/AGH/preprocess/fvecs_read.m", "content": "% Read a set of vectors stored in the fvec format (int + n * float)\r\n% The function returns a set of output vector (one vector per column)\r\n%\r\n% Syntax: \r\n% v = fvecs_read (filename) -> read all vectors\r\n% v = fvecs_read (filename, n) -> read n vectors \r\n% v = fvecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\r\nfunction v = fvecs_read (filename, bounds)\r\n\r\n% open the file and count the number of descriptors\r\nfid = fopen (filename, 'rb');\r\n \r\nif fid == -1\r\n error ('I/O error : Unable to open the file %s\\n', filename)\r\nend\r\n\r\n% Read the vector size\r\nd = fread (fid, 1, 'int');\r\nvecsizeof = 1 * 4 + d * 4;\r\n\r\n% Get the number of vectrors\r\nfseek (fid, 0, 1);\r\na = 1;\r\nbmax = ftell (fid) / vecsizeof;\r\nb = bmax;\r\n\r\nif nargin >= 2\r\n if length (bounds) == 1\r\n b = bounds;\r\n \r\n elseif length (bounds) == 2\r\n a = bounds(1);\r\n b = bounds(2); \r\n end\r\nend\r\n\r\nassert (a >= 1);\r\nif b > bmax\r\n b = bmax;\r\nend\r\n\r\nif b == 0 | b < a\r\n v = [];\r\n fclose (fid);\r\n return;\r\nend\r\n\r\n% compute the number of vectors that are really read and go in starting positions\r\nn = b - a + 1;\r\nfseek (fid, (a - 1) * vecsizeof, -1);\r\n\r\n% read n vectors\r\nv = fread (fid, (d + 1) * n, 'float=>single');\r\nv = reshape (v, d + 1, n);\r\n\r\n% Check if the first column (dimension of the vectors) is correct\r\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\r\nv = v (2:end, :);\r\n \r\nfclose (fid);\r\n\r\n" }, { "path": "algorithms/AGH/preprocess/get_anchor.m", "content": "function get_anchor(data_path,anchor_path,m)\ndataset = fvecs_read(data_path);\ndataset=dataset';\nn=size(dataset,1);\np=2000;\nidx=randsample(n,p);\nXtraining = dataset(idx,:);\ntic;\nopts = statset('Display','off','MaxIter',30);\n[idc, anchor] = kmeans(Xtraining, m, 'Options', opts, 'EmptyAction', 'singleton');\nsave([anchor_path '.mat'],'anchor');\nanchor_time=toc;\n\n%file = fopen('anchor_time.txt', 'a+');\n%fprintf(file,'%f #%s \\n',anchor_time,data_name);\n%fclose(file);\n\nend\n" }, { "path": "algorithms/AGH/preprocess/hammingDist.m", "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\nfunction Dh=hammingDist(B1, B2)\r\n%\r\n% Written by Rob Fergus\r\n% Compute hamming distance between two sets of samples (B1, B2)\r\n%\r\n% Dh=hammingDist(B1, B2);\r\n%\r\n% Input\r\n% B1, B2: compact bit vectors. Each datapoint is one row.\r\n% size(B1) = [ndatapoints1, nwords]\r\n% size(B2) = [ndatapoints2, nwords]\r\n% It is faster if ndatapoints1 < ndatapoints2\r\n% \r\n% Output\r\n% Dh = hamming distance. \r\n% size(Dh) = [ndatapoints1, ndatapoints2]\r\n%\r\n% example query\r\n% Dhamm = hammingDist(B2, B1);\r\n% this will give the same result than:\r\n% Dhamm = distMat(U2>0, U1>0).^2;\r\n% the size of the distance matrix is:\r\n% size(Dhamm) = [Ntest x Ntraining]\r\n%\r\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r\n\r\n% loop-up table:\r\nbit_in_char = uint16([...\r\n 0 1 1 2 1 2 2 3 1 2 2 3 2 3 3 4 1 2 2 3 2 3 ...\r\n 3 4 2 3 3 4 3 4 4 5 1 2 2 3 2 3 3 4 2 3 3 4 ...\r\n 3 4 4 5 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 1 2 ...\r\n 2 3 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 ...\r\n 3 4 4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 ...\r\n 5 6 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 1 2 2 3 ...\r\n 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 3 4 ...\r\n 4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 ...\r\n 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 2 3 3 4 3 4 ...\r\n 4 5 3 4 4 5 4 5 5 6 3 4 4 5 4 5 5 6 4 5 5 6 ...\r\n 5 6 6 7 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 4 5 ...\r\n 5 6 5 6 6 7 5 6 6 7 6 7 7 8]);\r\n\r\nn1 = size(B1,1);\r\n[n2, nwords] = size(B2);\r\n\r\nDh = zeros([n1 n2], 'uint16');\r\nfor j = 1:n1\r\n for n=1:nwords\r\n y = bitxor(B1(j,n),B2(:,n));\r\n Dh(j,:) = Dh(j,:) + bit_in_char(y+1);\r\n end\r\nend\r\n" }, { "path": "algorithms/AGH/preprocess/ivecs_read.m", "content": "% Read a set of vectors stored in the ivec format (int + n * int)\r\n% The function returns a set of output vector (one vector per column)\r\n%\r\n% Syntax: \r\n% v = ivecs_read (filename) -> read all vectors\r\n% v = ivecs_read (filename, n) -> read n vectors \r\n% v = ivecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\r\nfunction v = ivecs_read (filename, bounds)\r\n\r\n% open the file and count the number of descriptors\r\nfid = fopen (filename, 'rb');\r\n \r\nif fid == -1\r\n error ('I/O error : Unable to open the file %s\\n', filename)\r\nend\r\n\r\n% Read the vector size\r\nd = fread (fid, 1, 'int');\r\nvecsizeof = 1 * 4 + d * 4;\r\n\r\n% Get the number of vectrors\r\nfseek (fid, 0, 1);\r\na = 1;\r\nbmax = ftell (fid) / vecsizeof;\r\nb = bmax;\r\n\r\nif nargin >= 2\r\n if length (bounds) == 1\r\n b = bounds;\r\n \r\n elseif length (bounds) == 2\r\n a = bounds(1);\r\n b = bounds(2); \r\n end\r\nend\r\n\r\nassert (a >= 1);\r\nif b > bmax\r\n b = bmax;\r\nend\r\n\r\nif b == 0 | b < a\r\n v = [];\r\n fclose (fid);\r\n return;\r\nend\r\n\r\n% compute the number of vectors that are really read and go in starting positions\r\nn = b - a + 1;\r\nfseek (fid, (a - 1) * vecsizeof, -1);\r\n\r\n% read n vectors\r\nv = fread (fid, (d + 1) * n, 'int=>double');\r\nv = reshape (v, d + 1, n);\r\n\r\n% Check if the first column (dimension of the vectors) is correct\r\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\r\nv = v (2:end, :);\r\nfclose (fid);\r\n\r\n" }, { "path": "algorithms/AGH/preprocess/ivecs_write.m", "content": "% This function writes a vector from a file in the ivecs format\n\nfunction ivecs_write (filename, v)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'wb');\nd = size (v, 1);\nn = size (v, 2);\n\nfor i = 1:n\n \n % first write the vector size\n count = fwrite (fid, d, 'int');\n\n if count ~= 1 \n error ('Unable to write vector dimension: count !=1 \\n');\n end\n\n % write the vector components\n count = fwrite (fid, v(:,i), 'int');\n\n if count ~= d \n error ('Unable to write vector elements: count !=1 \\n');\n end\nend\n\nfclose (fid);\n" }, { "path": "algorithms/AGH/preprocess/main.m", "content": "% data_path: the full path of the dataset\n% m: the number of anchor\n% s: the number of the nearest anchors need to be considered\n% bit: the length of the hashcode\n\ndata_name='audio';\ndata_path=['../../../data/' data_name '_base.fvecs'];\n \nindex_path = ['../index/' data_name];\nmkdir(index_path);\n\nanchor_path=[index_path '/' data_name '_anchor'];\nL1_params_path=[index_path '/' data_name '_params.L1']; % the transformation parameters for 1-AGH\nL1_data_path=[index_path '/' data_name '_B1.L1'];% the binary format of the dataset\n\nL2_params_path=[index_path '/' data_name '_params.L2'];\nL2_data_path=[index_path '/' data_name '_B1.L2'];\n\nm=300;\ns=5;\nbit=8;\n\nget_anchor(data_path,anchor_path,m);\nmain_OneLayer(data_path,anchor_path,L1_params_path,L1_data_path,s,bit);\nmain_TwoLayer(data_path,anchor_path,L2_params_path,L2_data_path,s,bit);\n" }, { "path": "algorithms/AGH/preprocess/main_OneLayer.m", "content": "function main_c_OneLayer(data_path, anchor_path, params_path,B1_path,s,bit)\n\ndataset = fvecs_read(data_path); \ndataset=dataset'; % dataset = [n,d] \n\n[n,dim] = size(dataset);\n\nanchor=load([anchor_path '.mat']);\nanchor=anchor.anchor;\nm=size(anchor,1);\n\ntic;\n[Y, W, sigma] = OneLayerAGH_Train(dataset, anchor, bit, s, 0);\nB1 = compactbit(Y);\nindex_time=toc;\n \nfile = fopen(params_path, 'w');\nfprintf(file,'%d\\n',bit);\nfprintf(file,'%f\\n',sigma);\nfor t=1:m %anchor\n p_=anchor(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfor t=1:m %W\n p_=W(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfclose(file); \nivecs_write(B1_path,B1');\n\nend\n" }, { "path": "algorithms/AGH/preprocess/main_TwoLayer.m", "content": "function main_c_TwoLayer(data_path, anchor_path, params_path,B1_path,s,bit)\n\ntraindata = fvecs_read(data_path);\ntraindata=traindata';\n\n[n,dim] = size(traindata); \n\nanchor=load([anchor_path '.mat']);\nanchor=anchor.anchor;\nm=size(anchor,1);\n\n \ntic;\n[Y, W, thres, sigma] = TwoLayerAGH_Train(traindata, anchor, bit, s, 0);\nB1 = compactbit(Y);\nindex_time=toc;\n \nfile = fopen(params_path, 'w');\nfprintf(file,'%d\\n',bit);\nfprintf(file,'%f\\n',sigma);\nfor t=1:m %anchor\n p_=anchor(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfor t=1:m %W\n p_=W(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\n \nfor t=1:2 %Thres\n p_=thres(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfclose(file);\n \nivecs_write(B1_path,B1');\n \nend\n" }, { "path": "algorithms/AGH/preprocess/main_c_OneLayer.m~", "content": "function main_c_OneLayer(data_path, anchor_path,indices_path,B1_path,s,bit)\n\ndataset = fvecs_read(data_path); \ndataset=dataset'; % dataset = [n,d] \n\n[n,dim] = size(dataset);\n\nanchor=load([anchor_path '.mat']);\nanchor=anchor.anchor;\nm=size(anchor,1);\n\ntic;\n[Y, W, sigma] = OneLayerAGH_Train(dataset, anchor, bit, s, 0);\nB1 = compactbit(Y);\nindex_time=toc;\n \nfile = fopen(indices_path, 'w');\nfprintf(file,'%d\\n',bit);\nfprintf(file,'%f\\n',sigma);\nfor t=1:m %anchor\n p_=anchor(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfor t=1:m %W\n p_=W(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfclose(file); \nivecs_write(B1_path,B1');\n\nend\n" }, { "path": "algorithms/AGH/preprocess/main_c_TwoLayer.m~", "content": "function main_c_TwoLayer(data_path, anchor_path,indices_path,B1_path,s,bit)\n\ntraindata = fvecs_read(data_path);\ntraindata=traindata';\n\n[n,dim] = size(traindata); \n\nanchor=load(anchor_path);\nanchor=anchor.anchor;\nm=size(anchor,1);\n\n \ntic;\n[Y, W, thres, sigma] = TwoLayerAGH_Train(traindata, anchor, bit, s, 0);\nB1 = compactbit(Y);\nindex_time=toc;\n \nfile = fopen(indices_path, 'w');\nfprintf(file,'%d\\n',bit);\nfprintf(file,'%f\\n',sigma);\nfor t=1:m %anchor\n p_=anchor(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfor t=1:m %W\n p_=W(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\n \nfor t=1:2 %Thres\n p_=thres(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfclose(file);\n \nivecs_write(B1_path,B1');\n \nend\n" }, { "path": "algorithms/AGH/preprocess/sqdist.m", "content": "function d=sqdist(a,b)\r\n% SQDIST - computes squared Euclidean distance matrix\r\n% computes a rectangular matrix of pairwise distances\r\n% between points in A (given in columns) and points in B\r\n\r\n% NB: very fast implementation taken from Roland Bunschoten\r\n\r\naa = sum(a.*a,1); bb = sum(b.*b,1); ab = a'*b; \r\nd = abs(repmat(aa',[1 size(bb,2)]) + repmat(bb,[size(aa,2) 1]) - 2*ab);\r\n\r\n" }, { "path": "algorithms/AGH/script/searcher.sh", "content": "\ndata_path=\"../../../data\"\nresult_path=\"../results\"\nmkdir ${result_path}\n\ncd ../src\n\nk=20\n\nfor data in \"audio\"\ndo\n\ndataset_path=\"${data_path}/${data}_base.fvecs\"\nquery_path=\"${data_path}/${data}_query.fvecs\"\ngnd_path=\"${data_path}/${data}_groundtruth.ivecs\"\n \nfor layer in 1 2 # the layer of AGH\ndo\n\nparams_path=../index/${data}/${data}_params.L${layer} # file stored the transformation parameters\ndataset_binary_path=../index/${data}/${data}_B1.L${layer} # the file stored the binary dataset\nnbit=8 # the length of the hash code\nm=300 # the number of the anchors\nt=5 # the number of the nearest anchors used to build the app. graph\nradis=-2 # searching limited by the number of the maximum visited points\n\nfor c in 100 500 1000\ndo\n\n./AGH -s ${dataset_path} -q ${query_path} -g ${gnd_path} -k $k -a $m -t $t -l ${layer} -c $c -r ${radis} -b \"${dataset_binary_path}\" -p \"${params_path}\" >> \"${result_path}/${data}_${layer}AGH.txt\"\n\ndone\ndone\ndone\n\n" }, { "path": "algorithms/AGH/src/AGH.cpp", "content": "#include \"data_util.h\"\n#include \"AGH.h\"\n#include \"hammingDist.h\"\n#include \"eval.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nvector > points;\nvector > query;\nvector > gnds;\nvector maxChecks;\n\nint n;\nint nq;\nint dim;\nint k;\nint radis;\nint Layer;\nint anchor_size;\nint nearest_size;\n\nfloat diff_timeval(timeval t1, timeval t2) {\n return (float) (t1.tv_sec - t2.tv_sec) + (t1.tv_usec - t2.tv_usec) * 1e-6;\n}\n\nint main(int argc, char * argv[]) {\n\tconst struct option longopts[] ={\n\t {\"help\", no_argument, 0, 'h'},\n\t {\"dataset_filepath\", required_argument, 0, 's'},\n\t {\"query_filepath\" , required_argument, 0, 'q'},\n\t {\"groundtruth_filepath\", required_argument, 0, 'g'},\n\t {\"Layer\", required_argument, 0, 'l'},\n\t\t{\"k\", required_argument, 0, 'k'},\n\t\t{\"r\", required_argument, 0, 'r'},\n\t\t{\"c\", required_argument, 0, 'c'},\n\t\t{\"t\", required_argument, 0, 't'},\n\t\t{\"a\", required_argument, 0, 'a'},\n\t {\"dataset_binary_path\", required_argument, 0, 'b'},\n\t {\"params_path\", required_argument, 0, 'p'},\n\t };\n\t int ind;\n\t int iarg = 0;\n\t opterr = 1;\n\t int checks;\n\n\t char groundtruth_filepath[100] = \"\";\n\t char query_filepath[100] = \"\";\n\t char dataset_filepath[100] = \"\";\n\t char dataset_binary_path[100] = \"\";\n\t char params_path[100] = \"\";\n\t //char checks_path[20] =\"\";\n\n\n\t while (iarg != -1) {\n\t iarg = getopt_long(argc, argv, \"s:q:g:l:k:t:a:r:c:b:p:h\",\n\t longopts, &ind);\n\n\t switch (iarg) {\n\t case 's':\n\t if (optarg) {\n\t \tstrcpy(dataset_filepath, optarg);\n\t }\n\t break;\n\t case 'q':\n\t if (optarg) {\n\t \tstrcpy(query_filepath, optarg);\n\t }\n\t break;\n\t case 'g':\n\t if (optarg) {\n\t \tstrcpy(groundtruth_filepath, optarg);\n\t }\n\t break;\n\t case 'l':\n\t \t if (optarg) {\n\t \t Layer = atoi(optarg);\n\t \t }\n\t \t break;\n\t\t case 'k':\n\t \t if (optarg) {\n\t \t k = atoi(optarg);\n\t \t }\n\t \t break;\n\t case 'a':\n\t \t if (optarg) {\n\t \t anchor_size = atoi(optarg);\n\t \t }\n\t \t break;\n\t\t case 't':\n\t \t if (optarg) {\n\t \t nearest_size = atoi(optarg);\n\t \t }\n\t \t break;\n\t\t case 'c':\n\t \t if (optarg) {\n\t\t\t //strcpy(checks_path, optarg);\n\t\t\t checks = atoi(optarg);\n\t \t }\n\t \t break;\n\t\t case 'r':\n\t \t if (optarg) {\n\t \t radis = atoi(optarg);\n\t \t }\n\t \t break;\n\t case 'b':\n\t if (optarg) {\n\t \tstrcpy(dataset_binary_path, optarg);\n\t }\n\t break;\n\t case 'p':\n\t if (optarg) {\n\t \tstrcpy(params_path, optarg);\n\t }\n\t break;\n\t }\n\t }\n\t ReadPoints(dataset_filepath, &points, &n, &dim);\n\t ReadPoints(query_filepath, &query,&nq, &dim);\n\t ReadGroundtruth(groundtruth_filepath, &gnds, &nq, &k);\n \t //ReadChecks(checks_path,&maxChecks);\n\n\t //read SH params\n\t AGH AGH_;\n\t AGH_.ReadAGHParams(params_path,anchor_size,dim,Layer);\n\t int nbits = AGH_.nbits;\n\n\t vector > B1;\n\t ReadB1(dataset_binary_path,&B1,n);\n\t //SH_.compressSH(&points,&B1);\n\n\t HierarchicalClusteringIndex HCI_ ;\n\t HCI_.set_params(B1,32,4,100);\n\t if(radis==-2)\n\t \tHCI_.buildIndex();\n\n\t timeval start;\n gettimeofday(&start, NULL);\n\n\t // compress query\n\t vector > B2;\n\t if(Layer==1)\n\t\tAGH_.compressAGH_OneLayer(&query,&B2,nearest_size);\n\t else\n\t\tAGH_.compressAGH_TwoLayer(&query,&B2,nearest_size);\n\t\n\t timeval end;\n\t gettimeofday(&end, NULL);\n\t double transform_time = diff_timeval(end, start);\n\t\n\t gettimeofday(&start, NULL);\n\n\t vector > indices;\n\t vector > dists;\n\n if(radis==-1)\n\t\t hammingDist(&B2,&B1,&indices,checks);\n\t else if(radis==-2)\n\t\t HCI_.knnSearch(B2,indices,dists,checks,checks);\n\t else\n\t\t hammingDist_ByRange(&B2,&B1,&indices,radis);\n \n\t\t// get results after re-ranking\n\t vector > results;\n\t results = Search(&points, &query, &indices, k);\n\n gettimeofday(&end, NULL);\n double search_time = diff_timeval(end, start) + transform_time;\n\n\t float recall = compute_recall(&gnds, &results);\n\t\t \n\t cout<\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\n#ifndef AGH_H_\n#define AGH_H_\n\nclass AGH\n{\n\tpublic:\n\tint nbits;\n\tint m;\n\tint s;\n\tfloat sigma;\n\tvector > anchor;\n\tvector > W;\n\tvector > Thres;\n \n\tvoid ReadAGHParams(string filename, int m_,int dim, int isTwo)\n\t{\n\t\tm = m_;\n\t\tFILE* fp=fopen(filename.c_str(),\"r\");\n\t\t//read nbits\n\t\tfscanf(fp,\"%d\",&nbits);\n\n\t\t// read delta\n\t\tfscanf(fp,\"%f\",&sigma);\n\t\t\n\t\t//read anchor\n\t\tfor (int i=0;i tmp;\n\t\t\ttmp.resize(dim);\n\t\t\tfor(int d = 0; d < dim; ++d) \n\t\t\t{\n\t\t \t\tfscanf(fp,\"%f\",&tmp[d]);\n\t\t\t}\n\t\t\tanchor.push_back(tmp);\n\t\t}\n\n\t\t//read W\n\t\tif(isTwo==1)\n\t\t{\n\t\t\tfor (int i=0; i tmp;\n\t\t\t\ttmp.resize(nbits);\n\t\t\t\tfor(int d = 0; d < nbits; ++d)\n\t\t\t\t{\n\t\t\t\t\tfscanf(fp,\"%f\",&tmp[d]);\n\t\t\t\t}\n\t\t\t\tW.push_back(tmp);\n\t\t\t}\n\t\t}\n\t\telse\n\t\t{\n\t\t\tfor (int i=0; i tmp;\n\t\t\t\ttmp.resize(nbits/2);\n\t\t\t\tfor(int d = 0; d < nbits/2; ++d)\n\t\t\t\t{\n\t\t\t\t\tfscanf(fp,\"%f\",&tmp[d]);\n\t\t\t\t}\n\t\t\t\tW.push_back(tmp);\n\t\t\t}\n\t\t}\n\t\t\n\t\t//read Thres\n\t\tif(isTwo==2)\n\t\t{\n\t\t\tfor (int i=0; i<2; i++)\n\t\t\t{\n\t\t\t\tvector tmp;\n\t\t\t\ttmp.resize(nbits/2);\n\t\t\t\tfor(int d = 0; d < nbits/2; ++d)\n\t\t\t\t{\n\t\t\t\t\tfscanf(fp,\"%f\",&tmp[d]);\n\t\t\t\t}\n\t\t\t\tThres.push_back(tmp);\n\t\t\t}\n\t\t}\n\t}\n\t\n\tvoid compactBits(vector > U,vector >* B)\n\t{\n\t\t//U n*nbits\n\t\tint n = U.size();\n\t\tint nbits = U.at(0).size();\n\t\tfor(int i=0;i B_;\n\t\t\tunsigned tmp=0;\n\t\t\tfor(int j=0;j<=nbits;j++)\n\t\t\t{\n\t\t\t\tif(j==nbits)\n\t\t\t\t{\n\t\t\t\t\tB_.push_back(tmp);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif(j%8==0 && j!=0)\n\t\t\t\t{\n\t\t\t\t\tB_.push_back(tmp);\n\t\t\t\t\ttmp=0;\n\t\t\t\t}\n\t\t\t\tif(U.at(i)[j]>0)\n\t\t\t\t{\n\t\t\t\t\tunsigned pos=1<<(j%8);\n\t\t\t\t\ttmp |= pos;\n\t\t\t\t}\n\t\t\t}\n\t\t\tB->push_back(B_);\n\t\t}\n\t}\n\t\n\tstruct L_Neighbor{\n\t\tint pos;\n\t\tfloat val;\n\t\tL_Neighbor()\n\t\t{\n\t\t\tpos=0;\n\t\t\tval=-1;\n\t\t}\n\t};\n\n\tvoid UpdatePriorityList(L_Neighbor *BList, L_Neighbor tmp) {\n\t// find the location to insert\n\tint j;\n int i = s;\n while (i > 0) {\n j = i - 1;\n if (BList[j].val <= tmp.val && BList[j].val!=-1) break;\n i = j;\n }\n\tif(i < s)\n\t{\n \tj = s-1;\n \twhile (j > i) {\n \tBList[j] = BList[j-1];\n \t--j;\n \t}\n \tBList[i] = tmp;\n\t}\n\t}\n\tfloat get_distance(vector point, vector query)\n\t{\n\t\tint dim = query.size();\n\t\tfloat dist=0.0;\n\t\tfor(int i=0; i >* X, vector >* B,int s_)\n\t{\n\t\ts = s_;\n\t\tint nq = X->size();\n\t\tint dim = X->at(0).size();\n\t\t\n\t\tvector > Y;\n\t\t\n\t\tvector > Z;\n\t\tfor(int i=0; i Z_(m,0);\n\t\t\tZ.push_back(Z_);\n\n\t\t\tvector LNN(s);\n\t\t\tfor(int j=0; jat(i),anchor[j]);\n\t\t\t\tL_Neighbor L_;\n\t\t\t\tL_.pos=j;\n\t\t\t\tL_.val=dist;\n\t\t\t\tUpdatePriorityList(&LNN[0],L_);\n\t\t\t}\n\t\t\tfloat sum = 0.0;\n\t\t\tfor(int j=0;j > U;\n\t\tfor(int i=0; i U_;\n\t\t\tfor(int j=0; j >* X, vector >* B,int s_)\n\t{\n\t\ts = s_;\n\t\tint nq = X->size();\n\t\t\n\t\tvector > T;\n\t\t\n\t\tvector > Z;\n\t\tfor(int i=0; i Z_(m,0);\n\t\t\tZ.push_back(Z_);\n\n\t\t\tvector LNN(s);\n\t\t\tfor(int j=0; jat(i),anchor[j]);\n\t\t\t\tL_Neighbor L_;\n\t\t\t\tL_.pos=j;\n\t\t\t\tL_.val=dist;\n\t\t\t\tUpdatePriorityList(&LNN[0],L_);\n\t\t\t}\n\t\t\tfloat sum = 0.0;\n\t\t\tfor(int j=0;j > Y;\n\t\tfor(int i=0; i U_;\n\t\t\tU_.resize(nbits);\n\t\t\tfor(int j=0; j 0)\n\t\t\t\t{\n\t\t\t\t\tU_[nbits/2+j]=tmp-Thres[0][j];\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tU_[nbits/2+j]=-tmp+Thres[1][j];\n\t\t\t\t};\n\t\t\t}\n\t\t\tY.push_back(U_);\n\t\t}\n\t\tcompactBits(Y,B);\n\t}\n};\n\n#endif \n" }, { "path": "algorithms/AGH/src/Makefile", "content": "##############\n# Make the AGH project\n##############\n\nPROD\t:= DEBUG\nOPT := -O3 -march=native \nVERSION := \\\"0.1_${PROD}\\\"\nTARGETS := AGH\nSRCS := AGH.cpp\n\nCCFLAGS = ${OPT} -Wno-deprecated -ggdb -D${PROD} -I./ -DVERSION=${VERSION}\nLDFLAGS = ${OPT} -ggdb \nLIBS = \nCC\t= g++ \nOBJS := ${SRCS:.cpp=.o}\n\n\n.PHONY: all clean distclean \nall:: ${TARGETS} \n\nsrs: AGH.o\n\t${CC} ${LDFLAGS} -o $@ $^ ${LIBS}\n\n${OBJS}: %.o: %.cpp\n\t${CC} ${CCFLAGS} -o $@ -c $< \n\nclean:: \n\t-rm -f *~ *.o ${TARGETS}\n\ndistclean:: clean\n" }, { "path": "algorithms/AGH/src/center_chooser.h", "content": "#include \"random.h\"\n#include \"char_bit_cnt.h\"\n\nclass CenterChooser\n{\n\tvector* > dataset_;\npublic:\n\n\tvoid setDataset(vector >& dataset)\n {\n\t\tsize_t size_ = dataset.size();\n\n\t\tdataset_.resize(size_);\n\t\tfor (size_t i=0;isize();\n\t\tfor(int t=0; tat(t) ^ dataset_.at(p2)->at(t);\n\t\t\tdist += char_bit_cnt[y];\n\t\t}\n\t\treturn dist;\n\t} //*/\n\n\tvoid chooseCenters(int k, int* indices, int indices_length, int* centers, int& centers_length)\n\t{\n\t\tUniqueRandom r(indices_length);\n\n int index;\n for (index=0; index\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nvoid transpose(vector > X,vector >* Y)\n{\n\n\tint dim=X.size();\n\tint n=X[0].size();\n\n\tfor(int i=0;i y_;\n\t\tfor(int j=0;jpush_back(y_);\n\t}\n}\n\nvoid ReadPoints(char* filepath,vector >* points, int* len, int* d)\n{\n\tFILE * dfp = fopen(filepath, \"rb\");\n\tint dim;\n\tfread(&dim,sizeof(int),1,dfp);\n\tfseek(dfp, 0, SEEK_END);\n\toff_t size = ftell(dfp);\n fseek(dfp, 0, SEEK_SET);\n int n = (int)(size / (sizeof(int)+ sizeof(float)*dim));\n\t*d = dim;\n\t*len = n;\n\tpoints->resize(n);\n\tfor (int i=0;iat(i).resize(dim);\n \tfor(int j = 0; j < dim; ++j) {\n \t\tfloat buffer;\n\t\t\tfread(&(buffer), sizeof(float),1,dfp);\n \t\tpoints->at(i)[j] = buffer;\n \t}\n\t}\n}\n\n\nvoid ReadPoints(const string& filename, vector >* points, int count, int *dim) {\n\t//vector > X;\n\tifstream input;\n\tinput.open(filename.c_str(), ios::binary);\n\tif(!input.good()) {\n\t\tthrow std::logic_error(\"Invalid filename\");\n\t}\n\t//X.resize(count);\n\tpoints->resize(count);\n\tint dimension;\n\tfor(int pid = 0; pid < count; ++pid) {\n\t\tinput.read((char*)&dimension, sizeof(dimension));\n\t\tif(dimension <= 0) {\n\t\t\tthrow std::logic_error(\"Bad file content: non-positive dimension\");\n\t\t}\n\t//X.at(pid).resize(dimension);\n\tpoints->at(pid).resize(dimension);\n for(int d = 0; d < dimension; ++d) {\n \tfloat buffer;\n \tinput.read((char*)&(buffer), sizeof(float));\n \t//X.at(pid)[d] = buffer;\n \tpoints->at(pid)[d] = buffer;\n }\n }\n *dim = dimension;\n //points = &X;\n //transpose(X,points);\n}\n\nvoid ReadGroundtruth(char* filepath, vector >* gnds, int* len, int* d)\n{\n\tFILE * dfp = fopen(filepath, \"rb\");\n\tint k;\n\tfread(&k,sizeof(int),1,dfp);\n\tfseek(dfp, 0, SEEK_END);\n\toff_t size = ftell(dfp);\n fseek(dfp, 0, SEEK_SET);\n int n = (int)(size / (sizeof(int)+ sizeof(int)*k));\n\t*d = k;\n\t*len = n;\n\tgnds->resize(n);\n\tfor (int i=0;iat(i).resize(k);\n \tfor(int j = 0; j < k; ++j) {\n \t\tint buffer;\n\t\t\tfread(&(buffer), sizeof(int),1,dfp);\n \t\tgnds->at(i)[j] = buffer;\n \t}\n\t}\n}\n\nvoid ReadGroundtruth(const string& filename, vector >* gnds,int nq, int nn) {\n ifstream input;\n\tinput.open(filename.c_str(), ios::binary);\n if(!input.good()) {\n throw std::logic_error(\"Invalid filename\");\n }\n gnds->resize(nq);\n int k;\n for(int pid = 0; pid < nq; ++pid) {\n input.read((char*)&k, sizeof(k));\n if(k <= 0) {\n throw std::logic_error(\"Bad file content: non-positive dimension\");\n }\n gnds->at(pid).resize(k);\n for(int d = 0; d < k; ++d) {\n int buffer;\n input.read((char*)&(buffer), sizeof(int));\n gnds->at(pid)[d] = buffer;\n }\n }\n //*nn = k;\n}\n\nvoid ReadB1(const string& filename, vector >* B1,int n) {//\n ifstream input;\n\tinput.open(filename.c_str(), ios::binary);\n if(!input.good()) {\n throw std::logic_error(\"Invalid filename\");\n }\n B1->resize(n);\n int cbits;\n for(int pid = 0; pid < n; ++pid) {\n input.read((char*)&cbits, sizeof(cbits));\n if(cbits <= 0) {\n throw std::logic_error(\"Bad file content: non-positive dimension\");\n }\n B1->at(pid).resize(cbits);\n for(int d = 0; d < cbits; ++d) {\n int buffer;\n input.read((char*)&(buffer), sizeof(int));\n B1->at(pid)[d] = (unsigned)buffer;//(unsigned)\n }\n }\n}\n\n\nvoid ReadChecks(const string& filename, vector* checks) {\n FILE *fp = fopen(filename.c_str(), \"r\");\n int n;\n fscanf(fp, \"%d \", &n);\n checks->resize(n);\n int tmp;\n for (int i = 0; i < n; ++i) {\n fscanf(fp, \"%d \", &tmp);\n checks->at(i)=tmp;\n }\n}\n\n\n\n\n\n" }, { "path": "algorithms/AGH/src/dynamic_bitset.h", "content": "#include \n\nclass DynamicBitset\n{\npublic:\n /** @param only constructor we use in our code\n * @param the size of the bitset (in bits)\n */\n DynamicBitset(size_t size)\n {\n resize(size);\n reset();\n }\n\n /** Sets all the bits to 0\n */\n void clear()\n {\n std::fill(bitset_.begin(), bitset_.end(), 0);\n }\n\n void reset()\n {\n std::fill(bitset_.begin(), bitset_.end(), 0);\n }\n\n \n void resize(size_t size)\n {\n size_ = size;\n bitset_.resize(size / cell_bit_size_ + 1);\n }\n size_t size() const\n {\n return size_;\n }\n\n\tvoid set(size_t index)\n {\n bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_);\n }\n\n /** @param check if a bit is set\n * @param index the index of the bit to check\n * @return true if the bit is set\n */\n bool test(size_t index) const\n {\n return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0;\n }\n\nprivate:\n std::vector bitset_;\n size_t size_;\n static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t);\n};\n" }, { "path": "algorithms/AGH/src/eval.h", "content": "#ifndef EVAL_H_\n#define EVAL_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nfloat compute_recall(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tint avg = 0;\n\tfor (size_t i=0; iat(i)[j] == gnds->at(i)[k]) {\n\t\t\t\t\tavg ++;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn float(avg)/(nn*nq);\n}\n\nfloat compute_mean_reciprocal_rank(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[0]==results->at(i)[k])\n\t\t\t{\n\t\t\t\trate +=1.0/(k+1);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tavg+=rate;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_number_closer(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[j]==results->at(i)[k])\n\t\t\t\t{\n\t\t\t\t\trate +=(float)(j+1)/(k+1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tavg+=rate/nn;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_relative_distance_error(vector >* points, vector >* query, vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat count = 0.0;\n\tfor (size_t i=0; iat(gnds->at(i)[j]),query->at(i));\n\t\t\tfloat test_distance = get_distance(points->at(results->at(i)[j]),query->at(i));\n\t\t\tfloat d= (test_distance - min_distance )/min_distance;\n\t\t\tif(d >4)\n\t\t\t\tsum +=4;\n\t\t\telse\n\t\t\t\tsum +=d;\n\t\t}\n\t\tcount += sum /nn;\n\t}\n\treturn count/ nq;\n}\n\nfloat compute_mean_average_precision (vector >* gnds, vector >* results)\n{\n\tint nq = results->size();\n\tint nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (int i = 0; i < nq; ++i)\n\t{\n\t\tfloat rate = 0.0;\n\t\tint found = 0;\n\n\t\tstd::set gnd_row;\n \t\tfor(int j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(int j=0;j at(i)[j]) != gnd_row.end()) \n\t\t\t{\n\t\t\t\tfound ++;\n\t\t\t\trate += 1.0 * found/(j+1);\n \t\t}\n \t}\n\t\tsum += rate/nn;\n\t}\n\treturn sum / nq;\n}\n\nfloat compute_discounted_culmulative_gain (vector >* gnds, vector >* results) \n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (size_t i = 0; i < nq; ++i) \n\t{\n\t\tfloat rate = 0.0;\n\n\t\tstd::set gnd_row;\n \t\tfor(size_t j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(size_t j=0;j at(i)[j]) != gnd_row.end()) \n\t\t\t{\n\t\t\t\trate += 1.0 /log2(j+2);\n \t\t}\n \t}\n\t\tsum += rate;\n\t}\n\treturn sum / nq;\n}\n\nfloat compute_recall(vector >* gnds, vector >* results, float* recall)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tcout<<\"nq:\"<at(i)[j].id == gnds->at(i)[k]) {\n\t\t\t\t\tavg ++;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn float(avg)/(nn*nq);\n}\n\nfloat compute_mean_reciprocal_rank(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[0]==results->at(i)[k].id)\n\t\t\t{\n\t\t\t\trate +=1.0/(k+1);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tavg+=rate;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_number_closer(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[j]==results->at(i)[k].id)\n\t\t\t\t{\n\t\t\t\t\trate +=(float)(j+1)/(k+1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tavg+=rate/nn;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_relative_distance_error(vector >* points, vector >* query, vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat count = 0.0;\n\tfor (size_t i=0; iat(gnds->at(i)[j]),query->at(i));\n\t\t\tfloat test_distance=results->at(i)[j].dist;\n\t\t\tfloat d= (test_distance - min_distance )/min_distance;\n \n\t\t\tif(d >4)\n\t\t\t\tsum +=4;\n\t\t\telse\n\t\t\t\tsum +=d;\n\t\t}\n\t\tcount += sum /nn;\n\t}\n\treturn count/ nq;\n}\n\n\nfloat compute_mean_average_precision (vector >* gnds, vector >* results)\n{\n\tint nq = results->size();\n\tint nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (int i = 0; i < nq; ++i)\n\t{\n\t\tfloat rate = 0.0;\n\t\tint found = 0;\n\n\t\tstd::set gnd_row;\n \t\tfor(int j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(int j=0;j at(i)[j].id) != gnd_row.end()) \n\t\t\t{\n\t\t\t\tfound ++;\n\t\t\t\trate += 1.0 * found/(j+1);\n \t\t}\n \t}\n\t\tsum += rate/nn;\n\t}\n\treturn sum / nq;\n}\n\nfloat compute_discounted_culmulative_gain (vector >* gnds, vector >* results) \n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (size_t i = 0; i < nq; ++i) \n\t{\n\t\tfloat rate = 0.0;\n\n\t\tstd::set gnd_row;\n \t\tfor(size_t j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(size_t j=0;j at(i)[j].id) != gnd_row.end()) \n\t\t\t{\n\t\t\t\trate += 1.0 /log2(j+2);\n \t\t}\n \t}\n\t\tsum += rate;\n\t}\n\treturn sum / nq;\n}\n\n#endif /* EVAL_H_ */\n" }, { "path": "algorithms/AGH/src/hammingDist.h", "content": "#ifndef HAMMINGDIST_H_\n#define HAMMINGDIST_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"hierarchical_clustering_index.h\"\n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nstruct B_Neighbor{\n\tint id;\n\tint dist;\n\tB_Neighbor()\n\t{\n\t\tid=0;\n\t\tdist=-1;\n\t}\n\tint get_id(){return id;}\n};\n\nstruct E_Neighbor{\n\tint id;\n\tfloat dist;\n\tE_Neighbor()\n\t{\n\t\tid=0;\n\t\tdist=-1;\n\t}\n};\n\nfloat get_distance(vector point, vector query)\n{\n\tint dim = query.size();\n\tfloat dist=0.0;\n\tfor(int i=0; i >* B2,vector >* B1, vector >* Dist)\n{\n\tint n = B1->size();\n\tint nq = B2->size();\n\tint cbits = B2->at(0).size();\n\tfor(int i=0; i d_;\n\t\tfor(int j=0; jat(j)[t] ^ B2->at(i)[t];\n\t\t\t\ttmp += char_bit_cnt[y];\n\t\t\t}\n\t\t\td_.push_back(tmp);\n\t\t}\n\t\tDist->push_back(d_);\n\t}\n}\n\nvoid UpdatePriorityList_Binary (B_Neighbor *BList, B_Neighbor tmp, int len) {\n\t// find the location to insert\n\tint j;\n int i = len;\n while (i > 0) {\n j = i - 1;\n if (BList[j].dist <= tmp.dist && BList[j].dist!=-1) break;\n i = j;\n }\n\tif(i < len)\n\t{\n \tj = len-1;\n \twhile (j > i) {\n \tBList[j] = BList[j-1];\n \t--j;\n \t}\n \tBList[i] = tmp;\n\t}\n}\n\nvoid hammingDist(vector >* B2,vector >* B1, vector >* Dist,int nn)\n{\n\tint len = nn;\n\tint n = B1->size();\n\tint nq = B2->size();\n\tint cbits = B2->at(0).size();\n\n\tfor(int i=0; i BNN(len);\n\t\tfor(int j=0; jat(j)[t] ^ B2->at(i)[t];\n\t\t\t\ttmp += char_bit_cnt[y];\n\t\t\t}\n\n\t\t\tB_Neighbor b_;\n\t\t\tb_.id=j;\n\t\t\tb_.dist=tmp;\n\n\t\t\tUpdatePriorityList_Binary ( &BNN[0], b_, len);\n\t\t}\n\t\tvector d_;\n\t\tfor(int j=0;jpush_back(d_);\n\t}\n}\n\nvoid hammingDist_ByRange(vector >* B2,vector >* B1, vector >* IDs,int radis)\n{\n\tint n = B1->size();\n\tint nq = B2->size();\n\tint cbits = B2->at(0).size();\n\n\tfor(int i=0; i BNN;\n\t\tfor(int j=0; jat(j)[t] ^ B2->at(i)[t];\n\t\t\t\ttmp += char_bit_cnt[y];\n\t\t\t}\n\t\t\tif(tmp<=radis)\n\t\t\t\tBNN.push_back(j);\n\t\t}\n\t\tIDs->push_back(BNN);\n\t}\n}\n\nvoid UpdatePriorityList_Euclidean (E_Neighbor *BList, E_Neighbor tmp, int len) {\n\t// find the location to insert\n\tint j;\n int i = len;\n while (i > 0) {\n j = i - 1;\n if (BList[j].dist <= tmp.dist && BList[j].dist!=-1) break;\n i = j;\n }\n\tif(i < len)\n\t{\n \tj = len-1;\n \twhile (j > i) {\n \tBList[j] = BList[j-1];\n \t--j;\n \t}\n \tBList[i] = tmp;\n\t}\n}\n\nvector > Search(vector >* points, vector >* query, vector >* IDs, int K)\n{\n\tint nq=query->size();\n\tvector > results;\n\tfor(int i=0; i result;\n\t\tvector ENN(K);\n\t\tint len = IDs->at(i).size();\n\t\tvector ids_ = IDs->at(i);\n\t\tfor(int j=0; jat(i),points->at(ids_.at(j)));\n\t\t\tE_Neighbor e_;\n\t\t\te_.id=ids_.at(j);\n\t\t\te_.dist=dist;\n\t\t\tUpdatePriorityList_Euclidean (&ENN[0], e_, K);\n\t\t}\n\t\tfor(int j=0;j > Search(vector >* points, vector >* query, vector > IDs, int K)\n{\n\tint nq=query->size();\n\tvector > results;\n\tfor(int i=0; i ENN(K);\n\t\tint len = IDs.at(i).size();\n\t\tvector ids_ = IDs.at(i);\n\t\tfor(int j=0; jat(i),points->at(ids_.at(j)));\n\t\t\tE_Neighbor e_;\n\t\t\te_.id=ids_.at(j);\n\t\t\te_.dist=dist;\n\t\t\tUpdatePriorityList_Euclidean (&ENN[0], e_, K);\n\t\t}\n\t\t//for(int j=0;j\n#include \n\n\ntemplate \nclass Heap\n{\n\n /**\n * Storage array for the heap.\n * Type T must be comparable.\n */\n std::vector heap;\n int length;\n\n /**\n * Number of element in the heap\n */\n int count;\n\n\n\npublic:\n /**\n * Constructor.\n *\n * Params:\n * size = heap size\n */\n\n Heap(int size)\n {\n length = size;\n heap.reserve(length);\n count = 0;\n }\n\n /**\n *\n * Returns: heap size\n */\n int size()\n {\n return count;\n }\n\n /**\n * Tests if the heap is empty\n *\n * Returns: true is heap empty, false otherwise\n */\n bool empty()\n {\n return size()==0;\n }\n\n /**\n * Clears the heap.\n */\n void clear()\n {\n heap.clear();\n count = 0;\n }\n\n struct CompareT : public std::binary_function\n {\n bool operator()(const T& t_1, const T& t_2) const\n {\n return t_2 < t_1;\n }\n };\n\n /**\n * Insert a new element in the heap.\n *\n * We select the next empty leaf node, and then keep moving any larger\n * parents down until the right location is found to store this element.\n *\n * Params:\n * value = the new element to be inserted in the heap\n */\n void insert(const T& value)\n {\n /* If heap is full, then return without adding this element. */\n if (count == length) {\n return;\n }\n\n heap.push_back(value);\n static CompareT compareT;\n std::push_heap(heap.begin(), heap.end(), compareT);\n ++count;\n }\n\n\n\n /**\n * Returns the node of minimum value from the heap (top of the heap).\n *\n * Params:\n * value = out parameter used to return the min element\n * Returns: false if heap empty\n */\n bool popMin(T& value)\n {\n if (count == 0) {\n return false;\n }\n\n value = heap[0];\n static CompareT compareT;\n std::pop_heap(heap.begin(), heap.end(), compareT);\n heap.pop_back();\n --count;\n\n return true; /* Return old last node. */\n }\n};\n\n\ntemplate \nclass IntervalHeap\n{\n\tstruct Interval\n\t{\n\t\tT left;\n\t\tT right;\n\t};\n\n /**\n * Storage array for the heap.\n * Type T must be comparable.\n */\n std::vector heap;\n size_t capacity_;\n size_t size_;\n\npublic:\n /**\n * Constructor.\n *\n * Params:\n * size = heap size\n */\n\n IntervalHeap(int capacity) : capacity_(capacity), size_(0)\n {\n heap.resize(capacity/2 + capacity%2 + 1); // 1-based indexing\n }\n\n /**\n * @return Heap size\n */\n size_t size()\n {\n return size_;\n }\n\n /**\n * Tests if the heap is empty\n * @return true is heap empty, false otherwise\n */\n bool empty()\n {\n return size_==0;\n }\n\n /**\n * Clears the heap.\n */\n void clear()\n {\n size_ = 0;\n }\n\n void insert(const T& value)\n {\n /* If heap is full, then return without adding this element. */\n if (size_ == capacity_) {\n return;\n }\n\n // insert into the root\n if (size_<2) {\n \tif (size_==0) {\n \t\theap[1].left = value;\n \t\theap[1].right = value;\n \t}\n \telse {\n \t\tif (value1 && value < heap[par].left) {\n \t\theap[pos].left = heap[par].left;\n \t\tpos = par;\n \t\tpar = pos/2;\n \t}\n \theap[pos].left = value;\n \t++size_;\n\n \tif (size_%2) { // duplicate element in last position if size is odd\n \t\theap[last_pos].right = heap[last_pos].left;\n \t}\n }\n else {\n \tsize_t pos = last_pos;\n \tsize_t par = pos/2;\n \twhile (pos>1 && heap[par].right < value) {\n \t\theap[pos].right = heap[par].right;\n \t\tpos = par;\n \t\tpar = pos/2;\n \t}\n \theap[pos].right = value;\n \t++size_;\n\n \tif (size_%2) { // duplicate element in last position if size is odd\n \t\theap[last_pos].left = heap[last_pos].right;\n \t}\n }\n }\n\n\n /**\n * Returns the node of minimum value from the heap\n * @param value out parameter used to return the min element\n * @return false if heap empty\n */\n bool popMin(T& value)\n {\n if (size_ == 0) {\n return false;\n }\n\n value = heap[1].left;\n size_t last_pos = size_/2 + size_%2;\n T elem = heap[last_pos].left;\n\n if (size_ % 2) { // odd number of elements\n \t--last_pos;\n }\n else {\n \theap[last_pos].left = heap[last_pos].right;\n }\n --size_;\n if (size_<2) return true;\n\n size_t crt=1; // root node\n size_t child = crt*2;\n\n while (child <= last_pos) {\n \tif (child < last_pos && heap[child+1].left < heap[child].left) ++child; // pick the child with min\n\n \tif (!(heap[child].left\nclass BoundedHeap\n{\n\tIntervalHeap interval_heap_;\n\tsize_t capacity_;\npublic:\n\tBoundedHeap(size_t capacity) : interval_heap_(capacity), capacity_(capacity)\n\t{\n\n\t}\n\n /**\n * Returns: heap size\n */\n int size()\n {\n return interval_heap_.size();\n }\n\n /**\n * Tests if the heap is empty\n * Returns: true is heap empty, false otherwise\n */\n bool empty()\n {\n return interval_heap_.empty();\n }\n\n /**\n * Clears the heap.\n */\n void clear()\n {\n \tinterval_heap_.clear();\n }\n\n void insert(const T& value)\n {\n \tif (interval_heap_.size()==capacity_) {\n \t\tT max;\n \t\tinterval_heap_.getMax(max);\n \t\tif (max\n#include \n#include \n#include \n#include \n#include \"center_chooser.h\"\n//#include \"char_bit_cnt.h\"\n#include \"dynamic_bitset.h\"\n#include \"heap.h\"\n#include \"result_set.h\"\n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\n//typedef unsigned ElementType;\n//typedef int DistanceType;\n\nclass HierarchicalClusteringIndex\n{\n\tprivate:\n\n\tint branching_;\n int trees_;\n int leaf_max_size_;\n\n\tsize_t size_;\n \tsize_t veclen_;\n\tsize_t size_at_build_;\n\tsize_t last_id_;\n\n\tvector ids_;\n\tvector* > points_;\n\tCenterChooser Centers_;\n\t\n\t//DynamicBitset removed_points_;\n\n\tvoid setDataset(vector >& dataset)\n {\n \tsize_ = dataset.size();\n \t//cout<<\"size_: \"<at(0).size();\n \t//last_id_ = 0;\n\n \t//ids_.clear();\n \t//removed_points_.clear();\n \t//removed_ = false;\n \t//removed_count_ = 0;\n\n\t\tpoints_.resize(size_);\n\t\tfor (size_t i=0;iat(0)< >& inputData, int b=32,int t=4,int leaf=100)\n {\n branching_ = b;\n trees_ = t;\n leaf_max_size_ = leaf;\n\n\t\tsetDataset(inputData);\n\n Centers_.setDataset(inputData);\n\t\tDynamicBitset checked(size_);\n }\n\n\n /**\n * Builds the index\n */\n void buildIndex()\n {\n if (branching_<2) {\n cerr<<\"Branching factor must be at least 2\"< indices(size_);\n for (int i=0; iat(0)<<\" \"<at(0)<<\" \"<< get_distance(0,1)<* point; /** Point data */\n };\n\n\n\n\tstruct Node\n {\n \n \tvector* pivot; /** The cluster center */\n\n \tsize_t pivot_index;\n \n vector childs; /** Child nodes (only for non-terminal nodes) */\n \n vector points; /** Node points (only for terminal nodes) */\n\n ~Node()\n {\n \tfor(size_t i=0; i~Node();\n \t}\n };\n };\n\n\ttypedef Node* NodePtr;\n\tstruct BranchStruct\n\t{\n \tNode* node; /* Tree node at which search resumes */\n \tint mindist; /* Minimum distance to query for all nodes below. */\n\n \tBranchStruct() {}\n \tBranchStruct(Node* aNode, int dist) : node(aNode), mindist(dist)\n \t{//cout<<\"branch node:\"<pivot_index <<\" \"<pivot_index<size();\n\t\tfor(int t=0; tat(t) ^ points_.at(p2)->at(t);\n\t\t\tdist += char_bit_cnt[y];\n\t\t}\n\t\treturn dist;\n\t}\n\n\t\n\tint get_distance(const vector* p1, const vector* p2)\n\t{\n\t\tint dist=0;\n\t\tint cbits = p1->size();\n\t\tfor(int t=0; tat(t) ^ p2->at(t);\n\t\t\tdist += char_bit_cnt[y];\n\t\t}\n\t\treturn dist;\n\t}\n//*/\n\n\tvoid computeLabels(int* indices, int indices_length, int* centers, int centers_length, int* labels)\n {\n for (int i=0; inew_dist) {\n labels[i] = j;\n dist = new_dist;\n }\n }\n }\n }\n\n\tvoid computeClustering(Node* node, int* indices, int indices_length)\n {\n\t\t//cout<<\"begin:\"<points.resize(indices_length);\n for (int i=0;ipoints[i].index = indices[i];\n \tnode->points[i].point = points_.at(indices[i]);\n }\n node->childs.clear();\n\t\t\t//cout<<\"indices:\"<points.size()< centers(branching_);\n std::vector labels(indices_length);\n\n int centers_length;\n\t\tCenters_.chooseCenters(branching_, indices, indices_length, ¢ers[0], centers_length);\n\t\t//cout<<\"choose centers_length : \"<points.resize(indices_length);\n for (int i=0;ipoints[i].index = indices[i];\n \tnode->points[i].point = points_.at(indices[i]);\n }\n node->childs.clear();\n\t\t\t//cout<<\" centers: \"<points.size()<childs.resize(branching_);\n int start = 0;\n int end = start;\n for (int i=0; ichilds[i] = new Node();\n node->childs[i]->pivot_index = centers[i];\n node->childs[i]->pivot = points_.at(centers[i]);\n node->childs[i]->points.clear();\n computeClustering(node->childs[i],indices+start, end-start);\n start=end;\n }\n }\n\n\ttypedef BranchStruct BranchSt;\n\n\tvoid findNN(NodePtr node, ResultSet& result, const vector* vec, int& checks,const int maxChecks,\n\t Heap* heap, DynamicBitset& checked)\n\t{\n\t if (node->childs.empty())\n\t {\n\t if (checks>=maxChecks) {\n\t if (result.full()) return;\n\t }\n\n\t for (size_t i=0; ipoints.size(); ++i)\n\t {\n\t PointInfo& pointInfo = node->points[i];\n\t if (checked.test(pointInfo.index)) continue;\n\t int dist = get_distance(pointInfo.point, vec);\n\t result.addPoint(dist, pointInfo.index);\n\t checked.set(pointInfo.index);\n\t ++checks;\n\t }\n\t }\n\t else {\n\t int* domain_distances = new int[branching_];\n\t int best_index = 0;\n\t domain_distances[best_index] = get_distance(vec, node->childs[best_index]->pivot);\n\t for (int i=1; ichilds[i]->pivot);\n\t if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i]));\n\t }\n\t }\n\t delete[] domain_distances;\n\t findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked);\n\t }\n\t }\n\n\n\n\tvoid findNeighborsWithRemoved(ResultSet& result, const vector* vec, const int maxChecks)\n {\n //int maxChecks = searchParams.checks;\n\n // Priority queue storing intermediate branches in the best-bin-first search\n Heap* heap = new Heap(size_);\n\n DynamicBitset checked(size_);\n int checks = 0;\n for (int i=0; ipopMin(branch) && (checks >& queries, vector< vector >& indices,\tvector< vector >& dists,size_t knn,const int maxChecks)\n {\n if (indices.size() < queries.size() ) indices.resize(queries.size());\n\t\tif (dists.size() < queries.size() ) dists.resize(queries.size());\n\n\t\tint count = 0;\n\n\t\tfor (int i = 0; i < (int)queries.size(); i++)\n\t\t{\n\t\t\t\t//resultSet.clear();\n\t\t\t\tResultSet resultSet(knn);\n\t\t\t\tfindNeighborsWithRemoved(resultSet, &queries.at(i), maxChecks);\n\t\t\t\tsize_t n = std::min(resultSet.size(), knn);\n\t\t\t\tindices[i].resize(n);\n\t\t\t\tdists[i].resize(n);\n\t\t\t\tif (n>0)\n\t\t\t\t{\n\t\t\t\t\tresultSet.copy(&indices.at(i), &dists.at(i), n);\n\t\t\t\t}\n\t\t\t\tcount += n;\n\t\t}\n\t\treturn count;\n }\n\n\tvector tree_roots_;\n};\n\n" }, { "path": "algorithms/AGH/src/random.h", "content": "#include \n#include \n#include \n#include \n/**\n * Seeds the random number generator\n * @param seed Random seed\n */\ninline void seed_random(unsigned int seed)\n{\n srand(seed);\n}\n\n/*\n * Generates a random double value.\n */\n/**\n * Generates a random double value.\n * @param high Upper limit\n * @param low Lower limit\n * @return Random double value\n */\ninline double rand_double(double high = 1.0, double low = 0)\n{\n return low + ((high-low) * (std::rand() / (RAND_MAX + 1.0)));\n}\n\n/**\n * Generates a random integer value.\n * @param high Upper limit\n * @param low Lower limit\n * @return Random integer value\n */\ninline int rand_int(int high = RAND_MAX, int low = 0)\n{\n return low + (int) ( double(high-low) * (std::rand() / (RAND_MAX + 1.0)));\n}\n\n\nclass RandomGenerator\n{\npublic:\n ptrdiff_t operator() (ptrdiff_t i) { return rand_int(i); }\n};\n\n\n/**\n * Random number generator that returns a distinct number from\n * the [0,n) interval each time.\n */\nclass UniqueRandom\n{\n std::vector vals_;\n int size_;\n int counter_;\n\npublic:\n /**\n * Constructor.\n * @param n Size of the interval from which to generate\n * @return\n */\n UniqueRandom(int n)\n {\n init(n);\n }\n\n /**\n * Initializes the number generator.\n * @param n the size of the interval from which to generate random numbers.\n */\n void init(int n)\n {\n static RandomGenerator generator;\n // create and initialize an array of size n\n vals_.resize(n);\n size_ = n;\n for (int i = 0; i < size_; ++i) vals_[i] = i;\n\n // shuffle the elements in the array\n std::random_shuffle(vals_.begin(), vals_.end(), generator);\n\n counter_ = 0;\n }\n\n /**\n * Return a distinct random integer in greater or equal to 0 and less\n * than 'n' on each call. It should be called maximum 'n' times.\n * Returns: a random integer\n */\n int next()\n {\n if (counter_ == size_) {\n return -1;\n }\n else {\n return vals_[counter_++];\n }\n }\n};\n\n\n\n" }, { "path": "algorithms/AGH/src/result_set.h", "content": "#include \n#include \n#include \n#include \n#include \n#include \n\n\nstruct DistanceIndex\n{\n DistanceIndex(int dist, int index) :\n dist_(dist), index_(index)\n {\n }\n bool operator<(const DistanceIndex& dist_index) const\n {\n return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);\n }\n int dist_;\n int index_;\n};\n\n\nclass ResultSet\n{\npublic:\n\t//typedef DistanceIndex DistIndex;\n\n\tResultSet(size_t capacity_) :\n capacity_(capacity_)\n {\n\t\t// reserving capacity to prevent memory re-allocations\n\t\tdist_index_.reserve(capacity_);\n \tclear();\n }\n\n ~ResultSet()\n {\n }\n\n /**\n * Clears the result set\n */\n void clear()\n {\n dist_index_.clear();\n worst_dist_ = std::numeric_limits::max();\n is_full_ = false;\n }\n\n /**\n *\n * @return Number of elements in the result set\n */\n size_t size() const\n {\n return dist_index_.size();\n }\n\n /**\n * Radius search result set always reports full\n * @return\n */\n bool full()\n {\n return is_full_;\n }\n\n /**\n * Add another point to result set\n * @param dist distance to point\n * @param index index of point\n * Pre-conditions: capacity_>0\n */\n void addPoint(int dist, int index)\n {\n \tif (dist>=worst_dist_) return;\n\n \tif (dist_index_.size()==capacity_) {\n \t\t// if result set if filled to capacity, remove farthest element\n \t\tstd::pop_heap(dist_index_.begin(), dist_index_.end());\n \tdist_index_.pop_back();\n \t}\n\n \t// add new element\n \tdist_index_.push_back(DistanceIndex(dist,index));\n \tif (is_full_) { // when is_full_==true, we have a heap\n \t\tstd::push_heap(dist_index_.begin(), dist_index_.end());\n \t}\n\n \tif (dist_index_.size()==capacity_) {\n \t\tif (!is_full_) {\n \t\t\tstd::make_heap(dist_index_.begin(), dist_index_.end());\n \tis_full_ = true;\n \t\t}\n \t\t// we replaced the farthest element, update worst distance\n \tworst_dist_ = dist_index_[0].dist_;\n }\n }\n\n /**\n * Copy indices and distances to output buffers\n * @param indices\n * @param dists\n * @param num_elements Number of elements to copy\n * @param sorted Indicates if results should be sorted\n */\n void copy(vector* indices, vector* dists, size_t num_elements)\n {\n \t\t// std::sort_heap(dist_index_.begin(), dist_index_.end());\n \t\t// sort seems faster here, even though dist_index_ is a heap\n \t//std::sort(dist_index_.begin(), dist_index_.end());\n\n \tsize_t n = std::min(dist_index_.size(), num_elements);\n \tfor (size_t i=0; iat(i) = dist_index_[i].index_;\n \t\tdists->at(i) = dist_index_[i].dist_;\n \t}\n }\n\n int worstDist() const\n {\n \treturn worst_dist_;\n }\n\nprivate:\n size_t capacity_;\n int worst_dist_;\n std::vector dist_index_;\n bool is_full_;\n};\n" }, { "path": "algorithms/Annoy/README.html", "content": "\n\n\n \n \n \n \n \n\n\n

Annoy

\n

Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for approximate nearest neighbor search with L2 or angular distance. The version here is based on Annoy's source code obtained on 5 April 2016. Please refer to Annoy at GitHub for more details.

\n

Prerequisites

\n
    \n
  • linux system
    • \n
  • A sample data (e.g., audio) has been downloaded, which includes its data points, query points, and ground truth results. Note that we need to change data format later. All data files downloaded should be put in the ./knn_benchmark/data directory.
    • \n
\n

Compile (Annoy/src)

\n

build the Annoy trees construction and Annoy search programs.

\n
make
\n

Construct Annoy trees and conduct search (DPG/script)

\n
run.sh
\n

Note that, in our experiment paper, we vary the number of data points explored to achieve the trade-off between search speed and search quality (recall). This is exactly the same with KGraph.

\n\n\n" }, { "path": "algorithms/Annoy/README.md", "content": "# Annoy \n\nAnnoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python\nbindings to search for approximate nearest neighbor search with L2 or angular\ndistance. The version here is based on Annoy's source code obtained on 5\nApril 2016. Please refer to\n[**Annoy** at GitHub](https://github.com/spotify/annoy) for more details.\n\n\n\n## Prerequisites\n\n- linux system \n- A\n [sample data ](https://github.com/DBWangGroupUNSW/nns_benchmark/tree/master/data)\n (e.g., audio) has been downloaded, which includes its data points, query\n points, and ground truth results. Note that we need to change data format\n later. All data files downloaded should be put in the `./knn_benchmark/data`\n directory.\n\n \n## Compile (Annoy/src)\n\nbuild the Annoy trees construction and Annoy search programs.\n\n```\nmake\n```\n\n\n## Construct Annoy trees and conduct search (DPG/script)\n\n```\nrun.sh\n```\n\nNote that, in our experiment paper, we vary the number of data points explored to achieve the trade-off between search speed and search quality (recall). This is exactly the same with KGraph.\n\n\n" }, { "path": "algorithms/Annoy/script/indexer.sh", "content": "data_path=$1 # ./fvecs\ntrees=$2 # the number of tree\nindex_path=$3 \nindextime_path=$4\n\ncd ../src\n\n./indexer -i ${index_path} -d ${data_path} -o ${indextime_path} -t ${trees}\n" }, { "path": "algorithms/Annoy/script/run.sh", "content": "\nk=20\nntree=50\n\nmkdir ../index \nsh ./indexer.sh ../../../data/audio_base.fvecs ${ntree} ../index/audio.${ntree}tree ../index/index_${ntree}.txt\n\nmkdir ../result\n\nsh ./searcher.sh ../index/audio.${ntree}tree ../../../data/audio_query.fvecs ../../../data/audio_groundtruth.fvecs ${k} ../result/audio_${ntree}tree.txt\n\n" }, { "path": "algorithms/Annoy/script/searcher.sh", "content": "index_path=$1\nquery_path=$2 # ./fvecs\ngnd_path=$3 # ./ivecs\nk=$4\noutput_path=$5\n\ncd ../src\n\nfor stop in 100 300 400 500 800 1000\ndo\n\n./searcher -i ${index_path} -q ${query_path} -g ${gnd_path} -o ${output_path} -k ${k} -c ${stop} \ndone\n\n\n" }, { "path": "algorithms/Annoy/src/Makefile", "content": "CC=g++\nCCFLAGS=-O3 -lm -fPIC -fomit-frame-pointer -fno-stack-protector \nCPP_FILES=annoy.cpp\nOBJ_FILES=$(CPP_FILES:%.cpp=%.o)\n\n# Compile a single file.\n%.o: %.cpp %.h\n\t$(CC) $(CCFLAGS) -c $<\n\n# Main target.\nall: indexer searcher\n\trm -f *.o\n\nindexer: $(OBJ_FILES) indexer.o\n\trm -f $@\n\tg++ -fPIC $(OBJ_FILES) indexer.o -o $@\n\nsearcher: $(OBJ_FILES) searcher.o\n\trm -f $@\n\tg++ -fPIC $(OBJ_FILES) searcher.o -o $@\n\nclean: \n\trm -f *~ *.o\n\trm indexer searcher\n\n# Declare phony targets.\n.PHONY: all clean\n\n" }, { "path": "algorithms/Annoy/src/annoy.cpp", "content": "// Copyright (c) 2013 Spotify AB\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\"); you may not\n// use this file except in compliance with the License. You may obtain a copy of\n// the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n// License for the specific language governing permissions and limitations under\n// the License.\n\n#ifndef ANNOYLIB_H\n#define ANNOYLIB_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef __MINGW32__\n#include \"mman.h\"\n#include \n#else\n#include \n#endif\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \nusing namespace std;\nusing std::cout;\nusing std::endl;\n\n// This allows others to supply their own logger / error printer without\n// requiring Annoy to import their headers. See RcppAnnoy for a use case.\n#ifndef __ERROR_PRINTER_OVERRIDE__\n #define showUpdate(...) { fprintf(stderr, __VA_ARGS__ ); }\n#else\n #define showUpdate(...) { __ERROR_PRINTER_OVERRIDE__( __VA_ARGS__ ); }\n#endif\n\n#ifndef ANNOY_NODE_ATTRIBUTE\n #define ANNOY_NODE_ATTRIBUTE __attribute__((__packed__))\n // TODO: this is turned on by default, but may not work for all architectures! Need to investigate.\n#endif\n\n\nusing std::vector;\nusing std::string;\nusing std::pair;\nusing std::numeric_limits;\nusing std::make_pair;\n\nstruct RandRandom {\n // Default implementation of annoy-specific random number generator that uses rand() from standard library.\n // Owned by the AnnoyIndex, passed around to the distance metrics\n inline int flip() {\n // Draw random 0 or 1\n return rand() & 1;\n }\n inline size_t index(size_t n) {\n // Draw random integer between 0 and n-1 where n is at most the number of data points you have\n return rand() % n;\n }\n};\n\ntemplate\ninline T get_norm(T* v, int f) {\n T sq_norm = 0;\n for (int z = 0; z < f; z++)\n sq_norm += v[z] * v[z];\n return sqrt(sq_norm);\n}\n\ntemplate\ninline void normalize(T* v, int f) {\n T norm = get_norm(v, f);\n for (int z = 0; z < f; z++)\n v[z] /= norm;\n}\n\ntemplate\ninline void two_means(const vector& nodes, int f, Random& random, bool cosine, T* iv, T* jv) {\n /*\n This algorithm is a huge heuristic. Empirically it works really well, but I\n can't motivate it well. The basic idea is to keep two centroids and assign\n points to either one of them. We weight each centroid by the number of points\n assigned to it, so to balance it. \n */\n static int iteration_steps = 200;\n size_t count = nodes.size();\n\n size_t i = random.index(count);\n size_t j = random.index(count-1);\n j += (j >= i); // ensure that i != j\n std::copy(&nodes[i]->v[0], &nodes[i]->v[f], &iv[0]);\n std::copy(&nodes[j]->v[0], &nodes[j]->v[f], &jv[0]);\n if (cosine) { normalize(&iv[0], f); normalize(&jv[0], f); }\n\n int ic = 1, jc = 1;\n for (int l = 0; l < iteration_steps; l++) {\n size_t k = random.index(count);\n T di = ic * Distance::distance(&iv[0], nodes[k]->v, f),\n dj = jc * Distance::distance(&jv[0], nodes[k]->v, f);\n T norm = cosine ? get_norm(nodes[k]->v, f) : 1.0;\n if (di < dj) {\n for (int z = 0; z < f; z++)\n\tiv[z] = (iv[z] * ic + nodes[k]->v[z] / norm) / (ic + 1);\n ic++;\n } else if (dj < di) {\n for (int z = 0; z < f; z++)\n\tjv[z] = (jv[z] * jc + nodes[k]->v[z] / norm) / (jc + 1);\n jc++;\n }\n }\n}\n\n\nstruct Angular {\n template\n struct ANNOY_NODE_ATTRIBUTE Node {\n /*\n * We store a binary tree where each node has two things\n * - A vector associated with it\n * - Two children\n * All nodes occupy the same amount of memory\n * All nodes with n_descendants == 1 are leaf nodes.\n * A memory optimization is that for nodes with 2 <= n_descendants <= K,\n * we skip the vector. Instead we store a list of all descendants. K is\n * determined by the number of items that fits in the space of the vector.\n * For nodes with n_descendants == 1 the vector is a data point.\n * For nodes with n_descendants > K the vector is the normal of the split plane.\n * Note that we can't really do sizeof(node) because we cheat and allocate\n * more memory to be able to fit the vector outside\n */\n S n_descendants;\n S children[2]; // Will possibly store more than 2\n T v[1]; // We let this one overflow intentionally. Need to allocate at least 1 to make GCC happy\n };\n template\n static inline T distance(const T* x, const T* y, int f) {\n // want to calculate (a/|a| - b/|b|)^2\n // = a^2 / a^2 + b^2 / b^2 - 2ab/|a||b|\n // = 2 - 2cos\n T pp = 0, qq = 0, pq = 0;\n for (int z = 0; z < f; z++, x++, y++) {\n pp += (*x) * (*x);\n qq += (*y) * (*y);\n pq += (*x) * (*y);\n }\n T ppqq = pp * qq;\n if (ppqq > 0) return 2.0 - 2.0 * pq / sqrt(ppqq);\n else return 2.0; // cos is 0\n }\n template\n static inline T margin(const Node* n, const T* y, int f) {\n T dot = 0;\n for (int z = 0; z < f; z++)\n dot += n->v[z] * y[z];\n return dot;\n }\n template\n static inline bool side(const Node* n, const T* y, int f, Random& random) {\n T dot = margin(n, y, f);\n if (dot != 0)\n return (dot > 0);\n else\n return random.flip();\n }\n template\n static inline void create_split(const vector*>& nodes, int f, Random& random, Node* n) {\n vector best_iv(f, 0), best_jv(f, 0); // TODO: avoid allocation\n two_means >(nodes, f, random, true, &best_iv[0], &best_jv[0]);\n for (int z = 0; z < f; z++)\n n->v[z] = best_iv[z] - best_jv[z];\n normalize(n->v, f);\n }\n template\n static inline T normalized_distance(T distance) {\n // Used when requesting distances from Python layer\n // Turns out sometimes the squared distance is -0.0\n // so we have to make sure it's a positive number.\n return sqrt(std::max(distance, T(0)));\n }\n};\n\nstruct Euclidean {\n template\n struct ANNOY_NODE_ATTRIBUTE Node {\n S n_descendants;\n T a; // need an extra constant term to determine the offset of the plane\n S children[2];\n T v[1];\n };\n\n template\n static inline T distance(const T* x, const T* y, int f) {\n T d = 0.0;\n for (int i = 0; i < f; i++, x++, y++)\n d += ((*x) - (*y)) * ((*x) - (*y));\n return d;\n }\n template\n static inline T margin(const Node* n, const T* y, int f) {\n T dot = n->a;\n for (int z = 0; z < f; z++)\n dot += n->v[z] * y[z];\n return dot;\n }\n template\n static inline bool side(const Node* n, const T* y, int f, Random& random) {\n T dot = margin(n, y, f);\n if (dot != 0)\n return (dot > 0);\n else\n return random.flip();\n }\n template\n static inline void create_split(const vector*>& nodes, int f, Random& random, Node* n) {\n vector best_iv(f, 0), best_jv(f, 0);\n two_means >(nodes, f, random, false, &best_iv[0], &best_jv[0]);\n\n for (int z = 0; z < f; z++)\n n->v[z] = best_iv[z] - best_jv[z];\n normalize(n->v, f);\n n->a = 0.0;\n for (int z = 0; z < f; z++)\n n->a += -n->v[z] * (best_iv[z] + best_jv[z]) / 2;\n }\n template\n static inline T normalized_distance(T distance) {\n return sqrt(std::max(distance, T(0)));\n }\n};\n\ntemplate\nclass AnnoyIndexInterface {\n public:\n virtual ~AnnoyIndexInterface() {};\n virtual void add_item(S item, const T* w) = 0;\n virtual void build(int q) = 0;\n virtual bool save(const char* filename) = 0;\n virtual void unload() = 0;\n virtual bool load(const char* filename) = 0;\n virtual T get_distance(S i, S j) = 0;\n virtual void get_nns_by_item(S item, size_t n, size_t search_k, vector* result, vector* distances) = 0;\n virtual void get_nns_by_vector(const T* w, size_t n, size_t search_k, vector* result, vector* distances) = 0;\n virtual S get_n_items() = 0;\n virtual void verbose(bool v) = 0;\n virtual void get_item(S item, T* v) = 0;\n};\n\ntemplate\n class AnnoyIndex : public AnnoyIndexInterface {\n /*\n * We use random projection to build a forest of binary trees of all items.\n * Basically just split the hyperspace into two sides by a hyperplane,\n * then recursively split each of those subtrees etc.\n * We create a tree like this q times. The default q is determined automatically\n * in such a way that we at most use 2x as much memory as the vectors take.\n */\npublic:\n typedef Distance D;\n typedef typename D::template Node Node;\n\nprotected:\n int _f;\n size_t _s;\n S _n_items;\n Random _random;\n void* _nodes; // Could either be mmapped, or point to a memory buffer that we reallocate\n S _n_nodes;\n S _nodes_size;\n vector _roots;\n S _K;\n bool _loaded;\n bool _verbose;\n int _fd;\npublic:\n\n AnnoyIndex(int f) : _random() {\n _f = f;\n _s = offsetof(Node, v) + f * sizeof(T); // Size of each node\n _verbose = false;\n _K = (_s - offsetof(Node, children)) / sizeof(S); // Max number of descendants to fit into node\n reinitialize(); // Reset everything\n }\n ~AnnoyIndex() {\n unload();\n }\n\n void add_item(S item, const T* w) {\n _allocate_size(item + 1);\n Node* n = _get(item);\n\n n->children[0] = 0;\n n->children[1] = 0;\n n->n_descendants = 1;\n\n for (int z = 0; z < _f; z++)\n n->v[z] = w[z];\n\n if (item >= _n_items)\n _n_items = item + 1;\n }\n\n void build(int q) {\n if (_loaded) {\n // TODO: throw exception\n showUpdate(\"You can't build a loaded index\\n\");\n return;\n }\n cerr << endl;\n _n_nodes = _n_items;\n while (1) {\n if (q == -1 && _n_nodes >= _n_items * 2)\n break;\n if (q != -1 && _roots.size() >= (size_t)q)\n break;\n if (_verbose) showUpdate(\"pass %zd...\\n\", _roots.size());\n\n vector indices;\n for (S i = 0; i < _n_items; i++)\n indices.push_back(i);\n\n _roots.push_back(_make_tree(indices));\n cerr << \"*\";\n }\n cerr << endl;\n \n // Also, copy the roots into the last segment of the array\n // This way we can load them faster without reading the whole file\n _allocate_size(_n_nodes + (S)_roots.size());\n for (size_t i = 0; i < _roots.size(); i++)\n memcpy(_get(_n_nodes + (S)i), _get(_roots[i]), _s);\n _n_nodes += _roots.size();\n\n if (_verbose) showUpdate(\"has %d nodes\\n\", _n_nodes);\n }\n\n bool save(const char* filename) {\n FILE *f = fopen(filename, \"w\");\n if (f == NULL)\n return false;\n\n fwrite(_nodes, _s, _n_nodes, f);\n fclose(f);\n/*\n unload();\n return load(filename);*/\n\t return true;\n }\n\n void reinitialize() {\n _fd = 0;\n _nodes = NULL;\n _loaded = false;\n _n_items = 0;\n _n_nodes = 0;\n _nodes_size = 0;\n _roots.clear();\n }\n\n void unload() {\n if (_fd) {\n // we have mmapped data\n close(_fd);\n off_t size = _n_nodes * _s;\n munmap(_nodes, size);\n } else if (_nodes) {\n // We have heap allocated data\n free(_nodes);\n }\n reinitialize();\n if (_verbose) showUpdate(\"unloaded\\n\");\n }\n\n bool load(const char* filename) {\n FILE *f = fopen(filename, \"r\");\n\t\tfseek(f, 0, SEEK_END);\n\t\toff_t size = ftell(f);\n fseek(f, 0, SEEK_SET);\n _n_nodes = (S)(size / _s);\n _nodes = realloc(_nodes, _s * _n_nodes);\n\t\tfread(_nodes, _s, _n_nodes, f);\n\n // Find the roots by scanning the end of the file and taking the nodes with most descendants\n S m = -1;\n for (S i = _n_nodes - 1; i >= 0; i--) {\n S k = _get(i)->n_descendants;\n if (m == -1 || k == m) {\n _roots.push_back(i);\n m = k;\n } else {\n break;\n }\n }\n // hacky fix: since the last root precedes the copy of all roots, delete it\n if (_roots.size() > 1 && _get(_roots.front())->children[0] == _get(_roots.back())->children[0])\n _roots.pop_back();\n _loaded = true;\n _n_items = m;\n if (_verbose) showUpdate(\"found %lu roots with degree %d\\n\", _roots.size(), m);\n return true;\n }\n\n T get_distance(S i, S j) {\n const T* x = _get(i)->v;\n const T* y = _get(j)->v;\n return D::distance(x, y, _f);\n }\n\n void get_nns_by_item(S item, size_t n, size_t search_k, vector* result, vector* distances) {\n const Node* m = _get(item);\n _get_all_nns(m->v, n, search_k, result, distances);\n }\n\n void get_nns_by_vector(const T* w, size_t n, size_t search_k, vector* result, vector* distances) {\n _get_all_nns(w, n, search_k, result, distances);\n }\n S get_n_items() {\n return _n_items;\n }\n void verbose(bool v) {\n _verbose = v;\n }\n\n void get_item(S item, T* v) {\n Node* m = _get(item);\n std::copy(&m->v[0], &m->v[_f], v);\n }\n\nprotected:\n void _allocate_size(S n) {\n if (n > _nodes_size) {\n const double reallocation_factor = 1.3;\n S new_nodes_size = std::max(n,\n\t\t\t\t (S)((_nodes_size + 1) * reallocation_factor));\n if (_verbose) showUpdate(\"Reallocating to %d nodes\\n\", new_nodes_size);\n _nodes = realloc(_nodes, _s * new_nodes_size);\n memset((char *)_nodes + (_nodes_size * _s)/sizeof(char), 0, (new_nodes_size - _nodes_size) * _s);\n _nodes_size = new_nodes_size;\n }\n }\n\n inline Node* _get(S i) {\n return (Node*)((uint8_t *)_nodes + (_s * i));\n }\n\n S _make_tree(const vector& indices) {\n if (indices.size() == 1)\n return indices[0];\n\n if (indices.size() <= (size_t)_K) {\n _allocate_size(_n_nodes + 1);\n S item = _n_nodes++;\n Node* m = _get(item);\n m->n_descendants = (S)indices.size();\n\n // Using std::copy instead of a loop seems to resolve issues #3 and #13,\n // probably because gcc 4.8 goes overboard with optimizations.\n std::copy(indices.begin(), indices.end(), m->children);\n return item;\n }\n\n vector children;\n for (size_t i = 0; i < indices.size(); i++) {\n S j = indices[i];\n Node* n = _get(j);\n if (n)\n children.push_back(n);\n }\n\n \tvector children_indices[2];\n \tNode* m = (Node*)malloc(_s); // TODO: avoid\n \tD::create_split(children, _f, _random, m);\n\n \tfor (size_t i = 0; i < indices.size(); i++) {\n \tS j = indices[i];\n \tNode* n = _get(j);\n \tif (n) {\n \tbool side = D::side(m, n->v, _f, _random);\n \tchildren_indices[side].push_back(j);\n \t}\n }\n\n // If we didn't find a hyperplane, just randomize sides as a last option\n while (children_indices[0].size() == 0 || children_indices[1].size() == 0) {\n if (_verbose && indices.size() > 100000)\n showUpdate(\"Failed splitting %lu items\\n\", indices.size());\n\n children_indices[0].clear();\n children_indices[1].clear();\n\n // Set the vector to 0.0\n for (int z = 0; z < _f; z++)\n m->v[z] = 0.0;\n\n for (size_t i = 0; i < indices.size(); i++) {\n S j = indices[i];\n // Just randomize...\n children_indices[_random.flip()].push_back(j);\n }\n }\n\n int flip = (children_indices[0].size() > children_indices[1].size());\n //std::cout<n_descendants = (S)indices.size();\n for (int side = 0; side < 2; side++)\n // run _make_tree for the smallest child first (for cache locality)\n m->children[side^flip] = _make_tree(children_indices[side^flip]);\n\n _allocate_size(_n_nodes + 1);\n S item = _n_nodes++;\n memcpy(_get(item), m, _s);\n free(m);\n\n return item;\n }\n\n void _get_all_nns(const T* v, size_t n, size_t search_k, vector* result, vector* distances) {\n std::priority_queue > q;\n\n if (search_k == (size_t)-1)\n search_k = n * _roots.size(); // slightly arbitrary default value\n\n for (size_t i = 0; i < _roots.size(); i++) {\n q.push(make_pair(numeric_limits::infinity(), _roots[i]));\n }\n\n vector nns;\n while (nns.size() < search_k && !q.empty()) {\n const pair& top = q.top();\n T d = top.first;\n S i = top.second;\n Node* nd = _get(i);\n q.pop();\n if (nd->n_descendants == 1) {\n nns.push_back(i);\n } else if (nd->n_descendants <= _K) {\n const S* dst = nd->children;\n nns.insert(nns.end(), dst, &dst[nd->n_descendants]);\n } else {\n T margin = D::margin(nd, v, _f);\n q.push(make_pair(std::min(d, +margin), nd->children[1]));\n q.push(make_pair(std::min(d, -margin), nd->children[0]));\n }\n }\n\n // Get distances for all items\n // To avoid calculating distance multiple times for any items, sort by id\n sort(nns.begin(), nns.end());\n vector > nns_dist;\n S last = -1;\n for (size_t i = 0; i < nns.size(); i++) {\n S j = nns[i];\n if (j == last)\n continue;\n last = j;\n nns_dist.push_back(make_pair(D::distance(v, _get(j)->v, _f), j));\n }\n\n size_t m = nns_dist.size();\n size_t p = n < m ? n : m; // Return this many items\n std::partial_sort(&nns_dist[0], &nns_dist[p], &nns_dist[m]);\n for (size_t i = 0; i < p; i++) {\n if (distances)\n distances->push_back(D::normalized_distance(nns_dist[i].first));\n result->push_back(nns_dist[i].second);\n }\n\t\tresult->push_back(m);\n }\n};\n\n#endif\n// vim: tabstop=2 shiftwidth=2\n" }, { "path": "algorithms/Annoy/src/data.h", "content": "#ifndef WDONG_KGRAPH_DATA\n#define WDONG_KGRAPH_DATA\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef __GNUC__\n#ifdef __AVX__\n#define KGRAPH_MATRIX_ALIGN 32\n#else\n#ifdef __SSE2__\n#define KGRAPH_MATRIX_ALIGN 16\n#else\n#define KGRAPH_MATRIX_ALIGN 4\n#endif\n#endif\n#endif\n\nnamespace kgraph {\n\n /// L2 square distance with AVX instructions.\n /** AVX instructions have strong alignment requirement for t1 and t2.\n */\n extern float float_l2sqr_avx (float const *t1, float const *t2, unsigned dim);\n /// L2 square distance with SSE2 instructions.\n extern float float_l2sqr_sse2 (float const *t1, float const *t2, unsigned dim);\n /// L2 square distance for uint8_t with SSE2 instructions (for SIFT).\n extern float uint8_l2sqr_sse2 (uint8_t const *t1, uint8_t const *t2, unsigned dim);\n\n using std::vector;\n using std::runtime_error;\n\n /// namespace for various distance metrics.\n namespace metric {\n /// L2 square distance.\n struct l2sqr {\n template \n static float apply (T const *t1, T const *t2, unsigned dim) {\n\t //\t\t\t\tcout<<\"apply \\n\";\n float r = 0;\n for (unsigned i = 0; i < dim; ++i) {\n float v = float(t1[i]) - float(t2[i]);\n v *= v;\n r += v;\n }\n return r;\n }\n };\n /// L2 distance.\n struct l2 {\n template \n static float apply (T const *t1, T const *t2, unsigned dim) {\n return sqrt(l2sqr::apply(t1, t2, dim));\n }\n };\n }\n\n /// Matrix data.\n template \n class Matrix {\n unsigned col;\n unsigned row;\n size_t stride;\n char *data;\n\n void reset (unsigned r, unsigned c) {\n row = r;\n col = c;\n stride = (sizeof(T) * c + A - 1) / A * A;\n /*\n data.resize(row * stride);\n */\n if (data) free(data);\n data = (char *)memalign(A, row * stride); // SSE instruction needs data to be aligned\n if (!data) throw runtime_error(\"memalign\");\n }\n public:\n Matrix (): col(0), row(0), stride(0), data(0) {}\n Matrix (unsigned r, unsigned c): data(0) {\n reset(r, c);\n }\n ~Matrix () {\n if (data) free(data);\n }\n unsigned size () const {\n return row;\n }\n unsigned dim () const {\n return col;\n }\n size_t step () const {\n return stride;\n }\n void resize (unsigned r, unsigned c) {\n reset(r, c);\n }\n T const *operator [] (unsigned i) const {\n return reinterpret_cast(&data[stride * i]);\n }\n T *operator [] (unsigned i) {\n return reinterpret_cast(&data[stride * i]);\n }\n void zero () {\n memset(data, 0, row * stride);\n }\n void load (const std::string &path, unsigned dim, unsigned skip = 0, unsigned gap = 0) {\n std::ifstream is(path.c_str(), std::ios::binary);\n BOOST_VERIFY(is);\n is.seekg(0, std::ios::end);\n size_t size = is.tellg();\n size -= skip;\n unsigned line = sizeof(T) * dim + gap;\n unsigned N = size / line;\n reset(N, dim);\n zero();\n is.seekg(skip, std::ios::beg);\n for (unsigned i = 0; i < N; ++i) {\n is.read(&data[stride * i], sizeof(T) * dim);\n is.seekg(gap, std::ios::cur);\n }\n BOOST_VERIFY(is);\n }\n\n void load_lshkit (std::string const &path) {\n static const unsigned LSHKIT_HEADER = 3;\n std::ifstream is(path.c_str(), std::ios::binary);\n unsigned header[LSHKIT_HEADER]; /* entry size, row, col */\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n BOOST_VERIFY(header[0] == sizeof(T));\n is.close();\n unsigned D = header[2];\n unsigned skip = LSHKIT_HEADER * sizeof(unsigned);\n unsigned gap = 0;\n load(path, D, skip, gap);\n }\n\n void save_lshkit (std::string const &path) {\n std::ofstream os(path.c_str(), std::ios::binary);\n unsigned header[3];\n assert(sizeof header == 3*4);\n header[0] = sizeof(T);\n header[1] = row;\n header[2] = col;\n os.write((const char *)header, sizeof(header));\n for (unsigned i = 0; i < row; ++i) {\n os.write(&data[stride * i], sizeof(T) * col);\n }\n }\n };\n\n /// Matrix proxy to interface with 3rd party libraries (FLANN, OpenCV, NumPy).\n template \n class MatrixProxy {\n unsigned rows;\n unsigned cols; // # elements, not bytes, in a row,\n size_t stride; // # bytes in a row, >= cols * sizeof(element)\n uint8_t const *data;\n public:\n MatrixProxy (Matrix const &m)\n : rows(m.size()), cols(m.dim()), stride(m.step()), data(reinterpret_cast(m[0])) {\n }\n\n#ifndef __AVX__\n#ifdef FLANN_DATASET_H_\n /// Construct from FLANN matrix.\n MatrixProxy (flann::Matrix const &m)\n : rows(m.rows), cols(m.cols), stride(m.stride), data(m.data) {\n BOOST_VERIFY(stride % A == 0);\n }\n#endif\n#ifdef __OPENCV_CORE_HPP__\n /// Construct from OpenCV matrix.\n MatrixProxy (cv::Mat const &m)\n : rows(m.rows), cols(m.cols), stride(m.step), data(m.data) {\n BOOST_VERIFY(stride % A == 0);\n }\n#endif\n#ifdef NPY_NDARRAYOBJECT_H\n /// Construct from NumPy matrix.\n MatrixProxy (PyArrayObject *obj) {\n BOOST_VERIFY(obj->nd == 2);\n rows = obj->dimensions[0];\n cols = obj->dimensions[1];\n stride = obj->strides[0];\n data = reinterpret_cast(obj->data);\n BOOST_VERIFY(obj->descr->elsize == sizeof(DATA_TYPE));\n BOOST_VERIFY(stride % A == 0);\n BOOST_VERIFY(stride >= cols * sizeof(DATA_TYPE));\n }\n#endif\n#endif\n unsigned size () const {\n return rows;\n }\n unsigned dim () const {\n return cols;\n }\n DATA_TYPE const *operator [] (unsigned i) const {\n return reinterpret_cast(data + stride * i);\n }\n };\n\n /// Oracle for matrix data.\n /** DATA_TYPE can be Matrix or MatrixProxy,\n * DIST_TYPE should be one class within the namespace kgraph.metric.\n */\n template \n class MatrixOracle: public kgraph::IndexOracle {\n MatrixProxy proxy;\n public:\n class SearchOracle: public kgraph::SearchOracle {\n MatrixProxy proxy;\n DATA_TYPE const *query;\n public:\n SearchOracle (MatrixProxy const &p, DATA_TYPE const *q): proxy(p), query(q) {\n }\n virtual unsigned size () const {\n return proxy.size();\n }\n virtual float operator () (unsigned i) const {\n return DIST_TYPE::apply(proxy[i], query, proxy.dim());\n }\n };\n template \n MatrixOracle (MATRIX_TYPE const &m): proxy(m) {\n }\n virtual unsigned size () const {\n return proxy.size();\n }\n virtual float operator () (unsigned i, unsigned j) const {\n return DIST_TYPE::apply(proxy[i], proxy[j], proxy.dim());\n }\n\n\tvirtual float angle (unsigned orign, unsigned pivot, unsigned x) const { // return cos(\\theta), where \\theta is the angle between vector orign - pivot and orign - x\n\t float dot_prod = 0.0, norm = 0.0, norm2 = 0.0;\n\t float pi = 3.1415926;\n\t for (unsigned k = 0; k < proxy.dim(); ++k){\n\t dot_prod += (float)(proxy[pivot][k] - proxy[orign][k])* (float)(proxy[x][k] - proxy[orign][k]);\n\t norm += (float)(proxy[x][k] - proxy[orign][k]) * (proxy[x][k] - proxy[orign][k]);\n\t norm2 += (float)(proxy[pivot][k] - proxy[orign][k]) * (proxy[pivot][k] - proxy[orign][k]);\n\t }\n\t //\t if (dot_prod / sqrt(norm * norm2) < 0.001){\n\t // fprintf(stderr, \"IN: %d %d %d\", orign, pivot, x);\n\t //}\n\t //fprintf(stderr, \"%f \", dot_prod);\n\t //fprintf(stderr, \"%f\\n\", sqrt ( norm1 * norm2 ));\n\t return acos(dot_prod / sqrt(norm * norm2) ) * 180 / pi;\n\t}\n\tSearchOracle query (DATA_TYPE const *query) const {\n return SearchOracle(proxy, query);\n }\n };\n\n inline float AverageRecall (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n // compare\n unsigned found = 0;\n unsigned gs_n = 0;\n unsigned re_n = 0;\n while ((gs_n < K) && (re_n < K)) {\n if (gs_row[gs_n] < re_row[re_n]) {\n ++gs_n;\n }\n else if (gs_row[gs_n] == re_row[re_n]) {\n ++found;\n ++gs_n;\n ++re_n;\n }\n else {\n throw runtime_error(\"distance is unstable\");\n }\n }\n sum += float(found) / K;\n }\n return sum / result.size();\n }\n\tinline void AveragePrecision(Matrix const &gs, Matrix const &result, float *precision) {\n\t\tunsigned K = result.dim();\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n unsigned found = 0;\n\t\tunsigned gs_n = 0;\n unsigned re_n = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\twhile ((gs_n < K) && (re_n < K)) {\n if (gs_row[gs_n] < re_row[re_n]) {\n ++gs_n;\n }\n else if (gs_row[gs_n] == re_row[re_n]) {\n ++found;\n ++gs_n;\n ++re_n;\n }\n else {\n throw runtime_error(\"distance is unstable\");\n }\n }\n\t\t\tif (gs_row[0] == re_row[0]) {\n ++found;\n\n }\n }\n *precision = found / result.size();\n }\n\n\tinline void AverageAccuracy(Matrix const &gs, Matrix const &result, float *accuracy) \n\t{\n unsigned K = result.dim();\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n\n\t\tfloat avg = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n \tfloat dist = 0;\n\t\t\tunsigned gs_n = 0;\n unsigned re_n = 0;\n while ((gs_n < K) && (re_n < K)) {\n float d=(re_row[re_n]-gs_row[gs_n])/gs_row[gs_n];\n\t\t\t\tif(d<=4)\n\t\t\t\t\tdist += d;\n\t\t\t\telse\n\t\t\t\t\tdist+=4; \n \t\t\t\t++gs_n;\n ++re_n;\n\t\t\t}\n\t\t\tavg += dist/K;\n }\n *accuracy=avg / result.size();\n }\n\n\tinline float compute_number_closer (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tfor (unsigned gs_n=0;gs_n < K ;gs_n++)\n\t\t\t{\n\t\t\t\tfor(unsigned re_n=0; re_n < K; re_n++)\n\t\t\t\t{\n\t\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t\t{\n\t\t\t\t\t\trate += (float)(gs_n+1)/(re_n+1);\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n sum += rate/K;\n }\n return sum / result.size();\n }\n\n\tinline float compute_mean_reciprocal_rank (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tfor(unsigned re_n=0; re_n < K; re_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[0]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\trate += 1.0/(re_n+1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n sum += rate;\n }\n return sum / result.size();\n }\n\n\n\tinline float compute_mean_average_precision (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tunsigned re_n = 0;\n\t\t\tint count = 0;\n\t\t\tfor(unsigned gs_n=0; gs_n < K; gs_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\tcount++;\n\t\t\t\t\trate += 1.0*(count)/(gs_n+1);\n\t\t\t\t\tre_n ++;\n\t\t\t\t}\n\t\t\t}\n sum += rate/K;\n }\n return sum / result.size();\n }\n\n\tinline float compute_discounted_culmulative_gain (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tunsigned re_n = 0;\n\t\t\tfor(unsigned gs_n=0; gs_n < K; gs_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\trate += 1.0/log2(gs_n+2);\n\t\t\t\t\tre_n ++;\n\t\t\t\t}\n\t\t\t}\n sum += rate;\n }\n return sum / result.size();\n }\n\n inline void AverageMinDist (Matrix const &gs, Matrix const &result, float *MinDist,float *AvgDist,float *MaxDist) {\n\n BOOST_VERIFY(gs.size() >= result.size());\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n *MinDist += re_row[0]-gs_row[0];\n *MaxDist += abs(re_row[result.dim()]-gs_row[0]);\n for(unsigned j=0;j\n inline float l2sqr::apply (float const *t1, float const *t2, unsigned dim) {\n return float_l2sqr_avx(t1, t2, dim);\n }\n}}\n#else\n#ifdef __SSE2__\nnamespace kgraph { namespace metric {\n template <>\n inline float l2sqr::apply (float const *t1, float const *t2, unsigned dim) {\n return float_l2sqr_sse2(t1, t2, dim);\n }\n template <>\n inline float l2sqr::apply (uint8_t const *t1, uint8_t const *t2, unsigned dim) {\n return uint8_l2sqr_sse2(t1, t2, dim);\n }\n}}\n#endif\n#endif\n#endif\n#endif\n\n\n\n#endif\n\n" }, { "path": "algorithms/Annoy/src/indexer.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"annoy.cpp\"\n#include \"kissrandom.h\"\n\nusing namespace std;\nusing std::set;\n\nstruct item{\n\nunsigned id;\n};\n\nfloat diff_timeval(timeval t1, timeval t2) {\n return (float) (t1.tv_sec - t2.tv_sec) + (t1.tv_usec - t2.tv_usec) * 1e-6;\n}\n\n//static const unsigned LSHKIT_HEADER = 3;\n\ntypedef float T;\ntypedef unsigned S;\n\nfloat* load_data(const char* data_path, int* len, int* d)\n{\n\t// start to read data \n\tFILE * dfp = fopen(data_path, \"rb\");\n\tint dim;\n\tfread(&dim,sizeof(int),1,dfp);\n\tfseek(dfp, 0, SEEK_END);\n\toff_t size = ftell(dfp);\n fseek(dfp, 0, SEEK_SET);\n int n = (int)(size / (sizeof(int)+ sizeof(float)*dim));\n\t*d = dim;\n\t*len = n;\n\tfloat * data = new float [n*dim];\n\tfor (int i=0;i* index = new AnnoyIndex(dim);\n for (unsigned i = 0; i < N; ++i) { \n index->add_item(i, &data[i*dim]);//(T*) (data +i*dim*sizeof(T))\n }\n\n \ttimeval start;\n\tgettimeofday(&start, NULL);\n index->build(trees);\n\ttimeval end;\n\tgettimeofday(&end, NULL);\n\tT index_time = diff_timeval(end, start);\n\tFILE *ofp = fopen(output_path, \"a+\");\n fprintf(ofp,\"%.2f #trees_%d \\n\",index_time,trees);\t\n\tfclose(ofp);\n\n index->save(index_path);\n\n delete []data; \n\tdelete index;\n\n}\n" }, { "path": "algorithms/Annoy/src/kissrandom.h", "content": "#ifndef KISSRANDOM_H\n#define KISSRANDOM_H\n\n#include \n\n// KISS = \"keep it simple, stupid\", but high quality random number generator\n// http://www0.cs.ucl.ac.uk/staff/d.jones/GoodPracticeRNG.pdf -> \"Use a good RNG and build it into your code\"\n// http://mathforum.org/kb/message.jspa?messageID=6627731\n// https://de.wikipedia.org/wiki/KISS_(Zufallszahlengenerator)\n\n// 32 bit KISS\nstruct Kiss32Random {\n uint32_t x;\n uint32_t y;\n uint32_t z;\n uint32_t c;\n\n // seed must be != 0\n Kiss32Random(uint32_t seed = 123456789) {\n x = seed;\n y = 362436000;\n z = 521288629;\n c = 7654321;\n }\n\n uint32_t kiss() {\n // Linear congruence generator\n x = 69069 * x + 12345;\n\n // Xor shift\n y ^= y << 13;\n y ^= y >> 17;\n y ^= y << 5;\n\n // Multiply-with-carry\n uint64_t t = 698769069ULL * z + c;\n c = t >> 32;\n z = (uint32_t) t;\n\n return x + y + z;\n }\n inline int flip() {\n // Draw random 0 or 1\n return kiss() & 1;\n }\n inline size_t index(size_t n) {\n // Draw random integer between 0 and n-1 where n is at most the number of data points you have\n return kiss() % n;\n }\n};\n\n// 64 bit KISS. Use this if you have more than about 2^24 data points (\"big data\" ;) )\nstruct Kiss64Random {\n uint64_t x;\n uint64_t y;\n uint64_t z;\n uint64_t c;\n\n // seed must be != 0\n Kiss64Random(uint64_t seed = 1234567890987654321ULL) {\n x = seed;\n y = 362436362436362436ULL;\n z = 1066149217761810ULL;\n c = 123456123456123456ULL;\n }\n\n uint64_t kiss() {\n // Linear congruence generator\n z = 6906969069LL*z+1234567;\n\n // Xor shift\n y ^= (y<<13);\n y ^= (y>>17);\n y ^= (y<<43);\n\n // Multiply-with-carry (uint128_t t = (2^58 + 1) * x + c; c = t >> 64; x = (uint64_t) t)\n uint64_t t = (x<<58)+c;\n c = (x>>6);\n x += t;\n c += (x\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"annoy.cpp\"\n#include \"kissrandom.h\"\n\nusing namespace std;\nusing std::set;\n\nstruct item{\n\nunsigned id;\n};\n\nfloat diff_timeval(timeval t1, timeval t2) {\n return (float) (t1.tv_sec - t2.tv_sec) + (t1.tv_usec - t2.tv_usec) * 1e-6;\n}\n\nstatic const unsigned LSHKIT_HEADER = 3;\n\ntypedef float T;\ntypedef unsigned S;\n\ntemplate\nP* load_data(const char* data_path, int* len, int* d)\n{\n\t// start to read data \n\tFILE * dfp = fopen(data_path, \"rb\");\n\tint dim;\n\tfread(&dim,sizeof(int),1,dfp);\n\tfseek(dfp, 0, SEEK_END);\n\toff_t size = ftell(dfp);\n fseek(dfp, 0, SEEK_SET);\n int n = (int)(size / (sizeof(int)+ sizeof(P)*dim));\n\t*d = dim;\n\t*len = n;\n\tP * data = new P[n*dim];\n\tfor (int i=0;i* res, S k)\n{\n\tfloat ratio=0;\n\tstd::set gnd_row;\n\tfor(unsigned j=0; jat(j))!=gnd_row.end())\n\t\t\tratio++;\n\t}\n\treturn ratio/k;\n}\n\n\nT get_mAP(S * gt, vector* res, S k)\n{\n\tstd::set gs_set;\n\tint found_last=0;\n\tfloat mAP=0.0;\n\tfor(unsigned j=0;jat(t))!=gs_set.end())\n\t\t\t{\n\t\t\t\tcount++;\n\t\t\t}\n\t\tmAP += count*(count-found_last)*1.0/(j+1);\n\t\tfound_last=count;\n\t}\n return mAP/k;\n}\n\nint main(int argc, char *argv[]){\n\n\tconst struct option longopts[] ={\n\t {\"help\", no_argument, 0, 'h'},\n\t {\"data_path\", required_argument, 0, 'i'},\n\t {\"query_path\" , required_argument, 0, 'q'},\n\t {\"gnd_path\", required_argument, 0, 'g'},\n\t\t{\"output_path\", required_argument, 0, 'o'},\n\t\t{\"k\", required_argument, 0, 'k'},\n\t\t{\"c\", required_argument, 0, 'c'},\n\t };\n\t int ind;\n\t int iarg = 0;\n\t opterr = 1;\n\n\t char gnd_path[256] = \"\";\n\t char query_path[256] = \"\";\n\t char index_path[256] = \"\";\n\t char output_path[256]= \"\";\n\t int K;\n\t int nStop;\n\n\t while (iarg != -1) {\n\t iarg = getopt_long(argc, argv, \"i:q:g:o:k:c:h\",\n\t longopts, &ind);\n\n\t switch (iarg) {\n\t case 'i':\n\t if (optarg) {\n\t \tstrcpy(index_path, optarg);\n\t }\n\t break;\n\t case 'q':\n\t if (optarg) {\n\t \tstrcpy(query_path, optarg);\n\t }\n\t break;\n\t case 'g':\n\t if (optarg) {\n\t \tstrcpy(gnd_path, optarg);\n\t }\n\t break;\n\t case 'o':\n\t if (optarg) {\n\t \tstrcpy(output_path, optarg);\n\t }\n\t break;\n\t\t case 'k':\n\t \t if (optarg) {\n\t \t K = atoi(optarg);\n\t \t }\n\t \t break;\n\t\t case 'c':\n\t \t if (optarg) {\n\t\t\t nStop = atoi(optarg);\n\t \t }\n\t \t break;\n\t }\n\t}\n\t\n int dim, nq;\n T *query_data = load_data(query_path, &nq,&dim);\n\tS *gnd_data = load_data(gnd_path, &nq, &K);\n\n\tAnnoyIndex* index = new AnnoyIndex(dim);\n\tindex->load(index_path);\t\n\n\tassert( query_data != NULL );\n\tT recall=0;\n\tint search_N=0;\n\tT mAP =0;\n\ttimeval start;\n\ttimeval end;\n\tT search_time=0;\n\tFILE *ofp = fopen(output_path, \"a+\");\n\n\tfor ( int i=0; i result;\n\t\tvector distance;\n gettimeofday(&start, NULL);\n\t\t//index->get_nns_by_vector ((T*) (query_data +i*dim*sizeof(T)), K, nStop, &result, &distance);\n\t\tindex->get_nns_by_vector (&query_data[i*dim], K, nStop, &result, &distance);\n\t\tgettimeofday(&end, NULL);\n\t search_time += diff_timeval(end, start);\n\t\tsearch_N += result[K];\n\t\trecall += get_recall(&gnd_data[i*K],&result,K);\n\t\t\n\t\tmAP += get_mAP(&gnd_data[i*K],&result,K);\n\t}\n\t\n\trecall /= nq;\n\tsearch_N /= nq;\n\tmAP /= nq;\n\tsearch_time /= nq;\n\tfprintf(ofp,\"%.6f %.6f %.6f #N_%d %.6f \\n\",recall,0.0,search_time,search_N,mAP);\t\n\tfclose(ofp);\n\n\tif ( query_data != NULL ) delete []query_data;\n\n\tdelete index;\n}\n" }, { "path": "algorithms/DPG/README.html", "content": "\n\n\n \n \n \n \n \n\n\n

DPG

\n

Diversified Proximity Graph (DPG) is an extension of KGraph which maximizes the diversity of the K-NN graph

\n

Prerequisites

\n
    \n
  • A modern compiler that supports C++11 (e.g., gcc 4.7 and above)
    • \n
  • A linux system
    • \n
  • The boost library (dev version) installed. Note that we need to set the paths of the Boost installed in Makefile.
    • \n
  • A sample dataset (e.g., audio) is downloaded, including its data points, query points and ground truth results. Note that we need to change data format later. All data files downloaded should be put in the ./knn_benchmark/data directory.
    • \n
\n

Compile (DPG/src)

\n

The following script builds three program for K-NN graph construction (the same as KGraph), performs graph diversification (resulting in the new DPG index); they are DPG_index, DPG_diverse and DPG_search, respectively.

\n
build.sh
\n

Two data conversion programs, fvec2lshkit and lshkit2fvec, are also generated, which are copied to the data directory of the NNS benchmark (i.e, ./nns_benchmark/data)

\n

Construct Diversified Proxmity Graph (DPG) (DPG/script)

\n
build_index.sh
\n

Build K-NN graphs based on the data points, then diversify the K-NN graph to generate the DPG graph. To save the space, one may directly build a DPG graph by diversifying the K-NN graph constructed by KGraph (i.e., index files uder KGraph/index directory). Note that for a DPG graph with L=20 (i.e., 20 neighbors per data point), we need diversify a KGraph index created with L=40 (i.e., 40 neighbours per data point) because we only keep half of the neighbors to allow space to include reverse edges. By doing this, the size of DPG is guaranteed to be the same as KGraph in the worse case, and is typically smaller.

\n

Search on the K-NN graph (DPG/script)

\n
search.sh
\n

Note that, in our experiment paper, we tune the P value of DPG search (i.e., search queue size) to achieve the trade-off between search speed and search quality (recall). This is exactly the same with KGraph.

\n\n\n" }, { "path": "algorithms/DPG/README.md", "content": "# DPG\n\nDiversified Proximity Graph (**DPG**) is an extension of [**KGraph**](http://www.kgraph.org/) which maximizes the diversity of the K-NN graph \n\n## Prerequisites\n\n- A modern compiler that supports `C++11` (e.g., `gcc 4.7` and above)\n- A linux system \n- The `boost` library (dev version) installed. Note that we need to set the **paths** of the Boost installed in **Makefile**.\n- [A sample dataset](https://github.com/DBWangGroupUNSW/nns_benchmark/tree/master/data)\n (e.g., audio) is downloaded, including its data points, query points and\n ground truth results. Note that we need to change data format later. All data\n files downloaded should be put in the `./knn_benchmark/data` directory.\n\n \n## Compile (DPG/src)\n\nThe following script builds three program for K-NN graph construction (the same as KGraph),\nperforms graph diversification (resulting in the new DPG index); they are\n`DPG_index`, `DPG_diverse` and `DPG_search`, respectively. \n\n```\nbuild.sh\n```\n\nTwo data conversion programs, `fvec2lshkit` and `lshkit2fvec`, are also\ngenerated, which are copied to the data directory of the NNS benchmark (i.e,\n`./nns_benchmark/data`)\n\n\n## Construct Diversified Proxmity Graph (DPG) (DPG/script)\n\n```\nbuild_index.sh\n```\n\nBuild K-NN graphs based on the data points, then diversify the K-NN graph to generate the DPG graph. \nTo save the space, one may directly build a DPG graph by diversifying the K-NN\ngraph constructed by KGraph (i.e., index files uder KGraph/index directory).\nNote that for a DPG graph with `L=20` (i.e., 20 neighbors per data point), we\nneed diversify a KGraph index created with `L=40` (i.e., 40 neighbours per data\npoint) because we only keep half of the neighbors to allow space to include\nreverse edges. By doing this, the size of DPG is guaranteed to be the same as\nKGraph in the worse case, and is typically smaller. \n\n## Search on the K-NN graph (DPG/script)\n\n```\nsearch.sh\n```\n\nNote that, in our experiment paper, we tune the `P` value of DPG search (i.e.,\nsearch queue size) to achieve the trade-off between search speed and search\nquality (recall). This is exactly the same with KGraph.\n\n\n" }, { "path": "algorithms/DPG/script/build_index.sh", "content": "data_dir=\"../../../data\"\nindex_dir=\"../index\"\nbin_dir=\"../src\"\n\nmkdir ${index_dir} \n\n# Please sure the data ( data points, query points and ground truth files are located at ../../../data directory )\n\n# L1 and L2 is the length of the NN-list used by K-NN graph and DPG \n\nL1=40\nL2=20\n\nfor data in audio \ndo\n\n\n# first construct K-NN graph\n\n${bin_dir}/DPG_index --data ${data_dir}/${data}_base.lshkit --output ${index_dir}/${data}.kgraph_${L1} -S 20 -L ${L1} -K 20\n\n# do the diversification \n\n${bin_dir}/DPG_diverse --data ${data_dir}/${data}_base.lshkit --index ${index_dir}/${data}.kgraph_${L1} --output ${index_dir}/${data}.DPG_${L2} -L ${L2}\n\n\ndone\n\n\n" }, { "path": "algorithms/DPG/script/search.sh", "content": "\ndata_dir=\"../../../data\"\nindex_dir=\"../index\"\nbin_dir=\"../src\"\nresult_dir=\"../results\"\n\n\nmkdir ${result_dir}\n\n\nk=20\nL=20\n\n\nfor data in audio \ndo\n\nfor P in 1 2 5 10 20 40 50 100\ndo\n\n# show the result \n${bin_dir}/DPG_search --data ${data_dir}/${data}_base.lshkit --query ${data_dir}/${data}_query.lshkit --eval ${data_dir}/${data}_groundtruth.lshkit -K ${k} --index ${index_dir}/${data}.DPG_${L} -P ${P} \n\n# try again and keep the results into files \n\nresult=\"${data}_k${k}_P${P}.txt\" \n\n${bin_dir}/DPG_search --data ${data_dir}/${data}_base.lshkit --query ${data_dir}/${data}_query.lshkit --eval ${data_dir}/${data}_groundtruth.lshkit -K ${k} --index ${index_dir}/${data}.DPG_${L} -P ${P} > ${result_dir}/${result}\n\ndone\ndone\n\n\n\n" }, { "path": "algorithms/DPG/src/DPG_diverse.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nusing namespace boost::timer;\nusing namespace std;\nusing namespace boost;\nusing namespace kgraph;\nnamespace po = boost::program_options; \n\n#ifndef KGRAPH_VALUE_TYPE\n#define KGRAPH_VALUE_TYPE float\n#endif\n\n\ntypedef KGRAPH_VALUE_TYPE value_type;\n\nint main(int argc, char *argv[]){\n vector params;\n string input_data_path;\n string index_NNList_path;\n string index_DPG_path; \n unsigned L; \n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version number.\")\n (\"data\", po::value(&input_data_path), \"input data path\")\n (\"index\", po::value(&index_NNList_path), \"NNList index path\")\n (\"output\", po::value(&index_DPG_path), \"DPG index path\")\n (\",L\", po::value(&L)->default_value(default_K), \"\")\n ; \n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"index\", 1); \n p.add(\"output\", 1);\n\n po::variables_map vm; \n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || vm.count(\"data\") == 0 || vm.count(\"index\") == 0 || vm.count(\"output\") == 0) {\n cout << \"DPG_diverse \" << endl;\n cout << desc_visible << endl;\n return 0;\n }\n\n cerr << L << \" : L value\" << endl;\n\n Matrix data;\n data.load_lshkit(input_data_path.c_str());\n MatrixOracle oracle(data);\n\n KGraph *kgraph = KGraph::create();\n if (index_NNList_path.size() >0 )\n {\n kgraph->load(index_NNList_path.c_str()); \n }\n\n boost::timer::auto_cpu_timer timer;\n\n // start the diversification ?? ( seperately ) \n // add the diversification part here \n // cerr << \"Start the diversification process... (angular dissimilarity )\" << endl << endl;\n // kgraph->remove_near_edges(oracle, L); // here not knn's k, L is the length of NN list ( note that the true NN list length might be smaller than L) \n\n cerr << \"Start the diversification process... ( counting cuts )\" << endl << endl;\n kgraph->diversify_by_cut(oracle, L); // here not knn's k, L is the length of NN list ( note that the true NN list length might be smaller than L) \n\n\n // reverse the edges here \n cerr << \"Add reverse edges ...\" << endl << endl;\n kgraph->add_backward_edges();\n\n float time = timer.elapsed().wall / 1e9;\n\n if ( index_DPG_path.size() > 0 )\n kgraph->save( index_DPG_path.c_str());\n\n cout << time << \" # diversification time\" << endl;\n\n delete kgraph;\n\n}\n" }, { "path": "algorithms/DPG/src/DPG_index.cpp", "content": "#ifndef KGRAPH_VALUE_TYPE\n#define KGRAPH_VALUE_TYPE float\n#endif\n\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nusing namespace boost::timer;\nusing namespace std;\nusing namespace boost;\nusing namespace kgraph;\nnamespace po = boost::program_options; \n\n\ntypedef KGRAPH_VALUE_TYPE value_type;\n\nint main(int argc, char *argv[]) {\n string data_path;\n string output_path;\n KGraph::IndexParams params;\n unsigned D;\n unsigned skip;\n unsigned gap;\n unsigned synthetic;\n float noise;\n\n bool lshkit = true;\n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version information.\")\n (\"data\", po::value(&data_path), \"input path\")\n (\"output\", po::value(&output_path), \"output path\")\n (\",K\", po::value(¶ms.K)->default_value(default_K), \"number of nearest neighbor\")\n (\"controls,C\", po::value(¶ms.controls)->default_value(default_controls), \"number of control pounsigneds\")\n ;\n\n po::options_description desc_hidden(\"Expert options\");\n desc_hidden.add_options()\n (\"iterations,I\", po::value(¶ms.iterations)->default_value(default_iterations), \"\")\n (\",S\", po::value(¶ms.S)->default_value(default_S), \"\")\n (\",R\", po::value(¶ms.R)->default_value(default_R), \"\")\n (\",L\", po::value(¶ms.L)->default_value(default_L), \"\")\n (\"delta\", po::value(¶ms.delta)->default_value(default_delta), \"\")\n (\"recall\", po::value(¶ms.recall)->default_value(default_recall), \"\")\n (\"prune\", po::value(¶ms.prune)->default_value(default_prune), \"\")\n (\"noise\", po::value(&noise)->default_value(0), \"noise\")\n (\"seed\", po::value(¶ms.seed)->default_value(default_seed), \"\")\n (\"dim,D\", po::value(&D), \"dimension, see format\")\n (\"skip\", po::value(&skip)->default_value(0), \"see format\")\n (\"gap\", po::value(&gap)->default_value(0), \"see format\")\n (\"raw\", \"read raw binary file, need to specify D.\")\n (\"synthetic\", po::value(&synthetic)->default_value(0), \"generate synthetic data, for performance evaluation only, specify number of points\")\n ;\n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible).add(desc_hidden);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"output\", 1);\n\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm);\n\n if (vm.count(\"raw\") == 1) {\n lshkit = false;\n }\n\n if (vm.count(\"version\")) {\n cout << \"KGraph version \" << KGraph::version() << endl;\n return 0;\n }\n\n if (vm.count(\"help\")\n || (synthetic && (vm.count(\"dim\") == 0 || vm.count(\"data\")))\n || (!synthetic && (vm.count(\"data\") == 0 || (vm.count(\"dim\") == 0 && !lshkit)))) {\n cout << \"Usage: DPG_index [OTHER OPTIONS]... INPUT [OUTPUT]\" << endl;\n cout << desc_visible << endl;\n cout << desc_hidden << endl;\n return 0;\n }\n\n if (params.S == 0) {\n params.S = params.K;\n }\n\n if (lshkit && (synthetic == 0)) { // read dimension information from the data file\n static const unsigned LSHKIT_HEADER = 3;\n ifstream is(data_path.c_str(), ios::binary);\n unsigned header[LSHKIT_HEADER]; /* entry size, row, col */\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n BOOST_VERIFY(header[0] == sizeof(value_type));\n is.close();\n D = header[2];\n skip = LSHKIT_HEADER * sizeof(unsigned);\n gap = 0;\n }\n\n Matrix data;\n if (synthetic) {\n if (!std::is_floating_point::value) {\n throw runtime_error(\"synthetic data not implemented for non-floating-point values.\");\n }\n data.resize(synthetic, D);\n cerr << \"Generating synthetic data...\" << endl;\n default_random_engine rng(params.seed);\n uniform_real_distribution distribution(-1.0, 1.0);\n data.zero(); // important to do that\n for (unsigned i = 0; i < synthetic; ++i) {\n value_type *row = data[i];\n for (unsigned j = 0; j < D; ++j) {\n row[j] = distribution(rng);\n }\n }\n }\n else {\n data.load(data_path, D, skip, gap);\n }\n if (noise != 0) {\n if (!std::is_floating_point::value) {\n throw runtime_error(\"noise injection not implemented for non-floating-point value.\");\n }\n tr1::ranlux64_base_01 rng;\n double sum = 0, sum2 = 0;\n for (unsigned i = 0; i < data.size(); ++i) {\n for (unsigned j = 0; j < data.dim(); ++j) {\n value_type v = data[i][j];\n sum += v;\n sum2 += v * v;\n }\n }\n double total = double(data.size()) * data.dim();\n double avg2 = sum2 / total, avg = sum / total;\n double dev = sqrt(avg2 - avg * avg);\n cerr << \"Adding Gaussian noise w/ \" << noise << \"x sigma(\" << dev << \")...\" << endl;\n boost::normal_distribution gaussian(0, noise * dev);\n for (unsigned i = 0; i < data.size(); ++i) {\n for (unsigned j = 0; j < data.dim(); ++j) {\n data[i][j] += gaussian(rng);\n }\n }\n }\n\n MatrixOracle oracle(data);\n KGraph::IndexInfo info;\n KGraph *kgraph = KGraph::create(); //(oracle, params, &info);\n {\n auto_cpu_timer timer;\n kgraph->build(oracle, params, output_path.c_str(), &info);\n cerr << info.stop_condition << endl;\n }\n\n if (output_path.size()) {\n \n //Note that we modify the index save procedure to reduce index size \n //kgraph->save(output_path.c_str());\n }\n\n \n delete kgraph;\n\n return 0;\n}\n" }, { "path": "algorithms/DPG/src/DPG_search.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nusing namespace boost::timer;\nusing namespace std;\nusing namespace boost;\nusing namespace kgraph;\nnamespace po = boost::program_options; \n\n#ifndef KGRAPH_VALUE_TYPE\n#define KGRAPH_VALUE_TYPE float\n#endif\n\n\ntypedef KGRAPH_VALUE_TYPE value_type;\n\n\nint main(int argc, char *argv[]) {\n vector params;\n string input_path;\n string index_path;\n string query_path;\n string output_path;\n string init_path;\n string eval_path;\n unsigned K, M, P, T;\n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version number.\")\n (\"data\", po::value(&input_path), \"input path\")\n (\"index\", po::value(&index_path), \"index path\")\n (\"query\", po::value(&query_path), \"query path\")\n (\"output\", po::value(&output_path), \"output path\")\n (\"init\", po::value(&init_path), \"init path\")\n (\"eval\", po::value(&eval_path), \"eval path\")\n (\",K\", po::value(&K)->default_value(default_K), \"\")\n (\",M\", po::value(&M)->default_value(default_M), \"\")\n (\",P\", po::value(&P)->default_value(default_P), \"\")\n (\",T\", po::value(&T)->default_value(default_T), \"\")\n (\"linear\", \"\")\n ;\n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"index\", 1);\n p.add(\"query\", 1);\n p.add(\"output\", 1);\n\n po::variables_map vm; \n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm); \n\n if (vm.count(\"version\")) {\n cout << \"KGraph version \" << KGraph::version() << endl;\n return 0;\n }\n\n if (vm.count(\"help\") || vm.count(\"data\") == 0 || vm.count(\"index\") == 0 || vm.count(\"query\") == 0) {\n cout << \"DPG_search [output]\" << endl;\n cout << desc_visible << endl;\n return 0;\n }\n\n if (P < K) {\n P = K;\n }\n\n Matrix data;\n Matrix query;\n Matrix result; //(query.size(), U);\n unsigned init = 0;\n\n data.load_lshkit(input_path);\n query.load_lshkit(query_path);\n if (init_path.size()) {\n result.load_lshkit(init_path);\n BOOST_VERIFY(result.size() == query.size());\n init = result.dim();\n BOOST_VERIFY(init >= K);\n }\n MatrixOracle oracle(data);\n float recall = 0;\n float cost = 0;\n float time = 0;\n if (vm.count(\"linear\")) {\n boost::timer::auto_cpu_timer timer;\n result.resize(query.size(), K);\n// #pragma omp parallel for\n for (unsigned i = 0; i < query.size(); ++i) {\n oracle.query(query[i]).search(K, default_epsilon, result[i]);\n }\n cost = 1.0;\n time = timer.elapsed().wall / 1e9;\n }\n else {\n result.resize(query.size(), K);\n KGraph::SearchParams params;\n params.K = K;\n params.M = M;\n params.P = P;\n params.T = T;\n params.init = init;\n KGraph *kgraph = KGraph::create();\n kgraph->load(index_path.c_str());\n\n boost::timer::auto_cpu_timer timer;\n cerr << \"Searching...\" << endl;\n\n// #pragma omp parallel for reduction(+:cost)\n for (unsigned i = 0; i < query.size(); ++i) {\n KGraph::SearchInfo info;\n kgraph->search(oracle.query(query[i]), params, result[i], &info);\n cost += info.cost;\n }\n cost /= query.size();\n time = timer.elapsed().wall / 1e9;\n //cerr << \"Cost: \" << cost << endl;\n delete kgraph;\n }\n if (output_path.size()) {\n result.save_lshkit(output_path);\n }\n if (eval_path.size()) {\n Matrix gs;\n gs.load_lshkit(eval_path);\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(gs.size() >= query.size());\n kgraph::Matrix gs_dist(query.size(), K);\n kgraph::Matrix result_dist(query.size(), K);\n// #pragma omp parallel for\n for (unsigned i = 0; i < query.size(); ++i) {\n auto Q = oracle.query(query[i]);\n float *gs_dist_row = gs_dist[i];\n float *result_dist_row = result_dist[i];\n unsigned const *gs_row = gs[i];\n unsigned const *result_row = result[i];\n for (unsigned k = 0; k < K; ++k) {\n gs_dist_row[k] = Q(gs_row[k]);\n result_dist_row[k] = Q(result_row[k]);\n }\n sort(gs_dist_row, gs_dist_row + K);\n sort(result_dist_row, result_dist_row + K);\n }\n recall = AverageRecall(gs_dist, result_dist, K);\n }\n cout << \"Time: \" << time << \" Recall: \" << recall << \" Cost: \" << cost << endl;\n\n return 0;\n}" }, { "path": "algorithms/DPG/src/Makefile", "content": "CC=g++ \n\n#ARCH=-msse2\nOPT=-O3\n#OPENMP=-fopenmp\nboost_dir=\"/home/yingz/Software/boost_1_58_0/\"\nboost_lib=\"/usr/local/lib\"\n\nCXXFLAGS+=-fPIC -g -std=c++11 -I${boost_dir} -I. $(OPT) -L${boost_lib} $(OPT) $(ARCH) \nLDFLAGS+=-static $(OPENMP)\nLDLIBS+=-lboost_timer -lboost_chrono -lboost_system -lboost_program_options -lgomp -lm -lrt -L${boost_lib}\n\nCOMMON=kgraph.o metric.o RandGen.o\n\nHEADERS=kgraph.h kgraph-data.h RandGen.h\n\nPROGS=DPG_index DPG_diverse DPG_search\n\nRELEASE_SRC=Makefile LICENSE kgraph.h kgraph-data.h DPG_index.cpp DPG_search.cpp DPG_diverse.cpp\n\n$(PROGS): %:\t%.cpp $(HEADERS) $(COMMON)\n\t$(CXX) $(CXXFLAGS) $(LDFLAGS) -o $@ $*.cpp $(COMMON) $(LDLIBS)\n%.o:\t%.cpp $(HEADERS)\n\t$(CXX) $(CXXFLAGS) -c $*.cpp \n\nclean:\n\trm -f *.o\n\n\n\n\n" }, { "path": "algorithms/DPG/src/RandGen.cpp", "content": "/*\n * This file is part of SRS project.\n *\n * SRS is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n *\n * SRS is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with SRS. If not, see .\n *\n * Created by: Yifang Sun, Jianbin Qin\n * Last modified by: Yifang Sun, Jianbin Qin\n */\n\n#include \n#include \n#include \"RandGen.h\"\n\n/************************************************************\n *** Given a mean and a standard deviation, gaussian **\n ** generates a normally distributed random number **\n ** Algorithm: Polar Method, p. 104, Knuth, vol. 2 **\n ************************************************************/\n\nfloat gaussian(float mean, float sigma) {\n float v1, v2;\n float s;\n float x;\n\n do {\n v1 = 2 * uniform(0, 1) - 1;\n v2 = 2 * uniform(0, 1) - 1;\n s = v1 * v1 + v2 * v2;\n } while (s >= 1.);\n\n x = v1 * (float) sqrt(-2. * log(s) / s);\n\n /* x is normally distributed with mean 0 and sigma 1. */\n x = x * sigma + mean;\n\n return (x);\n}\n\n/************************************************************\n ** Generates a random number between _min and _max **\n ** uniformly **\n ** By Yufei Tao\n ************************************************************/\n\nfloat uniform(float _min, float _max) {\n int int_r = rand();\n long base = RAND_MAX - 1;\n float f_r = ((float) int_r) / base;\n return (_max - _min) * f_r + _min;\n}\n\n" }, { "path": "algorithms/DPG/src/RandGen.h", "content": "/*\n * This file is part of SRS project.\n *\n * SRS is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n *\n * SRS is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with SRS. If not, see .\n *\n * Created by: Yifang Sun, Jianbin Qin\n * Last modified by: Yifang Sun, Jianbin Qin\n */\n\n#ifndef RANDGEN_H_\n#define RANDGEN_H_\n\nfloat gaussian(float mean, float sigma);\nfloat uniform(float _min, float _max);\n\n#endif /* RANDGEN_H_ */\n" }, { "path": "algorithms/DPG/src/build.sh", "content": "make DPG_index\nmake DPG_diverse\nmake DPG_search\n\nmake clean\n" }, { "path": "algorithms/DPG/src/kgraph-data.h", "content": "#ifndef WDONG_KGRAPH_DATA\n#define WDONG_KGRAPH_DATA\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef __GNUC__\n#ifdef __AVX__\n#define KGRAPH_MATRIX_ALIGN 32\n#else\n#ifdef __SSE2__\n#define KGRAPH_MATRIX_ALIGN 16\n#else\n#define KGRAPH_MATRIX_ALIGN 4\n#endif\n#endif\n#endif\n\nnamespace kgraph {\n\n /// L2 square distance with AVX instructions.\n /** AVX instructions have strong alignment requirement for t1 and t2.\n */\n extern float float_l2sqr_avx (float const *t1, float const *t2, unsigned dim);\n /// L2 square distance with SSE2 instructions.\n extern float float_l2sqr_sse2 (float const *t1, float const *t2, unsigned dim);\n /// L2 square distance for uint8_t with SSE2 instructions (for SIFT).\n extern float uint8_l2sqr_sse2 (uint8_t const *t1, uint8_t const *t2, unsigned dim);\n\n using std::vector;\n using std::runtime_error;\n\n /// namespace for various distance metrics.\n namespace metric {\n /// L2 square distance.\n struct l2sqr {\n template \n static float apply (T const *t1, T const *t2, unsigned dim) {\n\t //\t\t\t\tcout<<\"apply \\n\";\n float r = 0;\n for (unsigned i = 0; i < dim; ++i) {\n float v = float(t1[i]) - float(t2[i]);\n v *= v;\n r += v;\n }\n return r;\n }\n };\n /// L2 distance.\n struct l2 {\n template \n static float apply (T const *t1, T const *t2, unsigned dim) {\n return sqrt(l2sqr::apply(t1, t2, dim));\n }\n };\n }\n\n /// Matrix data.\n template \n class Matrix {\n unsigned col;\n unsigned row;\n size_t stride;\n char *data;\n\n void reset (unsigned r, unsigned c) {\n row = r;\n col = c;\n stride = (sizeof(T) * c + A - 1) / A * A;\n /*\n data.resize(row * stride);\n */\n if (data) free(data);\n data = (char *)memalign(A, row * stride); // SSE instruction needs data to be aligned\n if (!data) throw runtime_error(\"memalign\");\n }\n public:\n Matrix (): col(0), row(0), stride(0), data(0) {}\n Matrix (unsigned r, unsigned c): data(0) {\n reset(r, c);\n }\n ~Matrix () {\n if (data) free(data);\n }\n unsigned size () const {\n return row;\n }\n unsigned dim () const {\n return col;\n }\n size_t step () const {\n return stride;\n }\n void resize (unsigned r, unsigned c) {\n reset(r, c);\n }\n T const *operator [] (unsigned i) const {\n return reinterpret_cast(&data[stride * i]);\n }\n T *operator [] (unsigned i) {\n return reinterpret_cast(&data[stride * i]);\n }\n void zero () {\n memset(data, 0, row * stride);\n }\n void load (const std::string &path, unsigned dim, unsigned skip = 0, unsigned gap = 0) {\n std::ifstream is(path.c_str(), std::ios::binary);\n BOOST_VERIFY(is);\n is.seekg(0, std::ios::end);\n size_t size = is.tellg();\n size -= skip;\n unsigned line = sizeof(T) * dim + gap;\n unsigned N = size / line;\n reset(N, dim);\n zero();\n is.seekg(skip, std::ios::beg);\n for (unsigned i = 0; i < N; ++i) {\n is.read(&data[stride * i], sizeof(T) * dim);\n is.seekg(gap, std::ios::cur);\n }\n BOOST_VERIFY(is);\n }\n\n void load_lshkit (std::string const &path) {\n static const unsigned LSHKIT_HEADER = 3;\n std::ifstream is(path.c_str(), std::ios::binary);\n unsigned header[LSHKIT_HEADER]; /* entry size, row, col */\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n BOOST_VERIFY(header[0] == sizeof(T));\n is.close();\n unsigned D = header[2];\n unsigned skip = LSHKIT_HEADER * sizeof(unsigned);\n unsigned gap = 0;\n load(path, D, skip, gap);\n }\n\n void save_lshkit (std::string const &path) {\n std::ofstream os(path.c_str(), std::ios::binary);\n unsigned header[3];\n assert(sizeof header == 3*4);\n header[0] = sizeof(T);\n header[1] = row;\n header[2] = col;\n os.write((const char *)header, sizeof(header));\n for (unsigned i = 0; i < row; ++i) {\n os.write(&data[stride * i], sizeof(T) * col);\n }\n }\n };\n\n /// Matrix proxy to interface with 3rd party libraries (FLANN, OpenCV, NumPy).\n template \n class MatrixProxy {\n unsigned rows;\n unsigned cols; // # elements, not bytes, in a row,\n size_t stride; // # bytes in a row, >= cols * sizeof(element)\n uint8_t const *data;\n public:\n MatrixProxy (Matrix const &m)\n : rows(m.size()), cols(m.dim()), stride(m.step()), data(reinterpret_cast(m[0])) {\n }\n\n#ifndef __AVX__\n#ifdef FLANN_DATASET_H_\n /// Construct from FLANN matrix.\n MatrixProxy (flann::Matrix const &m)\n : rows(m.rows), cols(m.cols), stride(m.stride), data(m.data) {\n BOOST_VERIFY(stride % A == 0);\n }\n#endif\n#ifdef __OPENCV_CORE_HPP__\n /// Construct from OpenCV matrix.\n MatrixProxy (cv::Mat const &m)\n : rows(m.rows), cols(m.cols), stride(m.step), data(m.data) {\n BOOST_VERIFY(stride % A == 0);\n }\n#endif\n#ifdef NPY_NDARRAYOBJECT_H\n /// Construct from NumPy matrix.\n MatrixProxy (PyArrayObject *obj) {\n BOOST_VERIFY(obj->nd == 2);\n rows = obj->dimensions[0];\n cols = obj->dimensions[1];\n stride = obj->strides[0];\n data = reinterpret_cast(obj->data);\n BOOST_VERIFY(obj->descr->elsize == sizeof(DATA_TYPE));\n BOOST_VERIFY(stride % A == 0);\n BOOST_VERIFY(stride >= cols * sizeof(DATA_TYPE));\n }\n#endif\n#endif\n unsigned size () const {\n return rows;\n }\n unsigned dim () const {\n return cols;\n }\n DATA_TYPE const *operator [] (unsigned i) const {\n return reinterpret_cast(data + stride * i);\n }\n };\n\n /// Oracle for matrix data.\n /** DATA_TYPE can be Matrix or MatrixProxy,\n * DIST_TYPE should be one class within the namespace kgraph.metric.\n */\n template \n class MatrixOracle: public kgraph::IndexOracle {\n MatrixProxy proxy;\n public:\n class SearchOracle: public kgraph::SearchOracle {\n MatrixProxy proxy;\n DATA_TYPE const *query;\n public:\n SearchOracle (MatrixProxy const &p, DATA_TYPE const *q): proxy(p), query(q) {\n }\n virtual unsigned size () const {\n return proxy.size();\n }\n virtual float operator () (unsigned i) const {\n return DIST_TYPE::apply(proxy[i], query, proxy.dim());\n }\n };\n template \n MatrixOracle (MATRIX_TYPE const &m): proxy(m) {\n }\n virtual unsigned size () const {\n return proxy.size();\n }\n virtual float operator () (unsigned i, unsigned j) const {\n return DIST_TYPE::apply(proxy[i], proxy[j], proxy.dim());\n }\n\n\tvirtual float angle (unsigned orign, unsigned pivot, unsigned x) const { // return cos(\\theta), where \\theta is the angle between vector orign - pivot and orign - x\n\t float dot_prod = 0.0, norm = 0.0, norm2 = 0.0;\n\t float pi = 3.1415926;\n\t for (unsigned k = 0; k < proxy.dim(); ++k){\n\t dot_prod += (float)(proxy[pivot][k] - proxy[orign][k])* (float)(proxy[x][k] - proxy[orign][k]);\n\t norm += (float)(proxy[x][k] - proxy[orign][k]) * (proxy[x][k] - proxy[orign][k]);\n\t norm2 += (float)(proxy[pivot][k] - proxy[orign][k]) * (proxy[pivot][k] - proxy[orign][k]);\n\t }\n\t //\t if (dot_prod / sqrt(norm * norm2) < 0.001){\n\t // fprintf(stderr, \"IN: %d %d %d\", orign, pivot, x);\n\t //}\n\t //fprintf(stderr, \"%f \", dot_prod);\n\t //fprintf(stderr, \"%f\\n\", sqrt ( norm1 * norm2 ));\n\t return acos(dot_prod / sqrt(norm * norm2) ) * 180 / pi;\n\t}\n\tSearchOracle query (DATA_TYPE const *query) const {\n return SearchOracle(proxy, query);\n }\n };\n\n inline float AverageRecall (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n // compare\n unsigned found = 0;\n unsigned gs_n = 0;\n unsigned re_n = 0;\n while ((gs_n < K) && (re_n < K)) {\n if (gs_row[gs_n] < re_row[re_n]) {\n ++gs_n;\n }\n else if (gs_row[gs_n] == re_row[re_n]) {\n ++found;\n ++gs_n;\n ++re_n;\n }\n else {\n throw runtime_error(\"distance is unstable\");\n }\n }\n sum += float(found) / K;\n }\n return sum / result.size();\n }\n\tinline void AveragePrecision(Matrix const &gs, Matrix const &result, float *precision) {\n\t\tunsigned K = result.dim();\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n unsigned found = 0;\n\t\tunsigned gs_n = 0;\n unsigned re_n = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\twhile ((gs_n < K) && (re_n < K)) {\n if (gs_row[gs_n] < re_row[re_n]) {\n ++gs_n;\n }\n else if (gs_row[gs_n] == re_row[re_n]) {\n ++found;\n ++gs_n;\n ++re_n;\n }\n else {\n throw runtime_error(\"distance is unstable\");\n }\n }\n\t\t\tif (gs_row[0] == re_row[0]) {\n ++found;\n\n }\n }\n *precision = found / result.size();\n }\n\n\tinline void AverageAccuracy(Matrix const &gs, Matrix const &result, float *accuracy) \n\t{\n unsigned K = result.dim();\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n\n\t\tfloat avg = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n \tfloat dist = 0;\n\t\t\tunsigned gs_n = 0;\n unsigned re_n = 0;\n while ((gs_n < K) && (re_n < K)) {\n float d=(re_row[re_n]-gs_row[gs_n])/gs_row[gs_n];\n\t\t\t\tif(d<=4)\n\t\t\t\t\tdist += d;\n\t\t\t\telse\n\t\t\t\t\tdist+=4; \n \t\t\t\t++gs_n;\n ++re_n;\n\t\t\t}\n\t\t\tavg += dist/K;\n }\n *accuracy=avg / result.size();\n }\n\n\tinline float compute_number_closer (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tfor (unsigned gs_n=0;gs_n < K ;gs_n++)\n\t\t\t{\n\t\t\t\tfor(unsigned re_n=0; re_n < K; re_n++)\n\t\t\t\t{\n\t\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t\t{\n\t\t\t\t\t\trate += (float)(gs_n+1)/(re_n+1);\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n sum += rate/K;\n }\n return sum / result.size();\n }\n\n\tinline float compute_mean_reciprocal_rank (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tfor(unsigned re_n=0; re_n < K; re_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[0]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\trate += 1.0/(re_n+1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n sum += rate;\n }\n return sum / result.size();\n }\n\n\n\tinline float compute_mean_average_precision (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tunsigned re_n = 0;\n\t\t\tint count = 0;\n\t\t\tfor(unsigned gs_n=0; gs_n < K; gs_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\tcount++;\n\t\t\t\t\trate += 1.0*(count)/(gs_n+1);\n\t\t\t\t\tre_n ++;\n\t\t\t\t}\n\t\t\t}\n sum += rate/K;\n }\n return sum / result.size();\n }\n\n\tinline float compute_discounted_culmulative_gain (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tunsigned re_n = 0;\n\t\t\tfor(unsigned gs_n=0; gs_n < K; gs_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\trate += 1.0/log2(gs_n+2);\n\t\t\t\t\tre_n ++;\n\t\t\t\t}\n\t\t\t}\n sum += rate;\n }\n return sum / result.size();\n }\n\n inline void AverageMinDist (Matrix const &gs, Matrix const &result, float *MinDist,float *AvgDist,float *MaxDist) {\n\n BOOST_VERIFY(gs.size() >= result.size());\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n *MinDist += re_row[0]-gs_row[0];\n *MaxDist += abs(re_row[result.dim()]-gs_row[0]);\n for(unsigned j=0;j\n inline float l2sqr::apply (float const *t1, float const *t2, unsigned dim) {\n return float_l2sqr_avx(t1, t2, dim);\n }\n}}\n#else\n#ifdef __SSE2__\nnamespace kgraph { namespace metric {\n template <>\n inline float l2sqr::apply (float const *t1, float const *t2, unsigned dim) {\n return float_l2sqr_sse2(t1, t2, dim);\n }\n template <>\n inline float l2sqr::apply (uint8_t const *t1, uint8_t const *t2, unsigned dim) {\n return uint8_l2sqr_sse2(t1, t2, dim);\n }\n}}\n#endif\n#endif\n#endif\n#endif\n\n\n\n#endif\n\n" }, { "path": "algorithms/DPG/src/kgraph.cpp", "content": "//static char const *kgraph_version = KGRAPH_VERSION \"-\" KGRAPH_BUILD_NUMBER \",\" KGRAPH_BUILD_ID;\nstatic char const *kgraph_version = \"deiversified proximity graph\";\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#define timer timer_for_boost_progress_t\n#include \n#undef timer\n#include \n#include \n#include \n#include \n#include \n#include \"boost/smart_ptr/detail/spinlock.hpp\"\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nnamespace kgraph {\n\n using namespace std;\n using namespace boost;\n using namespace boost::accumulators;\n\n unsigned verbosity = default_verbosity;\n\n typedef boost::detail::spinlock Lock;\n typedef std::lock_guard LockGuard;\n\n // generate size distinct random numbers < N\n template \n static void GenRandom (RNG &rng, unsigned *addr, unsigned size, unsigned N) {\n for (unsigned i = 0; i < size; ++i) {\n addr[i] = rng() % (N - size);\n }\n sort(addr, addr + size);\n for (unsigned i = 1; i < size; ++i) {\n if (addr[i] <= addr[i-1]) {\n addr[i] = addr[i-1] + 1;\n }\n }\n unsigned off = rng() % N;\n for (unsigned i = 0; i < size; ++i) {\n addr[i] = (addr[i] + off) % N;\n }\n }\n\n struct Neighbor {\n unsigned id;\n float dist;\n bool flag; // whether this entry is a newly found one \n Neighbor () {}\n Neighbor (unsigned i, float d, bool f = true): id(i), dist(d), flag(f) {\n }\n };\n\n static inline bool operator < (Neighbor const &n1, Neighbor const &n2) {\n if (n1.dist == n2.dist)\n\treturn n1.id < n2.id;\n else \n return n1.dist < n2.dist;\n }\n\n typedef vector Neighbors;\n\n // both pool and knn should be sorted in ascending order\n static float EvaluateRecall (Neighbors const &pool, Neighbors const &knn) {\n if (knn.empty()) return 1.0;\n unsigned found = 0;\n unsigned n_p = 0;\n unsigned n_k = 0;\n for (;;) {\n if (n_p >= pool.size()) break;\n if (n_k >= knn.size()) break;\n if (knn[n_k].dist < pool[n_p].dist) {\n ++n_k;\n }\n else if (knn[n_k].dist == pool[n_p].dist) {\n ++found;\n ++n_k;\n ++n_p;\n }\n else {\n cerr << \"Distance is unstable.\" << endl;\n cerr << \"Exact\";\n for (auto const &p: knn) {\n cerr << ' ' << p.id << ':' << p.dist;\n }\n cerr << endl;\n cerr << \"Approx\";\n for (auto const &p: pool) {\n cerr << ' ' << p.id << ':' << p.dist;\n }\n cerr << endl;\n throw runtime_error(\"distance is unstable\");\n }\n }\n return float(found) / knn.size(); //recall\n }\n\n static float EvaluateAccuracy (Neighbors const &pool, Neighbors const &knn) {\n unsigned m = std::min(pool.size(), knn.size());\n float sum = 0;\n unsigned cnt = 0;\n for (unsigned i = 0; i < m; ++i) {\n if (knn[i].dist > 0) {\n sum += abs(pool[i].dist - knn[i].dist) / knn[i].dist;\n ++cnt;\n }\n }\n return cnt > 0 ? sum / cnt: 0;\n }\n\n static float EvaluateOneRecall (Neighbors const &pool, Neighbors const &knn) {\n if (pool[0].dist == knn[0].dist) return 1.0;\n return 0;\n }\n \n static float EvaluateDelta (Neighbors const &pool, unsigned K) {\n unsigned c = 0;\n unsigned N = K;\n if (pool.size() < N) N = pool.size();\n for (unsigned i = 0; i < N; ++i) {\n if (pool[i].flag) ++c;\n }\n return float(c) / K; //the fraction of true in pool's flags\n }\n\n struct Control {\n unsigned id;\n Neighbors neighbors;\n };\n\n // try insert nn into the list\n // the array addr must contain at least K+1 entries:\n // addr[0..K-1] is a sorted list\n // addr[K] is as output parameter\n // * if nn is already in addr[0..K-1], return K+1\n // * Otherwise, do the equivalent of the following\n // put nn into addr[K]\n // make addr[0..K] sorted\n // return the offset of nn's index in addr (could be K)\n //\n // Special case: K == 0\n // addr[0] <- nn\n // return 0\n static inline unsigned UpdateKnnList (Neighbor *addr, unsigned K, Neighbor nn) {\n // find the location to insert\n unsigned j;\n unsigned i = K;\n while (i > 0) {\n j = i - 1;\n if (addr[j].dist <= nn.dist) break;\n i = j;\n }\n // check for equal ID\n unsigned l = i;\n while (l > 0) {\n j = l - 1;\n if (addr[j].dist < nn.dist) break;\n if (addr[j].id == nn.id) return K + 1;\n l = j;\n }\n // i <= K-1\n j = K;\n while (j > i) {\n addr[j] = addr[j-1];\n --j;\n }\n addr[i] = nn;\n return i;\n }\n\n void LinearSearch (IndexOracle const &oracle, unsigned i, unsigned K, vector *pnns) {\n vector nns(K+1);\n unsigned N = oracle.size();\n Neighbor nn;\n nn.id = 0;\n nn.flag = true; // we don't really use this\n unsigned k = 0;\n while (nn.id < N) {\n if (nn.id != i) {\n nn.dist = oracle(i, nn.id);\n UpdateKnnList(&nns[0], k, nn);\n if (k < K) ++k;\n }\n ++nn.id;\n }\n nns.resize(K);\n pnns->swap(nns);\n }\n\n unsigned SearchOracle::search (unsigned K, float epsilon, unsigned *ids, float *dists) const {\n vector nns(K+1);\n //cout<<\"oracle searching\"< epsilon) continue;\n UpdateKnnList(&nns[0], L, Neighbor(k, k_dist));\n if (L < K) ++L;\n }\n if (ids) {\n for (unsigned k = 0; k < L; ++k) {\n ids[k] = nns[k].id;\n }\n }\n if (dists) {\n for (unsigned k = 0; k < L; ++k) {\n dists[k] = nns[k].dist;\n }\n }\n return L;\n }\n\n void GenerateControl (IndexOracle const &oracle, unsigned C, unsigned K, vector *pcontrols) {\n vector controls(C);\n {\n vector index(oracle.size());\n int i = 0;\n for (unsigned &v: index) {\n v = i++;\n }\n random_shuffle(index.begin(), index.end());\n#ifdef PARALLEL \n#pragma omp parallel for\n#endif \n for (unsigned i = 0; i < C; ++i) {\n controls[i].id = index[i];\n LinearSearch(oracle, index[i], K, &controls[i].neighbors);\n }\n }\n pcontrols->swap(controls);\n }\n\n static char const *KGRAPH_MAGIC = \"KNNGRAPH\";\n static unsigned constexpr KGRAPH_MAGIC_SIZE = 8;\n static uint32_t constexpr VERSION_MAJOR = 2;\n static uint32_t constexpr VERSION_MINOR = 0;\n\n class KGraphImpl: public KGraph {\n protected:\n vector M;\n vector> graph;\n public:\n virtual ~KGraphImpl () {\n }\n virtual void load (char const *path) {\n BOOST_VERIFY(sizeof(unsigned) == sizeof(uint32_t));\n ifstream is(path, ios::binary);\n char magic[KGRAPH_MAGIC_SIZE];\n uint32_t major;\n uint32_t minor;\n uint32_t N;\n is.read(magic, sizeof(magic));\n is.read(reinterpret_cast(&major), sizeof(major));\n is.read(reinterpret_cast(&minor), sizeof(minor));\n if (major != VERSION_MAJOR) throw runtime_error(\"data version not supported.\");\n is.read(reinterpret_cast(&N), sizeof(N));\n if (!is) runtime_error(\"error reading index file.\");\n for (unsigned i = 0; i < KGRAPH_MAGIC_SIZE; ++i) {\n if (KGRAPH_MAGIC[i] != magic[i]) runtime_error(\"index corrupted.\");\n }\n graph.resize(N);\n M.resize(N);\n for (unsigned i = 0; i < graph.size(); ++i) {\n auto &knn = graph[i];\n unsigned K;\n is.read(reinterpret_cast(&M[i]), sizeof(M[i]));\n is.read(reinterpret_cast(&K), sizeof(K));\n if (!is) runtime_error(\"error reading index file.\");\n knn.resize(K);\n //modified by Yifang\n for (unsigned j = 0; j < K; ++j){\n is.read(reinterpret_cast(&knn[j].id), sizeof(knn[j].id));\n knn[j].flag = true;\n knn[j].dist = 0.0;\n }\n //is.read(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n }\n }\n\n virtual void merge(char const * graph_path, char const * id_path){\n\tifstream is(graph_path, ios::binary);\n\tchar magic[KGRAPH_MAGIC_SIZE];\n\tuint32_t major;\n\tuint32_t minor;\n\tuint32_t N;\n\tis.read(magic, sizeof(magic));\n\tis.read(reinterpret_cast(&major), sizeof(major));\n\tis.read(reinterpret_cast(&minor), sizeof(minor));\n\tif (major != VERSION_MAJOR) throw runtime_error(\"data version not supported.\");\n\tis.read(reinterpret_cast(&N), sizeof(N));\n\tif (!is) runtime_error(\"error reading index file.\");\n\tfor (unsigned i = 0; i < KGRAPH_MAGIC_SIZE; ++i) {\n\t if (KGRAPH_MAGIC[i] != magic[i]) runtime_error(\"index corrupted.\");\n\t}\n\tvector > small_graph;\n\tvector small_M;\n\tsmall_graph.resize(N);\n\tsmall_M.resize(N);\n\tfor (unsigned i = 0; i < small_graph.size(); ++i) {\n\t auto &knn = small_graph[i];\n\t unsigned K;\n\t is.read(reinterpret_cast(&small_M[i]), sizeof(small_M[i]));\n\t is.read(reinterpret_cast(&K), sizeof(K));\n\t if (!is) runtime_error(\"error reading index file.\");\n\t knn.resize(K);\n\t is.read(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n\t}\n\t//loaded the small graph\n\tFILE *fp = fopen(id_path, \"rb\");\n\tunsigned * id_arr = new unsigned[N];\n\tfread(id_arr, sizeof(unsigned), N, fp);\n\tfclose(fp);\n\n\tfor (unsigned i = 0; i < N; ++i){\n\t unsigned real_id = id_arr[i];\n\t auto &small_knn = small_graph[i];\n\t auto &knn = graph[real_id];\n\t if (M[real_id] != knn.size()){\n\t knn.resize(M[real_id]);\n\t }\n\t for (unsigned k = 0; k < small_M[i]; ++k){\n\t knn.push_back(Neighbor(id_arr[small_knn[k].id], small_knn[k].dist, false));\n\t }\n\t sort(knn.begin(), knn.end());\n\t for (unsigned k = 1; k < knn.size(); ++k){\n\t if (knn[k].id == knn[k-1].id){\n\t knn.erase(knn.begin() + k);\n\t k--;\n\t }\n\t }\n\t M[real_id] = knn.size();\n\t}\t\n }\n\n virtual void output_info(int const id){\n\t if (id > graph.size()){\n\t fprintf(stderr, \"out_of_index_bound!\\n\");\n\t return;\n\t }\n\t auto const &knn = graph[id];\n\t knn.size();\n\t \n\t fprintf(stderr, \"%d\\n%d\\n=================\\n\", M[id], knn.size());\n\t for (int i = 0; i < knn.size(); ++i){\n\t fprintf(stderr, \"%d %f\\n\", knn[i].id, knn[i].dist);\n\t }\n\t}\n\n\n struct hub_pair{\n\tunsigned id;\n\tint in_degree;\n\thub_pair(unsigned x, int y){\n\t id = x;\n\t in_degree = y;\n\t}\n\tbool operator<(const hub_pair& rhs) const{\n\t return in_degree > rhs.in_degree;\n\t}\n };\n \n struct rknn_pair{\n\tunsigned id;\n\tfloat dist;\n\trknn_pair(unsigned x, float y){\n\t id = x;\n\t dist = y;\n\t}\n };\n\n virtual void connectivity(unsigned const source, unsigned const dest){\n\tuint32_t N = graph.size();\n\tint level[N];\n\tfor (int i = 0; i < N; ++i){\n\t level[i] = 0;\n\t}\n\t\n\tqueue q;\n\tq.push(source);\n\tlevel[source] = 1;\n\tbool find = false;\n\twhile (!q.empty() && !find){\n\t unsigned cur_id = q.front();\n\t auto const &knn = graph[cur_id];\n\t q.pop();\n\t for (int i = 0; i < M[cur_id]; ++i){\n\t if (level[knn[i].id] != 0){\n\t continue;\n\t }\n\t q.push(knn[i].id);\n\t level[knn[i].id] = level[cur_id] + 1;\n\t if (knn[i].id == dest){\n\t find = true;\n\t fprintf(stderr, \"Found! Level = %d\\n\", level[knn[i].id]);\n\t }\n\t }\n\t}\n\tif (!find){\n\t fprintf(stderr, \"Not Found!\\n\");\n\t}\n }\n\n virtual void get_partitions(char const *output_path){\n\tvector > partitions;\n\tuint32_t N = graph.size();\n\tfor (int i = 0; i < N; ++i){\n\t if (i % 1000 == 0) {\n fprintf(stderr, \"\\r%d (%.3f\\%)\", i, 1.0 * i / N * 100);\n }\n\t auto const &knn = graph[i];\n\t int major_set_pos = -1;\n\t for (int j = 0; j < partitions.size(); ++j){\n\t if (find (partitions[j].begin(), partitions[j].end(), i) != partitions[j].end()){\n\t major_set_pos = j;\n\t break;\n\t }\n\t }\n\t if (major_set_pos == -1){\n\t vector new_set;\n\t new_set.push_back(i);\n\t partitions.push_back(new_set);\n\t major_set_pos = partitions.size() - 1;\n\t //fprintf(stderr, \"%d is in %d\\n\", i, major_set_pos);\n\t }\n\n\t //fprintf(stderr, \"i is in %d\\n\", major_set_pos);\n\t \n\t unsigned maxM = M[i];\n\t for (int j = 0; j < maxM; ++j){\n\t bool done = false;\n\t //test if knn[j] is in some partition\n\t for (int k = 0; k < partitions.size(); ++k){\n\t if (find (partitions[k].begin(), partitions[k].end(), knn[j].id) != partitions[k].end()){\n\t\tif (k == major_set_pos){\n\t\t done = true;\n\t\t break;\n\t\t} else {\n\t\t //merge\n\t\t //fprintf(stderr, \"markmerge\\n\");\n\t\t for ( auto it = partitions[k].begin(); it != partitions[k].end(); ++it ){\n\t\t //fprintf(stderr, \"markmerge %d %d\\n\", *it, partitions[major_set_pos].max_size());\n\t\t partitions[major_set_pos].push_back(*it);\n\t\t //fprintf(stderr, \"markmergeee\\n\");\n\t\t }\n\t\t //fprintf(stderr, \"markmerge done\\n\");\n\t\t //partitions[major_set_pos].insert(partitions[k].begin(), partitions[k].end());\n\t\t //vector temp = partitions.end();\n\t\t \n\t\t //\t\t partitions[k].swap(partitions.back());\n\t\t partitions[k].clear();\n\t\t for ( auto it = partitions.back().begin(); it != partitions.back().end(); ++it ){\n //fprintf(stderr, \"markmerge %d %d\\n\", *it, partitions[major_set_pos].max_size());\n partitions[k].push_back(*it);\n //fprintf(stderr, \"markmergeee\\n\");\n }\n\t\t partitions.back().clear();\n\t\t //partitions.erase(partitions.begin() + k);\n\t\t done = true;\n\t\t //fprintf(stderr, \"mark merge done\\n\");\n\t\t break;\n\t\t}\n\t }\n\t }\n\t if (!done){\n\t //add\n\t partitions[major_set_pos].push_back(knn[j].id);\n\t continue;\n\t }\n\t }\n\t //fprintf(stderr, \"mark2\\n\");\n\t}\n\tfprintf(stderr, \"%d\\n\", partitions.size());\n\t//\treturn partitions;\n\tint count = 0;\n\tfor (int i = 0; i < partitions.size(); ++i){\n\t if (partitions[i].size() != 0){\n\t fprintf(stderr, \"%d\\n\", partitions[i].size());\n\t count ++;\n\t }\n\t}\n\tfprintf(stderr, \"%d\\n\", count);\n\n }\n\n virtual void output_M (char const* filename) {\n FILE *fp = fopen(filename, \"wb\");\n unsigned N = graph.size();\n fwrite(&N, sizeof(unsigned), 1, fp);\n\tfor (int i = 0; i < N; ++i){\n unsigned len = M[i];\n fwrite(&len, sizeof(unsigned), 1, fp);\n }\n\tfclose(fp);\n }\n\n\n virtual void make_inverse_graph(){\n\tuint32_t N = graph.size();\n\tvector > inverse_graph;\n\tfor (unsigned i = 0; i < N; ++i){\n\t inverse_graph.push_back(vector());\n\t}\n\tfor (unsigned i = 0; i < N; ++i) {\n auto const &knn = graph[i];\n\t for (unsigned j = 0; j < M[i]; j++) {\n\t inverse_graph[knn[j].id].push_back(Neighbor(i, knn[j].dist, false));\n\t }\n\t}\n\tfor (unsigned i = 0; i < N; ++i) {\n\t M[i] = inverse_graph[i].size();\n\t}\n\tgraph = inverse_graph;\n }\n \n \n\n virtual void test_connectivity(unsigned dest){ // for inverse kgraph\n\tuint32_t N = graph.size();\n\tbool * traversed = new bool[N];\n\tqueue q;\n\tq.push(dest);\n\tmemset(traversed, 0, sizeof(bool)*N);\n\ttraversed[dest] = true;\n\tint count = 1;\n\t\n\twhile(!q.empty()){\n\t unsigned pivot = q.front();\n\t q.pop();\n\t auto const &knn = graph[pivot];\n\t for (unsigned i = 0; i < knn.size(); ++i){\n\t if (!traversed[knn[i].id]){\n\t q.push(knn[i].id);\n\t traversed[knn[i].id] = true;\n\t count++;\n\t }\n\t }\n\t}\n\n\tfprintf(stderr, \"%d points connected with pivot\\n\", count);\n }\n\n virtual void add_random_backward_edges(char const *data_path, char const *hubs_path, int const num_of_hubs, char const * start_points_path, unsigned num_of_query){\n uint32_t N = graph.size();\n\t//ofstream os(hubs_path, ios::binary);\n vector hubs;\n\tvector > rknn_graph;\n Matrix data;\n data.load_lshkit(data_path);\n\n\tint count = 0;\n\n for (unsigned i = 0; i < N; ++i) {\n\t hubs.push_back(hub_pair(i, 0));\n rknn_graph.push_back(vector());\n\t}\n\n\tfor (unsigned i = 0; i < N; ++i) {\n auto const &knn = graph[i];\n uint32_t K = knn.size();\n\t for (unsigned j=0; j ids;\n\tids.resize(num_of_query, num_of_hubs);\n Matrix hub_data;\n hub_data.resize(num_of_hubs, data.dim());\n\n\t//unsigned * ids = new unsigned[num_of_hubs];\n for (unsigned i = 0; i < num_of_hubs; ++i){\n //fprintf(stderr, \"%d: %d %d\\n\", i, hubs[i].id, hubs[i].in_degree);\n\t // add backward edges\n vector rknn_list = rknn_graph[hubs[i].id];\n\t count += rknn_list.size();\n for (unsigned j = 0; j < rknn_list.size(); ++j){\n graph[hubs[i].id].push_back(Neighbor(rknn_list[j].id, rknn_list[j].dist, true)); //rknn_list[j]);\n }\n\n for (unsigned j = 0; j < hub_data.dim(); ++j){\n hub_data[i][j] = data[hubs[i].id][j];\n }\n\n // write start point file\n\t for (int j = 0; j < num_of_query; ++j)\n ids[j][i] = hubs[i].id;\n //fprintf(stderr, \"%d: %d %d\\n\", i, ids[j][i], hubs[i].in_degree);\n sort(graph[hubs[i].id].begin(), graph[hubs[i].id].end());\n M[hubs[i].id] = rknn_list.size();\n\t}\n ids.save_lshkit(start_points_path);\n\thub_data.save_lshkit(hubs_path);\n // for (unsigned i = 0; i < num_of_query; ++i){\n // os2.write((const char *)ids, sizeof(unsigned) * header[2]);\n //}\n //delete[] ids;\n\tfprintf(stderr, \"inverse edges: %d\\n\", count);\n }\n\n virtual void add_backward_edges(){\n\tuint32_t N = graph.size();\n\t//ofstream os(hubs_path, ios::binary);\n\t//vector hubs;\n\tvector > rknn_graph;\n rknn_graph.resize(N);\n \n\tint count = 0;\n \n\t\n\tfor (unsigned i = 0; i < N; ++i) {\n\t auto const &knn = graph[i];\n\t uint32_t K = M[i]; //knn.size();\n\t for (unsigned j=0; j rknn_list = rknn_graph[i];\n\t count += rknn_list.size();\n\t \n for (unsigned j = 0; j < rknn_list.size(); ++j){\n\t graph[i].push_back(Neighbor(rknn_list[j].id, rknn_list[j].dist, true)); //rknn_list[j]);\n\t //sum += exp(-1 * sqrt(rknn_list[j].dist) * beta); // a function with dist\n\t }\n\n\t sort(graph[i].begin(), graph[i].end());\n\t for (unsigned j = 1; j < graph[i].size(); ++j){\n\t if (graph[i][j].id == graph[i][j - 1].id){\n\t graph[i].erase(graph[i].begin() + j);\n\t j--;\n\t }\n\t }\n\t \n M[i] = graph[i].size();\n\t}\n\tfprintf(stderr, \"inverse edges: %d\\n\", count);\n }\n\n\n virtual void compute_diversity(IndexOracle const &oracle, int d, float * norm){ // diversity\n\tif (oracle.size() != graph.size()){\n throw runtime_error(\"Build kgraph before adding long edges\");\n }\n\n uint32_t N = oracle.size();\n\tfloat * avg_arr = new float[N];\n\tuint32_t true_N = N;\n#pragma omp parallel for\n for (unsigned k = 0; k < N; k++){\n int len = M[k];\n\t int true_len = len * (len - 1) / 2;\n\t float sum_angle = 0.0;\n for (int i = 0; i < len; ++i){ // the first max_edge_num elements are the remaining ones\n\t if (graph[k][i].dist < 0.00001){\n\t true_len -= len - i - 1;\n\t continue;\n\t }\n for (int cand_pos = i + 1; cand_pos < len; cand_pos++){\n\t float angle = oracle.angle(k, graph[k][cand_pos].id, graph[k][i].id);\n\t if (!(angle > 0.00001)){\n\t\ttrue_len --;\n\t\tcontinue;\n\t }\n sum_angle += angle; //oracle.angle(k, graph[k][cand_pos].id, graph[k][i].id);\n }\n }\n\t if (true_len <= 0){\n\t avg_arr[k] = 0.0;\n\t true_N--;\n\t } else {\n\t avg_arr[k] = sum_angle / true_len;\n\t }\n\t //\t if (!(avg_arr[k] > 0.00001))\n\t // fprintf(stderr, \"%d %f %f\\n\", k, sum_angle, avg_arr[k]);\n\n }\n\n\tdouble avg_angle = 0.0;\n\tdouble normed_angle = 0.0;\n\tfor (unsigned k = 0; k < N; ++k){\n\t avg_angle = avg_angle + avg_arr[k];\n\t normed_angle += avg_arr[k] / norm[M[k] - 1];\n\t}\n\n\t//avg_angle /= N;\n\t//normed_angle /= N;\n\n\tcout << \"Average pairwise angle: \" << avg_angle / true_N << endl;\n\tcout << \"Normed pairwise angle: \" << normed_angle / N << endl;\n\n\tdelete[] avg_arr;\n }\n\n\nvirtual void diversify_by_cut(IndexOracle const &oracle, const int edge_num){\n\n if (oracle.size() != graph.size()){\n throw runtime_error(\"diversify_by_cut : inconsistent.\" );\n }\n \n uint32_t N = oracle.size();\n\n int cnt = 0;\n int nnt = 0;\n\n unsigned step = N / 100;\n cerr << endl << \"Progress : \";\n\n // #pragma omp parallel for\n for (unsigned k = 0; k < N; k++){\n\n if ( k % step == 0 ) cerr <<\"*\";\n\n\n // float *map = new float[N];\n // memset(map, 0, sizeof(float)*N);\n\n int len = graph[k].size();\n while(graph[k][len-1].id == 0){\n len--;\n }\n if (len > 2 * edge_num){\n len = 2 * edge_num;\n } \n \n // materialize the ditance here \n int* hit = new int[len];\n\n for ( int i=0; i< len; i++){\n graph[k][i].dist = oracle(k, graph[k][i].id );\n hit[i] = 0;\n } \n\n for ( int i=0; i < len-1; i++ ){\n // for ( int j=0; j < len; j++){\n for ( int j=i+1; j < len; j++){\n \n if ( i==j ) continue;\n\n Neighbor n_b = graph[k][i]; \n Neighbor n_k = graph[k][j];\n\n float dist = oracle(n_b.id, n_k.id);\n // cout << dist << \" : (\" << n_b.id << \" , \" << n_k.id << endl; \n if ( dist < n_k.dist ){\n hit[j]++;\n }\n }\n }\n\n\n // sort by the hits and find the cuts \n int* b_hit = new int[len];\n for ( int i=0; i< len; i++ )\n b_hit[i] = hit[i];\n\n // memcpy( b_hit, hit, sizeof(int)*len);\n\n sort(b_hit, b_hit + len );\n float cut = b_hit[edge_num];\n\n // update the neighbors by #hits \n Neighbor tmp[len];\n for ( int i=0; i < len; i++)\n tmp[i] = graph[k][i];\n\n\n cnt = 0;\n for ( int i=0; i < len; i++){ \n if ( hit[i] <= cut )\n graph[k][cnt++] = tmp[i];\n }\n\n graph[k].resize(edge_num); // reset the size of NN list \n\n delete []hit;\n delete []b_hit;\n\n /* \n if ( k % 100 == 0 ){\n for ( int i=0; i< len; i++){\n if ( hit[i]> cut )\n cout << hit[i] << \"* \";\n else\n cout << hit[i] << \" \";\n }\n cout << endl;\n }\n */ \n\n /*\n for (int i = 1; i < edge_num; ++i){ // the first max_edge_num elements are the remaining ones\n float max_sum_angle = -1.0;\n int max_pos = -1;\n for (int cand_pos = i; cand_pos < len; cand_pos++){\n map[graph[k][cand_pos].id] += oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n float sum_angle = map[graph[k][cand_pos].id];\n if (sum_angle > max_sum_angle){\n max_sum_angle = sum_angle;\n max_pos = cand_pos;\n }\n }\n Neighbor temp = graph[k][i];\n graph[k][i] = graph[k][max_pos];\n graph[k][max_pos] = temp;\n }\n */\n\n\n // graph[k].resize(edge_num); // reset the size of NN list \n\n // sort(graph[k].begin(), graph[k].end());\n \n M[k] = edge_num;\n \n // delete[] map;\n }\n\n cerr << endl;\n\n } // end of diversify_by_cut\n \n\n virtual float statistics(IndexOracle const &oracle, const int edge_num){ // diversity\n if (oracle.size() != graph.size()){\n throw runtime_error(\"in consistent ~!\");\n }\n\n uint32_t N = oracle.size();\n float avg_co (0.0);\n\n unsigned step = N / 100;\n cerr << \"Progress : \";\n\n for (unsigned k = 0; k < N; k++){\n\n if ( k % step == 0) cerr <<\"*\"; \n\n int len = graph[k].size();\n while(graph[k][len-1].id == 0){\n len--;\n }\n if (len > edge_num){\n len = edge_num;\n }\n\n\n // check each pair of neighbors \n long hit = 0; \n for ( int i=0; i < len; i++ ) \n for ( int j=i+1; j < len; j++ ){\n\n // check if the edge < id[i] and id[j] exisits in the K-NN-graph; now we assume \n bool exist = false;\n // check i's NBs \n unsigned id_1 = graph[k][i].id;\n unsigned id_2 = graph[k][j].id;\n for ( int s=0; s < len; s++)\n if (graph[id_1][s].id == id_2) {\n exist = true; break;\n }\n\n if ( !exist ) continue; \n\n for ( int s=0; s < len; s++)\n if (graph[id_2][s].id == id_1) {\n hit++; break;\n }\n } \n\n // also check j's NBs \n float ratio = 2.0*hit/(len*(len-1)); \n avg_co += ratio;\n\n } // end of K \n\n cerr << endl;\n\n avg_co = avg_co / N;\n\n return avg_co;\n \n } // end of function statistics \n \n\n virtual void remove_near_edges(IndexOracle const &oracle, const int edge_num){ // diversity\n\tif (oracle.size() != graph.size()){\n\t throw runtime_error(\"Build kgraph before adding long edges\");\n\t}\n\t\n\tuint32_t N = oracle.size();\n unsigned step = N / 100;\n cerr << \"Progress : \";\n\n //#pragma omp parallel for\n\tfor (unsigned k = 0; k < N; k++){\n\n if ( k % step == 0 ) cerr <<\"*\";\n\n\t //if (k % 1000 == 0) {\n\t // fprintf(stderr, \"\\r%d (%.3f\\%)\", k, 1.0 * k / N * 100);\n\t //}\n\t //auto const &knn = graph[k];\n //edge_num[k] /= 2;\n\t float * map = new float[N];\n\t memset(map, 0, sizeof(float)*N);\n\t int len = graph[k].size();\n\t while(graph[k][len-1].id == 0){\n\t len--;\n\t }\n\t if (len > 2 * edge_num){\n\t len = 2 * edge_num;\n\t } \n\t for (int i = 1; i < edge_num; ++i){ // the first max_edge_num elements are the remaining ones\n\t float max_sum_angle = -1.0;\n\t int max_pos = -1;\n\t for (int cand_pos = i; cand_pos < len; cand_pos++){\n\n\n // this is the version before sharing computation cost \n float sum_angle = 0.0;\n for (int ppp = 1; ppp < i; ++ppp){\n sum_angle += oracle.angle(k, graph[k][cand_pos].id, graph[k][ppp].id);\n }\n\n // map[graph[k][cand_pos].id] += oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n // float sum_angle = map[graph[k][cand_pos].id];\n\n\n\n\t if (sum_angle > max_sum_angle){\n\t\tmax_sum_angle = sum_angle;\n\t\tmax_pos = cand_pos;\n\t }\n\t }\n\t Neighbor temp = graph[k][i];\n\t graph[k][i] = graph[k][max_pos];\n\t graph[k][max_pos] = temp;\n\t }\n\t graph[k].resize(edge_num); // reset the size of NN list \n\n\t sort(graph[k].begin(), graph[k].end());\n\t M[k] = edge_num;\n\t delete[] map;\n\t }\n\n cerr << endl;\n\n }\n \n\n\n\n \n virtual void remove_near_edges_2(IndexOracle const &oracle, vector const &edge_num, unordered_set const &skip_set){ // diversification, maxmin\n if (oracle.size() != graph.size()){\n throw runtime_error(\"Build kgraph before adding long edges\");\n }\n\n uint32_t N = oracle.size();\n\n// #pragma omp parallel for\n for (unsigned k = 0; k < N; k++){\n\t if (skip_set.find(k) != skip_set.end()){\n continue;\n }\n //if (k % 1000 == 0) {\n // fprintf(stderr, \"\\r%d (%.3f\\%)\", k, 1.0 * k / N * 100);\n //}\n //auto const &knn = graph[k];\n float * map = new float[N];\n memset(map, 0, sizeof(float)*N);\n int len = graph[k].size();\n while(graph[k][len-1].dist == 0.0){\n len--;\n }\n for (int i = 1; i < edge_num[k]; ++i){ // the first max_edge_num elements are the remaining ones\n float min_angle = 10000000000.0;\n int min_pos = -1;\n for (int cand_pos = i; cand_pos < len; cand_pos++){\n\t float angle = oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n\t if (map[graph[k][cand_pos].id] == 0 || map[graph[k][cand_pos].id] < angle){\n\t\tmap[graph[k][cand_pos].id] = angle;\n\t }\n\t //map[graph[k][cand_pos].id] += oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n //float sum_angle = oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n if (min_angle > map[graph[k][cand_pos].id]){\n min_angle = map[graph[k][cand_pos].id];\n min_pos = cand_pos;\n }\n }\n Neighbor temp = graph[k][i];\n graph[k][i] = graph[k][min_pos];\n graph[k][min_pos] = temp;\n }\n graph[k].resize(edge_num[k]);\n sort(graph[k].begin(), graph[k].end());\n M[k] = edge_num[k];\n delete[] map;\n }\n }\n\n virtual void add_long_edges(IndexOracle const &oracle, int const rank_top_k, int const change_num){\n\t if (oracle.size() != graph.size()){\n\t throw runtime_error(\"Build kgraph before adding long edges\");\n\t }\n\n\t //fprintf(stderr, \"mark\\n\");\n\n\t uint32_t N = oracle.size();\n\t vector rank_id_vec; // index: rank; value: id\n\t vector id_rank_vec; // index: id; value: rank\n\t for (unsigned i = 0; i < N; ++i){\n\t rank_id_vec.push_back(i);\n\t }\n\t id_rank_vec.resize(N);\n\n\t //fprintf(stderr, \"mark\\n\");\n\n\t for (unsigned i = 0; i < N; ++i){ \n\t //fprintf(stderr, \"=======length: %d=========\\n\", graph[i].size());\n\t if (i % change_num == 0){\n\t // re-shuffle\n\t random_shuffle(rank_id_vec.begin(), rank_id_vec.end());\n\t for (unsigned j = 0; j < N; ++j){\n\t\tid_rank_vec[rank_id_vec[j]] = j;\n\t }\n\t }\n\t unsigned start = id_rank_vec[i] - rank_top_k / 2;\n\t if (start < 0) {\n\t start = 0;\n\t }\n\t unsigned end = id_rank_vec[i] + rank_top_k / 2;\n\t if (end >= N) {\n\t end = N - 1;\n\t }\n\t \n\t //fprintf(stderr, \"start = %d, end = %d\\n\", start, end);\n\n\t for (unsigned j = start; j <= end; ++j){\n\t //test if rank_id[j] should be added\n\t float dist = oracle(rank_id_vec[j], i);\n\t bool is_dominated = false;\n\t for (int k = 0; k < graph[i].size(); ++k){\n\t\t// if there exists a point in graph[i] that is closer to i in both distance and rank, \n\t\t// then the test point will be not added into the graph\n\t\tif (abs(j - id_rank_vec[i]) > abs(id_rank_vec[i] - id_rank_vec[graph[i][k].id]) && dist > graph[i][k].dist){\n\t\t is_dominated = true;\n\t\t break;\n\t\t}\n\t }\n\t if (!is_dominated){\n\t\tM[i] += 1;\n\t\tgraph[i].push_back(Neighbor(rank_id_vec[j], dist, true));\n\t }\n\t }\n\n\t //fprintf(stderr, \"length: %d\\n\", graph[i].size());\n\n\t sort(graph[i].begin(), graph[i].end());\n\t } // for\n\t //fprintf(stderr, \"final mark\\n\");\n\t}\n\n virtual void saver (char const *path) const {\n\tuint32_t N = graph.size();\n\t//fprintf(stderr,\"%d\\n\", N);\n\tofstream os(path, ios::binary);\n\tos.write(KGRAPH_MAGIC, KGRAPH_MAGIC_SIZE);\n\tos.write(reinterpret_cast(&VERSION_MAJOR), sizeof(VERSION_MAJOR));\n\tos.write(reinterpret_cast(&VERSION_MINOR), sizeof(VERSION_MINOR));\n\tos.write(reinterpret_cast(&N), sizeof(N));\n\tfor (unsigned i = 0; i < graph.size(); ++i) {\n\t auto const &knn = graph[i];\n\t uint32_t K = knn.size();\n\t //cout<(&K), sizeof(K));\n\t os.write(reinterpret_cast(&K), sizeof(K));\n\t os.write(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n\t}\n }\n\n virtual void save (char const *path) const {\n uint32_t N = graph.size();\n ofstream os(path, ios::binary);\n os.write(KGRAPH_MAGIC, KGRAPH_MAGIC_SIZE);\n os.write(reinterpret_cast(&VERSION_MAJOR), sizeof(VERSION_MAJOR));\n os.write(reinterpret_cast(&VERSION_MINOR), sizeof(VERSION_MINOR));\n os.write(reinterpret_cast(&N), sizeof(N));\n for (unsigned i = 0; i < graph.size(); ++i) {\n auto const &knn = graph[i];\n uint32_t K = knn.size();\n\t\t\t\t\n os.write(reinterpret_cast(&K), sizeof(K)); // modified by Yifang\n os.write(reinterpret_cast(&K), sizeof(K));\n for (unsigned j = 0; j < K; ++j){\n os.write(reinterpret_cast(&knn[j].id), sizeof(knn[j].id));\n }\n //os.write(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n }\n }\n\t\t\n virtual void get_hubs (char const *hubs_path,char const *knn_path) const {\n uint32_t N = graph.size();\n ofstream os(hubs_path, ios::binary);\n ofstream knn_out(knn_path, ios::binary);\n int hubs[N];\n\t memset(hubs,0,sizeof(int)*N);\n for (unsigned i = 0; i < N; ++i) {\n auto const &knn = graph[i];\n uint32_t K = knn.size();\n for (unsigned j=0;j K) {\n v.resize(K);\n }\n }\n return;\n vector> pruned(graph.size());\n vector> reachable(graph.size());\n vector added(graph.size());\n for (unsigned k = 0; k < K; ++k) {\n#pragma omp parallel for\n for (unsigned n = 0; n < graph.size(); ++n) {\n vector const &from = graph[n];\n if (from.size() <= k) continue;\n unsigned e = from[k];\n if (reachable[n].count(e)) {\n added[n] = false;\n }\n else {\n pruned[n].push_back(e);\n added[n] = true;\n }\n }\n // expand reachable\n#pragma omp parallel for\n for (unsigned n = 0; n < graph.size(); ++n) {\n vector const &to = pruned[n];\n set &nn = reachable[n];\n if (added[n]) {\n for (unsigned v: pruned[to.back()]) {\n nn.insert(v);\n }\n }\n for (unsigned v: to) {\n if (added[v]) {\n nn.insert(pruned[v].back());\n }\n }\n }\n }\n graph.swap(pruned);\n }\n */\n\n virtual void kgraph2dot (char * const &filename){\n\tstd::ofstream fs;\n\tfs.open(filename, std::ofstream::out);\n\n\t//\tfs << \"strict digraph g {\" << endl;\n\n\tfor (unsigned i = 0; i < graph.size(); ++i){\n\t for (unsigned j = 0; j < M[i]; ++j){\n\t fs << i << \" => \" << graph[i][j].id << endl; //\"[ label=\\\"\" << graph[i][j].dist << \"\\\" ];\" << endl;;\n\t }\n\t}\n\n\t//fs << \"}\" << endl;\n }\n\n virtual unsigned advanced_search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *pinfo, string const &info_path) const {// Matrix const &d_init,\n\t/*fprintf(stderr, \"all_dist=(%f\", oracle(0));\n\t for (int i = 1; i < oracle.size(); ++i){\n\t fprintf(stderr, \", %f\", oracle(i));\n\t\t}*/\n\t//fprintf(stderr, \"access_dist=(\");\n\t//fprintf(stdout, \"minimum_dist=(\");\n\n\tbool print_flag = true;\n\tstd::ofstream path_fs, mind_fs, curd_fs, nh_fs;\n\tstring file_name;\n\tif (info_path.empty()) {\n\t print_flag = false;\n\t}\n\t\n\tif (print_flag){\n\t file_name.assign(info_path);\n\t file_name += \"_path.txt\";\n\t path_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_mindist.txt\";\n\t mind_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_curdist.txt\";\n\t curd_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_nh.txt\";\n nh_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t}\n\t\n\tif (graph.size() > oracle.size()) {\n\t throw runtime_error(\"dataset larger than index\");\n\t}\n\tif (params.P >= graph.size()) {\n\t if (pinfo) {\n\t pinfo->updates = 0;\n\t pinfo->cost = 1.0;\n\t }\n\t return oracle.search(params.K, params.epsilon, ids, dists);\n\t}\n\tvector knn(params.K + params.P +1);\n\tvector init_knn(params.init);\n\tvector results;\n\tboost::dynamic_bitset<> flags(graph.size(), false);\n\n\tif (params.init && params.T > 1) {\n\t throw runtime_error(\"when init > 0, T must be 1.\");\n\t}\n\n\tunsigned seed = params.seed;\n\tunsigned updates = 0;\n\tif (seed == 0) seed = time(NULL);\n\tmt19937 rng(seed);\n\tunsigned n_comps = 0;\n\tfor (unsigned trial = 0; trial < params.T; ++trial) {\n\t unsigned L = params.init;\n\t //init>=K\n\t if (L == 0) { // generate random starting points\n\t vector random(params.P);\n\t GenRandom(rng, &random[0], random.size(), graph.size());\n\t for (unsigned s: random) {\n\t if (!flags[s]) {\n\t\tknn[L++].id = s;\n\t\t//flags[s] = true;\n\t }\n\t }\n\t for (unsigned k = 0; k < L; ++k) {\n\t flags[knn[k].id] = true;\n\t knn[k].flag = true;\n\t knn[k].dist = oracle(knn[k].id);\n\t \n\t }\n\t sort(knn.begin(), knn.begin() + L);\n\t }\n\t else { // user-provided starting points.\n\t BOOST_VERIFY(ids); //ids\n\t //std::cout<<\"set seed\"< knn.size()\n\t init_knn[l].id = ids[l]; //ids\n\t init_knn[l].flag = true;\n init_knn[l].dist = dists[l];\n\t //fprintf(stderr, \"%d: %d\\n\", l, knn[l].id);\n\t }\n\t //std::cout< init_fknn(graph.size());\n\t\t for (unsigned k = 0; k < graph.size(); ++k){\n\t\t init_fknn[k].id = k;\n\t\t\t init_fknn[k].flag = true;\n\t\t\t\t init_fknn[k].dist = oracle(k);\n\t\t\t\t\t }\n\t\t\t\t\t sort(init_fknn.begin(), init_fknn.end());\n\t\t\t\t\t\t //end delete\n\t\t\t\t\t\t */\n\t L = params.K;\n\t for (int ii = 0; ii < L; ++ii){\n\t //flags[init_fknn[graph.size() - 1 - ii].id] = true;\n\t //knn[L - 1 - ii] = init_fknn[graph.size() - 1 - ii];\n\t //recover\n\t flags[init_knn[ii].id] = true;\n\t knn[ii] = init_knn[ii];\n\t //end recover\n\t //fprintf(stderr, \"%d %f\\n\", knn[ii].id, knn[ii].dist);\n\t }\n\t }\n\n\t if (print_flag){\n\t for (unsigned k = 0; k < L; ++k){\n\t path_fs << \"-3 \" << knn[k].id << endl;\n\t mind_fs << knn[0].dist << endl;\n\t curd_fs << knn[k].dist << endl;\n\t \n\t nh_fs << M[knn[k].id] << \" \";\n\t unordered_set set;\n\t set.insert(knn[k].id);\n\t auto const &neighbors = graph[knn[k].id];\n\t for (unsigned l = 0; l < M[knn[k].id]; ++l){\n\t\tset.insert(neighbors[l].id);\n\t\tauto const &new_neighbors = graph[neighbors[l].id];\n\t\tfor (unsigned ll = 0; ll < M[neighbors[l].id]; ++ll){\n\t\t set.insert(new_neighbors[ll].id);\n\t\t}\n\t }\n\t nh_fs << set.size() << endl;\n\t }\n\t }\n\t \n\t unsigned k = 0;\n\t while (k < L) {\n\t unsigned nk = L;\n\t if (knn[k].flag) {\n\t knn[k].flag = false;\n\t unsigned cur = knn[k].id;\n\t //BOOST_VERIFY(cur < graph.size());\n\t unsigned maxM = M[cur];\n\t if (params.M > maxM) maxM = params.M;\n\t auto const &neighbors = graph[cur];\n\t if (maxM > neighbors.size()) {\n\t\tmaxM = neighbors.size();\n\t }\n\t for (unsigned m = 0; m < maxM; ++m) {\n\t\tunsigned id = neighbors[m].id;\n\t\t//BOOST_VERIFY(id < graph.size());\n\t\tif (flags[id]) continue;\n\t\tflags[id] = true;\n\t\t++n_comps;\n\t\t//if(n_comps >= params.Maxchecks)\n\t\t// {\n\t\t// break;\n\t\t// }\n\t\tfloat dist = oracle(id);\n\t\t \n\t\t/*if(n_comps == 1){\n\t\t fprintf(stderr, \"%f\", dist);\n\t\t\t } else {\n\t\t\t fprintf(stderr, \", %f\", dist);\n\t\t\t\t }*/\n\t\tNeighbor nn(id, dist);\n\t\tunsigned r = UpdateKnnList(&knn[0], L, nn);\n\t\tif (print_flag){\n\t\t path_fs << cur << \" \" << id << endl;\n\t\t mind_fs << knn[0].dist << endl;\n\t\t curd_fs << dist << endl;\n\n\t\t nh_fs << M[id] << \" \";\n\t\t unordered_set set;\n\t\t set.insert(id);\n\t\t auto const &nbors = graph[id];\n\t\t for (unsigned l_1 = 0; l_1 < M[id]; ++l_1){\n\t\t set.insert(nbors[l_1].id);\n\t\t auto const &new_neighbors = graph[nbors[l_1].id];\n\t\t for (unsigned ll = 0; ll < M[nbors[l_1].id]; ++ll){\n\t\t set.insert(new_neighbors[ll].id);\n\t\t }\n\t\t }\n\t\t nh_fs << set.size() << endl;\n\t\t}\n\t\t/*if(n_comps == 1){\n fprintf(stdout, \"%f\", knn[0].dist);\n } else {\n fprintf(stdout, \", %f\", knn[0].dist);\n\t\t\t }*/\n\t\tBOOST_VERIFY(r <= L);\n\t\t//if (r > L) continue;\n\t\tif (L + 1 < knn.size()) ++L;\n\t\tif (r < nk) {\n\t\t nk = r;\n\t\t}\n\t }\n\t //fprintf(stderr, \"}\\n\");\n\t }\n\t if (nk <= k) {\n\t k = nk;\n\t }\n\t else {\n\t ++k;\n\t }\n\t //if(n_comps >= params.Maxchecks)\n\t //{\n\t // break;\n\t //}\n\t }\n\t if (L > params.K) L = params.K;\n\t if (results.empty()) {\n\t results.reserve(params.K + 1);\n\t results.resize(L + 1);\n\t copy(knn.begin(), knn.begin() + L, results.begin());\n\t }\n\t else {\n\t // update results\n\t for (unsigned l = 0; l < L; ++l) {\n\t unsigned r = UpdateKnnList(&results[0], results.size() - 1, knn[l]);\n\t if (r < results.size() /* inserted */ && results.size() < (params.K + 1)) {\n\t\tresults.resize(results.size() + 1);\n\t }\n\t }\n\t }\n\t}\n\t// check epsilon\n\t{\n\t for (unsigned l = 0; l < results.size(); ++l) {\n\t if (results[l].dist > params.epsilon) {\n\t results.resize(l);\n\t break;\n\t }\n\t }\n\t}\n\tunsigned L = results.size() - 1;\n\tBOOST_VERIFY(L <= params.K);\n\t// check epsilon\n\tif (ids) {\n\t for (unsigned k = 0; k < L; ++k) {\n\t ids[k] = results[k].id;\n\t }\n\t}\n\tif (dists) {\n\t for (unsigned k = 0; k < L; ++k) {\n\t dists[k] = results[k].dist;\n\t }\n\t}\n\tif (pinfo) {\n\t pinfo->updates = updates;\n\t pinfo->cost = float(n_comps) / graph.size();\n\t pinfo->checks=n_comps;\n\t}\n\tif (print_flag){\n\t path_fs << \"-1\" << endl;\n\t for (unsigned k = 0; k < results.size() - 1; ++k){\n\t path_fs << results[k].id << endl; //fprintf(stderr, \"%d [color=lightblue, style=filled, label=\\\"top-%d\\\"]\\n\", results[k].id, k+1);\n\t }\n\t path_fs << \"-2\" << endl;\n\t mind_fs << \"-1\" << endl;\n\t curd_fs << \"-1\" << endl;\n\t nh_fs << \"-1\" << endl;\n\t}\n\t//fprintf(stderr, \")\\n\");\n\t//fprintf(stdout, \")\\n\");\n\t//fprintf(stderr, \")\\nplt.hist(kg_access_dist, bins=100, normed=False, alpha=0.75, label='accessed')\\nplt.hist(all_dist, bins=100, normed=False, alpha=0.5, label='all')\\nplt.title(\\\"Distribution of accessed points by KGraph\\\")\\nplt.xlabel(\\\"distance\\\")\\nplt.ylabel(\\\"Frequency\\\")\\nplt.legend(loc='upper right')\\nshow()\\n\");\n\treturn L;\n }\n\n virtual void find_lm (SearchOracle const &oracle) const {\n\tunsigned K = 10;\n\tunsigned N = oracle.size();\n\tunsigned * ordered_nn_id = new unsigned[N];\n\tunsigned * rank = new unsigned[N];\n\tfloat * ordered_nn_dist = new float[N];\n\tbool * is_lm = new bool[N];\n\tunordered_set top_set;\n\tint * top_cover = new int[K];\n\tbool * traverse = new bool[N];\n\tint counter = 0;\n\n\n\tqueue new_queue;\n\tqueue cur_queue;\n\t\n\toracle.search(N, default_epsilon, ordered_nn_id, ordered_nn_dist);\n\n\n\tfor (unsigned i = 0; i < N; ++i){\n\t rank[ordered_nn_id[i]] = i;\n\t is_lm[i] = true;\n\t traverse[i] = false;\n\t}\n\tfor (unsigned i = 0; i < K; ++i){\n\t top_set.insert(i);\n\t}\n\t\n\tfor (unsigned i = 0; i < N; ++i){\n\t auto const &neighbors = graph[i];\n\t for (unsigned l = 0; l < M[i]; ++l){\n\t if (rank[neighbors[l].id] < rank[i]){\n\t is_lm[i] = false;\n\t break;\n\t }\n\t }\n\t if (is_lm[i]){ // is LM, output something\n\t counter ++;\n\t fprintf(stderr, \"rank:\\t%d\\n\", rank[i]);\n\t fprintf(stderr, \"dist:\\t%f\\n\", ordered_nn_dist[rank[i]]);\n\t cur_queue.push(i);\n\t traverse[i] = true;\n\t if (top_set.find(rank[i]) != top_set.end()){\n\t top_cover[rank[i]] = 0;\n\t top_set.erase(rank[i]);\n\t }\n\t }\n\t}\n\n\t//\tfprintf(stderr, \"N=%d\\n\", N);\n\t//find coverage of top-K\n\tunsigned level = 0;\n\twhile(true){\n\t level++;\n\t //fprintf(stderr, \"mark2.1\\n\");\n\t while(!cur_queue.empty()){\n\t unsigned id = cur_queue.front();\n\t cur_queue.pop();\n\t auto const &neighbors = graph[id];\n\t //fprintf(stderr, \"mark2.5 %d\\n\", M[id]);\n\t for (unsigned l = 0; l < M[id]; ++l){\n\t //fprintf(stderr, \"%d\\n\", neighbors[l].id);\n\t //fprintf(stderr, \"%d\\n\", traverse[neighbors[l].id]);\n\t if (!traverse[neighbors[l].id]){\n\t\tnew_queue.push(neighbors[l].id);\n\t\ttraverse[neighbors[l].id] = true;\n\t\t//fprintf(stderr, \"-> %d\\n\", rank[neighbors[l].id]);\n\t\tif (rank[neighbors[l].id] < K && top_set.find(rank[neighbors[l].id]) != top_set.end()){\n\t\t top_cover[rank[neighbors[l].id]] = level;\n\t\t top_set.erase(rank[neighbors[l].id]);\n\t\t if (top_set.empty()){\n\t\t goto jump_out;\n\t\t }\n\t\t}\n\t }\n\t } // end for loop\n\t }\n\t cur_queue.swap(new_queue);\n\t}\n\n\n jump_out:\n\tfprintf(stderr, \"mark3\\n\");\n\tfprintf(stderr, \"# of LMins:\\t%d\\n\", counter);\n\tfor (unsigned i = 0; i < K; ++i){\n\t fprintf(stderr, \"top-%d is %d hops away from LMins\\n\", i + 1, top_cover[i]);\n\t}\n\t\n\tdelete[] ordered_nn_id;\n\tdelete[] rank;\n\tdelete[] ordered_nn_dist;\n\tdelete[] is_lm;\n\tdelete[] top_cover;\n }\n \n virtual unsigned search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *pinfo, string const &info_path) const {// Matrix const &d_init,\n\t /*fprintf(stderr, \"all_dist=(%f\", oracle(0));\n\t for (int i = 1; i < oracle.size(); ++i){\n\t fprintf(stderr, \", %f\", oracle(i));\n\t }*/\n\t //fprintf(stderr, \"access_dist=(\");\n\t //fprintf(stdout, \"minimum_dist=(\");\n\n\tbool print_flag = true;\n\tstd::ofstream path_fs, mind_fs, curd_fs, nh_fs;\n\tstring file_name;\n\tif (info_path.empty()) {\n\t print_flag = false;\n\t}\n\t\n\tif (print_flag){\n\t file_name.assign(info_path);\n\t file_name += \"_path.txt\";\n\t path_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_mindist.txt\";\n\t mind_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_curdist.txt\";\n\t curd_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_nh.txt\";\n nh_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t}\n\t\n\tif (graph.size() > oracle.size()) {\n\t throw runtime_error(\"dataset larger than index\");\n\t}\n\tif (params.P >= graph.size()) {\n\t if (pinfo) {\n\t pinfo->updates = 0;\n\t pinfo->cost = 1.0;\n\t }\n\t return oracle.search(params.K, params.epsilon, ids, dists);\n\t}\n vector knn(params.K + params.P +1);\n\t vector init_knn(params.init);\n vector results;\n boost::dynamic_bitset<> flags(graph.size(), false);\n\n if (params.init && params.T > 1) {\n throw runtime_error(\"when init > 0, T must be 1.\");\n }\n\n unsigned seed = params.seed;\n unsigned updates = 0;\n if (seed == 0) seed = time(NULL);\n mt19937 rng(seed);\n unsigned n_comps = 0;\n for (unsigned trial = 0; trial < params.T; ++trial) {\n unsigned L = params.init;\n //init>=K\n if (L == 0) { // generate random starting points\n vector random(params.P);\n GenRandom(rng, &random[0], random.size(), graph.size());\n for (unsigned s: random) {\n if (!flags[s]) {\n knn[L++].id = s;\n //flags[s] = true;\n }\n }\n\t\t for (unsigned k = 0; k < L; ++k) {\n\t\t flags[knn[k].id] = true;\n\t\t knn[k].flag = true;\n\t\t knn[k].dist = oracle(knn[k].id);\n\t\t \n\t\t }\n\t\t sort(knn.begin(), knn.begin() + L);\n }\n else { // user-provided starting points.\n BOOST_VERIFY(ids); //ids\n //std::cout<<\"set seed\"< knn.size()\n init_knn[l].id = ids[l]; //ids\n\t\t\t//fprintf(stderr, \"%d: %d\\n\", l, knn[l].id);\n }\n //std::cout< init_fknn(graph.size());\n\t\t for (unsigned k = 0; k < graph.size(); ++k){\n\t\t init_fknn[k].id = k;\n\t\t init_fknn[k].flag = true;\n\t\t init_fknn[k].dist = oracle(k);\n\t\t }\n\t\t sort(init_fknn.begin(), init_fknn.end());\n\t\t //end delete\n\t\t */\n\t\t L = params.K;\n\t\t for (int ii = 0; ii < L; ++ii){\n\t\t //flags[init_fknn[graph.size() - 1 - ii].id] = true;\n\t\t //knn[L - 1 - ii] = init_fknn[graph.size() - 1 - ii];\n\t\t //recover\n\t\t flags[init_knn[ii].id] = true;\n\t\t knn[ii] = init_knn[ii];\n\t\t //end recover\n\t\t //fprintf(stderr, \"%d %f\\n\", knn[ii].id, knn[ii].dist);\n\t\t }\n\t\t}\n\n\t\tif (print_flag){\n\t\t for (unsigned k = 0; k < L; ++k){\n\t\t path_fs << \"-3 \" << knn[k].id << endl;\n\t\t mind_fs << knn[0].dist << endl;\n\t\t curd_fs << knn[k].dist << endl;\n\t\t \n\t\t nh_fs << M[knn[k].id] << \" \";\n\t\t unordered_set set;\n\t\t set.insert(knn[k].id);\n\t\t auto const &neighbors = graph[knn[k].id];\n\t\t for (unsigned l = 0; l < M[knn[k].id]; ++l){\n\t\t set.insert(neighbors[l].id);\n\t\t auto const &new_neighbors = graph[neighbors[l].id];\n\t\t for (unsigned ll = 0; ll < M[neighbors[l].id]; ++ll){\n\t\t\tset.insert(new_neighbors[ll].id);\n\t\t }\n\t\t }\n\t\t nh_fs << set.size() << endl;\n\t\t }\n\t\t}\n\t\t\n\t\tunsigned k = 0;\n \n while (k < L) {\n unsigned nk = L;\n if (knn[k].flag) {\n knn[k].flag = false;\n unsigned cur = knn[k].id;\n\t\t\t//BOOST_VERIFY(cur < graph.size());\n unsigned maxM = M[cur];\n if (params.M > maxM) maxM = params.M;\n auto const &neighbors = graph[cur];\n if (maxM > neighbors.size()) {\n maxM = neighbors.size();\n }\n for (unsigned m = 0; m < maxM; ++m) {\n unsigned id = neighbors[m].id;\n //BOOST_VERIFY(id < graph.size());\n if (flags[id]) continue;\n flags[id] = true;\n ++n_comps;\n //if(n_comps >= params.Maxchecks)\n\t\t\t // {\n // break;\n\t\t\t // }\n float dist = oracle(id);\n\t\t\t \n\t\t\t /*if(n_comps == 1){\n\t\t\t fprintf(stderr, \"%f\", dist);\n\t\t\t } else {\n\t\t\t fprintf(stderr, \", %f\", dist);\n\t\t\t }*/\n\t\t\t Neighbor nn(id, dist);\n\t\t\t unsigned r = UpdateKnnList(&knn[0], L, nn);\n\t\t\t if (print_flag){\n path_fs << cur << \" \" << id << endl;\n mind_fs << knn[0].dist << endl;\n\t\t\t curd_fs << dist << endl;\n\n\t\t\t nh_fs << M[id] << \" \";\n\t\t\t unordered_set set;\n\t\t\t set.insert(id);\n\t\t\t auto const &nbors = graph[id];\n\t\t\t for (unsigned l_1 = 0; l_1 < M[id]; ++l_1){\n\t\t\t\tset.insert(nbors[l_1].id);\n\t\t\t\tauto const &new_neighbors = graph[nbors[l_1].id];\n\t\t\t\tfor (unsigned ll = 0; ll < M[nbors[l_1].id]; ++ll){\n\t\t\t\t set.insert(new_neighbors[ll].id);\n\t\t\t\t}\n\t\t\t }\n\t\t\t nh_fs << set.size() << endl;\n }\n\t\t\t /*if(n_comps == 1){\n fprintf(stdout, \"%f\", knn[0].dist);\n } else {\n fprintf(stdout, \", %f\", knn[0].dist);\n\t\t\t }*/\n BOOST_VERIFY(r <= L);\n //if (r > L) continue;\n if (L + 1 < knn.size()) ++L;\n if (r < nk) {\n nk = r;\n }\n }\n\t\t\t//fprintf(stderr, \"}\\n\");\n }\n if (nk <= k) {\n k = nk;\n }\n else {\n ++k;\n }\n //if(n_comps >= params.Maxchecks)\n\t\t //\t\t\t{\n // break;\n //}\n }\n if (L > params.K) L = params.K;\n if (results.empty()) {\n results.reserve(params.K + 1);\n results.resize(L + 1);\n copy(knn.begin(), knn.begin() + L, results.begin());\n }\n else {\n // update results\n for (unsigned l = 0; l < L; ++l) {\n unsigned r = UpdateKnnList(&results[0], results.size() - 1, knn[l]);\n if (r < results.size() /* inserted */ && results.size() < (params.K + 1)) {\n results.resize(results.size() + 1);\n }\n }\n }\n }\n\t //\t results.pop_back();\n // check epsilon\n {\n for (unsigned l = 0; l < results.size(); ++l) {\n if (results[l].dist > params.epsilon) {\n results.resize(l);\n break;\n }\n }\n }\n unsigned L = results.size() - 1;\n BOOST_VERIFY(L <= params.K);\n // check epsilon\n if (ids) {\n for (unsigned k = 0; k < L; ++k) {\n ids[k] = results[k].id;\n }\n }\n if (dists) {\n for (unsigned k = 0; k < L; ++k) {\n dists[k] = results[k].dist;\n }\n }\n if (pinfo) {\n pinfo->updates = updates;\n pinfo->cost = float(n_comps) / graph.size();\n pinfo->checks=n_comps;\n }\n\t if (print_flag){\n\t path_fs << \"-1\" << endl;\n\t for (unsigned k = 0; k < results.size() - 1; ++k){\n\t\tpath_fs << results[k].id << endl; //fprintf(stderr, \"%d [color=lightblue, style=filled, label=\\\"top-%d\\\"]\\n\", results[k].id, k+1);\n\t }\n\t path_fs << \"-2\" << endl;\n\t mind_fs << \"-1\" << endl;\n\t curd_fs << \"-1\" << endl;\n\t nh_fs << \"-1\" << endl;\n\t }\n\t //fprintf(stderr, \")\\n\");\n\t //fprintf(stdout, \")\\n\");\n\t //fprintf(stderr, \")\\nplt.hist(kg_access_dist, bins=100, normed=False, alpha=0.75, label='accessed')\\nplt.hist(all_dist, bins=100, normed=False, alpha=0.5, label='all')\\nplt.title(\\\"Distribution of accessed points by KGraph\\\")\\nplt.xlabel(\\\"distance\\\")\\nplt.ylabel(\\\"Frequency\\\")\\nplt.legend(loc='upper right')\\nshow()\\n\");\n return L;\n }\n\n\n virtual unsigned search_greedy (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *pinfo, string const &info_path) const {// Matrix const &d_init,\n /*fprintf(stderr, \"all_dist=(%f\", oracle(0));\n for (int i = 1; i < oracle.size(); ++i){\n fprintf(stderr, \", %f\", oracle(i));\n }*/\n //fprintf(stderr, \"access_dist=(\");\n //fprintf(stdout, \"minimum_dist=(\");\n\n bool print_flag = true;\n std::ofstream path_fs, mind_fs, curd_fs;\n string file_name;\n if (info_path.empty()) {\n print_flag = false;\n }\n \n if (print_flag){\n file_name.assign(info_path);\n file_name += \"_path.txt\";\n path_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n file_name.assign(info_path);\n file_name += \"_mindist.txt\";\n mind_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n file_name.assign(info_path);\n file_name += \"_curdist.txt\";\n curd_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n }\n \n if (graph.size() > oracle.size()) {\n throw runtime_error(\"dataset larger than index\");\n }\n if (params.P >= graph.size()) {\n if (pinfo) {\n pinfo->updates = 0;\n pinfo->cost = 1.0;\n }\n return oracle.search(params.K, params.epsilon, ids, dists);\n }\n vector knn(params.K + params.P +1);\n vector init_knn(params.init);\n vector results;\n boost::dynamic_bitset<> flags(graph.size(), false);\n\n if (params.init && params.T > 1) {\n throw runtime_error(\"when init > 0, T must be 1.\");\n }\n\n unsigned seed = params.seed;\n unsigned updates = 0;\n if (seed == 0) seed = time(NULL);\n mt19937 rng(seed);\n unsigned n_comps = 0;\n for (unsigned trial = 0; trial < params.T; ++trial) {\n unsigned L = params.init;\n //init>=K\n if (L == 0) { // generate random starting points\n vector random(params.P);\n GenRandom(rng, &random[0], random.size(), graph.size());\n for (unsigned s: random) {\n if (!flags[s]) {\n knn[L++].id = s;\n //flags[s] = true;\n }\n }\n for (unsigned k = 0; k < L; ++k) {\n flags[knn[k].id] = true;\n knn[k].flag = true;\n knn[k].dist = oracle(knn[k].id);\n }\n sort(knn.begin(), knn.begin() + L);\n }\n else { // user-provided starting points.\n BOOST_VERIFY(ids); //ids\n //std::cout<<\"set seed\"< knn.size()\n init_knn[l].id = ids[l]; //ids\n //fprintf(stderr, \"%d: %d\\n\", l, knn[l].id);\n }\n //std::cout< init_fknn(graph.size());\n for (unsigned k = 0; k < graph.size(); ++k){\n init_fknn[k].id = k;\n init_fknn[k].flag = true;\n init_fknn[k].dist = oracle(k);\n }\n sort(init_fknn.begin(), init_fknn.end());\n //end delete\n */\n L = params.K;\n for (int ii = 0; ii < L; ++ii){\n //flags[init_fknn[graph.size() - 1 - ii].id] = true;\n //knn[L - 1 - ii] = init_fknn[graph.size() - 1 - ii];\n //recover\n flags[init_knn[ii].id] = true;\n knn[ii] = init_knn[ii];\n //end recover\n //fprintf(stderr, \"%d %f\\n\", knn[ii].id, knn[ii].dist);\n }\n }\n\n if (print_flag){\n mind_fs << knn[0].dist << endl;\n curd_fs << knn[0].dist << endl;\n }\n \n unsigned k = 0;\n while (k < L) {\n unsigned nk = L;\n if (knn[k].flag) {\n knn[k].flag = false;\n unsigned cur = knn[k].id;\n //BOOST_VERIFY(cur < graph.size());\n unsigned maxM = M[cur];\n\t\t\tfloat thres = knn[k].dist;\n if (params.M > maxM) maxM = params.M;\n auto const &neighbors = graph[cur];\n if (maxM > neighbors.size()) {\n maxM = neighbors.size();\n }\n for (unsigned m = 0; m < maxM; ++m) {\n unsigned id = neighbors[m].id;\n //BOOST_VERIFY(id < graph.size());\n if (flags[id]) continue;\n flags[id] = true;\n ++n_comps;\n\t\t\t // if(n_comps >= params.Maxchecks)\n\t\t\t // {\n\t\t\t //break;\n\t\t\t // }\n float dist = oracle(id);\n\t\t\t if (dist > thres) {\n\t\t\t continue;\n\t\t\t }\n /*if(n_comps == 1){\n fprintf(stderr, \"%f\", dist);\n } else {\n fprintf(stderr, \", %f\", dist);\n }*/\n\t\t\t Neighbor nn(id, dist);\n\t\t\t unsigned r = UpdateKnnList(&knn[0], L, nn);\n\t\t\t if (print_flag){\n path_fs << cur << \" \" << id << endl;\n mind_fs << knn[0].dist << endl;\n\t\t\t curd_fs << dist << endl;\n }\n\t\t\t /*if(n_comps == 1){\n fprintf(stdout, \"%f\", knn[0].dist);\n\t\t\t } else {\n fprintf(stdout, \", %f\", knn[0].dist);\n\t\t\t }*/\n BOOST_VERIFY(r <= L);\n //if (r > L) continue;\n if (L + 1 < knn.size()) ++L;\n if (r < nk) {\n\t\t\t nk = r;\n }\n }\n\t\t\t//fprintf(stderr, \"}\\n\");\n }\n if (nk <= k) {\n k = nk;\n }\n else {\n ++k;\n }\n\t\t // if(n_comps >= params.Maxchecks)\n //{\n // break;\n\t\t // }\n }\n if (L > params.K) L = params.K;\n if (results.empty()) {\n results.reserve(params.K + 1);\n results.resize(L + 1);\n copy(knn.begin(), knn.begin() + L, results.begin());\n }\n else {\n // update results\n for (unsigned l = 0; l < L; ++l) {\n unsigned r = UpdateKnnList(&results[0], results.size() - 1, knn[l]);\n if (r < results.size() /* inserted */ && results.size() < (params.K + 1)) {\n results.resize(results.size() + 1);\n }\n }\n }\n }\n // check epsilon\n {\n for (unsigned l = 0; l < results.size(); ++l) {\n if (results[l].dist > params.epsilon) {\n results.resize(l);\n break;\n }\n }\n }\n unsigned L = results.size() - 1;\n BOOST_VERIFY(L <= params.K);\n // check epsilon\n if (ids) {\n for (unsigned k = 0; k < L; ++k) {\n ids[k] = results[k].id;\n }\n }\n if (dists) {\n for (unsigned k = 0; k < L; ++k) {\n dists[k] = results[k].dist;\n }\n }\n if (pinfo) {\n pinfo->updates = updates;\n pinfo->cost = float(n_comps) / graph.size();\n pinfo->checks=n_comps;\n }\n if (print_flag){\n path_fs << \"-1\" << endl;\n for (unsigned k = 0; k < results.size() - 1; ++k){\n path_fs << results[k].id << endl; //fprintf(stderr, \"%d [color=lightblue, style=filled, label=\\\"top-%d\\\"]\\n\", results[k].id, k+1);\n }\n path_fs << \"-2\" << endl;\n mind_fs << \"-1\" << endl;\n curd_fs << \"-1\" << endl;\n }\n //fprintf(stderr, \")\\n\");\n //fprintf(stdout, \")\\n\");\n //fprintf(stderr, \")\\nplt.hist(kg_access_dist, bins=100, normed=False, alpha=0.75, label='accessed')\\nplt.hist(all_dist, bins=100, normed=False, alpha=0.5, label='all')\\nplt.title(\\\"Distribution of accessed points by KGraph\\\")\\nplt.xlabel(\\\"distance\\\")\\nplt.ylabel(\\\"Frequency\\\")\\nplt.legend(loc='upper right')\\nshow()\\n\");\n return L;\n }\n\n virtual void get_nn (unsigned id, unsigned *nns, float *dist, unsigned *pM, unsigned *pL) const {\n BOOST_VERIFY(id < graph.size());\n auto const &v = graph[id];\n *pM = M[id];\n *pL = v.size();\n if (nns) {\n for (unsigned i = 0; i < v.size(); ++i) {\n nns[i] = v[i].id;\n }\n }\n if (dist) {\n for (unsigned i = 0; i < v.size(); ++i) {\n dist[i] = v[i].dist;\n }\n }\n }\n\n void prune1 () {\n for (unsigned i = 0; i < graph.size(); ++i) {\n if (graph[i].size() > M[i]) {\n graph[i].resize(M[i]);\n }\n }\n }\n\n void prune2 () {\n#if 0\n vector> new_graph(graph.size());\n vector new_M(graph.size());\n vector> reverse(graph.size());\n vector> todo(graph.size());\n unsigned L = 0;\n {\n cerr << \"Level 2 Prune, stage 1/2 ...\" << endl;\n progress_display progress(graph.size(), cerr);\n for (unsigned i = 0; i < graph.size(); ++i) {\n if (graph[i].size() > L) L = graph[i].size();\n todo[i] = unordered_set(graph[i].begin(), graph[i].end());\n ++progress;\n }\n }\n {\n cerr << \"Level 2 Prune, stage 2/2 ...\" << endl;\n progress_display progress(L, cerr);\n for (unsigned l = 0; l < L; ++l) {\n for (unsigned i = 0; i < graph.size(); ++i) {\n if (todo[i].empty()) continue;\n BOOST_VERIFY(l < graph[i].size());\n unsigned T = graph[i][l];\n if (todo[i].erase(T)) { // still there, need to be added\n new_graph[i].push_back(T);\n reverse[T].push_back(i);\n // mark newly reachable nodes\n for (auto n2: new_graph[T]) {\n todo[i].erase(n2);\n }\n for (auto r: reverse[i]) {\n todo[r].erase(T);\n }\n }\n if (l + 1 == M[i]) {\n new_M[i] = new_graph[i].size();\n }\n }\n ++progress;\n }\n }\n graph.swap(new_graph);\n M.swap(new_M);\n#endif\n }\n\n virtual void prune (IndexOracle const &oracle, unsigned level) {\n if (level & PRUNE_LEVEL_1) {\n prune1();\n }\n if (level & PRUNE_LEVEL_2) {\n prune2();\n }\n }\n };\n\n class KGraphConstructor: public KGraphImpl {\n // The neighborhood structure maintains a pool of near neighbors of an object.\n // The neighbors are stored in the pool. \"n\" (<=\"params.L\") is the number of valid entries\n // in the pool, with the beginning \"k\" (<=\"n\") entries sorted.\n struct Nhood { // neighborhood\n Lock lock;\n float radius; // distance of interesting range\n float radiusM;\n Neighbors pool;\n unsigned L; // # valid items in the pool, L + 1 <= pool.size()\n unsigned M; // we only join items in pool[0..M)\n bool found; // helped found new NN in this round\n vector nn_old;\n vector nn_new;\n vector rnn_old;\n vector rnn_new;\n\n // only non-readonly method which is supposed to be called in parallel\n unsigned parallel_try_insert (unsigned id, float dist) {\n if (dist > radius) return pool.size();\n LockGuard guard(lock);\n unsigned l = UpdateKnnList(&pool[0], L, Neighbor(id, dist, true));\n if (l <= L) { // inserted\n if (L + 1 < pool.size()) { // if l == L + 1, there's a duplicate\n ++L;\n }\n else {\n radius = pool[L-1].dist;\n }\n }\n return l;\n }\n\n // join should not be conflict with insert\n template \n void join (C callback) const {\n for (unsigned const i: nn_new) {\n for (unsigned const j: nn_new) {\n if (i < j) {\n callback(i, j);\n }\n }\n for (unsigned j: nn_old) {\n callback(i, j);\n }\n }\n }\n };\n\n IndexOracle const &oracle;\n IndexParams params;\n IndexInfo *pinfo;\n vector nhoods;\n size_t n_comps;\n\n void init () {\n unsigned N = oracle.size();\n unsigned seed = params.seed;\n mt19937 rng(seed);\n for (auto &nhood: nhoods) {\n nhood.nn_new.resize(params.S * 2);\n nhood.pool.resize(params.L+1);\n nhood.radius = numeric_limits::max();\n }\n #pragma omp parallel\n {\n #ifdef _OPENMP\n mt19937 rng(seed ^ omp_get_thread_num());\n #else\n mt19937 rng(seed);\n #endif\n vector random(params.S + 1);\n #pragma omp for\n for (unsigned n = 0; n < N; ++n) {\n auto &nhood = nhoods[n];\n Neighbors &pool = nhood.pool;\n GenRandom(rng, &nhood.nn_new[0], nhood.nn_new.size(), N); //nn_new 2*S random sampling \n GenRandom(rng, &random[0], random.size(), N); //random s+1\n nhood.L = params.S;\n nhood.M = params.S;\n\t\t\t\t\t//cout< 0;\n }\n n_comps += cc;\n }\n void update () {\n unsigned N = oracle.size();\n for (auto &nhood: nhoods) {\n nhood.nn_new.clear();\n nhood.nn_old.clear();\n nhood.rnn_new.clear();\n nhood.rnn_old.clear();\n nhood.radius = nhood.pool.back().dist;\n }\n //!!! compute radius2\n #pragma omp parallel for\n for (unsigned n = 0; n < N; ++n) {\n auto &nhood = nhoods[n];\n if (nhood.found) {\n unsigned maxl = std::min(nhood.M + params.S, nhood.L);\n unsigned c = 0;\n unsigned l = 0;\n while ((l < maxl) && (c < params.S)) {\n if (nhood.pool[l].flag) ++c;\n ++l;\n }\n nhood.M = l;\n }\n BOOST_VERIFY(nhood.M > 0);\n nhood.radiusM = nhood.pool[nhood.M-1].dist;\n }\n #pragma omp parallel for\n for (unsigned n = 0; n < N; ++n) {\n auto &nhood = nhoods[n];\n auto &nn_new = nhood.nn_new;\n auto &nn_old = nhood.nn_old;\n for (unsigned l = 0; l < nhood.M; ++l) {\n\t\t\t\t\t\n auto &nn = nhood.pool[l];\n auto &nhood_o = nhoods[nn.id]; // nhood on the other side of the edge\n if (nn.flag) {\n nn_new.push_back(nn.id);\n if (nn.dist > nhood_o.radiusM) {\n LockGuard guard(nhood_o.lock);\n nhood_o.rnn_new.push_back(n);\n }\n nn.flag = false;\n }\n else {\n nn_old.push_back(nn.id);\n if (nn.dist > nhood_o.radiusM) {\n LockGuard guard(nhood_o.lock);\n nhood_o.rnn_old.push_back(n);\n }\n }\n }\n }\n for (unsigned i = 0; i < N; ++i) {\n auto &nn_new = nhoods[i].nn_new;\n auto &nn_old = nhoods[i].nn_old;\n auto &rnn_new = nhoods[i].rnn_new;\n auto &rnn_old = nhoods[i].rnn_old;\n if (params.R && (rnn_new.size() > params.R)) {\n random_shuffle(rnn_new.begin(), rnn_new.end());\n rnn_new.resize(params.R);\n }\n nn_new.insert(nn_new.end(), rnn_new.begin(), rnn_new.end());\n if (params.R && (rnn_old.size() > params.R)) {\n random_shuffle(rnn_old.begin(), rnn_old.end());\n rnn_old.resize(params.R);\n }\n nn_old.insert(nn_old.end(), rnn_old.begin(), rnn_old.end());\n }\n }\n\npublic:\n KGraphConstructor (IndexOracle const &o, IndexParams const &p, IndexInfo *r, char const *path)\n : oracle(o), params(p), pinfo(r), nhoods(o.size()), n_comps(0)\n {\n boost::timer::cpu_timer timer;\n //params.check();\n unsigned N = oracle.size(); // size of dataset\n if (N <= params.K) throw runtime_error(\"K larger than dataset size\");\n\n vector controls; //struct control {unsigned id;Neighbors neighbors;}\n if (verbosity > 0) cerr << \"Generating control...\" << endl;\n GenerateControl(oracle, params.controls, params.K, &controls); //controls data points are sampled, and then calculate the true knn \n if (verbosity > 0) cerr << \"Initializing...\" << endl;\n // initialize nhoods\n init();\n\n // iterate until converge\n float total = N * float(N - 1) / 2;\n IndexInfo info;\n info.stop_condition = IndexInfo::ITERATION;\n info.recall = 0;\n info.accuracy = numeric_limits::max();\n info.cost = 0;\n info.iterations = 0;\n info.delta = 1.0;\n\n for (unsigned it = 0; (params.iterations <= 0) || (it < params.iterations); ++it) {\n ++info.iterations;\n join();\n {\n info.cost = n_comps / total;\n accumulator_set> one_exact;\n accumulator_set> one_approx;\n accumulator_set> one_recall;\n accumulator_set> recall;\n accumulator_set> accuracy;\n accumulator_set> M;\n accumulator_set> delta;\n for (auto const &nhood: nhoods) {\n M(nhood.M);\n delta(EvaluateDelta(nhood.pool, params.K));\n }\n for (auto const &c: controls) {\n one_approx(nhoods[c.id].pool[0].dist);\n one_exact(c.neighbors[0].dist);\n one_recall(EvaluateOneRecall(nhoods[c.id].pool, c.neighbors));\n recall(EvaluateRecall(nhoods[c.id].pool, c.neighbors));\n accuracy(EvaluateAccuracy(nhoods[c.id].pool, c.neighbors));\n }\n info.delta = mean(delta);\n info.recall = mean(recall);\n info.accuracy = mean(accuracy);\n info.M = mean(M);\n auto times = timer.elapsed();\n if (verbosity > 0) {\n\n cerr << \"iteration: \" << info.iterations\n << \" recall: \" << info.recall\n << \" accuracy: \" << info.accuracy\n << \" cost: \" << info.cost\n << \" M: \" << info.M\n << \" delta: \" << info.delta\n << \" time: \" << times.wall / 1e9\n << \" one-recall: \" << mean(one_recall)\n << \" one-ratio: \" << mean(one_approx) / mean(one_exact)\n << endl;\n }\n }\n if (info.delta <= params.delta) {\n info.stop_condition = IndexInfo::DELTA;\n break;\n }\n if (info.recall >= params.recall) {\n info.stop_condition = IndexInfo::RECALL;\n break;\n }\n update();\n }\n\n ofstream os(path, ios::binary);\n os.write(KGRAPH_MAGIC, KGRAPH_MAGIC_SIZE);\n os.write(reinterpret_cast(&VERSION_MAJOR), sizeof(VERSION_MAJOR));\n os.write(reinterpret_cast(&VERSION_MINOR), sizeof(VERSION_MINOR));\n os.write(reinterpret_cast(&N), sizeof(N));\n \n \n for (unsigned n = 0; n < N; ++n) {\n auto const &pool = nhoods[n].pool;\n unsigned K = params.L;\n os.write(reinterpret_cast(&K), sizeof(K));\n os.write(reinterpret_cast(&K), sizeof(K));\n for (unsigned j = 0; j < K; ++j){\n os.write(reinterpret_cast(&pool[j].id), sizeof(pool[j].id));\n }\n }\n /* // deleted by yifang\n M.resize(N);\n graph.resize(N);\n if (params.prune > 2) throw runtime_error(\"prune level not supported.\");\n for (unsigned n = 0; n < N; ++n) {\n auto &knn = graph[n];\n M[n] = nhoods[n].M;\n auto const &pool = nhoods[n].pool;\n\n unsigned K = params.L;\n knn.resize(K);\n for (unsigned k = 0; k < K; ++k) {\n knn[k].id = pool[k].id;\n knn[k].dist = pool[k].dist;\n }\n nhoods[n].pool.clear();\n }\n if (params.prune) {\n prune(o, params.prune);\n }\n if (pinfo) {\n *pinfo = info;\n }*/\n }\n\n };\n\nvoid KGraphImpl::build (IndexOracle const &oracle, IndexParams const ¶m, char const *path, IndexInfo *info) {\n KGraphConstructor con(oracle, param, info, path);\n /* //deleted by yifang\n M.swap(con.M);\n graph.swap(con.graph);\n */\n}\n\n KGraph *KGraph::create () {\n return new KGraphImpl;\n }\n\n char const* KGraph::version () {\n return kgraph_version;\n }\n}\n\n" }, { "path": "algorithms/DPG/src/kgraph.h", "content": "// Copyright (C) 2013-2015 Wei Dong . All Rights Reserved.\n//\n// \\mainpage KGraph: A Library for Efficient K-NN Search\n// \\author Wei Dong \\f$ wdong@wdong.org \\f$\n// \\author 2013-2015\n//\n\n#ifndef WDONG_KGRAPH\n#define WDONG_KGRAPH\n\n#include \n#include \n#include \n#include \n//#include \"kgraph-data.h\"\n\nusing namespace std;\n\nnamespace kgraph {\n static unsigned const default_iterations = 30;\n static unsigned const default_L = 100;\n static unsigned const default_K = 100;\n static unsigned const default_P = 100;\n static unsigned const default_M = 0;\n static unsigned const default_T = 1;\n static unsigned const default_S = 10;\n static unsigned const default_Maxchecks = 10;\n static unsigned const default_seedIndex = 1;\n static unsigned const default_R = 100;\n static unsigned const default_controls = 100;\n static unsigned const default_seed = 1998;\n static float const default_delta = 0.002;\n static float const default_recall = 0.99;\n static float const default_epsilon = 1e30;\n static unsigned const default_verbosity = 1;\n enum {\n PRUNE_LEVEL_1 = 1,\n PRUNE_LEVEL_2 = 2\n };\n static unsigned const default_prune = 0;\n\n /// Verbosity control\n /** Set verbosity = 0 to disable information output to stderr.\n */\n extern unsigned verbosity;\n\n /// Index oracle\n /** The index oracle is the user-supplied plugin that computes\n * the distance between two arbitrary objects in the dataset.\n * It is used for offline k-NN graph construction.\n */\n class IndexOracle {\n public:\n /// Returns the size of the dataset.\n virtual unsigned size () const = 0;\n /// Computes similarity\n /**\n * 0 <= i, j < size() are the index of two objects in the dataset.\n * This method return the distance between objects i and j.\n */\n virtual float operator () (unsigned i, unsigned j) const = 0;\n\tvirtual float angle (unsigned orign, unsigned pivot, unsigned x) const = 0;\n };\n\n /// Search oracle\n /** The search oracle is the user-supplied plugin that computes\n * the distance between the query and a arbitrary object in the dataset.\n * It is used for online k-NN search.\n */\n class SearchOracle {\n public:\n /// Returns the size of the dataset.\n virtual unsigned size () const = 0;\n /// Computes similarity\n /**\n * 0 <= i < size() are the index of an objects in the dataset.\n * This method return the distance between the query and object i.\n */\n virtual float operator () (unsigned i) const = 0;\n /// Search with brutal force.\n /**\n * Search results are guaranteed to be ranked in ascending order of distance.\n *\n * @param K Return at most K nearest neighbors.\n * @param epsilon Only returns nearest neighbors within distance epsilon.\n * @param ids Pointer to the memory where neighbor IDs are returned.\n * @param dists Pointer to the memory where distance values are returned, can be nullptr.\n */\n unsigned search (unsigned K, float epsilon, unsigned *ids, float *dists = nullptr) const;\n };\n\n /// The KGraph index.\n /** This is an abstract base class. Use KGraph::create to create an instance.\n */\n class KGraph {\n public:\n /// Indexing parameters.\n struct IndexParams {\n unsigned iterations; \n unsigned L;\n unsigned K;\n unsigned S;\n unsigned R;\n unsigned controls;\n unsigned seed;\n float delta;\n float recall;\n unsigned prune;\n\n /// Construct with default values.\n IndexParams (): iterations(default_iterations), L(default_L), K(default_K), S(default_S), R(default_R), controls(default_controls), seed(default_seed), delta(default_delta), recall(default_recall), prune(default_prune) {\n }\n };\n\n /// Search parameters.\n struct SearchParams {\n unsigned K;\n unsigned M;\n unsigned P;\n unsigned T;\n unsigned Maxchecks;\n float epsilon;\n unsigned seed;\n unsigned init;\n \n\n /// Construct with default values.\n SearchParams (): K(default_K), M(default_M), P(default_P), T(default_T), epsilon(default_epsilon), seed(1998), init(0) {\n }\n };\n\n /// Information and statistics of the indexing algorithm.\n struct IndexInfo {\n enum StopCondition {\n ITERATION = 0,\n DELTA,\n RECALL\n } stop_condition;\n unsigned iterations;\n float cost;\n float recall;\n float accuracy;\n float delta;\n float M;\n };\n\n /// Information and statistics of the search algorithm.\n struct SearchInfo {\n float cost;\n unsigned updates;\n int checks;\n };\n\n virtual ~KGraph () {\n }\n /// Load index from file.\n /**\n * @param path Path to the index file.\n */\n virtual void load (char const *path) = 0;\n /// Save index to file.\n /**sa\n * @param path Path to the index file.\n */\n virtual void save (char const *path) const = 0; // save to file\n\t virtual void saver (char const *path) const = 0;\n\t\tvirtual void get_hubs (char const *hubs_path,char const *knn_path) const = 0; // get_hubs\n\t\tvirtual void add_long_edges(IndexOracle const &oracle, int const rank_top_k, int const change_num) = 0; //add long edges\n\t\tvirtual void output_M (char const * filename) = 0;\n\t\tvirtual void output_info(int const id) = 0; // output info for id-th point\n\t\tvirtual void merge(char const * graph_path, char const * id_path) = 0;\n\t\tvirtual void test_connectivity(unsigned dest) = 0;\n\t\t\n virtual void remove_near_edges(IndexOracle const &oracle, const int edge_num) = 0; \n\n\n virtual float statistics(IndexOracle const &oracle, const int edge_num) = 0;\n virtual void diversify_by_cut(IndexOracle const &oracle, const int edge_num) = 0;\n\t\tvirtual void remove_near_edges_2(IndexOracle const &oracle, vector const &edge_num, unordered_set const &skip_set) = 0;\n\t\tvirtual void add_random_backward_edges(char const *data_path, char const *hubs_path, int const num_of_hubs, char const * start_points_path, unsigned num_of_query) = 0;\n\t\tvirtual void add_backward_edges() = 0;\n\t\tvirtual void get_partitions(char const *output_path) = 0;\n\t\tvirtual void compute_diversity(IndexOracle const &oracle, int d, float * norm) = 0;\n\t\tvirtual void connectivity(unsigned const source, unsigned const dest) = 0;\n\t\tvirtual void make_inverse_graph() = 0;\n\t\tvirtual void find_lm (SearchOracle const &oracle) const = 0;\n\t\tvirtual void kgraph2dot (char * const &filename) = 0;\n /// Build the index\n virtual void build (IndexOracle const &oracle, IndexParams const ¶ms, char const *path, IndexInfo *info = 0) = 0;\n /// Prune the index\n /**\n * Pruning makes the index smaller to save memory, and makes online search on the pruned index faster.\n * (The cost parameters of online search must be enlarged so accuracy is not reduced.)\n *\n * Currently only two pruning levels are supported:\n * - PRUNE_LEVEL_1 = 1: Only reduces index size, fast.\n * - PRUNE_LEVEL_2 = 2: For improve online search speed, slow.\n *\n * No pruning is done if level = 0.\n */\n virtual void prune (IndexOracle const &oracle, unsigned level) = 0;\n /// Online k-NN search.\n /**\n * Search results are guaranteed to be ranked in ascending order of distance.\n *\n * @param ids Pointer to the memory where neighbor IDs are stored, must have space to save params.K ids.\n */\n unsigned search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, SearchInfo *info = 0, string const &info_path = \"\") const {\n //std::cout <<\"h search \\n\";\n\t return search(oracle, params, ids, nullptr, info, info_path);// d_init, Matrix const &d_init,\n }\n\n\tunsigned search_greedy (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, SearchInfo *info = 0, string const &info_path = \"\") const {\n\t //std::cout <<\"h search \\n\";\n return search_greedy(oracle, params, ids, nullptr, info, info_path);// d_init, Matrix const &d_init,\n }\n /// Online k-NN search.\n /**\n * Search results are guaranteed to be ranked in ascending order of distance.\n *\n * @param ids Pointer to the memory where neighbor IDs are stored, must have space to save params.K values.\n * @param dists Pointer to the memory where distances are stored, must have space to save params.K values.\n */\n\tvirtual unsigned advanced_search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *info, string const &info_path) const = 0; //,Matrix const &d_init\n\n virtual unsigned search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *info, string const &info_path) const = 0; //,Matrix const &d_init\n\n\tvirtual unsigned search_greedy (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *info, string const &info_path) const = 0; //,Matrix const &d_init\n /// Constructor.\n static KGraph *create ();\n /// Returns version string.\n static char const* version ();\n\n /// Get offline computed k-NNs of a given object.\n /**\n * See the full version of get_nn.\n */\n virtual void get_nn (unsigned id, unsigned *nns, unsigned *M, unsigned *L) const {\n get_nn(id, nns, nullptr, M, L);\n }\n /// Get offline computed k-NNs of a given object.\n /**\n * The user must provide space to save IndexParams::L values.\n * The actually returned L could be smaller than IndexParams::L, and\n * M <= L is the number of neighbors KGraph thinks\n * could be most useful for online search, and is usually < L.\n * If the index has been pruned, the returned L could be smaller than\n * IndexParams::L used to construct the index.\n *\n * @params id Object ID whose neighbor information are returned.\n * @params nns Neighbor IDs, must have space to save IndexParams::L values. \n * @params dists Distance values, must have space to save IndexParams::L values.\n * @params M Useful number of neighbors, output only.\n * @params L Actually returned number of neighbors, output only.\n */\n virtual void get_nn (unsigned id, unsigned *nns, float *dists, unsigned *M, unsigned *L) const = 0;\n };\n}\n\n#if __cplusplus > 199711L\n#include \nnamespace kgraph {\n /// Oracle adapter for datasets stored in a vector-like container.\n /**\n * If the dataset is stored in a container of CONTAINER_TYPE that supports\n * - a size() method that returns the number of objects.\n * - a [] operator that returns the const reference to an object.\n * This class can be used to provide a wrapper to facilitate creating\n * the index and search oracles.\n *\n * The user must provide a callback function that takes in two\n * const references to objects and returns a distance value.\n */\n template \n class VectorOracle: public IndexOracle {\n public:\n typedef std::function METRIC_TYPE;\n private:\n CONTAINER_TYPE const &data;\n METRIC_TYPE dist;\n public:\n class VectorSearchOracle: public SearchOracle {\n CONTAINER_TYPE const &data;\n OBJECT_TYPE const query;\n METRIC_TYPE dist;\n public:\n VectorSearchOracle (CONTAINER_TYPE const &p, OBJECT_TYPE const &q, METRIC_TYPE m): data(p), query(q), dist(m) {\n }\n virtual unsigned size () const {\n return data.size();\n }\n virtual float operator () (unsigned i) const {\n return dist(data[i], query);\n }\n };\n /// Constructor.\n /**\n * @param d: the container that holds the dataset.\n * @param m: a callback function for distance computation. m(d[i], d[j]) must be\n * a valid expression to compute distance.\n */\n VectorOracle (CONTAINER_TYPE const &d, METRIC_TYPE m): data(d), dist(m) {\n }\n virtual unsigned size () const {\n return data.size();\n }\n virtual float operator () (unsigned i, unsigned j) const {\n return dist(data[i], data[j]);\n }\n /// Constructs a search oracle for query object q.\n VectorSearchOracle query (OBJECT_TYPE const &q) const {\n return VectorSearchOracle(data, q, dist);\n }\n };\n}\n#endif\n\n#endif\n\n" }, { "path": "algorithms/DPG/src/main.cpp", "content": "/* \n Copyright (C) 2010,2011 Wei Dong . All Rights Reserved.\n\n DISTRIBUTION OF THIS PROGRAM IN EITHER BINARY OR SOURCE CODE FORM MUST BE\n PERMITTED BY THE AUTHOR.\n*/\n#ifndef KGRAPH_VALUE_TYPE\n#define KGRAPH_VALUE_TYPE float\n#endif\n\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nusing namespace boost::timer;\nusing namespace std;\nusing namespace boost;\nusing namespace kgraph;\nnamespace po = boost::program_options; \n\n\ntypedef KGRAPH_VALUE_TYPE value_type;\n\nint main_index(int argc, char *argv[]) {\n string data_path;\n string output_path;\n KGraph::IndexParams params;\n unsigned D;\n unsigned skip;\n unsigned gap;\n unsigned synthetic;\n float noise;\n\n bool lshkit = true;\n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version information.\")\n (\"data\", po::value(&data_path), \"input path\")\n (\"output\", po::value(&output_path), \"output path\")\n (\",K\", po::value(¶ms.K)->default_value(default_K), \"number of nearest neighbor\")\n (\"controls,C\", po::value(¶ms.controls)->default_value(default_controls), \"number of control pounsigneds\")\n ;\n\n po::options_description desc_hidden(\"Expert options\");\n desc_hidden.add_options()\n (\"iterations,I\", po::value(¶ms.iterations)->default_value(default_iterations), \"\")\n (\",S\", po::value(¶ms.S)->default_value(default_S), \"\")\n (\",R\", po::value(¶ms.R)->default_value(default_R), \"\")\n (\",L\", po::value(¶ms.L)->default_value(default_L), \"\")\n (\"delta\", po::value(¶ms.delta)->default_value(default_delta), \"\")\n (\"recall\", po::value(¶ms.recall)->default_value(default_recall), \"\")\n (\"prune\", po::value(¶ms.prune)->default_value(default_prune), \"\")\n (\"noise\", po::value(&noise)->default_value(0), \"noise\")\n (\"seed\", po::value(¶ms.seed)->default_value(default_seed), \"\")\n (\"dim,D\", po::value(&D), \"dimension, see format\")\n (\"skip\", po::value(&skip)->default_value(0), \"see format\")\n (\"gap\", po::value(&gap)->default_value(0), \"see format\")\n (\"raw\", \"read raw binary file, need to specify D.\")\n (\"synthetic\", po::value(&synthetic)->default_value(0), \"generate synthetic data, for performance evaluation only, specify number of points\")\n ;\n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible).add(desc_hidden);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"output\", 1);\n\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm);\n\n if (vm.count(\"raw\") == 1) {\n lshkit = false;\n }\n\n if (vm.count(\"version\")) {\n cout << \"KGraph version \" << KGraph::version() << endl;\n return 0;\n }\n\n if (vm.count(\"help\")\n || (synthetic && (vm.count(\"dim\") == 0 || vm.count(\"data\")))\n || (!synthetic && (vm.count(\"data\") == 0 || (vm.count(\"dim\") == 0 && !lshkit)))) {\n cout << \"Usage: index [OTHER OPTIONS]... INPUT [OUTPUT]\" << endl;\n cout << desc_visible << endl;\n cout << desc_hidden << endl;\n return 0;\n }\n\n if (params.S == 0) {\n params.S = params.K;\n }\n\n if (lshkit && (synthetic == 0)) { // read dimension information from the data file\n static const unsigned LSHKIT_HEADER = 3;\n ifstream is(data_path.c_str(), ios::binary);\n unsigned header[LSHKIT_HEADER]; /* entry size, row, col */\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n BOOST_VERIFY(header[0] == sizeof(value_type));\n is.close();\n D = header[2];\n skip = LSHKIT_HEADER * sizeof(unsigned);\n gap = 0;\n }\n\n Matrix data;\n if (synthetic) {\n if (!std::is_floating_point::value) {\n throw runtime_error(\"synthetic data not implemented for non-floating-point values.\");\n }\n data.resize(synthetic, D);\n cerr << \"Generating synthetic data...\" << endl;\n default_random_engine rng(params.seed);\n uniform_real_distribution distribution(-1.0, 1.0);\n data.zero(); // important to do that\n for (unsigned i = 0; i < synthetic; ++i) {\n value_type *row = data[i];\n for (unsigned j = 0; j < D; ++j) {\n row[j] = distribution(rng);\n }\n }\n }\n else {\n data.load(data_path, D, skip, gap);\n }\n if (noise != 0) {\n if (!std::is_floating_point::value) {\n throw runtime_error(\"noise injection not implemented for non-floating-point value.\");\n }\n tr1::ranlux64_base_01 rng;\n double sum = 0, sum2 = 0;\n for (unsigned i = 0; i < data.size(); ++i) {\n for (unsigned j = 0; j < data.dim(); ++j) {\n value_type v = data[i][j];\n sum += v;\n sum2 += v * v;\n }\n }\n double total = double(data.size()) * data.dim();\n double avg2 = sum2 / total, avg = sum / total;\n double dev = sqrt(avg2 - avg * avg);\n cerr << \"Adding Gaussian noise w/ \" << noise << \"x sigma(\" << dev << \")...\" << endl;\n boost::normal_distribution gaussian(0, noise * dev);\n for (unsigned i = 0; i < data.size(); ++i) {\n for (unsigned j = 0; j < data.dim(); ++j) {\n data[i][j] += gaussian(rng);\n }\n }\n }\n\n MatrixOracle oracle(data);\n KGraph::IndexInfo info;\n KGraph *kgraph = KGraph::create(); //(oracle, params, &info);\n {\n auto_cpu_timer timer;\n kgraph->build(oracle, params, output_path.c_str(), &info);\n cerr << info.stop_condition << endl;\n }\n\n if (output_path.size()) {\n //kgraph->save(output_path.c_str());\n }\n\n\n /*\n // add the diversification part here \n cerr << \"Start the diversification process...\" << endl << endl;\n kgraph->remove_near_edges(oracle, params.L/2); // here not knn's k, L is the length of NN list ( note that the true NN list length might be smaller than L) \n\n\n // reverse the edges here \n cerr << \"Add reverse edges ...\" << endl << endl;\n kgraph->add_backward_edges();\n\n if (output_path.size()) {\n kgraph->save(output_path.c_str());\n }*/\n \n delete kgraph;\n\n return 0;\n}\n\nint main_statistic(int argc, char *argv[]){\n vector params;\n string input_data_path;\n string index_NNList_path;\n string index_DPG_path; \n unsigned L; \n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version number.\")\n (\"data\", po::value(&input_data_path), \"input data path\")\n (\"index\", po::value(&index_NNList_path), \"NNList index path\") \n (\",L\", po::value(&L)->default_value(default_K), \"\")\n ; \n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"index\", 1); \n\n\n po::variables_map vm; \n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || vm.count(\"data\") == 0 || vm.count(\"index\") == 0 ) {\n cout << \"graph_statistic -L NN_list size\" << endl;\n cout << desc_visible << endl;\n return 0;\n }\n\n cerr << L << \" : L value\" << endl;\n\n Matrix data;\n data.load_lshkit(input_data_path.c_str());\n MatrixOracle oracle(data);\n\n KGraph *kgraph = KGraph::create();\n if (index_NNList_path.size() >0 )\n {\n kgraph->load(index_NNList_path.c_str()); \n }\n\n // compute the co-efficient\n float co = kgraph->statistics( oracle , L );\n\n cout << co << \" # avg co-efficient \" << endl;\n\n}\n\nint main_diversify(int argc, char *argv[]){\n vector params;\n string input_data_path;\n string index_NNList_path;\n string index_DPG_path; \n unsigned L; \n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version number.\")\n (\"data\", po::value(&input_data_path), \"input data path\")\n (\"index\", po::value(&index_NNList_path), \"NNList index path\")\n (\"output\", po::value(&index_DPG_path), \"DPG index path\")\n (\",L\", po::value(&L)->default_value(default_K), \"\")\n ; \n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"index\", 1); \n p.add(\"output\", 1);\n\n po::variables_map vm; \n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || vm.count(\"data\") == 0 || vm.count(\"index\") == 0 || vm.count(\"output\") == 0) {\n cout << \"graph_diversify \" << endl;\n cout << desc_visible << endl;\n return 0;\n }\n\n cerr << L << \" : L value\" << endl;\n\n Matrix data;\n data.load_lshkit(input_data_path.c_str());\n MatrixOracle oracle(data);\n\n KGraph *kgraph = KGraph::create();\n if (index_NNList_path.size() >0 )\n {\n kgraph->load(index_NNList_path.c_str()); \n }\n\n boost::timer::auto_cpu_timer timer;\n\n // start the diversification ?? ( seperately ) \n // add the diversification part here \n // cerr << \"Start the diversification process... (angular dissimilarity )\" << endl << endl;\n // kgraph->remove_near_edges(oracle, L); // here not knn's k, L is the length of NN list ( note that the true NN list length might be smaller than L) \n\n cerr << \"Start the diversification process... ( counting cuts )\" << endl << endl;\n kgraph->diversify_by_cut(oracle, L); // here not knn's k, L is the length of NN list ( note that the true NN list length might be smaller than L) \n\n\n // reverse the edges here \n cerr << \"Add reverse edges ...\" << endl << endl;\n kgraph->add_backward_edges();\n\n float time = timer.elapsed().wall / 1e9;\n\n if ( index_DPG_path.size() > 0 )\n kgraph->save( index_DPG_path.c_str());\n\n cout << time << \" # diversification time\" << endl;\n\n delete kgraph;\n\n}\n\n\nint main_search(int argc, char *argv[]) {\n vector params;\n string input_path;\n string index_path;\n string query_path;\n string output_path;\n string init_path;\n string eval_path;\n unsigned K, M, P, T;\n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version number.\")\n (\"data\", po::value(&input_path), \"input path\")\n (\"index\", po::value(&index_path), \"index path\")\n (\"query\", po::value(&query_path), \"query path\")\n (\"output\", po::value(&output_path), \"output path\")\n (\"init\", po::value(&init_path), \"init path\")\n (\"eval\", po::value(&eval_path), \"eval path\")\n (\",K\", po::value(&K)->default_value(default_K), \"\")\n (\",M\", po::value(&M)->default_value(default_M), \"\")\n (\",P\", po::value(&P)->default_value(default_P), \"\")\n (\",T\", po::value(&T)->default_value(default_T), \"\")\n (\"linear\", \"\")\n ;\n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"index\", 1);\n p.add(\"query\", 1);\n p.add(\"output\", 1);\n\n po::variables_map vm; \n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm); \n\n if (vm.count(\"version\")) {\n cout << \"KGraph version \" << KGraph::version() << endl;\n return 0;\n }\n\n if (vm.count(\"help\") || vm.count(\"data\") == 0 || vm.count(\"index\") == 0 || vm.count(\"query\") == 0) {\n cout << \"search [output]\" << endl;\n cout << desc_visible << endl;\n return 0;\n }\n\n if (P < K) {\n P = K;\n }\n\n Matrix data;\n Matrix query;\n Matrix result; //(query.size(), U);\n unsigned init = 0;\n\n data.load_lshkit(input_path);\n query.load_lshkit(query_path);\n if (init_path.size()) {\n result.load_lshkit(init_path);\n BOOST_VERIFY(result.size() == query.size());\n init = result.dim();\n BOOST_VERIFY(init >= K);\n }\n MatrixOracle oracle(data);\n float recall = 0;\n float cost = 0;\n float time = 0;\n if (vm.count(\"linear\")) {\n boost::timer::auto_cpu_timer timer;\n result.resize(query.size(), K);\n// #pragma omp parallel for\n for (unsigned i = 0; i < query.size(); ++i) {\n oracle.query(query[i]).search(K, default_epsilon, result[i]);\n }\n cost = 1.0;\n time = timer.elapsed().wall / 1e9;\n }\n else {\n result.resize(query.size(), K);\n KGraph::SearchParams params;\n params.K = K;\n params.M = M;\n params.P = P;\n params.T = T;\n params.init = init;\n KGraph *kgraph = KGraph::create();\n kgraph->load(index_path.c_str());\n\n boost::timer::auto_cpu_timer timer;\n cerr << \"Searching...\" << endl;\n\n// #pragma omp parallel for reduction(+:cost)\n for (unsigned i = 0; i < query.size(); ++i) {\n KGraph::SearchInfo info;\n kgraph->search(oracle.query(query[i]), params, result[i], &info);\n cost += info.cost;\n }\n cost /= query.size();\n time = timer.elapsed().wall / 1e9;\n //cerr << \"Cost: \" << cost << endl;\n delete kgraph;\n }\n if (output_path.size()) {\n result.save_lshkit(output_path);\n }\n if (eval_path.size()) {\n Matrix gs;\n gs.load_lshkit(eval_path);\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(gs.size() >= query.size());\n kgraph::Matrix gs_dist(query.size(), K);\n kgraph::Matrix result_dist(query.size(), K);\n// #pragma omp parallel for\n for (unsigned i = 0; i < query.size(); ++i) {\n auto Q = oracle.query(query[i]);\n float *gs_dist_row = gs_dist[i];\n float *result_dist_row = result_dist[i];\n unsigned const *gs_row = gs[i];\n unsigned const *result_row = result[i];\n for (unsigned k = 0; k < K; ++k) {\n gs_dist_row[k] = Q(gs_row[k]);\n result_dist_row[k] = Q(result_row[k]);\n }\n sort(gs_dist_row, gs_dist_row + K);\n sort(result_dist_row, result_dist_row + K);\n }\n recall = AverageRecall(gs_dist, result_dist, K);\n }\n cout << \"Time: \" << time << \" Recall: \" << recall << \" Cost: \" << cost << endl;\n\n return 0;\n}\n\nint main(int argc, char *argv[])\n{\n cerr<< \"INDEX CONSTRUCTION...\" << endl;\n return main_index(argc, argv);\n \n // cerr<< \"Diversification ......\" << endl << endl; \n // return main_diversify( argc, argv);\n\n // cerr<< \"Report statistics ......\" << endl << endl; \n // return main_statistic( argc, argv);\n\n // default search \n // cerr<< \"SEARCH ... \" << endl;\n\t// return main_search(argc, argv); \n}\n\n\n\n" }, { "path": "algorithms/DPG/src/metric.cpp", "content": "#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n#include \n\n#ifdef __GNUC__\n#ifdef __AVX__\n#include \n#define AVX_L2SQR(addr1, addr2, dest, tmp1, tmp2) \\\n tmp1 = _mm256_loadu_ps(addr1);\\\n tmp2 = _mm256_loadu_ps(addr2);\\\n tmp1 = _mm256_sub_ps(tmp1, tmp2); \\\n tmp1 = _mm256_mul_ps(tmp1, tmp1); \\\n dest = _mm256_add_ps(dest, tmp1); \nnamespace kgraph {\nfloat float_l2sqr_avx (float const *t1, float const *t2, unsigned dim) {\n __m256 sum;\n __m256 l0, l1, l2, l3;\n __m256 r0, r1, r2, r3;\n unsigned D = (dim + 7) & ~7U; // # dim aligned up to 256 bits, or 8 floats\n unsigned DR = D % 32;\n unsigned DD = D - DR;\n const float *l = t1;\n const float *r = t2;\n const float *e_l = l + DD;\n const float *e_r = r + DD;\n float unpack[8] __attribute__ ((aligned (32))) = {0, 0, 0, 0, 0, 0, 0, 0};\n float ret = 0.0;\n sum = _mm256_load_ps(unpack);\n switch (DR) {\n case 24:\n AVX_L2SQR(e_l+16, e_r+16, sum, l2, r2);\n case 16:\n AVX_L2SQR(e_l+8, e_r+8, sum, l1, r1);\n case 8:\n AVX_L2SQR(e_l, e_r, sum, l0, r0);\n }\n for (unsigned i = 0; i < DD; i += 32, l += 32, r += 32) {\n AVX_L2SQR(l, r, sum, l0, r0);\n AVX_L2SQR(l + 8, r + 8, sum, l1, r1);\n AVX_L2SQR(l + 16, r + 16, sum, l2, r2);\n AVX_L2SQR(l + 24, r + 24, sum, l3, r3);\n }\n _mm256_storeu_ps(unpack, sum);\n ret = unpack[0] + unpack[1] + unpack[2] + unpack[3]\n + unpack[4] + unpack[5] + unpack[6] + unpack[7];\n return ret;//sqrt(ret);\n}\n}\n#endif\n#ifdef __SSE2__\n#include \n#define SSE_L2SQR(addr1, addr2, dest, tmp1, tmp2) \\\n tmp1 = _mm_load_ps(addr1);\\\n tmp2 = _mm_load_ps(addr2);\\\n tmp1 = _mm_sub_ps(tmp1, tmp2); \\\n tmp1 = _mm_mul_ps(tmp1, tmp1); \\\n dest = _mm_add_ps(dest, tmp1); \nnamespace kgraph {\nfloat float_l2sqr_sse2 (float const *t1, float const *t2, unsigned dim) {\n\t/*\t\n\t//cos dostance\n\tfloat dis=0;\n\tfloat x_ = 0;\n\tfloat y_ = 0; \n\tfor(unsigned i=0;i\nvoid print_128 (__m128i v) {\n static unsigned constexpr L = 16 / sizeof(T);\n T unpack[L] __attribute__ ((aligned (16)));\n _mm_store_si128((__m128i *)unpack, v);\n cout << '(' << int(unpack[0]);\n for (unsigned i = 1; i < L; ++i) {\n cout << ',' << int(unpack[i]);\n }\n cout << ')';\n}\n*/\n\n#define SSE_L2SQR_BYTE(addr1, addr2, sum, z) \\\n do { \\\n const __m128i o = _mm_load_si128((__m128i const *)(addr1));\\\n const __m128i p = _mm_load_si128((__m128i const *)(addr2));\\\n __m128i o1 = _mm_unpackhi_epi8(o,z); \\\n __m128i p1 = _mm_unpackhi_epi8(p,z); \\\n __m128i d = _mm_sub_epi16(o1, p1); \\\n sum = _mm_add_epi32(sum, _mm_madd_epi16(d, d)); \\\n o1 = _mm_unpacklo_epi8(o,z); \\\n p1 = _mm_unpacklo_epi8(p,z); \\\n d = _mm_sub_epi16(o1, p1); \\\n sum = _mm_add_epi32(sum, _mm_madd_epi16(d, d)); \\\n } while (false)\nnamespace kgraph {\nfloat uint8_l2sqr_sse2 (uint8_t const *t1, uint8_t const *t2, unsigned dim) {\n\tcout<<\"3\\n\"; \n\tunsigned D = (dim + 0xFU) & ~0xFU; // actual dimension used in calculation, 0-padded\n unsigned DR = D % 64; // process 32 dims per iteration\n unsigned DD = D - DR;\n const uint8_t *l = t1;\n const uint8_t *r = t2;\n const uint8_t *e_l = l + DD;\n const uint8_t *e_r = r + DD;\n int32_t unpack[4] __attribute__ ((aligned (16))) = {0, 0, 0, 0};\n __m128i sum = _mm_load_si128((__m128i *)unpack);\n const __m128i z = sum;\n switch (DR) {\n case 48:\n SSE_L2SQR_BYTE(e_l+32, e_r+32, sum, z);\n case 32:\n SSE_L2SQR_BYTE(e_l+16, e_r+16, sum, z);\n case 16:\n SSE_L2SQR_BYTE(e_l, e_r, sum, z);\n }\n for (unsigned i = 0; i < DD; i += 64, l += 64, r += 64) {\n SSE_L2SQR_BYTE(l, r, sum, z);\n SSE_L2SQR_BYTE(l + 16, r + 16, sum, z);\n SSE_L2SQR_BYTE(l + 32, r + 32, sum, z);\n SSE_L2SQR_BYTE(l + 48, r + 48, sum, z);\n }\n _mm_store_si128((__m128i *)unpack, sum);\n int32_t ret = unpack[0] + unpack[1] + unpack[2] + unpack[3];\n return float(ret);//sqrt(ret);\n}\n}\n#endif\n#endif\n" }, { "path": "algorithms/KGraph/README.html", "content": "\n\n\n \n \n \n \n \n\n\n

KGraph

\n

This is a modified version of KGraph from the KGraph source code obtained on 5 April 2016. The original version of KGraph which is continuously maintained by Dr. Wei Dong.

\n

Main differences:

\n
    \n

  1. We disabled SIMD and multi-threading techniques in KGraph. Note the besides the compiler flag, we also comment the SIMD related code in metric.cpp

    2. \n

  2. We reduce the index size of KGraph by not keeping the distances of the edges, which is not used in the search process.

    3. \n
\n

Prerequisites

\n
    \n
  • A modern compiler that supports C++11 (e.g., g++ 4.7 and above)
    • \n
  • A Linux System
    • \n
  • The boost library (dev version) installed. Note that we need to set the paths of the Boost installed in Makefile.
    • \n
  • A sample data (e.g., audio) is downloaded, including data points, query points and ground truth results. Note that all data files downloaded should be put to the ./knn_benchmark/data directory.
    • \n
\n

Compile (KGraph/src)

\n

build the index construction and search programs, namely kgraph_index and kgraph_search.

\n
build.sh
\n

Construct K-NN graph (KGraph/script)

\n

kgraph_index.sh

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Build K-NN graphs based on the data points, which is put to its index directory (i.e., ./nns_benchmark/index/kgraph). Note that the suffix number of the index file (e.g., kgraph_40 of audio.kgraph_40) represents the number of neighbors kept in the K-NN graph.

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Search on the K-NN graph (KGraph/script)

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kgraph_search.sh
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Note that, in our experiment paper, we use P value of KGraph search (i.e., search queue size) to achieve the trade-off between search speed and search quality (recall). The search performance (time and recall) results are kept in the KGraph/results directory.

\n\n\n" }, { "path": "algorithms/KGraph/README.md", "content": "# KGraph\n\nThis is a modified version of [**KGraph**](http://www.kgraph.org) from the\nKGraph source code obtained on 5 April 2016. The original version of **KGraph** which is continuously maintained by [Dr. Wei Dong](https://github.com/aaalgo).\n\nMain differences:\n\n 1. We disabled SIMD and multi-threading techniques in **KGraph**. Note the besides the compiler flag, we also comment the SIMD related code in `metric.cpp`\n \n 2. We reduce the index size of KGraph by not keeping the distances of the edges, which is not used in the search process. \n \n\n## Prerequisites\n- A modern compiler that supports `C++11` (e.g., `g++ 4.7` and above)\n- A Linux System \n- The boost library (dev version) installed. Note that we need to set the **paths** of the Boost installed in **Makefile**.\n- [A sample data](https://github.com/DBWangGroupUNSW/nns_benchmark/tree/master/data) (e.g., audio) is downloaded, including data points, query points and ground truth results. Note that all data files downloaded should be put to the `./knn_benchmark/data` directory.\n \n## Compile (KGraph/src)\n\nbuild the index construction and search programs, namely `kgraph_index` and `kgraph_search`.\n\n```\nbuild.sh\n```\n\n## Construct K-NN graph (KGraph/script) \n\n``` kgraph_index.sh ```\n\nBuild K-NN graphs based on the data points, which is put\nto its index directory (i.e., `./nns_benchmark/index/kgraph`). Note that the\nsuffix number of the index file (e.g., `kgraph_40` of `audio.kgraph_40`)\nrepresents the number of neighbors kept in the K-NN graph.\n\n## Search on the K-NN graph (KGraph/script)\n\n```\nkgraph_search.sh\n```\n\nNote that, in our experiment paper, we use `P` value of KGraph search (i.e.,\nsearch queue size) to achieve the trade-off between search speed and search\nquality (recall). The search performance (time and recall) results are kept in\nthe KGraph/results directory.\n\n" }, { "path": "algorithms/KGraph/script/kgraph_index.sh", "content": "data_dir=\"../../../data\"\nindex_dir=\"../index\"\nbin_dir=\"../src\"\n\nmkdir ${index_dir} \n\n\n# Please sure the data ( data points, query points and ground truth files are located at ../../../data directory )\n\nfor data in audio # sift \ndo\n\n# L is for lengh of the NN-list (i.e.,#neighbors materialized) of the K-NN graph\n# the K value of the input is the desirable k value in the k-NN search \n\nL=40\n\n${bin_dir}/kgraph_index --data ${data_dir}/${data}_base.lshkit --output ${index_dir}/${data}.kgraph_${L} -S 20 -L ${L} -K 20\n\ndone\n\n\n" }, { "path": "algorithms/KGraph/script/kgraph_search.sh", "content": "data_dir=\"../../../data\"\nindex_dir=\"../index\"\nbin_dir=\"../src\"\n\nresult_dir=\"../results\"\n\nmkdir ${result_dir}\n\n\nk=20\nL=40\n\n\nfor data in audio # sift \ndo\n\n# P is the queue size in the search \n\nfor P in 1 2 5 10 20 40 50 100\ndo\n\n# show the result \n${bin_dir}/kgraph_search --data ${data_dir}/${data}_base.lshkit --query ${data_dir}/${data}_query.lshkit --eval ${data_dir}/${data}_groundtruth.lshkit -K ${k} --index ${index_dir}/${data}.kgraph_${L} -P ${P} \n\n# try again and keep the results into files \n\nresult=\"${data}_k${k}_P${P}.txt\" \n\n${bin_dir}/kgraph_search --data ${data_dir}/${data}_base.lshkit --query ${data_dir}/${data}_query.lshkit --eval ${data_dir}/${data}_groundtruth.lshkit -K ${k} --index ${index_dir}/${data}.kgraph_${L} -P ${P} > ${result_dir}/${result}\n\ndone\ndone\n\n" }, { "path": "algorithms/KGraph/src/Makefile", "content": "CC=g++ \n\n#ARCH=-msse2\n#OPENMP=-fopenmp\n\nOPT=-O3\n\nboost_dir=\"/home/yingz/Software/boost_1_58_0/\"\nboost_lib=\"/usr/local/lib\"\n\nCXXFLAGS+=-fPIC -g -std=c++11 -I${boost_dir} -I. $(OPT) -L${boost_lib} $(OPT) $(ARCH) $(OPENMP)\nLDFLAGS+=-static $(OPENMP)\nLDLIBS+=-lboost_timer -lboost_chrono -lboost_system -lboost_program_options -lgomp -lm -lrt -L${boost_lib}\n\nCOMMON=kgraph.o metric.o RandGen.o\n\nHEADERS=kgraph.h kgraph-data.h RandGen.h\n\nPROGS=kgraph_index kgraph_search \n\nRELEASE_SRC=Makefile LICENSE kgraph.h kgraph-data.h kgraph_index.cpp kgraph_search.cpp \n\n$(PROGS): %:\t%.cpp $(HEADERS) $(COMMON)\n\t$(CXX) $(CXXFLAGS) $(LDFLAGS) -o $@ $*.cpp $(COMMON) $(LDLIBS)\n%.o:\t%.cpp $(HEADERS)\n\t$(CXX) $(CXXFLAGS) -c $*.cpp \n\nclean:\n\n\trm -f *.o\n\n" }, { "path": "algorithms/KGraph/src/RandGen.cpp", "content": "/*\n * This file is part of SRS project.\n *\n * SRS is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n *\n * SRS is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with SRS. If not, see .\n *\n * Created by: Yifang Sun, Jianbin Qin\n * Last modified by: Yifang Sun, Jianbin Qin\n */\n\n#include \n#include \n#include \"RandGen.h\"\n\n/************************************************************\n *** Given a mean and a standard deviation, gaussian **\n ** generates a normally distributed random number **\n ** Algorithm: Polar Method, p. 104, Knuth, vol. 2 **\n ************************************************************/\n\nfloat gaussian(float mean, float sigma) {\n float v1, v2;\n float s;\n float x;\n\n do {\n v1 = 2 * uniform(0, 1) - 1;\n v2 = 2 * uniform(0, 1) - 1;\n s = v1 * v1 + v2 * v2;\n } while (s >= 1.);\n\n x = v1 * (float) sqrt(-2. * log(s) / s);\n\n /* x is normally distributed with mean 0 and sigma 1. */\n x = x * sigma + mean;\n\n return (x);\n}\n\n/************************************************************\n ** Generates a random number between _min and _max **\n ** uniformly **\n ** By Yufei Tao\n ************************************************************/\n\nfloat uniform(float _min, float _max) {\n int int_r = rand();\n long base = RAND_MAX - 1;\n float f_r = ((float) int_r) / base;\n return (_max - _min) * f_r + _min;\n}\n\n" }, { "path": "algorithms/KGraph/src/RandGen.h", "content": "/*\n * This file is part of SRS project.\n *\n * SRS is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n *\n * SRS is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with SRS. If not, see .\n *\n * Created by: Yifang Sun, Jianbin Qin\n * Last modified by: Yifang Sun, Jianbin Qin\n */\n\n#ifndef RANDGEN_H_\n#define RANDGEN_H_\n\nfloat gaussian(float mean, float sigma);\nfloat uniform(float _min, float _max);\n\n#endif /* RANDGEN_H_ */\n" }, { "path": "algorithms/KGraph/src/build.sh", "content": "make kgraph_index \nmake kgraph_search\n\nmake clean\n\n" }, { "path": "algorithms/KGraph/src/kgraph-data.h", "content": "#ifndef WDONG_KGRAPH_DATA\n#define WDONG_KGRAPH_DATA\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef __GNUC__\n#ifdef __AVX__\n#define KGRAPH_MATRIX_ALIGN 32\n#else\n#ifdef __SSE2__\n#define KGRAPH_MATRIX_ALIGN 16\n#else\n#define KGRAPH_MATRIX_ALIGN 4\n#endif\n#endif\n#endif\n\nnamespace kgraph {\n\n /// L2 square distance with AVX instructions.\n /** AVX instructions have strong alignment requirement for t1 and t2.\n */\n extern float float_l2sqr_avx (float const *t1, float const *t2, unsigned dim);\n /// L2 square distance with SSE2 instructions.\n extern float float_l2sqr_sse2 (float const *t1, float const *t2, unsigned dim);\n /// L2 square distance for uint8_t with SSE2 instructions (for SIFT).\n extern float uint8_l2sqr_sse2 (uint8_t const *t1, uint8_t const *t2, unsigned dim);\n\n using std::vector;\n using std::runtime_error;\n\n /// namespace for various distance metrics.\n namespace metric {\n /// L2 square distance.\n struct l2sqr {\n template \n static float apply (T const *t1, T const *t2, unsigned dim) {\n\t //\t\t\t\tcout<<\"apply \\n\";\n float r = 0;\n for (unsigned i = 0; i < dim; ++i) {\n float v = float(t1[i]) - float(t2[i]);\n v *= v;\n r += v;\n }\n return r;\n }\n };\n /// L2 distance.\n struct l2 {\n template \n static float apply (T const *t1, T const *t2, unsigned dim) {\n return sqrt(l2sqr::apply(t1, t2, dim));\n }\n };\n }\n\n /// Matrix data.\n template \n class Matrix {\n unsigned col;\n unsigned row;\n size_t stride;\n char *data;\n\n void reset (unsigned r, unsigned c) {\n row = r;\n col = c;\n stride = (sizeof(T) * c + A - 1) / A * A;\n /*\n data.resize(row * stride);\n */\n if (data) free(data);\n data = (char *)memalign(A, row * stride); // SSE instruction needs data to be aligned\n if (!data) throw runtime_error(\"memalign\");\n }\n public:\n Matrix (): col(0), row(0), stride(0), data(0) {}\n Matrix (unsigned r, unsigned c): data(0) {\n reset(r, c);\n }\n ~Matrix () {\n if (data) free(data);\n }\n unsigned size () const {\n return row;\n }\n unsigned dim () const {\n return col;\n }\n size_t step () const {\n return stride;\n }\n void resize (unsigned r, unsigned c) {\n reset(r, c);\n }\n T const *operator [] (unsigned i) const {\n return reinterpret_cast(&data[stride * i]);\n }\n T *operator [] (unsigned i) {\n return reinterpret_cast(&data[stride * i]);\n }\n void zero () {\n memset(data, 0, row * stride);\n }\n void load (const std::string &path, unsigned dim, unsigned skip = 0, unsigned gap = 0) {\n std::ifstream is(path.c_str(), std::ios::binary);\n BOOST_VERIFY(is);\n is.seekg(0, std::ios::end);\n size_t size = is.tellg();\n size -= skip;\n unsigned line = sizeof(T) * dim + gap;\n unsigned N = size / line;\n reset(N, dim);\n zero();\n is.seekg(skip, std::ios::beg);\n for (unsigned i = 0; i < N; ++i) {\n is.read(&data[stride * i], sizeof(T) * dim);\n is.seekg(gap, std::ios::cur);\n }\n BOOST_VERIFY(is);\n }\n\n void load_lshkit (std::string const &path) {\n static const unsigned LSHKIT_HEADER = 3;\n std::ifstream is(path.c_str(), std::ios::binary);\n unsigned header[LSHKIT_HEADER]; /* entry size, row, col */\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n BOOST_VERIFY(header[0] == sizeof(T));\n is.close();\n unsigned D = header[2];\n unsigned skip = LSHKIT_HEADER * sizeof(unsigned);\n unsigned gap = 0;\n load(path, D, skip, gap);\n }\n\n void save_lshkit (std::string const &path) {\n std::ofstream os(path.c_str(), std::ios::binary);\n unsigned header[3];\n assert(sizeof header == 3*4);\n header[0] = sizeof(T);\n header[1] = row;\n header[2] = col;\n os.write((const char *)header, sizeof(header));\n for (unsigned i = 0; i < row; ++i) {\n os.write(&data[stride * i], sizeof(T) * col);\n }\n }\n };\n\n /// Matrix proxy to interface with 3rd party libraries (FLANN, OpenCV, NumPy).\n template \n class MatrixProxy {\n unsigned rows;\n unsigned cols; // # elements, not bytes, in a row,\n size_t stride; // # bytes in a row, >= cols * sizeof(element)\n uint8_t const *data;\n public:\n MatrixProxy (Matrix const &m)\n : rows(m.size()), cols(m.dim()), stride(m.step()), data(reinterpret_cast(m[0])) {\n }\n\n#ifndef __AVX__\n#ifdef FLANN_DATASET_H_\n /// Construct from FLANN matrix.\n MatrixProxy (flann::Matrix const &m)\n : rows(m.rows), cols(m.cols), stride(m.stride), data(m.data) {\n BOOST_VERIFY(stride % A == 0);\n }\n#endif\n#ifdef __OPENCV_CORE_HPP__\n /// Construct from OpenCV matrix.\n MatrixProxy (cv::Mat const &m)\n : rows(m.rows), cols(m.cols), stride(m.step), data(m.data) {\n BOOST_VERIFY(stride % A == 0);\n }\n#endif\n#ifdef NPY_NDARRAYOBJECT_H\n /// Construct from NumPy matrix.\n MatrixProxy (PyArrayObject *obj) {\n BOOST_VERIFY(obj->nd == 2);\n rows = obj->dimensions[0];\n cols = obj->dimensions[1];\n stride = obj->strides[0];\n data = reinterpret_cast(obj->data);\n BOOST_VERIFY(obj->descr->elsize == sizeof(DATA_TYPE));\n BOOST_VERIFY(stride % A == 0);\n BOOST_VERIFY(stride >= cols * sizeof(DATA_TYPE));\n }\n#endif\n#endif\n unsigned size () const {\n return rows;\n }\n unsigned dim () const {\n return cols;\n }\n DATA_TYPE const *operator [] (unsigned i) const {\n return reinterpret_cast(data + stride * i);\n }\n };\n\n /// Oracle for matrix data.\n /** DATA_TYPE can be Matrix or MatrixProxy,\n * DIST_TYPE should be one class within the namespace kgraph.metric.\n */\n template \n class MatrixOracle: public kgraph::IndexOracle {\n MatrixProxy proxy;\n public:\n class SearchOracle: public kgraph::SearchOracle {\n MatrixProxy proxy;\n DATA_TYPE const *query;\n public:\n SearchOracle (MatrixProxy const &p, DATA_TYPE const *q): proxy(p), query(q) {\n }\n virtual unsigned size () const {\n return proxy.size();\n }\n virtual float operator () (unsigned i) const {\n return DIST_TYPE::apply(proxy[i], query, proxy.dim());\n }\n };\n template \n MatrixOracle (MATRIX_TYPE const &m): proxy(m) {\n }\n virtual unsigned size () const {\n return proxy.size();\n }\n virtual float operator () (unsigned i, unsigned j) const {\n return DIST_TYPE::apply(proxy[i], proxy[j], proxy.dim());\n }\n\n\tvirtual float angle (unsigned orign, unsigned pivot, unsigned x) const { // return cos(\\theta), where \\theta is the angle between vector orign - pivot and orign - x\n\t float dot_prod = 0.0, norm = 0.0, norm2 = 0.0;\n\t float pi = 3.1415926;\n\t for (unsigned k = 0; k < proxy.dim(); ++k){\n\t dot_prod += (float)(proxy[pivot][k] - proxy[orign][k])* (float)(proxy[x][k] - proxy[orign][k]);\n\t norm += (float)(proxy[x][k] - proxy[orign][k]) * (proxy[x][k] - proxy[orign][k]);\n\t norm2 += (float)(proxy[pivot][k] - proxy[orign][k]) * (proxy[pivot][k] - proxy[orign][k]);\n\t }\n\t //\t if (dot_prod / sqrt(norm * norm2) < 0.001){\n\t // fprintf(stderr, \"IN: %d %d %d\", orign, pivot, x);\n\t //}\n\t //fprintf(stderr, \"%f \", dot_prod);\n\t //fprintf(stderr, \"%f\\n\", sqrt ( norm1 * norm2 ));\n\t return acos(dot_prod / sqrt(norm * norm2) ) * 180 / pi;\n\t}\n\tSearchOracle query (DATA_TYPE const *query) const {\n return SearchOracle(proxy, query);\n }\n };\n\n inline float AverageRecall (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n // compare\n unsigned found = 0;\n unsigned gs_n = 0;\n unsigned re_n = 0;\n while ((gs_n < K) && (re_n < K)) {\n if (gs_row[gs_n] < re_row[re_n]) {\n ++gs_n;\n }\n else if (gs_row[gs_n] == re_row[re_n]) {\n ++found;\n ++gs_n;\n ++re_n;\n }\n else {\n throw runtime_error(\"distance is unstable\");\n }\n }\n sum += float(found) / K;\n }\n return sum / result.size();\n }\n\tinline void AveragePrecision(Matrix const &gs, Matrix const &result, float *precision) {\n\t\tunsigned K = result.dim();\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n unsigned found = 0;\n\t\tunsigned gs_n = 0;\n unsigned re_n = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\twhile ((gs_n < K) && (re_n < K)) {\n if (gs_row[gs_n] < re_row[re_n]) {\n ++gs_n;\n }\n else if (gs_row[gs_n] == re_row[re_n]) {\n ++found;\n ++gs_n;\n ++re_n;\n }\n else {\n throw runtime_error(\"distance is unstable\");\n }\n }\n\t\t\tif (gs_row[0] == re_row[0]) {\n ++found;\n\n }\n }\n *precision = found / result.size();\n }\n\n\tinline void AverageAccuracy(Matrix const &gs, Matrix const &result, float *accuracy) \n\t{\n unsigned K = result.dim();\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n\n\t\tfloat avg = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n \tfloat dist = 0;\n\t\t\tunsigned gs_n = 0;\n unsigned re_n = 0;\n while ((gs_n < K) && (re_n < K)) {\n float d=(re_row[re_n]-gs_row[gs_n])/gs_row[gs_n];\n\t\t\t\tif(d<=4)\n\t\t\t\t\tdist += d;\n\t\t\t\telse\n\t\t\t\t\tdist+=4; \n \t\t\t\t++gs_n;\n ++re_n;\n\t\t\t}\n\t\t\tavg += dist/K;\n }\n *accuracy=avg / result.size();\n }\n\n\tinline float compute_number_closer (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tfor (unsigned gs_n=0;gs_n < K ;gs_n++)\n\t\t\t{\n\t\t\t\tfor(unsigned re_n=0; re_n < K; re_n++)\n\t\t\t\t{\n\t\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t\t{\n\t\t\t\t\t\trate += (float)(gs_n+1)/(re_n+1);\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n sum += rate/K;\n }\n return sum / result.size();\n }\n\n\tinline float compute_mean_reciprocal_rank (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tfor(unsigned re_n=0; re_n < K; re_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[0]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\trate += 1.0/(re_n+1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n sum += rate;\n }\n return sum / result.size();\n }\n\n\n\tinline float compute_mean_average_precision (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tunsigned re_n = 0;\n\t\t\tint count = 0;\n\t\t\tfor(unsigned gs_n=0; gs_n < K; gs_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\tcount++;\n\t\t\t\t\trate += 1.0*(count)/(gs_n+1);\n\t\t\t\t\tre_n ++;\n\t\t\t\t}\n\t\t\t}\n sum += rate/K;\n }\n return sum / result.size();\n }\n\n\tinline float compute_discounted_culmulative_gain (Matrix const &gs, Matrix const &result, unsigned K = 0) {\n if (K == 0) {\n K = result.dim();\n }\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(result.dim() >= K);\n BOOST_VERIFY(gs.size() >= result.size());\n float sum = 0;\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n\t\t\tfloat rate = 0.0;\n\t\t\tunsigned re_n = 0;\n\t\t\tfor(unsigned gs_n=0; gs_n < K; gs_n++)\n\t\t\t{\n\t\t\t\tif(gs_row[gs_n]==re_row[re_n])\n\t\t\t\t{\n\t\t\t\t\trate += 1.0/log2(gs_n+2);\n\t\t\t\t\tre_n ++;\n\t\t\t\t}\n\t\t\t}\n sum += rate;\n }\n return sum / result.size();\n }\n\n inline void AverageMinDist (Matrix const &gs, Matrix const &result, float *MinDist,float *AvgDist,float *MaxDist) {\n\n BOOST_VERIFY(gs.size() >= result.size());\n for (unsigned i = 0; i < result.size(); ++i) {\n float const *gs_row = gs[i];\n float const *re_row = result[i];\n *MinDist += re_row[0]-gs_row[0];\n *MaxDist += abs(re_row[result.dim()]-gs_row[0]);\n for(unsigned j=0;j\n inline float l2sqr::apply (float const *t1, float const *t2, unsigned dim) {\n return float_l2sqr_avx(t1, t2, dim);\n }\n}}\n#else\n#ifdef __SSE2__\nnamespace kgraph { namespace metric {\n template <>\n inline float l2sqr::apply (float const *t1, float const *t2, unsigned dim) {\n return float_l2sqr_sse2(t1, t2, dim);\n }\n template <>\n inline float l2sqr::apply (uint8_t const *t1, uint8_t const *t2, unsigned dim) {\n return uint8_l2sqr_sse2(t1, t2, dim);\n }\n}}\n#endif\n#endif\n#endif\n#endif\n\n\n\n#endif\n\n" }, { "path": "algorithms/KGraph/src/kgraph.cpp", "content": "//static char const *kgraph_version = KGRAPH_VERSION \"-\" KGRAPH_BUILD_NUMBER \",\" KGRAPH_BUILD_ID;\nstatic char const *kgraph_version = \"deiversified proximity graph\";\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#define timer timer_for_boost_progress_t\n#include \n#undef timer\n#include \n#include \n#include \n#include \n#include \n#include \"boost/smart_ptr/detail/spinlock.hpp\"\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nnamespace kgraph {\n\n using namespace std;\n using namespace boost;\n using namespace boost::accumulators;\n\n unsigned verbosity = default_verbosity;\n\n typedef boost::detail::spinlock Lock;\n typedef std::lock_guard LockGuard;\n\n // generate size distinct random numbers < N\n template \n static void GenRandom (RNG &rng, unsigned *addr, unsigned size, unsigned N) {\n for (unsigned i = 0; i < size; ++i) {\n addr[i] = rng() % (N - size);\n }\n sort(addr, addr + size);\n for (unsigned i = 1; i < size; ++i) {\n if (addr[i] <= addr[i-1]) {\n addr[i] = addr[i-1] + 1;\n }\n }\n unsigned off = rng() % N;\n for (unsigned i = 0; i < size; ++i) {\n addr[i] = (addr[i] + off) % N;\n }\n }\n\n struct Neighbor {\n unsigned id;\n float dist;\n bool flag; // whether this entry is a newly found one \n Neighbor () {}\n Neighbor (unsigned i, float d, bool f = true): id(i), dist(d), flag(f) {\n }\n };\n\n static inline bool operator < (Neighbor const &n1, Neighbor const &n2) {\n if (n1.dist == n2.dist)\n\treturn n1.id < n2.id;\n else \n return n1.dist < n2.dist;\n }\n\n typedef vector Neighbors;\n\n // both pool and knn should be sorted in ascending order\n static float EvaluateRecall (Neighbors const &pool, Neighbors const &knn) {\n if (knn.empty()) return 1.0;\n unsigned found = 0;\n unsigned n_p = 0;\n unsigned n_k = 0;\n for (;;) {\n if (n_p >= pool.size()) break;\n if (n_k >= knn.size()) break;\n if (knn[n_k].dist < pool[n_p].dist) {\n ++n_k;\n }\n else if (knn[n_k].dist == pool[n_p].dist) {\n ++found;\n ++n_k;\n ++n_p;\n }\n else {\n cerr << \"Distance is unstable.\" << endl;\n cerr << \"Exact\";\n for (auto const &p: knn) {\n cerr << ' ' << p.id << ':' << p.dist;\n }\n cerr << endl;\n cerr << \"Approx\";\n for (auto const &p: pool) {\n cerr << ' ' << p.id << ':' << p.dist;\n }\n cerr << endl;\n throw runtime_error(\"distance is unstable\");\n }\n }\n return float(found) / knn.size(); //recall\n }\n\n static float EvaluateAccuracy (Neighbors const &pool, Neighbors const &knn) {\n unsigned m = std::min(pool.size(), knn.size());\n float sum = 0;\n unsigned cnt = 0;\n for (unsigned i = 0; i < m; ++i) {\n if (knn[i].dist > 0) {\n sum += abs(pool[i].dist - knn[i].dist) / knn[i].dist;\n ++cnt;\n }\n }\n return cnt > 0 ? sum / cnt: 0;\n }\n\n static float EvaluateOneRecall (Neighbors const &pool, Neighbors const &knn) {\n if (pool[0].dist == knn[0].dist) return 1.0;\n return 0;\n }\n \n static float EvaluateDelta (Neighbors const &pool, unsigned K) {\n unsigned c = 0;\n unsigned N = K;\n if (pool.size() < N) N = pool.size();\n for (unsigned i = 0; i < N; ++i) {\n if (pool[i].flag) ++c;\n }\n return float(c) / K; //the fraction of true in pool's flags\n }\n\n struct Control {\n unsigned id;\n Neighbors neighbors;\n };\n\n // try insert nn into the list\n // the array addr must contain at least K+1 entries:\n // addr[0..K-1] is a sorted list\n // addr[K] is as output parameter\n // * if nn is already in addr[0..K-1], return K+1\n // * Otherwise, do the equivalent of the following\n // put nn into addr[K]\n // make addr[0..K] sorted\n // return the offset of nn's index in addr (could be K)\n //\n // Special case: K == 0\n // addr[0] <- nn\n // return 0\n static inline unsigned UpdateKnnList (Neighbor *addr, unsigned K, Neighbor nn) {\n // find the location to insert\n unsigned j;\n unsigned i = K;\n while (i > 0) {\n j = i - 1;\n if (addr[j].dist <= nn.dist) break;\n i = j;\n }\n // check for equal ID\n unsigned l = i;\n while (l > 0) {\n j = l - 1;\n if (addr[j].dist < nn.dist) break;\n if (addr[j].id == nn.id) return K + 1;\n l = j;\n }\n // i <= K-1\n j = K;\n while (j > i) {\n addr[j] = addr[j-1];\n --j;\n }\n addr[i] = nn;\n return i;\n }\n\n void LinearSearch (IndexOracle const &oracle, unsigned i, unsigned K, vector *pnns) {\n vector nns(K+1);\n unsigned N = oracle.size();\n Neighbor nn;\n nn.id = 0;\n nn.flag = true; // we don't really use this\n unsigned k = 0;\n while (nn.id < N) {\n if (nn.id != i) {\n nn.dist = oracle(i, nn.id);\n UpdateKnnList(&nns[0], k, nn);\n if (k < K) ++k;\n }\n ++nn.id;\n }\n nns.resize(K);\n pnns->swap(nns);\n }\n\n unsigned SearchOracle::search (unsigned K, float epsilon, unsigned *ids, float *dists) const {\n vector nns(K+1);\n //cout<<\"oracle searching\"< epsilon) continue;\n UpdateKnnList(&nns[0], L, Neighbor(k, k_dist));\n if (L < K) ++L;\n }\n if (ids) {\n for (unsigned k = 0; k < L; ++k) {\n ids[k] = nns[k].id;\n }\n }\n if (dists) {\n for (unsigned k = 0; k < L; ++k) {\n dists[k] = nns[k].dist;\n }\n }\n return L;\n }\n\n void GenerateControl (IndexOracle const &oracle, unsigned C, unsigned K, vector *pcontrols) {\n vector controls(C);\n {\n vector index(oracle.size());\n int i = 0;\n for (unsigned &v: index) {\n v = i++;\n }\n random_shuffle(index.begin(), index.end());\n#ifdef PARALLEL \n#pragma omp parallel for\n#endif \n for (unsigned i = 0; i < C; ++i) {\n controls[i].id = index[i];\n LinearSearch(oracle, index[i], K, &controls[i].neighbors);\n }\n }\n pcontrols->swap(controls);\n }\n\n static char const *KGRAPH_MAGIC = \"KNNGRAPH\";\n static unsigned constexpr KGRAPH_MAGIC_SIZE = 8;\n static uint32_t constexpr VERSION_MAJOR = 2;\n static uint32_t constexpr VERSION_MINOR = 0;\n\n class KGraphImpl: public KGraph {\n protected:\n vector M;\n vector> graph;\n public:\n virtual ~KGraphImpl () {\n }\n virtual void load (char const *path) {\n BOOST_VERIFY(sizeof(unsigned) == sizeof(uint32_t));\n ifstream is(path, ios::binary);\n char magic[KGRAPH_MAGIC_SIZE];\n uint32_t major;\n uint32_t minor;\n uint32_t N;\n is.read(magic, sizeof(magic));\n is.read(reinterpret_cast(&major), sizeof(major));\n is.read(reinterpret_cast(&minor), sizeof(minor));\n if (major != VERSION_MAJOR) throw runtime_error(\"data version not supported.\");\n is.read(reinterpret_cast(&N), sizeof(N));\n if (!is) runtime_error(\"error reading index file.\");\n for (unsigned i = 0; i < KGRAPH_MAGIC_SIZE; ++i) {\n if (KGRAPH_MAGIC[i] != magic[i]) runtime_error(\"index corrupted.\");\n }\n graph.resize(N);\n M.resize(N);\n for (unsigned i = 0; i < graph.size(); ++i) {\n auto &knn = graph[i];\n unsigned K;\n is.read(reinterpret_cast(&M[i]), sizeof(M[i]));\n is.read(reinterpret_cast(&K), sizeof(K));\n if (!is) runtime_error(\"error reading index file.\");\n knn.resize(K);\n //modified by Yifang\n for (unsigned j = 0; j < K; ++j){\n is.read(reinterpret_cast(&knn[j].id), sizeof(knn[j].id));\n knn[j].flag = true;\n knn[j].dist = 0.0;\n }\n //is.read(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n }\n }\n\n virtual void merge(char const * graph_path, char const * id_path){\n\tifstream is(graph_path, ios::binary);\n\tchar magic[KGRAPH_MAGIC_SIZE];\n\tuint32_t major;\n\tuint32_t minor;\n\tuint32_t N;\n\tis.read(magic, sizeof(magic));\n\tis.read(reinterpret_cast(&major), sizeof(major));\n\tis.read(reinterpret_cast(&minor), sizeof(minor));\n\tif (major != VERSION_MAJOR) throw runtime_error(\"data version not supported.\");\n\tis.read(reinterpret_cast(&N), sizeof(N));\n\tif (!is) runtime_error(\"error reading index file.\");\n\tfor (unsigned i = 0; i < KGRAPH_MAGIC_SIZE; ++i) {\n\t if (KGRAPH_MAGIC[i] != magic[i]) runtime_error(\"index corrupted.\");\n\t}\n\tvector > small_graph;\n\tvector small_M;\n\tsmall_graph.resize(N);\n\tsmall_M.resize(N);\n\tfor (unsigned i = 0; i < small_graph.size(); ++i) {\n\t auto &knn = small_graph[i];\n\t unsigned K;\n\t is.read(reinterpret_cast(&small_M[i]), sizeof(small_M[i]));\n\t is.read(reinterpret_cast(&K), sizeof(K));\n\t if (!is) runtime_error(\"error reading index file.\");\n\t knn.resize(K);\n\t is.read(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n\t}\n\t//loaded the small graph\n\tFILE *fp = fopen(id_path, \"rb\");\n\tunsigned * id_arr = new unsigned[N];\n\tfread(id_arr, sizeof(unsigned), N, fp);\n\tfclose(fp);\n\n\tfor (unsigned i = 0; i < N; ++i){\n\t unsigned real_id = id_arr[i];\n\t auto &small_knn = small_graph[i];\n\t auto &knn = graph[real_id];\n\t if (M[real_id] != knn.size()){\n\t knn.resize(M[real_id]);\n\t }\n\t for (unsigned k = 0; k < small_M[i]; ++k){\n\t knn.push_back(Neighbor(id_arr[small_knn[k].id], small_knn[k].dist, false));\n\t }\n\t sort(knn.begin(), knn.end());\n\t for (unsigned k = 1; k < knn.size(); ++k){\n\t if (knn[k].id == knn[k-1].id){\n\t knn.erase(knn.begin() + k);\n\t k--;\n\t }\n\t }\n\t M[real_id] = knn.size();\n\t}\t\n }\n\n virtual void output_info(int const id){\n\t if (id > graph.size()){\n\t fprintf(stderr, \"out_of_index_bound!\\n\");\n\t return;\n\t }\n\t auto const &knn = graph[id];\n\t knn.size();\n\t \n\t fprintf(stderr, \"%d\\n%d\\n=================\\n\", M[id], knn.size());\n\t for (int i = 0; i < knn.size(); ++i){\n\t fprintf(stderr, \"%d %f\\n\", knn[i].id, knn[i].dist);\n\t }\n\t}\n\n\n struct hub_pair{\n\tunsigned id;\n\tint in_degree;\n\thub_pair(unsigned x, int y){\n\t id = x;\n\t in_degree = y;\n\t}\n\tbool operator<(const hub_pair& rhs) const{\n\t return in_degree > rhs.in_degree;\n\t}\n };\n \n struct rknn_pair{\n\tunsigned id;\n\tfloat dist;\n\trknn_pair(unsigned x, float y){\n\t id = x;\n\t dist = y;\n\t}\n };\n\n virtual void connectivity(unsigned const source, unsigned const dest){\n\tuint32_t N = graph.size();\n\tint level[N];\n\tfor (int i = 0; i < N; ++i){\n\t level[i] = 0;\n\t}\n\t\n\tqueue q;\n\tq.push(source);\n\tlevel[source] = 1;\n\tbool find = false;\n\twhile (!q.empty() && !find){\n\t unsigned cur_id = q.front();\n\t auto const &knn = graph[cur_id];\n\t q.pop();\n\t for (int i = 0; i < M[cur_id]; ++i){\n\t if (level[knn[i].id] != 0){\n\t continue;\n\t }\n\t q.push(knn[i].id);\n\t level[knn[i].id] = level[cur_id] + 1;\n\t if (knn[i].id == dest){\n\t find = true;\n\t fprintf(stderr, \"Found! Level = %d\\n\", level[knn[i].id]);\n\t }\n\t }\n\t}\n\tif (!find){\n\t fprintf(stderr, \"Not Found!\\n\");\n\t}\n }\n\n virtual void get_partitions(char const *output_path){\n\tvector > partitions;\n\tuint32_t N = graph.size();\n\tfor (int i = 0; i < N; ++i){\n\t if (i % 1000 == 0) {\n fprintf(stderr, \"\\r%d (%.3f\\%)\", i, 1.0 * i / N * 100);\n }\n\t auto const &knn = graph[i];\n\t int major_set_pos = -1;\n\t for (int j = 0; j < partitions.size(); ++j){\n\t if (find (partitions[j].begin(), partitions[j].end(), i) != partitions[j].end()){\n\t major_set_pos = j;\n\t break;\n\t }\n\t }\n\t if (major_set_pos == -1){\n\t vector new_set;\n\t new_set.push_back(i);\n\t partitions.push_back(new_set);\n\t major_set_pos = partitions.size() - 1;\n\t //fprintf(stderr, \"%d is in %d\\n\", i, major_set_pos);\n\t }\n\n\t //fprintf(stderr, \"i is in %d\\n\", major_set_pos);\n\t \n\t unsigned maxM = M[i];\n\t for (int j = 0; j < maxM; ++j){\n\t bool done = false;\n\t //test if knn[j] is in some partition\n\t for (int k = 0; k < partitions.size(); ++k){\n\t if (find (partitions[k].begin(), partitions[k].end(), knn[j].id) != partitions[k].end()){\n\t\tif (k == major_set_pos){\n\t\t done = true;\n\t\t break;\n\t\t} else {\n\t\t //merge\n\t\t //fprintf(stderr, \"markmerge\\n\");\n\t\t for ( auto it = partitions[k].begin(); it != partitions[k].end(); ++it ){\n\t\t //fprintf(stderr, \"markmerge %d %d\\n\", *it, partitions[major_set_pos].max_size());\n\t\t partitions[major_set_pos].push_back(*it);\n\t\t //fprintf(stderr, \"markmergeee\\n\");\n\t\t }\n\t\t //fprintf(stderr, \"markmerge done\\n\");\n\t\t //partitions[major_set_pos].insert(partitions[k].begin(), partitions[k].end());\n\t\t //vector temp = partitions.end();\n\t\t \n\t\t //\t\t partitions[k].swap(partitions.back());\n\t\t partitions[k].clear();\n\t\t for ( auto it = partitions.back().begin(); it != partitions.back().end(); ++it ){\n //fprintf(stderr, \"markmerge %d %d\\n\", *it, partitions[major_set_pos].max_size());\n partitions[k].push_back(*it);\n //fprintf(stderr, \"markmergeee\\n\");\n }\n\t\t partitions.back().clear();\n\t\t //partitions.erase(partitions.begin() + k);\n\t\t done = true;\n\t\t //fprintf(stderr, \"mark merge done\\n\");\n\t\t break;\n\t\t}\n\t }\n\t }\n\t if (!done){\n\t //add\n\t partitions[major_set_pos].push_back(knn[j].id);\n\t continue;\n\t }\n\t }\n\t //fprintf(stderr, \"mark2\\n\");\n\t}\n\tfprintf(stderr, \"%d\\n\", partitions.size());\n\t//\treturn partitions;\n\tint count = 0;\n\tfor (int i = 0; i < partitions.size(); ++i){\n\t if (partitions[i].size() != 0){\n\t fprintf(stderr, \"%d\\n\", partitions[i].size());\n\t count ++;\n\t }\n\t}\n\tfprintf(stderr, \"%d\\n\", count);\n\n }\n\n virtual void output_M (char const* filename) {\n FILE *fp = fopen(filename, \"wb\");\n unsigned N = graph.size();\n fwrite(&N, sizeof(unsigned), 1, fp);\n\tfor (int i = 0; i < N; ++i){\n unsigned len = M[i];\n fwrite(&len, sizeof(unsigned), 1, fp);\n }\n\tfclose(fp);\n }\n\n\n virtual void make_inverse_graph(){\n\tuint32_t N = graph.size();\n\tvector > inverse_graph;\n\tfor (unsigned i = 0; i < N; ++i){\n\t inverse_graph.push_back(vector());\n\t}\n\tfor (unsigned i = 0; i < N; ++i) {\n auto const &knn = graph[i];\n\t for (unsigned j = 0; j < M[i]; j++) {\n\t inverse_graph[knn[j].id].push_back(Neighbor(i, knn[j].dist, false));\n\t }\n\t}\n\tfor (unsigned i = 0; i < N; ++i) {\n\t M[i] = inverse_graph[i].size();\n\t}\n\tgraph = inverse_graph;\n }\n \n \n\n virtual void test_connectivity(unsigned dest){ // for inverse kgraph\n\tuint32_t N = graph.size();\n\tbool * traversed = new bool[N];\n\tqueue q;\n\tq.push(dest);\n\tmemset(traversed, 0, sizeof(bool)*N);\n\ttraversed[dest] = true;\n\tint count = 1;\n\t\n\twhile(!q.empty()){\n\t unsigned pivot = q.front();\n\t q.pop();\n\t auto const &knn = graph[pivot];\n\t for (unsigned i = 0; i < knn.size(); ++i){\n\t if (!traversed[knn[i].id]){\n\t q.push(knn[i].id);\n\t traversed[knn[i].id] = true;\n\t count++;\n\t }\n\t }\n\t}\n\n\tfprintf(stderr, \"%d points connected with pivot\\n\", count);\n }\n\n virtual void add_random_backward_edges(char const *data_path, char const *hubs_path, int const num_of_hubs, char const * start_points_path, unsigned num_of_query){\n uint32_t N = graph.size();\n\t//ofstream os(hubs_path, ios::binary);\n vector hubs;\n\tvector > rknn_graph;\n Matrix data;\n data.load_lshkit(data_path);\n\n\tint count = 0;\n\n for (unsigned i = 0; i < N; ++i) {\n\t hubs.push_back(hub_pair(i, 0));\n rknn_graph.push_back(vector());\n\t}\n\n\tfor (unsigned i = 0; i < N; ++i) {\n auto const &knn = graph[i];\n uint32_t K = knn.size();\n\t for (unsigned j=0; j ids;\n\tids.resize(num_of_query, num_of_hubs);\n Matrix hub_data;\n hub_data.resize(num_of_hubs, data.dim());\n\n\t//unsigned * ids = new unsigned[num_of_hubs];\n for (unsigned i = 0; i < num_of_hubs; ++i){\n //fprintf(stderr, \"%d: %d %d\\n\", i, hubs[i].id, hubs[i].in_degree);\n\t // add backward edges\n vector rknn_list = rknn_graph[hubs[i].id];\n\t count += rknn_list.size();\n for (unsigned j = 0; j < rknn_list.size(); ++j){\n graph[hubs[i].id].push_back(Neighbor(rknn_list[j].id, rknn_list[j].dist, true)); //rknn_list[j]);\n }\n\n for (unsigned j = 0; j < hub_data.dim(); ++j){\n hub_data[i][j] = data[hubs[i].id][j];\n }\n\n // write start point file\n\t for (int j = 0; j < num_of_query; ++j)\n ids[j][i] = hubs[i].id;\n //fprintf(stderr, \"%d: %d %d\\n\", i, ids[j][i], hubs[i].in_degree);\n sort(graph[hubs[i].id].begin(), graph[hubs[i].id].end());\n M[hubs[i].id] = rknn_list.size();\n\t}\n ids.save_lshkit(start_points_path);\n\thub_data.save_lshkit(hubs_path);\n // for (unsigned i = 0; i < num_of_query; ++i){\n // os2.write((const char *)ids, sizeof(unsigned) * header[2]);\n //}\n //delete[] ids;\n\tfprintf(stderr, \"inverse edges: %d\\n\", count);\n }\n\n virtual void add_backward_edges(){\n\tuint32_t N = graph.size();\n\t//ofstream os(hubs_path, ios::binary);\n\t//vector hubs;\n\tvector > rknn_graph;\n rknn_graph.resize(N);\n \n\tint count = 0;\n \n\t\n\tfor (unsigned i = 0; i < N; ++i) {\n\t auto const &knn = graph[i];\n\t uint32_t K = M[i]; //knn.size();\n\t for (unsigned j=0; j rknn_list = rknn_graph[i];\n\t count += rknn_list.size();\n\t \n for (unsigned j = 0; j < rknn_list.size(); ++j){\n\t graph[i].push_back(Neighbor(rknn_list[j].id, rknn_list[j].dist, true)); //rknn_list[j]);\n\t //sum += exp(-1 * sqrt(rknn_list[j].dist) * beta); // a function with dist\n\t }\n\n\t sort(graph[i].begin(), graph[i].end());\n\t for (unsigned j = 1; j < graph[i].size(); ++j){\n\t if (graph[i][j].id == graph[i][j - 1].id){\n\t graph[i].erase(graph[i].begin() + j);\n\t j--;\n\t }\n\t }\n\t \n M[i] = graph[i].size();\n\t}\n\tfprintf(stderr, \"inverse edges: %d\\n\", count);\n }\n\n\n virtual void compute_diversity(IndexOracle const &oracle, int d, float * norm){ // diversity\n\tif (oracle.size() != graph.size()){\n throw runtime_error(\"Build kgraph before adding long edges\");\n }\n\n uint32_t N = oracle.size();\n\tfloat * avg_arr = new float[N];\n\tuint32_t true_N = N;\n#pragma omp parallel for\n for (unsigned k = 0; k < N; k++){\n int len = M[k];\n\t int true_len = len * (len - 1) / 2;\n\t float sum_angle = 0.0;\n for (int i = 0; i < len; ++i){ // the first max_edge_num elements are the remaining ones\n\t if (graph[k][i].dist < 0.00001){\n\t true_len -= len - i - 1;\n\t continue;\n\t }\n for (int cand_pos = i + 1; cand_pos < len; cand_pos++){\n\t float angle = oracle.angle(k, graph[k][cand_pos].id, graph[k][i].id);\n\t if (!(angle > 0.00001)){\n\t\ttrue_len --;\n\t\tcontinue;\n\t }\n sum_angle += angle; //oracle.angle(k, graph[k][cand_pos].id, graph[k][i].id);\n }\n }\n\t if (true_len <= 0){\n\t avg_arr[k] = 0.0;\n\t true_N--;\n\t } else {\n\t avg_arr[k] = sum_angle / true_len;\n\t }\n\t //\t if (!(avg_arr[k] > 0.00001))\n\t // fprintf(stderr, \"%d %f %f\\n\", k, sum_angle, avg_arr[k]);\n\n }\n\n\tdouble avg_angle = 0.0;\n\tdouble normed_angle = 0.0;\n\tfor (unsigned k = 0; k < N; ++k){\n\t avg_angle = avg_angle + avg_arr[k];\n\t normed_angle += avg_arr[k] / norm[M[k] - 1];\n\t}\n\n\t//avg_angle /= N;\n\t//normed_angle /= N;\n\n\tcout << \"Average pairwise angle: \" << avg_angle / true_N << endl;\n\tcout << \"Normed pairwise angle: \" << normed_angle / N << endl;\n\n\tdelete[] avg_arr;\n }\n\n\nvirtual void diversify_by_cut(IndexOracle const &oracle, const int edge_num){\n\n if (oracle.size() != graph.size()){\n throw runtime_error(\"diversify_by_cut : inconsistent.\" );\n }\n \n uint32_t N = oracle.size();\n\n int cnt = 0;\n int nnt = 0;\n\n unsigned step = N / 100;\n cerr << endl << \"Progress : \";\n\n // #pragma omp parallel for\n for (unsigned k = 0; k < N; k++){\n\n if ( k % step == 0 ) cerr <<\"*\";\n\n\n // float *map = new float[N];\n // memset(map, 0, sizeof(float)*N);\n\n int len = graph[k].size();\n while(graph[k][len-1].id == 0){\n len--;\n }\n if (len > 2 * edge_num){\n len = 2 * edge_num;\n } \n \n // materialize the ditance here \n int* hit = new int[len];\n\n for ( int i=0; i< len; i++){\n graph[k][i].dist = oracle(k, graph[k][i].id );\n hit[i] = 0;\n } \n\n for ( int i=0; i < len-1; i++ ){\n // for ( int j=0; j < len; j++){\n for ( int j=i+1; j < len; j++){\n \n if ( i==j ) continue;\n\n Neighbor n_b = graph[k][i]; \n Neighbor n_k = graph[k][j];\n\n float dist = oracle(n_b.id, n_k.id);\n // cout << dist << \" : (\" << n_b.id << \" , \" << n_k.id << endl; \n if ( dist < n_k.dist ){\n hit[j]++;\n }\n }\n }\n\n\n // sort by the hits and find the cuts \n int* b_hit = new int[len];\n for ( int i=0; i< len; i++ )\n b_hit[i] = hit[i];\n\n // memcpy( b_hit, hit, sizeof(int)*len);\n\n sort(b_hit, b_hit + len );\n float cut = b_hit[edge_num];\n\n // update the neighbors by #hits \n Neighbor tmp[len];\n for ( int i=0; i < len; i++)\n tmp[i] = graph[k][i];\n\n\n cnt = 0;\n for ( int i=0; i < len; i++){ \n if ( hit[i] <= cut )\n graph[k][cnt++] = tmp[i];\n }\n\n graph[k].resize(edge_num); // reset the size of NN list \n\n delete []hit;\n delete []b_hit;\n\n /* \n if ( k % 100 == 0 ){\n for ( int i=0; i< len; i++){\n if ( hit[i]> cut )\n cout << hit[i] << \"* \";\n else\n cout << hit[i] << \" \";\n }\n cout << endl;\n }\n */ \n\n /*\n for (int i = 1; i < edge_num; ++i){ // the first max_edge_num elements are the remaining ones\n float max_sum_angle = -1.0;\n int max_pos = -1;\n for (int cand_pos = i; cand_pos < len; cand_pos++){\n map[graph[k][cand_pos].id] += oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n float sum_angle = map[graph[k][cand_pos].id];\n if (sum_angle > max_sum_angle){\n max_sum_angle = sum_angle;\n max_pos = cand_pos;\n }\n }\n Neighbor temp = graph[k][i];\n graph[k][i] = graph[k][max_pos];\n graph[k][max_pos] = temp;\n }\n */\n\n\n // graph[k].resize(edge_num); // reset the size of NN list \n\n // sort(graph[k].begin(), graph[k].end());\n \n M[k] = edge_num;\n \n // delete[] map;\n }\n\n cerr << endl;\n\n } // end of diversify_by_cut\n \n\n virtual float statistics(IndexOracle const &oracle, const int edge_num){ // diversity\n if (oracle.size() != graph.size()){\n throw runtime_error(\"in consistent ~!\");\n }\n\n uint32_t N = oracle.size();\n float avg_co (0.0);\n\n unsigned step = N / 100;\n cerr << \"Progress : \";\n\n for (unsigned k = 0; k < N; k++){\n\n if ( k % step == 0) cerr <<\"*\"; \n\n int len = graph[k].size();\n while(graph[k][len-1].id == 0){\n len--;\n }\n if (len > edge_num){\n len = edge_num;\n }\n\n\n // check each pair of neighbors \n long hit = 0; \n for ( int i=0; i < len; i++ ) \n for ( int j=i+1; j < len; j++ ){\n\n // check if the edge < id[i] and id[j] exisits in the K-NN-graph; now we assume \n bool exist = false;\n // check i's NBs \n unsigned id_1 = graph[k][i].id;\n unsigned id_2 = graph[k][j].id;\n for ( int s=0; s < len; s++)\n if (graph[id_1][s].id == id_2) {\n exist = true; break;\n }\n\n if ( !exist ) continue; \n\n for ( int s=0; s < len; s++)\n if (graph[id_2][s].id == id_1) {\n hit++; break;\n }\n } \n\n // also check j's NBs \n float ratio = 2.0*hit/(len*(len-1)); \n avg_co += ratio;\n\n } // end of K \n\n cerr << endl;\n\n avg_co = avg_co / N;\n\n return avg_co;\n \n } // end of function statistics \n\n \n virtual void remove_near_edges(IndexOracle const &oracle, const int edge_num){ // diversity\n\tif (oracle.size() != graph.size()){\n\t throw runtime_error(\"Build kgraph before adding long edges\");\n\t}\n\t\n\tuint32_t N = oracle.size();\n unsigned step = N / 100;\n cerr << \"Progress : \";\n\n //#pragma omp parallel for\n\tfor (unsigned k = 0; k < N; k++){\n\n if ( k % step == 0 ) cerr <<\"*\";\n\n\t //if (k % 1000 == 0) {\n\t // fprintf(stderr, \"\\r%d (%.3f\\%)\", k, 1.0 * k / N * 100);\n\t //}\n\t //auto const &knn = graph[k];\n //edge_num[k] /= 2;\n\t float * map = new float[N];\n\t memset(map, 0, sizeof(float)*N);\n\t int len = graph[k].size();\n\t while(graph[k][len-1].id == 0){\n\t len--;\n\t }\n\t if (len > 2 * edge_num){\n\t len = 2 * edge_num;\n\t } \n\t for (int i = 1; i < edge_num; ++i){ // the first max_edge_num elements are the remaining ones\n\t float max_sum_angle = -1.0;\n\t int max_pos = -1;\n\t for (int cand_pos = i; cand_pos < len; cand_pos++){\n\t map[graph[k][cand_pos].id] += oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n\t float sum_angle = map[graph[k][cand_pos].id];\n\t if (sum_angle > max_sum_angle){\n\t\tmax_sum_angle = sum_angle;\n\t\tmax_pos = cand_pos;\n\t }\n\t }\n\t Neighbor temp = graph[k][i];\n\t graph[k][i] = graph[k][max_pos];\n\t graph[k][max_pos] = temp;\n\t }\n\t graph[k].resize(edge_num); // reset the size of NN list \n\n\t sort(graph[k].begin(), graph[k].end());\n\t M[k] = edge_num;\n\t delete[] map;\n\t}\n\n cerr << endl;\n\n }\n \n virtual void remove_near_edges_2(IndexOracle const &oracle, vector const &edge_num, unordered_set const &skip_set){ // diversification, maxmin\n if (oracle.size() != graph.size()){\n throw runtime_error(\"Build kgraph before adding long edges\");\n }\n\n uint32_t N = oracle.size();\n\n// #pragma omp parallel for\n for (unsigned k = 0; k < N; k++){\n\t if (skip_set.find(k) != skip_set.end()){\n continue;\n }\n //if (k % 1000 == 0) {\n // fprintf(stderr, \"\\r%d (%.3f\\%)\", k, 1.0 * k / N * 100);\n //}\n //auto const &knn = graph[k];\n float * map = new float[N];\n memset(map, 0, sizeof(float)*N);\n int len = graph[k].size();\n while(graph[k][len-1].dist == 0.0){\n len--;\n }\n for (int i = 1; i < edge_num[k]; ++i){ // the first max_edge_num elements are the remaining ones\n float min_angle = 10000000000.0;\n int min_pos = -1;\n for (int cand_pos = i; cand_pos < len; cand_pos++){\n\t float angle = oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n\t if (map[graph[k][cand_pos].id] == 0 || map[graph[k][cand_pos].id] < angle){\n\t\tmap[graph[k][cand_pos].id] = angle;\n\t }\n\t //map[graph[k][cand_pos].id] += oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n //float sum_angle = oracle.angle(k, graph[k][cand_pos].id, graph[k][i - 1].id);\n if (min_angle > map[graph[k][cand_pos].id]){\n min_angle = map[graph[k][cand_pos].id];\n min_pos = cand_pos;\n }\n }\n Neighbor temp = graph[k][i];\n graph[k][i] = graph[k][min_pos];\n graph[k][min_pos] = temp;\n }\n graph[k].resize(edge_num[k]);\n sort(graph[k].begin(), graph[k].end());\n M[k] = edge_num[k];\n delete[] map;\n }\n }\n\n virtual void add_long_edges(IndexOracle const &oracle, int const rank_top_k, int const change_num){\n\t if (oracle.size() != graph.size()){\n\t throw runtime_error(\"Build kgraph before adding long edges\");\n\t }\n\n\t //fprintf(stderr, \"mark\\n\");\n\n\t uint32_t N = oracle.size();\n\t vector rank_id_vec; // index: rank; value: id\n\t vector id_rank_vec; // index: id; value: rank\n\t for (unsigned i = 0; i < N; ++i){\n\t rank_id_vec.push_back(i);\n\t }\n\t id_rank_vec.resize(N);\n\n\t //fprintf(stderr, \"mark\\n\");\n\n\t for (unsigned i = 0; i < N; ++i){ \n\t //fprintf(stderr, \"=======length: %d=========\\n\", graph[i].size());\n\t if (i % change_num == 0){\n\t // re-shuffle\n\t random_shuffle(rank_id_vec.begin(), rank_id_vec.end());\n\t for (unsigned j = 0; j < N; ++j){\n\t\tid_rank_vec[rank_id_vec[j]] = j;\n\t }\n\t }\n\t unsigned start = id_rank_vec[i] - rank_top_k / 2;\n\t if (start < 0) {\n\t start = 0;\n\t }\n\t unsigned end = id_rank_vec[i] + rank_top_k / 2;\n\t if (end >= N) {\n\t end = N - 1;\n\t }\n\t \n\t //fprintf(stderr, \"start = %d, end = %d\\n\", start, end);\n\n\t for (unsigned j = start; j <= end; ++j){\n\t //test if rank_id[j] should be added\n\t float dist = oracle(rank_id_vec[j], i);\n\t bool is_dominated = false;\n\t for (int k = 0; k < graph[i].size(); ++k){\n\t\t// if there exists a point in graph[i] that is closer to i in both distance and rank, \n\t\t// then the test point will be not added into the graph\n\t\tif (abs(j - id_rank_vec[i]) > abs(id_rank_vec[i] - id_rank_vec[graph[i][k].id]) && dist > graph[i][k].dist){\n\t\t is_dominated = true;\n\t\t break;\n\t\t}\n\t }\n\t if (!is_dominated){\n\t\tM[i] += 1;\n\t\tgraph[i].push_back(Neighbor(rank_id_vec[j], dist, true));\n\t }\n\t }\n\n\t //fprintf(stderr, \"length: %d\\n\", graph[i].size());\n\n\t sort(graph[i].begin(), graph[i].end());\n\t } // for\n\t //fprintf(stderr, \"final mark\\n\");\n\t}\n\n virtual void saver (char const *path) const {\n\tuint32_t N = graph.size();\n\t//fprintf(stderr,\"%d\\n\", N);\n\tofstream os(path, ios::binary);\n\tos.write(KGRAPH_MAGIC, KGRAPH_MAGIC_SIZE);\n\tos.write(reinterpret_cast(&VERSION_MAJOR), sizeof(VERSION_MAJOR));\n\tos.write(reinterpret_cast(&VERSION_MINOR), sizeof(VERSION_MINOR));\n\tos.write(reinterpret_cast(&N), sizeof(N));\n\tfor (unsigned i = 0; i < graph.size(); ++i) {\n\t auto const &knn = graph[i];\n\t uint32_t K = knn.size();\n\t //cout<(&K), sizeof(K));\n\t os.write(reinterpret_cast(&K), sizeof(K));\n\t os.write(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n\t}\n }\n\n virtual void save (char const *path) const {\n uint32_t N = graph.size();\n ofstream os(path, ios::binary);\n os.write(KGRAPH_MAGIC, KGRAPH_MAGIC_SIZE);\n os.write(reinterpret_cast(&VERSION_MAJOR), sizeof(VERSION_MAJOR));\n os.write(reinterpret_cast(&VERSION_MINOR), sizeof(VERSION_MINOR));\n os.write(reinterpret_cast(&N), sizeof(N));\n for (unsigned i = 0; i < graph.size(); ++i) {\n auto const &knn = graph[i];\n uint32_t K = knn.size();\n\t\t\t\t\n os.write(reinterpret_cast(&K), sizeof(K)); // modified by Yifang\n os.write(reinterpret_cast(&K), sizeof(K));\n for (unsigned j = 0; j < K; ++j){\n os.write(reinterpret_cast(&knn[j].id), sizeof(knn[j].id));\n }\n //os.write(reinterpret_cast(&knn[0]), K * sizeof(knn[0]));\n }\n }\n\t\t\n virtual void get_hubs (char const *hubs_path,char const *knn_path) const {\n uint32_t N = graph.size();\n ofstream os(hubs_path, ios::binary);\n ofstream knn_out(knn_path, ios::binary);\n int hubs[N];\n\t memset(hubs,0,sizeof(int)*N);\n for (unsigned i = 0; i < N; ++i) {\n auto const &knn = graph[i];\n uint32_t K = knn.size();\n for (unsigned j=0;j K) {\n v.resize(K);\n }\n }\n return;\n vector> pruned(graph.size());\n vector> reachable(graph.size());\n vector added(graph.size());\n for (unsigned k = 0; k < K; ++k) {\n#pragma omp parallel for\n for (unsigned n = 0; n < graph.size(); ++n) {\n vector const &from = graph[n];\n if (from.size() <= k) continue;\n unsigned e = from[k];\n if (reachable[n].count(e)) {\n added[n] = false;\n }\n else {\n pruned[n].push_back(e);\n added[n] = true;\n }\n }\n // expand reachable\n#pragma omp parallel for\n for (unsigned n = 0; n < graph.size(); ++n) {\n vector const &to = pruned[n];\n set &nn = reachable[n];\n if (added[n]) {\n for (unsigned v: pruned[to.back()]) {\n nn.insert(v);\n }\n }\n for (unsigned v: to) {\n if (added[v]) {\n nn.insert(pruned[v].back());\n }\n }\n }\n }\n graph.swap(pruned);\n }\n */\n\n virtual void kgraph2dot (char * const &filename){\n\tstd::ofstream fs;\n\tfs.open(filename, std::ofstream::out);\n\n\t//\tfs << \"strict digraph g {\" << endl;\n\n\tfor (unsigned i = 0; i < graph.size(); ++i){\n\t for (unsigned j = 0; j < M[i]; ++j){\n\t fs << i << \" => \" << graph[i][j].id << endl; //\"[ label=\\\"\" << graph[i][j].dist << \"\\\" ];\" << endl;;\n\t }\n\t}\n\n\t//fs << \"}\" << endl;\n }\n\n virtual unsigned advanced_search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *pinfo, string const &info_path) const {// Matrix const &d_init,\n\t/*fprintf(stderr, \"all_dist=(%f\", oracle(0));\n\t for (int i = 1; i < oracle.size(); ++i){\n\t fprintf(stderr, \", %f\", oracle(i));\n\t\t}*/\n\t//fprintf(stderr, \"access_dist=(\");\n\t//fprintf(stdout, \"minimum_dist=(\");\n\n\tbool print_flag = true;\n\tstd::ofstream path_fs, mind_fs, curd_fs, nh_fs;\n\tstring file_name;\n\tif (info_path.empty()) {\n\t print_flag = false;\n\t}\n\t\n\tif (print_flag){\n\t file_name.assign(info_path);\n\t file_name += \"_path.txt\";\n\t path_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_mindist.txt\";\n\t mind_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_curdist.txt\";\n\t curd_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_nh.txt\";\n nh_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t}\n\t\n\tif (graph.size() > oracle.size()) {\n\t throw runtime_error(\"dataset larger than index\");\n\t}\n\tif (params.P >= graph.size()) {\n\t if (pinfo) {\n\t pinfo->updates = 0;\n\t pinfo->cost = 1.0;\n\t }\n\t return oracle.search(params.K, params.epsilon, ids, dists);\n\t}\n\tvector knn(params.K + params.P +1);\n\tvector init_knn(params.init);\n\tvector results;\n\tboost::dynamic_bitset<> flags(graph.size(), false);\n\n\tif (params.init && params.T > 1) {\n\t throw runtime_error(\"when init > 0, T must be 1.\");\n\t}\n\n\tunsigned seed = params.seed;\n\tunsigned updates = 0;\n\tif (seed == 0) seed = time(NULL);\n\tmt19937 rng(seed);\n\tunsigned n_comps = 0;\n\tfor (unsigned trial = 0; trial < params.T; ++trial) {\n\t unsigned L = params.init;\n\t //init>=K\n\t if (L == 0) { // generate random starting points\n\t vector random(params.P);\n\t GenRandom(rng, &random[0], random.size(), graph.size());\n\t for (unsigned s: random) {\n\t if (!flags[s]) {\n\t\tknn[L++].id = s;\n\t\t//flags[s] = true;\n\t }\n\t }\n\t for (unsigned k = 0; k < L; ++k) {\n\t flags[knn[k].id] = true;\n\t knn[k].flag = true;\n\t knn[k].dist = oracle(knn[k].id);\n\t \n\t }\n\t sort(knn.begin(), knn.begin() + L);\n\t }\n\t else { // user-provided starting points.\n\t BOOST_VERIFY(ids); //ids\n\t //std::cout<<\"set seed\"< knn.size()\n\t init_knn[l].id = ids[l]; //ids\n\t init_knn[l].flag = true;\n init_knn[l].dist = dists[l];\n\t //fprintf(stderr, \"%d: %d\\n\", l, knn[l].id);\n\t }\n\t //std::cout< init_fknn(graph.size());\n\t\t for (unsigned k = 0; k < graph.size(); ++k){\n\t\t init_fknn[k].id = k;\n\t\t\t init_fknn[k].flag = true;\n\t\t\t\t init_fknn[k].dist = oracle(k);\n\t\t\t\t\t }\n\t\t\t\t\t sort(init_fknn.begin(), init_fknn.end());\n\t\t\t\t\t\t //end delete\n\t\t\t\t\t\t */\n\t L = params.K;\n\t for (int ii = 0; ii < L; ++ii){\n\t //flags[init_fknn[graph.size() - 1 - ii].id] = true;\n\t //knn[L - 1 - ii] = init_fknn[graph.size() - 1 - ii];\n\t //recover\n\t flags[init_knn[ii].id] = true;\n\t knn[ii] = init_knn[ii];\n\t //end recover\n\t //fprintf(stderr, \"%d %f\\n\", knn[ii].id, knn[ii].dist);\n\t }\n\t }\n\n\t if (print_flag){\n\t for (unsigned k = 0; k < L; ++k){\n\t path_fs << \"-3 \" << knn[k].id << endl;\n\t mind_fs << knn[0].dist << endl;\n\t curd_fs << knn[k].dist << endl;\n\t \n\t nh_fs << M[knn[k].id] << \" \";\n\t unordered_set set;\n\t set.insert(knn[k].id);\n\t auto const &neighbors = graph[knn[k].id];\n\t for (unsigned l = 0; l < M[knn[k].id]; ++l){\n\t\tset.insert(neighbors[l].id);\n\t\tauto const &new_neighbors = graph[neighbors[l].id];\n\t\tfor (unsigned ll = 0; ll < M[neighbors[l].id]; ++ll){\n\t\t set.insert(new_neighbors[ll].id);\n\t\t}\n\t }\n\t nh_fs << set.size() << endl;\n\t }\n\t }\n\t \n\t unsigned k = 0;\n\t while (k < L) {\n\t unsigned nk = L;\n\t if (knn[k].flag) {\n\t knn[k].flag = false;\n\t unsigned cur = knn[k].id;\n\t //BOOST_VERIFY(cur < graph.size());\n\t unsigned maxM = M[cur];\n\t if (params.M > maxM) maxM = params.M;\n\t auto const &neighbors = graph[cur];\n\t if (maxM > neighbors.size()) {\n\t\tmaxM = neighbors.size();\n\t }\n\t for (unsigned m = 0; m < maxM; ++m) {\n\t\tunsigned id = neighbors[m].id;\n\t\t//BOOST_VERIFY(id < graph.size());\n\t\tif (flags[id]) continue;\n\t\tflags[id] = true;\n\t\t++n_comps;\n\t\t//if(n_comps >= params.Maxchecks)\n\t\t// {\n\t\t// break;\n\t\t// }\n\t\tfloat dist = oracle(id);\n\t\t \n\t\t/*if(n_comps == 1){\n\t\t fprintf(stderr, \"%f\", dist);\n\t\t\t } else {\n\t\t\t fprintf(stderr, \", %f\", dist);\n\t\t\t\t }*/\n\t\tNeighbor nn(id, dist);\n\t\tunsigned r = UpdateKnnList(&knn[0], L, nn);\n\t\tif (print_flag){\n\t\t path_fs << cur << \" \" << id << endl;\n\t\t mind_fs << knn[0].dist << endl;\n\t\t curd_fs << dist << endl;\n\n\t\t nh_fs << M[id] << \" \";\n\t\t unordered_set set;\n\t\t set.insert(id);\n\t\t auto const &nbors = graph[id];\n\t\t for (unsigned l_1 = 0; l_1 < M[id]; ++l_1){\n\t\t set.insert(nbors[l_1].id);\n\t\t auto const &new_neighbors = graph[nbors[l_1].id];\n\t\t for (unsigned ll = 0; ll < M[nbors[l_1].id]; ++ll){\n\t\t set.insert(new_neighbors[ll].id);\n\t\t }\n\t\t }\n\t\t nh_fs << set.size() << endl;\n\t\t}\n\t\t/*if(n_comps == 1){\n fprintf(stdout, \"%f\", knn[0].dist);\n } else {\n fprintf(stdout, \", %f\", knn[0].dist);\n\t\t\t }*/\n\t\tBOOST_VERIFY(r <= L);\n\t\t//if (r > L) continue;\n\t\tif (L + 1 < knn.size()) ++L;\n\t\tif (r < nk) {\n\t\t nk = r;\n\t\t}\n\t }\n\t //fprintf(stderr, \"}\\n\");\n\t }\n\t if (nk <= k) {\n\t k = nk;\n\t }\n\t else {\n\t ++k;\n\t }\n\t //if(n_comps >= params.Maxchecks)\n\t //{\n\t // break;\n\t //}\n\t }\n\t if (L > params.K) L = params.K;\n\t if (results.empty()) {\n\t results.reserve(params.K + 1);\n\t results.resize(L + 1);\n\t copy(knn.begin(), knn.begin() + L, results.begin());\n\t }\n\t else {\n\t // update results\n\t for (unsigned l = 0; l < L; ++l) {\n\t unsigned r = UpdateKnnList(&results[0], results.size() - 1, knn[l]);\n\t if (r < results.size() /* inserted */ && results.size() < (params.K + 1)) {\n\t\tresults.resize(results.size() + 1);\n\t }\n\t }\n\t }\n\t}\n\t// check epsilon\n\t{\n\t for (unsigned l = 0; l < results.size(); ++l) {\n\t if (results[l].dist > params.epsilon) {\n\t results.resize(l);\n\t break;\n\t }\n\t }\n\t}\n\tunsigned L = results.size() - 1;\n\tBOOST_VERIFY(L <= params.K);\n\t// check epsilon\n\tif (ids) {\n\t for (unsigned k = 0; k < L; ++k) {\n\t ids[k] = results[k].id;\n\t }\n\t}\n\tif (dists) {\n\t for (unsigned k = 0; k < L; ++k) {\n\t dists[k] = results[k].dist;\n\t }\n\t}\n\tif (pinfo) {\n\t pinfo->updates = updates;\n\t pinfo->cost = float(n_comps) / graph.size();\n\t pinfo->checks=n_comps;\n\t}\n\tif (print_flag){\n\t path_fs << \"-1\" << endl;\n\t for (unsigned k = 0; k < results.size() - 1; ++k){\n\t path_fs << results[k].id << endl; //fprintf(stderr, \"%d [color=lightblue, style=filled, label=\\\"top-%d\\\"]\\n\", results[k].id, k+1);\n\t }\n\t path_fs << \"-2\" << endl;\n\t mind_fs << \"-1\" << endl;\n\t curd_fs << \"-1\" << endl;\n\t nh_fs << \"-1\" << endl;\n\t}\n\t//fprintf(stderr, \")\\n\");\n\t//fprintf(stdout, \")\\n\");\n\t//fprintf(stderr, \")\\nplt.hist(kg_access_dist, bins=100, normed=False, alpha=0.75, label='accessed')\\nplt.hist(all_dist, bins=100, normed=False, alpha=0.5, label='all')\\nplt.title(\\\"Distribution of accessed points by KGraph\\\")\\nplt.xlabel(\\\"distance\\\")\\nplt.ylabel(\\\"Frequency\\\")\\nplt.legend(loc='upper right')\\nshow()\\n\");\n\treturn L;\n }\n\n virtual void find_lm (SearchOracle const &oracle) const {\n\tunsigned K = 10;\n\tunsigned N = oracle.size();\n\tunsigned * ordered_nn_id = new unsigned[N];\n\tunsigned * rank = new unsigned[N];\n\tfloat * ordered_nn_dist = new float[N];\n\tbool * is_lm = new bool[N];\n\tunordered_set top_set;\n\tint * top_cover = new int[K];\n\tbool * traverse = new bool[N];\n\tint counter = 0;\n\n\n\tqueue new_queue;\n\tqueue cur_queue;\n\t\n\toracle.search(N, default_epsilon, ordered_nn_id, ordered_nn_dist);\n\n\n\tfor (unsigned i = 0; i < N; ++i){\n\t rank[ordered_nn_id[i]] = i;\n\t is_lm[i] = true;\n\t traverse[i] = false;\n\t}\n\tfor (unsigned i = 0; i < K; ++i){\n\t top_set.insert(i);\n\t}\n\t\n\tfor (unsigned i = 0; i < N; ++i){\n\t auto const &neighbors = graph[i];\n\t for (unsigned l = 0; l < M[i]; ++l){\n\t if (rank[neighbors[l].id] < rank[i]){\n\t is_lm[i] = false;\n\t break;\n\t }\n\t }\n\t if (is_lm[i]){ // is LM, output something\n\t counter ++;\n\t fprintf(stderr, \"rank:\\t%d\\n\", rank[i]);\n\t fprintf(stderr, \"dist:\\t%f\\n\", ordered_nn_dist[rank[i]]);\n\t cur_queue.push(i);\n\t traverse[i] = true;\n\t if (top_set.find(rank[i]) != top_set.end()){\n\t top_cover[rank[i]] = 0;\n\t top_set.erase(rank[i]);\n\t }\n\t }\n\t}\n\n\t//\tfprintf(stderr, \"N=%d\\n\", N);\n\t//find coverage of top-K\n\tunsigned level = 0;\n\twhile(true){\n\t level++;\n\t //fprintf(stderr, \"mark2.1\\n\");\n\t while(!cur_queue.empty()){\n\t unsigned id = cur_queue.front();\n\t cur_queue.pop();\n\t auto const &neighbors = graph[id];\n\t //fprintf(stderr, \"mark2.5 %d\\n\", M[id]);\n\t for (unsigned l = 0; l < M[id]; ++l){\n\t //fprintf(stderr, \"%d\\n\", neighbors[l].id);\n\t //fprintf(stderr, \"%d\\n\", traverse[neighbors[l].id]);\n\t if (!traverse[neighbors[l].id]){\n\t\tnew_queue.push(neighbors[l].id);\n\t\ttraverse[neighbors[l].id] = true;\n\t\t//fprintf(stderr, \"-> %d\\n\", rank[neighbors[l].id]);\n\t\tif (rank[neighbors[l].id] < K && top_set.find(rank[neighbors[l].id]) != top_set.end()){\n\t\t top_cover[rank[neighbors[l].id]] = level;\n\t\t top_set.erase(rank[neighbors[l].id]);\n\t\t if (top_set.empty()){\n\t\t goto jump_out;\n\t\t }\n\t\t}\n\t }\n\t } // end for loop\n\t }\n\t cur_queue.swap(new_queue);\n\t}\n\n\n jump_out:\n\tfprintf(stderr, \"mark3\\n\");\n\tfprintf(stderr, \"# of LMins:\\t%d\\n\", counter);\n\tfor (unsigned i = 0; i < K; ++i){\n\t fprintf(stderr, \"top-%d is %d hops away from LMins\\n\", i + 1, top_cover[i]);\n\t}\n\t\n\tdelete[] ordered_nn_id;\n\tdelete[] rank;\n\tdelete[] ordered_nn_dist;\n\tdelete[] is_lm;\n\tdelete[] top_cover;\n }\n \n virtual unsigned search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *pinfo, string const &info_path) const {// Matrix const &d_init,\n\t /*fprintf(stderr, \"all_dist=(%f\", oracle(0));\n\t for (int i = 1; i < oracle.size(); ++i){\n\t fprintf(stderr, \", %f\", oracle(i));\n\t }*/\n\t //fprintf(stderr, \"access_dist=(\");\n\t //fprintf(stdout, \"minimum_dist=(\");\n\n\tbool print_flag = true;\n\tstd::ofstream path_fs, mind_fs, curd_fs, nh_fs;\n\tstring file_name;\n\tif (info_path.empty()) {\n\t print_flag = false;\n\t}\n\t\n\tif (print_flag){\n\t file_name.assign(info_path);\n\t file_name += \"_path.txt\";\n\t path_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_mindist.txt\";\n\t mind_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_curdist.txt\";\n\t curd_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t file_name.assign(info_path);\n file_name += \"_nh.txt\";\n nh_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n\t}\n\t\n\tif (graph.size() > oracle.size()) {\n\t throw runtime_error(\"dataset larger than index\");\n\t}\n\tif (params.P >= graph.size()) {\n\t if (pinfo) {\n\t pinfo->updates = 0;\n\t pinfo->cost = 1.0;\n\t }\n\t return oracle.search(params.K, params.epsilon, ids, dists);\n\t}\n vector knn(params.K + params.P +1);\n\t vector init_knn(params.init);\n vector results;\n boost::dynamic_bitset<> flags(graph.size(), false);\n\n if (params.init && params.T > 1) {\n throw runtime_error(\"when init > 0, T must be 1.\");\n }\n\n unsigned seed = params.seed;\n unsigned updates = 0;\n if (seed == 0) seed = time(NULL);\n mt19937 rng(seed);\n unsigned n_comps = 0;\n for (unsigned trial = 0; trial < params.T; ++trial) {\n unsigned L = params.init;\n //init>=K\n if (L == 0) { // generate random starting points\n vector random(params.P);\n GenRandom(rng, &random[0], random.size(), graph.size());\n for (unsigned s: random) {\n if (!flags[s]) {\n knn[L++].id = s;\n //flags[s] = true;\n }\n }\n\t\t for (unsigned k = 0; k < L; ++k) {\n\t\t flags[knn[k].id] = true;\n\t\t knn[k].flag = true;\n\t\t knn[k].dist = oracle(knn[k].id);\n\t\t \n\t\t }\n\t\t sort(knn.begin(), knn.begin() + L);\n }\n else { // user-provided starting points.\n BOOST_VERIFY(ids); //ids\n //std::cout<<\"set seed\"< knn.size()\n init_knn[l].id = ids[l]; //ids\n\t\t\t//fprintf(stderr, \"%d: %d\\n\", l, knn[l].id);\n }\n //std::cout< init_fknn(graph.size());\n\t\t for (unsigned k = 0; k < graph.size(); ++k){\n\t\t init_fknn[k].id = k;\n\t\t init_fknn[k].flag = true;\n\t\t init_fknn[k].dist = oracle(k);\n\t\t }\n\t\t sort(init_fknn.begin(), init_fknn.end());\n\t\t //end delete\n\t\t */\n\t\t L = params.K;\n\t\t for (int ii = 0; ii < L; ++ii){\n\t\t //flags[init_fknn[graph.size() - 1 - ii].id] = true;\n\t\t //knn[L - 1 - ii] = init_fknn[graph.size() - 1 - ii];\n\t\t //recover\n\t\t flags[init_knn[ii].id] = true;\n\t\t knn[ii] = init_knn[ii];\n\t\t //end recover\n\t\t //fprintf(stderr, \"%d %f\\n\", knn[ii].id, knn[ii].dist);\n\t\t }\n\t\t}\n\n\t\tif (print_flag){\n\t\t for (unsigned k = 0; k < L; ++k){\n\t\t path_fs << \"-3 \" << knn[k].id << endl;\n\t\t mind_fs << knn[0].dist << endl;\n\t\t curd_fs << knn[k].dist << endl;\n\t\t \n\t\t nh_fs << M[knn[k].id] << \" \";\n\t\t unordered_set set;\n\t\t set.insert(knn[k].id);\n\t\t auto const &neighbors = graph[knn[k].id];\n\t\t for (unsigned l = 0; l < M[knn[k].id]; ++l){\n\t\t set.insert(neighbors[l].id);\n\t\t auto const &new_neighbors = graph[neighbors[l].id];\n\t\t for (unsigned ll = 0; ll < M[neighbors[l].id]; ++ll){\n\t\t\tset.insert(new_neighbors[ll].id);\n\t\t }\n\t\t }\n\t\t nh_fs << set.size() << endl;\n\t\t }\n\t\t}\n\t\t\n\t\tunsigned k = 0;\n \n while (k < L) {\n unsigned nk = L;\n if (knn[k].flag) {\n knn[k].flag = false;\n unsigned cur = knn[k].id;\n\t\t\t//BOOST_VERIFY(cur < graph.size());\n unsigned maxM = M[cur];\n if (params.M > maxM) maxM = params.M;\n auto const &neighbors = graph[cur];\n if (maxM > neighbors.size()) {\n maxM = neighbors.size();\n }\n for (unsigned m = 0; m < maxM; ++m) {\n unsigned id = neighbors[m].id;\n //BOOST_VERIFY(id < graph.size());\n if (flags[id]) continue;\n flags[id] = true;\n ++n_comps;\n //if(n_comps >= params.Maxchecks)\n\t\t\t // {\n // break;\n\t\t\t // }\n float dist = oracle(id);\n\t\t\t \n\t\t\t /*if(n_comps == 1){\n\t\t\t fprintf(stderr, \"%f\", dist);\n\t\t\t } else {\n\t\t\t fprintf(stderr, \", %f\", dist);\n\t\t\t }*/\n\t\t\t Neighbor nn(id, dist);\n\t\t\t unsigned r = UpdateKnnList(&knn[0], L, nn);\n\t\t\t if (print_flag){\n path_fs << cur << \" \" << id << endl;\n mind_fs << knn[0].dist << endl;\n\t\t\t curd_fs << dist << endl;\n\n\t\t\t nh_fs << M[id] << \" \";\n\t\t\t unordered_set set;\n\t\t\t set.insert(id);\n\t\t\t auto const &nbors = graph[id];\n\t\t\t for (unsigned l_1 = 0; l_1 < M[id]; ++l_1){\n\t\t\t\tset.insert(nbors[l_1].id);\n\t\t\t\tauto const &new_neighbors = graph[nbors[l_1].id];\n\t\t\t\tfor (unsigned ll = 0; ll < M[nbors[l_1].id]; ++ll){\n\t\t\t\t set.insert(new_neighbors[ll].id);\n\t\t\t\t}\n\t\t\t }\n\t\t\t nh_fs << set.size() << endl;\n }\n\t\t\t /*if(n_comps == 1){\n fprintf(stdout, \"%f\", knn[0].dist);\n } else {\n fprintf(stdout, \", %f\", knn[0].dist);\n\t\t\t }*/\n BOOST_VERIFY(r <= L);\n //if (r > L) continue;\n if (L + 1 < knn.size()) ++L;\n if (r < nk) {\n nk = r;\n }\n }\n\t\t\t//fprintf(stderr, \"}\\n\");\n }\n if (nk <= k) {\n k = nk;\n }\n else {\n ++k;\n }\n //if(n_comps >= params.Maxchecks)\n\t\t //\t\t\t{\n // break;\n //}\n }\n if (L > params.K) L = params.K;\n if (results.empty()) {\n results.reserve(params.K + 1);\n results.resize(L + 1);\n copy(knn.begin(), knn.begin() + L, results.begin());\n }\n else {\n // update results\n for (unsigned l = 0; l < L; ++l) {\n unsigned r = UpdateKnnList(&results[0], results.size() - 1, knn[l]);\n if (r < results.size() /* inserted */ && results.size() < (params.K + 1)) {\n results.resize(results.size() + 1);\n }\n }\n }\n }\n\t //\t results.pop_back();\n // check epsilon\n {\n for (unsigned l = 0; l < results.size(); ++l) {\n if (results[l].dist > params.epsilon) {\n results.resize(l);\n break;\n }\n }\n }\n unsigned L = results.size() - 1;\n BOOST_VERIFY(L <= params.K);\n // check epsilon\n if (ids) {\n for (unsigned k = 0; k < L; ++k) {\n ids[k] = results[k].id;\n }\n }\n if (dists) {\n for (unsigned k = 0; k < L; ++k) {\n dists[k] = results[k].dist;\n }\n }\n if (pinfo) {\n pinfo->updates = updates;\n pinfo->cost = float(n_comps) / graph.size();\n pinfo->checks=n_comps;\n }\n\t if (print_flag){\n\t path_fs << \"-1\" << endl;\n\t for (unsigned k = 0; k < results.size() - 1; ++k){\n\t\tpath_fs << results[k].id << endl; //fprintf(stderr, \"%d [color=lightblue, style=filled, label=\\\"top-%d\\\"]\\n\", results[k].id, k+1);\n\t }\n\t path_fs << \"-2\" << endl;\n\t mind_fs << \"-1\" << endl;\n\t curd_fs << \"-1\" << endl;\n\t nh_fs << \"-1\" << endl;\n\t }\n\t //fprintf(stderr, \")\\n\");\n\t //fprintf(stdout, \")\\n\");\n\t //fprintf(stderr, \")\\nplt.hist(kg_access_dist, bins=100, normed=False, alpha=0.75, label='accessed')\\nplt.hist(all_dist, bins=100, normed=False, alpha=0.5, label='all')\\nplt.title(\\\"Distribution of accessed points by KGraph\\\")\\nplt.xlabel(\\\"distance\\\")\\nplt.ylabel(\\\"Frequency\\\")\\nplt.legend(loc='upper right')\\nshow()\\n\");\n return L;\n }\n\n\n virtual unsigned search_greedy (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *pinfo, string const &info_path) const {// Matrix const &d_init,\n /*fprintf(stderr, \"all_dist=(%f\", oracle(0));\n for (int i = 1; i < oracle.size(); ++i){\n fprintf(stderr, \", %f\", oracle(i));\n }*/\n //fprintf(stderr, \"access_dist=(\");\n //fprintf(stdout, \"minimum_dist=(\");\n\n bool print_flag = true;\n std::ofstream path_fs, mind_fs, curd_fs;\n string file_name;\n if (info_path.empty()) {\n print_flag = false;\n }\n \n if (print_flag){\n file_name.assign(info_path);\n file_name += \"_path.txt\";\n path_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n file_name.assign(info_path);\n file_name += \"_mindist.txt\";\n mind_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n file_name.assign(info_path);\n file_name += \"_curdist.txt\";\n curd_fs.open(file_name.c_str(), std::ofstream::out | std::ofstream::app);\n }\n \n if (graph.size() > oracle.size()) {\n throw runtime_error(\"dataset larger than index\");\n }\n if (params.P >= graph.size()) {\n if (pinfo) {\n pinfo->updates = 0;\n pinfo->cost = 1.0;\n }\n return oracle.search(params.K, params.epsilon, ids, dists);\n }\n vector knn(params.K + params.P +1);\n vector init_knn(params.init);\n vector results;\n boost::dynamic_bitset<> flags(graph.size(), false);\n\n if (params.init && params.T > 1) {\n throw runtime_error(\"when init > 0, T must be 1.\");\n }\n\n unsigned seed = params.seed;\n unsigned updates = 0;\n if (seed == 0) seed = time(NULL);\n mt19937 rng(seed);\n unsigned n_comps = 0;\n for (unsigned trial = 0; trial < params.T; ++trial) {\n unsigned L = params.init;\n //init>=K\n if (L == 0) { // generate random starting points\n vector random(params.P);\n GenRandom(rng, &random[0], random.size(), graph.size());\n for (unsigned s: random) {\n if (!flags[s]) {\n knn[L++].id = s;\n //flags[s] = true;\n }\n }\n for (unsigned k = 0; k < L; ++k) {\n flags[knn[k].id] = true;\n knn[k].flag = true;\n knn[k].dist = oracle(knn[k].id);\n }\n sort(knn.begin(), knn.begin() + L);\n }\n else { // user-provided starting points.\n BOOST_VERIFY(ids); //ids\n //std::cout<<\"set seed\"< knn.size()\n init_knn[l].id = ids[l]; //ids\n //fprintf(stderr, \"%d: %d\\n\", l, knn[l].id);\n }\n //std::cout< init_fknn(graph.size());\n for (unsigned k = 0; k < graph.size(); ++k){\n init_fknn[k].id = k;\n init_fknn[k].flag = true;\n init_fknn[k].dist = oracle(k);\n }\n sort(init_fknn.begin(), init_fknn.end());\n //end delete\n */\n L = params.K;\n for (int ii = 0; ii < L; ++ii){\n //flags[init_fknn[graph.size() - 1 - ii].id] = true;\n //knn[L - 1 - ii] = init_fknn[graph.size() - 1 - ii];\n //recover\n flags[init_knn[ii].id] = true;\n knn[ii] = init_knn[ii];\n //end recover\n //fprintf(stderr, \"%d %f\\n\", knn[ii].id, knn[ii].dist);\n }\n }\n\n if (print_flag){\n mind_fs << knn[0].dist << endl;\n curd_fs << knn[0].dist << endl;\n }\n \n unsigned k = 0;\n while (k < L) {\n unsigned nk = L;\n if (knn[k].flag) {\n knn[k].flag = false;\n unsigned cur = knn[k].id;\n //BOOST_VERIFY(cur < graph.size());\n unsigned maxM = M[cur];\n\t\t\tfloat thres = knn[k].dist;\n if (params.M > maxM) maxM = params.M;\n auto const &neighbors = graph[cur];\n if (maxM > neighbors.size()) {\n maxM = neighbors.size();\n }\n for (unsigned m = 0; m < maxM; ++m) {\n unsigned id = neighbors[m].id;\n //BOOST_VERIFY(id < graph.size());\n if (flags[id]) continue;\n flags[id] = true;\n ++n_comps;\n\t\t\t // if(n_comps >= params.Maxchecks)\n\t\t\t // {\n\t\t\t //break;\n\t\t\t // }\n float dist = oracle(id);\n\t\t\t if (dist > thres) {\n\t\t\t continue;\n\t\t\t }\n /*if(n_comps == 1){\n fprintf(stderr, \"%f\", dist);\n } else {\n fprintf(stderr, \", %f\", dist);\n }*/\n\t\t\t Neighbor nn(id, dist);\n\t\t\t unsigned r = UpdateKnnList(&knn[0], L, nn);\n\t\t\t if (print_flag){\n path_fs << cur << \" \" << id << endl;\n mind_fs << knn[0].dist << endl;\n\t\t\t curd_fs << dist << endl;\n }\n\t\t\t /*if(n_comps == 1){\n fprintf(stdout, \"%f\", knn[0].dist);\n\t\t\t } else {\n fprintf(stdout, \", %f\", knn[0].dist);\n\t\t\t }*/\n BOOST_VERIFY(r <= L);\n //if (r > L) continue;\n if (L + 1 < knn.size()) ++L;\n if (r < nk) {\n\t\t\t nk = r;\n }\n }\n\t\t\t//fprintf(stderr, \"}\\n\");\n }\n if (nk <= k) {\n k = nk;\n }\n else {\n ++k;\n }\n\t\t // if(n_comps >= params.Maxchecks)\n //{\n // break;\n\t\t // }\n }\n if (L > params.K) L = params.K;\n if (results.empty()) {\n results.reserve(params.K + 1);\n results.resize(L + 1);\n copy(knn.begin(), knn.begin() + L, results.begin());\n }\n else {\n // update results\n for (unsigned l = 0; l < L; ++l) {\n unsigned r = UpdateKnnList(&results[0], results.size() - 1, knn[l]);\n if (r < results.size() /* inserted */ && results.size() < (params.K + 1)) {\n results.resize(results.size() + 1);\n }\n }\n }\n }\n // check epsilon\n {\n for (unsigned l = 0; l < results.size(); ++l) {\n if (results[l].dist > params.epsilon) {\n results.resize(l);\n break;\n }\n }\n }\n unsigned L = results.size() - 1;\n BOOST_VERIFY(L <= params.K);\n // check epsilon\n if (ids) {\n for (unsigned k = 0; k < L; ++k) {\n ids[k] = results[k].id;\n }\n }\n if (dists) {\n for (unsigned k = 0; k < L; ++k) {\n dists[k] = results[k].dist;\n }\n }\n if (pinfo) {\n pinfo->updates = updates;\n pinfo->cost = float(n_comps) / graph.size();\n pinfo->checks=n_comps;\n }\n if (print_flag){\n path_fs << \"-1\" << endl;\n for (unsigned k = 0; k < results.size() - 1; ++k){\n path_fs << results[k].id << endl; //fprintf(stderr, \"%d [color=lightblue, style=filled, label=\\\"top-%d\\\"]\\n\", results[k].id, k+1);\n }\n path_fs << \"-2\" << endl;\n mind_fs << \"-1\" << endl;\n curd_fs << \"-1\" << endl;\n }\n //fprintf(stderr, \")\\n\");\n //fprintf(stdout, \")\\n\");\n //fprintf(stderr, \")\\nplt.hist(kg_access_dist, bins=100, normed=False, alpha=0.75, label='accessed')\\nplt.hist(all_dist, bins=100, normed=False, alpha=0.5, label='all')\\nplt.title(\\\"Distribution of accessed points by KGraph\\\")\\nplt.xlabel(\\\"distance\\\")\\nplt.ylabel(\\\"Frequency\\\")\\nplt.legend(loc='upper right')\\nshow()\\n\");\n return L;\n }\n\n virtual void get_nn (unsigned id, unsigned *nns, float *dist, unsigned *pM, unsigned *pL) const {\n BOOST_VERIFY(id < graph.size());\n auto const &v = graph[id];\n *pM = M[id];\n *pL = v.size();\n if (nns) {\n for (unsigned i = 0; i < v.size(); ++i) {\n nns[i] = v[i].id;\n }\n }\n if (dist) {\n for (unsigned i = 0; i < v.size(); ++i) {\n dist[i] = v[i].dist;\n }\n }\n }\n\n void prune1 () {\n for (unsigned i = 0; i < graph.size(); ++i) {\n if (graph[i].size() > M[i]) {\n graph[i].resize(M[i]);\n }\n }\n }\n\n void prune2 () {\n#if 0\n vector> new_graph(graph.size());\n vector new_M(graph.size());\n vector> reverse(graph.size());\n vector> todo(graph.size());\n unsigned L = 0;\n {\n cerr << \"Level 2 Prune, stage 1/2 ...\" << endl;\n progress_display progress(graph.size(), cerr);\n for (unsigned i = 0; i < graph.size(); ++i) {\n if (graph[i].size() > L) L = graph[i].size();\n todo[i] = unordered_set(graph[i].begin(), graph[i].end());\n ++progress;\n }\n }\n {\n cerr << \"Level 2 Prune, stage 2/2 ...\" << endl;\n progress_display progress(L, cerr);\n for (unsigned l = 0; l < L; ++l) {\n for (unsigned i = 0; i < graph.size(); ++i) {\n if (todo[i].empty()) continue;\n BOOST_VERIFY(l < graph[i].size());\n unsigned T = graph[i][l];\n if (todo[i].erase(T)) { // still there, need to be added\n new_graph[i].push_back(T);\n reverse[T].push_back(i);\n // mark newly reachable nodes\n for (auto n2: new_graph[T]) {\n todo[i].erase(n2);\n }\n for (auto r: reverse[i]) {\n todo[r].erase(T);\n }\n }\n if (l + 1 == M[i]) {\n new_M[i] = new_graph[i].size();\n }\n }\n ++progress;\n }\n }\n graph.swap(new_graph);\n M.swap(new_M);\n#endif\n }\n\n virtual void prune (IndexOracle const &oracle, unsigned level) {\n if (level & PRUNE_LEVEL_1) {\n prune1();\n }\n if (level & PRUNE_LEVEL_2) {\n prune2();\n }\n }\n };\n\n class KGraphConstructor: public KGraphImpl {\n // The neighborhood structure maintains a pool of near neighbors of an object.\n // The neighbors are stored in the pool. \"n\" (<=\"params.L\") is the number of valid entries\n // in the pool, with the beginning \"k\" (<=\"n\") entries sorted.\n struct Nhood { // neighborhood\n Lock lock;\n float radius; // distance of interesting range\n float radiusM;\n Neighbors pool;\n unsigned L; // # valid items in the pool, L + 1 <= pool.size()\n unsigned M; // we only join items in pool[0..M)\n bool found; // helped found new NN in this round\n vector nn_old;\n vector nn_new;\n vector rnn_old;\n vector rnn_new;\n\n // only non-readonly method which is supposed to be called in parallel\n unsigned parallel_try_insert (unsigned id, float dist) {\n if (dist > radius) return pool.size();\n LockGuard guard(lock);\n unsigned l = UpdateKnnList(&pool[0], L, Neighbor(id, dist, true));\n if (l <= L) { // inserted\n if (L + 1 < pool.size()) { // if l == L + 1, there's a duplicate\n ++L;\n }\n else {\n radius = pool[L-1].dist;\n }\n }\n return l;\n }\n\n // join should not be conflict with insert\n template \n void join (C callback) const {\n for (unsigned const i: nn_new) {\n for (unsigned const j: nn_new) {\n if (i < j) {\n callback(i, j);\n }\n }\n for (unsigned j: nn_old) {\n callback(i, j);\n }\n }\n }\n };\n\n IndexOracle const &oracle;\n IndexParams params;\n IndexInfo *pinfo;\n vector nhoods;\n size_t n_comps;\n\n void init () {\n unsigned N = oracle.size();\n unsigned seed = params.seed;\n mt19937 rng(seed);\n for (auto &nhood: nhoods) {\n nhood.nn_new.resize(params.S * 2);\n nhood.pool.resize(params.L+1);\n nhood.radius = numeric_limits::max();\n }\n #pragma omp parallel\n {\n #ifdef _OPENMP\n mt19937 rng(seed ^ omp_get_thread_num());\n #else\n mt19937 rng(seed);\n #endif\n vector random(params.S + 1);\n #pragma omp for\n for (unsigned n = 0; n < N; ++n) {\n auto &nhood = nhoods[n];\n Neighbors &pool = nhood.pool;\n GenRandom(rng, &nhood.nn_new[0], nhood.nn_new.size(), N); //nn_new 2*S random sampling \n GenRandom(rng, &random[0], random.size(), N); //random s+1\n nhood.L = params.S;\n nhood.M = params.S;\n\t\t\t\t\t//cout< 0;\n }\n n_comps += cc;\n }\n void update () {\n unsigned N = oracle.size();\n for (auto &nhood: nhoods) {\n nhood.nn_new.clear();\n nhood.nn_old.clear();\n nhood.rnn_new.clear();\n nhood.rnn_old.clear();\n nhood.radius = nhood.pool.back().dist;\n }\n //!!! compute radius2\n #pragma omp parallel for\n for (unsigned n = 0; n < N; ++n) {\n auto &nhood = nhoods[n];\n if (nhood.found) {\n unsigned maxl = std::min(nhood.M + params.S, nhood.L);\n unsigned c = 0;\n unsigned l = 0;\n while ((l < maxl) && (c < params.S)) {\n if (nhood.pool[l].flag) ++c;\n ++l;\n }\n nhood.M = l;\n }\n BOOST_VERIFY(nhood.M > 0);\n nhood.radiusM = nhood.pool[nhood.M-1].dist;\n }\n #pragma omp parallel for\n for (unsigned n = 0; n < N; ++n) {\n auto &nhood = nhoods[n];\n auto &nn_new = nhood.nn_new;\n auto &nn_old = nhood.nn_old;\n for (unsigned l = 0; l < nhood.M; ++l) {\n\t\t\t\t\t\n auto &nn = nhood.pool[l];\n auto &nhood_o = nhoods[nn.id]; // nhood on the other side of the edge\n if (nn.flag) {\n nn_new.push_back(nn.id);\n if (nn.dist > nhood_o.radiusM) {\n LockGuard guard(nhood_o.lock);\n nhood_o.rnn_new.push_back(n);\n }\n nn.flag = false;\n }\n else {\n nn_old.push_back(nn.id);\n if (nn.dist > nhood_o.radiusM) {\n LockGuard guard(nhood_o.lock);\n nhood_o.rnn_old.push_back(n);\n }\n }\n }\n }\n for (unsigned i = 0; i < N; ++i) {\n auto &nn_new = nhoods[i].nn_new;\n auto &nn_old = nhoods[i].nn_old;\n auto &rnn_new = nhoods[i].rnn_new;\n auto &rnn_old = nhoods[i].rnn_old;\n if (params.R && (rnn_new.size() > params.R)) {\n random_shuffle(rnn_new.begin(), rnn_new.end());\n rnn_new.resize(params.R);\n }\n nn_new.insert(nn_new.end(), rnn_new.begin(), rnn_new.end());\n if (params.R && (rnn_old.size() > params.R)) {\n random_shuffle(rnn_old.begin(), rnn_old.end());\n rnn_old.resize(params.R);\n }\n nn_old.insert(nn_old.end(), rnn_old.begin(), rnn_old.end());\n }\n }\n\npublic:\n KGraphConstructor (IndexOracle const &o, IndexParams const &p, IndexInfo *r, char const *path)\n : oracle(o), params(p), pinfo(r), nhoods(o.size()), n_comps(0)\n {\n boost::timer::cpu_timer timer;\n //params.check();\n unsigned N = oracle.size(); // size of dataset\n if (N <= params.K) throw runtime_error(\"K larger than dataset size\");\n\n vector controls; //struct control {unsigned id;Neighbors neighbors;}\n if (verbosity > 0) cerr << \"Generating control...\" << endl;\n GenerateControl(oracle, params.controls, params.K, &controls); //controls data points are sampled, and then calculate the true knn \n if (verbosity > 0) cerr << \"Initializing...\" << endl;\n // initialize nhoods\n init();\n\n // iterate until converge\n float total = N * float(N - 1) / 2;\n IndexInfo info;\n info.stop_condition = IndexInfo::ITERATION;\n info.recall = 0;\n info.accuracy = numeric_limits::max();\n info.cost = 0;\n info.iterations = 0;\n info.delta = 1.0;\n\n for (unsigned it = 0; (params.iterations <= 0) || (it < params.iterations); ++it) {\n ++info.iterations;\n join();\n {\n info.cost = n_comps / total;\n accumulator_set> one_exact;\n accumulator_set> one_approx;\n accumulator_set> one_recall;\n accumulator_set> recall;\n accumulator_set> accuracy;\n accumulator_set> M;\n accumulator_set> delta;\n for (auto const &nhood: nhoods) {\n M(nhood.M);\n delta(EvaluateDelta(nhood.pool, params.K));\n }\n for (auto const &c: controls) {\n one_approx(nhoods[c.id].pool[0].dist);\n one_exact(c.neighbors[0].dist);\n one_recall(EvaluateOneRecall(nhoods[c.id].pool, c.neighbors));\n recall(EvaluateRecall(nhoods[c.id].pool, c.neighbors));\n accuracy(EvaluateAccuracy(nhoods[c.id].pool, c.neighbors));\n }\n info.delta = mean(delta);\n info.recall = mean(recall);\n info.accuracy = mean(accuracy);\n info.M = mean(M);\n auto times = timer.elapsed();\n if (verbosity > 0) {\n\n cerr << \"iteration: \" << info.iterations\n << \" recall: \" << info.recall\n << \" accuracy: \" << info.accuracy\n << \" cost: \" << info.cost\n << \" M: \" << info.M\n << \" delta: \" << info.delta\n << \" time: \" << times.wall / 1e9\n << \" one-recall: \" << mean(one_recall)\n << \" one-ratio: \" << mean(one_approx) / mean(one_exact)\n << endl;\n }\n }\n if (info.delta <= params.delta) {\n info.stop_condition = IndexInfo::DELTA;\n break;\n }\n if (info.recall >= params.recall) {\n info.stop_condition = IndexInfo::RECALL;\n break;\n }\n update();\n }\n\n ofstream os(path, ios::binary);\n os.write(KGRAPH_MAGIC, KGRAPH_MAGIC_SIZE);\n os.write(reinterpret_cast(&VERSION_MAJOR), sizeof(VERSION_MAJOR));\n os.write(reinterpret_cast(&VERSION_MINOR), sizeof(VERSION_MINOR));\n os.write(reinterpret_cast(&N), sizeof(N));\n \n \n for (unsigned n = 0; n < N; ++n) {\n auto const &pool = nhoods[n].pool;\n unsigned K = params.L;\n os.write(reinterpret_cast(&K), sizeof(K));\n os.write(reinterpret_cast(&K), sizeof(K));\n for (unsigned j = 0; j < K; ++j){\n os.write(reinterpret_cast(&pool[j].id), sizeof(pool[j].id));\n }\n }\n /* // deleted by yifang\n M.resize(N);\n graph.resize(N);\n if (params.prune > 2) throw runtime_error(\"prune level not supported.\");\n for (unsigned n = 0; n < N; ++n) {\n auto &knn = graph[n];\n M[n] = nhoods[n].M;\n auto const &pool = nhoods[n].pool;\n\n unsigned K = params.L;\n knn.resize(K);\n for (unsigned k = 0; k < K; ++k) {\n knn[k].id = pool[k].id;\n knn[k].dist = pool[k].dist;\n }\n nhoods[n].pool.clear();\n }\n if (params.prune) {\n prune(o, params.prune);\n }\n if (pinfo) {\n *pinfo = info;\n }*/\n }\n\n };\n\nvoid KGraphImpl::build (IndexOracle const &oracle, IndexParams const ¶m, char const *path, IndexInfo *info) {\n KGraphConstructor con(oracle, param, info, path);\n /* //deleted by yifang\n M.swap(con.M);\n graph.swap(con.graph);\n */\n}\n\n KGraph *KGraph::create () {\n return new KGraphImpl;\n }\n\n char const* KGraph::version () {\n return kgraph_version;\n }\n}\n\n" }, { "path": "algorithms/KGraph/src/kgraph.h", "content": "// Copyright (C) 2013-2015 Wei Dong . All Rights Reserved.\n//\n// \\mainpage KGraph: A Library for Efficient K-NN Search\n// \\author Wei Dong \\f$ wdong@wdong.org \\f$\n// \\author 2013-2015\n//\n\n#ifndef WDONG_KGRAPH\n#define WDONG_KGRAPH\n\n#include \n#include \n#include \n#include \n//#include \"kgraph-data.h\"\n\nusing namespace std;\n\nnamespace kgraph {\n static unsigned const default_iterations = 30;\n static unsigned const default_L = 100;\n static unsigned const default_K = 100;\n static unsigned const default_P = 100;\n static unsigned const default_M = 0;\n static unsigned const default_T = 1;\n static unsigned const default_S = 10;\n static unsigned const default_Maxchecks = 10;\n static unsigned const default_seedIndex = 1;\n static unsigned const default_R = 100;\n static unsigned const default_controls = 100;\n static unsigned const default_seed = 1998;\n static float const default_delta = 0.002;\n static float const default_recall = 0.99;\n static float const default_epsilon = 1e30;\n static unsigned const default_verbosity = 1;\n enum {\n PRUNE_LEVEL_1 = 1,\n PRUNE_LEVEL_2 = 2\n };\n static unsigned const default_prune = 0;\n\n /// Verbosity control\n /** Set verbosity = 0 to disable information output to stderr.\n */\n extern unsigned verbosity;\n\n /// Index oracle\n /** The index oracle is the user-supplied plugin that computes\n * the distance between two arbitrary objects in the dataset.\n * It is used for offline k-NN graph construction.\n */\n class IndexOracle {\n public:\n /// Returns the size of the dataset.\n virtual unsigned size () const = 0;\n /// Computes similarity\n /**\n * 0 <= i, j < size() are the index of two objects in the dataset.\n * This method return the distance between objects i and j.\n */\n virtual float operator () (unsigned i, unsigned j) const = 0;\n\tvirtual float angle (unsigned orign, unsigned pivot, unsigned x) const = 0;\n };\n\n /// Search oracle\n /** The search oracle is the user-supplied plugin that computes\n * the distance between the query and a arbitrary object in the dataset.\n * It is used for online k-NN search.\n */\n class SearchOracle {\n public:\n /// Returns the size of the dataset.\n virtual unsigned size () const = 0;\n /// Computes similarity\n /**\n * 0 <= i < size() are the index of an objects in the dataset.\n * This method return the distance between the query and object i.\n */\n virtual float operator () (unsigned i) const = 0;\n /// Search with brutal force.\n /**\n * Search results are guaranteed to be ranked in ascending order of distance.\n *\n * @param K Return at most K nearest neighbors.\n * @param epsilon Only returns nearest neighbors within distance epsilon.\n * @param ids Pointer to the memory where neighbor IDs are returned.\n * @param dists Pointer to the memory where distance values are returned, can be nullptr.\n */\n unsigned search (unsigned K, float epsilon, unsigned *ids, float *dists = nullptr) const;\n };\n\n /// The KGraph index.\n /** This is an abstract base class. Use KGraph::create to create an instance.\n */\n class KGraph {\n public:\n /// Indexing parameters.\n struct IndexParams {\n unsigned iterations; \n unsigned L;\n unsigned K;\n unsigned S;\n unsigned R;\n unsigned controls;\n unsigned seed;\n float delta;\n float recall;\n unsigned prune;\n\n /// Construct with default values.\n IndexParams (): iterations(default_iterations), L(default_L), K(default_K), S(default_S), R(default_R), controls(default_controls), seed(default_seed), delta(default_delta), recall(default_recall), prune(default_prune) {\n }\n };\n\n /// Search parameters.\n struct SearchParams {\n unsigned K;\n unsigned M;\n unsigned P;\n unsigned T;\n unsigned Maxchecks;\n float epsilon;\n unsigned seed;\n unsigned init;\n \n\n /// Construct with default values.\n SearchParams (): K(default_K), M(default_M), P(default_P), T(default_T), epsilon(default_epsilon), seed(1998), init(0) {\n }\n };\n\n /// Information and statistics of the indexing algorithm.\n struct IndexInfo {\n enum StopCondition {\n ITERATION = 0,\n DELTA,\n RECALL\n } stop_condition;\n unsigned iterations;\n float cost;\n float recall;\n float accuracy;\n float delta;\n float M;\n };\n\n /// Information and statistics of the search algorithm.\n struct SearchInfo {\n float cost;\n unsigned updates;\n int checks;\n };\n\n virtual ~KGraph () {\n }\n /// Load index from file.\n /**\n * @param path Path to the index file.\n */\n virtual void load (char const *path) = 0;\n /// Save index to file.\n /**sa\n * @param path Path to the index file.\n */\n virtual void save (char const *path) const = 0; // save to file\n\t virtual void saver (char const *path) const = 0;\n\t\tvirtual void get_hubs (char const *hubs_path,char const *knn_path) const = 0; // get_hubs\n\t\tvirtual void add_long_edges(IndexOracle const &oracle, int const rank_top_k, int const change_num) = 0; //add long edges\n\t\tvirtual void output_M (char const * filename) = 0;\n\t\tvirtual void output_info(int const id) = 0; // output info for id-th point\n\t\tvirtual void merge(char const * graph_path, char const * id_path) = 0;\n\t\tvirtual void test_connectivity(unsigned dest) = 0;\n\t\tvirtual void remove_near_edges(IndexOracle const &oracle, const int edge_num) = 0;\n virtual float statistics(IndexOracle const &oracle, const int edge_num) = 0;\n virtual void diversify_by_cut(IndexOracle const &oracle, const int edge_num) = 0;\n\t\tvirtual void remove_near_edges_2(IndexOracle const &oracle, vector const &edge_num, unordered_set const &skip_set) = 0;\n\t\tvirtual void add_random_backward_edges(char const *data_path, char const *hubs_path, int const num_of_hubs, char const * start_points_path, unsigned num_of_query) = 0;\n\t\tvirtual void add_backward_edges() = 0;\n\t\tvirtual void get_partitions(char const *output_path) = 0;\n\t\tvirtual void compute_diversity(IndexOracle const &oracle, int d, float * norm) = 0;\n\t\tvirtual void connectivity(unsigned const source, unsigned const dest) = 0;\n\t\tvirtual void make_inverse_graph() = 0;\n\t\tvirtual void find_lm (SearchOracle const &oracle) const = 0;\n\t\tvirtual void kgraph2dot (char * const &filename) = 0;\n /// Build the index\n virtual void build (IndexOracle const &oracle, IndexParams const ¶ms, char const *path, IndexInfo *info = 0) = 0;\n /// Prune the index\n /**\n * Pruning makes the index smaller to save memory, and makes online search on the pruned index faster.\n * (The cost parameters of online search must be enlarged so accuracy is not reduced.)\n *\n * Currently only two pruning levels are supported:\n * - PRUNE_LEVEL_1 = 1: Only reduces index size, fast.\n * - PRUNE_LEVEL_2 = 2: For improve online search speed, slow.\n *\n * No pruning is done if level = 0.\n */\n virtual void prune (IndexOracle const &oracle, unsigned level) = 0;\n /// Online k-NN search.\n /**\n * Search results are guaranteed to be ranked in ascending order of distance.\n *\n * @param ids Pointer to the memory where neighbor IDs are stored, must have space to save params.K ids.\n */\n unsigned search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, SearchInfo *info = 0, string const &info_path = \"\") const {\n //std::cout <<\"h search \\n\";\n\t return search(oracle, params, ids, nullptr, info, info_path);// d_init, Matrix const &d_init,\n }\n\n\tunsigned search_greedy (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, SearchInfo *info = 0, string const &info_path = \"\") const {\n\t //std::cout <<\"h search \\n\";\n return search_greedy(oracle, params, ids, nullptr, info, info_path);// d_init, Matrix const &d_init,\n }\n /// Online k-NN search.\n /**\n * Search results are guaranteed to be ranked in ascending order of distance.\n *\n * @param ids Pointer to the memory where neighbor IDs are stored, must have space to save params.K values.\n * @param dists Pointer to the memory where distances are stored, must have space to save params.K values.\n */\n\tvirtual unsigned advanced_search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *info, string const &info_path) const = 0; //,Matrix const &d_init\n\n virtual unsigned search (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *info, string const &info_path) const = 0; //,Matrix const &d_init\n\n\tvirtual unsigned search_greedy (SearchOracle const &oracle, SearchParams const ¶ms, unsigned *ids, float *dists, SearchInfo *info, string const &info_path) const = 0; //,Matrix const &d_init\n /// Constructor.\n static KGraph *create ();\n /// Returns version string.\n static char const* version ();\n\n /// Get offline computed k-NNs of a given object.\n /**\n * See the full version of get_nn.\n */\n virtual void get_nn (unsigned id, unsigned *nns, unsigned *M, unsigned *L) const {\n get_nn(id, nns, nullptr, M, L);\n }\n /// Get offline computed k-NNs of a given object.\n /**\n * The user must provide space to save IndexParams::L values.\n * The actually returned L could be smaller than IndexParams::L, and\n * M <= L is the number of neighbors KGraph thinks\n * could be most useful for online search, and is usually < L.\n * If the index has been pruned, the returned L could be smaller than\n * IndexParams::L used to construct the index.\n *\n * @params id Object ID whose neighbor information are returned.\n * @params nns Neighbor IDs, must have space to save IndexParams::L values. \n * @params dists Distance values, must have space to save IndexParams::L values.\n * @params M Useful number of neighbors, output only.\n * @params L Actually returned number of neighbors, output only.\n */\n virtual void get_nn (unsigned id, unsigned *nns, float *dists, unsigned *M, unsigned *L) const = 0;\n };\n}\n\n#if __cplusplus > 199711L\n#include \nnamespace kgraph {\n /// Oracle adapter for datasets stored in a vector-like container.\n /**\n * If the dataset is stored in a container of CONTAINER_TYPE that supports\n * - a size() method that returns the number of objects.\n * - a [] operator that returns the const reference to an object.\n * This class can be used to provide a wrapper to facilitate creating\n * the index and search oracles.\n *\n * The user must provide a callback function that takes in two\n * const references to objects and returns a distance value.\n */\n template \n class VectorOracle: public IndexOracle {\n public:\n typedef std::function METRIC_TYPE;\n private:\n CONTAINER_TYPE const &data;\n METRIC_TYPE dist;\n public:\n class VectorSearchOracle: public SearchOracle {\n CONTAINER_TYPE const &data;\n OBJECT_TYPE const query;\n METRIC_TYPE dist;\n public:\n VectorSearchOracle (CONTAINER_TYPE const &p, OBJECT_TYPE const &q, METRIC_TYPE m): data(p), query(q), dist(m) {\n }\n virtual unsigned size () const {\n return data.size();\n }\n virtual float operator () (unsigned i) const {\n return dist(data[i], query);\n }\n };\n /// Constructor.\n /**\n * @param d: the container that holds the dataset.\n * @param m: a callback function for distance computation. m(d[i], d[j]) must be\n * a valid expression to compute distance.\n */\n VectorOracle (CONTAINER_TYPE const &d, METRIC_TYPE m): data(d), dist(m) {\n }\n virtual unsigned size () const {\n return data.size();\n }\n virtual float operator () (unsigned i, unsigned j) const {\n return dist(data[i], data[j]);\n }\n /// Constructs a search oracle for query object q.\n VectorSearchOracle query (OBJECT_TYPE const &q) const {\n return VectorSearchOracle(data, q, dist);\n }\n };\n}\n#endif\n\n#endif\n\n" }, { "path": "algorithms/KGraph/src/kgraph_index.cpp", "content": "#ifndef KGRAPH_VALUE_TYPE\n#define KGRAPH_VALUE_TYPE float\n#endif\n\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nusing namespace boost::timer;\nusing namespace std;\nusing namespace boost;\nusing namespace kgraph;\nnamespace po = boost::program_options; \n\n\ntypedef KGRAPH_VALUE_TYPE value_type;\n\nint main(int argc, char *argv[]) {\n string data_path;\n string output_path;\n KGraph::IndexParams params;\n unsigned D;\n unsigned skip;\n unsigned gap;\n unsigned synthetic;\n float noise;\n\n bool lshkit = true;\n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version information.\")\n (\"data\", po::value(&data_path), \"input path\")\n (\"output\", po::value(&output_path), \"output path\")\n (\",K\", po::value(¶ms.K)->default_value(default_K), \"number of nearest neighbor\")\n (\"controls,C\", po::value(¶ms.controls)->default_value(default_controls), \"number of control pounsigneds\")\n ;\n\n po::options_description desc_hidden(\"Expert options\");\n desc_hidden.add_options()\n (\"iterations,I\", po::value(¶ms.iterations)->default_value(default_iterations), \"\")\n (\",S\", po::value(¶ms.S)->default_value(default_S), \"\")\n (\",R\", po::value(¶ms.R)->default_value(default_R), \"\")\n (\",L\", po::value(¶ms.L)->default_value(default_L), \"\")\n (\"delta\", po::value(¶ms.delta)->default_value(default_delta), \"\")\n (\"recall\", po::value(¶ms.recall)->default_value(default_recall), \"\")\n (\"prune\", po::value(¶ms.prune)->default_value(default_prune), \"\")\n (\"noise\", po::value(&noise)->default_value(0), \"noise\")\n (\"seed\", po::value(¶ms.seed)->default_value(default_seed), \"\")\n (\"dim,D\", po::value(&D), \"dimension, see format\")\n (\"skip\", po::value(&skip)->default_value(0), \"see format\")\n (\"gap\", po::value(&gap)->default_value(0), \"see format\")\n (\"raw\", \"read raw binary file, need to specify D.\")\n (\"synthetic\", po::value(&synthetic)->default_value(0), \"generate synthetic data, for performance evaluation only, specify number of points\")\n ;\n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible).add(desc_hidden);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"output\", 1);\n\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm);\n\n if (vm.count(\"raw\") == 1) {\n lshkit = false;\n }\n\n if (vm.count(\"version\")) {\n cout << \"KGraph version \" << KGraph::version() << endl;\n return 0;\n }\n\n if (vm.count(\"help\")\n || (synthetic && (vm.count(\"dim\") == 0 || vm.count(\"data\")))\n || (!synthetic && (vm.count(\"data\") == 0 || (vm.count(\"dim\") == 0 && !lshkit)))) {\n cout << \"Usage: index [OTHER OPTIONS]... INPUT [OUTPUT]\" << endl;\n cout << desc_visible << endl;\n cout << desc_hidden << endl;\n return 0;\n }\n\n if (params.S == 0) {\n params.S = params.K;\n }\n\n if (lshkit && (synthetic == 0)) { // read dimension information from the data file\n static const unsigned LSHKIT_HEADER = 3;\n ifstream is(data_path.c_str(), ios::binary);\n unsigned header[LSHKIT_HEADER]; /* entry size, row, col */\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n BOOST_VERIFY(header[0] == sizeof(value_type));\n is.close();\n D = header[2];\n skip = LSHKIT_HEADER * sizeof(unsigned);\n gap = 0;\n }\n\n Matrix data;\n if (synthetic) {\n if (!std::is_floating_point::value) {\n throw runtime_error(\"synthetic data not implemented for non-floating-point values.\");\n }\n data.resize(synthetic, D);\n cerr << \"Generating synthetic data...\" << endl;\n default_random_engine rng(params.seed);\n uniform_real_distribution distribution(-1.0, 1.0);\n data.zero(); // important to do that\n for (unsigned i = 0; i < synthetic; ++i) {\n value_type *row = data[i];\n for (unsigned j = 0; j < D; ++j) {\n row[j] = distribution(rng);\n }\n }\n }\n else {\n data.load(data_path, D, skip, gap);\n }\n if (noise != 0) {\n if (!std::is_floating_point::value) {\n throw runtime_error(\"noise injection not implemented for non-floating-point value.\");\n }\n tr1::ranlux64_base_01 rng;\n double sum = 0, sum2 = 0;\n for (unsigned i = 0; i < data.size(); ++i) {\n for (unsigned j = 0; j < data.dim(); ++j) {\n value_type v = data[i][j];\n sum += v;\n sum2 += v * v;\n }\n }\n double total = double(data.size()) * data.dim();\n double avg2 = sum2 / total, avg = sum / total;\n double dev = sqrt(avg2 - avg * avg);\n cerr << \"Adding Gaussian noise w/ \" << noise << \"x sigma(\" << dev << \")...\" << endl;\n boost::normal_distribution gaussian(0, noise * dev);\n for (unsigned i = 0; i < data.size(); ++i) {\n for (unsigned j = 0; j < data.dim(); ++j) {\n data[i][j] += gaussian(rng);\n }\n }\n }\n\n MatrixOracle oracle(data);\n KGraph::IndexInfo info;\n KGraph *kgraph = KGraph::create(); //(oracle, params, &info);\n {\n auto_cpu_timer timer;\n kgraph->build(oracle, params, output_path.c_str(), &info);\n cerr << info.stop_condition << endl;\n }\n\n if (output_path.size()) {\n \n //Note that we modify the index save procedure to reduce index size \n //kgraph->save(output_path.c_str());\n }\n\n \n \n delete kgraph;\n\n return 0;\n}\n" }, { "path": "algorithms/KGraph/src/kgraph_search.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n\nusing namespace boost::timer;\nusing namespace std;\nusing namespace boost;\nusing namespace kgraph;\nnamespace po = boost::program_options; \n\n#ifndef KGRAPH_VALUE_TYPE\n#define KGRAPH_VALUE_TYPE float\n#endif\n\n\ntypedef KGRAPH_VALUE_TYPE value_type;\n\n\nint main(int argc, char *argv[]) {\n vector params;\n string input_path;\n string index_path;\n string query_path;\n string output_path;\n string init_path;\n string eval_path;\n unsigned K, M, P, T;\n\n po::options_description desc_visible(\"General options\");\n desc_visible.add_options()\n (\"help,h\", \"produce help message.\")\n (\"version,v\", \"print version number.\")\n (\"data\", po::value(&input_path), \"input path\")\n (\"index\", po::value(&index_path), \"index path\")\n (\"query\", po::value(&query_path), \"query path\")\n (\"output\", po::value(&output_path), \"output path\")\n (\"init\", po::value(&init_path), \"init path\")\n (\"eval\", po::value(&eval_path), \"eval path\")\n (\",K\", po::value(&K)->default_value(default_K), \"\")\n (\",M\", po::value(&M)->default_value(default_M), \"\")\n (\",P\", po::value(&P)->default_value(default_P), \"\")\n (\",T\", po::value(&T)->default_value(default_T), \"\")\n (\"linear\", \"\")\n ;\n\n po::options_description desc(\"Allowed options\");\n desc.add(desc_visible);\n\n po::positional_options_description p;\n p.add(\"data\", 1);\n p.add(\"index\", 1);\n p.add(\"query\", 1);\n p.add(\"output\", 1);\n\n po::variables_map vm; \n po::store(po::command_line_parser(argc, argv).options(desc).positional(p).run(), vm);\n po::notify(vm); \n\n if (vm.count(\"version\")) {\n cout << \"KGraph version \" << KGraph::version() << endl;\n return 0;\n }\n\n if (vm.count(\"help\") || vm.count(\"data\") == 0 || vm.count(\"index\") == 0 || vm.count(\"query\") == 0) {\n cout << \"DPG_search [output]\" << endl;\n cout << desc_visible << endl;\n return 0;\n }\n\n if (P < K) {\n P = K;\n }\n\n Matrix data;\n Matrix query;\n Matrix result; //(query.size(), U);\n unsigned init = 0;\n\n data.load_lshkit(input_path);\n query.load_lshkit(query_path);\n if (init_path.size()) {\n result.load_lshkit(init_path);\n BOOST_VERIFY(result.size() == query.size());\n init = result.dim();\n BOOST_VERIFY(init >= K);\n }\n MatrixOracle oracle(data);\n float recall = 0;\n float cost = 0;\n float time = 0;\n if (vm.count(\"linear\")) {\n boost::timer::auto_cpu_timer timer;\n result.resize(query.size(), K);\n// #pragma omp parallel for\n for (unsigned i = 0; i < query.size(); ++i) {\n oracle.query(query[i]).search(K, default_epsilon, result[i]);\n }\n cost = 1.0;\n time = timer.elapsed().wall / 1e9;\n }\n else {\n result.resize(query.size(), K);\n KGraph::SearchParams params;\n params.K = K;\n params.M = M;\n params.P = P;\n params.T = T;\n params.init = init;\n KGraph *kgraph = KGraph::create();\n kgraph->load(index_path.c_str());\n\n boost::timer::auto_cpu_timer timer;\n cerr << \"Searching...\" << endl;\n\n// #pragma omp parallel for reduction(+:cost)\n for (unsigned i = 0; i < query.size(); ++i) {\n KGraph::SearchInfo info;\n kgraph->search(oracle.query(query[i]), params, result[i], &info);\n cost += info.cost;\n }\n cost /= query.size();\n time = timer.elapsed().wall / 1e9;\n //cerr << \"Cost: \" << cost << endl;\n delete kgraph;\n }\n if (output_path.size()) {\n result.save_lshkit(output_path);\n }\n if (eval_path.size()) {\n Matrix gs;\n gs.load_lshkit(eval_path);\n BOOST_VERIFY(gs.dim() >= K);\n BOOST_VERIFY(gs.size() >= query.size());\n kgraph::Matrix gs_dist(query.size(), K);\n kgraph::Matrix result_dist(query.size(), K);\n// #pragma omp parallel for\n for (unsigned i = 0; i < query.size(); ++i) {\n auto Q = oracle.query(query[i]);\n float *gs_dist_row = gs_dist[i];\n float *result_dist_row = result_dist[i];\n unsigned const *gs_row = gs[i];\n unsigned const *result_row = result[i];\n for (unsigned k = 0; k < K; ++k) {\n gs_dist_row[k] = Q(gs_row[k]);\n result_dist_row[k] = Q(result_row[k]);\n }\n sort(gs_dist_row, gs_dist_row + K);\n sort(result_dist_row, result_dist_row + K);\n }\n recall = AverageRecall(gs_dist, result_dist, K);\n }\n cout << \"Time: \" << time << \" Recall: \" << recall << \" Cost: \" << cost << endl;\n\n return 0;\n}" }, { "path": "algorithms/KGraph/src/metric.cpp", "content": "#include \"kgraph.h\"\n#include \"kgraph-data.h\"\n#include \n\n#ifdef __GNUC__\n#ifdef __AVX__\n#include \n#define AVX_L2SQR(addr1, addr2, dest, tmp1, tmp2) \\\n tmp1 = _mm256_loadu_ps(addr1);\\\n tmp2 = _mm256_loadu_ps(addr2);\\\n tmp1 = _mm256_sub_ps(tmp1, tmp2); \\\n tmp1 = _mm256_mul_ps(tmp1, tmp1); \\\n dest = _mm256_add_ps(dest, tmp1); \nnamespace kgraph {\nfloat float_l2sqr_avx (float const *t1, float const *t2, unsigned dim) {\n __m256 sum;\n __m256 l0, l1, l2, l3;\n __m256 r0, r1, r2, r3;\n unsigned D = (dim + 7) & ~7U; // # dim aligned up to 256 bits, or 8 floats\n unsigned DR = D % 32;\n unsigned DD = D - DR;\n const float *l = t1;\n const float *r = t2;\n const float *e_l = l + DD;\n const float *e_r = r + DD;\n float unpack[8] __attribute__ ((aligned (32))) = {0, 0, 0, 0, 0, 0, 0, 0};\n float ret = 0.0;\n sum = _mm256_load_ps(unpack);\n switch (DR) {\n case 24:\n AVX_L2SQR(e_l+16, e_r+16, sum, l2, r2);\n case 16:\n AVX_L2SQR(e_l+8, e_r+8, sum, l1, r1);\n case 8:\n AVX_L2SQR(e_l, e_r, sum, l0, r0);\n }\n for (unsigned i = 0; i < DD; i += 32, l += 32, r += 32) {\n AVX_L2SQR(l, r, sum, l0, r0);\n AVX_L2SQR(l + 8, r + 8, sum, l1, r1);\n AVX_L2SQR(l + 16, r + 16, sum, l2, r2);\n AVX_L2SQR(l + 24, r + 24, sum, l3, r3);\n }\n _mm256_storeu_ps(unpack, sum);\n ret = unpack[0] + unpack[1] + unpack[2] + unpack[3]\n + unpack[4] + unpack[5] + unpack[6] + unpack[7];\n return ret;//sqrt(ret);\n}\n}\n#endif\n#ifdef __SSE2__\n#include \n#define SSE_L2SQR(addr1, addr2, dest, tmp1, tmp2) \\\n tmp1 = _mm_load_ps(addr1);\\\n tmp2 = _mm_load_ps(addr2);\\\n tmp1 = _mm_sub_ps(tmp1, tmp2); \\\n tmp1 = _mm_mul_ps(tmp1, tmp1); \\\n dest = _mm_add_ps(dest, tmp1); \nnamespace kgraph {\nfloat float_l2sqr_sse2 (float const *t1, float const *t2, unsigned dim) {\n\t/*\t\n\t//cos dostance\n\tfloat dis=0;\n\tfloat x_ = 0;\n\tfloat y_ = 0; \n\tfor(unsigned i=0;i\nvoid print_128 (__m128i v) {\n static unsigned constexpr L = 16 / sizeof(T);\n T unpack[L] __attribute__ ((aligned (16)));\n _mm_store_si128((__m128i *)unpack, v);\n cout << '(' << int(unpack[0]);\n for (unsigned i = 1; i < L; ++i) {\n cout << ',' << int(unpack[i]);\n }\n cout << ')';\n}\n*/\n\n#define SSE_L2SQR_BYTE(addr1, addr2, sum, z) \\\n do { \\\n const __m128i o = _mm_load_si128((__m128i const *)(addr1));\\\n const __m128i p = _mm_load_si128((__m128i const *)(addr2));\\\n __m128i o1 = _mm_unpackhi_epi8(o,z); \\\n __m128i p1 = _mm_unpackhi_epi8(p,z); \\\n __m128i d = _mm_sub_epi16(o1, p1); \\\n sum = _mm_add_epi32(sum, _mm_madd_epi16(d, d)); \\\n o1 = _mm_unpacklo_epi8(o,z); \\\n p1 = _mm_unpacklo_epi8(p,z); \\\n d = _mm_sub_epi16(o1, p1); \\\n sum = _mm_add_epi32(sum, _mm_madd_epi16(d, d)); \\\n } while (false)\nnamespace kgraph {\nfloat uint8_l2sqr_sse2 (uint8_t const *t1, uint8_t const *t2, unsigned dim) {\n\tcout<<\"3\\n\"; \n\tunsigned D = (dim + 0xFU) & ~0xFU; // actual dimension used in calculation, 0-padded\n unsigned DR = D % 64; // process 32 dims per iteration\n unsigned DD = D - DR;\n const uint8_t *l = t1;\n const uint8_t *r = t2;\n const uint8_t *e_l = l + DD;\n const uint8_t *e_r = r + DD;\n int32_t unpack[4] __attribute__ ((aligned (16))) = {0, 0, 0, 0};\n __m128i sum = _mm_load_si128((__m128i *)unpack);\n const __m128i z = sum;\n switch (DR) {\n case 48:\n SSE_L2SQR_BYTE(e_l+32, e_r+32, sum, z);\n case 32:\n SSE_L2SQR_BYTE(e_l+16, e_r+16, sum, z);\n case 16:\n SSE_L2SQR_BYTE(e_l, e_r, sum, z);\n }\n for (unsigned i = 0; i < DD; i += 64, l += 64, r += 64) {\n SSE_L2SQR_BYTE(l, r, sum, z);\n SSE_L2SQR_BYTE(l + 16, r + 16, sum, z);\n SSE_L2SQR_BYTE(l + 32, r + 32, sum, z);\n SSE_L2SQR_BYTE(l + 48, r + 48, sum, z);\n }\n _mm_store_si128((__m128i *)unpack, sum);\n int32_t ret = unpack[0] + unpack[1] + unpack[2] + unpack[3];\n return float(ret);//sqrt(ret);\n}\n}\n#endif\n#endif\n" }, { "path": "algorithms/NMSLIB/README.md", "content": "NonMetricSpaceLib\n======================================================\n\nThis is a modified version of NonMetricSpaceLib, which contains the implementation of HNSW, SW, NAPP and VP-Tree. The original version can be found form [here](https://github.com/searchivarius/nmslib).\n\nMain differences:\n\nWe disabled SIMD and multi-threading techniques. We also replace the compiler option -Ofast to -O3.\n\n##Prerequisites:\n\nA modern compiler that supports C++11: G++ 4.7, Intel compiler 14, Clang 3.4.\n- Boost (dev version).\n- CMake (version 2.8 or over is needed)\n- [Sample data](https://github.com/DBWangGroupUNSW/nns_benchmark/tree/master/data) (e.g., audio) is downloaded, including data points, query point. Note that the input format is .txt and the ground truth results can be generated before evaluating.\n\n##Compile\n\nTo compile, go to the directory code and type:\n\ncmake .\nmake \n\n##Build and Search\n\nrun_HNSW.sh, run_SW.sh, run_NAPP.sh, run_vptree.sh \n\ntype run_HNSW.sh, run_SW.sh, run_NAPP.sh, and run_vptree.sh to get the index and search results of Hierarchical Navigable Small World, Small World, NAAPP and VP-Tree. Note that vptree don't support to save the index and HNSW can't save the non-optimized index(skip_optimized_index=1).\n\nThe search performance (time and recall) results are kept in the NonMetricSpaceLib/results directory.\n\n" }, { "path": "algorithms/NMSLIB/code/CMakeFiles/3.2.3/CMakeCCompiler.cmake", "content": "set(CMAKE_C_COMPILER \"/opt/rh/devtoolset-2/root/usr/bin/cc\")\nset(CMAKE_C_COMPILER_ARG1 \"\")\nset(CMAKE_C_COMPILER_ID \"GNU\")\nset(CMAKE_C_COMPILER_VERSION \"4.8.2\")\nset(CMAKE_C_COMPILE_FEATURES \"c_function_prototypes;c_restrict;c_variadic_macros;c_static_assert\")\nset(CMAKE_C90_COMPILE_FEATURES \"c_function_prototypes\")\nset(CMAKE_C99_COMPILE_FEATURES \"c_restrict;c_variadic_macros\")\nset(CMAKE_C11_COMPILE_FEATURES \"c_static_assert\")\n\nset(CMAKE_C_PLATFORM_ID \"Linux\")\nset(CMAKE_C_SIMULATE_ID \"\")\nset(CMAKE_C_SIMULATE_VERSION \"\")\n\nset(CMAKE_AR \"/opt/rh/devtoolset-2/root/usr/bin/ar\")\nset(CMAKE_RANLIB 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COMPILER_VERSION_PATCH HEX(__CODEGEARC_VERSION__ & 0xFFFF)\n\n#elif defined(__BORLANDC__)\n# define COMPILER_ID \"Borland\"\n /* __BORLANDC__ = 0xVRR */\n# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8)\n# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF)\n\n#elif defined(__WATCOMC__) && __WATCOMC__ < 1200\n# define COMPILER_ID \"Watcom\"\n /* __WATCOMC__ = VVRR */\n# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100)\n# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10)\n# if (__WATCOMC__ % 10) > 0\n# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10)\n# endif\n\n#elif defined(__WATCOMC__)\n# define COMPILER_ID \"OpenWatcom\"\n /* __WATCOMC__ = VVRP + 1100 */\n# define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100)\n# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10)\n# if (__WATCOMC__ % 10) > 0\n# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10)\n# endif\n\n#elif defined(__SUNPRO_C)\n# define COMPILER_ID \"SunPro\"\n# if __SUNPRO_C >= 0x5100\n /* __SUNPRO_C = 0xVRRP */\n# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_C>>12)\n# define COMPILER_VERSION_MINOR HEX(__SUNPRO_C>>4 & 0xFF)\n# define COMPILER_VERSION_PATCH HEX(__SUNPRO_C & 0xF)\n# else\n /* __SUNPRO_CC = 0xVRP */\n# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_C>>8)\n# define COMPILER_VERSION_MINOR HEX(__SUNPRO_C>>4 & 0xF)\n# define COMPILER_VERSION_PATCH HEX(__SUNPRO_C & 0xF)\n# endif\n\n#elif defined(__HP_cc)\n# define COMPILER_ID \"HP\"\n /* __HP_cc = VVRRPP */\n# define COMPILER_VERSION_MAJOR DEC(__HP_cc/10000)\n# define COMPILER_VERSION_MINOR DEC(__HP_cc/100 % 100)\n# define COMPILER_VERSION_PATCH DEC(__HP_cc % 100)\n\n#elif defined(__DECC)\n# define COMPILER_ID \"Compaq\"\n /* __DECC_VER = VVRRTPPPP */\n# define COMPILER_VERSION_MAJOR DEC(__DECC_VER/10000000)\n# define COMPILER_VERSION_MINOR DEC(__DECC_VER/100000 % 100)\n# define COMPILER_VERSION_PATCH DEC(__DECC_VER % 10000)\n\n#elif defined(__IBMC__) && defined(__COMPILER_VER__)\n# define COMPILER_ID \"zOS\"\n /* __IBMC__ = VRP */\n# define COMPILER_VERSION_MAJOR DEC(__IBMC__/100)\n# define COMPILER_VERSION_MINOR DEC(__IBMC__/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(__IBMC__ % 10)\n\n#elif defined(__IBMC__) && !defined(__COMPILER_VER__) && __IBMC__ >= 800\n# define COMPILER_ID \"XL\"\n /* __IBMC__ = VRP */\n# define COMPILER_VERSION_MAJOR DEC(__IBMC__/100)\n# define COMPILER_VERSION_MINOR DEC(__IBMC__/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(__IBMC__ % 10)\n\n#elif defined(__IBMC__) && !defined(__COMPILER_VER__) && __IBMC__ < 800\n# define COMPILER_ID \"VisualAge\"\n /* __IBMC__ = VRP */\n# define COMPILER_VERSION_MAJOR DEC(__IBMC__/100)\n# define COMPILER_VERSION_MINOR DEC(__IBMC__/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(__IBMC__ % 10)\n\n#elif defined(__PGI)\n# define COMPILER_ID \"PGI\"\n# define COMPILER_VERSION_MAJOR DEC(__PGIC__)\n# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__)\n# if defined(__PGIC_PATCHLEVEL__)\n# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__)\n# endif\n\n#elif defined(_CRAYC)\n# define COMPILER_ID \"Cray\"\n# define COMPILER_VERSION_MAJOR DEC(_RELEASE)\n# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR)\n\n#elif defined(__TI_COMPILER_VERSION__)\n# define COMPILER_ID \"TI\"\n /* __TI_COMPILER_VERSION__ = VVVRRRPPP */\n# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000)\n# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000)\n# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000)\n\n#elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version)\n# define COMPILER_ID \"Fujitsu\"\n\n#elif defined(__TINYC__)\n# define COMPILER_ID \"TinyCC\"\n\n#elif defined(__SCO_VERSION__)\n# define COMPILER_ID \"SCO\"\n\n#elif defined(__clang__) && defined(__apple_build_version__)\n# define COMPILER_ID \"AppleClang\"\n# if defined(_MSC_VER)\n# define SIMULATE_ID \"MSVC\"\n# endif\n# define COMPILER_VERSION_MAJOR DEC(__clang_major__)\n# define COMPILER_VERSION_MINOR DEC(__clang_minor__)\n# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)\n# if defined(_MSC_VER)\n /* _MSC_VER = VVRR */\n# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)\n# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)\n# endif\n# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__)\n\n#elif defined(__clang__)\n# define COMPILER_ID \"Clang\"\n# if defined(_MSC_VER)\n# define SIMULATE_ID \"MSVC\"\n# endif\n# define COMPILER_VERSION_MAJOR DEC(__clang_major__)\n# define COMPILER_VERSION_MINOR DEC(__clang_minor__)\n# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)\n# if defined(_MSC_VER)\n /* _MSC_VER = VVRR */\n# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)\n# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)\n# endif\n\n#elif defined(__GNUC__)\n# define COMPILER_ID \"GNU\"\n# define COMPILER_VERSION_MAJOR DEC(__GNUC__)\n# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__)\n# if defined(__GNUC_PATCHLEVEL__)\n# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)\n# endif\n\n#elif defined(_MSC_VER)\n# define COMPILER_ID \"MSVC\"\n /* _MSC_VER = VVRR */\n# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100)\n# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100)\n# if defined(_MSC_FULL_VER)\n# if _MSC_VER >= 1400\n /* _MSC_FULL_VER = VVRRPPPPP */\n# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000)\n# else\n /* _MSC_FULL_VER = VVRRPPPP */\n# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000)\n# endif\n# endif\n# if defined(_MSC_BUILD)\n# define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD)\n# endif\n\n#elif defined(__VISUALDSPVERSION__) || defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__)\n# define COMPILER_ID \"ADSP\"\n#if defined(__VISUALDSPVERSION__)\n /* __VISUALDSPVERSION__ = 0xVVRRPP00 */\n# define COMPILER_VERSION_MAJOR HEX(__VISUALDSPVERSION__>>24)\n# define COMPILER_VERSION_MINOR HEX(__VISUALDSPVERSION__>>16 & 0xFF)\n# define COMPILER_VERSION_PATCH HEX(__VISUALDSPVERSION__>>8 & 0xFF)\n#endif\n\n#elif defined(__IAR_SYSTEMS_ICC__ ) || defined(__IAR_SYSTEMS_ICC)\n# define COMPILER_ID \"IAR\"\n\n#elif defined(SDCC)\n# define COMPILER_ID \"SDCC\"\n /* SDCC = VRP */\n# define COMPILER_VERSION_MAJOR DEC(SDCC/100)\n# define COMPILER_VERSION_MINOR DEC(SDCC/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(SDCC % 10)\n\n#elif defined(_SGI_COMPILER_VERSION) || defined(_COMPILER_VERSION)\n# define COMPILER_ID \"MIPSpro\"\n# if defined(_SGI_COMPILER_VERSION)\n /* _SGI_COMPILER_VERSION = VRP */\n# define COMPILER_VERSION_MAJOR DEC(_SGI_COMPILER_VERSION/100)\n# define COMPILER_VERSION_MINOR DEC(_SGI_COMPILER_VERSION/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(_SGI_COMPILER_VERSION % 10)\n# else\n /* _COMPILER_VERSION = VRP */\n# define COMPILER_VERSION_MAJOR DEC(_COMPILER_VERSION/100)\n# define COMPILER_VERSION_MINOR DEC(_COMPILER_VERSION/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(_COMPILER_VERSION % 10)\n# endif\n\n\n/* These compilers are either not known or too old to define an\n identification macro. Try to identify the platform and guess that\n it is the native compiler. */\n#elif defined(__sgi)\n# define COMPILER_ID \"MIPSpro\"\n\n#elif defined(__hpux) || defined(__hpua)\n# define COMPILER_ID \"HP\"\n\n#else /* unknown compiler */\n# define COMPILER_ID \"\"\n#endif\n\n/* Construct the string literal in pieces to prevent the source from\n getting matched. Store it in a pointer rather than an array\n because some compilers will just produce instructions to fill the\n array rather than assigning a pointer to a static array. */\nchar const* info_compiler = \"INFO\" \":\" \"compiler[\" COMPILER_ID \"]\";\n#ifdef SIMULATE_ID\nchar const* info_simulate = \"INFO\" \":\" \"simulate[\" SIMULATE_ID \"]\";\n#endif\n\n#ifdef __QNXNTO__\nchar const* qnxnto = \"INFO\" \":\" \"qnxnto[]\";\n#endif\n\n/* Identify known platforms by name. */\n#if defined(__linux) || defined(__linux__) || defined(linux)\n# define PLATFORM_ID \"Linux\"\n\n#elif defined(__CYGWIN__)\n# define PLATFORM_ID \"Cygwin\"\n\n#elif defined(__MINGW32__)\n# define PLATFORM_ID \"MinGW\"\n\n#elif defined(__APPLE__)\n# define PLATFORM_ID \"Darwin\"\n\n#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32)\n# define PLATFORM_ID \"Windows\"\n\n#elif defined(__FreeBSD__) || defined(__FreeBSD)\n# define PLATFORM_ID \"FreeBSD\"\n\n#elif defined(__NetBSD__) || defined(__NetBSD)\n# define PLATFORM_ID \"NetBSD\"\n\n#elif defined(__OpenBSD__) || defined(__OPENBSD)\n# define PLATFORM_ID \"OpenBSD\"\n\n#elif defined(__sun) || defined(sun)\n# define PLATFORM_ID \"SunOS\"\n\n#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__)\n# define PLATFORM_ID \"AIX\"\n\n#elif defined(__sgi) || defined(__sgi__) || defined(_SGI)\n# define PLATFORM_ID \"IRIX\"\n\n#elif defined(__hpux) || defined(__hpux__)\n# define PLATFORM_ID \"HP-UX\"\n\n#elif defined(__HAIKU__)\n# define PLATFORM_ID \"Haiku\"\n\n#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS)\n# define PLATFORM_ID \"BeOS\"\n\n#elif defined(__QNX__) || defined(__QNXNTO__)\n# define PLATFORM_ID \"QNX\"\n\n#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__)\n# define PLATFORM_ID \"Tru64\"\n\n#elif defined(__riscos) || defined(__riscos__)\n# define PLATFORM_ID \"RISCos\"\n\n#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__)\n# define PLATFORM_ID \"SINIX\"\n\n#elif defined(__UNIX_SV__)\n# define PLATFORM_ID \"UNIX_SV\"\n\n#elif defined(__bsdos__)\n# define PLATFORM_ID \"BSDOS\"\n\n#elif defined(_MPRAS) || defined(MPRAS)\n# define PLATFORM_ID \"MP-RAS\"\n\n#elif defined(__osf) || defined(__osf__)\n# define PLATFORM_ID \"OSF1\"\n\n#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv)\n# define PLATFORM_ID \"SCO_SV\"\n\n#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX)\n# define PLATFORM_ID \"ULTRIX\"\n\n#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX)\n# define PLATFORM_ID \"Xenix\"\n\n#elif defined(__WATCOMC__)\n# if defined(__LINUX__)\n# define PLATFORM_ID \"Linux\"\n\n# elif defined(__DOS__)\n# define PLATFORM_ID \"DOS\"\n\n# elif defined(__OS2__)\n# define PLATFORM_ID \"OS2\"\n\n# elif defined(__WINDOWS__)\n# define PLATFORM_ID \"Windows3x\"\n\n# else /* unknown platform */\n# define PLATFORM_ID \"\"\n# endif\n\n#else /* unknown platform */\n# define PLATFORM_ID \"\"\n\n#endif\n\n/* For windows compilers MSVC and Intel we can determine\n the architecture of the compiler being used. This is because\n the compilers do not have flags that can change the architecture,\n but rather depend on which compiler is being used\n*/\n#if defined(_WIN32) && defined(_MSC_VER)\n# if defined(_M_IA64)\n# define ARCHITECTURE_ID \"IA64\"\n\n# elif defined(_M_X64) || defined(_M_AMD64)\n# define ARCHITECTURE_ID \"x64\"\n\n# elif defined(_M_IX86)\n# define ARCHITECTURE_ID \"X86\"\n\n# elif defined(_M_ARM)\n# define ARCHITECTURE_ID \"ARM\"\n\n# elif defined(_M_MIPS)\n# define ARCHITECTURE_ID \"MIPS\"\n\n# elif defined(_M_SH)\n# define ARCHITECTURE_ID \"SHx\"\n\n# else /* unknown architecture */\n# define ARCHITECTURE_ID \"\"\n# endif\n\n#elif defined(__WATCOMC__)\n# if defined(_M_I86)\n# define ARCHITECTURE_ID \"I86\"\n\n# elif defined(_M_IX86)\n# define ARCHITECTURE_ID \"X86\"\n\n# else /* unknown architecture */\n# define ARCHITECTURE_ID \"\"\n# endif\n\n#else\n# define ARCHITECTURE_ID \"\"\n#endif\n\n/* Convert integer to decimal digit literals. */\n#define DEC(n) \\\n ('0' + (((n) / 10000000)%10)), \\\n ('0' + (((n) / 1000000)%10)), \\\n ('0' + (((n) / 100000)%10)), \\\n ('0' + (((n) / 10000)%10)), \\\n ('0' + (((n) / 1000)%10)), \\\n ('0' + (((n) / 100)%10)), \\\n ('0' + (((n) / 10)%10)), \\\n ('0' + ((n) % 10))\n\n/* Convert integer to hex digit literals. */\n#define HEX(n) \\\n ('0' + ((n)>>28 & 0xF)), \\\n ('0' + ((n)>>24 & 0xF)), \\\n ('0' + ((n)>>20 & 0xF)), \\\n ('0' + ((n)>>16 & 0xF)), \\\n ('0' + ((n)>>12 & 0xF)), \\\n ('0' + ((n)>>8 & 0xF)), \\\n ('0' + ((n)>>4 & 0xF)), \\\n ('0' + ((n) & 0xF))\n\n/* Construct a string literal encoding the version number components. */\n#ifdef COMPILER_VERSION_MAJOR\nchar const info_version[] = {\n 'I', 'N', 'F', 'O', ':',\n 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[',\n COMPILER_VERSION_MAJOR,\n# ifdef COMPILER_VERSION_MINOR\n '.', COMPILER_VERSION_MINOR,\n# ifdef COMPILER_VERSION_PATCH\n '.', COMPILER_VERSION_PATCH,\n# ifdef COMPILER_VERSION_TWEAK\n '.', COMPILER_VERSION_TWEAK,\n# endif\n# endif\n# endif\n ']','\\0'};\n#endif\n\n/* Construct a string literal encoding the version number components. */\n#ifdef SIMULATE_VERSION_MAJOR\nchar const info_simulate_version[] = {\n 'I', 'N', 'F', 'O', ':',\n 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[',\n SIMULATE_VERSION_MAJOR,\n# ifdef SIMULATE_VERSION_MINOR\n '.', SIMULATE_VERSION_MINOR,\n# ifdef SIMULATE_VERSION_PATCH\n '.', SIMULATE_VERSION_PATCH,\n# ifdef SIMULATE_VERSION_TWEAK\n '.', SIMULATE_VERSION_TWEAK,\n# endif\n# endif\n# endif\n ']','\\0'};\n#endif\n\n/* Construct the string literal in pieces to prevent the source from\n getting matched. Store it in a pointer rather than an array\n because some compilers will just produce instructions to fill the\n array rather than assigning a pointer to a static array. */\nchar const* info_platform = \"INFO\" \":\" \"platform[\" PLATFORM_ID \"]\";\nchar const* info_arch = \"INFO\" \":\" \"arch[\" ARCHITECTURE_ID \"]\";\n\n\n\n\n/*--------------------------------------------------------------------------*/\n\n#ifdef ID_VOID_MAIN\nvoid main() {}\n#else\nint main(int argc, char* argv[])\n{\n int require = 0;\n require += info_compiler[argc];\n require += info_platform[argc];\n require += info_arch[argc];\n#ifdef COMPILER_VERSION_MAJOR\n require += info_version[argc];\n#endif\n#ifdef SIMULATE_ID\n require += info_simulate[argc];\n#endif\n#ifdef SIMULATE_VERSION_MAJOR\n require += info_simulate_version[argc];\n#endif\n (void)argv;\n return require;\n}\n#endif\n" }, { "path": "algorithms/NMSLIB/code/CMakeFiles/3.2.3/CompilerIdCXX/CMakeCXXCompilerId.cpp", "content": "/* This source file must have a .cpp extension so that all C++ compilers\n recognize the extension without flags. Borland does not know .cxx for\n example. */\n#ifndef __cplusplus\n# error \"A C compiler has been selected for C++.\"\n#endif\n\n\n/* Version number components: V=Version, R=Revision, P=Patch\n Version date components: YYYY=Year, MM=Month, DD=Day */\n\n#if defined(__COMO__)\n# define COMPILER_ID \"Comeau\"\n /* __COMO_VERSION__ = VRR */\n# define COMPILER_VERSION_MAJOR DEC(__COMO_VERSION__ / 100)\n# define COMPILER_VERSION_MINOR DEC(__COMO_VERSION__ % 100)\n\n#elif defined(__INTEL_COMPILER) || defined(__ICC)\n# define COMPILER_ID \"Intel\"\n# if defined(_MSC_VER)\n# define SIMULATE_ID \"MSVC\"\n# endif\n /* __INTEL_COMPILER = VRP */\n# define COMPILER_VERSION_MAJOR DEC(__INTEL_COMPILER/100)\n# define COMPILER_VERSION_MINOR DEC(__INTEL_COMPILER/10 % 10)\n# if defined(__INTEL_COMPILER_UPDATE)\n# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER_UPDATE)\n# else\n# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER % 10)\n# endif\n# if defined(__INTEL_COMPILER_BUILD_DATE)\n /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */\n# define COMPILER_VERSION_TWEAK DEC(__INTEL_COMPILER_BUILD_DATE)\n# endif\n# if defined(_MSC_VER)\n /* _MSC_VER = VVRR */\n# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)\n# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)\n# endif\n\n#elif defined(__PATHCC__)\n# define COMPILER_ID \"PathScale\"\n# define COMPILER_VERSION_MAJOR DEC(__PATHCC__)\n# define COMPILER_VERSION_MINOR DEC(__PATHCC_MINOR__)\n# if defined(__PATHCC_PATCHLEVEL__)\n# define COMPILER_VERSION_PATCH DEC(__PATHCC_PATCHLEVEL__)\n# endif\n\n#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__)\n# define COMPILER_ID \"Embarcadero\"\n# define COMPILER_VERSION_MAJOR HEX(__CODEGEARC_VERSION__>>24 & 0x00FF)\n# define COMPILER_VERSION_MINOR HEX(__CODEGEARC_VERSION__>>16 & 0x00FF)\n# define COMPILER_VERSION_PATCH HEX(__CODEGEARC_VERSION__ & 0xFFFF)\n\n#elif defined(__BORLANDC__)\n# define COMPILER_ID \"Borland\"\n /* __BORLANDC__ = 0xVRR */\n# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8)\n# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF)\n\n#elif defined(__WATCOMC__) && __WATCOMC__ < 1200\n# define COMPILER_ID \"Watcom\"\n /* __WATCOMC__ = VVRR */\n# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100)\n# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10)\n# if (__WATCOMC__ % 10) > 0\n# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10)\n# endif\n\n#elif defined(__WATCOMC__)\n# define COMPILER_ID \"OpenWatcom\"\n /* __WATCOMC__ = VVRP + 1100 */\n# define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100)\n# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10)\n# if (__WATCOMC__ % 10) > 0\n# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10)\n# endif\n\n#elif defined(__SUNPRO_CC)\n# define COMPILER_ID \"SunPro\"\n# if __SUNPRO_CC >= 0x5100\n /* __SUNPRO_CC = 0xVRRP */\n# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>12)\n# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xFF)\n# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF)\n# else\n /* __SUNPRO_CC = 0xVRP */\n# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>8)\n# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xF)\n# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF)\n# endif\n\n#elif defined(__HP_aCC)\n# define COMPILER_ID \"HP\"\n /* __HP_aCC = VVRRPP */\n# define COMPILER_VERSION_MAJOR DEC(__HP_aCC/10000)\n# define COMPILER_VERSION_MINOR DEC(__HP_aCC/100 % 100)\n# define COMPILER_VERSION_PATCH DEC(__HP_aCC % 100)\n\n#elif defined(__DECCXX)\n# define COMPILER_ID \"Compaq\"\n /* __DECCXX_VER = VVRRTPPPP */\n# define COMPILER_VERSION_MAJOR DEC(__DECCXX_VER/10000000)\n# define COMPILER_VERSION_MINOR DEC(__DECCXX_VER/100000 % 100)\n# define COMPILER_VERSION_PATCH DEC(__DECCXX_VER % 10000)\n\n#elif defined(__IBMCPP__) && defined(__COMPILER_VER__)\n# define COMPILER_ID \"zOS\"\n /* __IBMCPP__ = VRP */\n# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100)\n# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10)\n\n#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ >= 800\n# define COMPILER_ID \"XL\"\n /* __IBMCPP__ = VRP */\n# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100)\n# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10)\n\n#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ < 800\n# define COMPILER_ID \"VisualAge\"\n /* __IBMCPP__ = VRP */\n# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100)\n# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10)\n# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10)\n\n#elif defined(__PGI)\n# define COMPILER_ID \"PGI\"\n# define COMPILER_VERSION_MAJOR DEC(__PGIC__)\n# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__)\n# if defined(__PGIC_PATCHLEVEL__)\n# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__)\n# endif\n\n#elif defined(_CRAYC)\n# define COMPILER_ID \"Cray\"\n# define COMPILER_VERSION_MAJOR DEC(_RELEASE)\n# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR)\n\n#elif defined(__TI_COMPILER_VERSION__)\n# define COMPILER_ID \"TI\"\n /* __TI_COMPILER_VERSION__ = VVVRRRPPP */\n# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000)\n# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000)\n# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000)\n\n#elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version)\n# define COMPILER_ID \"Fujitsu\"\n\n#elif defined(__SCO_VERSION__)\n# define COMPILER_ID \"SCO\"\n\n#elif defined(__clang__) && defined(__apple_build_version__)\n# define COMPILER_ID \"AppleClang\"\n# if defined(_MSC_VER)\n# define SIMULATE_ID \"MSVC\"\n# endif\n# define COMPILER_VERSION_MAJOR DEC(__clang_major__)\n# define COMPILER_VERSION_MINOR DEC(__clang_minor__)\n# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)\n# if defined(_MSC_VER)\n /* _MSC_VER = VVRR */\n# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)\n# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)\n# endif\n# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__)\n\n#elif defined(__clang__)\n# define COMPILER_ID \"Clang\"\n# if defined(_MSC_VER)\n# define SIMULATE_ID \"MSVC\"\n# endif\n# define COMPILER_VERSION_MAJOR DEC(__clang_major__)\n# define COMPILER_VERSION_MINOR DEC(__clang_minor__)\n# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)\n# if defined(_MSC_VER)\n /* _MSC_VER = VVRR */\n# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)\n# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)\n# endif\n\n#elif defined(__GNUC__)\n# define COMPILER_ID \"GNU\"\n# define COMPILER_VERSION_MAJOR DEC(__GNUC__)\n# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__)\n# if defined(__GNUC_PATCHLEVEL__)\n# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)\n# endif\n\n#elif defined(_MSC_VER)\n# define COMPILER_ID \"MSVC\"\n /* _MSC_VER = VVRR */\n# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100)\n# define 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future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec63957620.dir/build.make CMakeFiles/cmTryCompileExec63957620.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec63957620.dir/flags.make' has modification time 0.0035 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec63957620.dir/CheckSymbolExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec63957620.dir/CheckSymbolExists.c.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CheckSymbolExists.c\nLinking C executable cmTryCompileExec63957620\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec63957620.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec63957620.dir/CheckSymbolExists.c.o -o cmTryCompileExec63957620 -rdynamic \nCMakeFiles/cmTryCompileExec63957620.dir/CheckSymbolExists.c.o: In function `main':\nCheckSymbolExists.c:(.text+0x16): undefined reference to `pthread_create'\ncollect2: error: ld returned 1 exit status\ngmake[1]: *** [cmTryCompileExec63957620] Error 1\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: *** [cmTryCompileExec63957620/fast] Error 2\n\nFile /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CheckSymbolExists.c:\n/* */\n#include \n\nint main(int argc, char** argv)\n{\n (void)argv;\n#ifndef pthread_create\n return ((int*)(&pthread_create))[argc];\n#else\n (void)argc;\n return 0;\n#endif\n}\n\nDetermining if the function pthread_create exists in the pthreads failed with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec3176298737/fast\"\ngmake: Warning: File `Makefile' has modification time 0.01 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec3176298737.dir/build.make CMakeFiles/cmTryCompileExec3176298737.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec3176298737.dir/flags.make' has modification time 0.0053 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec3176298737.dir/CheckFunctionExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create -o CMakeFiles/cmTryCompileExec3176298737.dir/CheckFunctionExists.c.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CheckFunctionExists.c\nLinking C executable cmTryCompileExec3176298737\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec3176298737.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTryCompileExec3176298737.dir/CheckFunctionExists.c.o -o cmTryCompileExec3176298737 -rdynamic -lpthreads \n/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/ld: cannot find -lpthreads\ncollect2: error: ld returned 1 exit status\ngmake[1]: *** [cmTryCompileExec3176298737] Error 1\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: *** [cmTryCompileExec3176298737/fast] Error 2\n\n\nDetermining if the pthread_create exist failed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec1167864682/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec1167864682.dir/build.make CMakeFiles/cmTryCompileExec1167864682.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec1167864682.dir/CheckSymbolExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec1167864682.dir/CheckSymbolExists.c.o -c /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp/CheckSymbolExists.c\nLinking C executable cmTryCompileExec1167864682\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec1167864682.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec1167864682.dir/CheckSymbolExists.c.o -o cmTryCompileExec1167864682 -rdynamic \nCMakeFiles/cmTryCompileExec1167864682.dir/CheckSymbolExists.c.o: In function `main':\nCheckSymbolExists.c:(.text+0x16): undefined reference to `pthread_create'\ncollect2: error: ld returned 1 exit status\ngmake[1]: *** [cmTryCompileExec1167864682] Error 1\ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\ngmake: *** [cmTryCompileExec1167864682/fast] Error 2\n\nFile /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp/CheckSymbolExists.c:\n/* */\n#include \n\nint main(int argc, char** argv)\n{\n (void)argv;\n#ifndef pthread_create\n return ((int*)(&pthread_create))[argc];\n#else\n (void)argc;\n return 0;\n#endif\n}\n\nDetermining if the function pthread_create exists in the pthreads failed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec1614964294/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec1614964294.dir/build.make CMakeFiles/cmTryCompileExec1614964294.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec1614964294.dir/CheckFunctionExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create -o CMakeFiles/cmTryCompileExec1614964294.dir/CheckFunctionExists.c.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CheckFunctionExists.c\nLinking C executable cmTryCompileExec1614964294\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec1614964294.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTryCompileExec1614964294.dir/CheckFunctionExists.c.o -o cmTryCompileExec1614964294 -rdynamic -lpthreads \n/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/ld: cannot find -lpthreads\ncollect2: error: ld returned 1 exit status\ngmake[1]: *** [cmTryCompileExec1614964294] Error 1\ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\ngmake: *** [cmTryCompileExec1614964294/fast] Error 2\n\n\nDetermining if the pthread_create exist failed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec1791637194/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec1791637194.dir/build.make CMakeFiles/cmTryCompileExec1791637194.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec1791637194.dir/CheckSymbolExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec1791637194.dir/CheckSymbolExists.c.o -c /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp/CheckSymbolExists.c\nLinking C executable cmTryCompileExec1791637194\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec1791637194.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec1791637194.dir/CheckSymbolExists.c.o -o cmTryCompileExec1791637194 -rdynamic \nCMakeFiles/cmTryCompileExec1791637194.dir/CheckSymbolExists.c.o: In function `main':\nCheckSymbolExists.c:(.text+0x16): undefined reference to `pthread_create'\ncollect2: error: ld returned 1 exit status\ngmake[1]: *** [cmTryCompileExec1791637194] Error 1\ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\ngmake: *** [cmTryCompileExec1791637194/fast] Error 2\n\nFile /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp/CheckSymbolExists.c:\n/* */\n#include \n\nint main(int argc, char** argv)\n{\n (void)argv;\n#ifndef pthread_create\n return ((int*)(&pthread_create))[argc];\n#else\n (void)argc;\n return 0;\n#endif\n}\n\nDetermining if the function pthread_create exists in the pthreads failed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec3258096989/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec3258096989.dir/build.make CMakeFiles/cmTryCompileExec3258096989.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec3258096989.dir/CheckFunctionExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create -o CMakeFiles/cmTryCompileExec3258096989.dir/CheckFunctionExists.c.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CheckFunctionExists.c\nLinking C executable cmTryCompileExec3258096989\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec3258096989.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTryCompileExec3258096989.dir/CheckFunctionExists.c.o -o cmTryCompileExec3258096989 -rdynamic -lpthreads \n/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/ld: cannot find -lpthreads\ncollect2: error: ld returned 1 exit status\ngmake[1]: *** [cmTryCompileExec3258096989] Error 1\ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\ngmake: *** [cmTryCompileExec3258096989/fast] Error 2\n\n\n" }, { "path": "algorithms/NMSLIB/code/CMakeFiles/CMakeOutput.log", "content": "The system is: Linux - 2.6.32-573.12.1.el6.x86_64 - x86_64\nCompiling the C compiler identification source file \"CMakeCCompilerId.c\" succeeded.\nCompiler: /opt/rh/devtoolset-2/root/usr/bin/cc \nBuild flags: \nId flags: \n\nThe output was:\n0\n\n\nCompilation of the C compiler identification source \"CMakeCCompilerId.c\" produced \"a.out\"\n\nThe C compiler identification is GNU, found in \"/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/3.2.3/CompilerIdC/a.out\"\n\nCompiling the CXX compiler identification source file \"CMakeCXXCompilerId.cpp\" succeeded.\nCompiler: /opt/rh/devtoolset-2/root/usr/bin/c++ \nBuild flags: \nId flags: \n\nThe output was:\n0\n\n\nCompilation of the CXX compiler identification source \"CMakeCXXCompilerId.cpp\" produced \"a.out\"\n\nThe CXX compiler identification is GNU, found in \"/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/3.2.3/CompilerIdCXX/a.out\"\n\nDetermining if the C compiler works passed with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec685482307/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec685482307.dir/build.make CMakeFiles/cmTryCompileExec685482307.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec685482307.dir/testCCompiler.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec685482307.dir/testCCompiler.c.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/testCCompiler.c\nLinking C executable cmTryCompileExec685482307\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec685482307.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec685482307.dir/testCCompiler.c.o -o cmTryCompileExec685482307 -rdynamic \ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\n\n\nDetecting C compiler ABI info compiled with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec317719069/fast\"\ngmake: Warning: File `Makefile' has modification time 0.0017 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec317719069.dir/build.make CMakeFiles/cmTryCompileExec317719069.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec317719069.dir/progress.make' has modification time 0.0065 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec317719069.dir/CMakeCCompilerABI.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec317719069.dir/CMakeCCompilerABI.c.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CMakeCCompilerABI.c\nLinking C executable cmTryCompileExec317719069\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec317719069.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc -v CMakeFiles/cmTryCompileExec317719069.dir/CMakeCCompilerABI.c.o -o cmTryCompileExec317719069 -rdynamic \nUsing built-in 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--with-mpc=/builddir/build/BUILD/gcc-4.8.2-20140120/obj-x86_64-redhat-linux/mpc-install --with-tune=generic --with-arch_32=i686 --build=x86_64-redhat-linux\nThread model: posix\ngcc version 4.8.2 20140120 (Red Hat 4.8.2-15) (GCC) \nCOMPILER_PATH=/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/\nLIBRARY_PATH=/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/../../../../lib64/:/lib/../lib64/:/usr/lib/../lib64/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/../../../:/lib/:/usr/lib/\nCOLLECT_GCC_OPTIONS='-v' '-o' 'cmTryCompileExec317719069' '-rdynamic' '-mtune=generic' '-march=x86-64'\n 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Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\n Feature record: C_FEATURE:1c_function_prototypes\n Feature record: C_FEATURE:1c_restrict\n Feature record: C_FEATURE:1c_static_assert\n Feature record: C_FEATURE:1c_variadic_macros\n\n\nDetecting C [-std=c99] compiler features compiled with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec3521544553/fast\"\ngmake: Warning: File `Makefile' has modification time 0.0034 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec3521544553.dir/build.make CMakeFiles/cmTryCompileExec3521544553.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec3521544553.dir/progress.make' has modification time 0.0079 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec3521544553.dir/feature_tests.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -std=c99 -o CMakeFiles/cmTryCompileExec3521544553.dir/feature_tests.c.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/feature_tests.c\nLinking C executable cmTryCompileExec3521544553\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec3521544553.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec3521544553.dir/feature_tests.c.o -o cmTryCompileExec3521544553 -rdynamic \ngmake[1]: warning: Clock skew detected. Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\n Feature record: C_FEATURE:1c_function_prototypes\n Feature record: C_FEATURE:1c_restrict\n Feature record: C_FEATURE:0c_static_assert\n Feature record: C_FEATURE:1c_variadic_macros\n\n\nDetecting C [-std=c90] compiler features compiled with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec3695376473/fast\"\ngmake: Warning: File `Makefile' has modification time 0.003 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec3695376473.dir/build.make CMakeFiles/cmTryCompileExec3695376473.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec3695376473.dir/progress.make' has modification time 0.0074 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec3695376473.dir/feature_tests.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -std=c90 -o CMakeFiles/cmTryCompileExec3695376473.dir/feature_tests.c.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/feature_tests.c\nLinking C executable cmTryCompileExec3695376473\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec3695376473.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec3695376473.dir/feature_tests.c.o -o cmTryCompileExec3695376473 -rdynamic \ngmake[1]: warning: Clock skew detected. Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\n Feature record: C_FEATURE:1c_function_prototypes\n Feature record: C_FEATURE:0c_restrict\n Feature record: C_FEATURE:0c_static_assert\n Feature record: C_FEATURE:0c_variadic_macros\nDetermining if the CXX compiler works passed with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec3770998589/fast\"\ngmake: Warning: File `Makefile' has modification time 0.0044 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec3770998589.dir/build.make CMakeFiles/cmTryCompileExec3770998589.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec3770998589.dir/progress.make' has modification time 0.0075 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding CXX object CMakeFiles/cmTryCompileExec3770998589.dir/testCXXCompiler.cxx.o\n/opt/rh/devtoolset-2/root/usr/bin/c++ -o CMakeFiles/cmTryCompileExec3770998589.dir/testCXXCompiler.cxx.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/testCXXCompiler.cxx\nLinking CXX executable cmTryCompileExec3770998589\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec3770998589.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/c++ CMakeFiles/cmTryCompileExec3770998589.dir/testCXXCompiler.cxx.o -o cmTryCompileExec3770998589 -rdynamic \ngmake[1]: warning: Clock skew detected. Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\nDetecting CXX compiler ABI info compiled with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec2289776478/fast\"\ngmake: Warning: File `Makefile' has modification time 0.0035 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec2289776478.dir/build.make CMakeFiles/cmTryCompileExec2289776478.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec2289776478.dir/progress.make' has modification time 0.0061 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding CXX object CMakeFiles/cmTryCompileExec2289776478.dir/CMakeCXXCompilerABI.cpp.o\n/opt/rh/devtoolset-2/root/usr/bin/c++ -o CMakeFiles/cmTryCompileExec2289776478.dir/CMakeCXXCompilerABI.cpp.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CMakeCXXCompilerABI.cpp\nLinking CXX executable cmTryCompileExec2289776478\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec2289776478.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/c++ -v CMakeFiles/cmTryCompileExec2289776478.dir/CMakeCXXCompilerABI.cpp.o -o cmTryCompileExec2289776478 -rdynamic \nUsing built-in specs.\nCOLLECT_GCC=/opt/rh/devtoolset-2/root/usr/bin/c++\nCOLLECT_LTO_WRAPPER=/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/lto-wrapper\nTarget: x86_64-redhat-linux\nConfigured with: ../configure --prefix=/opt/rh/devtoolset-2/root/usr --mandir=/opt/rh/devtoolset-2/root/usr/share/man --infodir=/opt/rh/devtoolset-2/root/usr/share/info --with-bugurl=http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared --enable-threads=posix --enable-checking=release --with-system-zlib --enable-__cxa_atexit --disable-libunwind-exceptions --enable-gnu-unique-object --enable-linker-build-id --enable-languages=c,c++,fortran,lto --enable-plugin --with-linker-hash-style=gnu --enable-initfini-array --disable-libgcj --with-isl=/builddir/build/BUILD/gcc-4.8.2-20140120/obj-x86_64-redhat-linux/isl-install --with-cloog=/builddir/build/BUILD/gcc-4.8.2-20140120/obj-x86_64-redhat-linux/cloog-install --with-mpc=/builddir/build/BUILD/gcc-4.8.2-20140120/obj-x86_64-redhat-linux/mpc-install --with-tune=generic --with-arch_32=i686 --build=x86_64-redhat-linux\nThread model: posix\ngcc version 4.8.2 20140120 (Red Hat 4.8.2-15) (GCC) \nCOMPILER_PATH=/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/\nLIBRARY_PATH=/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/../../../../lib64/:/lib/../lib64/:/usr/lib/../lib64/:/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/../../../:/lib/:/usr/lib/\nCOLLECT_GCC_OPTIONS='-v' '-o' 'cmTryCompileExec2289776478' '-rdynamic' '-shared-libgcc' '-mtune=generic' '-march=x86-64'\n /opt/rh/devtoolset-2/root/usr/libexec/gcc/x86_64-redhat-linux/4.8.2/collect2 --build-id --no-add-needed --eh-frame-hdr --hash-style=gnu -m elf_x86_64 -export-dynamic -dynamic-linker /lib64/ld-linux-x86-64.so.2 -o cmTryCompileExec2289776478 /usr/lib/../lib64/crt1.o /usr/lib/../lib64/crti.o /opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/crtbegin.o -L/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2 -L/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/../../../../lib64 -L/lib/../lib64 -L/usr/lib/../lib64 -L/opt/rh/devtoolset-2/root/usr/lib/gcc/x86_64-redhat-linux/4.8.2/../../.. 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Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\n\n\n Feature record: CXX_FEATURE:0cxx_aggregate_default_initializers\n Feature record: CXX_FEATURE:1cxx_alias_templates\n Feature record: CXX_FEATURE:1cxx_alignas\n Feature record: CXX_FEATURE:1cxx_alignof\n Feature record: CXX_FEATURE:1cxx_attributes\n Feature record: CXX_FEATURE:0cxx_attribute_deprecated\n Feature record: CXX_FEATURE:1cxx_auto_type\n Feature record: CXX_FEATURE:0cxx_binary_literals\n Feature record: CXX_FEATURE:1cxx_constexpr\n Feature record: CXX_FEATURE:0cxx_contextual_conversions\n Feature record: CXX_FEATURE:1cxx_decltype\n Feature record: CXX_FEATURE:0cxx_decltype_auto\n Feature record: CXX_FEATURE:1cxx_decltype_incomplete_return_types\n Feature record: CXX_FEATURE:1cxx_default_function_template_args\n Feature record: CXX_FEATURE:1cxx_defaulted_functions\n Feature record: CXX_FEATURE:1cxx_defaulted_move_initializers\n Feature record: CXX_FEATURE:1cxx_delegating_constructors\n Feature record: CXX_FEATURE:1cxx_deleted_functions\n Feature record: CXX_FEATURE:0cxx_digit_separators\n Feature record: CXX_FEATURE:1cxx_enum_forward_declarations\n Feature record: CXX_FEATURE:1cxx_explicit_conversions\n Feature record: CXX_FEATURE:1cxx_extended_friend_declarations\n Feature record: CXX_FEATURE:1cxx_extern_templates\n Feature record: CXX_FEATURE:1cxx_final\n Feature record: CXX_FEATURE:1cxx_func_identifier\n Feature record: CXX_FEATURE:1cxx_generalized_initializers\n Feature record: CXX_FEATURE:0cxx_generic_lambdas\n Feature record: CXX_FEATURE:1cxx_inheriting_constructors\n Feature record: CXX_FEATURE:1cxx_inline_namespaces\n Feature record: CXX_FEATURE:1cxx_lambdas\n Feature record: CXX_FEATURE:0cxx_lambda_init_captures\n Feature record: CXX_FEATURE:1cxx_local_type_template_args\n Feature record: CXX_FEATURE:1cxx_long_long_type\n Feature record: CXX_FEATURE:1cxx_noexcept\n Feature record: CXX_FEATURE:1cxx_nonstatic_member_init\n Feature record: CXX_FEATURE:1cxx_nullptr\n Feature record: CXX_FEATURE:1cxx_override\n Feature record: CXX_FEATURE:1cxx_range_for\n Feature record: CXX_FEATURE:1cxx_raw_string_literals\n Feature record: CXX_FEATURE:1cxx_reference_qualified_functions\n Feature record: CXX_FEATURE:0cxx_relaxed_constexpr\n Feature record: CXX_FEATURE:0cxx_return_type_deduction\n Feature record: CXX_FEATURE:1cxx_right_angle_brackets\n Feature record: CXX_FEATURE:1cxx_rvalue_references\n Feature record: CXX_FEATURE:1cxx_sizeof_member\n Feature record: CXX_FEATURE:1cxx_static_assert\n Feature record: CXX_FEATURE:1cxx_strong_enums\n Feature record: CXX_FEATURE:1cxx_template_template_parameters\n Feature record: CXX_FEATURE:1cxx_thread_local\n Feature record: CXX_FEATURE:1cxx_trailing_return_types\n Feature record: CXX_FEATURE:1cxx_unicode_literals\n Feature record: CXX_FEATURE:1cxx_uniform_initialization\n Feature record: CXX_FEATURE:1cxx_unrestricted_unions\n Feature record: CXX_FEATURE:1cxx_user_literals\n Feature record: CXX_FEATURE:0cxx_variable_templates\n Feature record: CXX_FEATURE:1cxx_variadic_macros\n Feature record: CXX_FEATURE:1cxx_variadic_templates\n\n\nDetecting CXX [-std=c++11] compiler features compiled with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec3107561267/fast\"\ngmake: Warning: File `Makefile' has modification time 0.0039 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec3107561267.dir/build.make CMakeFiles/cmTryCompileExec3107561267.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec3107561267.dir/progress.make' has modification time 0.0064 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding CXX object CMakeFiles/cmTryCompileExec3107561267.dir/feature_tests.cxx.o\n/opt/rh/devtoolset-2/root/usr/bin/c++ -std=c++11 -o CMakeFiles/cmTryCompileExec3107561267.dir/feature_tests.cxx.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/feature_tests.cxx\nLinking CXX executable cmTryCompileExec3107561267\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec3107561267.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/c++ CMakeFiles/cmTryCompileExec3107561267.dir/feature_tests.cxx.o -o cmTryCompileExec3107561267 -rdynamic \ngmake[1]: warning: Clock skew detected. Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\n Feature record: CXX_FEATURE:0cxx_aggregate_default_initializers\n Feature record: CXX_FEATURE:1cxx_alias_templates\n Feature record: CXX_FEATURE:1cxx_alignas\n Feature record: CXX_FEATURE:1cxx_alignof\n Feature record: CXX_FEATURE:1cxx_attributes\n Feature record: CXX_FEATURE:0cxx_attribute_deprecated\n Feature record: CXX_FEATURE:1cxx_auto_type\n Feature record: CXX_FEATURE:0cxx_binary_literals\n Feature record: CXX_FEATURE:1cxx_constexpr\n Feature record: CXX_FEATURE:0cxx_contextual_conversions\n Feature record: CXX_FEATURE:1cxx_decltype\n Feature record: CXX_FEATURE:0cxx_decltype_auto\n Feature record: CXX_FEATURE:1cxx_decltype_incomplete_return_types\n Feature record: CXX_FEATURE:1cxx_default_function_template_args\n Feature record: CXX_FEATURE:1cxx_defaulted_functions\n Feature record: CXX_FEATURE:1cxx_defaulted_move_initializers\n Feature record: CXX_FEATURE:1cxx_delegating_constructors\n Feature record: CXX_FEATURE:1cxx_deleted_functions\n Feature record: CXX_FEATURE:0cxx_digit_separators\n Feature record: CXX_FEATURE:1cxx_enum_forward_declarations\n Feature record: CXX_FEATURE:1cxx_explicit_conversions\n Feature record: CXX_FEATURE:1cxx_extended_friend_declarations\n Feature record: CXX_FEATURE:1cxx_extern_templates\n Feature record: CXX_FEATURE:1cxx_final\n Feature record: CXX_FEATURE:1cxx_func_identifier\n Feature record: CXX_FEATURE:1cxx_generalized_initializers\n Feature record: CXX_FEATURE:0cxx_generic_lambdas\n Feature record: CXX_FEATURE:1cxx_inheriting_constructors\n Feature record: CXX_FEATURE:1cxx_inline_namespaces\n Feature record: CXX_FEATURE:1cxx_lambdas\n Feature record: CXX_FEATURE:0cxx_lambda_init_captures\n Feature record: CXX_FEATURE:1cxx_local_type_template_args\n Feature record: CXX_FEATURE:1cxx_long_long_type\n Feature record: CXX_FEATURE:1cxx_noexcept\n Feature record: CXX_FEATURE:1cxx_nonstatic_member_init\n Feature record: CXX_FEATURE:1cxx_nullptr\n Feature record: CXX_FEATURE:1cxx_override\n Feature record: CXX_FEATURE:1cxx_range_for\n Feature record: CXX_FEATURE:1cxx_raw_string_literals\n Feature record: CXX_FEATURE:1cxx_reference_qualified_functions\n Feature record: CXX_FEATURE:0cxx_relaxed_constexpr\n Feature record: CXX_FEATURE:0cxx_return_type_deduction\n Feature record: CXX_FEATURE:1cxx_right_angle_brackets\n Feature record: CXX_FEATURE:1cxx_rvalue_references\n Feature record: CXX_FEATURE:1cxx_sizeof_member\n Feature record: CXX_FEATURE:1cxx_static_assert\n Feature record: CXX_FEATURE:1cxx_strong_enums\n Feature record: CXX_FEATURE:1cxx_template_template_parameters\n Feature record: CXX_FEATURE:1cxx_thread_local\n Feature record: CXX_FEATURE:1cxx_trailing_return_types\n Feature record: CXX_FEATURE:1cxx_unicode_literals\n Feature record: CXX_FEATURE:1cxx_uniform_initialization\n Feature record: CXX_FEATURE:1cxx_unrestricted_unions\n Feature record: CXX_FEATURE:1cxx_user_literals\n Feature record: CXX_FEATURE:0cxx_variable_templates\n Feature record: CXX_FEATURE:1cxx_variadic_macros\n Feature record: CXX_FEATURE:1cxx_variadic_templates\n\n\nDetecting CXX [-std=c++98] compiler features compiled with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec1247648529/fast\"\ngmake: Warning: File `Makefile' has modification time 0.0016 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec1247648529.dir/build.make CMakeFiles/cmTryCompileExec1247648529.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec1247648529.dir/progress.make' has modification time 0.0057 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding CXX object CMakeFiles/cmTryCompileExec1247648529.dir/feature_tests.cxx.o\n/opt/rh/devtoolset-2/root/usr/bin/c++ -std=c++98 -o CMakeFiles/cmTryCompileExec1247648529.dir/feature_tests.cxx.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/feature_tests.cxx\nLinking CXX executable cmTryCompileExec1247648529\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec1247648529.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/c++ CMakeFiles/cmTryCompileExec1247648529.dir/feature_tests.cxx.o -o cmTryCompileExec1247648529 -rdynamic \ngmake[1]: warning: Clock skew detected. Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\n Feature record: CXX_FEATURE:0cxx_aggregate_default_initializers\n Feature record: CXX_FEATURE:0cxx_alias_templates\n Feature record: CXX_FEATURE:0cxx_alignas\n Feature record: CXX_FEATURE:0cxx_alignof\n Feature record: CXX_FEATURE:0cxx_attributes\n Feature record: CXX_FEATURE:0cxx_attribute_deprecated\n Feature record: CXX_FEATURE:0cxx_auto_type\n Feature record: CXX_FEATURE:0cxx_binary_literals\n Feature record: CXX_FEATURE:0cxx_constexpr\n Feature record: CXX_FEATURE:0cxx_contextual_conversions\n Feature record: CXX_FEATURE:0cxx_decltype\n Feature record: CXX_FEATURE:0cxx_decltype_auto\n Feature record: CXX_FEATURE:0cxx_decltype_incomplete_return_types\n Feature record: CXX_FEATURE:0cxx_default_function_template_args\n Feature record: CXX_FEATURE:0cxx_defaulted_functions\n Feature record: CXX_FEATURE:0cxx_defaulted_move_initializers\n Feature record: CXX_FEATURE:0cxx_delegating_constructors\n Feature record: CXX_FEATURE:0cxx_deleted_functions\n Feature record: CXX_FEATURE:0cxx_digit_separators\n Feature record: CXX_FEATURE:0cxx_enum_forward_declarations\n Feature record: CXX_FEATURE:0cxx_explicit_conversions\n Feature record: CXX_FEATURE:0cxx_extended_friend_declarations\n Feature record: CXX_FEATURE:0cxx_extern_templates\n Feature record: CXX_FEATURE:0cxx_final\n Feature record: CXX_FEATURE:0cxx_func_identifier\n Feature record: CXX_FEATURE:0cxx_generalized_initializers\n Feature record: CXX_FEATURE:0cxx_generic_lambdas\n Feature record: CXX_FEATURE:0cxx_inheriting_constructors\n Feature record: CXX_FEATURE:0cxx_inline_namespaces\n Feature record: CXX_FEATURE:0cxx_lambdas\n Feature record: CXX_FEATURE:0cxx_lambda_init_captures\n Feature record: CXX_FEATURE:0cxx_local_type_template_args\n Feature record: CXX_FEATURE:0cxx_long_long_type\n Feature record: CXX_FEATURE:0cxx_noexcept\n Feature record: CXX_FEATURE:0cxx_nonstatic_member_init\n Feature record: CXX_FEATURE:0cxx_nullptr\n Feature record: CXX_FEATURE:0cxx_override\n Feature record: CXX_FEATURE:0cxx_range_for\n Feature record: CXX_FEATURE:0cxx_raw_string_literals\n Feature record: CXX_FEATURE:0cxx_reference_qualified_functions\n Feature record: CXX_FEATURE:0cxx_relaxed_constexpr\n Feature record: CXX_FEATURE:0cxx_return_type_deduction\n Feature record: CXX_FEATURE:0cxx_right_angle_brackets\n Feature record: CXX_FEATURE:0cxx_rvalue_references\n Feature record: CXX_FEATURE:0cxx_sizeof_member\n Feature record: CXX_FEATURE:0cxx_static_assert\n Feature record: CXX_FEATURE:0cxx_strong_enums\n Feature record: CXX_FEATURE:1cxx_template_template_parameters\n Feature record: CXX_FEATURE:0cxx_thread_local\n Feature record: CXX_FEATURE:0cxx_trailing_return_types\n Feature record: CXX_FEATURE:0cxx_unicode_literals\n Feature record: CXX_FEATURE:0cxx_uniform_initialization\n Feature record: CXX_FEATURE:0cxx_unrestricted_unions\n Feature record: CXX_FEATURE:0cxx_user_literals\n Feature record: CXX_FEATURE:0cxx_variable_templates\n Feature record: CXX_FEATURE:0cxx_variadic_macros\n Feature record: CXX_FEATURE:0cxx_variadic_templates\nDetermining if files pthread.h exist passed with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec2326696968/fast\"\ngmake: Warning: File `Makefile' has modification time 0.011 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec2326696968.dir/build.make CMakeFiles/cmTryCompileExec2326696968.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec2326696968.dir/flags.make' has modification time 0.0062 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec2326696968.dir/CheckIncludeFiles.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec2326696968.dir/CheckIncludeFiles.c.o -c /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CheckIncludeFiles.c\nLinking C executable cmTryCompileExec2326696968\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec2326696968.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec2326696968.dir/CheckIncludeFiles.c.o -o cmTryCompileExec2326696968 -rdynamic \ngmake[1]: warning: Clock skew detected. Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\nDetermining if the function pthread_create exists in the pthread passed with the following output:\nChange Dir: /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec1176357958/fast\"\ngmake: Warning: File `Makefile' has modification time 0.0093 s in the future\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec1176357958.dir/build.make CMakeFiles/cmTryCompileExec1176357958.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake[1]: Warning: File `CMakeFiles/cmTryCompileExec1176357958.dir/flags.make' has modification time 0.0059 s in the future\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec1176357958.dir/CheckFunctionExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create -o CMakeFiles/cmTryCompileExec1176357958.dir/CheckFunctionExists.c.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CheckFunctionExists.c\nLinking C executable cmTryCompileExec1176357958\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec1176357958.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTryCompileExec1176357958.dir/CheckFunctionExists.c.o -o cmTryCompileExec1176357958 -rdynamic -lpthread \ngmake[1]: warning: Clock skew detected. Your build may be incomplete.\ngmake[1]: Leaving directory `/home/yzhang4/HD/home/liwen/Documents/NMSLIB/nmslib-1.5.1/similarity_search/CMakeFiles/CMakeTmp'\ngmake: warning: Clock skew detected. Your build may be incomplete.\n\n\nDetermining if files pthread.h exist passed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec3784435972/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec3784435972.dir/build.make CMakeFiles/cmTryCompileExec3784435972.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec3784435972.dir/CheckIncludeFiles.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec3784435972.dir/CheckIncludeFiles.c.o -c /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp/CheckIncludeFiles.c\nLinking C executable cmTryCompileExec3784435972\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec3784435972.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec3784435972.dir/CheckIncludeFiles.c.o -o cmTryCompileExec3784435972 -rdynamic \ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\n\n\nDetermining if the function pthread_create exists in the pthread passed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec1739529121/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec1739529121.dir/build.make CMakeFiles/cmTryCompileExec1739529121.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec1739529121.dir/CheckFunctionExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create -o CMakeFiles/cmTryCompileExec1739529121.dir/CheckFunctionExists.c.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CheckFunctionExists.c\nLinking C executable cmTryCompileExec1739529121\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec1739529121.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTryCompileExec1739529121.dir/CheckFunctionExists.c.o -o cmTryCompileExec1739529121 -rdynamic -lpthread \ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/CMakeFiles/CMakeTmp'\n\n\nDetermining if files pthread.h exist passed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec2023778701/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec2023778701.dir/build.make CMakeFiles/cmTryCompileExec2023778701.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec2023778701.dir/CheckIncludeFiles.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -o CMakeFiles/cmTryCompileExec2023778701.dir/CheckIncludeFiles.c.o -c /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp/CheckIncludeFiles.c\nLinking C executable cmTryCompileExec2023778701\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec2023778701.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc CMakeFiles/cmTryCompileExec2023778701.dir/CheckIncludeFiles.c.o -o cmTryCompileExec2023778701 -rdynamic \ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\n\n\nDetermining if the function pthread_create exists in the pthread passed with the following output:\nChange Dir: /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp\n\nRun Build Command:\"/usr/bin/gmake\" \"cmTryCompileExec625074925/fast\"\n/usr/bin/gmake -f CMakeFiles/cmTryCompileExec625074925.dir/build.make CMakeFiles/cmTryCompileExec625074925.dir/build\ngmake[1]: Entering directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_progress_report /home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp/CMakeFiles 1\nBuilding C object CMakeFiles/cmTryCompileExec625074925.dir/CheckFunctionExists.c.o\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create -o CMakeFiles/cmTryCompileExec625074925.dir/CheckFunctionExists.c.o -c /home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/share/cmake-3.2/Modules/CheckFunctionExists.c\nLinking C executable cmTryCompileExec625074925\n/home/yzhang4/HD/home/liwen/Downloads/software/CMake3.2/bin/cmake -E cmake_link_script CMakeFiles/cmTryCompileExec625074925.dir/link.txt --verbose=1\n/opt/rh/devtoolset-2/root/usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTryCompileExec625074925.dir/CheckFunctionExists.c.o -o cmTryCompileExec625074925 -rdynamic -lpthread \ngmake[1]: Leaving directory `/home/yzhang4/HD/release/algorithms/NMSLIB/code/CMakeFiles/CMakeTmp'\n\n\n" }, { "path": "algorithms/NMSLIB/code/CMakeFiles/Makefile.cmake", "content": "# CMAKE generated file: DO NOT EDIT!\n# Generated by \"Unix Makefiles\" Generator, CMake Version 3.2\n\n# The 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\"\\\\1\\\\2\"\n # COMPILER_VERSION ${COMPILER_VERSION})\n\n set(${_OUTPUT_VERSION} ${COMPILER_VERSION} PARENT_SCOPE)\nendfunction()\n\nif(NOT WIN32)\n CXX_COMPILER_DUMPVERSION(CXX_COMPILER_VERSION)\nendif()\n#message(FATAL_ERROR \"stopping... compiler version is: ${CMAKE_CXX_COMPILER_ID} ${CXX_COMPILER_VERSION}\")\n\n\nif(\"${CMAKE_CXX_COMPILER_ID}\" STREQUAL \"GNU\")\n # require at least gcc 4.7\n if (CXX_COMPILER_VERSION VERSION_LESS 4.7)\n message(FATAL_ERROR \"GCC version must be at least 4.7!\")\n endif()\n # Uncomment the following lines to see how the code compiles without AVX,SSE4.2 and/or SSE2\n #set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Ofast -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -fopenmp -fpic -march=x86-64\")\n #set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Ofast -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -fopenmp -fpic -march=core2\")\n #set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Ofast -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -fopenmp -fpic -msse4.2\")\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Wcast-align -O3 -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -Wl,--no-as-needed -fpic\") #-Ofast -fopenmp\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -Wcast-align -ggdb -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -Wl,--no-as-needed -fpic\")\nelseif(\"${CMAKE_CXX_COMPILER_ID}\" STREQUAL \"Intel\")\n if (CXX_COMPILER_VERSION VERSION_LESS 14.0.1)\n message(FATAL_ERROR \"Intel version must be at least 14.0.1!\")\n endif()\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -O3 -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -ggdb -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\nelseif(\"${CMAKE_CXX_COMPILER_ID}\" STREQUAL \"Clang\")\n if (CXX_COMPILER_VERSION VERSION_LESS 4.2.1)\n message(FATAL_ERROR \"Clang version must be at least 3.4 (GCC >= 4.2.1 equivalent)!\")\n endif()\n if (CMAKE_SYSTEM_NAME MATCHES Darwin)\n # MACOSX\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Wcast-align -O3 -DNDEBUG -std=c++11 -DHAVE_CXX0X -fpic\")\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -Wcast-align -ggdb -DNDEBUG -std=c++11 -DHAVE_CXX0X -fpic\")\n set (WITHOUT_LSH YES)\n set (WITHOUT_SIMD YES)\n else()\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Wcast-align -O3 -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -Wcast-align -ggdb -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\n endif()\n #message(FATAL_ERROR \"CLANG ${CMAKE_SYSTEM_NAME}\")\nelseif(WIN32)\n # TODO add support for later versions?\n if(NOT MSVC12)\n message(FATAL_ERROR \"On Windows, only MSVC version 12 is supported!\") \n endif()\n set (WITHOUT_LSH YES)\nelse ()\n message(FATAL_ERROR \"Please, use GCC, Clang, or the Intel compiler!\")\nendif()\n\nif (WITHOUT_LSH)\n message(STATUS \"Will build without LSH\")\n add_definitions (-DWITHOUT_LSH=1)\nendif()\n\nif (WITHOUT_SIMD)\n message(STATUS \"Will build without SIMD\")\n add_definitions (-DWITHOUT_SIMD=1)\nendif()\n\n\nif (NOT CMAKE_BUILD_TYPE)\n set (CMAKE_BUILD_TYPE Release CACHE STRING \"Build types: Release Debug\" FORCE)\nendif (NOT CMAKE_BUILD_TYPE)\nmessage (STATUS \"Build type: ${CMAKE_BUILD_TYPE}\")\n\nset (CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR})\n\n# GSL is needed by lshkit, yet NO lshkit for Win32 and MACOSX \nif ((NOT WITHOUT_LSH) AND (NOT WIN32))\n find_package (GSL REQUIRED)\n if (GSL_FOUND)\n message (STATUS \"Found GSL.\")\n include_directories (${GSL_INCLUDE_DIRS} ${GSLCBLAS_INCLUDE_DIRS})\n set (LIBS ${LIBS} ${GSL_LIBRARIES} ${GSLCBLAS_LIBRARIES})\n else (GSL_FOUND)\n message (FATAL_ERROR \"Could not locate GSL.\")\n endif (GSL_FOUND)\n\n include_directories (${PROJECT_SOURCE_DIR}/lshkit/include)\n add_subdirectory (lshkit)\nendif()\n\nfind_package(Eigen3 3 REQUIRED)\nif (EIGEN3_FOUND)\n message (STATUS \"Found Eigen3.\")\n include_directories (${EIGEN3_INCLUDE_DIR})\nelse ()\n message (FATAL_ERROR \"Could not locate Eigen3.\")\nendif ()\n\n# Can be used to hardwire boost location.\n#set (BOOST_ROOT $ENV{HOME}/boost_1_48_0)\n#set (Boost_INCLUDE_DIR ${BOOST_ROOT})\n#set (Boost_LIBRARY_DIR ${BOOST_ROOT}/stage/lib)\n\nfind_package (Boost 1.48 COMPONENTS system filesystem program_options REQUIRED)\n\nif (Boost_FOUND)\n message (STATUS \"Found BOOST.\")\n include_directories (${Boost_INCLUDE_DIR})\n include_directories (${PROJECT_SOURCE_DIR}/lshkit/include ${PROJECT_SOURCE_DIR}/src ${Boost_INCLUDE_DIR})\n link_directories (${Boost_LIBRARY_DIR})\nelse ()\n message (FATAL_ERROR \"Could not locate BOOST.\")\nendif ()\n\nadd_subdirectory (src)\nadd_subdirectory (test)\n\nmessage (STATUS \"Compiler: ${CMAKE_CXX_COMPILER_ID} ${CXX_COMPILER_VERSION}\")\n" }, { "path": "algorithms/NMSLIB/code/CMakeLists.txt~", "content": "#\n# Non-metric Space Library\n#\n# Authors: Bilegsaikhan Naidan, Leonid Boytsov.\n#\n# This code is released under the\n# Apache License Version 2.0 http://www.apache.org/licenses/.\n#\n#\n\ncmake_minimum_required (VERSION 2.8)\n\nproject (NonMetricSpaceLib)\n\n#\n# Runs compiler with \"-dumpversion\" and parses major/minor\n# version with a regex.\n#\n# Taken&Modified from Boost.cmake\n#\nfunction(CXX_COMPILER_DUMPVERSION _OUTPUT_VERSION)\n\n exec_program(${CMAKE_CXX_COMPILER}\n ARGS ${CMAKE_CXX_COMPILER_ARG1} -dumpversion\n OUTPUT_VARIABLE COMPILER_VERSION\n )\n #string(REGEX REPLACE \"([0-9])\\\\.([0-9])(\\\\.[0-9])?\" \"\\\\1\\\\2\"\n # COMPILER_VERSION ${COMPILER_VERSION})\n\n set(${_OUTPUT_VERSION} ${COMPILER_VERSION} PARENT_SCOPE)\nendfunction()\n\nif(NOT WIN32)\n CXX_COMPILER_DUMPVERSION(CXX_COMPILER_VERSION)\nendif()\n#message(FATAL_ERROR \"stopping... compiler version is: ${CMAKE_CXX_COMPILER_ID} ${CXX_COMPILER_VERSION}\")\n\n\nif(\"${CMAKE_CXX_COMPILER_ID}\" STREQUAL \"GNU\")\n # require at least gcc 4.7\n if (CXX_COMPILER_VERSION VERSION_LESS 4.7)\n message(FATAL_ERROR \"GCC version must be at least 4.7!\")\n endif()\n # Uncomment the following lines to see how the code compiles without AVX,SSE4.2 and/or SSE2\n #set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Ofast -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -fopenmp -fpic -march=x86-64\")\n #set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Ofast -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -fopenmp -fpic -march=core2\")\n #set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Ofast -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -fopenmp -fpic -msse4.2\")\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Wcast-align -O3 -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -Wl,--no-as-needed -fpic\") #-Ofast -fopenmp\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -Wcast-align -ggdb -lm -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -Wl,--no-as-needed -fpic\")\nelseif(\"${CMAKE_CXX_COMPILER_ID}\" STREQUAL \"Intel\")\n if (CXX_COMPILER_VERSION VERSION_LESS 14.0.1)\n message(FATAL_ERROR \"Intel version must be at least 14.0.1!\")\n endif()\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -O3 -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -ggdb -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\nelseif(\"${CMAKE_CXX_COMPILER_ID}\" STREQUAL \"Clang\")\n if (CXX_COMPILER_VERSION VERSION_LESS 4.2.1)\n message(FATAL_ERROR \"Clang version must be at least 3.4 (GCC >= 4.2.1 equivalent)!\")\n endif()\n if (CMAKE_SYSTEM_NAME MATCHES Darwin)\n # MACOSX\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Wcast-align -O3 -DNDEBUG -std=c++11 -DHAVE_CXX0X -fpic\")\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -Wcast-align -ggdb -DNDEBUG -std=c++11 -DHAVE_CXX0X -fpic\")\n set (WITHOUT_LSH YES)\n set (WITHOUT_SIMD YES)\n else()\n set (CMAKE_CXX_FLAGS_RELEASE \"-Wall -Wunreachable-code -Wcast-align -O3 -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\n set (CMAKE_CXX_FLAGS_DEBUG \"-Wall -Wunreachable-code -Wcast-align -ggdb -DNDEBUG -std=c++11 -DHAVE_CXX0X -march=native -fpic\")\n endif()\n #message(FATAL_ERROR \"CLANG ${CMAKE_SYSTEM_NAME}\")\nelseif(WIN32)\n # TODO add support for later versions?\n if(NOT MSVC12)\n message(FATAL_ERROR \"On Windows, only MSVC version 12 is supported!\") \n endif()\n set (WITHOUT_LSH YES)\nelse ()\n message(FATAL_ERROR \"Please, use GCC, Clang, or the Intel compiler!\")\nendif()\n\nif (WITHOUT_LSH)\n message(STATUS \"Will build without LSH\")\n add_definitions (-DWITHOUT_LSH=1)\nendif()\n\nif (WITHOUT_SIMD)\n message(STATUS \"Will build without SIMD\")\n add_definitions (-DWITHOUT_SIMD=1)\nendif()\n\n\nif (NOT CMAKE_BUILD_TYPE)\n set (CMAKE_BUILD_TYPE Release CACHE STRING \"Build types: Release Debug\" FORCE)\nendif (NOT CMAKE_BUILD_TYPE)\nmessage (STATUS \"Build type: ${CMAKE_BUILD_TYPE}\")\n\nset (CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR})\n\n# GSL is needed by lshkit, yet NO lshkit for Win32 and MACOSX \nif ((NOT WITHOUT_LSH) AND (NOT WIN32))\n find_package (GSL REQUIRED)\n if (GSL_FOUND)\n message (STATUS \"Found GSL.\")\n include_directories (${GSL_INCLUDE_DIRS} ${GSLCBLAS_INCLUDE_DIRS})\n set (LIBS ${LIBS} ${GSL_LIBRARIES} ${GSLCBLAS_LIBRARIES})\n else (GSL_FOUND)\n message (FATAL_ERROR \"Could not locate GSL.\")\n endif (GSL_FOUND)\n\n include_directories (${PROJECT_SOURCE_DIR}/lshkit/include)\n add_subdirectory (lshkit)\nendif()\n\nfind_package(Eigen3 3 REQUIRED)\nif (EIGEN3_FOUND)\n message (STATUS \"Found Eigen3.\")\n include_directories (${EIGEN3_INCLUDE_DIR})\nelse ()\n message (FATAL_ERROR \"Could not locate Eigen3.\")\nendif ()\n\n# Can be used to hardwire boost location.\n#set (BOOST_ROOT $ENV{HOME}/boost_1_48_0)\n#set (Boost_INCLUDE_DIR ${BOOST_ROOT})\n#set (Boost_LIBRARY_DIR ${BOOST_ROOT}/stage/lib)\n\nfind_package (Boost 1.48 COMPONENTS system filesystem program_options REQUIRED)\n\nif (Boost_FOUND)\n message (STATUS \"Found BOOST.\")\n include_directories (${Boost_INCLUDE_DIR})\n include_directories (${PROJECT_SOURCE_DIR}/lshkit/include ${PROJECT_SOURCE_DIR}/src ${Boost_INCLUDE_DIR})\n link_directories (${Boost_LIBRARY_DIR})\nelse ()\n message (FATAL_ERROR \"Could not locate BOOST.\")\nendif ()\n\nadd_subdirectory (src)\nadd_subdirectory (test)\n\nmessage (STATUS \"Compiler: ${CMAKE_CXX_COMPILER_ID} ${CXX_COMPILER_VERSION}\")\n" }, { "path": "algorithms/NMSLIB/code/FindEigen3.cmake", "content": "# - Try to find Eigen3 lib\n#\n# This module supports requiring a minimum version, e.g. you can do\n# find_package(Eigen3 3.1.2)\n# to require version 3.1.2 or newer of Eigen3.\n#\n# Once done this will define\n#\n# EIGEN3_FOUND - system has eigen lib with correct version\n# EIGEN3_INCLUDE_DIR - the eigen include directory\n# EIGEN3_VERSION - eigen version\n\n# Copyright (c) 2006, 2007 Montel Laurent, \n# Copyright (c) 2008, 2009 Gael Guennebaud, \n# Copyright (c) 2009 Benoit Jacob \n# Redistribution and use is allowed according to the terms of the 2-clause BSD license.\n\nif(NOT Eigen3_FIND_VERSION)\n if(NOT Eigen3_FIND_VERSION_MAJOR)\n set(Eigen3_FIND_VERSION_MAJOR 2)\n endif(NOT Eigen3_FIND_VERSION_MAJOR)\n if(NOT Eigen3_FIND_VERSION_MINOR)\n set(Eigen3_FIND_VERSION_MINOR 91)\n endif(NOT Eigen3_FIND_VERSION_MINOR)\n if(NOT Eigen3_FIND_VERSION_PATCH)\n set(Eigen3_FIND_VERSION_PATCH 0)\n endif(NOT Eigen3_FIND_VERSION_PATCH)\n\n set(Eigen3_FIND_VERSION \"${Eigen3_FIND_VERSION_MAJOR}.${Eigen3_FIND_VERSION_MINOR}.${Eigen3_FIND_VERSION_PATCH}\")\nendif(NOT Eigen3_FIND_VERSION)\n\nmacro(_eigen3_check_version)\n file(READ \"${EIGEN3_INCLUDE_DIR}/Eigen/src/Core/util/Macros.h\" _eigen3_version_header)\n\n string(REGEX MATCH \"define[ \\t]+EIGEN_WORLD_VERSION[ \\t]+([0-9]+)\" _eigen3_world_version_match \"${_eigen3_version_header}\")\n set(EIGEN3_WORLD_VERSION \"${CMAKE_MATCH_1}\")\n string(REGEX MATCH \"define[ \\t]+EIGEN_MAJOR_VERSION[ \\t]+([0-9]+)\" _eigen3_major_version_match \"${_eigen3_version_header}\")\n set(EIGEN3_MAJOR_VERSION \"${CMAKE_MATCH_1}\")\n string(REGEX MATCH \"define[ \\t]+EIGEN_MINOR_VERSION[ \\t]+([0-9]+)\" _eigen3_minor_version_match \"${_eigen3_version_header}\")\n set(EIGEN3_MINOR_VERSION \"${CMAKE_MATCH_1}\")\n\n set(EIGEN3_VERSION ${EIGEN3_WORLD_VERSION}.${EIGEN3_MAJOR_VERSION}.${EIGEN3_MINOR_VERSION})\n if(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})\n set(EIGEN3_VERSION_OK FALSE)\n else(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})\n set(EIGEN3_VERSION_OK TRUE)\n endif(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})\n\n if(NOT EIGEN3_VERSION_OK)\n\n message(STATUS \"Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}, \"\n \"but at least version ${Eigen3_FIND_VERSION} is required\")\n endif(NOT EIGEN3_VERSION_OK)\nendmacro(_eigen3_check_version)\n\nif (EIGEN3_INCLUDE_DIR)\n\n # in cache already\n _eigen3_check_version()\n set(EIGEN3_FOUND ${EIGEN3_VERSION_OK})\n\nelse (EIGEN3_INCLUDE_DIR)\n\n find_path(EIGEN3_INCLUDE_DIR NAMES signature_of_eigen3_matrix_library\n PATHS\n ${CMAKE_INSTALL_PREFIX}/include\n ${KDE4_INCLUDE_DIR}\n PATH_SUFFIXES eigen3 eigen\n )\n\n if(EIGEN3_INCLUDE_DIR)\n _eigen3_check_version()\n endif(EIGEN3_INCLUDE_DIR)\n\n include(FindPackageHandleStandardArgs)\n find_package_handle_standard_args(Eigen3 DEFAULT_MSG EIGEN3_INCLUDE_DIR EIGEN3_VERSION_OK)\n\n mark_as_advanced(EIGEN3_INCLUDE_DIR)\n\nendif(EIGEN3_INCLUDE_DIR)\n\n" }, { "path": "algorithms/NMSLIB/code/FindGSL.cmake", "content": "# Try to find gnu scientific library GSL\n# See \n# http://www.gnu.org/software/gsl/ and \n# http://gnuwin32.sourceforge.net/packages/gsl.htm\n#\n# Once run this will define: \n# \n# GSL_FOUND = system has GSL lib\n#\n# GSL_LIBRARIES = full path to the libraries\n# on Unix/Linux with additional linker flags from \"gsl-config --libs\"\n# \n# CMAKE_GSL_CXX_FLAGS = Unix compiler flags for GSL, essentially \"`gsl-config --cxxflags`\"\n#\n# GSL_INCLUDE_DIR = where to find headers \n#\n# GSL_LINK_DIRECTORIES = link directories, useful for rpath on Unix\n# GSL_EXE_LINKER_FLAGS = rpath on Unix\n#\n# Felix Woelk 07/2004\n# Jan Woetzel\n#\n# www.mip.informatik.uni-kiel.de\n# --------------------------------\n\nIF(WIN32)\n # JW tested with gsl-1.8, Windows XP, MSVS 7.1, MSVS 8.0\n SET(GSL_POSSIBLE_ROOT_DIRS\n ${GSL_ROOT_DIR}\n $ENV{GSL_ROOT_DIR}\n ${GSL_DIR}\n ${GSL_HOME} \n $ENV{GSL_DIR}\n $ENV{GSL_HOME}\n $ENV{EXTERN_LIBS_DIR}/gsl\n $ENV{EXTRA}\n # \"C:/home/jw/source2/gsl-1.8\"\n )\n FIND_PATH(GSL_INCLUDE_DIR\n NAMES gsl/gsl_cdf.h gsl/gsl_randist.h\n PATHS ${GSL_POSSIBLE_ROOT_DIRS}\n PATH_SUFFIXES include\n DOC \"GSL header include dir\"\n )\n \n FIND_LIBRARY(GSL_GSL_LIBRARY\n NAMES gsl libgsl\n PATHS ${GSL_POSSIBLE_ROOT_DIRS}\n PATH_SUFFIXES lib\n DOC \"GSL library dir\" ) \n \n FIND_LIBRARY(GSL_GSLCBLAS_LIBRARY\n NAMES gslcblas libgslcblas\n PATHS ${GSL_POSSIBLE_ROOT_DIRS}\n PATH_SUFFIXES lib\n DOC \"GSL cblas library dir\" )\n \n SET(GSL_LIBRARIES ${GSL_GSL_LIBRARY} ${GSL_GSLCBLAS_LIBRARY})\n\n #MESSAGE(\"DBG\\n\"\n # \"GSL_GSL_LIBRARY=${GSL_GSL_LIBRARY}\\n\"\n # \"GSL_GSLCBLAS_LIBRARY=${GSL_GSLCBLAS_LIBRARY}\\n\"\n # \"GSL_LIBRARIES=${GSL_LIBRARIES}\")\n\n\nELSE(WIN32)\n \n IF(UNIX) \n SET(GSL_CONFIG_PREFER_PATH \n \"$ENV{GSL_DIR}/bin\"\n \"$ENV{GSL_DIR}\"\n \"$ENV{GSL_HOME}/bin\" \n \"$ENV{GSL_HOME}\" \n CACHE STRING \"preferred path to GSL (gsl-config)\")\n FIND_PROGRAM(GSL_CONFIG gsl-config\n ${GSL_CONFIG_PREFER_PATH}\n /usr/bin/\n )\n # MESSAGE(\"DBG GSL_CONFIG ${GSL_CONFIG}\")\n \n IF (GSL_CONFIG) \n \n MESSAGE(STATUS \"GSL using gsl-config ${GSL_CONFIG}\")\n # set CXXFLAGS to be fed into CXX_FLAGS by the user:\n EXEC_PROGRAM(${GSL_CONFIG}\n ARGS --cflags\n OUTPUT_VARIABLE GSL_CXX_FLAGS )\n #SET(GSL_CXX_FLAGS \"`${GSL_CONFIG} --cflags`\")\n \n # set INCLUDE_DIRS to prefix+include\n EXEC_PROGRAM(${GSL_CONFIG}\n ARGS --prefix\n OUTPUT_VARIABLE GSL_PREFIX)\n SET(GSL_INCLUDE_DIR ${GSL_PREFIX}/include CACHE STRING INTERNAL)\n\n # set link libraries and link flags\n \n #SET(GSL_LIBRARIES \"`${GSL_CONFIG} --libs`\")\n \n # extract link dirs for rpath \n EXEC_PROGRAM(${GSL_CONFIG}\n ARGS --libs\n OUTPUT_VARIABLE GSL_CONFIG_LIBS )\n SET(GSL_LIBRARIES \"${GSL_CONFIG_LIBS}\")\n\n # split off the link dirs (for rpath)\n # use regular expression to match wildcard equivalent \"-L*\"\n # with is a space or a semicolon\n STRING(REGEX MATCHALL \"[-][L]([^ ;])+\" \n GSL_LINK_DIRECTORIES_WITH_PREFIX \n \"${GSL_CONFIG_LIBS}\" )\n # MESSAGE(\"DBG GSL_LINK_DIRECTORIES_WITH_PREFIX=${GSL_LINK_DIRECTORIES_WITH_PREFIX}\")\n\n # remove prefix -L because we need the pure directory for LINK_DIRECTORIES\n \n IF (GSL_LINK_DIRECTORIES_WITH_PREFIX)\n STRING(REGEX REPLACE \"[-][L]\" \"\" GSL_LINK_DIRECTORIES ${GSL_LINK_DIRECTORIES_WITH_PREFIX} )\n ENDIF (GSL_LINK_DIRECTORIES_WITH_PREFIX)\n SET(GSL_EXE_LINKER_FLAGS \"-Wl,-rpath,${GSL_LINK_DIRECTORIES}\" CACHE STRING INTERNAL)\n # MESSAGE(\"DBG GSL_LINK_DIRECTORIES=${GSL_LINK_DIRECTORIES}\")\n # MESSAGE(\"DBG GSL_EXE_LINKER_FLAGS=${GSL_EXE_LINKER_FLAGS}\")\n\n # ADD_DEFINITIONS(\"-DHAVE_GSL\")\n # SET(GSL_DEFINITIONS \"-DHAVE_GSL\")\n MARK_AS_ADVANCED(\n GSL_CXX_FLAGS\n GSL_INCLUDE_DIR\n GSL_LIBRARIES\n GSL_LINK_DIRECTORIES\n GSL_DEFINITIONS\n\t)\n MESSAGE(STATUS \"Using GSL from ${GSL_PREFIX}\")\n \n ELSE(GSL_CONFIG)\n \n INCLUDE(UsePkgConfig) #needed for PKGCONFIG(...)\n\n MESSAGE(STATUS \"GSL using pkgconfig\")\n # PKGCONFIG(gsl includedir libdir linkflags cflags)\n PKGCONFIG(gsl GSL_INCLUDE_DIR GSL_LINK_DIRECTORIES GSL_LIBRARIES GSL_CXX_FLAGS)\n IF(GSL_INCLUDE_DIR)\n\tMARK_AS_ADVANCED(\n GSL_CXX_FLAGS\n GSL_INCLUDE_DIR\n GSL_LIBRARIES\n GSL_LINK_DIRECTORIES\n\t )\n\t \n ELSE(GSL_INCLUDE_DIR) \n\tMESSAGE(\"FindGSL.cmake: gsl-config/pkg-config gsl not found. Please set it manually. 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prefix\nif(NOT DEFINED CMAKE_INSTALL_PREFIX)\n set(CMAKE_INSTALL_PREFIX \"/usr/local\")\nendif()\nstring(REGEX REPLACE \"/$\" \"\" CMAKE_INSTALL_PREFIX \"${CMAKE_INSTALL_PREFIX}\")\n\n# Set the install configuration name.\nif(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)\n if(BUILD_TYPE)\n string(REGEX REPLACE \"^[^A-Za-z0-9_]+\" \"\"\n CMAKE_INSTALL_CONFIG_NAME \"${BUILD_TYPE}\")\n else()\n set(CMAKE_INSTALL_CONFIG_NAME \"Release\")\n endif()\n message(STATUS \"Install configuration: \\\"${CMAKE_INSTALL_CONFIG_NAME}\\\"\")\nendif()\n\n# Set the component getting installed.\nif(NOT CMAKE_INSTALL_COMPONENT)\n if(COMPONENT)\n message(STATUS \"Install component: \\\"${COMPONENT}\\\"\")\n set(CMAKE_INSTALL_COMPONENT \"${COMPONENT}\")\n else()\n set(CMAKE_INSTALL_COMPONENT)\n endif()\nendif()\n\n# Install shared libraries without execute permission?\nif(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)\n set(CMAKE_INSTALL_SO_NO_EXE \"0\")\nendif()\n\nif(NOT CMAKE_INSTALL_LOCAL_ONLY)\n # Include the install script for each subdirectory.\n include(\"/home/yzhang4/HD/release/algorithms/NMSLIB/code/lshkit/cmake_install.cmake\")\n include(\"/home/yzhang4/HD/release/algorithms/NMSLIB/code/src/cmake_install.cmake\")\n include(\"/home/yzhang4/HD/release/algorithms/NMSLIB/code/test/cmake_install.cmake\")\n\nendif()\n\nif(CMAKE_INSTALL_COMPONENT)\n set(CMAKE_INSTALL_MANIFEST \"install_manifest_${CMAKE_INSTALL_COMPONENT}.txt\")\nelse()\n set(CMAKE_INSTALL_MANIFEST \"install_manifest.txt\")\nendif()\n\nstring(REPLACE \";\" \"\\n\" CMAKE_INSTALL_MANIFEST_CONTENT\n \"${CMAKE_INSTALL_MANIFEST_FILES}\")\nfile(WRITE \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/${CMAKE_INSTALL_MANIFEST}\"\n \"${CMAKE_INSTALL_MANIFEST_CONTENT}\")\n" }, { "path": "algorithms/NMSLIB/code/include/cluster_util.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2015\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef CLUSTER_UTIL\n#define CLUSTER_UTIL\n\n#include \n#include \n#include \n\n#include \n#include \n\nnamespace similarity {\n\n const size_t MAX_CLARANS_ITER_QTY = 1000; // 1000 is quite a lot, usually an algorithm converges locally in a dozen of attempts\n const size_t MAX_METAITER_CLARANS_ITER_QTY = 20; // This is a maximum number of iterations for a single meta-iteration\n const size_t CLARANS_SWAP_ATTEMPTS = 20;\n const size_t CLARANS_SAMPLE_QTY = 10;\n const size_t CLARANS_RAND_RESTART_QTY = 5;\n const size_t REDUCTIVE_CLARANS_MIN_CLUSTER_SIZE = 5;\n\n const size_t SAMPLE_LIST_CLUST_DEFAULT_SAMPLE_QTY = 1000000;\n\n using std::vector;\n using std::string;\n\n const string CLUST_TYPE_CLARAN = \"clarans\";\n const string CLUST_TYPE_REDUCT_CLARAN = \"reduct_clarans\";\n const string CLUST_TYPE_FIRMAL = \"firmal\";\n\n template \n class ClusterUtils {\n public:\n\n /*\n * IMPORTANT NOTE on all clustring methods:\n * vClustAssign is always sorted by the distance to the cluster in the ascending order.\n */\n\n /*\n * A variant of the k-medoid clustering called CLARANS. First described in:\n *\n * Raymond T. Ng and Jiawei Han. 1994.\n * Efficient and Effective Clustering Methods for Spatial Data Mining.\n * In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB '94)\n */\n static void doCLARANS(bool PrintProgress,\n const Space& space, // A space object\n ObjectVector data, // A list of objects, it is copied\n size_t centerQty, // Number of clusters/centers\n ObjectVector& vCenters, // Centers\n vector>>& vClusterAssign, // Cluster assignment\n IdTypeUnsign inClusterSwapAttempts = CLARANS_SWAP_ATTEMPTS, // Number of random swaps to find a better cluster center\n IdTypeUnsign inClusterSampleQty = CLARANS_SAMPLE_QTY, // Number of random points to estimate if the swap was beneficial\n size_t randRestQty = CLARANS_RAND_RESTART_QTY,// A number of random restarts\n IdTypeUnsign maxIterQty = MAX_CLARANS_ITER_QTY, // A maximum number of iterations\n double errMinDiff = 1e-4 // Stop iterating if the total configuration\n // cost doesn't decrease by at least this value\n );\n\n /*\n * CLARANS applied to ever-diminishing chunks of data. Processing is split into meta-iterations.\n * In one meta-iteration, we carry out a few (<=maxIterQty) CLARANS iterations.\n * Then, we keep only some of the points (namely keepFrac*data.size()) and cluster the remaining ones\n * recursively. Unlike regular CLARANS, the number of random restarts is 1.\n * We carry out at most maxMetaIterQty iterations. Some points remain unassigned.\n */\n static void doReductiveCLARANS(bool PrintProgress,\n const Space& space, // A space object\n ObjectVector data, // A list of objects, it is copied\n IdTypeUnsign maxMetaIterQty, // A maximum number of meta iterations\n float keepFrac, // Percentage of assigned points kept after a meta-iteration is finished\n size_t centerQty, // Number of clusters/centers\n ObjectVector&vCentersGlobal, // Centers\n vector>>& vClusterAssign, // Cluster assignment\n ObjectVector& vUnassigned, // Unassigned points\n IdTypeUnsign inClusterSwapAttempts = CLARANS_SWAP_ATTEMPTS, // Number of random swaps to find a better cluster center\n IdTypeUnsign inClusterSampleQty = CLARANS_SAMPLE_QTY, // Number of random points to estimate if the swap was beneficial\n IdTypeUnsign maxIterQty = MAX_METAITER_CLARANS_ITER_QTY, // A maximum number of iterations in one meta-iteration\n double errMinDiff = 1e-4 // Stop iterating if the total configuration\n // cost doesn't decrease by at least this value\n );\n\n // FIRMAL: fixed radius multi-attempt linkage\n static void doFIRMAL(bool PrintProgress,\n const Space& space, // A space object\n ObjectVector data, // A list of objects, it is copied\n float ExpCenterQty, // A hint for the number of clusters, the acutal # fo clusters can be much longer\n ObjectVector& vCenters, // Centers\n vector>>& vClusterAssign, // Cluster assignment\n ObjectVector& vUnassigned, // Unassigned points\n size_t SearchCloseIterQty, // A # of search iterations to find a point that is close to already selected centers\n // For good performance it should be in the order of sqrt(data.size())\n size_t SampleDistQty = SAMPLE_LIST_CLUST_DEFAULT_SAMPLE_QTY,\n bool bUseAllClustersInIter = true\n );\n\n // Sort data points inside each cluster in the order of increasing distance to the cluster center.\n static void sortInsideClustersDist(const vector>>& vClusterAssign);\n\n static void printClusterStat(const Space& space,\n const vector>>&vClustAssign, // Cluster assignment\n IdTypeUnsign sampleQty // a number of point to sample in each cluster\n\n );\n static void printAndVerifyClusterStat(const Space& space,\n const ObjectVector& vCenters,\n const vector>>&vClustAssign, // Cluster assignment\n IdTypeUnsign sampleQty // a number of point to sample in each cluster\n );\n\n };\n} // namespace similarity\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/distcomp.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef DISTCOMP_HPP\n#define DISTCOMP_HPP\n\n#include \n\n#include \n#include \n#include \n#include \n\n#include \"permutation_type.h\"\n\nnamespace similarity {\n\nusing std::max;\n\n/*\n * LInf norm\n */\n\ntemplate T LInfNormStandard(const T *p1, const T *p2, size_t qty);\ntemplate T LInfNorm(const T *p1, const T *p2, size_t qty) ;\ntemplate T LInfNormSIMD(const T* pVect1, const T* pVect2, size_t qty);\n\n\n/*\n * L1 norm\n */\n\ntemplate T L1NormStandard(const T *p1, const T *p2, size_t qty);\ntemplate T L1Norm(const T *p1, const T *p2, size_t qty) ;\ntemplate T L1NormSIMD(const T* pVect1, const T* pVect2, size_t qty);\n\n\n/*\n * L2 (Eucledian) norm\n */\n\ntemplate T L2NormStandard(const T *p1, const T *p2, size_t qty);\ntemplate T L2Norm(const T* pVect1, const T* pVect2, size_t qty);\ntemplate T L2NormSIMD(const T* pVect1, const T* pVect2, size_t qty);\n\nfloat L2SqrSIMD(const float* pVect1, const float* pVect2, size_t qty);\n\n/*\n * Scalar product related distances \n */\ntemplate T AngularDistance(const T *p1, const T *p2, size_t qty);\ntemplate T CosineSimilarity(const T *p1, const T *p2, size_t qty);\n// Scalar product divided by vector Euclidean norms\ntemplate T NormScalarProduct(const T *p1, const T *p2, size_t qty);\ntemplate T NormScalarProductSIMD(const T *p1, const T *p2, size_t qty);\n\n// Scalar product that is not normalized \ntemplate T ScalarProduct(const T *p1, const T *p2, size_t qty);\ntemplate T ScalarProductSIMD(const T *p1, const T *p2, size_t qty);\n\n// Fast scalar product between sparse vectors (using SIMD)\nfloat ScalarProductFast(const char* pData1, size_t len1, const char* pData2, size_t len2);\n\n/*\n * Itakura-Saito distance\n */\ntemplate T ItakuraSaito(const T* pVect1, const T* pVect2, size_t qty);\n\n/* \n * Precomp means that qty logarithms are precomputed \n * and stored right after qty original vector values.\n * That's the layout is:\n * x1 ... x1_qty log(x1) log(x2) ... log(x_qty)\n */ \n\ntemplate T ItakuraSaitoPrecomp(const T* pVect1, const T* pVect2, size_t qty);\ntemplate T ItakuraSaitoPrecompSIMD(const T* pVect1, const T* pVect2, size_t qty);\n\n/*\n * KL-divergence\n * Should be applied ONLY to vectors such that:\n * 1) ALL elements are positive\n * 2) the sum of elements is ONE\n */\ntemplate T KLStandard(const T* pVect1, const T* pVect2, size_t qty);\n/*\n * This is a less efficient version of KLStandard that computes the difference\n * of logarithms as opposed to log of the ratio. Yet, it has better accuracy.\n */\ntemplate T KLStandardLogDiff(const T* pVect1, const T* pVect2, size_t qty);\n// Precomputed logs\ntemplate T KLPrecomp(const T* pVect1, const T* pVect2, size_t qty);\n// Same as KLPrecomp, but uses SIMD to make computation faster.\ntemplate T KLPrecompSIMD(const T* pVect1, const T* pVect2, size_t qty);\n\n/*\n * Generalized KL-divergence\n * Should be applied ONLY to vectors such that:\n * 1) ALL elements are positive\n * 2) Yet, the sum of elements IS NOT NECESSARILY one.\n *\n */\ntemplate T KLGeneralStandard(const T* pVect1, const T* pVect2, size_t qty);\n// Precomputed logs\ntemplate T KLGeneralPrecomp(const T* pVect1, const T* pVect2, size_t qty);\n// Same as KLPrecomp, but uses SIMD to make computation faster.\ntemplate T KLGeneralPrecompSIMD(const T* pVect1, const T* pVect2, size_t qty);\n\n/*\n * Computes logarithms and stores them after qty values of pVect.\n * NOTE 1: pVect should have 2 * qty elements!!! but only\n * the first qty elements are used to compute the logarithms.\n * After this function completes, the would look like:\n * x1 ... x1_qty log(x1) log(x2) ... log(x_qty)\n *\n * NOTE 2: if the number is <=0, its log is computed as the minimum possible numbers,\n * rather than minus infinity.\n */\ntemplate void PrecompLogarithms(T* pVect, size_t qty) {\n for (size_t i = 0; i < qty; i++) { \n //pVect[i + qty] = (pVect[i] > 0) ? log(pVect[i]): std::numeric_limits::lowest();\n pVect[i + qty] = (pVect[i] > 0) ? log(pVect[i]): T(-1e5f);\n }\n}\n\n/*\n * Jensen-Shannon divergence \n *\n * The squared root of JS-divergence is a metric.\n * \n * Österreicher, Ferdinand, and Igor Vajda. \n * \"A new class of metric divergences on probability spaces and its applicability in statistics.\" \n * Annals of the Institute of Statistical Mathematics 55.3 (2003): 639-653.\n *\n *\n * Endres, Dominik Maria, and Johannes E. Schindelin. \n * \"A new metric for probability distributions.\" \n * Information Theory, IEEE Transactions on 49.7 (2003): 1858-1860.\n *\n */\ntemplate T JSStandard(const T *pVect1, const T *pVect2, size_t qty);\n// Precomputed logs\ntemplate T JSPrecomp(const T *pVect1, const T *pVect2, size_t qty);\n// Precomputed logs, one log is approximate\ntemplate T JSPrecompApproxLog(const T *pVect1, const T *pVect2, size_t qty);\n\n// Precomputed logs & inverse values\ntemplate T JSPrecompDivApproxLog(const T *pVect1, const T *pVect2, size_t qty);\n\ntemplate T JSPrecompSIMDApproxLog(const T* pVect1, const T* pVect2, size_t qty);\n\n/*\n * Slower versions of LP-distance\n */\ntemplate T LPGenericDistance(const T* x, const T* y, const int length, const T p);\n\ntemplate T LPGenericDistanceOptim(const T* x, const T* y, const int length, const T p);\n\n\n/*\n * Rank correlations\n */\n\ntypedef int (*IntDistFuncPtr)(const PivotIdType* x, const PivotIdType* y, size_t qty);\n\nint SpearmanFootrule(const PivotIdType* x, const PivotIdType* y, size_t qty);\nint SpearmanRho(const PivotIdType* x, const PivotIdType* y, size_t qty);\nint SpearmanFootruleSIMD(const PivotIdType* x, const PivotIdType* y, size_t qty);\nint SpearmanRhoSIMD(const PivotIdType* x, const PivotIdType* y, size_t qty);\n\n//unsigned BitHamming(const uint32_t* a, const uint32_t* b, size_t qty);\n\n#include \"simdutils.h\"\n\n\nunsigned inline BitHamming(const uint32_t* a, const uint32_t* b, size_t qty) {\n unsigned res = 0;\n\n for (size_t i=0; i < qty; ++i) {\n // __builtin_popcount quickly computes the number on 1s\n res += __builtin_popcount(a[i] ^ b[i]);\n }\n\n return res;\n}\n\n}\n\n/*\n * Edit distances\n */\n#include \"distcomp_edist.h\"\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/distcomp_edist.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef DISTCOMP_EDIST_HPP\n#define DISTCOMP_EDIST_HPP\n\nnamespace similarity {\n\n/* \n * The maximum number of elements that will be kept on the stack\n * by the function levenshtein. \n *\n * TODO:@leo If there are too many threads, we might run out stack memory.\n * but it is probably extremely unlikely with the buffer of this size.\n *\n */\n\n#define MAX_LEVEN_BUFFER_QTY 512\n\ntemplate int levenshtein(const T* p1, size_t len1, const T* p2, size_t len2) ;\ntemplate int levenshtein(const T &s1, const T & s2) {\n return levenshtein(s1.c_str(), s1.size(), s2.c_str(), s2.size());\n}\n\n\n}\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/eval_metrics.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef EVAL_METRICS_H\n#define EVAL_METRICS_H\n\n#include \n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"gold_standard.h\"\n\nnamespace similarity {\n\nusing std::vector;\nusing std::sort;\nusing std::ostream;\nusing std::unordered_set;\n\ntemplate \nbool ApproxEqualElem(const ResultEntry& elem1, const ResultEntry& elem2) {\n return elem1.mId == elem2.mId || ApproxEqual(elem1.mDist, elem2.mDist);\n}\n\ntemplate \nstruct EvalMetricsBase {\n virtual double operator()(double ExactResultSize,\n const vector>& SortedAllEntries, const unordered_set& ExactResultIds,\n const vector>& ApproxEntries, const unordered_set& ApproxResultIds\n ) const = 0;\n /* \n * An auxilliar function that aligns exact and approximate answers.\n * It used to compute error approximation metrics.\n */\n template \n static void iterate(AccumObj& obj,\n const vector>& SortedAllEntries, const unordered_set& ExactResultIds,\n const vector>& ApproxEntries, const unordered_set& ApproxResultIds\n ) {\n for (size_t k = 0, p = 0; k < ApproxEntries.size() && p < SortedAllEntries.size(); ++k) {\n const auto& elemApprox = ApproxEntries[k];\n const auto& elemExact = SortedAllEntries[p];\n /*\n * There is no guarantee that the floating point arithmetic\n * produces consistent results. For instance, we can call the distance\n * function twice with the same object pointers, but get slightly\n * different results.\n */\n if (elemApprox.mDist - elemExact.mDist < 0\n && !ApproxEqualElem(elemApprox, elemExact)\n ) {\n double mx = std::abs(std::max(ApproxEntries[k].mDist, SortedAllEntries[p].mDist));\n double mn = std::abs(std::min(ApproxEntries[k].mDist, SortedAllEntries[p].mDist));\n \n for (size_t i = 0; i < std::min(SortedAllEntries.size(), ApproxEntries.size()); ++i ) {\n LOG(LIB_INFO) << \"Ex: \" << SortedAllEntries[i].mDist << \" id = \" << SortedAllEntries[i].mId <<\n \" -> Apr: \"<< ApproxEntries[i].mDist << \" id = \" << ApproxEntries[i].mId << \n \" 1 - ratio: \" << (1 - mn/mx) << \" diff: \" << (mx - mn);\n }\n LOG(LIB_FATAL) << \"bug: the approximate query should not return objects \"\n << \"that are closer to the query than object returned by \"\n << \"(exact) sequential searching!\"\n << \" Approx: \" << elemApprox.mDist << \" id = \" << elemApprox.mId \n << \" Exact: \" << elemExact.mDist << \" id = \" << elemExact.mId;\n }\n // At this point the distance to the true answer is either <= than the distance to the approximate answer,\n // or the distance to the true answer is slightly larger due to non-determinism of floating point arithmetic\n size_t LastEqualP = p;\n if (p < SortedAllEntries.size() && \n ApproxEqualElem(elemApprox, elemExact)) {\n ++p;\n } else {\n while (p < SortedAllEntries.size() && \n SortedAllEntries[p].mDist < ApproxEntries[k].mDist &&\n !ApproxEqualElem(SortedAllEntries[p], ApproxEntries[k]))\n {\n ++p;\n ++LastEqualP;\n }\n }\n if (p < k) {\n for (size_t i = 0; i < std::min(SortedAllEntries.size(), ApproxEntries.size()); ++i ) {\n LOG(LIB_INFO) << \"E: \" << SortedAllEntries[i].mDist << \" -> \" << ApproxEntries[i].mDist;\n }\n LOG(LIB_FATAL) << \"bug: p = \" << p << \" k = \" << k;\n }\n CHECK(p >= k);\n obj(k, LastEqualP);\n }\n }\n};\n\n\ntemplate \nstruct EvalRecall : public EvalMetricsBase {\n /*\n * A classic recall measure\n */\n double operator()(double ExactResultSize,\n const vector>& SortedAllEntries, const unordered_set& ExactResultIds,\n const vector>& ApproxEntries, const unordered_set& ApproxResultIds\n ) const {\n if (ExactResultIds.empty()) return 1.0;\n double recall = 0.0;\n for (auto it = ApproxResultIds.begin(); it != ApproxResultIds.end(); ++it) {\n recall += ExactResultIds.count(*it);\n }\n return recall / ExactResultSize;\n }\n};\n\ntemplate \nstruct EvalNumberCloser : public EvalMetricsBase {\n /*\n * Number of the nearest neighbors or range search answers that are closer to the query\n * than the closest element returned by the search.\n */\n double operator()(double ExactResultSize,\n const vector>& SortedAllEntries, const unordered_set& ExactResultIds,\n const vector>& ApproxEntries, const unordered_set& ApproxResultIds\n ) const {\n if (ExactResultIds.empty()) return 0.0;\n if (ApproxEntries.empty()) return min(ExactResultSize, static_cast(SortedAllEntries.size()));\n \n double NumberCloser = 0;\n\n // 2. Compute the number of points closer to the 1-NN then the first result.\n CHECK(!ApproxEntries.empty());\n for (size_t p = 0; p < SortedAllEntries.size(); ++p) {\n if (SortedAllEntries[p].mDist >= ApproxEntries[0].mDist || ApproxEqualElem(SortedAllEntries[p], ApproxEntries[0])) break;\n ++NumberCloser;\n }\n\n return NumberCloser;\n }\n};\n\ntemplate \nstruct EvalPrecisionOfApprox : public EvalMetricsBase {\n /*\n * Precision of approximation.\n *\n * Proposed in:\n * Zezula, P., Savino, P., Amato, G., Rabitti, F., \n * Approximate similarity retrieval with m-trees. \n * The VLDB Journal 7(4) (December 1998) 275-293\n *\n * Formally, the precision of approximation is equal to:\n * 1/K sum_{i=1}^K i/pos(i)\n */\n struct AccumPrecisionOfApprox {\n double PrecisionOfApprox_ = 0;\n void operator()(size_t k, size_t LastEqualP) {\n PrecisionOfApprox_ += static_cast(k + 1) / (LastEqualP + 1);\n }\n };\n\n double operator()(double ExactResultSize,\n const vector>& SortedAllEntries, const unordered_set& ExactResultIds,\n const vector>& ApproxEntries, const unordered_set& ApproxResultIds\n ) const {\n if (ExactResultIds.empty()) return 1.0;\n if (ApproxEntries.empty()) return 0.0;\n\n AccumPrecisionOfApprox res;\n\n EvalMetricsBase::iterate(res, SortedAllEntries, ExactResultIds, ApproxEntries, ApproxResultIds);\n \n return res.PrecisionOfApprox_ / ApproxEntries.size();\n }\n};\n\ntemplate \nstruct EvalLogRelPosError : public EvalMetricsBase {\n struct AccumLogRelPossError {\n double LogRelPosError_ = 0;\n void operator()(size_t k, size_t LastEqualP) {\n LogRelPosError_ += log(static_cast(LastEqualP + 1) / (k + 1));\n }\n };\n\n double operator()(double ExactResultSize,\n const vector>& SortedAllEntries, const unordered_set& ExactResultIds,\n const vector>& ApproxEntries, const unordered_set& ApproxResultIds\n ) const {\n if (ExactResultIds.empty()) return 0.0;\n if (ApproxEntries.empty()) return log(min(ExactResultSize, static_cast(SortedAllEntries.size())));\n\n AccumLogRelPossError res;\n\n EvalMetricsBase::iterate(res, SortedAllEntries, ExactResultIds, ApproxEntries, ApproxResultIds);\n \n return res.LogRelPosError_ / ApproxEntries.size();\n }\n};\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/eval_results.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef EVAL_RESULTS_H\n#define EVAL_RESULTS_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"utils.h\"\n#include \"space.h\"\n#include \"object.h\"\n#include \"index.h\"\n#include \"knnqueue.h\"\n#include \"eval_metrics.h\"\n\nnamespace similarity {\n\nusing std::unordered_set;\nusing std::unordered_map;\nusing std::vector;\nusing std::sort;\n\n\n\ntemplate \nclass EvalResults {\npublic:\n EvalResults(const typename similarity::Space& space,\n const typename similarity::KNNQuery* query,\n const GoldStandard& gs) : K_(0), SortedAllEntries_(gs.GetSortedEntries()) {\n GetKNNData(query);\n ComputeMetrics(query->QueryObject()->label());\n }\n\n EvalResults(const typename similarity::Space& space,\n const typename similarity::RangeQuery* query,\n const GoldStandard& gs) : K_(0), SortedAllEntries_(gs.GetSortedEntries()) {\n GetRangeData(query);\n ComputeMetrics(query->QueryObject()->label());\n }\n\n /* \n * For all metrics below, pos(i) denotes a position of the \n * i-th approximate result in the exact list of neighbors.\n * That is, a k=pos(i) means that the i-th element returned\n * by a search method is, in truth, a k-th nearest neighbor.\n */\n\n /* \n * GetNumCloser() makes most sense only for 1-NN search,\n * because it computes the number of points closer than\n * the nearest point found by a method.\n * \n * Formally is it equal to pos(0) - 1.\n *\n * This metric was proposed in:\n * L. Cayton. Fast nearest neighbor retrieval for bregman divergences. \n * Twenty-Fifth International Conference on Machine Learning (ICML), 2008.\n *\n */\n double GetNumCloser() const { return NumberCloser_;}\n\n /*\n * Recall of the closets (1-NN) entry\n */\n double GetRecallAt1() const { return RecallAt1_;}\n\n /*\n * An average logarithm of a relative position error.\n * Just exponentiate to get a geometric mean of relative position errors.\n *\n * Formally, it is equal to:\n * 1/K sum_{i=1}^K log(pos(i))\n */\n double GetLogRelPos() const { return LogRelPosError_;}\n /*\n * Just the classic recall value\n */\n double GetRecall() const { return Recall_; }\n /*\n * Classification correctness\n */\n ClassResult GetClassCorrect() const { return ClassCorrect_; }\n /*\n * Precision of approximation.\n *\n * Zezula, P., Savino, P., Amato, G., Rabitti, F., \n * Approximate similarity retrieval with m-trees. \n * The VLDB Journal 7(4) (December 1998) 275-293\n *\n * Formally, the precision of approximation is equal to:\n * 1/K sum_{i=1}^K i/pos(i)\n */\n double GetPrecisionOfApprox() const { return PrecisionOfApprox_; }\nprivate:\n size_t K_;\n\n /* \n * In k-NN search the k-neighborhood may be definied ambiguously.\n * It can happen if several points are at the same distance from the query point.\n * All these points are included into the ExactResultIds_, yet, their number can be > K_.\n */\n void GetKNNData(const KNNQuery* query) {\n K_ = query->GetK();\n for (size_t i = 0; i < SortedAllEntries_.size(); ++i) {\n if (i < K_ || (K_ && ApproxEqual(SortedAllEntries_[i].mDist, SortedAllEntries_[K_-1].mDist))) {\n ExactResultIds_.insert(SortedAllEntries_[i].mId);\n }\n else break; // SortedAllEntries_ are sorted by distance\n }\n\n unique_ptr> ResQ(query->Result()->Clone());\n\n while (!ResQ->Empty()) {\n const Object* ResObject = reinterpret_cast(ResQ->TopObject());\n CHECK(ResObject);\n /*\n * A search method can potentially return duplicate records.\n * We simply ignore duplicates during evaluation.\n */\n if (ApproxResultIds_.find(ResObject->id()) == ApproxResultIds_.end()) {\n ApproxResultIds_.insert(ResObject->id());\n ApproxEntries_.insert(ApproxEntries_.begin(), \n ResultEntry(ResObject->id(), ResObject->label(), ResQ->TopDistance()));\n }\n ResQ->Pop();\n }\n // ApproxEntries_ should be sorted\n }\n\n void GetRangeData(const RangeQuery* query) {\n for (size_t i = 0; i < SortedAllEntries_.size(); ++i) {\n if (SortedAllEntries_[i].mDist <= query->Radius()) ExactResultIds_.insert(SortedAllEntries_[i].mId);\n else break; // SortedAllEntries_ are sorted by distance\n }\n\n const ObjectVector& ResQ = *query->Result();\n const std::vector& ResQDists = *query->ResultDists();\n\n CHECK(ResQ.size() == ResQDists.size());\n\n for (size_t i = 0; i < ResQ.size(); ++i) {\n const Object* ResObject = ResQ[i];\n CHECK(ResObject);\n // We should not have any duplicates!\n if (ApproxResultIds_.find(ResObject->id()) == ApproxResultIds_.end()) {\n ApproxResultIds_.insert(ResObject->id());\n ApproxEntries_.insert(ApproxEntries_.begin(), \n ResultEntry(ResObject->id(), ResObject->label(), ResQDists[i]));\n }\n }\n\n std::sort(ApproxEntries_.begin(), ApproxEntries_.end());\n }\n\n void ComputeMetrics(LabelType queryLabel) {\n size_t ExactResultSize = K_ ? min(K_,ExactResultIds_.size()) /* If the data set is tiny\n there may be less than K_\n answers */\n :\n ExactResultIds_.size();\n\n ClassCorrect_ = kClassUnknown;\n Recall_ = EvalRecall()(ExactResultSize, SortedAllEntries_, ExactResultIds_, ApproxEntries_, ApproxResultIds_);\n NumberCloser_ = EvalNumberCloser()(ExactResultSize, SortedAllEntries_, ExactResultIds_, ApproxEntries_, ApproxResultIds_);\n RecallAt1_ = NumberCloser_ > 0.1 ? 0.0 : 1;\n PrecisionOfApprox_ = EvalPrecisionOfApprox()(ExactResultSize, SortedAllEntries_, ExactResultIds_, ApproxEntries_, ApproxResultIds_);\n LogRelPosError_ = EvalLogRelPosError()(ExactResultSize, SortedAllEntries_, ExactResultIds_, ApproxEntries_, ApproxResultIds_);\n\n // 2 Obtain class result\n if (queryLabel >= 0) {\n unordered_map hClassQty;\n vector> vClassQty;\n\n for (size_t k = 0; k < ApproxEntries_.size(); ++k) {\n hClassQty[ApproxEntries_[k].mLabel]++;\n }\n for (auto elem:hClassQty) {\n /* \n * Revert here: qty now should go first:\n * the minus sign will make sort put entries\n * with the largest qty first.\n */\n vClassQty.push_back(make_pair(-elem.second, elem.first));\n }\n sort(vClassQty.begin(), vClassQty.end());\n if (!vClassQty.empty()) {\n ClassCorrect_ = vClassQty[0].second == queryLabel ? kClassCorrect : kClassWrong;\n }\n }\n }\n\n double RecallAt1_;\n double NumberCloser_;\n double LogRelPosError_;\n double Recall_;\n ClassResult ClassCorrect_;\n double PrecisionOfApprox_;\n\n std::vector> ApproxEntries_;\n std::unordered_set ApproxResultIds_;\n std::unordered_set ExactResultIds_;\n\n\n /* \n * SortedAllEntries_ include all database entries sorted in the order\n * of increasing distance from the query.\n */\n const std::vector>& SortedAllEntries_;\n};\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/experimentconf.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _EXPERIMENT_CONFIG_H_\n#define _EXPERIMENT_CONFIG_H_\n\n#include \n#include \n#include \n#include \n\n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::stringstream;\nusing std::vector;\nusing std::unordered_map;\n\ntemplate \nclass ExperimentConfig {\npublic:\n ExperimentConfig(Space& space,\n const string& datafile,\n const string& queryfile,\n unsigned TestSetQty, // The # of times the datafile is randomly divided into the query and the test set\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQueryToRun,\n const typename std::vector& knn,\n float eps,\n const typename std::vector& range)\n : space_(space),\n datafile_(datafile),\n queryfile_(queryfile),\n pExternalData_(NULL),\n pExternalQuery_(NULL),\n noQueryData_(queryfile.empty()),\n testSetToRunQty_(TestSetQty),\n testSetQty_(TestSetQty),\n maxNumData_(MaxNumData),\n maxNumQuery_(MaxNumQueryToRun),\n maxNumQueryToRun_(MaxNumQueryToRun),\n range_(range),\n knn_(knn),\n eps_(eps),\n dataSetWasRead_(false) {\n if (noQueryData_) {\n if (!testSetToRunQty_) {\n throw runtime_error(\n \"Bad configuration. One should either specify a query file/data, \"\n \" or the number of test sets obtained by bootstrapping \"\n \"(random division into query and data files).\");\n }\n }\n }\n\n ExperimentConfig(Space& space,\n const ObjectVector& externalData,\n const ObjectVector& externalQuery,\n unsigned TestSetQty, // The # of times the datafile is randomly divided into the query and the test set\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQueryToRun,\n const typename std::vector& knn,\n float eps,\n const typename std::vector& range)\n : space_(space),\n datafile_(\"\"),\n queryfile_(\"\"),\n pExternalData_(&externalData),\n pExternalQuery_(&externalQuery),\n noQueryData_(externalQuery.empty()),\n testSetToRunQty_(TestSetQty),\n testSetQty_(TestSetQty),\n maxNumData_(MaxNumData),\n maxNumQuery_(MaxNumQueryToRun),\n maxNumQueryToRun_(MaxNumQueryToRun),\n range_(range),\n knn_(knn),\n eps_(eps),\n dataSetWasRead_(false) {\n if (noQueryData_) {\n if (!testSetToRunQty_) {\n throw runtime_error(\n \"Bad configuration. One should either specify a query file/data, \"\n \" or the number of test sets obtained by bootstrapping \"\n \"(random division into query and data files).\");\n }\n }\n }\n\n ~ExperimentConfig() {\n for (auto it = origData_.begin(); it != origData_.end(); ++it) {\n delete *it;\n }\n for (auto it = origQuery_.begin(); it != origQuery_.end(); ++it) {\n delete *it;\n }\n }\n\n void PrintInfo() const;\n void SelectTestSet(int SetNum);\n int GetTestSetToRunQty() const {\n if (!noQueryData_) return 1;\n return testSetToRunQty_;\n }\n int GetTestSetTotalQty() const {\n if (!noQueryData_) return 1;\n return testSetQty_;\n }\n size_t GetOrigDataQty() const { return origData_.size(); }\n const Space& GetSpace() const { return space_; }\n Space& GetSpace() { return space_; }\n const ObjectVector& GetDataObjects() const { return dataobjects_; }\n const ObjectVector& GetQueryObjects() const { return queryobjects_; }\n const typename std::vector& GetKNN() const { return knn_; }\n float GetEPS() const { return eps_; }\n const typename std::vector& GetRange() const { return range_; }\n int GetQueryToRunQty() const {\n return noQueryData_ ? maxNumQueryToRun_ :\n static_cast(origQuery_.size());\n }\n int GetTotalQueryQty() const {\n return noQueryData_ ? maxNumQuery_:\n static_cast(origQuery_.size());\n }\n\n /*\n * Read/Write : save/retrieve some of the config information.\n */\n void Write(ostream& controlStream, ostream& binaryStream);\n /* \n * If this function is called (i.e., the cache is read), it should be read\n * before the dataset is read, because data/query splits are stored in cache.\n * TODO: this is an evil form of delayed (and ordered) initialization,\n * we'll need to get rid of this some day. Instead, we should do\n * all the work in the constructor.\n *\n * For now, as a protection from changing the sequence of calls,\n * we have a special flag (dataSetWasRead_). However, this is not\n * an ideal solution, in particular, b/c the bug will be noticed\n * only at run-time.\n * \n */\n void Read(istream& controlStream, istream& binaryStream, size_t& cacheDataSetQty);\n\n void ReadDataset();\nprivate:\n Space& space_;\n ObjectVector dataobjects_;\n ObjectVector queryobjects_;\n ObjectVector origData_;\n ObjectVector origQuery_;\n vector origDataAssignment_; // >=0 denotes an index of the test set, -1 denotes data points\n unordered_map cachedDataAssignment_;\n string datafile_;\n string queryfile_;\n const ObjectVector* pExternalData_; \n const ObjectVector* pExternalQuery_; \n bool noQueryData_;\n unsigned testSetToRunQty_;\n unsigned testSetQty_;\n\n IdTypeUnsign maxNumData_;\n IdTypeUnsign maxNumQuery_;\n IdTypeUnsign maxNumQueryToRun_;\n\n\n vector range_; // range search parameter\n vector knn_; // knn search parameters\n float eps_; // knn search parameter\n\n bool dataSetWasRead_;\n\n void CopyExternal(const ObjectVector& src, ObjectVector& dst, size_t maxQty);\n};\n\n}\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/experiments.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _EXPERIMENTS_H\n#define _EXPERIMENTS_H\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"global.h\"\n#include \"object.h\"\n#include \"memory.h\"\n#include \"ztimer.h\"\n#include \"utils.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n#include \"index.h\"\n#include \"logging.h\"\n#include \"methodfactory.h\"\n#include \"eval_results.h\"\n#include \"meta_analysis.h\"\n#include \"query_creator.h\"\n\nnamespace similarity {\n\nusing std::vector;\nusing std::shared_ptr;\nusing std::string;\nusing std::stringstream;\nusing std::mutex;\nusing std::thread;\nusing std::ref;\nusing std::lock_guard;\nusing std::unique_ptr;\n\ntemplate \nclass Experiments {\npublic:\n typedef Index IndexType;\n\n static void RunAll(bool LogInfo, \n unsigned ThreadTestQty,\n size_t TestSetId,\n const GoldStandardManager& managerGS,\n vector>& ExpResRange,\n vector>& ExpResKNN,\n const ExperimentConfig& config,\n IndexType& Method,\n const vector>& QueryTimeParams) {\n\n if (LogInfo) LOG(LIB_INFO) << \">>>> TestSetId: \" << TestSetId;\n if (LogInfo) LOG(LIB_INFO) << \">>>> Will use: \" << ThreadTestQty << \" threads in efficiency testing\";\n if (LogInfo) config.PrintInfo();\n\n if (!config.GetRange().empty()) {\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n const dist_t radius = config.GetRange()[i];\n RangeCreator cr(radius);\n Execute, RangeCreator>(LogInfo,\n ThreadTestQty, TestSetId,\n managerGS.GetRangeGS(i),\n ExpResRange[i], config, cr, \n Method, QueryTimeParams);\n }\n }\n\n if (!config.GetKNN().empty()) {\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n const size_t K = config.GetKNN()[i];\n KNNCreator cr(K, config.GetEPS());\n Execute, KNNCreator>(LogInfo,\n ThreadTestQty, TestSetId,\n managerGS.GetKNNGS(i),\n ExpResKNN[i], config, cr, \n Method, QueryTimeParams);\n }\n }\n if (LogInfo) LOG(LIB_INFO) << \"experiment done at \" << LibGetCurrentTime();\n }\n\n template \n struct BenchmarkThreadParams {\n BenchmarkThreadParams(\n mutex& UpdateStat,\n unsigned ThreadQty,\n unsigned QueryPart,\n size_t TestSetId, \n std::vector& ExpRes,\n const ExperimentConfig& config,\n const QueryCreatorType& QueryCreator,\n const Index& Method,\n unsigned MethNum,\n vector& SearchTime,\n vector& AvgNumDistComp,\n vector& max_result_size,\n vector& avg_result_size,\n vector& DistCompQty) :\n UpdateStat_(UpdateStat),\n ThreadQty_(ThreadQty),\n QueryPart_(QueryPart),\n TestSetId_(TestSetId),\n ExpRes_(ExpRes),\n config_(config),\n QueryCreator_(QueryCreator),\n Method_(Method),\n MethNum_(MethNum),\n SearchTime_(SearchTime),\n\n AvgNumDistComp_(AvgNumDistComp),\n max_result_size_(max_result_size),\n avg_result_size_(avg_result_size),\n DistCompQty_(DistCompQty)\n {}\n\n mutex& UpdateStat_;\n unsigned ThreadQty_;\n unsigned QueryPart_;\n size_t TestSetId_;\n std::vector& ExpRes_;\n const ExperimentConfig& config_;\n const QueryCreatorType& QueryCreator_;\n const Index& Method_;\n unsigned MethNum_;\n vector& SearchTime_;\n\n vector& AvgNumDistComp_;\n vector& max_result_size_;\n vector& avg_result_size_;\n vector& DistCompQty_;\n\n vector queryIds;\n vector> queries; // queries with results\n };\n\n template \n struct BenchmarkThread {\n void operator ()(BenchmarkThreadParams& prm) {\n size_t numquery = prm.config_.GetQueryObjects().size();\n\n WallClockTimer wtm;\n\n wtm.reset();\n\n unsigned MethNum = prm.MethNum_;\n unsigned QueryPart = prm.QueryPart_;\n unsigned ThreadQty = prm.ThreadQty_;\n\n for (size_t q = 0; q < numquery; ++q) {\n if ((q % ThreadQty) == QueryPart) {\n unique_ptr query(prm.QueryCreator_(prm.config_.GetSpace(), \n prm.config_.GetQueryObjects()[q]));\n uint64_t t1 = wtm.split();\n prm.Method_.Search(query.get());\n uint64_t t2 = wtm.split();\n\n {\n lock_guard g(prm.UpdateStat_);\n\n prm.ExpRes_[MethNum]->AddDistComp(prm.TestSetId_, query->DistanceComputations());\n prm.ExpRes_[MethNum]->AddQueryTime(prm.TestSetId_, (1.0*t2 - t1)/1e3);\n\n\n prm.DistCompQty_[MethNum] += query->DistanceComputations();\n prm.avg_result_size_[MethNum] += query->ResultSize();\n\n if (query->ResultSize() > prm.max_result_size_[MethNum]) {\n prm.max_result_size_[MethNum] = query->ResultSize();\n }\n\n prm.queryIds.push_back(q);\n prm.queries.push_back(std::move(query));\n }\n }\n }\n }\n };\n\n template \n static void Execute(bool LogInfo, unsigned ThreadTestQty, size_t TestSetId,\n const vector>> &goldStand,\n std::vector& ExpRes,\n const ExperimentConfig& config,\n const QueryCreatorType& QueryCreator,\n IndexType& Method,\n const vector>& QueryTimeParams) {\n size_t numquery = config.GetQueryObjects().size();\n unsigned MethQty = QueryTimeParams.size();\n\n if (LogInfo) LOG(LIB_INFO) << \"##### Query type: \" << QueryType::Type();\n if (LogInfo) LOG(LIB_INFO) << \">>>> query params = \" << QueryCreator.ParamsForPrint();\n if (LogInfo) LOG(LIB_INFO) << \">>>> Computing efficiency metrics \";\n if (LogInfo) LOG(LIB_INFO) << \">>>> # of query time parameters: \" << MethQty;\n\n vector SearchTime(MethQty); \n\n vector ClassAccuracy(MethQty);\n vector Recall(MethQty);\n vector NumCloser(MethQty);\n vector RecallAt1(MethQty);\n vector LogPosErr(MethQty);\n vector PrecisionOfApprox(MethQty);\n vector SystemTimeElapsed(MethQty);\n\n uint64_t SeqSearchTime = 0;\n\n vector AvgNumDistComp(MethQty);\n vector ImprDistComp(MethQty);\n vector max_result_size(MethQty);\n vector avg_result_size(MethQty);\n vector DistCompQty(MethQty);\n\n mutex UpdateStat;\n\n config.GetSpace().SetQueryPhase();\n\n for (size_t MethNum = 0; MethNum < QueryTimeParams.size(); ++MethNum) {\n /* \n * Setting query time parameters must be done \n * before running any tests, in particular, because\n * the function SetQueryTimeParams is NOT supposed to be THREAD-SAFE. \n */\n const AnyParams& qtp = *QueryTimeParams[MethNum];\n Method.SetQueryTimeParams(qtp);\n\n LOG(LIB_INFO) << \">>>> Query-Time Parameters: \" << qtp.ToString();\n\n if (LogInfo) LOG(LIB_INFO) << \">>>> Efficiency test for: \"<< Method.StrDesc();\n\n WallClockTimer wtm;\n\n wtm.reset();\n\n if (!ThreadTestQty) ThreadTestQty = 1;\n\n vector*> ThreadParams(ThreadTestQty);\n vector Threads(ThreadTestQty);\n AutoVectDel> DelThreadParams(ThreadParams);\n\n\n for (unsigned QueryPart = 0; QueryPart < ThreadTestQty; ++QueryPart) {\n ThreadParams[QueryPart] = new BenchmarkThreadParams(\n UpdateStat,\n ThreadTestQty,\n QueryPart,\n TestSetId, \n ExpRes,\n config,\n QueryCreator,\n Method,\n MethNum,\n SearchTime,\n AvgNumDistComp,\n max_result_size,\n avg_result_size,\n DistCompQty);\n }\n\n if (ThreadTestQty> 1) {\n for (unsigned QueryPart = 0; QueryPart < ThreadTestQty; ++QueryPart) {\n Threads[QueryPart] = std::thread(BenchmarkThread(), \n ref(*ThreadParams[QueryPart]));\n }\n for (unsigned QueryPart = 0; QueryPart < ThreadTestQty; ++QueryPart) {\n Threads[QueryPart].join();\n }\n } else {\n CHECK(ThreadTestQty == 1);\n BenchmarkThread()(*ThreadParams[0]);\n }\n\n wtm.split();\n\n SearchTime[MethNum] = wtm.elapsed();\n\n AvgNumDistComp[MethNum] = static_cast(DistCompQty[MethNum])/numquery;\n ImprDistComp[MethNum] = config.GetDataObjects().size() / AvgNumDistComp[MethNum];\n\n ExpRes[MethNum]->SetImprDistComp(TestSetId, ImprDistComp[MethNum]);\n\n avg_result_size[MethNum] /= static_cast(numquery);\n\n if (LogInfo) LOG(LIB_INFO) << \">>>> Computing effectiveness metrics for \" << Method.StrDesc();\n\n for (unsigned QueryPart = 0; QueryPart < ThreadTestQty; ++QueryPart) {\n const BenchmarkThreadParams* params = ThreadParams[QueryPart];\n \n for (size_t qi = 0; qi < params->queries.size(); ++qi) {\n size_t q = params->queryIds[qi] ;\n const QueryType* pQuery = params->queries[qi].get();\n\n unique_ptr queryGS(QueryCreator(config.GetSpace(), config.GetQueryObjects()[q]));\n\n const GoldStandard& QueryGS = *goldStand[q];\n\n EvalResults Eval(config.GetSpace(), pQuery, QueryGS);\n\n NumCloser[MethNum] += Eval.GetNumCloser();\n RecallAt1[MethNum] += Eval.GetRecallAt1();\n LogPosErr[MethNum] += Eval.GetLogRelPos();\n Recall[MethNum] += Eval.GetRecall();\n double addAccuracy = (Eval.GetClassCorrect() == kClassCorrect ? 1:0);\n ClassAccuracy[MethNum]+= addAccuracy;\n PrecisionOfApprox[MethNum] += Eval.GetPrecisionOfApprox();\n\n ExpRes[MethNum]->AddPrecisionOfApprox(TestSetId, Eval.GetPrecisionOfApprox());\n ExpRes[MethNum]->AddRecall(TestSetId, Eval.GetRecall());\n ExpRes[MethNum]->AddClassAccuracy(TestSetId, addAccuracy);\n ExpRes[MethNum]->AddLogRelPosError(TestSetId, Eval.GetLogRelPos());\n ExpRes[MethNum]->AddNumCloser(TestSetId, Eval.GetNumCloser());\n ExpRes[MethNum]->AddRecallAt1(TestSetId, Eval.GetRecallAt1());\n\n }\n }\n }\n\n config.GetSpace().SetIndexPhase();\n\n /* \n * Sequential search times should be computed only once.\n */\n for (size_t q = 0; q < numquery; ++q) {\n const GoldStandard& QueryGS = *goldStand[q];\n SeqSearchTime += QueryGS.GetSeqSearchTime();\n }\n\n for (size_t MethNum = 0; MethNum < QueryTimeParams.size(); ++MethNum) {\n double timeSec = SearchTime[MethNum]/double(1e6);\n double queryPerSec = numquery / timeSec;\n\n if (LogInfo) {\n LOG(LIB_INFO) << \"=========================================\";\n LOG(LIB_INFO) << \">>>> Index type is \"<< Method.StrDesc();\n LOG(LIB_INFO) << \"=========================================\";\n LOG(LIB_INFO) << \">>>> max # results = \" << max_result_size[MethNum];\n LOG(LIB_INFO) << \">>>> avg # results = \" << avg_result_size[MethNum];\n LOG(LIB_INFO) << \">>>> # of distance computations = \" << AvgNumDistComp[MethNum];\n LOG(LIB_INFO) << \">>>> Impr in # of dist comp: \" << ImprDistComp[MethNum];\n LOG(LIB_INFO) << \"=========================================\";\n LOG(LIB_INFO) << \">>>> Time elapsed: \" << timeSec << \" sec\";\n LOG(LIB_INFO) << \">>>> # of queries per sec: : \" << queryPerSec;\n LOG(LIB_INFO) << \">>>> Avg time per query: \" << (timeSec/1e3/numquery) << \" msec\";\n LOG(LIB_INFO) << \">>>> System time elapsed: \" << (SystemTimeElapsed[MethNum]/double(1e6)) << \" sec\";\n LOG(LIB_INFO) << \"=========================================\";\n }\n\n // This number is adjusted for the number of threads!\n double ImprEfficiency = static_cast(SeqSearchTime)/(SearchTime[MethNum]*ThreadTestQty);\n\n ExpRes[MethNum]->SetImprEfficiency(TestSetId, ImprEfficiency);\n ExpRes[MethNum]->SetQueryPerSec(TestSetId, queryPerSec);\n\n Recall[MethNum] /= static_cast(numquery);\n ClassAccuracy[MethNum] /= static_cast(numquery);\n NumCloser[MethNum] /= static_cast(numquery);\n RecallAt1[MethNum] /= static_cast(numquery);\n LogPosErr[MethNum] /= static_cast(numquery);\n PrecisionOfApprox[MethNum] /= static_cast(numquery);\n \n if (LogInfo) {\n LOG(LIB_INFO) << \"=========================================\";\n LOG(LIB_INFO) << \">>>> # of test threads: \" << ThreadTestQty;\n LOG(LIB_INFO) << \">>>> Seq. search time elapsed: \" << (SeqSearchTime/double(1e6)) << \" sec\";\n LOG(LIB_INFO) << \">>>> Avg Seq. search time per query: \" << (SeqSearchTime/double(1e3)/numquery) << \" msec\";\n LOG(LIB_INFO) << \">>>> Impr. in Efficiency = \" << ImprEfficiency;\n LOG(LIB_INFO) << \">>>> Recall = \" << Recall[MethNum];\n LOG(LIB_INFO) << \">>>> PrecisionOfApprox = \" << PrecisionOfApprox[MethNum];\n LOG(LIB_INFO) << \">>>> RelPosError = \" << exp(LogPosErr[MethNum]);\n LOG(LIB_INFO) << \">>>> NumCloser = \" << NumCloser[MethNum];\n LOG(LIB_INFO) << \">>>> RecallAt1 = \" << RecallAt1[MethNum];\n LOG(LIB_INFO) << \">>>> Class. accuracy = \" << ClassAccuracy[MethNum];\n }\n }\n\n if (LogInfo) LOG(LIB_INFO) << \"#### Finished \" << QueryType::Type() << \" \" << LibGetCurrentTime();\n }\n};\n\n} // namespace similarity\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/factory/init_methods.h", "content": "#ifndef INIT_METHODS_H\n#define INIT_METHODS_H\n/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"methodfactory.h\"\n\n\n#if !defined(WITHOUT_LSH) && !defined(_MSC_VER)\n// These guys won't work on Windows and MACOSX yet\n#include \"factory/method/lsh.h\"\n#include \"factory/method/lsh_multiprobe.h\"\n#endif\n#include \"factory/method/dummy.h\"\n#include \"factory/method/bbtree.h\"\n#include \"factory/method/ghtree.h\"\n#include \"factory/method/list_clusters.h\"\n#include \"factory/method/nonmetr_list_clust.h\"\n#include \"factory/method/multi_index.h\"\n#include \"factory/method/multi_vantage_point_tree.h\"\n#include \"factory/method/perm_bin_vptree.h\"\n#include \"factory/method/perm_index_incr_bin.h\"\n#include \"factory/method/perm_lsh_bin.h\"\n#include \"factory/method/permutation_inverted_index.h\"\n#include \"factory/method/permutation_prefix_index.h\"\n#include \"factory/method/pivot_neighb_invindx.h\"\n#include \"factory/method/proj_vptree.h\"\n#include \"factory/method/projection_index_incremental.h\"\n#include \"factory/method/seqsearch.h\"\n#include \"factory/method/small_world_rand.h\"\n#include \"factory/method/hnsw.h\"\n#include \"factory/method/spatial_approx_tree.h\"\n#include \"factory/method/vptree.h\"\n#include \"factory/method/omedrank.h\"\n#if !defined(_MSC_VER)\n#include \"factory/method/nndes.h\"\n#endif\n\nnamespace similarity {\n\n\ninline void initMethods() {\n // a dummy method\n REGISTER_METHOD_CREATOR(float, METH_DUMMY, CreateDummy)\n REGISTER_METHOD_CREATOR(double, METH_DUMMY, CreateDummy)\n REGISTER_METHOD_CREATOR(int, METH_DUMMY, CreateDummy)\n \n // bbtree\n REGISTER_METHOD_CREATOR(float, METH_BBTREE, CreateBBTree)\n REGISTER_METHOD_CREATOR(double, METH_BBTREE, CreateBBTree)\n\n // ghtree\n REGISTER_METHOD_CREATOR(float, METH_GHTREE, CreateGHTree)\n REGISTER_METHOD_CREATOR(double, METH_GHTREE, CreateGHTree)\n REGISTER_METHOD_CREATOR(int, METH_GHTREE, CreateGHTree)\n\n // list of clusters\n REGISTER_METHOD_CREATOR(float, METH_LIST_CLUSTERS, CreateListClusters)\n REGISTER_METHOD_CREATOR(double, METH_LIST_CLUSTERS, CreateListClusters)\n REGISTER_METHOD_CREATOR(int, METH_LIST_CLUSTERS, CreateListClusters)\n\n#if !defined(WITHOUT_LSH) && !defined(_MSC_VER)\n// These guys won't work on Windows and MACOSX yet\n // Regular LSH\n REGISTER_METHOD_CREATOR(float, METH_LSH_CAUCHY, CreateLSHCauchy)\n REGISTER_METHOD_CREATOR(float, METH_LSH_GAUSSIAN, CreateLSHGaussian)\n REGISTER_METHOD_CREATOR(float, METH_LSH_THRESHOLD, CreateLSHThreshold)\n\n // Multiprobe LSH\n REGISTER_METHOD_CREATOR(float, METH_LSH_MULTIPROBE, CreateLSHMultiprobe)\n REGISTER_METHOD_CREATOR(float, METH_LSH_MULTIPROBE_SYN, CreateLSHMultiprobe)\n#endif\n\n // Multi-vantage point tree\n REGISTER_METHOD_CREATOR(float, METH_MVPTREE, CreateMultiVantagePointTree)\n REGISTER_METHOD_CREATOR(double, METH_MVPTREE, CreateMultiVantagePointTree)\n REGISTER_METHOD_CREATOR(int, METH_MVPTREE, CreateMultiVantagePointTree)\n\n // Vptree over binarized permutations\n REGISTER_METHOD_CREATOR(float, METH_PERM_BIN_VPTREE, CreatePermutationBinVPTree)\n REGISTER_METHOD_CREATOR(double, METH_PERM_BIN_VPTREE, CreatePermutationBinVPTree)\n REGISTER_METHOD_CREATOR(int, METH_PERM_BIN_VPTREE, CreatePermutationBinVPTree)\n\n // Sequential-search binarized permutation index with incremental sorting\n REGISTER_METHOD_CREATOR(float, METH_PERMUTATION_INC_SORT_BIN, CreatePermutationIndexIncrementalBin)\n REGISTER_METHOD_CREATOR(double, METH_PERMUTATION_INC_SORT_BIN, CreatePermutationIndexIncrementalBin)\n REGISTER_METHOD_CREATOR(int, METH_PERMUTATION_INC_SORT_BIN, CreatePermutationIndexIncrementalBin)\n REGISTER_METHOD_CREATOR(float, METH_PERMUTATION_INC_SORT_BIN_SYN, CreatePermutationIndexIncrementalBin)\n REGISTER_METHOD_CREATOR(double, METH_PERMUTATION_INC_SORT_BIN_SYN, CreatePermutationIndexIncrementalBin)\n REGISTER_METHOD_CREATOR(int, METH_PERMUTATION_INC_SORT_BIN_SYN, CreatePermutationIndexIncrementalBin)\n\n // LSH based on binarized permutations\n REGISTER_METHOD_CREATOR(float, METH_PERMUTATION_LSH_BIN, CreatePermutationIndexLSHBin)\n REGISTER_METHOD_CREATOR(double, METH_PERMUTATION_LSH_BIN, CreatePermutationIndexLSHBin)\n REGISTER_METHOD_CREATOR(int, METH_PERMUTATION_LSH_BIN, CreatePermutationIndexLSHBin)\n\n // Inverted index over permutations\n REGISTER_METHOD_CREATOR(float, METH_PERM_INVERTED_INDEX, CreatePermInvertedIndex)\n REGISTER_METHOD_CREATOR(double, METH_PERM_INVERTED_INDEX, CreatePermInvertedIndex)\n REGISTER_METHOD_CREATOR(int, METH_PERM_INVERTED_INDEX, CreatePermInvertedIndex)\n REGISTER_METHOD_CREATOR(float, METH_PERM_INVERTED_INDEX_SYN, CreatePermInvertedIndex)\n REGISTER_METHOD_CREATOR(double, METH_PERM_INVERTED_INDEX_SYN, CreatePermInvertedIndex)\n REGISTER_METHOD_CREATOR(int, METH_PERM_INVERTED_INDEX_SYN, CreatePermInvertedIndex)\n\n // Prefix index over permutations\n REGISTER_METHOD_CREATOR(float, METH_PERMUTATION_PREFIX_IND, CreatePermutationPrefixIndex)\n REGISTER_METHOD_CREATOR(double, METH_PERMUTATION_PREFIX_IND, CreatePermutationPrefixIndex)\n REGISTER_METHOD_CREATOR(int, METH_PERMUTATION_PREFIX_IND, CreatePermutationPrefixIndex)\n REGISTER_METHOD_CREATOR(float, METH_PERMUTATION_PREFIX_IND_SYN, CreatePermutationPrefixIndex)\n REGISTER_METHOD_CREATOR(double, METH_PERMUTATION_PREFIX_IND_SYN, CreatePermutationPrefixIndex)\n REGISTER_METHOD_CREATOR(int, METH_PERMUTATION_PREFIX_IND_SYN, CreatePermutationPrefixIndex)\n\n // Inverted index over permutation-based neighborhoods\n REGISTER_METHOD_CREATOR(float, METH_PIVOT_NEIGHB_INVINDEX, CreatePivotNeighbInvertedIndex)\n REGISTER_METHOD_CREATOR(double, METH_PIVOT_NEIGHB_INVINDEX, CreatePivotNeighbInvertedIndex)\n REGISTER_METHOD_CREATOR(int, METH_PIVOT_NEIGHB_INVINDEX, CreatePivotNeighbInvertedIndex)\n REGISTER_METHOD_CREATOR(float, METH_PIVOT_NEIGHB_INVINDEX_SYN, CreatePivotNeighbInvertedIndex)\n REGISTER_METHOD_CREATOR(double, METH_PIVOT_NEIGHB_INVINDEX_SYN, CreatePivotNeighbInvertedIndex)\n REGISTER_METHOD_CREATOR(int, METH_PIVOT_NEIGHB_INVINDEX_SYN, CreatePivotNeighbInvertedIndex)\n\n // Rank aggregation approach (omedrank) by Fagin et al\n REGISTER_METHOD_CREATOR(float, METH_OMEDRANK, CreateOMedRank)\n REGISTER_METHOD_CREATOR(double, METH_OMEDRANK, CreateOMedRank)\n REGISTER_METHOD_CREATOR(int, METH_OMEDRANK, CreateOMedRank)\n\n // VP-tree built over projections\n REGISTER_METHOD_CREATOR(float, METH_PROJ_VPTREE, CreateProjVPTree)\n REGISTER_METHOD_CREATOR(double, METH_PROJ_VPTREE, CreateProjVPTree)\n REGISTER_METHOD_CREATOR(int, METH_PROJ_VPTREE, CreateProjVPTree)\n\n \n // Sequential-search projection index with incremental sorting\n REGISTER_METHOD_CREATOR(float, METH_PROJECTION_INC_SORT, CreateProjectionIndexIncremental)\n REGISTER_METHOD_CREATOR(double, METH_PROJECTION_INC_SORT, CreateProjectionIndexIncremental)\n REGISTER_METHOD_CREATOR(int, METH_PROJECTION_INC_SORT, CreateProjectionIndexIncremental)\n\n\n // Just sequential searching\n REGISTER_METHOD_CREATOR(float, METH_SEQ_SEARCH, CreateSeqSearch)\n REGISTER_METHOD_CREATOR(double, METH_SEQ_SEARCH, CreateSeqSearch)\n REGISTER_METHOD_CREATOR(int, METH_SEQ_SEARCH, CreateSeqSearch)\n REGISTER_METHOD_CREATOR(float, METH_SEQ_SEARCH_SYN, CreateSeqSearch)\n REGISTER_METHOD_CREATOR(double, METH_SEQ_SEARCH_SYN, CreateSeqSearch)\n REGISTER_METHOD_CREATOR(int, METH_SEQ_SEARCH_SYN, CreateSeqSearch)\n\n // Small-word (KNN-graph) with randomly generated neighborhood-networks\n REGISTER_METHOD_CREATOR(float, METH_SMALL_WORLD_RAND, CreateSmallWorldRand)\n REGISTER_METHOD_CREATOR(double, METH_SMALL_WORLD_RAND, CreateSmallWorldRand)\n REGISTER_METHOD_CREATOR(int, METH_SMALL_WORLD_RAND, CreateSmallWorldRand)\n REGISTER_METHOD_CREATOR(float, METH_SMALL_WORLD_RAND_SYN, CreateSmallWorldRand)\n REGISTER_METHOD_CREATOR(double, METH_SMALL_WORLD_RAND_SYN, CreateSmallWorldRand)\n REGISTER_METHOD_CREATOR(int, METH_SMALL_WORLD_RAND_SYN, CreateSmallWorldRand)\n\n REGISTER_METHOD_CREATOR(float, METH_HNSW, CreateHnsw)\n REGISTER_METHOD_CREATOR(double, METH_HNSW, CreateHnsw)\n REGISTER_METHOD_CREATOR(int, METH_HNSW, CreateHnsw)\n\n REGISTER_METHOD_CREATOR(float, METH_SMALL_WORLD_RAND_SPLIT, CreateSmallWorldRandSplit)\n REGISTER_METHOD_CREATOR(double, METH_SMALL_WORLD_RAND_SPLIT, CreateSmallWorldRandSplit)\n REGISTER_METHOD_CREATOR(int, METH_SMALL_WORLD_RAND_SPLIT, CreateSmallWorldRandSplit)\n\n#if !defined(_MSC_VER)\n // Another KNN-graph, which is computed via NN-descent\n REGISTER_METHOD_CREATOR(float, METH_NNDES, CreateNNDescent)\n REGISTER_METHOD_CREATOR(double, METH_NNDES, CreateNNDescent)\n REGISTER_METHOD_CREATOR(int, METH_NNDES, CreateNNDescent)\n\n#endif\n // SA-tree\n REGISTER_METHOD_CREATOR(float, METH_SATREE, CreateSATree)\n REGISTER_METHOD_CREATOR(double, METH_SATREE, CreateSATree)\n REGISTER_METHOD_CREATOR(int, METH_SATREE, CreateSATree)\n\n // VP-tree, piecewise-polynomial approximation of the decision rule\n REGISTER_METHOD_CREATOR(int, METH_VPTREE, CreateVPTree)\n REGISTER_METHOD_CREATOR(float, METH_VPTREE, CreateVPTree)\n REGISTER_METHOD_CREATOR(double, METH_VPTREE, CreateVPTree)\n\n // A multi-index combination\n REGISTER_METHOD_CREATOR(float, METH_MULT_INDEX, CreateMultiIndex)\n REGISTER_METHOD_CREATOR(double, METH_MULT_INDEX, CreateMultiIndex)\n REGISTER_METHOD_CREATOR(int, METH_MULT_INDEX, CreateMultiIndex)\n\n // Non-metric clustering\n REGISTER_METHOD_CREATOR(float, METH_NON_METR_LISTCLUST, CreateNonMetrListClust)\n REGISTER_METHOD_CREATOR(double, METH_NON_METR_LISTCLUST, CreateNonMetrListClust)\n REGISTER_METHOD_CREATOR(int, METH_NON_METR_LISTCLUST, CreateNonMetrListClust)\n\n}\n\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/init_spaces.h", "content": "#ifndef INIT_SPACES_H\n#define INIT_SPACES_H\n/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"spacefactory.h\"\n\n#include \"factory/space/space_edist.h\"\n#include \"factory/space/space_bit_hamming.h\"\n#include \"factory/space/space_bregman.h\"\n#include \"factory/space/space_dummy.h\"\n#include \"factory/space/space_js.h\"\n#include \"factory/space/space_lp.h\"\n#include \"factory/space/space_scalar.h\"\n#include \"factory/space/space_sparse_lp.h\"\n#include \"factory/space/space_sparse_scalar.h\"\n#include \"factory/space/space_savch.h\"\n#include \"factory/space/space_word_embed.h\"\n#if !defined(_MSC_VER)\n#include \"factory/space/space_sqfd.h\"\n#endif\n\nnamespace similarity {\n\n\ninline void initSpaces() {\n // Registering a dummy space\n REGISTER_SPACE_CREATOR(int, SPACE_DUMMY, CreateDummy)\n REGISTER_SPACE_CREATOR(float, SPACE_DUMMY, CreateDummy)\n REGISTER_SPACE_CREATOR(double, SPACE_DUMMY, CreateDummy)\n\n // Registering binary/bit Hamming\n REGISTER_SPACE_CREATOR(int, SPACE_BIT_HAMMING, CreateBitHamming)\n\n // Registering the Levensthein-distance: regular and normalized\n REGISTER_SPACE_CREATOR(int, SPACE_LEVENSHTEIN, CreateLevenshtein)\n REGISTER_SPACE_CREATOR(float, SPACE_LEVENSHTEIN_NORM, CreateLevenshteinNormalized)\n\n // Registering Bregman divergences\n REGISTER_SPACE_CREATOR(float, SPACE_KLDIV_FAST, CreateKLDivFast)\n REGISTER_SPACE_CREATOR(double, SPACE_KLDIV_FAST, CreateKLDivFast)\n REGISTER_SPACE_CREATOR(float, SPACE_KLDIV_FAST_RIGHT_QUERY, CreateKLDivFastRightQuery)\n REGISTER_SPACE_CREATOR(double, SPACE_KLDIV_FAST_RIGHT_QUERY, CreateKLDivFastRightQuery)\n REGISTER_SPACE_CREATOR(float, SPACE_KLDIVGEN_FAST, CreateKLDivGenFast)\n REGISTER_SPACE_CREATOR(double, SPACE_KLDIVGEN_FAST, CreateKLDivGenFast)\n REGISTER_SPACE_CREATOR(float, SPACE_KLDIVGEN_SLOW, CreateKLDivGenSlow)\n REGISTER_SPACE_CREATOR(double, SPACE_KLDIVGEN_SLOW, CreateKLDivGenSlow)\n REGISTER_SPACE_CREATOR(float, SPACE_KLDIVGEN_FAST_RIGHT_QUERY, CreateKLDivGenFastRightQuery)\n REGISTER_SPACE_CREATOR(double, SPACE_KLDIVGEN_FAST_RIGHT_QUERY, CreateKLDivGenFastRightQuery)\n REGISTER_SPACE_CREATOR(float, SPACE_ITAKURASAITO_FAST, CreateItakuraSaitoFast)\n REGISTER_SPACE_CREATOR(double, SPACE_ITAKURASAITO_FAST, CreateItakuraSaitoFast)\n\n // Jensen-Shannon functions and their derivatives\n REGISTER_SPACE_CREATOR(float, SPACE_JS_DIV_SLOW, CreateJSDivSlow)\n REGISTER_SPACE_CREATOR(double, SPACE_JS_DIV_SLOW, CreateJSDivSlow)\n REGISTER_SPACE_CREATOR(float, SPACE_JS_DIV_FAST, CreateJSDivFastPrecomp)\n REGISTER_SPACE_CREATOR(double, SPACE_JS_DIV_FAST, CreateJSDivFastPrecomp)\n REGISTER_SPACE_CREATOR(float, SPACE_JS_DIV_FAST_APPROX, CreateJSDivFastPrecompApprox)\n REGISTER_SPACE_CREATOR(double, SPACE_JS_DIV_FAST_APPROX, CreateJSDivFastPrecompApprox)\n\n REGISTER_SPACE_CREATOR(float, SPACE_JS_METR_SLOW, CreateJSMetricSlow)\n REGISTER_SPACE_CREATOR(double, SPACE_JS_METR_SLOW, CreateJSMetricSlow)\n REGISTER_SPACE_CREATOR(float, SPACE_JS_METR_FAST, CreateJSMetricFastPrecomp)\n REGISTER_SPACE_CREATOR(double, SPACE_JS_METR_FAST, CreateJSMetricFastPrecomp)\n REGISTER_SPACE_CREATOR(float, SPACE_JS_METR_FAST_APPROX, CreateJSMetricFastPrecompApprox)\n REGISTER_SPACE_CREATOR(double, SPACE_JS_METR_FAST_APPROX, CreateJSMetricFastPrecompApprox)\n\n // Word embeddings\n REGISTER_SPACE_CREATOR(float, SPACE_WORD_EMBED, CreateWordEmbed)\n REGISTER_SPACE_CREATOR(double, SPACE_WORD_EMBED, CreateWordEmbed)\n\n // LP spaces\n REGISTER_SPACE_CREATOR(float, SPACE_L, CreateL)\n REGISTER_SPACE_CREATOR(double, SPACE_L, CreateL)\n REGISTER_SPACE_CREATOR(float, SPACE_LINF, CreateLINF)\n REGISTER_SPACE_CREATOR(double, SPACE_LINF, CreateLINF)\n REGISTER_SPACE_CREATOR(float, SPACE_L1, CreateL1)\n REGISTER_SPACE_CREATOR(double, SPACE_L1, CreateL1)\n REGISTER_SPACE_CREATOR(float, SPACE_L2, CreateL2)\n REGISTER_SPACE_CREATOR(double, SPACE_L2, CreateL2)\n\n // Scalar product and related distances\n // Dense\n REGISTER_SPACE_CREATOR(float, SPACE_COSINE_SIMILARITY, CreateCosineSimilarity)\n REGISTER_SPACE_CREATOR(double, SPACE_COSINE_SIMILARITY, CreateCosineSimilarity)\n REGISTER_SPACE_CREATOR(float, SPACE_ANGULAR_DISTANCE, CreateAngularDistance)\n REGISTER_SPACE_CREATOR(double, SPACE_ANGULAR_DISTANCE, CreateAngularDistance)\n\n // Sparse\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_L, CreateSparseL)\n REGISTER_SPACE_CREATOR(double, SPACE_SPARSE_L, CreateSparseL)\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_LINF, CreateSparseLINF)\n REGISTER_SPACE_CREATOR(double, SPACE_SPARSE_LINF, CreateSparseLINF)\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_L1, CreateSparseL1)\n REGISTER_SPACE_CREATOR(double, SPACE_SPARSE_L1, CreateSparseL1)\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_L2, CreateSparseL2)\n REGISTER_SPACE_CREATOR(double, SPACE_SPARSE_L2, CreateSparseL2)\n\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_COSINE_SIMILARITY, CreateSparseCosineSimilarity)\n REGISTER_SPACE_CREATOR(double, SPACE_SPARSE_COSINE_SIMILARITY, CreateSparseCosineSimilarity)\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_ANGULAR_DISTANCE, CreateSparseAngularDistance)\n REGISTER_SPACE_CREATOR(double, SPACE_SPARSE_ANGULAR_DISTANCE, CreateSparseAngularDistance)\n\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_COSINE_SIMILARITY_FAST, CreateSparseCosineSimilarityFast)\n REGISTER_SPACE_CREATOR(float, SPACE_SPARSE_ANGULAR_DISTANCE_FAST, CreateSparseAngularDistanceFast)\n\n\n REGISTER_SPACE_CREATOR(float, \"savch\", CreateSavch)\n\n#if !defined(_MSC_VER)\n // Signature Quadratic Form Distance\n REGISTER_SPACE_CREATOR(float, SPACE_SQFD_HEURISTIC_FUNC, CreateSqfdHeuristicFunc)\n REGISTER_SPACE_CREATOR(double, SPACE_SQFD_HEURISTIC_FUNC, CreateSqfdHeuristicFunc)\n REGISTER_SPACE_CREATOR(float, SPACE_SQFD_MINUS_FUNC, CreateSqfdMinusFunc)\n REGISTER_SPACE_CREATOR(double, SPACE_SQFD_MINUS_FUNC, CreateSqfdMinusFunc)\n REGISTER_SPACE_CREATOR(float, SPACE_SQFD_GAUSSIAN_FUNC, CreateSqfdGaussianFunc)\n REGISTER_SPACE_CREATOR(double, SPACE_SQFD_GAUSSIAN_FUNC, CreateSqfdGaussianFunc)\n#endif\n}\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/bbtree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_BBTREE_H_\n#define _FACTORY_BBTREE_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateBBTree(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n\n return new BBTree(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/dummy.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_DUMMY_H_\n#define _FACTORY_DUMMY_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateDummy(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new DummyMethod(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/ghtree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_GH_TREE_H_\n#define _FACTORY_GH_TREE_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateGHTree(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n\n return new GHTree(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/hnsw.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n /*\n *\n * A Hierarchical Navigable Small World (HNSW) approach.\n *\n * The main publication is (available on arxiv: http://arxiv.org/abs/1603.09320):\n * \"Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs\" by Yu. A. Malkov, D. A. Yashunin\n * This code was contributed by Yu. A. Malkov. It also was used in tests from the paper.\n *\n *\n */\n\n#pragma once\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\n template \n Index* CreateHnsw(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new Hnsw(PrintProgress, space, DataObjects);\n }\n\n/*\n * End of creating functions.\n */\n\n}\n\n\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/list_clusters.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_LIST_CLUSTERS_H_\n#define _FACTORY_LIST_CLUSTERS_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateListClusters(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n\n return new ListClusters(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/lsh.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_LSH_\n#define _FACTORY_LSH_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateLSHCauchy(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n\n int SpaceSelector = 1;\n CHECK_MSG(SpaceType == \"l1\", \"LSH (Cauchy) works only with L1\");\n\n return new LSHCauchy(\n space,\n DataObjects,\n SpaceSelector\n );\n}\n\ntemplate \nIndex* CreateLSHGaussian(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n int SpaceSelector = 2;\n CHECK_MSG(SpaceType == \"l2\", \"LSH (Guassian) works only with L2\");\n\n return new LSHGaussian(\n space,\n DataObjects,\n SpaceSelector\n );\n}\n\ntemplate \nIndex* CreateLSHThreshold(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n int SpaceSelector = 1;\n CHECK_MSG(SpaceType == \"l1\", \"LSH (Threshold) works only with L1\");\n\n return new LSHThreshold(\n space,\n DataObjects,\n SpaceSelector);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/lsh_multiprobe.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_LSH_MULTIPROBE_H_\n#define _FACTORY_LSH_MULTIPROBE_H_\n\n#include \n\n#include \"logging.h\"\n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateLSHMultiprobe(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n CHECK_MSG(SpaceType == \"l2\", \"Multiprobe LSH works only with L2\");\n\n return new MultiProbeLSH(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/multi_index.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_MULTI_INDEX_H_\n#define _FACTORY_MULTI_INDEX_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateMultiIndex(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new MultiIndex(PrintProgress, SpaceType, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/multi_vantage_point_tree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_MULTI_VPTREE_H_\n#define _FACTORY_MULTI_VPTREE_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateMultiVantagePointTree(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new MultiVantagePointTree(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/nndes.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_NNDES_H_\n#define _FACTORY_NNDES_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateNNDescent(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new NNDescentMethod(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/nonmetr_list_clust.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_NONMETR_LIST_CLUST_H\n#define _FACTORY_NONMETR_LIST_CLUST_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateNonMetrListClust(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new NonMetrListClust(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/omedrank.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_OMEDRANK_H_\n#define _FACTORY_OMEDRANK_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateOMedRank(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new OMedRank(PrintProgress,\n space,\n DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/perm_bin_vptree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PERM_BIN_VPTREE_H_\n#define _FACTORY_PERM_BIN_VPTREE_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreatePermutationBinVPTree(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n\n return new PermBinVPTree(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/perm_index_incr_bin.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PERM_INDEX_INCR_BIN_H_\n#define _FACTORY_PERM_INDEX_INCR_BIN_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreatePermutationIndexIncrementalBin(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& dataObjects) {\n\n return new PermutationIndexIncrementalBin(\n PrintProgress,\n space,\n dataObjects);\n\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/perm_lsh_bin.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PERM_INDEX_LSH_BIN_H_\n#define _FACTORY_PERM_INDEX_LSH_BIN_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreatePermutationIndexLSHBin(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& dataObjects) {\n return new PermutationIndexLSHBin(PrintProgress,\n space,\n dataObjects);\n\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/permutation_inverted_index.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PERM_INV_INDEX_H_\n#define _FACTORY_PERM_INV_INDEX_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreatePermInvertedIndex(\n bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new PermutationInvertedIndex(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/permutation_prefix_index.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PERM_PREF_INDEX_H_\n#define _FACTORY_PERM_PREF_INDEX_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreatePermutationPrefixIndex(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new PermutationPrefixIndex(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/pivot_neighb_invindx.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2010--2013\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PIVOT_NEIGHB_H_\n#define _FACTORY_PIVOT_NEIGHB_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreatePivotNeighbInvertedIndex(\n bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n \n return new PivotNeighbInvertedIndex(\n PrintProgress,\n space,\n DataObjects\n );\n}\n\n/*\n * End of creating functions.\n */\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/proj_vptree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PROJ_VPTREE_H\n#define _FACTORY_PROJ_VPTREE_H\n\n#include \n#include \n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateProjVPTree(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new ProjectionVPTree(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/projection_index_incremental.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_PROJ_INDEX_INCR_H_\n#define _FACTORY_PROJ_INDEX_INCR_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateProjectionIndexIncremental(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new ProjectionIndexIncremental(PrintProgress,\n space,\n DataObjects);\n\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/seqsearch.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_SEQ_SEARCH_H\n#define _FACTORY_SEQ_SEARCH_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateSeqSearch(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new SeqSearch(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/small_world_rand.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_SMALL_WORLD_RAND_H_\n#define _FACTORY_SMALL_WORLD_RAND_H_\n\n#include \n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateSmallWorldRand(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new SmallWorldRand(PrintProgress, space, DataObjects);\n}\n\ntemplate \nIndex* CreateSmallWorldRandSplit(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new SmallWorldRandSplit(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/spatial_approx_tree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_SAT_H_\n#define _FACTORY_SAT_H_\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nIndex* CreateSATree(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new SpatialApproxTree(space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/method/vptree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _FACTORY_VPTREE_H_\n#define _FACTORY_VPTREE_H_\n\n#include \"searchoracle.h\"\n#include \n\nnamespace similarity {\n\n/* \n * We have different creating functions, \n * b/c there are different pruning methods.\n */\ntemplate \nIndex* CreateVPTree(bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n return new VPTree>(PrintProgress, space, DataObjects);\n}\n\n/*\n * End of creating functions.\n */\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_bit_hamming.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef FACTORY_SPACE_BIT_HAMMING_H\n#define FACTORY_SPACE_BIT_HAMMING_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ninline Space* CreateBitHamming(const AnyParams& /* ignoring params */) {\n return new SpaceBitHamming();\n}\n\n/*\n * End of creating functions.\n */\n}\n\n#endif\n\n\n\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_bregman.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef FACTORY_SPACE_BREGMAN_H\n#define FACTORY_SPACE_BREGMAN_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateKLDivFast(const AnyParams& /* ignoring params */) {\n return new KLDivFast();\n}\n\ntemplate \nSpace* CreateKLDivFastRightQuery(const AnyParams& /* ignoring params */) {\n return new KLDivFastRightQuery();\n}\n\ntemplate \nSpace* CreateKLDivGenFast(const AnyParams& /* ignoring params */) {\n return new KLDivGenFast();\n}\n\ntemplate \nSpace* CreateKLDivGenFastRightQuery(const AnyParams& /* ignoring params */) {\n return new KLDivGenFastRightQuery();\n}\n\ntemplate \nSpace* CreateKLDivGenSlow(const AnyParams& /* ignoring params */) {\n return new KLDivGenSlow();\n}\n\ntemplate \nSpace* CreateItakuraSaitoFast(const AnyParams& /* ignoring params */) {\n return new ItakuraSaitoFast();\n}\n\n\n/*\n * End of creating functions.\n */\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_dummy.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_DUMMY_H\n#define FACTORY_SPACE_DUMMY_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateDummy(const AnyParams& AllParams) {\n AnyParamManager pmgr(AllParams);\n\n int param1, param2;\n\n pmgr.GetParamRequired(\"param1\", param1);\n pmgr.GetParamRequired(\"param2\", param2);\n\n pmgr.CheckUnused();\n\n return new SpaceDummy(param1, param2);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_edist.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_EDIST_H\n#define FACTORY_SPACE_EDIST_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\nSpace* CreateLevenshtein(const AnyParams& AllParams) {\n return new SpaceLevenshtein();\n}\n\nSpace* CreateLevenshteinNormalized(const AnyParams& AllParams) {\n return new SpaceLevenshteinNormalized();\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_js.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_JS_H\n#define FACTORY_SPACE_JS_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateJSDivSlow(const AnyParams& /* ignoring params */) {\n return new SpaceJSDiv(SpaceJSDiv::kJSSlow);\n}\n\ntemplate \nSpace* CreateJSDivFastPrecomp(const AnyParams& /* ignoring params */) {\n return new SpaceJSDiv(SpaceJSDiv::kJSFastPrecomp);\n}\n\ntemplate \nSpace* CreateJSDivFastPrecompApprox(const AnyParams& /* ignoring params */) {\n return new SpaceJSDiv(SpaceJSDiv::kJSFastPrecompApprox);\n}\n\n\ntemplate \nSpace* CreateJSMetricSlow(const AnyParams& /* ignoring params */) {\n return new SpaceJSMetric(SpaceJSMetric::kJSSlow);\n}\n\ntemplate \nSpace* CreateJSMetricFastPrecomp(const AnyParams& /* ignoring params */) {\n return new SpaceJSMetric(SpaceJSMetric::kJSFastPrecomp);\n}\n\ntemplate \nSpace* CreateJSMetricFastPrecompApprox(const AnyParams& /* ignoring params */) {\n return new SpaceJSMetric(SpaceJSMetric::kJSFastPrecompApprox);\n}\n\n\n\n/*\n * End of creating functions.\n */\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_lp.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_LP_H\n#define FACTORY_SPACE_LP_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateLINF(const AnyParams& /* ignoring params */) {\n // Chebyshev Distance\n return new SpaceLp(-1);\n\n}\ntemplate \nSpace* CreateL1(const AnyParams& /* ignoring params */) {\n return new SpaceLp(1);\n}\ntemplate \nSpace* CreateL2(const AnyParams& /* ignoring params */) {\n return new SpaceLp(2);\n}\n\ntemplate \nSpace* CreateL(const AnyParams& AllParams) {\n AnyParamManager pmgr(AllParams);\n\n dist_t p;\n\n pmgr.GetParamRequired(\"p\", p);\n\n return new SpaceLp(p);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_savch.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_SAVCH_H\n#define FACTORY_SPACE_SAVCH_H\n\n#include \n#include \n\n#include \n\nnamespace similarity {\n\nusing std::sqrt;\n\nconst int DELTA=1;\nconst int POINTS_IN_W=10;\nconst int POINTS_IN_H=10;\nconst int HISTO_SIZE=8;\n\n#define DISTANCE CHI_SQUARE\n\n/*\n * Creating functions.\n */\n\ntemplate \nstruct SavchSpace {\n ElementType accum_dist (ElementType a, ElementType b) const {\n ElementType res = 0;\n#if DISTANCE==MANHATTEN\n res = std::abs(a-b);\n#elif DISTANCE==EUC\n res =(a-b)*(a-b);\n#elif DISTANCE==CHI_SQUARE\n if((a+b)>0)\n res=(a-b)*(a-b)/(a+b);\n#endif\n return res;\n };\n\n ElementType operator()(const ElementType* x, const ElementType* y, size_t qty) const {\n const ElementType *lhs, *rhs;\n ElementType result = 0;\n for (int i = 0; i < POINTS_IN_H; ++i) {\n size_t iMin = i >= DELTA ? i - DELTA : 0;\n size_t iMax = i + DELTA;\n if (iMax >= POINTS_IN_H)\n iMax = POINTS_IN_H - 1;\n for (int j = 0; j < POINTS_IN_W; ++j) {\n size_t jMin = j >= DELTA ? j - DELTA : 0;\n size_t jMax = j + DELTA;\n if (jMax >= POINTS_IN_W)\n jMax = POINTS_IN_W - 1;\n ElementType minSum = numeric_limits::max();\n size_t i1 = i, j1 = j;\n lhs = y + (i1*POINTS_IN_W + j1)*HISTO_SIZE;\n for (size_t i2 = iMin; i2 <= iMax; ++i2){\n for (size_t j2 = jMin; j2 <= jMax; ++j2){\n rhs = x + (i2*POINTS_IN_W + j2)*HISTO_SIZE;\n ElementType curSum = 0;\n const ElementType* llhs = lhs;\n for (size_t ind = 0; ind < HISTO_SIZE; ++ind){\n float d1 = *llhs++;\n float d2 = *(rhs + ind);\n\n //curSum += accum_dist(d1, d2, 1);\n curSum += accum_dist(d1, d2);\n }\n if (curSum<0)\n curSum = 0;\n if (minSum>curSum){\n minSum = curSum;\n }\n }\n }\n result += sqrt(minSum);\n }\n }\n return result;\n }\n};\n\ntemplate \nSpace* CreateSavch(const AnyParams& /* ignoring params */) {\n return new VectorSpaceGen>();\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_scalar.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_SCALAR_H\n#define FACTORY_SPACE_SCALAR_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateCosineSimilarity(const AnyParams& /* ignoring params */) {\n // Cosine Similarity\n return new SpaceCosineSimilarity();\n}\n\n\ntemplate \nSpace* CreateAngularDistance(const AnyParams& /* ignoring params */) {\n // Cosine Similarity\n return new SpaceAngularDistance();\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_sparse_lp.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_SPARSE_LP_H\n#define FACTORY_SPACE_SPARSE_LP_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateSparseLINF(const AnyParams& /* ignoring params */) {\n // Chebyshev Distance\n return new SpaceSparseLp(-1);\n}\n\ntemplate \nSpace* CreateSparseL1(const AnyParams& /* ignoring params */) {\n return new SpaceSparseLp(1);\n}\n\ntemplate \nSpace* CreateSparseL2(const AnyParams& /* ignoring params */) {\n return new SpaceSparseLp(2);\n}\n\ntemplate \nSpace* CreateSparseL(const AnyParams& AllParams) {\n AnyParamManager pmgr(AllParams);\n\n dist_t p;\n\n pmgr.GetParamRequired(\"p\", p);\n\n return new SpaceSparseLp(p);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_sparse_scalar.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_SPARSE_SCALAR_H\n#define FACTORY_SPACE_SPARSE_SCALAR_H\n\n#include \n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateSparseCosineSimilarity(const AnyParams& /* ignoring params */) {\n // Cosine Similarity\n return new SpaceSparseCosineSimilarity();\n}\n\n\ntemplate \nSpace* CreateSparseAngularDistance(const AnyParams& /* ignoring params */) {\n // Cosine Similarity\n return new SpaceSparseAngularDistance();\n}\n\nSpace* CreateSparseCosineSimilarityFast(const AnyParams& /* ignoring params */) {\n // Cosine Similarity\n return new SpaceSparseCosineSimilarityFast();\n}\n\n\nSpace* CreateSparseAngularDistanceFast(const AnyParams& /* ignoring params */) {\n // Cosine Similarity\n return new SpaceSparseAngularDistanceFast();\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_sqfd.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_SQFD_H\n#define FACTORY_SPACE_SQFD_H\n\n#if !defined(_MSC_VER)\n\n#include \"space/space_sqfd.h\"\n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateSqfdHeuristicFunc(const AnyParams& AllParams) {\n AnyParamManager pmgr(AllParams);\n\n dist_t alpha;\n pmgr.GetParamRequired(\"alpha\", alpha);\n\n return new SpaceSqfd(new SqfdHeuristicFunction(alpha));\n}\n\ntemplate \nSpace* CreateSqfdMinusFunc(const AnyParams& ignore) {\n return new SpaceSqfd(new SqfdMinusFunction);\n}\n\ntemplate \nSpace* CreateSqfdGaussianFunc(const AnyParams& AllParams) {\n AnyParamManager pmgr(AllParams);\n\n dist_t alpha;\n pmgr.GetParamRequired(\"alpha\", alpha);\n\n return new SpaceSqfd(new SqfdGaussianFunction(alpha));\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/factory/space/space_word_embed.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2015\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef FACTORY_SPACE_WORD_EMBED_H\n#define FACTORY_SPACE_WORD_EMBED_H\n\n#include \n\nnamespace similarity {\n\n/*\n * Creating functions.\n */\n\ntemplate \nSpace* CreateWordEmbed(const AnyParams& AllParams) {\n AnyParamManager pmgr(AllParams);\n\n EmbedDistSpace distType;\n\n string s;\n\n pmgr.GetParamRequired(\"dist\", s);\n\n ToLower(s); \n\n if (SPACE_WORD_EMBED_DIST_L2 == s) \n distType = kEmbedDistL2;\n else if (SPACE_WORD_EMBED_DIST_COSINE == s)\n distType = kEmbedDistCosine;\n else throw runtime_error(\"Unsupported/unknown distance type for embeddings: '\" + s + \"'\");\n\n pmgr.CheckUnused();\n\n return new WordEmbedSpace(distType);\n}\n\n/*\n * End of creating functions.\n */\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/flexbuff.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2015\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#ifndef FLEX_MINIMUM_ALLOC_BUFF_H\n#define FLEX_MINIMUM_ALLOC_BUFF_H\n\n/*\n * Allocates an array\n *\n * 1) on the stack, if the number of elements doesn't exceed iMaxStackQty\n * 2) on the heap (and on the stack), if the number of elements does exceed iMaxStackQty\n *\n * One allocation can be relatively expensive (dozens or hundreds CPU cycles). However,\n * the amortized allocation cost can be quite small, if, most of the time, we deal\n * with small data that can be allocated on the stack.\n *\n * A word of caution: \n * i) Use small values for iMaxStackQty (so that allocated memory is in the order of 1-4KB or smaller),\n * ii) The stack has limited size (by default on Linux it's 8 MB). \n * iii) Therefore, the more threads you use the more likely the program will crash.\n */\n#define DECLARE_FLEXIBLE_BUFF(ElemType, varName, iBuffQty, iMaxStackQty) \\\n std::unique_ptr varName ## HeapBuff; \\\n ElemType varName ## LocalBuff[iMaxStackQty];\\\n ElemType* varName = varName ## LocalBuff ;\\\n if (iBuffQty > iMaxStackQty) {\\\n varName ## HeapBuff.reset(new ElemType[iBuffQty]);\\\n varName = varName ## HeapBuff .get() ;\\\n }\n\n/*\n\n Some dumb code that can be used to test the above (also, e.g, with valgrind). \n Unfortunately, you can't do with a unit test.\n\n// Change to 1 to see if the delete was actually called\n#if 0\nvoid operator delete ( void* ptr ) { cout << \"delete called\" << endl; free( ptr ); }\nvoid operator delete[]( void* ptr ) { cout << \"delete[] called\" << endl; free( ptr ); }\n#endif\n\n\nint main(int,char**) {\n // Maximum default stack size is 8192MB or 2048 ints\n // One must be careful here not to exceed this number\n const unsigned ELEM_QTY = 1024 * 32;\n\n size_t bufQty = ELEM_QTY +1, stackQty = ELEM_QTY ;\n DECLARE_FLEXIBLE_BUFF(int, var1, bufQty, stackQty);\n for (int i = 0; i < bufQty; ++i) var1[i] = 255;\n\n bufQty = ELEM_QTY ; stackQty = ELEM_QTY ;\n DECLARE_FLEXIBLE_BUFF(int, var2, bufQty, stackQty);\n for (int i = 0; i < bufQty; ++i) var2[i] = 255;\n\n DECLARE_FLEXIBLE_BUFF(char, var3, 1025, 1024);\n for (int i = 0; i < 1025; ++i) var3[i] = 255;\n\n DECLARE_FLEXIBLE_BUFF(double, var4, ELEM_QTY * 2, ELEM_QTY);\n for (int i = 0; i < ELEM_QTY * 2; ++i) var4[i] = -1;\n}\n\n*/\n\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/floatdiff.h", "content": "// -*- c++ -*-\n//\n// Copyright 2005, Google Inc.\n// All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are\n// met:\n//\n// * Redistributions of source code must retain the above copyright\n// notice, this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above\n// copyright notice, this list of conditions and the following disclaimer\n// in the documentation and/or other materials provided with the\n// distribution.\n// * Neither the name of Google Inc. nor the names of its\n// contributors may be used to endorse or promote products derived from\n// this software without specific prior written permission.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n// \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n// Authors: Zhanyong Wan, Sean Mcafee\n//\n// Taken from The Google C++ Testing Framework (Google Test).\n// Modified for the discussion by Fred Richards:\n// http://www.working-software.com/node/35\n//\n// Leo(nid) Boytsov slightly improved the class by \n// 1) Making ULP_diff public\n// 2) Allowing to specify the maximum number in ULPs to be considered equal\n// (and removing the default maxUlp)\n \n#ifndef FLOATING_POINT_DIFF_H\n#define FLOATING_POINT_DIFF_H\n \n#include \n#include \n \n \nnamespace\n{\n template \n struct TypeWithSize\n {\n typedef void UInt;\n };\n \n template <>\n struct TypeWithSize<4>\n {\n typedef uint32_t UInt;\n };\n \n template <>\n struct TypeWithSize<8>\n {\n typedef uint64_t UInt;\n };\n}\n \n \ntemplate \nclass FloatingPointDiff\n{\npublic:\n typedef typename TypeWithSize::UInt Bits;\n \n static const size_t kBitCount = 8*sizeof(RawType);\n static const size_t kFracBitCount = std::numeric_limits::digits - 1;\n static const size_t kExpBitCount = kBitCount - 1 - kFracBitCount;\n \n static const Bits kSignBitMask = static_cast(1) << (kBitCount - 1);\n static const Bits kFracBitMask = ~static_cast(0) >> (kExpBitCount + 1);\n static const Bits kExpBitMask = ~(kSignBitMask | kFracBitMask);\n \n explicit FloatingPointDiff(const RawType& x) : value_(x) {}\n \n //\n // Now checking for NaN to match == behavior.\n //\n bool AlmostEquals(const FloatingPointDiff& rhs, size_t maxUlps) const {\n if (is_nan() || rhs.is_nan()) return false;\n return ULP_diff(bits_, rhs.bits_) <= maxUlps;\n }\n\n Bits ULP_diff(const FloatingPointDiff& rhs) const {\n return ULP_diff(bits_, rhs.bits_);\n }\n \n bool is_nan() const {\n return ((kExpBitMask & bits_) == kExpBitMask) &&\n\t ((kFracBitMask & bits_) != 0);\n }\n \nprivate:\n Bits SignAndMagnitudeToBiased(const Bits& sam) const {\n if (kSignBitMask & sam) {\n return ~sam + 1; // two's complement\n } else {\n return kSignBitMask | sam; // * 2\n }\n }\n \n Bits ULP_diff(const Bits& sam1, const Bits& sam2) const\n {\n const Bits biased1 = SignAndMagnitudeToBiased(sam1);\n const Bits biased2 = SignAndMagnitudeToBiased(sam2);\n \n return (biased1 >= biased2) ? (biased1 - biased2) : (biased2 - biased1);\n }\n \n union {\n RawType value_;\n Bits bits_;\n };\n};\n \n#endif // FLOATING_POINT_DIFF_H\n" }, { "path": "algorithms/NMSLIB/code/include/global.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _GLOBAL_H_\n#define _GLOBAL_H_\n\n#include \n#include \n#include \n\nstatic const int kMaxFilenameLength = 255;\n\n#define ARRAY_SIZE(x) (static_cast(sizeof(x)/sizeof(*x)))\n\n#ifdef __GNUC__\n#define DEPRECATED(func) __attribute__ ((deprecated)) func\n#elif defined(_MSC_VER)\n#define DEPRECATED(func) __declspec(deprecated) func\n#else\n#pragma message(\"WARNING: You need to implement DEPRECATED for this compiler\")\n#define DEPRECATED(func) func\n#endif\n\nnamespace similarity {\n\n#undef DISABLE_COPY_AND_ASSIGN\n#define DISABLE_COPY_AND_ASSIGN(Type) \\\n explicit Type(const Type&); \\\n Type& operator=(const Type&)\n\n/*\n * On windows, disabling copy&assign generates way to many warnings C4661\n * Let's disable this warning.\n */\n#if defined(_MSC_VER)\n #pragma warning(disable: 4661)\n#endif\n\n} // namespace similarity\n\n#endif // _GLOBAL_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/gold_standard.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef GOLD_STANDARD_H\n#define GOLD_STANDARD_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"query_creator.h\"\n#include \"space.h\"\n#include \"utils.h\"\n#include \"ztimer.h\"\n\n#define SEQ_SEARCH_TIME \"SeqSearchTime\"\n#define SEQ_GS_QTY \"GoldStandQty\"\n#define GS_NOTE_FIELD \"Note\"\n#define GS_TEST_SET_ID \"TestSetId\"\n#define GS_THREAD_TEST_QTY \"ThreadTestQty\"\n\nnamespace similarity {\n\nusing std::vector;\nusing std::ostream;\nusing std::istream;\nusing std::unique_ptr;\nusing std::thread;\nusing std::ref;\n\n\ntemplate \nstruct ResultEntry {\n IdType mId;\n LabelType mLabel;\n dist_t mDist;\n ResultEntry(IdType id = 0, LabelType label = 0, dist_t dist = 0) \n : mId(id), mLabel(label), mDist(dist) {}\n\n /*\n * Reads entry in the binary format (can't read if the data was written\n * on the machine with another type of endianness.)\n */\n void readBinary(istream& in) {\n in.read(reinterpret_cast(&mId), sizeof mId);\n in.read(reinterpret_cast(&mLabel), sizeof mLabel);\n in.read(reinterpret_cast(&mDist), sizeof mDist);\n }\n /*\n * Saves entry in the binary format, see the comment\n * on the endianness above.\n */\n void writeBinary(ostream& out) const {\n out.write(reinterpret_cast(&mId), sizeof mId);\n out.write(reinterpret_cast(&mLabel), sizeof mLabel);\n out.write(reinterpret_cast(&mDist), sizeof mDist);\n }\n bool operator<(const ResultEntry& o) const {\n if (mDist != o.mDist) return mDist < o.mDist;\n return mId < o.mId;\n }\n bool operator==(const ResultEntry& o) const {\n return mId == o.mId && \n mDist == o.mDist && \n mLabel == o.mLabel;\n }\n};\n\ntemplate \nostream& operator<<(ostream& out, const ResultEntry& e) {\n return out << \"[\" << e.mId << \" lab=\" << e.mLabel << \" dist=\" << e.mDist << \"]\";\n}\n\nenum ClassResult {\n kClassUnknown,\n kClassCorrect,\n kClassWrong,\n};\n\ntemplate \nclass GoldStandard {\npublic:\n GoldStandard(){}\n GoldStandard(const typename similarity::Space& space,\n const ObjectVector& datapoints,\n typename similarity::Query* query,\n float maxKeepEntryCoeff\n ) {\n DoSeqSearch(space, datapoints, query);\n size_t maxKeepEntryQty =\n std::min((size_t)std::round(query->ResultSize()*maxKeepEntryCoeff),\n SortedAllEntries_.size());\n\n if (maxKeepEntryQty != 0) {\n CHECK(maxKeepEntryQty <= SortedAllEntries_.size())\n vector> tmp(SortedAllEntries_.begin(),\n SortedAllEntries_.begin() + maxKeepEntryQty);\n /* \n * The swap trick actually leads to memory being freed.\n * If we simply erase the extra entries, vector still keeps the memory!\n */\n SortedAllEntries_.swap(tmp);\n }\n }\n /*\n * See the endianness comment.\n */\n void Write(ostream& controlStream, ostream& binaryStream) const {\n\n WriteField(controlStream, SEQ_SEARCH_TIME, ConvertToString(SeqSearchTime_));\n WriteField(controlStream, SEQ_GS_QTY, ConvertToString(SortedAllEntries_.size()));\n for (size_t i = 0; i < SortedAllEntries_.size(); ++i) {\n SortedAllEntries_[i].writeBinary(binaryStream);\n }\n }\n\n void Read(istream& controlStream, istream& binaryStream) {\n string s;\n ReadField(controlStream, SEQ_SEARCH_TIME, s);\n ConvertFromString(s, SeqSearchTime_);\n ReadField(controlStream, SEQ_GS_QTY, s);\n size_t qty = 0;\n ConvertFromString(s, qty);\n SortedAllEntries_.resize(qty);\n for (size_t i = 0; i < qty; ++i)\n SortedAllEntries_[i].readBinary(binaryStream);\n }\n uint64_t GetSeqSearchTime() const { return SeqSearchTime_; }\n\n /*\n * SortedAllEntries_ include all database entries sorted in the order\n * of increasing distance from the query.\n */\n const vector>& GetSortedEntries() const { return SortedAllEntries_;}\nprivate:\n void DoSeqSearch(const Space& space,\n const ObjectVector& datapoints,\n typename similarity::Query* pQuery) {\n WallClockTimer wtm;\n\n wtm.reset();\n\n SortedAllEntries_.resize(datapoints.size());\n const Object* pQueryObj = pQuery->QueryObject();\n\n for (size_t i = 0; i < datapoints.size(); ++i) {\n // Distance can be asymmetric, but the query is always on the right side\n SortedAllEntries_[i] = ResultEntry(datapoints[i]->id(),\n datapoints[i]->label(),\n space.IndexTimeDistance(datapoints[i], pQueryObj));\n pQuery->CheckAndAddToResult(SortedAllEntries_[i].mDist, datapoints[i]);\n }\n\n wtm.split();\n\n SeqSearchTime_ = wtm.elapsed();\n\n std::sort(SortedAllEntries_.begin(), SortedAllEntries_.end());\n }\n\n uint64_t SeqSearchTime_;\n\n vector> SortedAllEntries_;\n};\n\ntemplate \nstruct GoldStandardThreadParams {\n const ExperimentConfig& config_;\n const QueryCreatorType& QueryCreator_;\n float maxKeepEntryCoeff_;\n const unsigned ThreadQty_;\n const unsigned GoldStandPart_;\n vector>>& vGoldStand_;\n GoldStandardThreadParams(const ExperimentConfig& config,\n const QueryCreatorType& QueryCreator,\n float maxKeepEntryCoeff,\n unsigned ThreadQty,\n unsigned GoldStandPart,\n vector>>& vGoldStand) : config_(config),\n QueryCreator_(QueryCreator),\n maxKeepEntryCoeff_(maxKeepEntryCoeff),\n ThreadQty_(ThreadQty),\n GoldStandPart_(GoldStandPart),\n vGoldStand_(vGoldStand) {}\n};\n\ntemplate \nstruct GoldStandardThread {\n void operator ()(GoldStandardThreadParams& prm) {\n size_t numquery = prm.config_.GetQueryObjects().size();\n\n unsigned GoldStandPart = prm.GoldStandPart_;\n unsigned ThreadQty = prm.ThreadQty_;\n\n for (size_t q = 0; q < numquery; ++q) {\n if ((q % ThreadQty) == GoldStandPart) {\n unique_ptr> query(prm.QueryCreator_(prm.config_.GetSpace(),\n prm.config_.GetQueryObjects()[q]));\n\n prm.vGoldStand_[q].reset(new GoldStandard(prm.config_.GetSpace(),\n prm.config_.GetDataObjects(),\n query.get(),\n prm.maxKeepEntryCoeff_));\n }\n }\n }\n};\n\ntemplate \nclass GoldStandardManager {\npublic:\n GoldStandardManager(const ExperimentConfig& config) :\n config_(config),\n vvGoldStandardRange_(config_.GetRange().size()),\n vvGoldStandardKNN_(config_.GetKNN().size()) {}\n // Both read and Compute can be called multiple times\n // if maxKeepEntryCoeff is non-zero, it defines how many GS entries (relative to the result set size) we memorize\n void Compute(size_t threadQty, float maxKeepEntryCoeff) {\n threadQty = std::max(size_t(1), threadQty);\n LOG(LIB_INFO) << \"Computing gold standard data using \" << threadQty << \" threads, keeping \" << maxKeepEntryCoeff<< \" times result set size entries\";;\n for (size_t i = 0; i < config_.GetRange().size(); ++i) {\n vvGoldStandardRange_[i].clear();\n const dist_t radius = config_.GetRange()[i];\n RangeCreator cr(radius);\n procOneSet(cr, vvGoldStandardRange_[i], threadQty, maxKeepEntryCoeff);\n }\n for (size_t i = 0; i < config_.GetKNN().size(); ++i) {\n vvGoldStandardKNN_[i].clear();\n const size_t K = config_.GetKNN()[i];\n KNNCreator cr(K, config_.GetEPS());\n procOneSet(cr, vvGoldStandardKNN_[i], threadQty, maxKeepEntryCoeff);\n }\n }\n void Read(istream& controlStream, istream& binaryStream,\n size_t queryQty,\n size_t& testSetId,\n size_t& savedThreadQty) {\n LOG(LIB_INFO) << \"Reading gold standard data from cache\";\n string s;\n ReadField(controlStream, GS_TEST_SET_ID, s);\n ConvertFromString(s, testSetId);\n ReadField(controlStream, GS_THREAD_TEST_QTY, savedThreadQty);\n for (size_t i = 0; i < vvGoldStandardRange_.size(); ++i) {\n ReadField(controlStream, GS_NOTE_FIELD, s);\n vvGoldStandardRange_[i].clear();\n readOneGS(controlStream, binaryStream,\n queryQty, vvGoldStandardRange_[i]);\n }\n for (size_t i = 0; i < vvGoldStandardKNN_.size(); ++i) {\n ReadField(controlStream, GS_NOTE_FIELD, s);\n vvGoldStandardKNN_[i].clear();\n readOneGS(controlStream, binaryStream,\n queryQty, vvGoldStandardKNN_[i]);\n }\n }\n void Write(ostream& controlStream, ostream& binaryStream,\n size_t testSetId, size_t threadTestQty) {\n WriteField(controlStream, GS_TEST_SET_ID, ConvertToString(testSetId));\n WriteField(controlStream,GS_THREAD_TEST_QTY, threadTestQty);\n // GS_NOTE_FIELD & GS_TEST_SET_ID are for informational purposes only\n for (size_t i = 0; i < vvGoldStandardRange_.size(); ++i) {\n WriteField(controlStream, GS_NOTE_FIELD,\n \"range radius=\" + ConvertToString(config_.GetRange()[i]));\n writeOneGS(controlStream, binaryStream,\n vvGoldStandardRange_[i]);\n }\n for (size_t i = 0; i < vvGoldStandardKNN_.size(); ++i) {\n WriteField(controlStream, GS_NOTE_FIELD,\n \"k=\" + ConvertToString(config_.GetKNN()[i]) +\n \"eps=\" + ConvertToString(config_.GetEPS()));\n writeOneGS(controlStream, binaryStream,\n vvGoldStandardKNN_[i]);\n }\n }\n const vector>> &GetRangeGS(size_t i) const {\n return vvGoldStandardRange_[i];\n }\n const vector>> &GetKNNGS(size_t i) const {\n return vvGoldStandardKNN_[i];\n }\nprivate:\n const ExperimentConfig& config_;\n vector>>> vvGoldStandardRange_;\n vector>>> vvGoldStandardKNN_;\n\n void writeOneGS(ostream& controlStream, ostream& binaryStream,\n const vector>>& oneGS) {\n for(const unique_ptr>& obj : oneGS) {\n obj->Write(controlStream, binaryStream);\n }\n }\n\n void readOneGS(istream& controlStream, istream& binaryStream,\n size_t queryQty,\n vector>>& oneGS) {\n oneGS.resize(queryQty);\n for (size_t k = 0; k < queryQty; ++k) {\n unique_ptr> gs(new GoldStandard());\n gs->Read(controlStream, binaryStream);\n oneGS[k].reset(gs.release());\n }\n }\n\n template \n void procOneSet(const QueryCreatorType& QueryCreator,\n vector>>& vGoldStand,\n size_t threadQty,\n float maxKeepEntryCoeff) {\n size_t queryQty = config_.GetQueryObjects().size();\n vGoldStand.resize(queryQty);\n\n vector>> GoldStandParams(threadQty);\n\n for (unsigned GoldPart = 0; GoldPart < threadQty; ++GoldPart) {\n GoldStandParams[GoldPart].reset(\n new GoldStandardThreadParams(config_,\n QueryCreator,\n maxKeepEntryCoeff,\n threadQty,\n GoldPart,\n vGoldStand));\n }\n\n if (threadQty == 1) {\n GoldStandardThread()(*GoldStandParams[0]);\n } else {\n vector Threads(threadQty);\n\n for (unsigned GoldPart = 0; GoldPart < threadQty; ++GoldPart) {\n Threads[GoldPart] = thread(GoldStandardThread(),ref(*GoldStandParams[GoldPart]));\n }\n\n for (unsigned GoldPart = 0; GoldPart < threadQty; ++GoldPart) {\n Threads[GoldPart].join();\n }\n }\n }\n};\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/idtype.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *_\n */\n\n#ifndef ID_TYPE_H\n#define ID_TYPE_H\n\n#include \n\nnamespace similarity {\n\nusing std::numeric_limits;\n\n/* \n * We are not gonna have billions of records or labels in the foreseeable future\n * Negative values would represent missing labels and ids. In principle, it is probably\n * easy to switch to 8-byte IDs by simply changing below typedef declarations.\n * However, some caution needs to be excecised because\n * 1) We want to preserve alignment (extra padding in the Object may be needed (see the comment in object.h).\n * 2) We need to carefully check all the contexts where IDs are used. It is not impossible that some\n * code declares IDs as int-type.\n */\ntypedef int32_t IdType;\ntypedef uint32_t IdTypeUnsign;\nconst IdTypeUnsign MAX_DATASET_QTY = numeric_limits::max();\n\ntypedef int32_t LabelType;\n\n#define LABEL_PREFIX \"label:\"\n#define EMPTY_LABEL numeric_limits::min()\n\nconst size_t ID_SIZE = sizeof(IdType);\nconst size_t LABEL_SIZE = sizeof(LabelType);\nconst size_t DATALENGTH_SIZE = sizeof(size_t);\n\n} // namespace similarity\n\n#endif // _OBJECT_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/incremental_quick_select.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _INCREMENTAL_QUICK_SELECT_H_\n#define _INCREMENTAL_QUICK_SELECT_H_\n\n#include \n#include \n#include \"global.h\"\n\nnamespace similarity {\n\ntemplate \nclass IncrementalQuickSelect {\n public:\n explicit IncrementalQuickSelect(std::vector& x)\n : x_(x), n_(static_cast(x.size())), idx_(0) {\n stk_.push(n_);\n }\n\n ~IncrementalQuickSelect() {}\n\n T GetNext() {\n CHECK(idx_ <= stk_.top());\n if (stk_.top() == idx_) {\n stk_.pop();\n return x_[idx_];\n } else {\n int pivot_idx = idx_; // OR random[idx, stk.top() - 1]\n int pivot_idx_final = Partition(pivot_idx, idx_, stk_.top() - 1);\n stk_.push(pivot_idx_final);\n return GetNext();\n }\n }\n\n inline bool Next() {\n ++idx_;\n return idx_ < n_;\n }\n\n private:\n\n int Partition(int pivot_idx, int left, int right) {\n T pivot = x_[pivot_idx];\n for (;;) {\n while (x_[left] < pivot) ++left;\n while (x_[right] > pivot) --right;\n\n if (left >= right) {\n break;\n } else if (x_[left] == x_[right]) {\n ++left;\n } else if (left < right) {\n std::swap(x_[left], x_[right]);\n }\n }\n return right;\n }\n\n std::vector& x_;\n const int n_;\n int idx_;\n std::stack stk_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(IncrementalQuickSelect);\n};\n\n} // namespace similarity\n\n#endif // _INCREMENTAL_QUICK_SELECT_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/index.h", "content": "/**\n * Non-metric Space Library\n *#\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _INDEX_STRUCTURE_H_\n#define _INDEX_STRUCTURE_H_\n\n#include \n#include \n#include \n\n#include \"params.h\"\n#include \"object.h\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\n\n/*\n * This error message is used to report the situation\n * when a previously used index is used with a different\n * data set\n */\nconst string DATA_MUTATION_ERROR_MSG =\n \"A previously saved index is apparently used with a different data set, a different data set split, and/or a different gold standard file!\";\nconst string METHOD_DESC=\"MethodDesc\";\nconst string LINE_QTY=\"LineQty\";\n\ntemplate \nclass RangeQuery;\n\ntemplate \nclass KNNQuery;\n\n/*\n * Abstract class for all index structures\n */\ntemplate \nclass Index {\npublic:\n // Create an index using given parameters\n virtual void CreateIndex(const AnyParams& indexParams) = 0;\n // SaveIndex is not necessarily implemented\n virtual void SaveIndex(const string& location) {\n throw runtime_error(\"SaveIndex is not implemented for method: \" + StrDesc());\n }\n // LoadIndex is not necessarily implemented\n virtual void LoadIndex(const string& location) {\n throw runtime_error(\"LoadIndex is not implemented for method: \" + StrDesc());\n }\n virtual ~Index() {}\n /*\n * There are two type of search methods: a range search and a k-Nearest Neighbor search.\n * In both cases, you can specify an optional starting data point/object. Some methods, e.g.,\n * based on proximity graphs, can use this information to make the search faster and/or more \n * accurate. \n */\n virtual void Search(RangeQuery* query, IdType startObj = -1) const = 0;\n virtual void Search(KNNQuery* query, IdType startObj = -1) const = 0;\n // Get the description of the method\n virtual const string StrDesc() const = 0;\n // Set query-time parameters\n virtual void SetQueryTimeParams(const AnyParams& params) = 0;\n // Reset query-time parameters so that they have default values\n virtual void ResetQueryTimeParams() { SetQueryTimeParams(getEmptyParams()); }\n /*\n * In rare cases, mostly when we wrap up 3rd party methods,\n * we simply duplicate the data set. This function\n * let the experimentation code know this, so it could\n * adjust the memory consumption of the index.\n */\n virtual bool DuplicateData() const { return false; }\n\nprivate:\n template \n void GenericSearch(QueryType* query, IdType) const;\n\n};\n\n} // namespace similarity\n\n#endif // _METRIC_INDEX_STRUCTURE_H_\n" }, { "path": "algorithms/NMSLIB/code/include/init.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _INIT_H_\n#define _INIT_H_\n\n/*\n * This function should be called only *ONCE*,\n * but before actually using library functionality.\n */\n\n#include \"logging.h\"\n\nnamespace similarity {\n\n void initLibrary(LogChoice choice = LIB_LOGNONE, const char*pLogFile = NULL);\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/knnquery.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _KNNQUERY_H_\n#define _KNNQUERY_H_\n\n#include \"object.h\"\n#include \"query.h\"\n\nnamespace similarity {\n\ntemplate \nclass KNNQueue;\n\ntemplate \nclass Index;\n\ntemplate \nclass Space;\n\ntemplate \nclass KNNQuery : public Query {\n public:\n ~KNNQuery();\n KNNQuery(const Space& space, const Object* query_object, const unsigned K, float eps = 0);\n\n const KNNQueue* Result() const;\n virtual dist_t Radius() const;\n unsigned ResultSize() const;\n unsigned GetK() const { return K_; }\n float GetEPS() const { return eps_; }\n\n void Reset();\n bool CheckAndAddToResult(const dist_t distance, const Object* object);\n bool CheckAndAddToResult(const Object* object);\n size_t CheckAndAddToResult(const ObjectVector& bucket);\n\n bool Equals(const KNNQuery* query) const;\n void Print() const;\n static std::string Type() { return \"K-NN\"; }\n\n protected:\n unsigned K_;\n float eps_;\n KNNQueue* result_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(KNNQuery);\n};\n\n} // namespace similarity\n\n#endif // _QUERY_TYPES_H_\n" }, { "path": "algorithms/NMSLIB/code/include/knnqueue.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _KNN_QUEUE_H_\n#define _KNN_QUEUE_H_\n\n#include \n#include \n#include \n#include \n\n#include \"global.h\"\n#include \"object.h\"\n\nnamespace similarity {\n\ntemplate \nclass KNNQueue {\n public:\n KNNQueue(unsigned K) : K_(K) {}\n\n ~KNNQueue() {}\n\n void Reset() { QueueType empty; std::swap(empty, queue_); }\n\n bool Empty() const { return queue_.empty(); }\n\n size_t Size() const { return queue_.size(); }\n\n dist_t TopDistance() const {\n return queue_.empty()\n ? std::numeric_limits::max()\n : queue_.top().first;\n }\n\n const Object* TopObject() const { return queue_.top().second; }\n\n const Object* Pop() {\n const Object* top_object = TopObject();\n queue_.pop();\n return top_object;\n }\n\n void Push(const dist_t distance, const Object* object) {\n if (Size() < K_) {\n queue_.push(QueueElement(distance, object));\n } else {\n if (TopDistance() > distance) {\n Pop();\n queue_.push(QueueElement(distance, object));\n }\n }\n }\n\n KNNQueue* Clone() const {\n KNNQueue* clone = new KNNQueue(K_);\n clone->queue_ = queue_;\n return clone;\n }\n\n private:\n typedef std::pair QueueElement;\n typedef std::priority_queue QueueType;\n\n QueueType queue_;\n unsigned K_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(KNNQueue);\n};\n\n} // namespace similarity\n\n#endif // _KNN_QUEUE_H_\n" }, { "path": "algorithms/NMSLIB/code/include/logging.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _LOGGING_H_\n#define _LOGGING_H_\n\n#include \n#include \n#include \n#include \n#include \n\nusing std::ostream;\nusing std::ofstream;\nusing std::cerr;\nusing std::stringstream;\nusing std::runtime_error;\n\nenum LogSeverity {LIB_INFO, LIB_WARNING, LIB_ERROR, LIB_FATAL};\nenum LogChoice {LIB_LOGNONE, LIB_LOGFILE, LIB_LOGSTDERR};\n\nstd::string LibGetCurrentTime();\n\n// write log to file\nvoid InitializeLogger(LogChoice choice = LIB_LOGNONE, const char* logfile = NULL);\n\nclass Logger {\n public:\n Logger(LogSeverity severity, const std::string& file, int line, const char* function);\n ~Logger();\n\n static std::ostream& stream() { return *currstrm_ ; }\n\n private:\n LogSeverity severity_;\n\n static ofstream logfile_;\n\n static ostream* currstrm_;\n\n // If choice != LIB_LOGFILE the second argument is ignored\n friend void InitializeLogger(LogChoice choice, const char* logfile);\n};\n\nclass RuntimeErrorWrapper {\n public:\n RuntimeErrorWrapper(const std::string& file, int line, const char* function);\n\n stringstream& stream() { return currstrm_ ; }\n\n private:\n stringstream currstrm_;\n};\n\n\n#define LOG(severity) \\\n Logger(severity, __FILE__, __LINE__, __FUNCTION__).stream()\n\n\n#define CHECK(condition) \\\n if (!(condition)) {\\\n LOG(LIB_ERROR) << \"Check failed: \" << #condition; \\\n throw runtime_error(\"Check failed: it's either a bug or inconsistent data!\"); \\\n }\n\n#define CHECK_MSG(condition,message) \\\n if (!(condition)) {\\\n LOG(LIB_ERROR) << \"Check failed: \" << #condition; \\\n throw runtime_error(\"Check failed: \" + string(message)); \\\n }\n\n// debug only check and log\n#ifndef NDEBUG\n#define DCHECK(condition) CHECK(condition)\n#define DLOG(severity) LOG(severity)\n#else\n// NDEBUG disables standard C assertions\n#define DCHECK(condition) while (0) CHECK(condition)\n#define DLOG(severity) while (0) LOG(severity)\n#endif\n\n#define PREPARE_RUNTIME_ERR(var) \\\n RuntimeErrorWrapper var(__FILE__, __LINE__, __FUNCTION__); var.stream()\n#define THROW_RUNTIME_ERR(var) \\\n throw runtime_error(var.stream().str())\n\n#endif // _LOGGING_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/memory.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _MEMORY_USAGE_H_\n#define _MEMORY_USAGE_H_\n\nnamespace similarity {\n\nclass MemUsage {\n public:\n MemUsage();\n\n /** returns the current virtual memory size of\n the process with pid in MB **/\n double get_vmsize();\n\n private:\n char status_file_[50];\n};\n\n} // namespace similarity\n\n#endif // _MEMORY_USAGE_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/meta_analysis.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _META_ANALYSIS_H\n#define _META_ANALYSIS_H\n\n#include \n#include \n\n#include \"utils.h\"\n\nnamespace similarity {\n\nusing std::vector;\nusing std::string;\n\n/*\n * A helper class to integrate results\n */\nclass MetaAnalysis {\npublic:\n MetaAnalysis(size_t TestSetQty, double zVal = 1.96) : zVal_(zVal) {\n PrecisionOfApprox_ .resize(TestSetQty);\n Recall_ .resize(TestSetQty);\n ClassAccuracy_ .resize(TestSetQty);\n LogRelPosError_ .resize(TestSetQty);\n NumCloser_ .resize(TestSetQty);\n RecallAt1_ .resize(TestSetQty);\n QueryTime_ .resize(TestSetQty);\n DistComp_ .resize(TestSetQty);\n ImprEfficiency_ .resize(TestSetQty);\n ImprDistComp_ .resize(TestSetQty);\n Mem_ .resize(TestSetQty);\n IndexTime_ .resize(TestSetQty);\n LoadTime_ .resize(TestSetQty);\n SaveTime_ .resize(TestSetQty);\n QueryPerSec_ .resize(TestSetQty);\n }\n\n // Let's protect Add* functions, b/c them can be called from different threads \n void AddRecall(size_t SetId, double Recall) {\n Recall_[SetId].push_back(Recall);\n }\n void AddPrecisionOfApprox(size_t SetId, double PrecisionOfApprox) {\n PrecisionOfApprox_[SetId].push_back(PrecisionOfApprox);\n }\n void AddClassAccuracy(size_t SetId, double ClassAccuracy) {\n ClassAccuracy_[SetId].push_back(ClassAccuracy);\n }\n void AddLogRelPosError(size_t SetId, double LogRelPosError) {\n LogRelPosError_[SetId].push_back(LogRelPosError);\n }\n void AddNumCloser(size_t SetId, double NumCloser) {\n NumCloser_[SetId].push_back(NumCloser);\n }\n void AddRecallAt1(size_t SetId, double RecallAt1) {\n RecallAt1_[SetId].push_back(RecallAt1);\n }\n void AddQueryTime(size_t SetId, double QueryTime) {\n QueryTime_[SetId].push_back(QueryTime);\n }\n void AddDistComp(size_t SetId, double DistComp) {\n DistComp_[SetId].push_back(DistComp);\n }\n void SetMem(size_t SetId, double Mem) {\n Mem_[SetId] = Mem;\n }\n void SetIndexTime(size_t SetId, double IndexTime) {\n IndexTime_[SetId] = IndexTime;\n }\n void SetLoadTime(size_t SetId, double LoadTime) {\n LoadTime_[SetId] = LoadTime;\n }\n void SetSaveTime(size_t SetId, double SaveTime) {\n SaveTime_[SetId] = SaveTime;\n }\n void SetQueryPerSec(size_t SetId, double QueryPerSec) {\n QueryPerSec_[SetId] = QueryPerSec;\n }\n void SetImprEfficiency(size_t SetId, double ImprEfficiency) {\n ImprEfficiency_[SetId] = ImprEfficiency;\n }\n void SetImprDistComp(size_t SetId, double ImprDistComp) {\n ImprDistComp_[SetId] = ImprDistComp;\n }\n\n void ComputeAll() {\n ComputeOneSimple(\"Recall\", Recall_, RecallAvg, RecallConfMin, RecallConfMax);\n ComputeOneSimple(\"PrecisionOfApprox\", PrecisionOfApprox_, PrecisionOfApproxAvg, PrecisionOfApproxConfMin, PrecisionOfApproxConfMax);\n ComputeOneSimple(\"ClassAccuracy\", ClassAccuracy_, ClassAccuracyAvg, ClassAccuracyConfMin, ClassAccuracyConfMax);\n ComputeOneSimple(\"LogRelPosError\", LogRelPosError_, LogRelPosErrorAvg, LogRelPosErrorConfMin, LogRelPosErrorConfMax);\n ComputeOneSimple(\"NumCloser\", NumCloser_, NumCloserAvg, NumCloserConfMin, NumCloserConfMax);\n ComputeOneSimple(\"RecallAt1\", RecallAt1_, RecallAt1Avg, RecallAt1ConfMin, RecallAt1ConfMax);\n ComputeOneMeta(\"QueryTime\", QueryTime_, QueryTimeAvg, QueryTimeConfMin, QueryTimeConfMax);\n ComputeOneMeta(\"DistComp\", DistComp_, DistCompAvg, DistCompConfMin, DistCompConfMax);\n\n ComputeOneSimple(\"ImprEfficiency\", ImprEfficiency_, ImprEfficiencyAvg, ImprEfficiencyConfMin, ImprEfficiencyConfMax);\n ComputeOneSimple(\"ImprDistComp\", ImprDistComp_, ImprDistCompAvg, ImprDistCompConfMin, ImprDistCompConfMax);\n ComputeOneSimple(\"MemUsage\", Mem_, MemAvg, MemConfMin, MemConfMax);\n ComputeOneSimple(\"IndexTime\", IndexTime_, IndexTimeAvg, IndexTimeConfMin, IndexTimeConfMax);\n ComputeOneSimple(\"LoadTime\", LoadTime_, LoadTimeAvg, LoadTimeConfMin, LoadTimeConfMax);\n ComputeOneSimple(\"SaveTime\", SaveTime_, SaveTimeAvg, SaveTimeConfMin, SaveTimeConfMax);\n ComputeOneSimple(\"QueryPerSec\", QueryPerSec_, QueryPerSecAvg, QueryPerSecConfMin, QueryPerSecConfMax);\n }\n\n double GetRecallAvg() const { return RecallAvg;} \n double GetRecallConfMin() const{return RecallConfMin;}; \n double GetRecallConfMax() const { return RecallConfMax;}\n\n double GetPrecisionOfApproxAvg() const { return PrecisionOfApproxAvg;} \n double GetPrecisionOfApproxConfMin() const{return PrecisionOfApproxConfMin;}; \n double GetPrecisionOfApproxConfMax() const { return PrecisionOfApproxConfMax;}\n\n double GetClassAccuracyAvg() const { return ClassAccuracyAvg;} \n double GetClassAccuracyConfMin() const{return ClassAccuracyConfMin;}; \n double GetClassAccuracyConfMax() const { return ClassAccuracyConfMax;}\n\n double GetRelPosErrorAvg() const { return exp(LogRelPosErrorAvg);} \n double GetRelPosErrorConfMin() const{return exp(LogRelPosErrorConfMin);}; \n double GetRelPosErrorConfMax() const { return exp(LogRelPosErrorConfMax);}\n\n double GetNumCloserAvg() const { return NumCloserAvg;} \n double GetNumCloserConfMin() const{return NumCloserConfMin;}; \n double GetNumCloserConfMax() const { return NumCloserConfMax;}\n\n double GetRecallAt1Avg() const { return RecallAt1Avg;} \n double GetRecallAt1ConfMin() const{return RecallAt1ConfMin;}; \n double GetRecallAt1ConfMax() const { return RecallAt1ConfMax;}\n\n double GetImprEfficiencyAvg() const { return ImprEfficiencyAvg;} \n double GetImprEfficiencyConfMin() const{return ImprEfficiencyConfMin;}; \n double GetImprEfficiencyConfMax() const { return ImprEfficiencyConfMax;}\n\n double GetImprDistCompAvg() const { return ImprDistCompAvg;} \n double GetImprDistCompConfMin() const{return ImprDistCompConfMin;}; \n double GetImprDistCompConfMax() const { return ImprDistCompConfMax;}\n\n double GetMemAvg() const { return MemAvg;} \n double GetMemConfMin() const{return MemConfMin;}; \n double GetMemConfMax() const { return MemConfMax;}\n\n double GetIndexTimeAvg() const { return IndexTimeAvg;} \n double GetIndexTimeConfMin() const{return IndexTimeConfMin;}; \n double GetIndexTimeConfMax() const { return IndexTimeConfMax;}\n\n double GetLoadTimeAvg() const { return LoadTimeAvg;} \n double GetLoadTimeConfMin() const{return LoadTimeConfMin;}; \n double GetLoadTimeConfMax() const { return LoadTimeConfMax;}\n\n double GetSaveTimeAvg() const { return SaveTimeAvg;} \n double GetSaveTimeConfMin() const{return SaveTimeConfMin;}; \n double GetSaveTimeConfMax() const { return SaveTimeConfMax;}\n\n double GetQueryPerSecAvg() const { return QueryPerSecAvg;} \n double GetQueryPerSecConfMin() const{return QueryPerSecConfMin;}; \n double GetQueryPerSecConfMax() const { return QueryPerSecConfMax;}\n\n double GetQueryTimeAvg() const { return QueryTimeAvg;} \n double GetQueryTimeConfMin() const{return QueryTimeConfMin;}; \n double GetQueryTimeConfMax() const { return QueryTimeConfMax;}\n\n double GetDistCompAvg() const { return DistCompAvg;} \n double GetDistCompConfMin() const{return DistCompConfMin;}; \n double GetDistCompConfMax() const { return DistCompConfMax;}\nprivate:\ndouble RecallAvg, RecallConfMin, RecallConfMax;\ndouble PrecisionOfApproxAvg, PrecisionOfApproxConfMin, PrecisionOfApproxConfMax;\ndouble ClassAccuracyAvg, ClassAccuracyConfMin, ClassAccuracyConfMax;\ndouble LogRelPosErrorAvg, LogRelPosErrorConfMin, LogRelPosErrorConfMax;\ndouble NumCloserAvg, NumCloserConfMin, NumCloserConfMax;\ndouble RecallAt1Avg, RecallAt1ConfMin, RecallAt1ConfMax;\ndouble QueryTimeAvg, QueryTimeConfMin, QueryTimeConfMax;\ndouble DistCompAvg, DistCompConfMin, DistCompConfMax;\ndouble ImprEfficiencyAvg, ImprEfficiencyConfMin, ImprEfficiencyConfMax;\ndouble ImprDistCompAvg, ImprDistCompConfMin, ImprDistCompConfMax;\ndouble MemAvg, MemConfMin, MemConfMax;\ndouble IndexTimeAvg, IndexTimeConfMin, IndexTimeConfMax;\ndouble LoadTimeAvg, LoadTimeConfMin, LoadTimeConfMax;\ndouble SaveTimeAvg, SaveTimeConfMin, SaveTimeConfMax;\ndouble QueryPerSecAvg, QueryPerSecConfMin, QueryPerSecConfMax;\ndouble zVal_;\n\nvector> Recall_; \nvector> PrecisionOfApprox_; \nvector> ClassAccuracy_; \nvector> LogRelPosError_; \nvector> NumCloser_; \nvector> RecallAt1_; \nvector> QueryTime_; \nvector> DistComp_; \nvector ImprEfficiency_; \nvector ImprDistComp_; \nvector Mem_; \nvector IndexTime_; \nvector LoadTime_; \nvector SaveTime_; \nvector QueryPerSec_; \n\nMetaAnalysis(){} // be private!\n\nvoid ComputeOneSimple(const string &Name, \n const vector>& vals, double& avg, double& ConfMin, double& ConfMax) {\n vector avgVals;\n for (size_t i = 0; i < vals.size(); ++i) {\n avgVals.push_back(Mean(&vals[i][0], vals[i].size()));\n }\n ComputeOneSimple(Name, avgVals, avg, ConfMin, ConfMax);\n}\n\nvoid ComputeOneSimple(const string& Name, \n const vector& vals, double& avg, double& ConfMin, double& ConfMax) {\n avg = Mean(&vals[0], vals.size());\n#if 0\n LOG(INFO) << \".... \" << Name << \" \" << avg << \" qty: \" << vals.size();\n#endif\n double var = vals.size()> 1 ? Variance(&vals[0], vals.size(), avg) : 0;\n double sigma = sqrt(var/vals.size());\n // 5% confidence interval (assuming normal distrbution).\n ConfMin = avg - zVal_ * sigma;\n ConfMax = avg + zVal_ * sigma;\n}\n\nvoid ComputeOneMeta(const string &Name, \n const vector>& vals, double& avg, double& ConfMin, double& ConfMax) {\n avg = 0;\n\n /*\n * Fixed-effect meta-analysis.\n * See L. Hedges, and J. Vevea, 1998, Fixed and Random-Effects Models in Meta-Analysis \n *\n */\n\n double SumW = 0;\n\n for (size_t i = 0; i < vals.size(); ++i) {\n double mean = Mean(&vals[i][0], vals[i].size());\n\n#if 0\n LOG(INFO) << \".... \" << Name << \" \" << mean << \" qty: \" << vals[i].size();\n#endif\n /* \n * Sometimes variance is very small or zero,\n * this causes some kind of a numerical *flow.\n * Let's pretend the variance is never too small.\n */\n double var = std::max(1e-5, Variance(&vals[i][0], vals[i].size(), mean));\n var /= vals[i].size();\n double w = 1/var;\n SumW += w;\n avg += w * mean;\n }\n\n double sigma = sqrt(1/SumW);\n avg /= SumW;\n\n#if 0\n LOG(INFO) << \".... \" << Name << \" mean: \" << avg << \" SumW: \" << SumW;\n#endif\n\n // 5% confidence interval (assuming normal distrbution).\n ConfMin = avg - zVal_ * sigma;\n ConfMax = avg + zVal_ * sigma;\n}\n};\n\n\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/bbtree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is based on the BBTREE implementation written by Lawrence Cayton\n *\n * The algorithms were originally published in the papers:\n * 1) L. Cayton. Fast nearest neighbor retrieval for bregman divergences. \n * Twenty-Fifth International Conference on Machine Learning (ICML), 2008. \n *\n * 2) L. Cayton. Efficient bregman range search. \n * Advances in Neural Information Processing Systems 22 (NIPS), 2009. \n *\n * See https://github.com/lcayton/bbtree and http://lcayton.com/code.html\n *\n * Because the original code is released under the terms of the GNU General Public License,\n * we had to release this file under the GNU license as well. \n *\n */\n\n#ifndef _BBTREE_H_\n#define _BBTREE_H_\n\n#include \"index.h\"\n#include \"params.h\"\n\n#define METH_BBTREE \"bbtree\"\n\nnamespace similarity {\n\ntemplate \nclass Space;\n\ntemplate \nclass BregmanDiv;\n\ntemplate \nclass BBTree : public Index {\n public:\n BBTree(const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n void SetQueryTimeParams(const AnyParams& params) override {\n AnyParamManager pmgr(params);\n pmgr.GetParamOptional(\"maxLeavesToVisit\", MaxLeavesToVisit_, FAKE_MAX_LEAVES_TO_VISIT);\n\n LOG(LIB_INFO) << \"Set bbtree query-time parameters:\";\n LOG(LIB_INFO) << \"maxLeavesToVisit\" << MaxLeavesToVisit_;\n pmgr.CheckUnused();\n }\n\n ~BBTree();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n virtual bool DuplicateData() const override { return ChunkBucket_; }\n private:\n class BBNode {\n public:\n BBNode(const BregmanDiv* div,\n const ObjectVector& data, size_t bucket_size, bool use_optim);\n ~BBNode();\n\n inline bool IsLeaf() const;\n\n template \n bool RecBinSearch(const BregmanDiv* div,\n Object* query_gradient, \n QueryType* query, dist_t mindist_est,\n dist_t l=0.0, dist_t r=1.0, int depth=0) const;\n\n template \n bool NeedToSearch(const BregmanDiv* div,\n Object* query_gradient, \n QueryType* query, dist_t mindist_est,\n dist_t div_query_to_center) const;\n\n template \n void LeftSearch(const BregmanDiv* div,\n Object* query_gradient, QueryType* query,\n int& MaxLeavesToVisit_) const;\n\n void SelectCenters(const ObjectVector& data, ObjectVector& centers);\n\n void FindSplitKMeans(const BregmanDiv* div, \n const ObjectVector& data,\n ObjectVector& bucket_left, \n ObjectVector& bucket_right);\n\n private:\n enum { kMaxRetry = 10 };\n\n Object* center_;\n Object* center_gradf_;\n dist_t covering_radius_;\n bool is_leaf_;\n ObjectVector* bucket_;\n char* CacheOptimizedBucket_;\n BBNode* left_child_;\n BBNode* right_child_;\n DISABLE_COPY_AND_ASSIGN(BBNode);\n };\n\n const ObjectVector& data_;\n\n unique_ptr root_node_;\n size_t BucketSize_;\n int MaxLeavesToVisit_;\n bool ChunkBucket_;\n const BregmanDiv* BregmanDivSpace_;\n DISABLE_COPY_AND_ASSIGN(BBTree);\n};\n\n} // namespace similarity\n\n#endif // _BBTREE_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/dummy.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _DUMMY_METHOD_H_\n#define _DUMMY_METHOD_H_\n\n#include \n#include \n\n#include \"index.h\"\n\n#define METH_DUMMY \"dummy\"\n\nnamespace similarity {\n\nusing std::string;\n\n/*\n * This is a dummy, i.e., zero-functionality search method,\n * which can be used as a template to create fully functional\n * search method.\n */\n\ntemplate \nclass DummyMethod : public Index {\n public:\n /*\n * The constructor here space and data-objects' references,\n * which are guaranteed to be be valid during testing.\n * So, we can memorize them safely.\n */\n DummyMethod(Space& space, \n const ObjectVector& data) : data_(data), space_(space) {}\n\n /*\n * This function is supposed to create a search index (or call a \n * function to create it)!\n */\n void CreateIndex(const AnyParams& IndexParams) override {\n AnyParamManager pmgr(IndexParams);\n pmgr.GetParamOptional(\"doSeqSearch\", \n bDoSeqSearch_, \n // One should always specify the default value of an optional parameter!\n false\n );\n // Check if a user specified extra parameters, which can be also misspelled variants of existing ones\n pmgr.CheckUnused();\n // Always call ResetQueryTimeParams() to set query-time parameters to their default values\n this->ResetQueryTimeParams();\n }\n\n /*\n * One can implement functions for index serialization and reading.\n * However, this is not required.\n */\n // SaveIndex is not necessarily implemented\n virtual void SaveIndex(const string& location) override {\n throw runtime_error(\"SaveIndex is not implemented for method: \" + StrDesc());\n }\n // LoadIndex is not necessarily implemented\n virtual void LoadIndex(const string& location) override {\n throw runtime_error(\"LoadIndex is not implemented for method: \" + StrDesc());\n }\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n\n ~DummyMethod(){};\n\n /* \n * Just the name of the method, consider printing crucial parameter values\n */\n const std::string StrDesc() const override { \n stringstream str;\n str << \"Dummy method: \" << (bDoSeqSearch_ ? \" does seq. search \" : \" does nothing (really dummy)\"); \n return str.str();\n }\n\n /* \n * One needs to implement two search functions.\n */\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n /*\n * In rare cases, mostly when we wrap up 3rd party methods,\n * we simply duplicate the data set. This function\n * let the experimentation code know this, so it could\n * adjust the memory consumption of the index.\n *\n * Note, however, that this method doesn't create any data duplicates.\n */\n virtual bool DuplicateData() const override { return false; }\n\n private:\n bool data_duplicate_;\n const ObjectVector& data_;\n Space& space_;\n bool bDoSeqSearch_;\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(DummyMethod);\n};\n\n} // namespace similarity\n\n#endif // _DUMMY_METHOD_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/ghtree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _METRIC_GHTREE_H_\n#define _METRIC_GHTREE_H_\n\n#include \"index.h\"\n#include \"params.h\"\n\n#define METH_GHTREE \"ghtree\"\n\nnamespace similarity {\n\n/* \n * J. K. Uhlmann. Satisfying general proximity/similarity queries with metric trees, 1991\n */\n\ntemplate \nclass Space;\n\ntemplate \nclass GHTree : public Index {\n public:\n GHTree(const Space& space,\n const ObjectVector& data,\n bool use_random_center = true);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~GHTree();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType ) const override;\n void Search(KNNQuery* query, IdType ) const override;\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override {\n AnyParamManager pmgr(QueryTimeParams);\n pmgr.GetParamOptional(\"maxLeavesToVisit\", MaxLeavesToVisit_, FAKE_MAX_LEAVES_TO_VISIT);\n LOG(LIB_INFO) << \"Set GH-tree query-time parameters:\";\n LOG(LIB_INFO) << \"maxLeavesToVisit=\" << MaxLeavesToVisit_;\n pmgr.CheckUnused();\n }\n\n private:\n class GHNode {\n public:\n GHNode(const Space& space, const ObjectVector& data,\n size_t bucket_size, bool chunk_bucket,\n const bool use_random_center);\n ~GHNode();\n\n template \n void GenericSearch(QueryType* query, int& MaxLeavesToVisit);\n\n private:\n\n const Object* pivot1_;\n const Object* pivot2_;\n GHNode* left_child_;\n GHNode* right_child_;\n ObjectVector* bucket_;\n char* CacheOptimizedBucket_;\n friend class GHTree;\n };\n\n const Space& space_;\n const ObjectVector& data_;\n bool use_random_center_;\n unique_ptr root_;\n\n size_t BucketSize_;\n int MaxLeavesToVisit_;\n bool ChunkBucket_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(GHTree);\n};\n\n} // namespace similarity\n\n#endif // _METRIC_GHTREE_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/hnsw.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n /*\n *\n * A Hierarchical Navigable Small World (HNSW) approach.\n *\n * The main publication is (available on arxiv: http://arxiv.org/abs/1603.09320):\n * \"Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs\" by Yu. A. Malkov, D. A. Yashunin \n * This code was contributed by Yu. A. Malkov. It also was used in tests from the paper.\n *\n *\n */\n#pragma once\n\n#include \"index.h\"\n#include \"params.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\n#define METH_HNSW \"hnsw\"\n#define METH_HNSW_SYN \"Hierarchical_NSW\"\n\nnamespace similarity {\n\n using std::string;\n using std::vector;\n using std::thread;\n using std::mutex;\n using std::unique_lock;\n using std::condition_variable;\n using std::ref;\n\n template \n class Space;\n class VisitedListPool;\n template class HnswNodeDistCloser;\n template class HnswNodeDistFarther;\n\n class HnswNode {\n public:\n HnswNode(const Object *Obj, size_t id) {\n data_ = Obj;\n id_ = id;\n }\n ~HnswNode() {};\n\n template \n void getNeighborsByDistanceHeuristic(priority_queue> &resultSet1, const int NN, const Space* space) {\n if (resultSet1.size() < NN) {\n return;\n }\n priority_queue> resultSet;\n priority_queue> templist;\n vector> returnlist;\n while (resultSet1.size() > 0)\n {\n resultSet.emplace(resultSet1.top().getDistance(), resultSet1.top().getMSWNodeHier());\n resultSet1.pop();\n }\n while (resultSet.size()) {\n\n if (returnlist.size() >= NN)\n break;\n HnswNodeDistFarther curen = resultSet.top();\n dist_t dist_to_query = curen.getDistance();\n resultSet.pop();\n bool good = true;\n for (HnswNodeDistFarther curen2 : returnlist) {\n dist_t curdist = space->IndexTimeDistance(curen2.getMSWNodeHier()->getData(), curen.getMSWNodeHier()->getData());\n\n if (curdist <= dist_to_query) {\n good = false;\n break;\n }\n }\n if (good)\n returnlist.push_back(curen);\n else\n templist.push(curen);\n\n }\n\n while (returnlist.size() < NN && templist.size() > 0) {\n returnlist.push_back(templist.top());\n templist.pop();\n }\n\n for (HnswNodeDistFarther curen2 : returnlist) {\n resultSet1.emplace(curen2.getDistance(), curen2.getMSWNodeHier());\n }\n\n };\n /*\n * Experimental method for selection of neighbors based on local search. However is peforms worse than the previous method\n * Note that it works correctly only in a single thread!!!\n */\n template \n void getNeighborsByMiniGreedySearches(priority_queue> &resultSet1, const int NN, const Space* space, int level) {\n if (resultSet1.size() < NN) {\n return;\n }\n priority_queue> inputSet;\n priority_queue> templist;\n\n set result;\n\n while (resultSet1.size() > 0)\n {\n inputSet.emplace(resultSet1.top().getDistance(), resultSet1.top().getMSWNodeHier());\n resultSet1.pop();\n }\n result.insert(inputSet.top().getMSWNodeHier());\n inputSet.pop();\n while (inputSet.size()) {\n if (result.size() >= NN)\n break;\n bool good = true;\n HnswNodeDistFarther curen = inputSet.top();\n inputSet.pop();\n dist_t curdist = curen.getDistance();\n HnswNode *curNode = curen.getMSWNodeHier();\n\n bool changed = true;\n while (changed) {\n changed = false;\n\n const vector& neighbor = curNode->getAllFriends(level);\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n _mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n }\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n const Object *currObj = (*iter)->getData();\n dist_t d = space->IndexTimeDistance(this->data_, currObj);\n if (d < curdist) {\n curdist = d;\n curNode = *iter;\n changed = true;\n }\n }\n if (result.count(curNode)) {\n good = false;\n break;\n }\n }\n if (good)\n result.insert(curen.getMSWNodeHier());\n else\n templist.push(curen);\n\n }\n\n while (result.size() < NN && templist.size() > 0) {\n result.insert(templist.top().getMSWNodeHier());\n templist.pop();\n }\n resultSet1.empty();\n for (HnswNode *curNode : result) {\n resultSet1.emplace(0, curNode);\n }\n\n };\n template \n void addFriendlevel(int level, HnswNode* element, const Space* space, int delaunay_type) {\n\n unique_lock lock(accessGuard_);\n for (int i = 0; i < allFriends[level].size(); i++)\n if (allFriends[level][i] == element) {\n cerr << \"This should not happen. For some reason the elements is already added\";\n return;\n }\n allFriends[level].push_back(element);\n bool shrink = false;\n if (level > 0)\n if (allFriends[level].size() > maxsize)\n shrink = true;\n else\n shrink = false;\n else\n if (allFriends[level].size() > maxsize0)\n shrink = true;\n else\n shrink = false;\n if (shrink)\n {\n if (delaunay_type > 0) {\n priority_queue> resultSet;\n for (int i = 1; i < allFriends[level].size(); i++) {\n resultSet.emplace(space->IndexTimeDistance(this->getData(), allFriends[level][i]->getData()), allFriends[level][i]);\n }\n if (delaunay_type == 1)\n this->getNeighborsByDistanceHeuristic(resultSet, resultSet.size() - 1, space);\n else if (delaunay_type == 2)\n this->getNeighborsByMiniGreedySearches(resultSet, resultSet.size() - 1, space, level);\n allFriends[level].clear();\n\n while (resultSet.size()) {\n allFriends[level].push_back(resultSet.top().getMSWNodeHier());\n resultSet.pop();\n\n }\n }\n else {\n dist_t max = space->IndexTimeDistance(this->getData(), allFriends[level][0]->getData());\n int maxi = 0;\n for (int i = 1; i < allFriends[level].size(); i++) {\n\n dist_t curd = space->IndexTimeDistance(this->getData(), allFriends[level][i]->getData());\n if (curd > max) {\n max = curd;\n maxi = i;\n }\n }\n allFriends[level].erase(allFriends[level].begin() + maxi);\n }\n }\n\n\n\n\n }\n\n public: void init(int level1, int maxFriends, int maxfriendslevel0) {\n\n level = level1;\n maxsize = maxFriends;\n maxsize0 = maxfriendslevel0;\n allFriends.resize(level + 1);\n for (int i = 0; i <= level; i++) {\n allFriends[i].reserve(maxsize + 1);\n }\n allFriends[0].reserve(maxsize0 + 1);\n\n }\n public: void copyDataAndLevel0LinksToOptIndex(char *mem1, size_t offsetlevels, size_t offsetData) {\n char *mem = mem1;\n //Level\n *((int *)(mem)) = level;\n mem += sizeof(int);\n\n char *memlevels = mem1 + offsetlevels;\n\n char *memt = memlevels;\n *((int *)(memt)) = (int)allFriends[0].size();\n memt += sizeof(int);\n for (size_t j = 0; j < allFriends[0].size(); j++) {\n *((int *)(memt)) = (int)allFriends[0][j]->getId();\n memt += sizeof(int);\n }\n mem = mem1 + offsetData;\n memcpy(mem, data_->buffer(), data_->bufferlength());\n\n return;\n }\n public: void copyHigherLevelLinksToOptIndex(char *mem1, size_t offsetlevels) {\n char *mem = mem1;\n\n *((int *)(mem)) = level;\n mem += sizeof(int);\n\n char *memlevels = mem1 + offsetlevels;\n\n for (int i = 1; i <= level; i++) {\n\n char *memt = memlevels;\n *((int *)(memt)) = (int)allFriends[i].size();\n\n memt += sizeof(int);\n for (size_t j = 0; j < allFriends[i].size(); j++) {\n *((int *)(memt)) = (int)allFriends[i][j]->getId();\n memt += sizeof(int);\n }\n memlevels += (1 + maxsize)* sizeof(int);\n };\n return;\n }\n const Object* getData() const {\n return data_;\n }\n size_t getId() const { return id_; }\n const vector& getAllFriends(int level) const {\n return allFriends[level];\n }\n\n mutex accessGuard_;\n public:\n\n private:\n const Object* data_;\n public:size_t id_;\n vector> allFriends;\n\n int maxsize0;\n int maxsize;\n int level;\n\n\n };\n\n\n //----------------------------------\n template \n class HnswNodeDistFarther {\n public:\n HnswNodeDistFarther() {\n distance = 0;\n element = NULL;\n }\n HnswNodeDistFarther(dist_t di, HnswNode* node) {\n distance = di;\n element = node;\n }\n ~HnswNodeDistFarther() {}\n dist_t getDistance() const { return distance; }\n HnswNode* getMSWNodeHier() const { return element; }\n bool operator< (const HnswNodeDistFarther &obj1) const {\n return (distance > obj1.getDistance());\n }\n\n private:\n dist_t distance;\n HnswNode* element;\n };\n\n template \n class EvaluatedMSWNodeInt {\n public:\n EvaluatedMSWNodeInt() {\n distance = 0;\n element = NULL;\n }\n EvaluatedMSWNodeInt(dist_t di, int element1) {\n distance = di;\n element = element1;\n }\n ~EvaluatedMSWNodeInt() {}\n dist_t getDistance() const { return distance; }\n int getMSWNodeHier() const { return element; }\n bool operator< (const EvaluatedMSWNodeInt &obj1) const {\n return (distance < obj1.getDistance());\n }\n\n private:\n dist_t distance;\n public:int element;\n };\n\n template \n class HnswNodeDistCloser {\n public:\n HnswNodeDistCloser() {\n distance = 0;\n element = NULL;\n }\n HnswNodeDistCloser(dist_t di, HnswNode* node) {\n distance = di;\n element = node;\n }\n ~HnswNodeDistCloser() {}\n dist_t getDistance() const { return distance; }\n HnswNode* getMSWNodeHier() const { return element; }\n bool operator< (const HnswNodeDistCloser &obj1) const {\n return (distance < obj1.getDistance());\n }\n\n private:\n dist_t distance;\n HnswNode* element;\n };\n\n template \n class Hnsw : public Index {\n public:\n virtual void SaveIndex(const string &location) override;\n\n virtual void LoadIndex(const string &location) override;\n\n Hnsw(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~Hnsw();\n\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams&) override;\n private:\n\n\n\n typedef std::vector ElementList;\n void baseSearchAlgorithm(KNNQuery* query);\n void listPassingModifiedAlgorithm(KNNQuery* query);\n void SearchL2Custom(KNNQuery* query);\n void SearchCosineNormalized(KNNQuery* query);\n\n \n\n private: \tint getRandomLevel(double revSize) {\n std::uniform_real_distribution distribution(0.0, 1.0);\n double r = -log(distribution(*generator)) * revSize;\n return (int)r;\n }\n\n public:\n void kSearchElementsWithAttemptsLevel(const Space* space,\n const Object* queryObj, size_t NN,\n std::priority_queue>& resultSet, HnswNode* ep, int level) const;\n void add(const Space* space, HnswNode *newElement);\n void addToElementListSynchronized(HnswNode *newElement);\n\n void link(HnswNode* first, HnswNode* second, int level, const Space* space, int delaunay_type) {\n // We have to pass the Space, since we need to know what elements can be deleted from the list\n first->addFriendlevel(level, second, space, delaunay_type);\n second->addFriendlevel(level, first, space, delaunay_type);\n }\n\n //\n private:\n\n std::default_random_engine *generator;\n size_t M_;\n size_t maxM_;\n size_t maxM0_;\n size_t efConstruction_;\n size_t ef_;\n size_t searchMethod_;\n size_t indexThreadQty_;\n VisitedListPool *visitedlistpool;\n const Space& space_;\n bool PrintProgress_;\n int delaunay_type_;\n double mult_;\n int maxlevel_;\n HnswNode *enterpoint_;\n unsigned int enterpointId_;\n unsigned int totalElementsStored_;\n\n const ObjectVector& data_;// We do not copy objects\n //ObjectVector data_; // We copy all the data\n\n mutable mutex ElListGuard_;\n mutable mutex MaxLevelGuard_;\n ElementList ElList_;\n\n int vectorlength_ = 0;\n int dist_func_type_;\n bool iscosine_ = false;\n size_t offsetData_, offsetLevel0_;\n char *data_level0_memory_;\n char **linkLists_;\n size_t memoryPerObject_;\n float (*fstdistfunc_)(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes);\n protected:\n DISABLE_COPY_AND_ASSIGN(Hnsw);\n };\n\n\n class VisitedList {\n public: unsigned int curV;\n public:\tunsigned int *mass;\n public: unsigned int numelements;\n public:\n VisitedList(int numelements1) {\n curV = 0;\n numelements = numelements1;\n mass = new unsigned int[numelements];\n }\n void reset() {\n curV++;\n if (curV == 0)\n {\n memset(mass, 0, sizeof(int)*numelements);\n }\n };\n ~VisitedList() {\n\n delete mass;\n }\n };\n ///////////////////////////////////////////////////////////\n //\n // Class for multi-threaded pool-management of VisitedLists\n //\n /////////////////////////////////////////////////////////\n\n class VisitedListPool {\n vector listPool;\n vector curV;\n deque pool;\n mutex poolguard;\n int maxpools;\n int numelements;\n public:\n VisitedListPool(int initmaxpools, int numelements1) {\n numelements = numelements1;\n for (int i = 0; i < initmaxpools; i++)\n pool.push_front(new VisitedList(numelements));\n }\n VisitedList * getFreeVisitedList() {\n unique_lock lock(poolguard);\n if (pool.size() > 0) {\n VisitedList *rez = pool.front();\n rez->reset();\n pool.pop_front();\n return rez;\n }\n else {\n return new VisitedList(numelements);\n }\n\n };\n void releaseVisitedList(VisitedList *vl) {\n unique_lock lock(poolguard);\n pool.push_front(vl);\n };\n ~VisitedListPool() {\n LOG(LIB_INFO) << \"Total \" << pool.size() << \" lists allocated\";\n while (pool.size()) {\n VisitedList *rez = pool.front();\n pool.pop_front();\n delete rez;\n }\n };\n };\n\n\n\n\n\n\n\n}\n" }, { "path": "algorithms/NMSLIB/code/include/method/lcstrategy.h", "content": "#ifndef _LIST_OF_CLUSTERS_STRAT_H_\n#define _LIST_OF_CLUSTERS_STRAT_H_\n\n#include \n\nnamespace similarity {\n\nenum class ListClustersStrategy {\n kRandom, // random\n kClosestPrevCenter, // closest to the previous selected center\n kFarthestPrevCenter, // farthest from the previous selected center\n kMinSumDistPrevCenters, // minimize the sum of dist-s to prev.centers\n kMaxSumDistPrevCenters // maximize the sum of dist-s to prev.centers\n};\n\ninline std::ostream& operator<<(std::ostream& o, ListClustersStrategy v) {\n switch (v) {\n case ListClustersStrategy::kRandom:\n return o << \"random\";\n case ListClustersStrategy::kClosestPrevCenter:\n return o << \"closestPrevCenter\";\n case ListClustersStrategy::kFarthestPrevCenter:\n return o << \"farthestPrevCenter\";\n case ListClustersStrategy::kMinSumDistPrevCenters:\n return o << \"minSumDistPrevCenters\";\n case ListClustersStrategy::kMaxSumDistPrevCenters:\n return o << \"maxSumDistPrevCenters\";\n }\n return o << \"unknown\";\n}\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/list_clusters.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _LIST_OF_CLUSTERS_H_\n#define _LIST_OF_CLUSTERS_H_\n\n#include \"index.h\"\n#include \"lcstrategy.h\"\n#include \"params.h\"\n\n#define METH_LIST_CLUSTERS \"list_clusters\"\n\nnamespace similarity {\n\n/* \n * E. Chavez and G. Navarro.\n * A compact space decomposition for effective a metric indexing. \n * Pattern Recognition Letters, 26(9):1363-1376, 2005\n *\n * The method also resembles canopy clustering: https://en.wikipedia.org/wiki/Canopy_clustering_algorithm\n *\n * Note that similar ideas were also proposed earlier:\n *\n * 1) DynDex: a dynamic and non-metric space indexer, KS Goh, B Li, E Chang, 2002.\n * 2) C. Li, E. Chang, H. Garcia-Molina, and G. Wiederhold. \n * Clustering for approximate similarity search in high-dimensional\n * 3) E. Chavez and G. Navarro. \n * A compact space decomposition for effective a metric indexing. 2005\n */\n\ntemplate \nclass Space;\n\ntemplate \nclass ListClusters : public Index {\n public:\n ListClusters(const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& MethParams) override;\n ~ListClusters();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n static const Object* SelectNextCenter(\n DistObjectPairVector& remaining,\n ListClustersStrategy strategy);\n\n virtual bool DuplicateData() const override { return ChunkBucket_; }\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n private:\n\n template \n void GenSearch(QueryType* query) const;\n\n class Cluster {\n public:\n Cluster(const Object* center);\n ~Cluster();\n\n void OptimizeBucket();\n void AddObject(const Object* object,\n const dist_t dist);\n\n const Object* GetCenter();\n const dist_t GetCoveringRadius();\n const ObjectVector& GetBucket();\n\n template \n void Search(QueryType* query) const;\n\n private:\n const Object* center_;\n dist_t covering_radius_;\n char* CacheOptimizedBucket_;\n ObjectVector* bucket_;\n int MaxLeavesToVisit_;\n };\n\n const Space& space_;\n const ObjectVector& data_;\n\n std::vector cluster_list_;\n\n ListClustersStrategy Strategy_; \n bool UseBucketSize_;\n size_t BucketSize_;\n dist_t Radius_;\n int MaxLeavesToVisit_;\n bool ChunkBucket_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(ListClusters);\n};\n\n} // namespace similarity\n\n#endif // _METRIC_GHTREE_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/lsh.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _LSH_H_\n#define _LSH_H_\n\n#include \"index.h\"\n#include \"space.h\"\n#include \"lshkit.h\"\n\n#define METH_LSH_THRESHOLD \"lsh_threshold\"\n#define METH_LSH_CAUCHY \"lsh_cauchy\"\n#define METH_LSH_GAUSSIAN \"lsh_gaussian\"\n\nnamespace similarity {\n\n// this class is a wrapper around lshkit and\n// but lshkit can handle only float!\n\ntemplate \nclass Space;\n\ntemplate \nusing TailRepeatHash = lshkit::Tail>;\n\nusing TailRepeatHashThreshold = TailRepeatHash;\nusing TailRepeatHashCauchy = TailRepeatHash;\nusing TailRepeatHashGaussian = TailRepeatHash;\n\ntemplate \nclass ParameterCreator {\n public:\n static typename lsh_t::Parameter GetParameter(\n const lshkit::FloatMatrix& matrix,\n unsigned H, unsigned M, float W) {\n throw runtime_error(\"not allowed: dummy parameter creator\");\n return lsh_t::Parameter();\n }\n\n static std::string StrDesc() {\n return \"ParameterCreator\";\n }\n};\n\ntemplate <>\nclass ParameterCreator {\n public:\n static TailRepeatHashThreshold::Parameter GetParameter(\n const lshkit::FloatMatrix& matrix,\n unsigned H, unsigned M, float W) {\n TailRepeatHashThreshold::Parameter param;\n param.range = H;\n param.repeat = M;\n param.dim = matrix.getDim();\n param.min = std::numeric_limits::max();\n param.max = std::numeric_limits::min();\n for (int i = 0; i < matrix.getSize(); ++i) {\n for (int j = 0; j < matrix.getDim(); ++j) {\n if (matrix[i][j] < param.min) param.min = matrix[i][j];\n if (matrix[i][j] > param.max) param.max = matrix[i][j];\n }\n }\n LOG(LIB_INFO) << \"min \" << param.min << \" max \" << param.max;\n return param;\n }\n\n static std::string StrDesc() {\n return \"ParameterCreator (l1 distance)\";\n }\n};\n\ntemplate <>\nclass ParameterCreator {\n public:\n static TailRepeatHashCauchy::Parameter GetParameter(\n const lshkit::FloatMatrix& matrix,\n unsigned H, unsigned M, float W) {\n TailRepeatHashCauchy::Parameter param;\n param.range = H;\n param.repeat = M;\n param.dim = matrix.getDim();\n param.W = W;\n return param;\n }\n\n static std::string StrDesc() {\n return \"ParameterCreator (l1 distance)\";\n }\n};\n\ntemplate <>\nclass ParameterCreator {\n public:\n static TailRepeatHashGaussian::Parameter GetParameter(\n const lshkit::FloatMatrix& matrix,\n unsigned H, unsigned M, float W) {\n TailRepeatHashGaussian::Parameter param;\n param.range = H;\n param.repeat = M;\n param.dim = matrix.getDim();\n param.W = W;\n return param;\n }\n\n static std::string StrDesc() {\n return \"ParameterCreator (l2 distance)\";\n }\n};\n\n\ntemplate \nclass LSH : public Index {\n public:\n LSH(const Space& space,\n const ObjectVector& data,\n int P // lp (l1 or l2)\n );\n\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~LSH();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType ) const override;\n void Search(KNNQuery* query, IdType ) const override;\n void SetQueryTimeParams(const AnyParams& params) override {};\n\n // LSH does copy all data\n virtual bool DuplicateData() const override { return true; }\n private:\n typedef lshkit::LshIndex, unsigned> LshIndexType;\n\n const ObjectVector& data_;\n int p_;\n lshkit::FloatMatrix* matrix_;\n LshIndexType* index_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(LSH);\n};\n\n// for l1 distance\ntemplate \nusing LSHThreshold = LSH>;\n\n// for l1 distance\ntemplate \nusing LSHCauchy = LSH>;\n\n// for l2 distance\ntemplate \nusing LSHGaussian = LSH>;\n\n} // namespace similarity\n\n#endif // _LSH_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/lsh_multiprobe.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _LSH_MULTI_PROBE_H_\n#define _LSH_MULTI_PROBE_H_\n\n#include \"index.h\"\n#include \"space.h\"\n#include \"lshkit.h\"\n\n#define METH_LSH_MULTIPROBE_SYN \"lsh_multiprobe\"\n#define METH_LSH_MULTIPROBE \"mplsh\"\n\nnamespace similarity {\n\n// this class is a wrapper around lshkit and\n// but lshkit can handle only float!\n\ntemplate \nclass Space;\n\ntemplate \nclass MultiProbeLSH : public Index {\n public:\n MultiProbeLSH(const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n ~MultiProbeLSH();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams& params) override {};\n\n // LSH does copy all data\n virtual bool DuplicateData() const override { return true; }\n private:\n typedef lshkit::MultiProbeLshIndex LshIndexType;\n\n const ObjectVector& data_;\n int dim_;\n lshkit::FloatMatrix* matrix_;\n LshIndexType* index_;\n unsigned T_;\n float R_;\n \n void CreateIndexInternal(\n // for FitData()\n unsigned N1, // number of points to use\n unsigned P, // number of pairs to sample\n unsigned Q, // number of queries to sample\n unsigned K, // search for K neighbors neighbors\n unsigned F, // divide the sample to F folds\n // for MPLSHTune()\n unsigned N2, // dataset size\n double R, // desired recall\n // other config\n unsigned L, // # of hash tables\n unsigned T, // # of bins probed in each hash table\n unsigned H, // hash table size\n int M, // # of hash functions\n float W // width\n );\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(MultiProbeLSH);\n};\n\n} // namespace similarity\n\n#endif // _LSH_MULTI_PROBE_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/lsh_space.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _LSH_SPACE_H_\n#define _LSH_SPACE_H_\n\n#include \n#include \"knnquery.h\"\n\nnamespace similarity {\n\nfloat LSHLp(const float* x, const float* y, const int dim, const int p);\n\ntemplate \nclass LSHLpSpace {\n public:\n LSHLpSpace(unsigned dim, unsigned p, KNNQuery* query)\n : dim_(dim), p_(p), query_(query) {\n }\n\n ~LSHLpSpace() {}\n\n float operator()(const float* x, const float* y) const {\n query_->AddDistanceComputations(1);\n return LSHLp(x, y, dim_, p_);\n }\n\n private:\n unsigned dim_;\n unsigned p_;\n KNNQuery* query_;\n};\n\nfloat LSHMultiProbeLp(const float* x, const float* y, const int dim);\n\ntemplate \nclass LSHMultiProbeLpSpace {\n public:\n LSHMultiProbeLpSpace(unsigned dim, KNNQuery* query)\n : dim_(dim), query_(query) {\n }\n\n ~LSHMultiProbeLpSpace() {}\n\n float operator()(const float* x, const float* y) const {\n query_->AddDistanceComputations(1);\n return LSHMultiProbeLp(x, y, dim_);\n }\n\n private:\n unsigned dim_;\n KNNQuery* query_;\n};\n\n\n} // namespace similarity\n\n#endif // _LSH_SPACE_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/multi_index.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _MULT_INDEX_H_\n#define _MULT_INDEX_H_\n\n#include \n\n#include \"index.h\"\n#include \"params.h\"\n#include \"methodfactory.h\"\n\n#define METH_MULT_INDEX \"mult_index\"\n\nnamespace similarity {\n\n/*\n * This is a generic method that can create multiple copies of any method indices.\n * During search, results obtained from all the copies are aggregated.\n * This allows us to achieve higher recall when an approximate search method is used.\n * In other words, using multiple copies of the index created using the same method \n * permits to trade efficiency for effectiveness. However, this makes sense only for the\n * methods where creation of the index is NON-deterministic, e.g., when\n * pivots are created randomly.\n * \n */\n\ntemplate class Space;\n\ntemplate \nclass MultiIndex : public Index {\n public:\n MultiIndex(bool PrintProgress,\n const string& SpaceType,\n Space& space, \n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~MultiIndex();\n\n const std::string StrDesc() const override;\n\n void Search(RangeQuery* query, IdType ) const override;\n void Search(KNNQuery* query, IdType ) const override;\n\n virtual void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n protected:\n\n std::vector*> indices_;\n Space& space_;\n const ObjectVector& data_;\n string SpaceType_;\n bool PrintProgress_;\n size_t IndexQty_;\n string MethodName_;\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/multi_vantage_point_tree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _MULTI_VANTAGE_POINT_TREE_H_\n#define _MULTI_VANTAGE_POINT_TREE_H_\n\n#include \"index.h\"\n#include \"params.h\"\n\n#define METH_MVPTREE \"mvptree\"\n\nnamespace similarity {\n\n/* \n * (Binary version of ) Multi-Vantage Point Tree\n * T. Bozkaya and M. Ozsoyoglu,\n * Indexing large metric spaces for similarity search queries\n */\n\ntemplate \nclass Space;\n\ntemplate \nclass MultiVantagePointTree : public Index {\n public:\n MultiVantagePointTree(const Space& space,\n const ObjectVector& data);\n void CreateIndex(const AnyParams& IndexParams) override;\n ~MultiVantagePointTree();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override {\n AnyParamManager pmgr(QueryTimeParams);\n pmgr.GetParamOptional(\"maxLeavesToVisit\", MaxLeavesToVisit_, FAKE_MAX_LEAVES_TO_VISIT);\n LOG(LIB_INFO) << \"Set MVP-tree query-time parameters:\";\n LOG(LIB_INFO) << \"maxLeavesToVisit\" << MaxLeavesToVisit_;\n pmgr.CheckUnused();\n }\n\n virtual bool DuplicateData() const override { return ChunkBucket_; }\n private:\n struct Entry;\n\n typedef std::vector Dists;\n typedef std::vector Entries;\n\n struct Entry {\n const Object* object;\n Dists path;\n dist_t d1;\n dist_t d2;\n explicit Entry(const Object* object) : object(object) {}\n };\n\n struct Dist1AscComparator {\n bool operator()(const Entry& e1, const Entry& e2) const {\n return e1.d1 < e2.d1;\n }\n };\n\n struct Dist2AscComparator {\n bool operator()(const Entry& e1, const Entry& e2) const {\n return e1.d2 < e2.d2;\n }\n };\n\n class Node {\n public:\n Node(const Object* pivot1, const Object* pivot2, const bool isleaf)\n : pivot1_(pivot1),\n pivot2_(pivot2),\n isleaf_(isleaf) {}\n virtual ~Node() {}\n inline bool isleaf() const { return isleaf_; }\n\n protected:\n const Object* pivot1_;\n const Object* pivot2_;\n const bool isleaf_;\n friend class MultiVantagePointTree;\n };\n\n class InternalNode : public Node {\n public:\n InternalNode(const Object* pivot1,\n const Object* pivot2,\n dist_t m1,\n dist_t m21,\n dist_t m22)\n : Node(pivot1, pivot2, false),\n m1_(m1),\n m21_(m21),\n m22_(m22),\n child1_(NULL),\n child2_(NULL),\n child3_(NULL),\n child4_(NULL) {}\n\n ~InternalNode() {\n delete child1_;\n delete child2_;\n delete child3_;\n delete child4_;\n }\n private:\n dist_t m1_;\n dist_t m21_, m22_;\n Node* child1_;\n Node* child2_;\n Node* child3_;\n Node* child4_;\n friend class MultiVantagePointTree;\n };\n\n class LeafNode : public Node {\n public:\n LeafNode(const Object* pivot1, const Object* pivot2, Entries& entries, bool ChunkBucket)\n : Node(pivot1, pivot2, true),\n entries_(entries), CacheOptimizedBucket_(NULL), bucket_(NULL) {\n if (ChunkBucket && entries.size()) {\n // TODO (@leo) Figure out whether and (why?) chunking does not improve performance\n ObjectVector TmpData(entries.size());\n\n for (unsigned i = 0; i < entries.size(); ++i) {\n TmpData[i] = entries[i].object;\n }\n\n CreateCacheOptimizedBucket(TmpData, CacheOptimizedBucket_, bucket_);\n\n for (unsigned i = 0; i < entries.size(); ++i) {\n entries[i].object = (*bucket_)[i];\n }\n }\n }\n ~LeafNode() {\n ClearBucket(CacheOptimizedBucket_, bucket_);\n }\n\n private:\n Entries entries_;\n char* CacheOptimizedBucket_; \n ObjectVector* bucket_;\n friend class MultiVantagePointTree;\n };\n\n Node* BuildTree(const Space* space, Entries& entries);\n\n template \n void GenericSearch(Node* node, QueryType* query, Dists& path, size_t query_path_len, int& MaxLeavesToVisit) const;\n\n const Space& space_;\n const ObjectVector& data_;\n unique_ptr root_; // root node\n\n size_t MaxPathLength_; // the number of distances for the data\n // points to be kept at the leaves (P)\n size_t BucketSize_; // the maximum fanout for the leaf nodes (K)\n bool ChunkBucket_;\n int MaxLeavesToVisit_;\n\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(MultiVantagePointTree);\n};\n\n} // namespace similarity\n\n#endif // _MULTI_VANTAGE_POINT_TREE_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/multi_vantage_point_tree_utils.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _MULTI_VANTAGE_POINT_TREE_UTILS_H_\n#define _MULTI_VANTAGE_POINT_TREE_UTILS_H_\n\nnamespace similarity {\n\ntemplate \nT Remove(std::vector& array, int index) {\n T result = array[index];\n array.erase(array.begin() + index);\n return result;\n}\n\ntemplate \nT SplitByMedian(std::vector& data,\n std::vector& split1,\n std::vector& split2) {\n CHECK(!data.empty());\n size_t index_of_median = data.size() >> 1;\n T median = data[index_of_median];\n size_t i = 0;\n for ( ; i <= index_of_median; ++i) {\n split1.push_back(data[i]);\n }\n for ( ; i < data.size(); ++i) {\n split2.push_back(data[i]);\n }\n std::vector().swap(data);\n return median;\n}\n\n} // namespace similarity\n\n#endif // _MULTI_VANTAGE_POINT_TREE_UTILS_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/nndes.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * This is a wrapper class for the Wei Dong implementation of https://code.google.com/p/nndes/,\n * which also contains some of the original code from Wei Dong's repository. \n * Wei Dong, Charikar Moses, and Kai Li. 2011. Efficient k-nearest neighbor graph construction for generic similarity measures. \n * In Proceedings of the 20th international conference on World wide web (WWW '11). ACM, New York, NY, USA, 577-586. \n * \n * The Wei Dong's code can be redistributed given that the license (see below) is retained in the source code.\n */\n/*\nCopyright (C) 2010,2011 Wei Dong \nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n#ifndef NNDES_METHOD_H_\n#define NNDES_METHOD_H_\n\n#include \n#include \n#include \n\n#include \"index.h\"\n#include \"space.h\"\n\n#include \"nndes/nndes-common.h\"\n#include \"nndes/nndes.h\"\n\n#define METH_NNDES \"nndes\"\n\nnamespace similarity {\n\nusing std::string;\n\ntemplate \nclass NNDescentMethod : public Index {\n public:\n NNDescentMethod(bool PrintProgress,\n const Space& space, \n const ObjectVector& data);\n void CreateIndex(const AnyParams& IndexParams) override;\n ~NNDescentMethod(){};\n\n /* \n * Just the name of the method, consider printing crucial parameter values\n */\n const std::string StrDesc() const override { \n stringstream str;\n str << \"NNDescentMethod method: \";\n return str.str();\n }\n\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n virtual void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n\n class SpaceOracle {\n public:\n SpaceOracle(const Space& space, const ObjectVector& data) :\n space_(space), data_(data) {}\n inline dist_t operator()(IdType id1, IdType id2) const {\n return space_.IndexTimeDistance(data_.at(id1), data_.at(id2));\n }\n private:\n const Space& space_;\n const ObjectVector& data_;\n };\n private:\n void SearchGreedy(KNNQuery* query) const;\n void SearchSmallWorld(KNNQuery* query) const;\n\n typedef pair EvaluatedNode;\n\n\n const Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n size_t NN_; // K in the original Wei Dong's code nndes.cpp\n size_t efSearch_;\n size_t controlQty_; // control in the original Wei Dong's code nndes.cpp\n size_t iterationQty_; // iteration in the original Wei Dong's code nndes.cpp\n float rho_;\n float delta_;\n\n SpaceOracle nndesOracle_;\n unique_ptr> nndesObj_;\n\n size_t initSearchAttempts_;\n bool greedy_;\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(NNDescentMethod);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/nonmetr_list_clust.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef NONMETR_LIST_CLUST_H\n#define NONMETR_LIST_CLUST_H\n\n#include \n#include \n#include \n\n#include \"index.h\"\n#include \"object.h\"\n\n#define METH_NON_METR_LISTCLUST \"nonmetr_list_clust\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::shared_ptr;\n\n/*\n * A non-metric indexing based on one-level clustering. There are papers for \n * metric and non-metric indexes exploited this idea, e.g.:\n * \n * 1) DynDex: a dynamic and non-metric space indexer, KS Goh, B Li, E Chang, 2002.\n * 2) C. Li, E. Chang, H. Garcia-Molina, and G. Wiederhold. \n * Clustering for approximate similarity search in high-dimensional\n * 3) E. Chavez and G. Navarro. \n * A compact space decomposition for effective a metric indexing. 2005\n */\n\ntemplate \nclass NonMetrListClust : public Index {\n public:\n NonMetrListClust(bool printProgress,\n Space& space,\n const ObjectVector& data) : printProgress_(printProgress), data_(data), space_(space) {\n maxObjId_ = 0;\n for (const Object* o: data) {\n maxObjId_ = max(maxObjId_, o->id());\n CHECK_MSG(o->id() >= 0, \"Bug: detected negative object id\");\n }\n }\n\n void CreateIndex(const AnyParams& IndexParams) override;\n // SaveIndex is not necessarily implemented\n virtual void SaveIndex(const string& location) override {\n throw runtime_error(\"SaveIndex is not implemented for method: \" + StrDesc());\n }\n // LoadIndex is not necessarily implemented\n virtual void LoadIndex(const string& location) override {\n throw runtime_error(\"LoadIndex is not implemented for method: \" + StrDesc());\n }\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n\n ~NonMetrListClust(){};\n\n const std::string StrDesc() const override { \n return string(\"list of clusters for non-metric indexing\");\n }\n\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n virtual bool DuplicateData() const override { return false; }\n\n private:\n bool printProgress_;\n const ObjectVector& data_;\n Space& space_;\n\n size_t db_scan_;\n \n ObjectVector vCenters_; // Cluster centers\n vector>> vClusterAssign_; // Cluster assignment\n ObjectVector vUnassigned_;\n IdType maxObjId_;\n\n template \n void GenericSearch(QueryType* query, IdType) const;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(NonMetrListClust);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/omedrank.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * Patent ALERT: even though the code is released under the liberal Apache 2 license,\n * the underlying search method is patented. Therefore, it's free to use in research,\n * but may be problematic in a production setting.\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _OMEDRANK_METHOD_H_\n#define _OMEDRANK_METHOD_H_\n\n#include \n#include \n#include \n#include \n\n#include \"index.h\"\n#include \"projection.h\"\n#include \"ported_boost_progress.h\"\n\n#define METH_OMEDRANK \"omedrank\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\n\n/*\n * This is an implementation of Fagin's OMEDRANK method for arbitrary spaces.\n * The main difference is that we allow the user to select several types of\n * projections, while the original Fagin's OMEDRANK relies only on the random\n * projections.\n *\n * Patent: \n * Efficient similarity search and classification via rank aggregation\n * United States Patent Application 20040249831\n *\n * Paper: \n * Fagin, Ronald, Ravi Kumar, and Dandapani Sivakumar. \n * \"Efficient similarity search and classification via rank aggregation.\" \n * Proceedings of the 2003 ACM SIGMOD international conference on Management of data. ACM, 2003.\n *\n * In this implementation, we introduce several modifications:\n * 1) Book-keeping (i.e., counting the number of occurrences) relies on a well-known ScanCount algorithm.\n * 2) The inverted file is split into small parts. In doing so, we aim to\n * achieve better caching properties of the counter array used in ScanCount.\n * \n * For an example of using ScanCount see, e.g.:\n *\n * Li, Chen, Jiaheng Lu, and Yiming Lu. \n * \"Efficient merging and filtering algorithms for approximate string searches.\" \n * In Data Engineering, 2008. ICDE 2008. \n * IEEE 24th International Conference on, pp. 257-266. IEEE, 2008.\n */\n\ntemplate \nclass OMedRank : public Index {\n public:\n OMedRank(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n virtual ~OMedRank(){};\n\n const std::string StrDesc() const override { return \"omedrank\" ; }\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n private:\n void IndexChunk(size_t chunkId, ProgressDisplay* displayBar);\n\n const Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n size_t num_pivot_;\n size_t num_pivot_search_;\n size_t chunk_index_size_;\n size_t index_qty_;\n size_t db_scan_;\n bool skip_check_;\n string proj_type_;\n size_t interm_dim_; // used only for sparse vector spaces\n size_t K_;\n size_t knn_amp_;\n float\t\t\t\t\t db_scan_frac_;\n float min_freq_;\n std::unique_ptr> projection_;\n\n struct ObjectInvEntry {\n IdType id_;\n float pivot_dist_;\n ObjectInvEntry(IdType id, dist_t pivot_dist) : id_(id), pivot_dist_(pivot_dist) {} \n bool operator<(const ObjectInvEntry& o) const { \n if (pivot_dist_ != o.pivot_dist_) return pivot_dist_ < o.pivot_dist_; \n return id_ < o.id_;\n };\n };\n\n typedef vector PostingList;\n\n vector>> posting_lists_;\n \n // Heuristics: try to read db_scan_fraction/index_qty entries from each index part\n // or alternatively K * knn_amp_ entries, for KNN-search\n size_t computeDbScan(size_t K) const {\n if (knn_amp_) { return min(K * knn_amp_, data_.size()); }\n return static_cast(db_scan_frac_ * data_.size());\n }\n\n template void GenSearch(QueryType* query, size_t K) const;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(OMedRank);\n};\n\n} // namespace similarity\n\n#endif // _OMEDRANK_METHOD_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/perm_bin_vptree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PERM_BIN_VPTREE_H_\n#define _PERM_BIN_VPTREE_H_\n\n#include \n\n#include \"index.h\"\n#include \"space/space_bit_hamming.h\"\n#include \"permutation_utils.h\"\n#include \"vptree.h\"\n#include \"params.h\"\n#include \"searchoracle.h\"\n\n#define METH_PERM_BIN_VPTREE \"perm_bin_vptree\"\n\nnamespace similarity {\n\n/*\n * Brief permutation index. Binarized permutations are stored in the VP-tree.\n *\n * Similar to :\n *\n * A brief index for proximity searching\n * ES Téllez, E Chávez, A Camarena-Ibarrola \n *\n * The difference from their work: we search permutations using APPROXIMATE near-neighbor search.\n *\n */\ntemplate \nclass PermBinVPTree : public Index {\n public:\n PermBinVPTree(bool PrintProgress,\n Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~PermBinVPTree();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n private:\n\n Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n size_t bin_threshold_;\n size_t bin_perm_word_qty_;\n size_t db_scan_qty_;\n ObjectVector pivots_;\n ObjectVector BinPermData_;\n\n unique_ptr>> VPTreeIndex_;\n unique_ptr VPTreeSpace_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(PermBinVPTree);\n};\n\n} // namespace similarity\n\n#endif // _PERM_BIN_VPTREE_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/perm_index_incr_bin.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PERMUTATION_INDEX_INCREMENTAL_BINARY_H_\n#define _PERMUTATION_INDEX_INCREMENTAL_BINARY_H_\n\n#include \n#include \"index.h\"\n#include \"permutation_utils.h\"\n\n\n#define METH_PERMUTATION_INC_SORT_BIN \"perm_incsort_bin\"\n#define METH_PERMUTATION_INC_SORT_BIN_SYN \"perm_bin_incsort\"\n\nnamespace similarity {\n\n/* \n * A simplified version of the brief permutation index.\n * Unlike the original version, all binarized permutations\n * are sought for sequentially.\n * \n * A brief index for proximity searching\n * ES Téllez, E Chávez, A Camarena-Ibarrola\n */\n\ntemplate \nclass PermutationIndexIncrementalBin : public Index {\n public:\n PermutationIndexIncrementalBin(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n ~PermutationIndexIncrementalBin();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n virtual void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n\n private:\n const Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n ObjectVector pivot_;\n\n size_t num_pivot_;\n size_t bin_threshold_;\n float db_scan_frac_; \n size_t knn_amp_;\n size_t bin_perm_word_qty_;\n bool use_sort_;\n size_t max_hamming_dist_;\n bool skip_checking_;\n\n std::vector permtable_;\n \n size_t computeDbScan(size_t K) const {\n if (knn_amp_) { return min(K * knn_amp_, data_.size()); }\n return static_cast(db_scan_frac_ * data_.size());\n }\n\n template void GenSearch(QueryType* query, size_t K) const;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(PermutationIndexIncrementalBin);\n};\n\n} // namespace similarity\n\n#endif // _PERMUTATION_INDEX_INCREMENTAL_BINARY_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/perm_lsh_bin.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PERMUTATION_INDEX_LSH_BIN_\n#define _PERMUTATION_INDEX_LSH_BIN_\n\n#include \n#include \"index.h\"\n#include \"permutation_utils.h\"\n\n\n#define METH_PERMUTATION_LSH_BIN \"perm_lsh_bin\"\n\nnamespace similarity {\n\n/* \n * A simplified version of the LSH based on binarized permutations.\n * \n * Tellez, Eric Sadit, and Edgar Chavez. \n * \"On locality sensitive hashing in metric spaces.\" \n * Proceedings of the SISAP 2010.\n */\n\ntemplate \nclass PermutationIndexLSHBin : public Index {\n public:\n PermutationIndexLSHBin(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& MethParams) override;\n ~PermutationIndexLSHBin();\n\n const std::string StrDesc() const override { return \"LSH (binary permutations)\"; }\n void Search(RangeQuery* query, IdType) const override {GenSearch(query);}\n void Search(KNNQuery* query, IdType) const override {GenSearch(query);}\n\n void SetQueryTimeParams(const AnyParams &) override {}\n private:\n const Space& space_;\n const ObjectVector& data_;\n bool printProgress_;\n\n size_t num_pivot_;\n size_t bin_threshold_;\n size_t bit_sample_qty_;\n size_t num_hash_;\n size_t hash_table_size_;\n vector pivots_;\n vector> bit_sample_flags_;\n\n vector*>> hash_tables_;\n\n template void GenSearch(QueryType* query) const;\n\n /*\n * If pQuery is not NULL, distances are computed via the query.\n */\n size_t computeHashValue(size_t hashId, const Object* pObj, Query* pQuery) const {\n Permutation perm_q;\n if (pQuery) {\n GetPermutation(pivots_[hashId], pQuery, &perm_q);\n } else {\n GetPermutation(pivots_[hashId], space_, pObj, &perm_q);\n }\n size_t res = 0, flag = 1;\n for (size_t i = 0; i < num_pivot_; ++i) \n if (bit_sample_flags_[hashId][i]) {\n if (perm_q[i] >= bin_threshold_) {\n res |= flag;\n }\n flag <<= 1;\n }\n return res % hash_table_size_;\n }\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(PermutationIndexLSHBin);\n};\n\n} // namespace similarity\n\n#endif \n\n" }, { "path": "algorithms/NMSLIB/code/include/method/permutation_inverted_index.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PERM_INVERTED_INDEX_H_\n#define _PERM_INVERTED_INDEX_H_\n\n#include \n#include \"index.h\"\n#include \"permutation_utils.h\"\n\n#define METH_PERM_INVERTED_INDEX \"perm_inv_indx\"\n#define METH_PERM_INVERTED_INDEX_SYN \"mi-file\"\n\n#define USE_MAP_THRESHOLD 0.01\n\nnamespace similarity {\n\n/*\n * Giuseppe Amato and Pasquale Savino,\n * Approximate Similarity Search in Metric Spaces Using Inverted Files,\n * Infoscale (2008)\n *\n * Later dubbed as MI-File (metric inverted file).\n */\n\ntemplate \nclass PermutationInvertedIndex : public Index {\n public:\n PermutationInvertedIndex(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& params) override;\n ~PermutationInvertedIndex();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n \n virtual void SetQueryTimeParams(const AnyParams& params) override;\n\n private:\n const Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n float db_scan_frac_;\n size_t num_pivot_; // overall number of pivots\n size_t num_pivot_index_; // ki in the original paper\n size_t num_pivot_search_; // ks in the original paper\n size_t max_pos_diff_;\n size_t knn_amp_;\n ObjectVector pivot_;\n\n size_t computeDbScan(size_t K) const {\n if (knn_amp_) { return min(K * knn_amp_, data_.size()); }\n return static_cast(db_scan_frac_ * data_.size());\n }\n\n struct ObjectInvEntry {\n IdType id_;\n int pos_;\n ObjectInvEntry(IdType id, int pos) : id_(id), pos_(pos) {} \n bool operator<(const ObjectInvEntry& o) const { \n if (pos_ != o.pos_) return pos_ < o.pos_; \n return id_ < o.id_;\n };\n };\n\n typedef std::vector PostingList;\n\n std::vector posting_lists_;\n\n // K==0 for range search\n template void GenSearch(QueryType* query, size_t K) const;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(PermutationInvertedIndex);\n};\n\n} // namespace similarity\n\n#endif // _INVERTED_INDEX_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/permutation_prefix_index.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PERMUTATION_PREFIX_INDEX_H_\n#define _PERMUTATION_PREFIX_INDEX_H_\n\n#include \n#include \"index.h\"\n#include \"permutation_utils.h\"\n\n#define METH_PERMUTATION_PREFIX_IND \"perm_prefix\"\n#define METH_PERMUTATION_PREFIX_IND_SYN \"pp-index\"\n\nnamespace similarity {\n\n/*\n * Permutation Prefix Index (PP-index): \n * Esuli, 2012.\n * Using Permutation Prefixes for Efficient and Scalable Approximate Similarity Search.\n */\n\ntemplate \nclass Space;\n\nclass PrefixTree;\n\ntemplate \nclass PermutationPrefixIndex : public Index {\n public:\n PermutationPrefixIndex(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n ~PermutationPrefixIndex();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n virtual bool DuplicateData() const override { return chunkBucket_; }\n private:\n\n size_t computeDbScan(size_t K) const {\n if (knn_amp_) { return min(K * knn_amp_, data_.size()); }\n return static_cast(min(min_candidate_, data_.size()));\n }\n\n\n template \n void GenSearch(QueryType* query, size_t K) const;\n\n const Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n // permutation prefix length (l in the original paper) in (0, num_pivot]\n size_t num_pivot_;\n size_t prefix_length_;\n size_t min_candidate_;\n size_t knn_amp_;\n // min # of candidates to be selected (z in the original paper)\n ObjectVector pivot_;\n unique_ptr prefixtree_;\n bool chunkBucket_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(PermutationPrefixIndex);\n};\n\n} // namespace similarity\n\n#endif // _PERMUTATION_PREFIX_INDEX_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/pivot_neighb_common.h", "content": "\n#ifndef NONMETRICSPACELIB_PIVOT_NEIGHB_COMMON_H_H\n#define NONMETRICSPACELIB_PIVOT_NEIGHB_COMMON_H_H\n\n#include \n#include \n\nnamespace similarity {\n\nusing std::vector;\n\n#define PERM_PROC_FAST_SCAN \"scan\"\n#define PERM_PROC_MAP \"map\"\n#define PERM_PROC_MERGE \"merge\"\n\nstruct IdCount {\n size_t id;\n size_t qty;\n\n IdCount(size_t id = 0, int qty = 0) : id(id), qty(qty) { }\n};\n\ntypedef vector VectIdCount;\n\ntypedef vector PostingListInt;\n\ninline void postListUnion(const VectIdCount &lst1, const PostingListInt lst2, VectIdCount &res) {\n res.clear();\n res.reserve((lst1.size() + lst2.size()) / 2);\n auto i1 = lst1.begin();\n auto i2 = lst2.begin();\n\n while (i1 != lst1.end() && i2 != lst2.end()) {\n size_t id2 = static_cast(*i2);\n if (i1->id < id2) {\n res.push_back(*i1);\n ++i1;\n } else if (i1->id > id2) {\n res.push_back(IdCount(id2, 1));\n ++i2;\n } else {\n res.push_back(IdCount(i1->id, i1->qty + 1));\n ++i1;\n ++i2;\n }\n }\n while (i1 != lst1.end()) {\n res.push_back(*i1);\n ++i1;\n }\n while (i2 != lst2.end()) {\n res.push_back(IdCount(*i2, 1));\n ++i2;\n }\n}\n\n}\n\n#endif //NONMETRICSPACELIB_PIVOT_NEIGHB_COMMON_H_H\n" }, { "path": "algorithms/NMSLIB/code/include/method/pivot_neighb_invindx.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2010--2013\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PIVOT_NEIGHBORHOOD_INVINDEX_H\n#define _PIVOT_NEIGHBORHOOD_INVINDEX_H\n\n#include \n#include \n\n#include \"index.h\"\n#include \"permutation_utils.h\"\n#include \"ported_boost_progress.h\"\n\n#define METH_PIVOT_NEIGHB_INVINDEX \"pivot_neighb_invindx\"\n#define METH_PIVOT_NEIGHB_INVINDEX_SYN \"napp\"\n\n#include \n\nnamespace similarity {\n\nusing std::vector;\nusing std::mutex;\n\n/*\n * Neighborhood-APProximation Index (NAPP).\n *\n * The main idea of the method (indexing K most closest pivots using an inverted file)\n * was taken from the paper:\n *\n * Eric Sadit Tellez, Edgar Chavez and Gonzalo Navarro,\n * Succinct Nearest Neighbor Search, SISAP 2011\n *\n * In this implementation, we introduce several modifications:\n * 1) The inverted file is split into small parts. In doing so, we aim to\n * achieve better caching properties of the counter array used in ScanCount.\n * 2) The index is not compressed (though it could be)\n * 3) Instead of the adaptive union algorithm, we use a well-known ScanCount algorithm (by default). \n * The overall time spent on processing of the inverted file is 20-30% of the overall\n * search time. Thus, the retrieval time cannot be substantially improved by\n * replacing ScanCount with even better approach (should one exist).\n * 4) We also implemented several other simple algorithms for posting processing, to compare\n * against ScanCount. For instance, the merge-sort union algorithms is about 2-3 times as slow.\n *\n * For an example of using ScanCount see, e.g.:\n *\n * Li, Chen, Jiaheng Lu, and Yiming Lu. \n * \"Efficient merging and filtering algorithms for approximate string searches.\" \n * In Data Engineering, 2008. ICDE 2008. \n * IEEE 24th International Conference on, pp. 257-266. IEEE, 2008.\n */\n\ntemplate \nclass PivotNeighbInvertedIndex : public Index {\n public:\n PivotNeighbInvertedIndex(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n\n virtual void CreateIndex(const AnyParams& IndexParams) override;\n virtual void SaveIndex(const string &location) override;\n virtual void LoadIndex(const string &location) override;\n\n ~PivotNeighbInvertedIndex();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n \n void IndexChunk(size_t chunkId, ProgressDisplay*, mutex&);\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n private:\n\n const ObjectVector& data_;\n const Space& space_;\n bool PrintProgress_;\n bool recreate_points_;\n\n size_t chunk_index_size_;\n size_t K_;\n size_t knn_amp_;\n float db_scan_frac_;\n size_t num_prefix_; // K in the original paper\n size_t min_times_; // t in the original paper\n bool use_sort_;\n bool skip_checking_;\n size_t index_thread_qty_;\n size_t num_pivot_;\n string pivot_file_;\n\n enum eAlgProctype {\n kScan,\n kMap,\n kMerge\n } inv_proc_alg_;\n\n string toString(eAlgProctype type) {\n if (type == kScan) return PERM_PROC_FAST_SCAN;\n if (type == kMap) return PERM_PROC_MAP;\n if (type == kMerge) return PERM_PROC_MERGE;\n return \"unknown\";\n }\n\n ObjectVector pivot_;\n vector pivot_pos_;\n\n ObjectVector genPivot_; // generated pivots\n\n size_t computeDbScan(size_t K, size_t chunkQty) const {\n size_t totalDbScan = static_cast(db_scan_frac_ * data_.size());\n if (knn_amp_) { \n totalDbScan = K * knn_amp_;\n }\n totalDbScan = min(totalDbScan, data_.size());\n CHECK_MSG(chunkQty, \"Bug or inconsistent parameters: the number of index chunks cannot be zero!\");\n return (totalDbScan + chunkQty - 1) / chunkQty;\n }\n \n vector>> posting_lists_;\n\n template void GenSearch(QueryType* query, size_t K) const;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(PivotNeighbInvertedIndex);\n};\n\n} // namespace similarity\n\n#endif // _PERMUTATION_SUCCINCT_INDEX_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/proj_vptree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PROJ_VPTREE_H_\n#define _PROJ_VPTREE_H_\n\n#include \n\n#include \"index.h\"\n#include \"space/space_lp.h\"\n#include \"space/space_vector.h\"\n#include \"method/vptree.h\"\n#include \"params.h\"\n#include \"projection.h\"\n#include \"searchoracle.h\"\n\n#define METH_PROJ_VPTREE \"proj_vptree\"\n\nnamespace similarity {\n\n/*\n * The VP-tree build over projections.\n */\ntemplate \nclass ProjectionVPTree : public Index {\n public:\n ProjectionVPTree(bool PrintProgress, \n Space& space,\n const ObjectVector& data);\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~ProjectionVPTree();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType ) const override;\n void Search(KNNQuery* query, IdType ) const override;\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n private:\n\n\n Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n size_t K_;\n size_t knn_amp_;\n float\t\t\t\t\t db_scan_frac_;\n\n size_t computeDbScan(size_t K) const {\n if (knn_amp_) { return min(K * knn_amp_, data_.size()); }\n return static_cast(db_scan_frac_ * data_.size());\n }\n\n unique_ptr > projObj_;\n ObjectVector projData_;\n size_t projDim_;\n\n Object* ProjectOneVect(size_t targSpaceId,\n const Query* pQuery,\n const Object* pSrcObj) const;\n\n unique_ptr>> VPTreeIndex_;\n unique_ptr> VPTreeSpace_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(ProjectionVPTree);\n};\n\n} // namespace similarity\n\n#endif // _PROJ_VPTREE_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/projection_index_incremental.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PROJECTION_INDEX_INCREMENTAL_H_\n#define _PROJECTION_INDEX_INCREMENTAL_H_\n\n#include \n#include \n\n#include \"index.h\"\n#include \"projection.h\"\n#include \"space/space_vector.h\"\n\n// If set, projected vectors are stored contiguously in memory\n#define PROJ_CONTIGUOUS_STORAGE\n\n#define METH_PROJECTION_INC_SORT \"proj_incsort\"\n\nnamespace similarity {\n\n/* \n * The following filter-and-refine method is inspired by the paper of Chavez et al (see below). \n * The main difference is that this method supports several transformations of the source objects into vectors.\n * In other words, we select dbScanFract vectors whose projection vector is close to the projection \n * of the query. Sorting is done via the incremental quicksort. There is an additional parameter:\n * the maximum allowed distance in the projected space between the query and the data point projection\n * (which is not in the referenced paper).\n *\n * Edgar Chávez et al., Effective Proximity Retrieval by Ordering Permutations.\n * IEEE Trans. Pattern Anal. Mach. Intell. (2008)\n */\n\ntemplate \nclass ProjectionIndexIncremental : public Index {\n public:\n ProjectionIndexIncremental(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n void CreateIndex(const AnyParams& IndexParams) override;\n ~ProjectionIndexIncremental();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n \n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override;\n private:\n\n const Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n float max_proj_dist_;\n bool use_priority_queue_;\n size_t K_;\n size_t knn_amp_;\n float\t\t\t\t\t db_scan_frac_;\n size_t proj_dim_;\n bool use_cosine_;\n string proj_descr_;\n unique_ptr > proj_obj_;\n\n#ifdef PROJ_CONTIGUOUS_STORAGE\n std::vector proj_vects_;\n#else\n std::vector> proj_vects_;\n#endif\n \n size_t computeDbScan(size_t K) const {\n if (knn_amp_) { return min(K * knn_amp_, data_.size()); }\n return static_cast(db_scan_frac_ * data_.size());\n }\n\n template void GenSearch(QueryType* query, size_t K) const;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(ProjectionIndexIncremental);\n};\n\n} // namespace similarity\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/seqsearch.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _SEQSEARCH_H_\n#define _SEQSEARCH_H_\n\n#include \n\n#include \"index.h\"\n\n#define METH_SEQ_SEARCH \"brute_force\"\n#define METH_SEQ_SEARCH_SYN \"seq_search\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\n\n// Sequential search\n\ntemplate \nclass SeqSearch : public Index {\n public:\n SeqSearch(Space& space, const ObjectVector& data);\n void CreateIndex(const AnyParams& ) override;\n virtual ~SeqSearch();\n\n const std::string StrDesc() const override { return \"Sequential search\"; }\n\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams& params) override {}\n private:\n Space& space_;\n const ObjectVector& origData_;\n char* cacheOptimizedBucket_;\n\n ObjectVector* pData_;\n bool multiThread_;\n IdTypeUnsign threadQty_;\n vector vvThreadData;\n\n const ObjectVector& getData() const { return pData_ != NULL ? *pData_ : origData_; }\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(SeqSearch);\n};\n\n} // namespace similarity\n\n#endif // _SEQSEARCH_H_\n" }, { "path": "algorithms/NMSLIB/code/include/method/small_world_rand.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SMALL_WORLD_RAND_H_\n#define _SMALL_WORLD_RAND_H_\n\n#include \"index.h\"\n#include \"params.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\n#define METH_SMALL_WORLD_RAND \"sw-graph\"\n#define METH_SMALL_WORLD_RAND_SYN \"small_world_rand\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\nusing std::thread;\nusing std::mutex;\nusing std::unique_lock;\nusing std::condition_variable;\nusing std::ref;\n\ntemplate \nclass Space;\n\n/*\n *\n * A small world approach. It builds the knn-graph incrementally and relies on \n * a straightforward randomized algorithm to insert an element. \n * \n * The main publication is as follows, but the basic algorithm was also presented as SISAP'12: \n * Malkov, Y., Ponomarenko, A., Logvinov, A., & Krylov, V., 2014. \n * Approximate nearest neighbor algorithm based on navigable small world graphs. Information Systems, 45, 61-68. \n *\n */\n\n//----------------------------------\nclass MSWNode{\npublic:\n MSWNode(const Object *Obj, size_t id) {\n data_ = Obj;\n id_ = id;\n }\n ~MSWNode(){};\n void removeAllFriends(){\n friends.clear();\n }\n /* \n * 1. The list of friend pointers is sorted.\n * 2. If bCheckForDup == true addFriend checks for\n * duplicates using binary searching (via pointer comparison).\n */\n void addFriend(MSWNode* element, bool bCheckForDup) {\n unique_lock lock(accessGuard_);\n\n if (bCheckForDup) {\n auto it = lower_bound(friends.begin(), friends.end(), element);\n if (it == friends.end() || (*it) != element) {\n friends.insert(it, element);\n }\n } else {\n friends.push_back(element);\n }\n }\n const Object* getData() const {\n return data_;\n }\n size_t getId() const { return id_; }\n /* \n * THIS NOTE APPLIES ONLY TO THE INDEXING PHASE:\n *\n * Before getting access to the friends,\n * one needs to lock the mutex accessGuard_\n * The mutex can be released ONLY when\n * we exit the scope that has access to\n * the reference returned by getAllFriends()\n */\n const vector& getAllFriends() const {\n return friends;\n }\n\n mutex accessGuard_;\n\nprivate:\n const Object* data_;\n size_t id_;\n vector friends;\n};\n//----------------------------------\ntemplate \nclass EvaluatedMSWNodeReverse{\npublic:\n EvaluatedMSWNodeReverse() {\n distance = 0;\n element = NULL;\n }\n EvaluatedMSWNodeReverse(dist_t di, MSWNode* node) {\n distance = di;\n element = node;\n }\n ~EvaluatedMSWNodeReverse(){}\n dist_t getDistance() const {return distance;}\n MSWNode* getMSWNode() const {return element;}\n bool operator< (const EvaluatedMSWNodeReverse &obj1) const {\n return (distance > obj1.getDistance());\n }\n\nprivate:\n dist_t distance;\n MSWNode* element;\n};\n\ntemplate \nclass EvaluatedMSWNodeDirect{\npublic:\n EvaluatedMSWNodeDirect() {\n distance = 0;\n element = NULL;\n }\n EvaluatedMSWNodeDirect(dist_t di, MSWNode* node) {\n distance = di;\n element = node;\n }\n ~EvaluatedMSWNodeDirect(){}\n dist_t getDistance() const {return distance;}\n MSWNode* getMSWNode() const {return element;}\n bool operator< (const EvaluatedMSWNodeDirect &obj1) const {\n return (distance < obj1.getDistance());\n }\n\nprivate:\n dist_t distance;\n MSWNode* element;\n};\n\n//----------------------------------\ntemplate \nclass SmallWorldRand : public Index {\npublic:\n virtual void SaveIndex(const string &location) override;\n\n virtual void LoadIndex(const string &location) override;\n\n SmallWorldRand(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~SmallWorldRand();\n\n typedef std::vector ElementList;\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n MSWNode* getRandomEntryPoint() const;\n MSWNode* getRandomEntryPointLocked() const;\n size_t getEntryQtyLocked() const;\n \n void searchForIndexing(const Object *queryObj,\n std::priority_queue> &resultSet) const;\n void add(MSWNode *newElement);\n void addCriticalSection(MSWNode *newElement);\n void link(MSWNode* first, MSWNode* second){\n // addFriend checks for duplicates if the second argument is true\n first->addFriend(second, true);\n second->addFriend(first, true);\n }\n\n void SetQueryTimeParams(const AnyParams& ) override;\nprivate:\n\n size_t NN_;\n size_t efConstruction_;\n size_t efSearch_;\n size_t initIndexAttempts_;\n size_t initSearchAttempts_;\n size_t indexThreadQty_;\n string pivotFile_;\n ObjectVector pivots_;\n\n const Space& space_;\n ObjectVector data_; // We copy all the data\n bool PrintProgress_;\n\n mutable mutex ElListGuard_;\n ElementList ElList_;\n\n\n void SearchOld(KNNQuery* query) const;\n void SearchV1Merge(KNNQuery* query) const;\n \n enum AlgoType { kOld, kV1Merge };\n\n AlgoType searchAlgoType_;\n\nprotected:\n\n DISABLE_COPY_AND_ASSIGN(SmallWorldRand);\n};\n\n\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/small_world_rand_split.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SMALL_WORLD_RAND_SPLIT_H_\n#define _SMALL_WORLD_RAND_SPLIT_H_\n\n#include \"index.h\"\n#include \"params.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\n#define METH_SMALL_WORLD_RAND_SPLIT \"sw-graph-split\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\nusing std::thread;\nusing std::mutex;\nusing std::unique_lock;\nusing std::condition_variable;\nusing std::ref;\n\ntemplate \nclass Space;\n\n/*\n *\n * A small world approach. It builds the knn-graph incrementally and relies on \n * a straightforward randomized algorithm to insert an element. \n * The index can be split into chunks (to make indexing faster). Usually,\n * this comes at the expense of search efficiency.\n * \n * The main publication is as follows, but the basic algorithm was also presented as SISAP'12: \n * Malkov, Y., Ponomarenko, A., Logvinov, A., & Krylov, V., 2014. \n * Approximate nearest neighbor algorithm based on navigable small world graphs. Information Systems, 45, 61-68. \n *\n */\n\n//----------------------------------\n\n//----------------------------------\ntemplate \nclass SmallWorldRandSplit : public Index {\npublic:\n class MSWNode{\n public:\n MSWNode(const Object *Obj, size_t id) {\n data_ = Obj;\n id_ = id;\n }\n ~MSWNode(){};\n void removeAllFriends(){\n friends.clear();\n }\n /*\n * 1. The list of friend pointers is sorted.\n * 2. If bCheckForDup == true addFriend checks for\n * duplicates using binary searching (via pointer comparison).\n */\n void addFriend(MSWNode* element, bool bCheckForDup) {\n unique_lock lock(accessGuard_);\n\n if (bCheckForDup) {\n auto it = lower_bound(friends.begin(), friends.end(), element);\n if (it == friends.end() || (*it) != element) {\n friends.insert(it, element);\n }\n } else {\n friends.push_back(element);\n }\n }\n const Object* getData() const {\n return data_;\n }\n size_t getId() const { return id_; }\n void setId(size_t id) { id_ = id; }\n /*\n * THIS NOTE APPLIES ONLY TO THE INDEXING PHASE:\n *\n * Before getting access to the friends,\n * one needs to lock the mutex accessGuard_\n * The mutex can be released ONLY when\n * we exit the scope that has access to\n * the reference returned by getAllFriends()\n */\n const vector& getAllFriends() const {\n return friends;\n }\n\n mutex accessGuard_;\n\n private:\n const Object* data_;\n size_t id_;\n vector friends;\n };\n //----------------------------------\n class EvaluatedMSWNodeReverse{\n public:\n EvaluatedMSWNodeReverse() {\n distance = 0;\n element = NULL;\n }\n EvaluatedMSWNodeReverse(dist_t di, MSWNode* node) {\n distance = di;\n element = node;\n }\n ~EvaluatedMSWNodeReverse(){}\n dist_t getDistance() const {return distance;}\n MSWNode* getMSWNode() const {return element;}\n bool operator< (const EvaluatedMSWNodeReverse &obj1) const {\n return (distance > obj1.getDistance());\n }\n\n private:\n dist_t distance;\n MSWNode* element;\n };\n\n class EvaluatedMSWNodeDirect{\n public:\n EvaluatedMSWNodeDirect() {\n distance = 0;\n element = NULL;\n }\n EvaluatedMSWNodeDirect(dist_t di, MSWNode* node) {\n distance = di;\n element = node;\n }\n ~EvaluatedMSWNodeDirect(){}\n dist_t getDistance() const {return distance;}\n MSWNode* getMSWNode() const {return element;}\n bool operator< (const EvaluatedMSWNodeDirect &obj1) const {\n return (distance < obj1.getDistance());\n }\n\n private:\n dist_t distance;\n MSWNode* element;\n };\n\n virtual void SaveIndex(const string &location) override;\n\n virtual void LoadIndex(const string &location) override;\n\n SmallWorldRandSplit(bool PrintProgress,\n const Space& space,\n const ObjectVector& data);\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~SmallWorldRandSplit();\n\n typedef std::vector ElementList;\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n MSWNode* getRandomEntryPoint(size_t start, size_t end) const;\n MSWNode* getRandomEntryPointLocked(size_t start, size_t end) const;\n size_t getEntryQtyLocked() const;\n \n void searchForIndexing(const Object *queryObj,\n const size_t chunkStart, const size_t chunkEnd, size_t randomEntryPointEnd,\n vector& visitedBitset,\n std::priority_queue &resultSet) const;\n void add(MSWNode *newElement, const size_t chunkStart, const size_t chunkEnd, vector& visitedBitset);\n void link(MSWNode* first, MSWNode* second){\n // addFriend checks for duplicates if the second argument is true\n first->addFriend(second, true);\n second->addFriend(first, true);\n }\n\n void SetQueryTimeParams(const AnyParams& ) override;\n\nprivate:\n\n size_t NN_;\n size_t efConstruction_;\n size_t efSearch_;\n size_t initIndexAttempts_;\n size_t initSearchAttempts_;\n size_t indexThreadQty_;\n size_t chunkIndexSize_;\n\n const Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n\n mutable mutex ElListGuard_;\n ElementList ElList_;\n\nprotected:\n\n DISABLE_COPY_AND_ASSIGN(SmallWorldRandSplit);\n};\n\n\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/spatial_approx_tree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPATIAL_APPROXIMATION_TREE_H_\n#define _SPATIAL_APPROXIMATION_TREE_H_\n\n#include \n\n#include \"index.h\"\n#include \"params.h\"\n\n#define METH_SATREE \"satree\"\n\nnamespace similarity {\n\n/* \n * (Static) Spatial Approximation Tree\n * G. Navarro: Searching in metric spaces by spatial approximation\n * VLDB Journal 11: pp 28-46 (2002)\n */\n\ntemplate \nclass Space;\n\ntemplate \nclass SpatialApproxTree : public Index {\n public:\n SpatialApproxTree(const Space& space,\n const ObjectVector& data);\n\n void CreateIndex(const AnyParams&) override;\n\n ~SpatialApproxTree();\n\n const std::string StrDesc() const override;\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n void SetQueryTimeParams(const AnyParams&) override {}\n\n private:\n struct SATKnn;\n class SATNode;\n\n const Space& space_;\n const ObjectVector data_;\n \n unique_ptr root_;\n};\n\n} // namespace similarity\n\n#endif // _SPATIAL_APPROXIMATION_TREE_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/method/vptree.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _VPTREE_H_\n#define _VPTREE_H_\n\n#include \n#include \n#include \n\n#include \"index.h\"\n#include \"params.h\"\n#include \"ported_boost_progress.h\"\n\n#define METH_VPTREE \"vptree\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\nusing std::unique_ptr;\n\n// Vantage point tree\n\ntemplate class Space;\n\ntemplate \nclass VPTree : public Index {\n public:\n VPTree(bool PrintProgress,\n Space& space,\n const ObjectVector& data,\n bool use_random_center = true);\n\n void CreateIndex(const AnyParams& IndexParams) override;\n\n ~VPTree();\n\n const std::string StrDesc() const override;\n\n void Search(RangeQuery* query, IdType) const override;\n void Search(KNNQuery* query, IdType) const override;\n\n const vector& getQueryTimeParams() const { return QueryTimeParams_; }\n\n\n void SetQueryTimeParams(const AnyParams& QueryTimeParams) override {\n AnyParamManager pmgr(QueryTimeParams);\n oracle_.SetQueryTimeParams(pmgr); \n pmgr.GetParamOptional(\"maxLeavesToVisit\", MaxLeavesToVisit_, FAKE_MAX_LEAVES_TO_VISIT);\n LOG(LIB_INFO) << \"Set VP-tree query-time parameters:\";\n LOG(LIB_INFO) << \"maxLeavesToVisit=\" << MaxLeavesToVisit_;\n pmgr.CheckUnused();\n }\n\n virtual bool DuplicateData() const override { return ChunkBucket_; }\n private:\n\n class VPNode {\n public:\n // We want trees to be balanced\n const size_t BalanceConst = 4; \n\n VPNode(unsigned level,\n ProgressDisplay* progress_bar,\n const SearchOracle& oracle,\n const Space& space, const ObjectVector& data,\n size_t BucketSize, bool ChunkBucket,\n bool use_random_center);\n ~VPNode();\n\n template \n void GenericSearch(QueryType* query, int& MaxLeavesToVisit) const;\n\n private:\n void CreateBucket(bool ChunkBucket, const ObjectVector& data, \n ProgressDisplay* progress_bar);\n const SearchOracle& oracle_; // The search oracle must be accessed by reference,\n // so that VP-tree may be able to change its parameters\n const Object* pivot_;\n /* \n * Even if dist_t is double, or long double\n * storing the median as the single-precision number (i.e., float)\n * should be good enough.\n */\n float mediandist_;\n VPNode* left_child_;\n VPNode* right_child_;\n ObjectVector* bucket_;\n char* CacheOptimizedBucket_;\n\n friend class VPTree;\n };\n\n Space& space_;\n const ObjectVector& data_;\n bool PrintProgress_;\n bool use_random_center_;\n\n SearchOracle oracle_;\n unique_ptr root_;\n size_t BucketSize_;\n int MaxLeavesToVisit_;\n bool ChunkBucket_;\n\n vector QueryTimeParams_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(VPTree);\n};\n\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/method/vptree_utils.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _VPTREE_UTILS_H_\n#define _VPTREE_UTILS_H_\n\n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\ninline size_t SelectVantagePoint(const ObjectVector& data, bool use_random_center) {\n CHECK(!data.empty());\n return use_random_center ? RandomInt() % data.size() : data.size() - 1;\n}\n\n/* \n * A median is a pair:\n * a distance + an index.\n */\ntemplate \ninline DistObjectPair GetMedian(const DistObjectPairVector& dp) {\n CHECK(!dp.empty());\n dist_t val = 0;\n size_t index;\n if ((dp.size() & 1) == 1) { // odd\n index = dp.size() / 2;\n val = dp[index].first;\n } else { //even\n CHECK(dp.size() >= 2);\n index = dp.size() / 2;\n val = static_cast((static_cast(dp[index - 1].first) \n + static_cast(dp[index ].first)) / 2.0);\n }\n return DistObjectPair(val, dp[index].second); \n}\n\n/* \n * This function find for approximate quantile boundaries.\n * It wasn't meant to get quantiles exactly. Furthermore,\n * the method may return fewer indices than the # of quantiles.\n*/\ntemplate \ninline std::vector EstimateQuantileIndices(\n const typename std::vector >& dp,\n const std::vector quant) {\n CHECK(!dp.empty());\n std::vector res;\n size_t N = dp.size();\n\n size_t prevIndx = 0;\n\n for(auto it = quant.begin(); it != quant.end(); ++it) {\n size_t indx = static_cast(round(*it * N));\n if (indx < N && indx > prevIndx) {\n dist_t dist = dp[indx].first;\n prevIndx = indx;\n // If there is a series of entries with equal distances, go to the end of the list!\n while (indx < N && dp[indx].first == dist) {\n prevIndx = indx++;\n }\n res.push_back(prevIndx);\n }\n }\n\n return res;\n}\n\n} // namespace similarity\n\n#endif // _VPTREE_UTILS_H_\n" }, { "path": "algorithms/NMSLIB/code/include/methodfactory.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef METHOD_FACTORY_H\n#define METHOD_FACTORY_H\n\n#include \n#include \n\n#include \"index.h\"\n#include \"space.h\"\n#include \"logging.h\"\n\nnamespace similarity {\n\nusing std::string;\n\n#define REGISTER_METHOD_CREATOR(type, name, func)\\\n MethodFactoryRegistry::Instance().Register(name, func); \n \n\ntemplate \nclass MethodFactoryRegistry {\npublic:\n typedef Index* (*CreateFuncPtr)(\n bool PrintProgress,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects);\n\n static MethodFactoryRegistry& Instance() {\n static MethodFactoryRegistry elem;\n return elem;\n }\n\n void Register(const string& MethodName, CreateFuncPtr func) {\n LOG(LIB_INFO) << \"Registering at the factory, method: \" << MethodName << \" distance type: \" << DistTypeName();\n Creators_[MethodName] = func;\n }\n\n Index* CreateMethod(bool PrintProgress,\n const string& MethName,\n const string& SpaceType,\n Space& space,\n const ObjectVector& DataObjects) {\n if (Creators_.count(MethName)) {\n return Creators_[MethName](PrintProgress, SpaceType, space, DataObjects);\n } else {\n PREPARE_RUNTIME_ERR(err) << \"It looks like the method \" << MethName << \n \" is not defined for the distance type : \" << DistTypeName();\n THROW_RUNTIME_ERR(err);\n }\n return NULL;\n }\nprivate:\n map Creators_;\n};\n\n}\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/nndes/nndes-common.h", "content": "/*\nCopyright (C) 2010,2011 Wei Dong \nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n#ifndef WDONG_NNDESCENT_COMMON\n#define WDONG_NNDESCENT_COMMON\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#define USE_SPINLOCK 1\n#ifdef USE_SPINLOCK\n#include \n#include \"boost/smart_ptr/detail/spinlock.hpp\"\n#endif\n#ifdef _OPENMP\n#include \n#endif\n\nnamespace similarity {\n\n using std::vector;\n using std::numeric_limits;\n\n#define SYMMETRIC 1\n\n#ifdef _OPENMP\n#if SYMMETRIC\n#define NEED_LOCK 1\n#endif\n#endif\n\n#if NEED_LOCK\n#ifdef USE_SPINLOCK\n class Mutex {\n boost::detail::spinlock lock;\n public:\n void init () {\n }\n void set () {\n lock.lock();\n }\n \n void unset () {\n lock.unlock();\n }\n friend class ScopedLock;\n };\n\n class ScopedLock: public std::lock_guard {\n public:\n ScopedLock (Mutex &mutex): std::lock_guard(mutex.lock) {\n \n }\n };\n#else\n class Mutex {\n omp_lock_t *lock;\n public:\n Mutex (): lock(0) {\n }\n\n void init () {\n lock = new omp_lock_t;\n omp_init_lock(lock);\n }\n\n ~Mutex () {\n if (lock) {\n omp_destroy_lock(lock);\n delete lock;\n }\n }\n\n void set () {\n omp_set_lock(lock);\n }\n\n void unset () {\n omp_unset_lock(lock);\n }\n\n friend class ScopedLock;\n };\n class ScopedLock {\n omp_lock_t *lock;\n public:\n ScopedLock (Mutex &mutex) {\n lock = mutex.lock;\n omp_set_lock(lock);\n }\n ~ScopedLock () {\n omp_unset_lock(lock);\n }\n };\n#endif\n#else\n class Mutex {\n public:\n void init () {};\n void set () {};\n void unset () {};\n };\n class ScopedLock {\n public:\n ScopedLock (Mutex &) {\n }\n };\n#endif\n\n struct KNNEntry\n {\n static const int BAD = -1; //numeric_limits::max();\n int key;\n float dist; \n bool flag;\n bool match (const KNNEntry &e) const { return key == e.key; }\n KNNEntry (int key_, float dist_, bool flag_ = true) : key(key_), dist(dist_), flag(flag_) {}\n KNNEntry () : dist(numeric_limits::max()) { }\n void reset () { key = BAD; dist = numeric_limits::max(); }\n friend bool operator < (const KNNEntry &e1, const KNNEntry &e2)\n {\n return e1.dist < e2.dist;\n }\n };\n\n class KNN: public vector\n {\n int K;\n Mutex mutex;\n public:\n typedef KNNEntry Element;\n typedef vector Base;\n\n void init (int k) {\n mutex.init();\n K = k;\n this->resize(k);\n BOOST_FOREACH(KNNEntry &e, *this) {\n e.reset();\n }\n }\n\n int update (Element t)\n {\n //ScopedLock ll(mutex);\n mutex.set();\n int i = this->size() - 1;\n int j;\n if (!(t < this->back())) {\n mutex.unset();\n return -1;\n }\n for (;;)\n {\n if (i == 0) break;\n j = i - 1;\n if (this->at(j).match(t)) {\n mutex.unset();\n return -1;\n }\n if (this->at(j) < t) break;\n i = j;\n }\n\n j = this->size() - 1;\n for (;;)\n {\n if (j == i) break;\n this->at(j) = this->at(j-1);\n --j;\n }\n this->at(i) = t;\n mutex.unset();\n return i;\n }\n\n void update_unsafe (Element t)\n {\n int i = this->size() - 1;\n int j;\n if (!(t < this->back())) return;\n for (;;)\n {\n if (i == 0) break;\n j = i - 1;\n if (this->at(j).match(t)) {\n return;\n }\n if (this->at(j) < t) break;\n i = j;\n }\n\n j = this->size() - 1;\n for (;;)\n {\n if (j == i) break;\n this->at(j) = this->at(j-1);\n --j;\n }\n this->at(i) = t;\n }\n\n void lock() {\n mutex.set();\n }\n\n void unlock() {\n mutex.unset();\n }\n };\n\n static inline float recall (const int *knn, const KNN &ans, int K) {\n int match = 0;\n for (int i = 0; i < K; ++i) {\n for (int j = 0; j < K; ++j) {\n if (knn[i] == ans[j].key) {\n ++match;\n break;\n }\n }\n }\n return float(match) / K;\n }\n\n class Random {\n boost::mt19937 rng;\n public:\n Random () {\n }\n void seed (int s) {\n rng.seed(s);\n }\n ptrdiff_t operator () (ptrdiff_t i) {\n return rng() % i;\n }\n };\n}\n\n#endif \n\n" }, { "path": "algorithms/NMSLIB/code/include/nndes/nndes.h", "content": "/*\nCopyright (C) 2010,2011 Wei Dong \nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n#ifndef WDONG_NNDESCENT\n#define WDONG_NNDESCENT\n\n#include \"nndes-common.h\"\n#include \"ported_boost_progress.h\"\n\nnamespace similarity {\n\n using std::cout;\n using std::unique_ptr;\n using std::vector;\n using std::swap;\n\n#ifndef NNDES_SHOW_PROGRESS\n#define NNDES_SHOW_PROGRESS 1\n#endif\n\n // Normally one would use GRAPH_BOTH,\n // GRAPH_KNN & GRAPH_RNN are for experiments only.\n static const int GRAPH_NONE = 0, GRAPH_KNN = 1, GRAPH_RNN = 2, GRAPH_BOTH = 4;\n typedef int GraphOption;\n\n // The main NN-Descent class.\n // Instead of the actual dataset, the class takes a distance oracle\n // as input. Given two data item ids, the oracle returns the distance\n // between the two.\n template \n class NNDescent {\n private:\n const ORACLE &oracle;\n int N; // # points\n int K; // K-NN to find\n int S; // # of NNs to use for exploration\n GraphOption option;\n vector nn; // K-NN approximation\n\n // We maintain old and newly added KNN/RNN items\n // separately for incremental processing:\n // we need to compare two new ones\n // and a new one to an old one, but not two old ones as they\n // must have been compared already.\n vector > nn_old;\n vector > nn_new;\n vector > rnn_old;\n vector > rnn_new;\n\n // total number of comparisons done.\n long long int cost;\n\n // This function decides of it's necessary to compare two\n // points. Obviously a point should not compare against itself.\n // Another potential usage of this function is to record all\n // pairs that have already be compared, so that when seen in the future,\n // then same pair doesn't have be compared again.\n bool mark (int p1, int p2) {\n return p1 == p2;\n }\n\n // Compare two points and update their KNN list of necessary.\n // Return the number of comparisons done (0 or 1).\n int update (int p1, int p2) {\n if (mark(p1, p2)) return 0;\n // KNN::update is synchronized by a lock\n // keep an order is necessary to avoid deadlock.\n if (p1 > p2) swap(p1, p2);\n float dist = oracle(p1, p2);\n nn[p1].update(KNN::Element(p2, dist, true));\n nn[p2].update(KNN::Element(p1, dist, true));\n return 1;\n }\n\n public:\n const vector &getNN() const {\n return nn;\n }\n\n long long int getCost () const {\n return cost;\n }\n\n NNDescent (int N_, int K_, float S_, const ORACLE &oracle_,\n GraphOption opt = GRAPH_BOTH)\n : oracle(oracle_), N(N_), K(K_), S(K * S_), option(opt), nn(N_),\n nn_old(N_), nn_new(N_), rnn_old(N_), rnn_new(N_), cost(0)\n {\n for (int i = 0; i < N; ++i) {\n nn[i].init(K);\n // random initial edges\n if ((option & GRAPH_KNN) || (option & GRAPH_BOTH)) {\n nn_new[i].resize(S);\n BOOST_FOREACH(int &u, nn_new[i]) {\n u = RandomInt() % N;\n }\n }\n if ((option & GRAPH_RNN) || (option & GRAPH_BOTH)) {\n rnn_new[i].resize(S);\n BOOST_FOREACH(int &u, rnn_new[i]) {\n u = RandomInt() % N;\n }\n }\n }\n }\n\n // An iteration contains two parts:\n // local join\n // identify the newly detected NNs.\n int iterate (bool PrintProgress) {\n\n#if NNDES_SHOW_PROGRESS\n unique_ptr progress(\n PrintProgress? new ProgressDisplay(N, cerr): NULL);\n#endif\n\n long long int cc = 0;\n // local joins\n#pragma omp parallel for default(shared) reduction(+:cc)\n for (int i = 0; i < N; ++i) {\n\n // The following loops are bloated to deal with all\n // the experimental setups. Otherwise they should\n // be really simple.\n if (option & (GRAPH_KNN | GRAPH_BOTH)) {\n BOOST_FOREACH(int j, nn_new[i]) {\n BOOST_FOREACH(int k, nn_new[i]) {\n if (j >= k) continue;\n cc += update(j, k);\n }\n BOOST_FOREACH(int k, nn_old[i]) {\n cc += update(j, k);\n }\n }\n }\n if (option & (GRAPH_RNN | GRAPH_BOTH)) {\n BOOST_FOREACH(int j, rnn_new[i]) {\n BOOST_FOREACH(int k, rnn_new[i]) {\n if (j >= k) continue;\n cc += update(j, k);\n }\n BOOST_FOREACH(int k, rnn_old[i]) {\n cc += update(j, k);\n }\n }\n }\n if (option & GRAPH_BOTH) {\n BOOST_FOREACH(int j, nn_new[i]) {\n BOOST_FOREACH(int k, rnn_old[i]) {\n cc += update(j, k);\n }\n BOOST_FOREACH(int k, rnn_new[i]) {\n cc += update(j, k);\n }\n }\n BOOST_FOREACH(int j, nn_old[i]) {\n BOOST_FOREACH(int k, rnn_new[i]) {\n cc += update(j, k);\n }\n }\n }\n\n#if NNDES_SHOW_PROGRESS\n#pragma omp critical \n if (progress) ++(*progress);\n#endif\n }\n\n cost += cc;\n\n int t = 0;\n//#pragma omp parallel for default(shared) reduction(+:t)\n for (int i = 0; i < N; ++i) {\n\n nn_old[i].clear();\n nn_new[i].clear();\n rnn_old[i].clear();\n rnn_new[i].clear();\n\n // find the new ones\n for (int j = 0; j < K; ++j) {\n KNN::Element &e = nn[i][j];\n if (e.key == KNN::Element::BAD) continue;\n if (e.flag){\n nn_new[i].push_back(j);\n }\n else {\n nn_old[i].push_back(e.key);\n }\n }\n\n t += nn_new[i].size();\n // sample\n if (nn_new[i].size() > unsigned(S)) {\n random_shuffle(nn_new[i].begin(), nn_new[i].end());\n nn_new[i].resize(S);\n }\n BOOST_FOREACH(int &v, nn_new[i]) {\n nn[i][v].flag = false;\n v = nn[i][v].key;\n }\n }\n\n // symmetrize\n if ((option & GRAPH_RNN) || (option & GRAPH_BOTH)) {\n for (int i = 0; i < N; ++i) {\n BOOST_FOREACH(int e, nn_old[i]) {\n rnn_old[e].push_back(i);\n }\n BOOST_FOREACH(int e, nn_new[i]) {\n rnn_new[e].push_back(i);\n }\n }\n }\n\n//#pragma omp parallel for default(shared) reduction(+:t)\n for (int i = 0; i < N; ++i) {\n if (rnn_old[i].size() > unsigned(S)) {\n random_shuffle(rnn_old[i].begin(), rnn_old[i].end());\n rnn_old[i].resize(S);\n }\n if (rnn_new[i].size() > unsigned(S)) {\n random_shuffle(rnn_new[i].begin(), rnn_new[i].end());\n rnn_new[i].resize(S);\n }\n }\n\n return t;\n }\n };\n\n\n}\n\n#endif \n\n" }, { "path": "algorithms/NMSLIB/code/include/object.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _OBJECT_H_\n#define _OBJECT_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"global.h\"\n#include \"idtype.h\"\n#include \"logging.h\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::stringstream;\nusing std::numeric_limits;\n\n/* \n * Structure of object: | 4-byte id | 4-byte label | 8-byte datasize | data ........ |\n * We need data to be aligned on 8-byte boundaries.\n * \n * TODO 1) this all apparenlty hinges on the assumption that malloc() gives addresses\n * that are 8-bye aligned. So, this is related to issue #9\n * 2) even though GCC doesn't complain, using a single char buffer may break aliasing rules\n *\n * See also http://searchivarius.org/blog/what-you-must-know-about-alignment-21st-century\n */\n\nclass Object {\n public:\n explicit Object(char* buffer) : buffer_(buffer), memory_allocated_(false) {}\n\n Object(IdType id, LabelType label, size_t datalength, const void* data) {\n buffer_ = new char[ID_SIZE + LABEL_SIZE + DATALENGTH_SIZE + datalength];\n CHECK(buffer_ != NULL);\n memory_allocated_ = true;\n char* ptr = buffer_;\n memcpy(ptr, &id, ID_SIZE);\n ptr += ID_SIZE;\n memcpy(ptr, &label, LABEL_SIZE); \n ptr += LABEL_SIZE;\n memcpy(ptr, &datalength, DATALENGTH_SIZE);\n ptr += DATALENGTH_SIZE;\n if (data != NULL) {\n memcpy(ptr, data, datalength);\n } else {\n memset(ptr, 0, datalength);\n }\n }\n\n ~Object() {\n if (memory_allocated_) {\n delete[] buffer_;\n }\n }\n\n static Object* CreateNewEmptyObject(size_t datalength) {\n // the caller is responsible for releasing the pointer\n Object* empty_object = new Object(-1, -1, datalength, NULL);\n CHECK(empty_object != NULL);\n return empty_object;\n }\n\n Object* Clone() const {\n Object* clone = new Object(id(), label(), datalength(), data());\n return clone;\n }\n\n inline IdType id() const { return *(reinterpret_cast(buffer_)); }\n inline LabelType label() const { return *(reinterpret_cast(buffer_ + LABEL_SIZE)); }\n inline size_t datalength() const { return *(reinterpret_cast(buffer_ + LABEL_SIZE + ID_SIZE));}\n inline const char* data() const { return buffer_ + ID_SIZE + LABEL_SIZE+ DATALENGTH_SIZE; }\n inline char* data() { return buffer_ + ID_SIZE + LABEL_SIZE+ DATALENGTH_SIZE; }\n\n inline const char* buffer() const { return buffer_; }\n inline size_t bufferlength() const { return ID_SIZE + LABEL_SIZE+ DATALENGTH_SIZE + datalength(); }\n\n void Print() const {\n LOG(LIB_INFO) << \"id = \" << id()\n << \"\\tlabel = \" << label()\n << \"\\tdatalength = \" << datalength()\n << \"\\tbuffer = \" << buffer()\n << \"\\tdata = \" << data();\n }\n\n /*\n * Extracts label from the beginning of the input string and modifies the string by removing label information.\n * If no label is present, the input string remains unchanged.\n */\n static LabelType extractLabel(string& fileLine) {\n static string labelPrefix = LABEL_PREFIX; // thread-safe in C++11\n\n LabelType res = EMPTY_LABEL;\n if (fileLine.size() > labelPrefix.size() + 1 &&\n fileLine.substr(0, labelPrefix.size()) == labelPrefix) {\n int p = -1;\n\n for (size_t i = labelPrefix.size(); i < fileLine.size(); ++i) {\n if (isspace(fileLine[i])) {\n p = i; \n break;\n }\n }\n if (p >= 0) {\n size_t j = p;\n // j is the first non-white space char\n while(j < fileLine.size() && isspace(fileLine[j])) ++j;\n\n stringstream numstr(fileLine.substr(labelPrefix.size(), p - labelPrefix.size()));\n\n if (!(numstr >> res) || !numstr.eof()) {\n PREPARE_RUNTIME_ERR(err) << \"Cannot extract label from the file line: '\" << fileLine << \"'\";\n THROW_RUNTIME_ERR(err);\n }\n\n fileLine = fileLine.substr(j);\n \n } else {\n PREPARE_RUNTIME_ERR(err) << \"No space is found after the label definition in the file line: '\" << fileLine << \"'\";\n THROW_RUNTIME_ERR(err);\n }\n }\n return res;\n }\n /*\n * Adds a label to the beginning of the string.\n */\n static void addLabel(string& fileLine, LabelType label) {\n stringstream str;\n str << LABEL_PREFIX << label << \" \";\n fileLine.insert(0, str.str()); \n }\n private:\n char* buffer_;\n bool memory_allocated_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(Object);\n};\n\n\n/* \n * Clearing memory: we will use some smart pointer here (somewhen).\n * can't use standard shared_ptr, b/c they have\n * performance issues.\n * see, e.g.: http://nerds-central.blogspot.com/2012/03/sharedptr-performance-issues-and.html \n */\ntypedef std::vector ObjectVector;\n\ninline size_t DataSpaceUsed(const ObjectVector &vect) {\n size_t res = 0;\n for (const auto elem: vect) res += elem->datalength();\n return res;\n}\n\ninline size_t TotalSpaceUsed(const ObjectVector &vect) {\n size_t res = 0;\n for (const auto elem: vect) res += elem->bufferlength();\n return res;\n}\n\n/* \n * The caller is repsonsible for deleting:\n * 1) bucket\n * 2) Object pointers stored in the bucket\n */\ninline void CreateCacheOptimizedBucket(const ObjectVector& data, \n char*& CacheOptimizedBucket, \n ObjectVector*& bucket) {\n if (data.empty()) {\n /* TODO @leo Normally this wouldn't happen\n * However, some methods, e.g., list of clusters\n * with KLDiv may produce empty clusters.\n */\n LOG(LIB_WARNING) << \"Empty bucket!\"; \n }\n CacheOptimizedBucket = new char [TotalSpaceUsed(data)];\n char *p = CacheOptimizedBucket;\n bucket = new ObjectVector(data.size());\n\n for(size_t i = 0; i < data.size(); ++i) {\n memcpy(p, data[i]->buffer(), data[i]->bufferlength());\n (*bucket)[i] = new Object(const_cast(p));\n p += data[i]->bufferlength();\n }\n}\n\ninline void ClearBucket(char* CacheOptimizedBucket,\n ObjectVector* bucket) {\n if (CacheOptimizedBucket) {\n for(auto i:(*bucket)) {\n delete i;\n }\n }\n delete [] CacheOptimizedBucket;\n delete bucket;\n}\n\ntypedef std::list ObjectList;\n\ntemplate\nusing DistObjectPair = std::pair;\n\ntemplate \nusing DistObjectPairVector = std::vector>;\n\n\ntemplate \nstruct DistObjectPairAscComparator {\n bool operator()(const DistObjectPair& x,\n const DistObjectPair& y) const {\n return x.first < y.first;\n }\n};\n\ntemplate \nstruct DistObjectPairDescComparator {\n bool operator()(const DistObjectPair& x,\n const DistObjectPair& y) const {\n return x.first > y.first;\n }\n};\n\nstruct ObjectIdAscComparator {\n inline bool operator()(const Object* x, const Object* y) const {\n return x->id() < y->id();\n }\n};\n\n/*\n * We do not support very large data sets.\n */\ninline void CheckDataSize(const ObjectVector &data) {\n if (data.size() > MAX_DATASET_QTY) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: the number of data elements (\" << data.size() << \") is too big, \"\n << \"bigger than \" << MAX_DATASET_QTY;\n }\n}\n\n/*\n * Creates a recoding array to efficiently map object IDs to their\n * positions in the data vector. The array-based mapping is\n * quite space-efficient, because the largest object ID is rouhgly\n * equal to the number of data vector elements. The array based mapping\n * also permits extremely fast lookups.\n */\ninline void CreateObjIdToPosMapper(const ObjectVector& data, std::vector& mapper) {\n CheckDataSize(data);\n IdType maxId = -1;\n for (const Object* pObj : data) {\n CHECK_MSG(pObj->id() >= 0, \"Bug: encountered negative object ID\");\n maxId = std::max(maxId, pObj->id());\n }\n mapper.resize(maxId);\n std::fill(mapper.begin(), mapper.end(), -1);\n for (IdTypeUnsign i = 0; i < data.size(); ++i) {\n mapper[data[i]->id()] = i;\n }\n}\n\n\n\n} // namespace similarity\n\n#endif // _OBJECT_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/params.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef PARAMS_H\n#define PARAMS_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"logging.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\nusing std::multimap;\nusing std::set;\nusing std::stringstream;\nusing std::shared_ptr;\nusing std::unique_ptr;\n\n\n#define FAKE_MAX_LEAVES_TO_VISIT std::numeric_limits::max() \n\nclass AnyParams {\npublic:\n /* \n * Each element of the description array is in the form:\n * =\n */\n AnyParams(const vector& Desc) :ParamNames(0), ParamValues(0) {\n set seen;\n for (unsigned i = 0; i < Desc.size(); ++i) {\n vector OneParamPair;\n if (!SplitStr(Desc[i], OneParamPair, '=') ||\n OneParamPair.size() != 2) {\n stringstream err;\n err << \"Wrong format of an argument: '\" << Desc[i] << \"' should be in the format: =\";\n LOG(LIB_ERROR) << err.str();\n throw runtime_error(err.str());\n }\n const string& Name = OneParamPair[0];\n const string& sVal = OneParamPair[1];\n\n if (seen.count(Name)) {\n string err = \"Duplicate parameter: \" + Name;\n LOG(LIB_ERROR) << err;\n throw runtime_error(err);\n }\n seen.insert(Name);\n\n ParamNames.push_back(Name);\n ParamValues.push_back(sVal);\n }\n }\n \n /* \n * Compare parameters against parameters in the other parameter container.\n * In doing so, IGNORE parameters from the exception list.\n */\n bool equalsIgnoreInList(const AnyParams& that, const vector& ExceptList) { \n /*\n * These loops are reasonably efficient, unless\n * we have thousands of parameters in the exception\n * list (which realistically won't happen)\n */\n \n vector> vals[2], inter;\n const AnyParams* objRefs[2] = {this, &that};\n\n\n for (size_t objId = 0; objId < 2; ++objId) {\n const AnyParams& obj = *objRefs[objId];\n \n for (size_t i = 0; i < obj.ParamNames.size(); ++i) {\n const string& name = obj.ParamNames[i];\n if (find(ExceptList.begin(), ExceptList.end(), name) == ExceptList.end()) {\n vals[objId].push_back(make_pair(name, obj.ParamValues[i]));\n }\n } \n sort(vals[objId].begin(), vals[objId].end());\n }\n \n inter.resize(ParamNames.size() + that.ParamNames.size());\n size_t qty = set_intersection(vals[0].begin(), vals[0].end(),\n vals[1].begin(), vals[1].end(),\n inter.begin()) - inter.begin();\n /*\n * We computed the size of the intersection.\n * If it is equal to the size of either of the original \n * param lists (entries in the exception lists are excluded\n * at this point), then both sets are equal.\n */\n return qty == vals[0].size() && qty == vals[1].size(); \n }\n\n string ToString() const {\n stringstream res;\n for (unsigned i = 0; i < ParamNames.size(); ++i) {\n if (i) res << \",\";\n res << ParamNames[i] << \"=\" << ParamValues[i];\n }\n return res.str();\n }\n\n template \n void ChangeParam(const string& Name, const ParamType& Value) {\n for (unsigned i = 0; i < ParamNames.size(); ++i) \n if (Name == ParamNames[i]) {\n stringstream str;\n\n str << Value;\n ParamValues[i] = str.str();\n return;\n }\n string err = \"Parameter not found: \" + Name;\n LOG(LIB_ERROR) << err;\n throw runtime_error(err);\n } \n\n template \n void AddChangeParam(const string& Name, const ParamType& Value) {\n stringstream str;\n str << Value;\n \n for (unsigned i = 0; i < ParamNames.size(); ++i) \n if (Name == ParamNames[i]) {\n ParamValues[i] = str.str();\n return;\n }\n\n ParamNames.push_back(Name);\n ParamValues.push_back(str.str());\n }\n\n AnyParams(){}\n AnyParams(const vector& Names, const vector& Values) \n : ParamNames(Names), ParamValues(Values) {}\n AnyParams(const AnyParams& that)\n : ParamNames(that.ParamNames), ParamValues(that.ParamValues) {}\n\n vector ParamNames;\n vector ParamValues;\n\n};\n\nconst inline AnyParams& getEmptyParams() {\n static AnyParams empty;\n return empty;\n}\n\nclass AnyParamManager {\npublic:\n AnyParamManager(const AnyParams& params) : params(params), seen() {\n if (params.ParamNames.size() != params.ParamValues.size()) {\n string err = \"Bug: different # of parameters and values\";\n LOG(LIB_ERROR) << err;\n throw runtime_error(err);\n }\n }\n\n template \n void GetParamRequired(const string& Name, ParamType& Value) {\n GetParam(Name, Value, true);\n }\n\n template \n void GetParamOptional(const string& Name, ParamType& Value, const DefaultType& DefaultValue) {\n Value=DefaultValue;\n GetParam(Name, Value, false);\n }\n\n /*\n * Takes a list of exceptions and extracts all parameter values, \n * except parameters from the exceptions' list. The extracted parameters\n * are added to the list of parameters already seen.\n */\n AnyParams ExtractParametersExcept(const vector& ExceptList) {\n set except(ExceptList.begin(), ExceptList.end());\n\n vector names, values;\n\n for (size_t i = 0; i < params.ParamNames.size(); ++i) {\n const string& name = params.ParamNames[i];\n if (except.count(name) == 0) { // Not on the exception list\n names.push_back(name);\n values.push_back(params.ParamValues[i]);\n seen.insert(name);\n }\n }\n\n return AnyParams(names, values);\n }\n\n /*\n * Extract all parameter values, whose values are on the list.\n * The extracted parameters are added to the list of parameters already seen.\n */\n AnyParams ExtractParameters(const vector& CheckList) {\n set except(CheckList.begin(), CheckList.end());\n\n vector names, values;\n\n for (size_t i = 0; i < params.ParamNames.size(); ++i) {\n const string& name = params.ParamNames[i];\n if (except.count(name)) { // On the list\n names.push_back(name);\n values.push_back(params.ParamValues[i]);\n seen.insert(name);\n }\n }\n\n return AnyParams(names, values);\n }\n\n bool hasParam(const string& name) {\n for (const string& s: params.ParamNames)\n if (s == name) return true;\n return false;\n };\n\n void CheckUnused() {\n bool bFail = false;\n for (size_t i = 0; i < params.ParamNames.size(); ++i) {\n const string& name = params.ParamNames[i];\n if (seen.count(name) == 0) {\n bFail = true;\n LOG(LIB_ERROR) << \"Unknown parameter: '\" << name << \"'\";\n }\n }\n if (bFail) throw runtime_error(\"Unknown parameters found!\"); \n }\n\n const AnyParams& GetAllParams() const { return params; }\n\n void copySeen(AnyParamManager& other) {\n for (const string s: seen) other.seen.insert(s);\n }\n\nprivate:\n const AnyParams& params;\n set seen;\n\n template \n void GetParam(const string& Name, ParamType& Value, bool bRequired) {\n bool bFound = false;\n /* \n * This loop is reasonably efficient, unless\n * we have thousands of parameters (which realistically won't happen)\n */\n for (size_t i =0; i < params.ParamNames.size(); ++i) \n if (Name == params.ParamNames[i]) {\n bFound = true; \n ConvertStrToValue(params.ParamValues[i], Value);\n }\n\n if (bFound) {\n seen.insert(Name);\n }\n\n if (!bFound) {\n if (bRequired) {\n stringstream err;\n err << \"Mandatory parameter: '\" << Name << \"' is missing!\";\n LOG(LIB_ERROR) << err.str();\n throw runtime_error(err.str());\n }\n } \n }\n\n template \n void ConvertStrToValue(const string& s, ParamType& Value);\n};\n\n\ntemplate \ninline void AnyParamManager::ConvertStrToValue(const string& s, ParamType& Value) {\n stringstream str(s);\n\n if (!(str>>Value) || !str.eof()) {\n stringstream err;\n err << \"Failed to convert value '\" << s << \"' from type: \" << typeid(Value).name();\n LOG(LIB_ERROR) << err.str();\n throw runtime_error(err.str());\n }\n}\n\ntemplate <>\ninline void AnyParamManager::ConvertStrToValue(const string& str, string& Value) {\n Value = str;\n}\n\n// Parse space name/parameters\nvoid ParseSpaceArg(const string& str, string& SpaceType, vector& SpaceDesc);\n// Take a comma-separated list of parameters and split them.\nvoid ParseArg(const string& str, vector& vDesc);\n\n};\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/params_cmdline.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef PARAMS_CMDLINE_H\n#define PARAMS_CMDLINE_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"logging.h\"\n#include \"utils.h\"\n#include \"params.h\"\n\nnamespace similarity {\n\nvoid ParseCommandLine(int argc, char *argv[], bool& bPrintProgress,\n string& LogFile,\n string& LoadIndexLoc,\n string& SaveIndexLoc,\n string& DistType,\n string& SpaceType,\n shared_ptr& SpaceParams,\n unsigned& ThreadTestQty,\n bool& AppendToResFile, \n string& ResFilePrefix,\n unsigned& TestSetQty,\n string& DataFile,\n string& QueryFile,\n string& CacheGSFilePrefix,\n float& MaxCacheGSQty,\n unsigned& MaxNumData,\n unsigned& MaxNumQuery,\n vector& knn,\n float& eps,\n string& RangeArg,\n string& MethodName,\n shared_ptr& IndexTimeParams,\n vector>& QueryTimeParams);\n};\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/params_def.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef PARAMS_DEF_H\n#define PARAMS_DEF_H\n\n// Definition of most command-line parameters\n#define HELP_PARAM_OPT \"help,h\"\n#define HELP_PARAM_MSG \"produce help message\"\n\n#define SPACE_TYPE_PARAM_OPT \"spaceType,s\"\n#define SPACE_TYPE_PARAM_MSG \"space type, e.g., l1, l2, lp\"\n\n#define DIST_TYPE_PARAM_OPT \"distType\"\n#define DIST_TYPE_PARAM_MSG \"distance value type: int, float, double\"\n\n#define DATA_FILE_PARAM_OPT \"dataFile,i\"\n#define DATA_FILE_PARAM_MSG \"input data file\"\n\n#define MAX_NUM_DATA_PARAM_OPT \"maxNumData,D\"\n#define MAX_NUM_DATA_PARAM_MSG \"if non-zero, only the first maxNumData elements are used\"\n#define MAX_NUM_DATA_PARAM_DEFAULT 0\n\n#define QUERY_FILE_PARAM_OPT \"queryFile,q\"\n#define QUERY_FILE_PARAM_MSG \"query file\"\n#define QUERY_FILE_PARAM_DEFAULT \"\"\n\n#define LOAD_INDEX_PARAM_OPT \"loadIndex,L\"\n#define LOAD_INDEX_PARAM_MSG \"a location to load the index from \"\n#define LOAD_INDEX_PARAM_DEFAULT \"\"\n\n#define SAVE_INDEX_PARAM_OPT \"saveIndex,S\"\n#define SAVE_INDEX_PARAM_MSG \"a location to save the index to \"\n#define SAVE_INDEX_PARAM_DEFAULT \"\"\n\n#define CACHE_PREFIX_GS_PARAM_OPT \"cachePrefixGS,g\"\n#define CACHE_PREFIX_GS_PARAM_MSG \"a prefix of gold standard cache files\"\n#define CACHE_PREFIX_GS_PARAM_DEFAULT \"\"\n\n#define MAX_CACHE_GS_QTY_PARAM_OPT \"maxCacheGSRelativeQty\"\n#define MAX_CACHE_GS_QTY_PARAM_MSG \"a maximum number of gold standard entries to compute/cache, note that it is relative to the number of result entries.\"\n#define MAX_CACHE_GS_QTY_PARAM_DEFAULT 10.0\n\n#define LOG_FILE_PARAM_OPT \"logFile,l\"\n#define LOG_FILE_PARAM_MSG \"log file\"\n#define LOG_FILE_PARAM_DEFAULT \"\"\n\n#define MAX_NUM_QUERY_PARAM_OPT \"maxNumQuery,Q\"\n#define MAX_NUM_QUERY_PARAM_MSG \"if non-zero, use maxNumQuery query elements (required in the case of bootstrapping)\"\n#define MAX_NUM_QUERY_PARAM_DEFAULT 0\n\n#define TEST_SET_QTY_PARAM_OPT \"testSetQty,b\"\n#define TEST_SET_QTY_PARAM_MSG \"# of test sets obtained by bootstrapping; ignored if queryFile is specified\"\n#define TEST_SET_QTY_PARAM_DEFAULT 0\n\n#define KNN_PARAM_OPT \"knn,k\"\n#define KNN_PARAM_MSG \"comma-separated values of K for the k-NN search\"\n\n#define RANGE_PARAM_OPT \"range,r\"\n#define RANGE_PARAM_MSG \"comma-separated radii for the range searches\"\n\n#define EPS_PARAM_OPT \"eps\"\n#define EPS_PARAM_MSG \"the parameter for the eps-approximate k-NN search.\"\n#define EPS_PARAM_DEFAULT 0.0\n\n#define QUERY_TIME_PARAMS_PARAM_OPT \"queryTimeParams,t\"\n#define QUERY_TIME_PARAMS_PARAM_MSG \"query-time method(s) parameters in the format:\\nparam1=value1,param2=value2,...,paramK=valueK\"\n\n#define INDEX_TIME_PARAMS_PARAM_OPT \"createIndex,c\"\n#define INDEX_TIME_PARAMS_PARAM_MSG \"index-time method(s) parameters in the format:\\nparam1=value1,param2=value2,...,paramK=valueK\"\n\n#define METHOD_PARAM_OPT \"method,m\"\n#define METHOD_PARAM_MSG \"method/index name\"\n#define METHOD_PARAM_DEFAULT \"\"\n\n#define THREAD_TEST_QTY_PARAM_OPT \"threadTestQty\"\n#define THREAD_TEST_QTY_PARAM_MSG \"# of threads during querying\"\n#define THREAD_TEST_QTY_PARAM_DEFAULT 1\n\n#define OUT_FILE_PREFIX_PARAM_OPT \"outFilePrefix,o\"\n#define OUT_FILE_PREFIX_PARAM_MSG \"output file prefix\"\n#define OUT_FILE_PREFIX_PARAM_DEFAULT \"\"\n\n#define APPEND_TO_RES_FILE_PARAM_OPT \"appendToResFile,a\"\n#define APPEND_TO_RES_FILE_PARAM_MSG \"do not override information in results files, append new data\"\n\n#define NO_PROGRESS_PARAM_OPT \"noProgressBar\"\n#define NO_PROGRESS_PARAM_MSG \"suppress displaying (mostly indexing) progress bars (for some methods)\"\n\n// Server/client parameters \n\n#define DEBUG_PARAM_OPT \"debug,D\"\n#define DEBUG_PARAM_MSG \"Print debug messages?\"\n\n#define PORT_PARAM_OPT \"port,p\"\n#define PORT_PARAM_MSG \"TCP/IP port number\"\n\n#define ADDR_PARAM_OPT \"addr,a\"\n#define ADDR_PARAM_MSG \"TCP/IP server address\"\n\n#define THREAD_PARAM_OPT \"threadQty\"\n#define THREAD_PARAM_MSG \"A number of server threads\"\n\n#define RET_EXT_ID_PARAM_OPT \"retExternId,e\"\n#define RET_EXT_ID_PARAM_MSG \"Return external IDs?\"\n\n#define RET_OBJ_PARAM_OPT \"retObj,o\"\n#define RET_OBJ_PARAM_MSG \"Return string representation of found objects?\"\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/permutation_type.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PERM_TYPE_H\n#define _PERM_TYPE_H\n\n#include \n#include \n\nnamespace similarity {\n\n\n/* \n * Should be a (signed) int32_t type!!!\n * We don't use integer of a smaller range, due to possible overflow.\n *\n * Don't change it, or the code will be broken.\n * In particular, SIMD-based correlation functions won't work\n */\ntypedef int32_t PivotIdType;\n\ntypedef std::vector Permutation;\n\n\n}\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/permutation_utils.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PERMUTATION_UTILS_H_\n#define _PERMUTATION_UTILS_H_\n\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"permutation_type.h\"\n#include \"distcomp.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\nusing std::max;\n\ntemplate \nusing DistInt = std::pair; // \n\ntypedef std::pair IntInt; // \n\ntemplate \nvoid GetPermutationPivot(const ObjectVector& data,\n const Space& space,\n const size_t num_pivot,\n ObjectVector* pivot,\n std::vector* pivot_pos = NULL) {\n if (num_pivot >= data.size()) {\n throw runtime_error(\"The data set in the space \" + space.StrDesc() +\n \"is to small to select enough pivots\");\n }\n std::unordered_set pivot_idx;\n for (size_t i = 0; i < num_pivot; ++i) {\n int p = RandomInt() % data.size();\n for (size_t rep = 0; pivot_idx.count(p) != 0; ++rep) {\n if (rep > MAX_RAND_ITER_BEFORE_GIVE_UP) {\n throw runtime_error(\"Cannot find a unique pivot, perhaps, the data set is too small.\");\n }\n p = RandomInt() % data.size();\n }\n pivot_idx.insert(p);\n if (pivot_pos != NULL) pivot_pos->push_back(p);\n pivot->push_back(data[p]);\n }\n}\n\ntemplate \nvoid GetPermutation(const ObjectVector& pivot, const Space& space,\n const Object* object, Permutation* p) {\n // get pivot id\n std::vector> dists;\n for (size_t i = 0; i < pivot.size(); ++i) {\n dists.push_back(std::make_pair(space.IndexTimeDistance(pivot[i], object), static_cast(i)));\n }\n std::sort(dists.begin(), dists.end());\n // dists.second = pivot id i.e. \\Pi_o(i)\n\n // get pivot id's pos i.e. position in \\Pi_o(i) = \\Pi^{-1}(i)\n std::vector pivot_idx;\n for (size_t i = 0; i < pivot.size(); ++i) {\n pivot_idx.push_back(std::make_pair(dists[i].second, static_cast(i)));\n }\n std::sort(pivot_idx.begin(), pivot_idx.end());\n // pivot_idx.second = pos of pivot (which is needed for computing the Rho func)\n for (size_t i = 0; i < pivot.size(); ++i) {\n p->push_back(static_cast(pivot_idx[i].second));\n }\n}\n\ntemplate \nvoid GetPermutation(const ObjectVector& pivot,\n const Query* query,\n Permutation* p) {\n std::vector> dists;\n for (size_t i = 0; i < pivot.size(); ++i) {\n /* Distance can be asymmetric, pivot should be always on the left side */\n dists.push_back(std::make_pair(query->DistanceObjLeft(pivot[i]),\n static_cast(i)));\n }\n std::sort(dists.begin(), dists.end());\n std::vector pivot_idx;\n for (size_t i = 0; i < pivot.size(); ++i) {\n pivot_idx.push_back(std::make_pair(dists[i].second,\n static_cast(i)));\n }\n std::sort(pivot_idx.begin(), pivot_idx.end());\n for (size_t i = 0; i < pivot.size(); ++i) {\n\t p->push_back(static_cast(pivot_idx[i].second));\n }\n}\n\n\n\n\n// Permutation Prefix Index\n\ntemplate \nvoid GetPermutationPPIndex(const ObjectVector& pivot,\n const Space& space,\n const Object* object,\n Permutation* p) {\n // get pivot id\n std::vector> dists;\n for (size_t i = 0; i < pivot.size(); ++i) {\n dists.push_back(std::make_pair(space.IndexTimeDistance(pivot[i], object),\n static_cast(i)));\n }\n std::sort(dists.begin(), dists.end());\n // dists.second = pivot id i.e. \\Pi_o(i)\n\n for (size_t i = 0; i < pivot.size(); ++i) {\n p->push_back(dists[i].second);\n }\n}\n\ntemplate class QueryType, typename dist_t>\nvoid GetPermutationPPIndex(const ObjectVector& pivot,\n QueryType* query,\n Permutation* p) {\n std::vector> dists;\n for (size_t i = 0; i < pivot.size(); ++i) {\n /* Distance can be asymmetric, pivot should be always on the left side */\n dists.push_back(std::make_pair(query->DistanceObjLeft(pivot[i]),\n static_cast(i)));\n }\n std::sort(dists.begin(), dists.end());\n for (size_t i = 0; i < pivot.size(); ++i) {\n p->push_back(dists[i].second);\n }\n}\n\n/*\n * Create a binary version of the permutation.\n */\ninline void Binarize(const vector &perm, const PivotIdType thresh, vector&bin_perm) {\n size_t bin_perm_word_qty = (perm.size() + 31)/32;\n\n bin_perm.resize(bin_perm_word_qty);\n fill(bin_perm.begin(), bin_perm.end(), 0);\n\n for (size_t i = 0; i < perm.size(); ++i) {\n bool b =perm[i] >= thresh;\n\n if (b) {\n bin_perm[i/32] |= (1<<(i%32)) ;\n }\n }\n}\n\n} // namespace similarity\n\n#endif // _PERMUTATION_UTILS_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/ported_boost_progress.h", "content": "// boost progress.hpp header file ------------------------------------------//\n\n// Copyright Beman Dawes 1994-99. Distributed under the Boost\n// Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\n\n// See http://www.boost.org/libs/timer for documentation.\n\n// Revision History\n// 18 Feb 15 @Leo removed extra functionality and added this thing\n// as one of our headers. One Windows, the core should build\n// without Boost!\n// 1 Dec 01 Add leading progress display strings (suggested by Toon Knapen)\n// 20 May 01 Introduce several static_casts<> to eliminate warning messages\n// (Fixed by Beman, reported by Herve Bronnimann)\n// 12 Jan 01 Change to inline implementation to allow use without library\n// builds. See docs for more rationale. (Beman Dawes) \n// 22 Jul 99 Name changed to .hpp\n// 16 Jul 99 Second beta\n// 6 Jul 99 Initial boost version\n\n#ifndef PORTED_BOOST_PROGRESS_HPP\n#define PORTED_BOOST_PROGRESS_HPP\n\n#include // for ostream, cout, etc\n#include // for string\n\nnamespace similarity {\n\n// progress_display --------------------------------------------------------//\n\n// progress_display displays an appropriate indication of \n// progress at an appropriate place in an appropriate form.\n\n// NOTE: (Jan 12, 2001) Tried to change unsigned long to boost::uintmax_t, but\n// found some compilers couldn't handle the required conversion to double.\n// Reverted to unsigned long until the compilers catch up. \n\nclass ProgressDisplay {\n public:\n explicit ProgressDisplay( unsigned long expected_count,\n std::ostream & os = std::cout,\n const std::string & s1 = \"\\n\", //leading strings\n const std::string & s2 = \"\",\n const std::string & s3 = \"\" )\n // os is hint; implementation may ignore, particularly in embedded systems\n : m_os(os), m_s1(s1), m_s2(s2), m_s3(s3) { restart(expected_count); }\n\n void restart( unsigned long expected_count )\n // Effects: display appropriate scale\n // Postconditions: count()==0, expected_count()==expected_count\n {\n _count = _next_tic_count = _tic = 0;\n _expected_count = expected_count;\n\n m_os << m_s1 << \"0% 10 20 30 40 50 60 70 80 90 100%\\n\"\n << m_s2 << \"|----|----|----|----|----|----|----|----|----|----|\"\n << std::endl // endl implies flush, which ensures display\n << m_s3;\n if ( !_expected_count ) _expected_count = 1; // prevent divide by zero\n } // restart\n\n unsigned long operator+=( unsigned long increment )\n // Effects: Display appropriate progress tic if needed.\n // Postconditions: count()== original count() + increment\n // Returns: count().\n {\n if ( (_count += increment) >= _next_tic_count ) { display_tic(); }\n return _count;\n }\n\n unsigned long operator++() { return operator+=( 1 ); }\n unsigned long count() const { return _count; }\n unsigned long expected_count() const { return _expected_count; }\n\n private:\n std::ostream & m_os; // may not be present in all imps\n const std::string m_s1; // string is more general, safer than \n const std::string m_s2; // const char *, and efficiency or size are\n const std::string m_s3; // not issues\n\n unsigned long _count, _expected_count, _next_tic_count;\n unsigned int _tic;\n void display_tic()\n {\n // use of floating point ensures that both large and small counts\n // work correctly. static_cast<>() is also used several places\n // to suppress spurious compiler warnings. \n unsigned int tics_needed =\n static_cast(\n (static_cast(_count)/_expected_count)*50.0 );\n do { m_os << '*' << std::flush; } while ( ++_tic < tics_needed );\n _next_tic_count = \n static_cast((_tic/50.0)*_expected_count);\n if ( _count == _expected_count ) {\n if ( _tic < 51 ) m_os << '*';\n m_os << std::endl;\n }\n } // display_tic\n};\n\n}\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/pow.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef POW_HPP\n#define POW_HPP\n\n/*\n * An efficient computation of exponentiation to an INTEGER power.\n * See, Leonid's blog entry for details:\n *\n * http://searchivarius.org/blog/how-fast-are-our-math-libraries \n *\n */\n\n#include \n\nnamespace similarity {\n\ntemplate \ninline T EfficientPow(T Base, unsigned Exp) {\n if (Exp == 0) return 1;\n if (Exp == 1) return Base; \n if (Exp == 2) return Base * Base; \n if (Exp == 3) return Base * Base * Base; \n if (Exp == 4) {\n Base *= Base;\n return Base * Base;\n }\n\n T res = Base;\n\n if (Exp == 5) {\n Base *= Base;\n return res * Base * Base;\n }\n\n if (Exp == 6) {\n Base *= Base;\n res = Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 7) {\n Base *= Base;\n res *= Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 8) {\n Base *= Base;\n Base *= Base;\n Base *= Base;\n return Base;\n }\n\n if (Exp == 9) {\n Base *= Base;\n Base *= Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 10) {\n Base *= Base;\n res = Base;\n Base *= Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 11) {\n Base *= Base;\n res *= Base;\n Base *= Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 12) {\n Base *= Base;\n Base *= Base;\n res = Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 13) {\n Base *= Base;\n Base *= Base;\n res *= Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 14) {\n Base *= Base;\n res = Base;\n Base *= Base;\n res *= Base;\n Base *= Base;\n return res * Base;\n }\n\n if (Exp == 15) {\n Base *= Base;\n res *= Base;\n Base *= Base;\n res *= Base;\n Base *= Base;\n return res * Base;\n }\n\n res *= res;\n res *= res;\n res *= res;\n res *= res;\n\n if (Exp == 16) {\n return res;\n }\n\n Exp -= 16;\n\n while (true) {\n if (Exp & 1) res *= Base;\n Exp >>= 1;\n if (Exp) {\n Base *= Base;\n } else {\n return res;\n }\n }\n\n return res;\n}\n\n\n\n}\n\ntemplate \ninline T EfficientFractPowUtil(T Base, uint64_t Exp, uint64_t MaxK) {\n if (Exp == 0) return 1; // pow == 0 \n if (Exp == MaxK) return Base; // pow == 1\n\n uint64_t Mask1 = MaxK - 1;\n uint64_t Mask2 = MaxK >>= 1;\n\n T res = 1.0;\n\n while (true) {\n Base = sqrt(Base);\n\n if (Exp & Mask2) res *= Base;\n\n Exp = (Exp << 1) & Mask1;\n\n if (!Exp) return res;\n }\n\n return res;\n}\n\ntemplate \ninline T EfficientFractPow(T Base, T FractExp, unsigned NumDig) {\n CHECK(FractExp <= 1 && NumDig);\n uint64_t MaxK = uint64_t(1) << NumDig;\n uint64_t Exp = static_cast(std::ceil(FractExp * MaxK));\n\n return EfficientFractPowUtil(Base, Exp, MaxK);\n}\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/projection.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _PROJECTION_H_\n#define _PROJECTION_H_\n\n#include \"distcomp.h\"\n#include \"space.h\"\n#include \"object.h\"\n\n#include \n#include \n\n// Classic random projections using random orthonormal vectors\n#define PROJ_TYPE_RAND \"rand\" \n// Distance to random reference points\n#define PROJ_TYPE_RAND_REF_POINT \"randrefpt\" \n// FastMap (project on lines defined by two randomly selected points)\n#define PROJ_TYPE_FAST_MAP \"fastmap\" \n// Integer value permutations\n#define PROJ_TYPE_PERM \"perm\"\n// Integer value truncted permutations\n#define PROJ_TYPE_PERM_TRUNC \"permtrunc\"\n// Binarized permutations: note that the result is nevertheless saved as floating-point vector\n#define PROJ_TYPE_PERM_BIN \"permbin\"\n// Dense vectors remain unchanged, sparsed vectors are \"hashed\" into dense ones\n#define PROJ_TYPE_VECTOR_DENSE \"densevect\"\n// Trivial projection\n#define PROJ_TYPE_NONE \"none\"\n\nnamespace similarity {\n\ntemplate \nclass Projection {\npublic:\n /*\n * Create a projection helper class that inherits from the current one.\n */\n static Projection* createProjection(const Space& space,\n const ObjectVector& data,\n std::string projType,\n size_t nProjDim,\n /*\n For sparse vector spaces and random projections\n is used to create an intermediate dense vector representations.\n It can be set to zero (or otherwise) if\n 1) The source space is a dense vector space\n 2) A projection is of the following type:\n i) FastMap;\n ii) Random reference points;\n iii) Permutations.\n */\n size_t nDstDim,\n unsigned binThreshold);\n /*\n * A function to create a projection. It should be implemented in child classes.\n * Note that the following:\n *\n * 1) we only support projection to single-precision floating point vectors.\n * 2) Space objects can be used to compute distances only during indexing time.\n * At search time, all distance computations are proxied through a query object.\n * During the indexing time, the query parameter can be NULL, but during\n * search time, one needs to supply the actual query.\n *\n * TODO: @leo This is actually not such a good approach, we need a separate\n * object to proxy distance computations. It should not be the query!\n * Then, functions like compProj will not need to use both the pObj (at\n * index time) and the pQuery (at search time).\n *\n */\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const = 0;\n\nprotected:\n static dist_t DistanceObjLeft(const Space& space,\n const Query* pQuery,\n const Object* pRefObj, // reference object\n const Object* pObj // the object to project\n ) {\n /*\n * Object (in this case pivot) is the left argument.\n * At search time, the right argument of the distance will be the query point\n * and pivot again is the left argument.\n */\n return NULL == pQuery ?\n space.IndexTimeDistance(pRefObj, pObj)\n :\n pQuery->DistanceObjLeft(pRefObj);\n }\n static void fillIntermBuffer(const Space& space,\n const Object* pObj,\n size_t nIntermDim,\n vector& intermBuffer) {\n\n /*\n * For dense vector spaces CreateDenseVectFromObj does nothing useful\n * (and intermDim_ == srcDim_),\n * however, we introduced this function to have the\n * uniform interface for sparse and dense vector spaces.\n */\n space.CreateDenseVectFromObj(pObj, &intermBuffer[0], nIntermDim);\n }\n};\n\n}\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/query.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _QUERY_H_\n#define _QUERY_H_\n\n#include \"object.h\"\n\nnamespace similarity {\n\ntemplate \nclass Space;\n\ntemplate \nclass Query {\n public:\n Query(const Space& space, const Object* query_object);\n virtual ~Query();\n\n const Object* QueryObject() const;\n uint64_t DistanceComputations() const;\n void AddDistanceComputations(uint64_t DistComp) { distance_computations_ += DistComp; }\n\n void ResetStats();\n virtual dist_t Distance(const Object* object1, const Object* object2) const;\n // Distance can be asymmetric!\n virtual dist_t DistanceObjLeft(const Object* object) const;\n virtual dist_t DistanceObjRight(const Object* object) const;\n\n virtual void Reset() = 0;\n virtual dist_t Radius() const = 0;\n virtual unsigned ResultSize() const = 0;\n virtual bool CheckAndAddToResult(const dist_t distance, const Object* object) = 0;\n virtual void Print() const = 0;\n\n protected:\n const Space& space_;\n const Object* query_object_;\n mutable uint64_t distance_computations_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(Query);\n};\n\n} // namespace similarity\n\n#endif // _QUERY_H_\n" }, { "path": "algorithms/NMSLIB/code/include/query_creator.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _QUERY_CREATOR_H_\n#define _QUERY_CREATOR_H_\n\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n\n\nnamespace similarity {\n\ntemplate \nclass RangeCreator {\npublic:\n RangeCreator(dist_t radius) : radius_(radius){}\n RangeQuery* operator()(const Space& space,\n const Object* query_object) const {\n return new RangeQuery(space, query_object, radius_);\n }\n std::string ParamsForPrint() const {\n std::stringstream str;\n str << \"Radius = \" << radius_;\n return str.str();\n }\n dist_t radius_;\n};\n\ntemplate \nclass KNNCreator {\npublic:\n KNNCreator(size_t K, float eps) : K_(K), eps_(eps) {}\n KNNQuery* operator()(const Space& space,\n const Object* query_object) const {\n return new KNNQuery(space, query_object, K_, eps_);\n }\n\n std::string ParamsForPrint() const {\n std::stringstream str;\n str << \"K = \" << K_ << \" Epsilon = \" << eps_;\n return str.str();\n }\n dist_t K_;\n float eps_;\n};\n\n} // namespace similarity\n\n#endif // _QUERY_H_\n" }, { "path": "algorithms/NMSLIB/code/include/randproj_util.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _RANDPROJ_UTILS_H_\n#define _RANDPROJ_UTILS_H_\n\n#include \n\n#include \"distcomp.h\"\n\nnamespace similarity {\n\nusing std::vector;\n\n/*\n * These are the classic random projections that rely on random normal numbers.\n * Vectors are subsequently normalized using the Gram-Schmidt algorithm (therefore,\n * nDstDim should be <= nSrcDim). \n * For a more detailed discussion see, e.g.:\n *\n * Dasgupta, Sanjoy. \"Experiments with random projection.\" \n * Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence. 2000.\n *\n */ \n\ntemplate void initRandProj(size_t nSrcDim, size_t nDstDim,\n bool bDoOrth, vector>& projMatr);\ntemplate void compRandProj(const vector>& projMatr,\n const dist_t* pSrcVect, size_t nSrcDim,\n dist_t* pDstVect, size_t nDstDim);\n\n\n}\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/rangequery.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _RANGEQUERY_H_\n#define _RANGEQUERY_H_\n\n#include \n#include \"object.h\"\n#include \"query.h\"\n\nnamespace similarity {\n\ntemplate \nclass RangeQuery : public Query {\n public:\n RangeQuery(const Space& space, const Object* query_object, const dist_t radius);\n\n const ObjectVector* Result() const;\n const std::vector* ResultDists() const { return &resultDists_; }\n std::set ResultSet() const ;\n dist_t Radius() const;\n unsigned ResultSize() const;\n\n void Reset();\n bool CheckAndAddToResult(const dist_t distance, const Object* object);\n bool CheckAndAddToResult(const Object* object);\n size_t CheckAndAddToResult(const ObjectVector& bucket);\n bool Equals(const RangeQuery* query) const;\n void Print() const;\n static std::string Type() { return \"RANGE\"; }\n\n private:\n dist_t radius_;\n ObjectVector result_;\n std::vector resultDists_;\n\n // disable copy and assign\n DISABLE_COPY_AND_ASSIGN(RangeQuery);\n};\n\n} // namespace similarity\n\n#endif // _RANGEQUERY_H_\n" }, { "path": "algorithms/NMSLIB/code/include/report.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef REPORT_H\n#define REPORT_H\n\n#include \n#include \n\n#include \"meta_analysis.h\"\n#include \"experiments.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n\nnamespace similarity {\n using std::string;\n using std::endl;\n using std::stringstream;\n\n template \n inline string produceHumanReadableReport(\n const ExperimentConfig& config,\n MetaAnalysis& ExpRes,\n const string& MethDescStr,\n const string& IndexParamStr,\n const string& QueryTimeParamStr) {\n stringstream Print;\n/*\n Print << std::endl << \n \"===================================\" << std::endl;\n Print << MethDescStr << std::endl;\n Print << \"Index parameters: \" << IndexParamStr << std::endl;\n Print << \"Query-time parameters: \" << QueryTimeParamStr << std::endl;\n Print << \"===================================\" << std::endl;\n Print << \"# of points: \" << config.GetDataObjects().size() << std::endl;\n Print << \"# of queries: \" << config.GetQueryToRunQty() << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"Recall: \" << round3(ExpRes.GetRecallAvg()) << \" -> \" << \"[\" << round3(ExpRes.GetRecallConfMin()) << \" \" << round3(ExpRes.GetRecallConfMax()) << \"]\" << std::endl;\n Print << \"Recall@1: \" << round3(ExpRes.GetRecallAt1Avg()) << \" -> \" << \"[\" << round3(ExpRes.GetRecallAt1ConfMin()) << \" \" << round3(ExpRes.GetRecallAt1ConfMax()) << \"]\" << std::endl;\n Print << \"PrecisionOfApprox: \" << round3(ExpRes.GetPrecisionOfApproxAvg()) << \" -> \" << \"[\" << round3(ExpRes.GetPrecisionOfApproxConfMin()) << \" \" << round3(ExpRes.GetPrecisionOfApproxConfMax()) << \"]\" << std::endl;\n Print << \"ClassAccuracy: \" << ExpRes.GetClassAccuracyAvg() << \" -> \" << \"[\" << ExpRes.GetClassAccuracyConfMin() << \" \" << ExpRes.GetClassAccuracyConfMax() << \"]\" << std::endl;\n Print << \"RelPosError: \" << round2(ExpRes.GetRelPosErrorAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetRelPosErrorConfMin()) << \" \\t\" << round2(ExpRes.GetRelPosErrorConfMax()) << \"]\" << std::endl;\n Print << \"NumCloser: \" << round2(ExpRes.GetNumCloserAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetNumCloserConfMin()) << \" \\t\" << round2(ExpRes.GetNumCloserConfMax()) << \"]\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"QueryTime: \" << round2(ExpRes.GetQueryTimeAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetQueryTimeConfMin()) << \" \\t\" << round2(ExpRes.GetQueryTimeConfMax()) << \"]\" << std::endl;\n Print << \"QueryPerSec: \" << round2(ExpRes.GetQueryPerSecAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetQueryPerSecConfMin()) << \" \\t\" << round2(ExpRes.GetQueryPerSecConfMax()) << \"]\" << std::endl;\n Print << \"DistComp: \" << round2(ExpRes.GetDistCompAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetDistCompConfMin()) << \" \\t\" << round2(ExpRes.GetDistCompConfMax()) << \"]\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"ImprEfficiency: \" << round2(ExpRes.GetImprEfficiencyAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetImprEfficiencyConfMin()) << \" \\t\" << round2(ExpRes.GetImprEfficiencyConfMax()) << \"]\" << std::endl;\n Print << \"ImprDistComp: \" << round2(ExpRes.GetImprDistCompAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetImprDistCompAvg()) << \" \\t\"<< round2(ExpRes.GetImprDistCompConfMax()) << \"]\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"Memory Usage: \" << round2(ExpRes.GetMemAvg()) << \" MB\" << std::endl;\n Print << \"Index Time: \" << round2(ExpRes.GetIndexTimeAvg()) << \" sec\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n */\n\n\tPrint.setf(ios::fixed);\n\tPrint << round3(ExpRes.GetRecallAvg()) << \" \" << round6(ExpRes.GetQueryTimeAvg()/1000.0) << \" \"<\n#include \n\n#include \"meta_analysis.h\"\n#include \"experiments.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n\nnamespace similarity {\n using std::string;\n using std::endl;\n using std::stringstream;\n\n template \n inline string produceHumanReadableReport(\n const ExperimentConfig& config,\n MetaAnalysis& ExpRes,\n const string& MethDescStr,\n const string& IndexParamStr,\n const string& QueryTimeParamStr) {\n stringstream Print;\n/*\n Print << std::endl << \n \"===================================\" << std::endl;\n Print << MethDescStr << std::endl;\n Print << \"Index parameters: \" << IndexParamStr << std::endl;\n Print << \"Query-time parameters: \" << QueryTimeParamStr << std::endl;\n Print << \"===================================\" << std::endl;\n Print << \"# of points: \" << config.GetDataObjects().size() << std::endl;\n Print << \"# of queries: \" << config.GetQueryToRunQty() << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"Recall: \" << round3(ExpRes.GetRecallAvg()) << \" -> \" << \"[\" << round3(ExpRes.GetRecallConfMin()) << \" \" << round3(ExpRes.GetRecallConfMax()) << \"]\" << std::endl;\n Print << \"Recall@1: \" << round3(ExpRes.GetRecallAt1Avg()) << \" -> \" << \"[\" << round3(ExpRes.GetRecallAt1ConfMin()) << \" \" << round3(ExpRes.GetRecallAt1ConfMax()) << \"]\" << std::endl;\n Print << \"PrecisionOfApprox: \" << round3(ExpRes.GetPrecisionOfApproxAvg()) << \" -> \" << \"[\" << round3(ExpRes.GetPrecisionOfApproxConfMin()) << \" \" << round3(ExpRes.GetPrecisionOfApproxConfMax()) << \"]\" << std::endl;\n Print << \"ClassAccuracy: \" << ExpRes.GetClassAccuracyAvg() << \" -> \" << \"[\" << ExpRes.GetClassAccuracyConfMin() << \" \" << ExpRes.GetClassAccuracyConfMax() << \"]\" << std::endl;\n Print << \"RelPosError: \" << round2(ExpRes.GetRelPosErrorAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetRelPosErrorConfMin()) << \" \\t\" << round2(ExpRes.GetRelPosErrorConfMax()) << \"]\" << std::endl;\n Print << \"NumCloser: \" << round2(ExpRes.GetNumCloserAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetNumCloserConfMin()) << \" \\t\" << round2(ExpRes.GetNumCloserConfMax()) << \"]\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"QueryTime: \" << round2(ExpRes.GetQueryTimeAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetQueryTimeConfMin()) << \" \\t\" << round2(ExpRes.GetQueryTimeConfMax()) << \"]\" << std::endl;\n Print << \"QueryPerSec: \" << round2(ExpRes.GetQueryPerSecAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetQueryPerSecConfMin()) << \" \\t\" << round2(ExpRes.GetQueryPerSecConfMax()) << \"]\" << std::endl;\n Print << \"DistComp: \" << round2(ExpRes.GetDistCompAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetDistCompConfMin()) << \" \\t\" << round2(ExpRes.GetDistCompConfMax()) << \"]\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"ImprEfficiency: \" << round2(ExpRes.GetImprEfficiencyAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetImprEfficiencyConfMin()) << \" \\t\" << round2(ExpRes.GetImprEfficiencyConfMax()) << \"]\" << std::endl;\n Print << \"ImprDistComp: \" << round2(ExpRes.GetImprDistCompAvg()) << \" -> \" << \"[\" << round2(ExpRes.GetImprDistCompAvg()) << \" \\t\"<< round2(ExpRes.GetImprDistCompConfMax()) << \"]\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n Print << \"Memory Usage: \" << round2(ExpRes.GetMemAvg()) << \" MB\" << std::endl;\n Print << \"Index Time: \" << round2(ExpRes.GetIndexTimeAvg()) << \" sec\" << std::endl;\n Print << \"------------------------------------\" << std::endl;\n */\n\n\tPrint.setf(ios::fixed);\n\tPrint << round3(ExpRes.GetRecallAvg()) << \" \" << round6(ExpRes.GetQueryTimeAvg()/1000.0) << \" \"<\n#include \"object.h\"\n\nnamespace similarity {\n\n/*\n * This version of intrinsic dimensionality is defined in \n * E. Chavez, G. Navarro, R. Baeza-Yates, and J. L. Marroquin, 2001, Searching in metric spaces.\n *\n * Note that this measure may be irrelevant in non-metric spaces.\n */\ntemplate \nvoid ComputeIntrinsicDimensionality(const Space& space, \n const ObjectVector& dataset,\n double& IntrDim,\n double& DistMean,\n double& DistSigma,\n size_t SampleQty = 1000000) {\n std::vector dist;\n DistMean = 0;\n for (size_t n = 0; n < SampleQty; ++n) {\n size_t r1 = RandomInt() % dataset.size();\n size_t r2 = RandomInt() % dataset.size();\n CHECK(r1 < dataset.size());\n CHECK(r2 < dataset.size());\n const Object* obj1 = dataset[r1];\n const Object* obj2 = dataset[r2];\n dist_t d = space.IndexTimeDistance(obj1, obj2);\n dist.push_back(d);\n if (ISNAN(d)) {\n /* \n * TODO: @leo Dump object contents here. To this end,\n * we need to subclass objects, so that sparse\n * vectors, dense vectors and other objects\n * can implement their own dump function.\n */\n throw runtime_error(\"!!! Bug: a distance returned NAN!\");\n }\n DistMean += d;\n }\n DistMean /= double(SampleQty);\n DistSigma = 0;\n for (size_t i = 0; i < SampleQty; ++i) {\n DistSigma += (dist[i] - DistMean) * (dist[i] - DistMean);\n }\n DistSigma /= double(SampleQty);\n IntrDim = DistMean * DistMean / (2 * DistSigma);\n DistSigma = sqrt(DistSigma);\n}\n\ntemplate \nvoid ReportIntrinsicDimensionality(const string& reportName,\n const Space& space, \n const ObjectVector& dataset,\n size_t SampleQty = 1000000) {\n double DistMean, DistSigma, IntrDim;\n\n ComputeIntrinsicDimensionality(space, dataset,\n IntrDim,\n DistMean,\n DistSigma,\n SampleQty);\n\n LOG(LIB_INFO) << \"### \" << reportName;\n LOG(LIB_INFO) << \"### intrinsic dim: \" << IntrDim;\n LOG(LIB_INFO) << \"### distance mean: \" << DistMean;\n LOG(LIB_INFO) << \"### distance sigma: \" << DistSigma;\n}\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/searchoracle.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef SEARCH_ORACLE_HPP\n#define SEARCH_ORACLE_HPP\n\n#include \n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"space.h\"\n#include \"pow.h\"\n#include \"params.h\"\n#include \"experimentconf.h\"\n#include \"logging.h\"\n\n#define EXP_LEFT_PARAM \"expLeft\"\n#define EXP_RIGHT_PARAM \"expRight\"\n#define ALPHA_LEFT_PARAM \"alphaLeft\"\n#define ALPHA_RIGHT_PARAM \"alphaRight\"\n#define MIN_EXP_PARAM \"minExp\"\n#define MAX_EXP_PARAM \"maxExp\"\n#define DESIRED_RECALL_PARAM \"desiredRecall\"\n#define TUNE_K_PARAM \"tuneK\"\n#define TUNE_R_PARAM \"tuneR\"\n#define TUNE_QTY_PARAM \"tuneQty\"\n\n#define MAX_CACHE_GS_QTY_PARAM \"maxCacheGSQty\"\n#define MAX_ITER_PARAM \"maxIter\"\n#define MAX_REC_DEPTH_PARAM \"maxRecDepth\"\n#define STEP_N_PARAM \"stepN\"\n#define ADD_RESTART_QTY_PARAM \"addRestartQty\"\n#define FULL_FACTOR_PARAM \"fullFactor\"\n\nnamespace similarity {\n\nconst size_t MIN_EXP_DEFAULT = 1;\nconst size_t MAX_EXP_DEFAULT = 1;\n\nconst size_t MAX_CACHE_GS_QTY_DEFAULT = 1000;\nconst size_t MAX_ITER_DEFAULT = 10;\nconst size_t MAX_REC_DEPTH_DEFAULT = 6;\nconst size_t STEP_N_DEFAULT = 2;\nconst size_t ADD_RESTART_QTY_DEFAULT = 4;\nconst double FULL_FACTOR_DEFAULT = 8.0;\n\nusing std::string; \nusing std::vector; \nusing std::stringstream;\n\nenum OptimMetric {IMPR_DIST_COMP,\n IMPR_EFFICIENCY,\n IMPR_INVALID};\n\n#define OPTIM_METRIC_PARAMETER \"metric\"\n\n#define OPTIM_IMPR_DIST_COMP \"dist\"\n#define OPTIM_IMPR_EFFICIENCY \"time\"\n#define OPTIM_METRIC_DEFAULT OPTIM_IMPR_DIST_COMP \n\ninline OptimMetric getOptimMetric(const string& s) {\n string s1 = s;\n ToLower(s1);\n if (s1 == OPTIM_IMPR_DIST_COMP) return IMPR_DIST_COMP;\n if (s1 == OPTIM_IMPR_EFFICIENCY) return IMPR_EFFICIENCY;\n return IMPR_INVALID;\n}\n\ninline string getOptimMetricName(OptimMetric metr) {\n if (IMPR_DIST_COMP == metr) return \"improvement in dist. comp\";\n if (IMPR_EFFICIENCY == metr) return \"improvement in efficiency\";\n throw runtime_error(\"Bug: Invalid optimization metric name\");\n}\n\n/*\n * Basic pruning oracles are built on the idea that you can relax the pruning criterion\n * in a kd-tree or a vp-tree.\n *\n * First, this idea was proposed by P.N. Yianilos in 1999:\n * Peter N. Yianilos, Locally lifting the curse of dimensionality for nearest neighbor search.\n *\n * It was later generalized to metric spaces. The introduced technique was called\n * \"stretching of the triangle inequality\". Stretching was governed by a single coefficient alpha,\n * so that the classic metric-space VP-tree pruning rule:\n *\n * MaxDist <= | M - d(q, pivot) |\n *\n * was replaced by:\n *\n * MaxDist <= alpha | M - d(q, pivot) |\n *\n * Here, M is a median distance from data points to the pivot and MaxDist\n * is the minimum distance from an object to query encountered during the search\n * (prior to encountering the current pivot/node), which plays a role of the \n * query radius.\n *\n * Stretching of the triangle inequality was described in:\n *\n * Probabilistic proximity search: \n * Fighting the curse of dimensionality in metric spaces\n * E Chavez, G Navarro \n *\n * Boytsov and Bilegsaikhan showed that a more generic pruning is needed if we want to\n * search in generic metric spaces, where the distance is not symmetric. This more generic\n * pruning method can also be more efficient in metric spaces than the originally proposed \n * stretching rule.\n *\n * More specifically, two potentially different stretching coefficients alphaLeft and alphaRight\n * are used for the left and the right partition, respectively. \n *\n * The results were published in:\n * Boytsov, Leonid, and Bilegsaikhan Naidan. \n * \"Learning to prune in metric and non-metric spaces.\" Advances in Neural Information Processing Systems. 2013.\n * However, the tunning procedure itself was later slightly improved and, most importantly, modified to\n * to tune to a specific recall using only a sample of the data.\n *\n * Another small extension is to support a polynomial approximation of the pruning rule.\n * This works best for low-dimensional spaces. For high dimensional spaces, the linear rule is not worse.\n * In the left subtree we prune if:\n * MaxDist <= alphaLeft | M - d(q, pivot) |^expLeft\n * In the right subtree we prune if:\n * MaxDist <= alphaRight | M - d(q, pivot) |^expRight\n */\n\nenum VPTreeVisitDecision { kVisitLeft = 1, kVisitRight = 2, kVisitBoth = 3 };\n\ntemplate \nclass PolynomialPruner {\npublic:\n static std::string GetName() { return \"polynomial pruner\"; }\n PolynomialPruner(Space& space, const ObjectVector& data, bool bPrintProgres) : \n space_(space), data_(data), printProgress_(bPrintProgres), \n alpha_left_(1), exp_left_(1), alpha_right_(1), exp_right_(1),\n alpha_left_default_(1), exp_left_default_(1), alpha_right_default_(1), exp_right_default_(1) {}\n // It's important to pass parameters only by reference here!\n void SetQueryTimeParams(AnyParamManager& pmgr);\n void SetIndexTimeParams(AnyParamManager& pmgr);\n\n vector GetQueryTimeParamNames() const {\n vector res = {ALPHA_LEFT_PARAM, EXP_LEFT_PARAM, ALPHA_RIGHT_PARAM, EXP_RIGHT_PARAM};\n\n return res;\n }\n \n void LogParams() {\n LOG(LIB_INFO) << ALPHA_LEFT_PARAM << \" = \" << alpha_left_ << \" \" << EXP_LEFT_PARAM << \" = \" << exp_left_;\n LOG(LIB_INFO) << ALPHA_RIGHT_PARAM << \" = \" << alpha_right_ << \" \" << EXP_RIGHT_PARAM << \" = \" << exp_right_;\n }\n\n inline VPTreeVisitDecision Classify(dist_t distQueryPivot, dist_t MaxDist, dist_t MedianDist) const {\n\n /*\n * If the median is in both subtrees (e.g., this is often the case of a discrete metric)\n * and the distance to the pivot is MedianDist, we need to visit both subtrees.\n * Hence, we check for the strict inequality!!! Even if MaxDist == 0, \n * for the case of dist == MedianDist, 0 < 0 may be false. \n * Thus, we visit both subtrees. \n */\n if (distQueryPivot <= MedianDist) {\n double diff = double(MedianDist - distQueryPivot);\n double expDiff = EfficientPow(diff, exp_left_);\n //LOG(LIB_INFO) << \" ### \" << diff << \" -> \" << expDiff;\n if (double(MaxDist) < alpha_left_ * expDiff) return (kVisitLeft);\n }\n if (distQueryPivot >= MedianDist) {\n double diff = double(distQueryPivot - MedianDist); \n double expDiff = EfficientPow(diff, exp_right_);\n //LOG(LIB_INFO) << \" ### \" << diff << \" -> \" << expDiff;\n if (double(MaxDist) < alpha_right_* expDiff) return (kVisitRight);\n }\n\n return (kVisitBoth);\n }\n string Dump() { \n stringstream str;\n\n str << ALPHA_LEFT_PARAM << \": \" << alpha_left_ << \" ExponentLeft: \" << exp_left_ << \" \" <<\n ALPHA_RIGHT_PARAM << \": \" << alpha_right_ << \" ExponentRight: \" << exp_right_ ;\n return str.str();\n }\nprivate:\n Space& space_;\n const ObjectVector data_;\n bool printProgress_;\n\n double alpha_left_;\n unsigned exp_left_;\n double alpha_right_;\n unsigned exp_right_;\n\n double alpha_left_default_;\n unsigned exp_left_default_;\n double alpha_right_default_;\n unsigned exp_right_default_;\n};\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/simddebug.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SIMD_DEBUG_H_\n#define _SIMD_DEBUG_H_\n\n\n#ifdef __SSE4_2__\n#include \n#include \n#include \n\n#include \n#include \n\nusing std::cout;\nusing std::endl;\nusing std::string;\n\nnamespace similarity {\n\n// Some SIMD debug printing\ninline void print4_ps(__m128 val, const string& desc=\"\") {\n float tmp[4];\n _mm_storeu_ps(tmp, val);\n cout << \"SIMD debug 4 floats: \" << \"(\" << desc <<\") \";\n for (int i = 0; i < 4; ++i) {\n cout << \" \" << tmp[i] << \" \";\n }\n cout << endl;\n}\n\ninline void print4_pd(__m128d val, const string& desc=\"\") {\n double tmp[2];\n _mm_storeu_pd(tmp, val);\n cout << \"SIMD debug 2 doubles: \" << \"(\" << desc <<\") \";\n for (int i = 0; i < 2; ++i) {\n cout << \" \" << tmp[i] << \" \";\n }\n cout << endl;\n}\n\ninline void print4_si(__m128i val, const string& desc=\"\") {\n int tmp[4];\n\n _mm_storeu_si128(reinterpret_cast<__m128i*>(tmp), val);\n cout << \"SIMD debug 4 ints: \" << \"(\" << desc <<\") \";\n for (int i = 0; i < 4; ++i) {\n cout << \" \" << tmp[i] << \" \";\n }\n cout << endl;\n}\n\n#endif\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/simdutils.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan, Leonid Boytsov.\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n/* \n * Note that __GNUC__ is also defined for Intel and Clang,\n * which do understand __attribute__ ((aligned(16)))\n */\n\n#if defined(__GNUC__)\n#define PORTABLE_ALIGN16 __attribute__((aligned(16)))\n#else\n#define PORTABLE_ALIGN16 __declspec(align(16))\n#endif\n\n#ifndef WITHOUT_SIMD\n\n// On Win64 SSE2 is always enabled\n// http://stackoverflow.com/questions/1067630/sse2-option-in-visual-c-x64\n#if defined(__SSE2__) || defined(__AVX__) || defined(_MSC_VER)\n#define PORTABLE_SSE2\n#endif\n\n// Unfortunately on Win32/64, there is not separate option for SSE4\n#if defined(__SSE4_2__) || defined(__AVX__)\n#define PORTABLE_SSE4\n#endif\n\n#endif // WITHOUT_SIMD\n\n\n#ifdef PORTABLE_SSE4\n#include \n#include \n#include \n\n\n/*\n * Based on explanations/suggestions from here\n * http://stackoverflow.com/questions/5526658/intel-sse-why-does-mm-extract-ps-return-int-instead-of-float\n */\n\n#define MM_EXTRACT_DOUBLE(v,i) _mm_cvtsd_f64(_mm_shuffle_pd(v, v, _MM_SHUFFLE2(0, i)))\n#define MM_EXTRACT_FLOAT(v,i) _mm_cvtss_f32(_mm_shuffle_ps(v, v, _MM_SHUFFLE(0, 0, 0, i)))\n\n#endif\n\n#ifdef _MSC_VER\n#include \n\n#define __builtin_popcount(t) __popcnt(t)\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/sort_arr_bi.h", "content": "#ifndef SORT_ARR_BI_H\n#define SORT_ARR_BI_H\n/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2015\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n\n#include \n#include \n#include \n#include \n#include \n\n/*\n * This is not a fully functional heap and this is done on purpose.\n */\ntemplate \nclass SortArrBI {\n public:\n class Item {\n public:\n KeyType key;\n bool used = false;\n DataType data;\n\n Item() {}\n Item(const KeyType& k) : key(k) {}\n Item(const KeyType& k, const DataType& d) : key(k), data(d) {}\n\n bool operator < (const Item& i2) const {\n return key < i2.key;\n }\n };\n using value_type = DataType;\n\n SortArrBI(size_t max_elem) : v_(max_elem) {\n if (max_elem <= 0) {\n throw std::runtime_error(\"The maximum number of elements in MinHeapPseudoBI should be > 0\");\n }\n }\n\n // resize may invalidate the reference returned by get_data() !!!!\n void resize(size_t max_elem) {\n v_.resize(max_elem);\n }\n\n // push_unsorted_grow may invalidate the reference returned by get_data() !!!!\n void push_unsorted_grow(const KeyType& key, const DataType& data) {\n if (num_elems_ + 1 >= v_.size()) resize(num_elems_+1);\n v_[num_elems_].used = false;\n v_[num_elems_].key = key;\n v_[num_elems_].data = data;\n num_elems_++;\n }\n\n KeyType top_key() {\n return v_[num_elems_-1].key;\n }\n\n void sort() {\n _mm_prefetch(&v_[0], _MM_HINT_T0);\n std::sort(v_.begin(), v_.begin() + num_elems_);\n }\n\n void swap(size_t x, size_t y) {\n std::swap(v_[x], v_[y]);\n }\n\n // Checking for duplicate IDs isn't the responsiblity of this function\n // it also assumes a non-empty array\n size_t push_or_replace_non_empty(const KeyType& key, const DataType& data) {\n // num_elems_ > 0\n unsigned curr = num_elems_ - 1;\n if (v_[curr].key < key) return num_elems_;\n\n while (curr > 0) {\n unsigned j = curr - 1;\n if (v_[j].key <= key) break;\n curr = j;\n }\n\n if (num_elems_ < v_.size()) num_elems_++;\n // curr + 1 <= num_elems_\n _mm_prefetch(&v_[curr], _MM_HINT_T0);\n memmove(&v_[curr+1], &v_[curr], (num_elems_ - (1 + curr)) * sizeof(v_[0]));\n \n v_[curr].used = false;\n v_[curr].key = key;\n v_[curr].data = data;\n return curr;\n }\n\n // In-place merge\n size_t merge_with_sorted_items(Item* items, size_t item_qty) {\n if (!item_qty) return num_elems_;\n\n if (item_qty > v_.size()) item_qty = v_.size();\n //if (item_qty == 1) return push_or_replace_non_empty_exp(items[0].key, items[0].data);\n\n const size_t left_qty = v_.size() - num_elems_;\n\n if (left_qty >= item_qty) {\n memcpy(&v_[num_elems_], items, item_qty * sizeof(Item));\n\n std::inplace_merge(v_.begin(), v_.begin() + num_elems_, v_.begin() + num_elems_ + item_qty);\n num_elems_ += item_qty;\n\n } else {\n // Here left_qty < item_qty\n size_t remove_qty = 0;\n while (item_qty > left_qty+ remove_qty &&\n num_elems_ > remove_qty && // This entails that num_elems_ - remove_qty - 1 >= 0\n items[left_qty + remove_qty].key < v_[num_elems_ - remove_qty - 1].key) {\n remove_qty++;\n }\n memcpy(&v_[num_elems_- remove_qty], items, (left_qty + remove_qty)* sizeof(Item));\n\n // Note that num_elems_ + left_qty == v_.size()\n std::inplace_merge(v_.begin(), v_.begin() + num_elems_- remove_qty, v_.end());\n num_elems_ = v_.size(); // filling out the buffer completely\n }\n\n size_t ret = 0;\n while (ret < num_elems_ && v_[ret].used) ++ret;\n return ret;\n }\n\n size_t push_or_replace_non_empty_exp(const KeyType& key, const DataType& data) {\n // num_elems_ > 0\n unsigned curr = num_elems_ - 1;\n if (v_[curr].key <= key) return num_elems_;\n unsigned prev = curr;\n\n unsigned d=1;\n // always curr >= d\n while (curr > 0 && v_[curr].key > key) {\n prev = curr;\n curr -= d;\n d *= 2;\n if (d > curr) d = curr;\n }\n\n _mm_prefetch(&v_[curr], _MM_HINT_T0);\n if (curr < prev) {\n curr = std::lower_bound(&v_[curr], &v_[prev], Item(key)) - &v_[0];\n }\n\n if (num_elems_ < v_.size()) num_elems_++;\n // curr + 1 <= num_elems_\n\n memmove(&v_[curr+1], &v_[curr], (num_elems_ - (1 + curr)) * sizeof(v_[0]));\n\n v_[curr].used = false;\n v_[curr].key = key;\n v_[curr].data = data;\n return curr;\n }\n\n\n const std::vector& get_data() const {\n return this->v_;\n }\n\n std::vector& get_data() {\n return this->v_;\n }\n\n size_t size() const { return num_elems_; }\n \n protected:\n\n std::vector v_;\n size_t num_elems_ = 0;\n};\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_bit_hamming.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_BIT_HAMMING_H_\n#define _SPACE_BIT_HAMMING_H_\n\n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"distcomp.h\"\n\n#define SPACE_BIT_HAMMING \"bit_hamming\"\n\nnamespace similarity {\n\nclass SpaceBitHamming : public Space {\n public:\n explicit SpaceBitHamming() {}\n virtual ~SpaceBitHamming() {}\n\n /** Standard functions to read/write/create objects */ \n // Create an object from string representation.\n virtual unique_ptr CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpState) const;\n // Create a string representation of an object.\n virtual string CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const;\n // Open a file for reading, fetch a header (if there is any) and memorize an input state\n virtual unique_ptr OpenReadFileHeader(const string& inputFile) const;\n // Open a file for writing, write a header (if there is any) and memorize an output state\n virtual unique_ptr OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outputFile) const;\n /*\n * Read a string representation of the next object in a file as well\n * as its label. Return false, on EOF.\n */\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const;\n /** End of standard functions to read/write/create objects */\n\n /*\n * Used only for testing/debugging: compares objects approximately. Floating point numbers\n * should be nearly equal. Integers and strings should coincide exactly.\n */\n virtual bool ApproxEqual(const Object& obj1, const Object& obj2) const;\n\n virtual std::string StrDesc() const { return \"Hamming (bit-storage) space\"; }\n virtual void CreateDenseVectFromObj(const Object* obj, int* pVect,\n size_t nElem) const {\n throw runtime_error(\"Cannot create a dense vector for the space: \" + StrDesc());\n }\n virtual size_t GetElemQty(const Object* object) const {return 0;}\n virtual Object* CreateObjFromVect(IdType id, LabelType label, std::vector& InpVect) const {\n InpVect.push_back(InpVect.size());\n return CreateObjFromVectInternal(id, label, InpVect);\n }\n protected:\n virtual Object* CreateObjFromVectInternal(IdType id, LabelType label, const std::vector& InpVect) const;\n Object* CreateObjFromBitMaskVect(IdType id, LabelType label, const std::vector& bitMaskVect) const;\n virtual int HiddenDistance(const Object* obj1, const Object* obj2) const;\n void ReadBitMaskVect(std::string line, LabelType& label, std::vector& v) const;\n\n DISABLE_COPY_AND_ASSIGN(SpaceBitHamming);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_bregman.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_BREGMAN_H_\n#define _SPACE_BREGMAN_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_lp.h\"\n\n#define SPACE_KLDIV_FAST \"kldivfast\"\n#define SPACE_KLDIV_FAST_RIGHT_QUERY \"kldivfastrq\" \n\n#define SPACE_KLDIVGEN_FAST \"kldivgenfast\"\n#define SPACE_KLDIVGEN_FAST_RIGHT_QUERY \"kldivgenfastrq\" \n#define SPACE_KLDIVGEN_SLOW \"kldivgenslow\"\n\n#define SPACE_ITAKURASAITO_FAST \"itakurasaitofast\"\n#define SPACE_ITAKURASAITO_FAST_RIGHT_QUERY \"itakurasaitofastrq\" \n#define SPACE_ITAKURASAITO_SLOW \"itakurasaitoslow\"\n\nnamespace similarity {\n\ntemplate \nclass BregmanDiv : public VectorSpace {\n public:\n explicit BregmanDiv() {}\n virtual ~BregmanDiv() {}\n\n /* computes the Bregman generator function */\n virtual dist_t Function(const Object* object) const = 0;\n /* computes the gradient of the generator function at point \"object\" */\n virtual Object* GradientFunction(const Object* object) const = 0;\n /* computes the inverse gradient of the generator function at point \"object\" */\n virtual Object* InverseGradientFunction(const Object* object) const = 0;\n\n virtual std::string StrDesc() const = 0;\n\n /* \n * Returns the number of vector elements stored in the object.\n * It may not be possible to get this number using Object->datalength(),\n * because the object can store additional data such as precomputed\n * logarithms\n */\n virtual size_t GetElemQty(const Object* object) const = 0;\n\n /*\n * However, all the Bregman divergences have similar storage scheme:\n * The original values of the vectors are stored in the begining\n * of the object. Thus, known the number of elements one can always\n * extract them.\n */\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n return VectorSpace::\n CreateVectFromObjSimpleStorage(__func__, obj, pDstVect, nElem);\n }\n\n virtual Object* Mean(const ObjectVector& data) const;\n\n static inline\n const BregmanDiv* ConvertFrom(const Space* space) {\n const BregmanDiv* div =\n dynamic_cast*>(space);\n if (div != NULL) {\n return div;\n } else {\n PREPARE_RUNTIME_ERR(err) << \"Space \" << space->StrDesc() << \" is not Bregman divergence\";\n THROW_RUNTIME_ERR(err);\n }\n }\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const = 0;\n};\n\ntemplate \nclass KLDivAbstract : public BregmanDiv {\n public:\n explicit KLDivAbstract() {}\n virtual ~KLDivAbstract() {}\n\n virtual dist_t Function(const Object* object) const;\n virtual Object* GradientFunction(const Object* object) const;\n virtual Object* InverseGradientFunction(const Object* object) const;\n\n virtual std::string StrDesc() const = 0;\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const = 0;\n virtual size_t GetElemQty(const Object* object) const = 0;\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const = 0;\n};\n\ntemplate \nclass KLDivGenSlow : public KLDivAbstract {\n public:\n explicit KLDivGenSlow() {}\n virtual ~KLDivGenSlow() {}\n\n virtual std::string StrDesc() const { return \"Generalized Kullback-Leibler divergence\"; }\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n\n virtual size_t GetElemQty(const Object* object) const { return object->datalength()/ sizeof(dist_t); }\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* object1, const Object* object2) const;\n DISABLE_COPY_AND_ASSIGN(KLDivGenSlow);\n};\n\ntemplate \nclass KLDivGenFast : public KLDivAbstract {\n public:\n explicit KLDivGenFast() {}\n virtual ~KLDivGenFast() {}\n\n virtual Object* InverseGradientFunction(const Object* object) const;\n virtual std::string StrDesc() const { return \"Generalized Kullback-Leibler divergence (precomputed logs)\"; }\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n virtual size_t GetElemQty(const Object* object) const { return object->datalength()/ sizeof(dist_t)/ 2; }\n virtual Object* Mean(const ObjectVector& data) const;\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* object1, const Object* object2) const;\n DISABLE_COPY_AND_ASSIGN(KLDivGenFast);\n};\n\ntemplate \nclass ItakuraSaitoFast : public BregmanDiv {\n public:\n explicit ItakuraSaitoFast() {}\n virtual ~ItakuraSaitoFast() {}\n\n virtual dist_t Function(const Object* object) const;\n virtual Object* InverseGradientFunction(const Object* object) const;\n virtual Object* GradientFunction(const Object* object) const;\n\n virtual std::string StrDesc() const { return \"Itakura-Saito (precomputed logs)\"; }\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n virtual size_t GetElemQty(const Object* object) const { return object->datalength()/ sizeof(dist_t)/ 2; }\n virtual Object* Mean(const ObjectVector& data) const;\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* object1, const Object* object2) const;\n DISABLE_COPY_AND_ASSIGN(ItakuraSaitoFast);\n};\n\ntemplate \nclass KLDivGenFastRightQuery : public VectorSpace {\n public:\n explicit KLDivGenFastRightQuery() {}\n virtual ~KLDivGenFastRightQuery() {}\n\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n return VectorSpace::\n CreateVectFromObjSimpleStorage(__func__, obj, pDstVect, nElem);\n }\n\n virtual std::string StrDesc() const { return \"Generalized Kullback-Leibler divergence, right queries (precomputed logs)\"; }\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n virtual size_t GetElemQty(const Object* object) const { return object->datalength()/ sizeof(dist_t)/ 2; }\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* object1, const Object* object2) const;\n DISABLE_COPY_AND_ASSIGN(KLDivGenFastRightQuery);\n};\n\ntemplate \nclass KLDivFast: public VectorSpace {\n public:\n explicit KLDivFast() {}\n virtual ~KLDivFast() {}\n\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n return VectorSpace::\n CreateVectFromObjSimpleStorage(__func__, obj, pDstVect, nElem);\n }\n\n virtual std::string StrDesc() const { return \"Kullback-Leibler divergence (precomputed logs)\"; }\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n virtual size_t GetElemQty(const Object* object) const { return object->datalength()/ sizeof(dist_t)/ 2; }\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* object1, const Object* object2) const;\n DISABLE_COPY_AND_ASSIGN(KLDivFast);\n};\n\ntemplate \nclass KLDivFastRightQuery: public VectorSpace {\n public:\n explicit KLDivFastRightQuery() {}\n virtual ~KLDivFastRightQuery() {}\n\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n return VectorSpace::\n CreateVectFromObjSimpleStorage(__func__, obj, pDstVect, nElem);\n }\n\n virtual std::string StrDesc() const { return \"Kullback-Leibler divergence, right queries (precomputed logs)\"; }\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n virtual size_t GetElemQty(const Object* object) const { return object->datalength()/ sizeof(dist_t)/ 2; }\n protected:\n // Should not be directly accessible\n virtual dist_t HiddenDistance(const Object* object1, const Object* object2) const;\n DISABLE_COPY_AND_ASSIGN(KLDivFastRightQuery);\n};\n\n} // namespace similarity\n\n#endif // _METRIC_SPACE_H_\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_dummy.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_DUMMY_H_\n#define _SPACE_DUMMY_H_\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_vector.h\"\n#include \"distcomp.h\"\n\n#define SPACE_DUMMY \"dummy\"\n\nnamespace similarity {\n\n\n/*\n * This is a dummy, i.e., zero-functionality space,\n * which can be used as a template to create fully functional\n * space.\n */\n\ntemplate \nclass SpaceDummy : public Space {\n public:\n // A constructor can have arbitrary params\n explicit SpaceDummy(int param1, int param2) : param1_(param1), param2_(param2) {\n LOG(LIB_INFO) << \"Created \" << StrDesc();\n }\n virtual ~SpaceDummy() {}\n\n /* \n * Space name: It will be used in result files.\n * Consider, including all the parameters where you\n * print the space name:\n */\n virtual std::string StrDesc() const {\n stringstream str;\n str << \"DummySpace param1=\" << param1_ << \" param2=\" << param2_;\n return str.str();\n }\n\n /** Standard functions to read/write/create objects */ \n /*\n * Create an object from a (possibly binary) string.\n * If the input state pointer isn't null, we check\n * if the new vector is consistent with previously read vectors.\n * For example, when we start reading vectors,\n * we don't know the number of elements. When, we see the first\n * vector, we memorize dimensionality. If a subsequently read\n * vector has a different dimensionality, an exception will be thrown.\n */\n virtual unique_ptr CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpState) const;\n // Create a string representation of an object\n // The string representation may include external ID.\n virtual string CreateStrFromObj(const Object* pObj, const string& externId) const;\n // Open a file for reading, fetch a header (if there is any) and memorize an input state\n virtual unique_ptr OpenReadFileHeader(const string& inputFile) const;\n // Open a file for writing, write a header (if there is any) and memorize an output state\n virtual unique_ptr OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outputFile) const;\n /*\n * Read a string representation of the next object in a file as well\n * as its label. Return false, on EOF.\n */\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const;\n /* \n * Write a string representation of the next object to a file. We totally delegate\n * this to a Space object, because it may package the string representation, by\n * e.g., in the form of an XML fragment.\n */\n virtual void WriteNextObj(const Object& obj, const string& externId, DataFileOutputState &) const;\n /** End of standard functions to read/write/create objects */ \n\n /*\n * Used only for testing/debugging: compares objects approximately. Floating point numbers\n * should be nearly equal. Integers and strings should coincide exactly.\n */\n virtual bool ApproxEqual(const Object& obj1, const Object& obj2) const {\n return true; // In an actual, non-dummy class, you should return the result\n // of an actual comparison. Here 'true' is used only to make it compile.\n }\n\n /*\n * CreateDenseVectFromObj and GetElemQty() are only needed, if\n * one wants to use methods with random projections.\n */\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pVect,\n size_t nElem) const {\n throw runtime_error(\"Cannot create vector for the space: \" + StrDesc());\n }\n virtual size_t GetElemQty(const Object* object) const {return 0;}\n protected:\n /*\n * This function should always be protected.\n * Only children and friends of the Space class\n * should be able to access it.\n */\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const;\n private:\n \n /* \n * We typically use a trailing underscore to distinguish between\n * class-member variables from non-member variables.\n */\n int param1_;\n int param2_;\n /*\n * One should forbid making copies of the Space object\n */\n DISABLE_COPY_AND_ASSIGN(SpaceDummy);\n};\n\n\n} // namespace similarity\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/space/space_js.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_JS_H_\n#define _SPACE_JS_H_\n\n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_vector.h\"\n\n#define SPACE_JS_DIV_SLOW \"jsdivslow\"\n#define SPACE_JS_DIV_FAST \"jsdivfast\"\n#define SPACE_JS_DIV_FAST_APPROX \"jsdivfastapprox\"\n\n#define SPACE_JS_METR_SLOW \"jsmetrslow\"\n#define SPACE_JS_METR_FAST \"jsmetrfast\"\n#define SPACE_JS_METR_FAST_APPROX \"jsmetrfastapprox\"\n\nnamespace similarity {\n\ntemplate \nclass SpaceJSBase : public VectorSpace {\n public:\n enum JSType {kJSSlow, kJSFastPrecomp, kJSFastPrecompApprox};\n\n explicit SpaceJSBase(JSType type) : type_(type) {}\n virtual ~SpaceJSBase() {}\n\n virtual std::string StrDesc() const = 0;\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n\n virtual size_t GetElemQty(const Object* object) const {\n size_t tmp = object->datalength()/ sizeof(dist_t);\n return (type_ == kJSSlow) ? tmp : tmp / 2;\n }\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n return VectorSpace::\n CreateVectFromObjSimpleStorage(__func__, obj, pDstVect, nElem);\n\n }\n protected:\n dist_t JensenShannonFunc(const Object* obj1, const Object* obj2) const;\n JSType GetType() const { return type_; }\n private:\n JSType type_;\n};\n\ntemplate \nclass SpaceJSDiv : public SpaceJSBase {\n public:\n explicit SpaceJSDiv(typename SpaceJSBase::JSType type) : SpaceJSBase(type) {}\n virtual ~SpaceJSDiv() {}\n\n virtual std::string StrDesc() const {\n std::stringstream stream;\n stream << \"Jensen-Shannon divergence: type code = \" << SpaceJSBase::GetType();\n return stream.str();\n }\n\n protected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const {\n return SpaceJSBase::JensenShannonFunc(obj1, obj2);\n }\n private:\n DISABLE_COPY_AND_ASSIGN(SpaceJSDiv);\n};\n\ntemplate \nclass SpaceJSMetric : public SpaceJSBase {\n public:\n explicit SpaceJSMetric(typename SpaceJSBase::JSType type) : SpaceJSBase(type) {}\n virtual ~SpaceJSMetric() {}\n\n virtual std::string StrDesc() const {\n std::stringstream stream;\n stream << \"Jensen-Shannon metric: type code = \" << SpaceJSBase::GetType();\n return stream.str();\n }\n protected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const {\n return sqrt(SpaceJSBase::JensenShannonFunc(obj1, obj2));\n }\n private:\n DISABLE_COPY_AND_ASSIGN(SpaceJSMetric);\n};\n\n\n} // namespace similarity\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/space/space_leven.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_LEVENSHTEIN_H_\n#define _SPACE_LEVENSHTEIN_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \"distcomp.h\"\n#include \"space/space_string.h\"\n\nnamespace similarity {\n\n#define SPACE_LEVENSHTEIN \"leven\"\n#define SPACE_LEVENSHTEIN_NORM \"normleven\"\n\nclass SpaceLevenshtein : public StringSpace {\n public:\n explicit SpaceLevenshtein() {}\n virtual ~SpaceLevenshtein() {}\n virtual std::string StrDesc() const { return \"Levenshtein distance\"; }\n protected:\n virtual int HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj2->datalength() > 0);\n const char* x = reinterpret_cast(obj1->data());\n const char* y = reinterpret_cast(obj2->data());\n const size_t len1 = obj1->datalength() / sizeof(char);\n const size_t len2 = obj2->datalength() / sizeof(char);\n\n return levenshtein(x, len1, y, len2);\n }\n DISABLE_COPY_AND_ASSIGN(SpaceLevenshtein);\n};\n\nclass SpaceLevenshteinNormalized : public StringSpace {\n public:\n explicit SpaceLevenshteinNormalized() {}\n virtual ~SpaceLevenshteinNormalized() {}\n virtual std::string StrDesc() const { return \"Normalized Levenshtein distance\"; }\n protected:\n virtual float HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj2->datalength() > 0);\n const char* x = reinterpret_cast(obj1->data());\n const char* y = reinterpret_cast(obj2->data());\n const size_t len1 = obj1->datalength() / sizeof(char);\n const size_t len2 = obj2->datalength() / sizeof(char);\n\n if (0 == len1 && 0 == len2) return 0;\n CHECK(len1 || len2);\n return float(levenshtein(x, len1, y, len2))/std::max(len1, len2);\n }\n DISABLE_COPY_AND_ASSIGN(SpaceLevenshteinNormalized);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_lp.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_LP_H_\n#define _SPACE_LP_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_vector.h\"\n#include \"distcomp.h\"\n\n#define SPACE_L \"lp\"\n#define SPACE_LINF \"linf\"\n#define SPACE_L1 \"l1\"\n#define SPACE_L2 \"l2\"\n\nnamespace similarity {\n\n\n\ntemplate \nclass SpaceLpDist {\npublic:\n explicit SpaceLpDist(dist_t pf) : p_(static_cast(pf)), pf_(pf), custom_(false) {\n if (fabs(dist_t(p_) - pf_) < numeric_limits::min()) {\n custom_ = p_ == -1 || p_ == 1 || p_ == 2;\n }\n }\n\n dist_t operator()(const dist_t* x, const dist_t* y, size_t length) const {\n CHECK(p_ >= 0 || -1 == p_);\n\n if (custom_) {\n if (p_ == -1) {\n return LInfNormSIMD(x, y, length);\n } else if (p_ == 1) {\n return L1NormSIMD(x, y, length);\n } else if (p_ == 2) {\n return L2NormSIMD(x, y, length);\n } \n }\n\n /* \n * This one will be relatively efficient for integer-valued p_,\n * but not if p_ is an arbitrary value.\n */\n return LPGenericDistanceOptim(x, y, length, dist_t(pf_));\n }\n dist_t getP() const { return pf_; }\n bool getCustom() const { return custom_; }\nprivate:\n int p_;\n dist_t pf_; \n bool custom_; // Do we use a custom implementation for l=0,1,2?\n};\n\ntemplate \nclass SpaceLp : public VectorSpaceSimpleStorage {\n public:\n explicit SpaceLp(dist_t p) : distObj_(p) {}\n virtual ~SpaceLp() {}\n\n virtual std::string StrDesc() const;\n protected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const;\n private:\n SpaceLpDist distObj_;\n DISABLE_COPY_AND_ASSIGN(SpaceLp);\n};\n\n\n} // namespace similarity\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/space/space_rank_correl.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _RANK_CORREL_SPACE_H_\n#define _RANK_CORREL_SPACE_H_\n\n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_vector.h\"\n#include \"permutation_type.h\"\n\nnamespace similarity {\n\ntemplate \nclass RankCorrelVectorSpace : public VectorSpace {\n public:\n explicit RankCorrelVectorSpace() {}\n\n virtual std::string StrDesc() const { return \"rank correlation vector space\"; }\n protected:\n // Should not be directly accessible\n virtual PivotIdType HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const PivotIdType* x = reinterpret_cast(obj1->data());\n const PivotIdType* y = reinterpret_cast(obj2->data());\n const size_t length = obj1->datalength() / sizeof(size_t);\n\n return RankCorrelDistFunc(x, y, length);\n }\n DISABLE_COPY_AND_ASSIGN(RankCorrelVectorSpace);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_scalar.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_SCALAR_H_\n#define _SPACE_SCALAR_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_vector.h\"\n#include \"distcomp.h\"\n\n\n#define SPACE_COSINE_SIMILARITY \"cosinesimil\"\n#define SPACE_ANGULAR_DISTANCE \"angulardist\"\n\nnamespace similarity {\n\ntemplate \nclass SpaceCosineSimilarity : public VectorSpaceSimpleStorage {\npublic:\n SpaceCosineSimilarity() {}\n virtual std::string StrDesc() const {\n return \"CosineSimilarity\";\n }\nprotected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const;\n DISABLE_COPY_AND_ASSIGN(SpaceCosineSimilarity);\n};\n\ntemplate \nclass SpaceAngularDistance : public VectorSpaceSimpleStorage {\npublic:\n SpaceAngularDistance() {}\n virtual std::string StrDesc() const {\n return \"AngularDistance\";\n }\n virtual size_t GetElemQty(const Object* object) const {\n return object->datalength()/ sizeof(dist_t);\n }\n virtual void createVectFromObj(const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n return VectorSpace::\n CreateVectFromObjSimpleStorage(__func__, obj, pDstVect, nElem);\n }\n\nprotected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const;\n DISABLE_COPY_AND_ASSIGN(SpaceAngularDistance);\n};\n\n\n} // namespace similarity\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/space/space_sparse_lp.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_SPARSE_LP_H_\n#define _SPACE_SPARSE_LP_H_\n\n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_lp.h\"\n#include \"space_sparse_vector.h\"\n\n#define SPACE_SPARSE_L \"lp_sparse\"\n#define SPACE_SPARSE_LINF \"linf_sparse\"\n#define SPACE_SPARSE_L1 \"l1_sparse\"\n#define SPACE_SPARSE_L2 \"l2_sparse\"\n\nnamespace similarity {\n\ntemplate \nclass SpaceSparseLp : public SpaceSparseVectorSimpleStorage {\n public:\n explicit SpaceSparseLp(dist_t p) : distObj_(p) {}\n virtual ~SpaceSparseLp() {}\n\n virtual std::string StrDesc() const;\n\n protected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const;\n private:\n SpaceLpDist distObj_;\n DISABLE_COPY_AND_ASSIGN(SpaceSparseLp);\n};\n\n\n} // namespace similarity\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/space/space_sparse_scalar.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_SPARSE_SCALAR_H_\n#define _SPACE_SPARSE_SCALAR_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_sparse_vector.h\"\n#include \"distcomp.h\"\n\n#define SPACE_SPARSE_COSINE_SIMILARITY \"cosinesimil_sparse\"\n#define SPACE_SPARSE_ANGULAR_DISTANCE \"angulardist_sparse\"\n\nnamespace similarity {\n\ntemplate \nclass SpaceSparseAngularDistance : public SpaceSparseVectorSimpleStorage {\n public:\n explicit SpaceSparseAngularDistance() {}\n virtual ~SpaceSparseAngularDistance() {}\n\n virtual std::string StrDesc() const {\n return \"AngularDistance\";\n }\n\n protected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const {\n return SpaceSparseVectorSimpleStorage::\n ComputeDistanceHelper(obj1, obj2, distObjAngular_);\n }\n private:\n struct SpaceAngularDist {\n dist_t operator()(const dist_t* x, const dist_t* y, size_t length) const {\n dist_t val = AngularDistance(x, y, length);\n // TODO: @leo shouldn't happen any more, but let's keep this check here for a while\n if (std::isnan(val)) throw runtime_error(\"SpaceAngularDist Bug: NAN dist!!!!\");\n return val;\n }\n };\n SpaceAngularDist distObjAngular_;\n DISABLE_COPY_AND_ASSIGN(SpaceSparseAngularDistance);\n};\n\ntemplate \nclass SpaceSparseCosineSimilarity : public SpaceSparseVectorSimpleStorage {\n public:\n explicit SpaceSparseCosineSimilarity() {}\n virtual ~SpaceSparseCosineSimilarity() {}\n\n virtual std::string StrDesc() const {\n return \"CosineSimilarity\";\n }\n\n protected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const {\n return SpaceSparseVectorSimpleStorage::\n ComputeDistanceHelper(obj1, obj2, distObjCosineSimilarity_);\n }\n private:\n struct SpaceCosineSimilarityDist {\n dist_t operator()(const dist_t* x, const dist_t* y, size_t length) const {\n dist_t val = CosineSimilarity(x, y, length);\n // TODO: @leo shouldn't happen any more, but let's keep this check here for a while\n if (std::isnan(val)) throw runtime_error(\"SpaceCosineSimilarityDist Bug: NAN dist!!!!\");\n return val;\n }\n };\n SpaceCosineSimilarityDist distObjCosineSimilarity_;\n DISABLE_COPY_AND_ASSIGN(SpaceSparseCosineSimilarity);\n};\n\n\n} // namespace similarity\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/space/space_sparse_scalar_fast.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_SPARSE_SCALAR_FAST_H_\n#define _SPACE_SPARSE_SCALAR_FAST_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_sparse_vector_inter.h\"\n#include \"distcomp.h\"\n\n\n#define SPACE_SPARSE_COSINE_SIMILARITY_FAST \"cosinesimil_sparse_fast\"\n#define SPACE_SPARSE_ANGULAR_DISTANCE_FAST \"angulardist_sparse_fast\"\n\nnamespace similarity {\n\nclass SpaceSparseCosineSimilarityFast : public SpaceSparseVectorInter {\npublic:\n explicit SpaceSparseCosineSimilarityFast(){}\n virtual std::string StrDesc() const {\n return \"CosineSimilarity (fast)\";\n }\nprotected:\n virtual float HiddenDistance(const Object* obj1, const Object* obj2) const;\n DISABLE_COPY_AND_ASSIGN(SpaceSparseCosineSimilarityFast);\n};\n\nclass SpaceSparseAngularDistanceFast : public SpaceSparseVectorInter {\npublic:\n explicit SpaceSparseAngularDistanceFast(){}\n virtual std::string StrDesc() const {\n return \"AngularDistance (fast)\";\n }\nprotected:\n virtual float HiddenDistance(const Object* obj1, const Object* obj2) const;\n DISABLE_COPY_AND_ASSIGN(SpaceSparseAngularDistanceFast);\n};\n\n\n} // namespace similarity\n\n#endif \n" }, { "path": "algorithms/NMSLIB/code/include/space/space_sparse_vector.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_SPARSE_VECTOR_H_\n#define _SPACE_SPARSE_VECTOR_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"distcomp.h\"\n\nnamespace similarity {\n\nusing std::vector;\nusing std::fill;\n\ntemplate \nstruct SparseVectElem { \n uint32_t id_;\n dist_t val_;\n SparseVectElem(uint32_t id = 0, dist_t val = 0) : id_(id), val_(val) {}\n bool operator<(const SparseVectElem& that) const {\n return id_ < that.id_;\n }\n bool operator==(const SparseVectElem& that) const {\n return id_ == that.id_ && val_ == that.val_;\n }\n bool operator!=(const SparseVectElem& that) const {\n return !operator==(that);\n }\n};\n\ntemplate \nostream& operator<<(ostream& out, SparseVectElem e) {\n return out << \"[\" << e.id_ << \": \" << e.val_ << \"]\";\n}\n\n/* \n * The maximum number of sparse elements that will be kept on the stack\n * by the function ComputeDistanceHelper. \n *\n * TODO:@leo If there are too many threads, we might run out stack memory.\n * but it is probably extremely unlikely with the buffer of this size.\n *\n */\n#define MAX_BUFFER_QTY 8192\n\ntemplate \nclass SpaceSparseVector : public Space {\npublic:\n typedef SparseVectElem ElemType;\n\n explicit SpaceSparseVector() {}\n virtual ~SpaceSparseVector() {}\n\n /** Standard functions to read/write/create objects */ \n // Create a string representation of an object.\n virtual unique_ptr CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpState) const;\n virtual string CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const;\n // Open a file for reading, fetch a header (if there is any) and memorize an input state\n virtual unique_ptr OpenReadFileHeader(const string& inputFile) const;\n // Open a file for writing, write a header (if there is any) and memorize an output state\n virtual unique_ptr OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outputFile) const;\n /*\n * Read a string representation of the next object in a file as well\n * as its label. Return false, on EOF.\n */\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const;\n /** End of standard functions to read/write/create objects */\n\n /*\n * Used only for testing/debugging: compares objects approximately. Floating point numbers\n * should be nearly equal. Integers and strings should coincide exactly.\n */\n virtual bool ApproxEqual(const Object& obj1, const Object& obj2) const;\n\n /* \n * Different implementations of the sparse vector space will pack elements differently.\n * Hence, they have to provide their own procedures to create an Object as\n * well as extract elements from an Object.\n */\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const vector& InpVect) const = 0;\n // Sparse vectors have no fixed dimensionality\n virtual size_t GetElemQty(const Object* object) const {return 0;}\n\n virtual dist_t ScalarProduct(const Object* obj1, const Object* obj2) const = 0;\nprotected:\n void ReadSparseVec(std::string line, size_t line_num, IdType& label, vector& v) const;\n virtual void CreateVectFromObj(const Object* obj, vector& v) const = 0;\n DISABLE_COPY_AND_ASSIGN(SpaceSparseVector);\n};\n\ntemplate \nclass SpaceSparseVectorSimpleStorage : public SpaceSparseVector {\npublic:\n typedef SparseVectElem ElemType;\n\n virtual ~SpaceSparseVectorSimpleStorage() {}\n explicit SpaceSparseVectorSimpleStorage() {}\n\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pVect,\n size_t nElem) const {\n static std::hash indexHash;\n fill(pVect, pVect + nElem, static_cast(0));\n const ElemType* beg = reinterpret_cast(obj->data());\n const ElemType* const end =\n reinterpret_cast(obj->data() + obj->datalength());\n for (;beg < end;++beg) {\n size_t idx = indexHash(beg->id_) % nElem;\n pVect[idx] += beg->val_;\n }\n }\n\n Object* CreateObjFromVect(IdType id, LabelType label, const vector& InpVect) const {\n return new Object(id, label, InpVect.size() * sizeof(ElemType), &InpVect[0]);\n };\n\n virtual dist_t ScalarProduct(const Object* obj1, const Object* obj2) const {\n SpaceNormScalarProduct distObjNormSP;\n return SpaceSparseVectorSimpleStorage::\n ComputeDistanceHelper(obj1, obj2, distObjNormSP);\n }\nprotected:\n DISABLE_COPY_AND_ASSIGN(SpaceSparseVectorSimpleStorage);\n\n virtual void CreateVectFromObj(const Object* obj, vector& v) const {\n const ElemType* beg = reinterpret_cast(obj->data());\n const ElemType* const end =\n reinterpret_cast(obj->data() + obj->datalength());\n size_t qty = end - beg;\n v.resize(qty);\n for (size_t i = 0; i < qty; ++i) v[i] = beg[i];\n }\n\n struct SpaceNormScalarProduct {\n dist_t operator()(const dist_t* x, const dist_t* y, size_t length) const {\n return NormScalarProduct(x, y, length);\n }\n };\n\n \n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const = 0;\n\n /* \n * This helper function converts a dense vector to a sparse one and then calls a generic distance function.\n * It can be used only with simple-storage sparse vector spaces, not with\n * children of SpaceSparseVectorInter.\n */\n template \n static dist_t ComputeDistanceHelper(const Object* obj1, const Object* obj2, \n const DistObjType distObj, dist_t missingValue = 0) {\n dist_t *mem1 = NULL, *mem2 = NULL;\n dist_t buf1[MAX_BUFFER_QTY], buf2[MAX_BUFFER_QTY];\n dist_t *vect1 = buf1;\n dist_t *vect2 = buf2;\n\n dist_t res = 0;\n\n CHECK(obj1->datalength() > 0);\n CHECK(obj2->datalength() > 0);\n const ElemType* beg1 = reinterpret_cast(obj1->data());\n const ElemType* beg2 = reinterpret_cast(obj2->data());\n const ElemType* end1 = reinterpret_cast(obj1->data() + obj1->datalength());\n const ElemType* end2 = reinterpret_cast(obj2->data() + obj2->datalength());\n const size_t qty1 = obj1->datalength() / sizeof(ElemType);\n const size_t qty2 = obj2->datalength() / sizeof(ElemType);\n const size_t maxQty = qty1 + qty2;\n\n /* \n * We hope not to allocate memory frequently, b/c memory allocation can be expensive\n * TODO:@leo check how expensive are memory allocations, perhaps, this fear is not substantiated\n */ \n try {\n if (maxQty > MAX_BUFFER_QTY) {\n vect1 = mem1 = new dist_t[maxQty];\n vect2 = mem2 = new dist_t[maxQty];\n }\n\n size_t qty = 0;\n \n const ElemType* it1 = beg1, *it2 = beg2; \n\n while (it1 < end1 && it2 < end2) {\n if (it1->id_ == it2->id_) {\n vect1[qty] = it1->val_;\n vect2[qty] = it2->val_;\n ++it1;\n ++it2;\n ++qty; \n } else if (it1->id_ < it2->id_) {\n vect1[qty] = it1->val_;\n vect2[qty] = missingValue;\n ++qty;\n ++it1;\n } else {\n vect1[qty] = missingValue;\n vect2[qty] = it2->val_;\n ++qty;\n ++it2;\n }\n }\n \n while (it1 < end1) {\n vect1[qty] = it1->val_;\n vect2[qty] = missingValue;\n ++qty;\n ++it1;\n }\n \n while (it2 < end2) {\n vect1[qty] = missingValue;\n vect2[qty] = it2->val_;\n ++qty;\n ++it2;\n }\n\n if (qty > maxQty) {\n LOG(LIB_ERROR) << qty1;\n LOG(LIB_ERROR) << qty2;\n LOG(LIB_ERROR) << qty;\n }\n CHECK(qty <= maxQty);\n\n res = distObj(vect1, vect2, qty);\n\n } catch (...) {\n delete [] mem1;\n delete [] mem2;\n }\n\n delete [] mem1;\n delete [] mem2;\n\n return res;\n }\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_sparse_vector_inter.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_SPARSE_VECTOR_INTER_H_\n#define _SPACE_SPARSE_VECTOR_INTER_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"distcomp.h\"\n#include \"space_sparse_vector.h\"\n\nnamespace similarity {\n\nusing std::vector;\n\n/*\n *\n * This helper base class is different from the SpaceSparseVectorSimpleStorage class\n * in that it stores sparse vectors in a special format (divided into blocks).\n *\n * This makes it possible to quickly compute distances, \n * where computations involves elements present in both vectors.\n *\n * One example is the scalar product.\n *\n */\ntemplate \nclass SpaceSparseVectorInter : public SpaceSparseVector {\n public:\n explicit SpaceSparseVectorInter(){}\n typedef SparseVectElem ElemType;\n\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pVect,\n size_t nElem) const;\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const vector& InpVect) const;\n virtual void CreateVectFromObj(const Object* obj, vector& v) const ;\n /*\n * Overriding the function from the base class\n */\n virtual dist_t ScalarProduct(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj2->datalength() > 0);\n\n return ScalarProductFast(obj1->data(), obj1->datalength(),\n obj2->data(), obj2->datalength());\n }\n protected:\n DISABLE_COPY_AND_ASSIGN(SpaceSparseVectorInter);\n\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const = 0;\n};\n\n/* \n *\n * Modifying ids, so we don't have ids for which id % 65536 == 0\n *\n * The SIMD intersection algorithm won't be able to handle such ids, \n * because a faster version of _mm_cmpistrm requires that all ids \n * are non-zero.\n *\n */\ninline size_t removeBlockZeros(size_t id) {\n // This way we don't have ids for which id % 65536 == 0\n return (id / 65535) * 65536 + (id % 65535) + 1;\n};\n\n/* \n * A conversion that reverts removeBlockZeros\n * It works only for the numbers where id % 65536 != 0,\n * which makes sense as removeBlockZeros can't produce a multiple of 65536.\n *\n *\n * One may doubt if this reverse conversion actually works (we \n * do have a unit test to verify this). However, it's not \n * hard to see why the conversion is correct using basic\n * modulo and integer division arithmetic (see below).\n *\n * Note 1:\n * One way to represent id produced by removeBlockZeros(origId):\n * id = (origId / 65535) * 65536 + term_smaller_than_65536\n * Therefore: origId / 65536 is equal to id / 65536\n * This deletion clearly \"removes\" the term smaller than 65536.\n * \n * Note 2:\n * Another way to represent id obtained as removeBlockZeros(origId):\n * id = a_multiple_of_65536 + (origId % 65535) + 1\n * Note that the sum of second and the third terms is smaller than 65536.\n * Therefore id % 65536 = (origId % 65535) + 1\n *\n * Combining Note 1 & 2, we obtain that:\n * addBlockZeros(id) = (origId / 65535) + origId % 65535 = origId\n * \n * Q.E.D.\n */\ninline size_t addBlockZeros(size_t id) {\n return (id / 65536) * 65535 + (id % 65536) - 1;\n}\n\ntemplate \ninline void ParseSparseElementHeader(const char* pBuff, \n size_t& rBlockQty,\n dist_t& rSqSum,\n const size_t*& rpBlockQtys,\n const size_t*& rpBlockOffs, \n const char*& rpBlockBegin) {\n const size_t* pQty = reinterpret_cast(pBuff);\n rBlockQty = *pQty;\n const dist_t* pSqSum = reinterpret_cast(pQty + 1);\n rSqSum = *pSqSum;\n rpBlockQtys = reinterpret_cast(pSqSum + 1);\n rpBlockOffs = rpBlockQtys + rBlockQty;\n\n rpBlockBegin = reinterpret_cast(rpBlockOffs + rBlockQty);\n} \n\ntemplate \ninline void UnpackSparseElements(const char* pBuff, size_t dataLen,\n vector>& OutVect)\n{\n typedef SparseVectElem ElemType;\n\n size_t blockQty = 0;\n dist_t SqSum = 0;\n const size_t* pBlockQty = NULL;\n const size_t* pBlockOff = NULL;\n\n const char* pBlockBegin = NULL;\n\n ParseSparseElementHeader(pBuff, blockQty, SqSum, \n pBlockQty, \n pBlockOff, \n pBlockBegin);\n\n size_t elemSize = 2 + sizeof(dist_t);\n\n for (size_t i = 0; i < blockQty; ++i) {\n size_t qty = pBlockQty[i];\n size_t offset = pBlockOff[i];\n\n const uint16_t* pBlockIds = reinterpret_cast(pBlockBegin);\n const dist_t* pValBegin = reinterpret_cast(pBlockIds + qty);\n\n for (size_t k = 0; k < qty; ++k) {\n OutVect.push_back(ElemType(addBlockZeros(pBlockIds[k] + offset), pValBegin[k]));\n }\n\n pBlockBegin += elemSize * qty;\n }\n\n CHECK(pBlockBegin - pBuff == (ssize_t)dataLen);\n}\n\ntemplate \ninline void PackSparseElements(const vector>& InpVect, \n char*& prBuff, size_t& dataSize) {\n typedef SparseVectElem ElemType;\n\n vector> blocks;\n vector blockOffsets;\n\n uint32_t prevBlockId = numeric_limits::max();\n\n vector currBlock;\n\n size_t totQty = 0;\n dist_t sqSum = 0;\n\n for (size_t i = 0; i < InpVect.size(); ++i) {\n\t uint32_t id = static_cast // Blocks are never too large\n\t\t\t\t\t\t(removeBlockZeros(InpVect[i].id_));\n\n\t\n uint32_t blockId = static_cast // Blocks are never too large\n\t\t\t\t\t\t(id / 65536);\n\n id &= 65535; // in-block id will contain only the last two bytes\n\n if (blockId != prevBlockId && !currBlock.empty()) {\n blocks.push_back(currBlock);\n blockOffsets.push_back(prevBlockId * 65536);\n currBlock.clear();\n }\n\n prevBlockId = blockId;\n currBlock.push_back(ElemType(id, InpVect[i].val_));\n sqSum += InpVect[i].val_ * InpVect[i].val_;\n ++totQty;\n }\n blocks.push_back(currBlock);\n blockOffsets.push_back(prevBlockId * 65536);\n\n CHECK(InpVect.size() == totQty);\n\n /* \n * How much memory do we need?\n *\n * i) A header will store the number of blocks. \n * ii) For each block we keep the # of elements\n * iii) The sum of squared element values.\n * iv) Each element has a 2-byte id and a sizeof(dist_t) value\n *\n */\n size_t elemSize = 2 + sizeof(dist_t);\n dataSize = sizeof(size_t) + // number of blocks\n sizeof(dist_t) + // sum of squared elements\n 2 * sizeof(size_t) * (blocks.size()) + // block qtys & offsets\n elemSize * InpVect.size(); \n\n prBuff = new char[dataSize]; \n\n size_t* pQty = reinterpret_cast(prBuff);\n\n /*\n * Store meta information.\n */\n *pQty = blocks.size(); \n\n dist_t* pSqSum = reinterpret_cast(pQty + 1);\n *pSqSum = sqSum;\n size_t* pBlockQtyOff = reinterpret_cast(pSqSum + 1);\n\n for (size_t i = 0; i < blocks.size(); ++i) {\n *pBlockQtyOff++ = blocks[i].size(); // qty\n }\n for (size_t i = 0; i < blocks.size(); ++i) {\n *pBlockQtyOff++ = blockOffsets[i]; // offset\n }\n /*\n * Store block data.\n */\n uint16_t* pBlockIds = reinterpret_cast(pBlockQtyOff);\n\n for (size_t i = 0; i < blocks.size(); ++i) {\n dist_t* pBlockVals = reinterpret_cast(pBlockIds + blocks[i].size());\n\n for (size_t k = 0; k < blocks[i].size(); ++k) {\n *pBlockIds++ = blocks[i][k].id_;\n *pBlockVals++ = blocks[i][k].val_;\n }\n\n pBlockIds = reinterpret_cast(pBlockVals);\n }\n\n CHECK(reinterpret_cast(pBlockIds) - prBuff == (ssize_t)dataSize); \n}\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_sqfd.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_SQFD_H_\n#define _SPACE_SQFD_H_\n\n// Don't use on windows\n#if !defined(_MSC_VER)\n\n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"distcomp.h\"\n\n#define SPACE_SQFD_HEURISTIC_FUNC \"sqfd_heuristic_func\"\n#define SPACE_SQFD_MINUS_FUNC \"sqfd_minus_func\"\n#define SPACE_SQFD_GAUSSIAN_FUNC \"sqfd_gaussian_func\"\n\n#define FAKE_FILE_NAME \"fake_file\"\n\nnamespace similarity {\n\ntemplate \nclass SqfdFunction {\n public:\n virtual ~SqfdFunction() {}\n virtual dist_t f(const dist_t* p1, const dist_t* p2, const int sz) const = 0;\n virtual std::string StrDesc() const = 0;\n virtual SqfdFunction* Clone() const = 0;\n};\n\ntemplate \nclass SqfdMinusFunction : public SqfdFunction {\n public:\n dist_t f(const dist_t* p1, const dist_t* p2, const int sz) const {\n return -L2NormSIMD(p1, p2, sz);\n }\n std::string StrDesc() const {\n return \"minus function\";\n }\n virtual SqfdFunction* Clone() const {\n return new SqfdMinusFunction(); // No parameters!!!!\n }\n};\n\ntemplate \nclass SqfdHeuristicFunction : public SqfdFunction {\n public:\n SqfdHeuristicFunction(float alpha) : alpha_(alpha) {}\n dist_t f(const dist_t* p1, const dist_t* p2, const int sz) const {\n return 1.0 / (alpha_ + L2NormSIMD(p1, p2, sz));\n }\n std::string StrDesc() const {\n std::stringstream stream;\n stream << \"heuristic function alpha=\" << alpha_;\n return stream.str();\n }\n virtual SqfdFunction* Clone() const {\n return new SqfdHeuristicFunction(alpha_);\n }\n private:\n float alpha_;\n};\n\ntemplate \nclass SqfdGaussianFunction : public SqfdFunction {\n public:\n SqfdGaussianFunction(float alpha) : alpha_(alpha) {}\n dist_t f(const dist_t* p1, const dist_t* p2, const int sz) const {\n const dist_t d = L2NormSIMD(p1, p2, sz);\n return exp(-alpha_ * d * d);\n }\n std::string StrDesc() const {\n std::stringstream stream;\n stream << \"gaussian function alpha=\" << alpha_;\n return stream.str();\n }\n virtual SqfdFunction* Clone() const {\n return new SqfdGaussianFunction(alpha_);\n }\n private:\n float alpha_;\n};\n\ntemplate \nclass SpaceSqfd : public Space {\n public:\n SpaceSqfd(SqfdFunction* func);\n virtual ~SpaceSqfd();\n\n std::string StrDesc() const;\n\n /** Standard functions to read/write/create objects */ \n /*\n * Create an object from string representation.\n * If the input state pointer isn't null, we check\n * if the new vector is consistent with previous output.\n * If now previous output was seen, the state vector may be\n * updated. For example, when we start reading vectors,\n * we don't know the number of elements. When, we see the first\n * vector, we memorize dimensionality. If a subsequently read\n * vector has a different dimensionality, an exception will be thrown.\n */\n virtual unique_ptr CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpState) const;\n // Create a string representation of an object.\n // It doesn't recreate the file name, b/c we don't memorize it.\n // Instead it prints a fake entry.\n virtual string CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const;\n // Open a file for reading, fetch a header (if there is any) and memorize an input state\n virtual unique_ptr OpenReadFileHeader(const string& inputFile) const;\n // Open a file for writing, write a header (if there is any) and memorize an output state\n virtual unique_ptr OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outputFile) const;\n /*\n * Read a string representation of the next object in a file as well\n * as its label. Return false, on EOF.\n */\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const;\n /** End of standard functions to read/write/create objects */\n\n /*\n * Used only for testing/debugging: compares objects approximately. Floating point numbers\n * should be nearly equal. Integers and strings should coincide exactly.\n */\n virtual bool ApproxEqual(const Object& obj1, const Object& obj2) const;\n\n /*\n * CreateDenseVectFromObj and GetElemQty() are only needed, if\n * one wants to use methods with random projections.\n */\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pVect,\n size_t nElem) const {\n throw runtime_error(\"Cannot create vector for the space: \" + StrDesc());\n }\n virtual size_t GetElemQty(const Object* object) const {return 0;}\n protected:\n DISABLE_COPY_AND_ASSIGN(SpaceSqfd);\n\n dist_t HiddenDistance(\n const Object* obj1,\n const Object* obj2) const;\n private:\n SqfdFunction* func_;\n};\n\n} // namespace similarity\n\n#endif\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_string.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_STRING_H_\n#define _SPACE_STRING_H_\n\n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n\nnamespace similarity {\n\nusing std::string;\n\n/*\n * TODO: @leo/@bileg Currently we support only char, but need to support char32_t and UTF-8 as well. \n */\ntemplate \nclass StringSpace : public Space {\n public:\n explicit StringSpace() {}\n virtual ~StringSpace() {}\n\n /** Standard functions to read/write/create objects */ \n // Create an object from string representation.\n virtual unique_ptr CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpState) const {\n // TODO double-check that sizeof(char) should always be 1 as guaranteed by the C++ standard\n // then sizeof can be removed\n return unique_ptr(new Object(id, label, s.size() * sizeof(char), s.c_str()));\n }\n // Create a string representation of an object.\n virtual string CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const;\n // Open a file for reading, fetch a header (if there is any) and memorize an input state\n virtual unique_ptr OpenReadFileHeader(const string& inputFile) const ;\n // Open a file for writing, write a header (if there is any) and memorize an output state\n virtual unique_ptr OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outputFile) const ;\n // Read a string representation of the next object in a file\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const;\n // Write a string representation of the next object to a file\n virtual void WriteNextObj(const Object& obj, const string& externId, DataFileOutputState &) const;\n /** End of standard functions to read/write/create objects */ \n\n /*\n * Used only for testing/debugging: compares objects approximately. Floating point numbers\n * should be nearly equal. Integers and strings should coincide exactly.\n */\n virtual bool ApproxEqual(const Object& obj1, const Object& obj2) const;\n\n virtual string StrDesc() const = 0;\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pVect,\n size_t nElem) const {\n throw runtime_error(\"Cannot create vector for the space: \" + StrDesc());\n }\n virtual size_t GetElemQty(const Object* object) const {return 0;}\n protected:\n DISABLE_COPY_AND_ASSIGN(StringSpace);\n\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const = 0;\n void ReadStr(std::string line, LabelType& label, std::string& str, size_t* pLineNum) const;\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_vector.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_VECTOR_H_\n#define _SPACE_VECTOR_H_\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::unique_ptr;\n\ntemplate \nclass VectorSpace : public Space {\n public:\n explicit VectorSpace() {}\n virtual ~VectorSpace() {}\n\n /** Standard functions to read/write/create objects */ \n virtual unique_ptr CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpState) const;\n // Create a string representation of an object.\n virtual string CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const;\n // Open a file for reading, fetch a header (if there is any) and memorize an input state\n virtual unique_ptr OpenReadFileHeader(const string& inputFile) const;\n // Open a file for writing, write a header (if there is any) and memorize an output state\n virtual unique_ptr OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outputFile) const;\n /*\n * Read a string representation of the next object in a file as well\n * as its label. Return false, on EOF.\n */\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const;\n /** End of standard functions to read/write/create objects */ \n\n /*\n * Used only for testing/debugging: compares objects approximately. Floating point numbers\n * should be nearly equal. Integers and strings should coincide exactly.\n */\n virtual bool ApproxEqual(const Object& obj1, const Object& obj2) const;\n\n virtual Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const;\n virtual size_t GetElemQty(const Object* object) const = 0;\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pVect,\n size_t nElem) const = 0;\n\n static void ReadVec(std::string line, LabelType& label, std::vector& v);\n\nprotected:\n DISABLE_COPY_AND_ASSIGN(VectorSpace);\n\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const = 0;\n\n void CreateVectFromObjSimpleStorage(const char *pFuncName,\n const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n const dist_t* pSrcVec = reinterpret_cast(obj->data());\n const size_t len = GetElemQty(obj);\n if (nElem > len) {\n std::stringstream err;\n err << pFuncName << \" The number of requested elements \"\n << nElem << \" is larger than the actual number of elements \" << len;\n throw runtime_error(err.str());\n }\n for (size_t i = 0; i < nElem; ++i) pDstVect[i]=pSrcVec[i];\n }\n};\n\ntemplate \nclass VectorSpaceSimpleStorage : public VectorSpace {\n public:\n virtual ~VectorSpaceSimpleStorage() {}\n explicit VectorSpaceSimpleStorage() {}\n virtual size_t GetElemQty(const Object* object) const {\n return object->datalength()/ sizeof(dist_t);\n }\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pDstVect,\n size_t nElem) const {\n VectorSpace::\n CreateVectFromObjSimpleStorage(__func__, obj, pDstVect, nElem);\n }\nprivate:\n DISABLE_COPY_AND_ASSIGN(VectorSpaceSimpleStorage);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_vector_gen.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _SPACE_VECTOR_GEN_H_\n#define _SPACE_VECTOR_GEN_H_\n\n#include \n#include \n#include \n\n#include \n\nnamespace similarity {\n\n/*\n * A generic vector space: can be used with\n * an arbitrary distance function.\n */ \ntemplate \nclass VectorSpaceGen : public VectorSpaceSimpleStorage {\n public:\n virtual ~VectorSpaceGen() {}\n explicit VectorSpaceGen() {}\n\n virtual void CreateDataset(ObjectVector& dataset, \n const vector>& sourceData) {\n fillDataSet(dataset, sourceData, NULL);\n }\n\n virtual void CreateDataset(ObjectVector& dataset, \n const vector>& sourceData,\n const vector& labels) {\n fillDataSet(dataset, sourceData, &labels);\n }\n virtual std::string StrDesc() const {\n return \"custom space\";\n }\n Object* CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n return VectorSpace::CreateObjFromVect(id, label, InpVect);\n };\n protected:\n DISABLE_COPY_AND_ASSIGN(VectorSpaceGen);\n\n DistObjType distObj_;\n\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n const size_t length = obj1->datalength() / sizeof(dist_t);\n\n return distObj_(x, y, length);\n }\n \n void fillDataSet(ObjectVector& dataset, \n const vector>& sourceData,\n const vector* pLabels) {\n size_t dim = 0;\n\n for (size_t index = 0; index < sourceData.size(); ++index) {\n const vector& oneElem = sourceData[index];\n\n size_t currDim = oneElem.size();\n\n if (!dim) dim = currDim;\n else {\n if (dim != currDim) {\n PREPARE_RUNTIME_ERR(err) << \"The # of vector elements (\" << currDim << \")\" <<\n \" doesn't match the # of elements in previous lines. (\" << dim << \" )\" <<\n \"Found mismatch, index : \" << (index + 1);\n THROW_RUNTIME_ERR(err);\n }\n }\n LabelType label = pLabels ? (*pLabels)[index] : -1 ;\n dataset.push_back(CreateObjFromVect(index, label, oneElem));\n }\n }\n\n};\n\n} // namespace similarity\n\n\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space/space_word_embed.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _SPACE_WORD_EMBED_H_\n#define _SPACE_WORD_EMBED_H_\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"space_vector.h\"\n\n#define SPACE_WORD_EMBED \"word_embed\"\n#define SPACE_WORD_EMBED_DIST_L2 \"l2\"\n#define SPACE_WORD_EMBED_DIST_COSINE \"cosine\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::unique_ptr;\n\nenum EmbedDistSpace { kEmbedDistL2 = 0, kEmbedDistCosine = 1};\n\ntemplate \nclass WordEmbedSpace : public VectorSpaceSimpleStorage {\n public:\n explicit WordEmbedSpace(EmbedDistSpace distType) : distType_(distType) {}\n virtual ~WordEmbedSpace() {}\n\n /** Standard functions to read/write/create objects */ \n // Create a string representation of an object.\n virtual string CreateStrFromObj(const Object* pObj, const string& externId) const;\n /*\n * Read a string representation of the next object in a file as well\n * as its label. Return false, on EOF.\n */\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const;\n /** End of standard functions to read/write/create objects */ \n virtual string StrDesc() const;\n protected:\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const;\n EmbedDistSpace distType_;\n\n DISABLE_COPY_AND_ASSIGN(WordEmbedSpace);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/space.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2015\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef SPACE_H\n#define SPACE_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \"global.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"logging.h\"\n#include \"permutation_type.h\"\n\n#define LABEL_PREFIX \"label:\"\n\n#define DIST_TYPE_INT \"int\"\n#define DIST_TYPE_FLOAT \"float\"\n#define DIST_TYPE_DOUBLE \"double\"\n\nnamespace similarity {\n\nusing std::map;\nusing std::string;\nusing std::ifstream;\nusing std::ofstream;\nusing std::pair;\nusing std::make_pair;\nusing std::runtime_error;\nusing std::hash;\n\ntemplate \nclass ExperimentConfig;\n\ntemplate \nconst char* DistTypeName();\n\ntemplate <> inline const char* DistTypeName() { return \"FLOAT\"; }\ntemplate <> inline const char* DistTypeName() { return \"DOUBLE\"; }\ntemplate <> inline const char* DistTypeName() { return \"INT\"; }\ntemplate inline const char* DistTypeName() { return typeid(dist_t).name(); }\n\ntemplate \nclass Query;\n\ntemplate \nclass KNNQuery;\n\ntemplate \nclass RangeQuery;\n\ntemplate \nclass Experiments;\n\nstruct DataFileInputState {\n virtual void Close() {};\n virtual ~DataFileInputState(){};\n};\n\nstruct DataFileInputStateOneFile : public DataFileInputState {\n virtual void Close() { inp_file_.close(); }\n DataFileInputStateOneFile(const string& inpFileName) :\n inp_file_(inpFileName.c_str()), line_num_(0) {\n\n if (!inp_file_) {\n PREPARE_RUNTIME_ERR(err) << \"Cannot open file: \" << inpFileName << \" for reading\";\n THROW_RUNTIME_ERR(err);\n }\n\n inp_file_.exceptions(ios::badbit);\n }\n virtual ~DataFileInputStateOneFile(){};\n ifstream inp_file_;\n size_t line_num_;\n};\n\nstruct DataFileInputStateVec : public DataFileInputStateOneFile {\n DataFileInputStateVec(const string& inpFileName) : DataFileInputStateOneFile(inpFileName), dim_(0) { } \n unsigned dim_;\n};\n\nstruct DataFileOutputState {\n DataFileOutputState(const string& outputFile) : out_file_(outputFile.c_str()) {\n out_file_.exceptions(std::ios::badbit | std::ios::failbit);\n }\n virtual void Close() { out_file_.close(); }\n virtual ~DataFileOutputState(){};\n ofstream out_file_;\n};\n\ntemplate \nclass Space {\n public:\n explicit Space() {}\n virtual ~Space() {}\n // This function is public and it is not supposed to be used in the query-mode\n dist_t IndexTimeDistance(const Object* obj1, const Object* obj2) const {\n if (!bIndexPhase) {\n throw runtime_error(string(\"The public function \") + __func__ + \n \" function is accessible only during the indexing phase!\");\n }\n return HiddenDistance(obj1, obj2);\n }\n\n virtual dist_t ProxyDistance(const Object* obj1, const Object* obj2) const {\n throw runtime_error(\"Not supported!\");\n }\n\n virtual string StrDesc() const = 0;\n\n /** Standard functions to read/write/create objects */ \n /*\n * Create an object from string representation.\n * If the input state pointer isn't null, we check\n * if the new vector is consistent with previous output.\n * If now previous output was seen, the state vector may be\n * updated. For example, when we start reading vectors,\n * we don't know the number of elements. When, we see the first\n * vector, we memorize dimensionality. If a subsequently read\n * vector has a different dimensionality, an exception will be thrown.\n */\n virtual unique_ptr CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpState) const = 0;\n // Create a string representation of an object.\n virtual string CreateStrFromObj(const Object* pObj, const string& externId) const = 0;\n // Open a file for reading, fetch a header (if there is any) and memorize an input state\n virtual unique_ptr OpenReadFileHeader(const string& inputFile) const = 0;\n // Open a file for writing, write a header (if there is any) and memorize an output state\n virtual unique_ptr OpenWriteFileHeader(const ObjectVector& dataset, \n const string& outputFile) const = 0;\n /*\n * Read a string representation of the next object in a file as well\n * as its label. Return false, on EOF.\n */\n virtual bool ReadNextObjStr(DataFileInputState &, string& strObj, LabelType& label, string& externId) const = 0;\n /* \n * Write a string representation of the next object to a file. We totally delegate\n * this to a Space object, because it may package the string representation, by\n * e.g., in the form of an XML fragment.\n */\n virtual void WriteNextObj(const Object& obj, const string& externId, DataFileOutputState &outState) const {\n outState.out_file_ << CreateStrFromObj(&obj, externId) << endl;\n }\n /*\n * This function allows setting space parameters (and creating additional parameter-dependent data structures)\n * based on the content of the input file.\n */\n virtual void UpdateParamsFromFile(DataFileInputState& inpState) {}\n /** End of standard functions to read/write/create objects as well as update state parameters */ \n\n /*\n * Used only for testing/debugging: compares objects approximately. Floating point numbers\n * should be nearly equal. Integers and strings should coincide exactly.\n */\n virtual bool ApproxEqual(const Object& obj1, const Object& obj2) const = 0;\n\n unique_ptr ReadDataset(ObjectVector& dataset,\n vector& vExternIds,\n const string& inputFile,\n const IdTypeUnsign MaxNumObjects = MAX_DATASET_QTY) const;\n void WriteDataset(const ObjectVector& dataset,\n const vector& vExternIds,\n const string& inputFile,\n const IdTypeUnsign MaxNumObjects = MAX_DATASET_QTY) const;\n\n /*\n * For some real-valued or integer-valued *DENSE* vector spaces this function\n * returns the number of vector elements. For all other spaces, it returns\n * 0.\n *\n * TODO: @leo This seems to be related to issue #7\n * https://github.com/searchivarius/NonMetricSpaceLib/issues/7\n *\n * With a proper hierarchy of Object classes, getDimension() would\n * be a function of an object, not of a space. At some point Object\n * must become smarter. Now it's a dumb container, while all the heavy\n * lifting is done by a Space object.\n *\n */\n virtual size_t GetElemQty(const Object* obj) const = 0;\n /*\n * For some dense vector spaces this function extracts the first nElem\n * elements from the object. If nElem > getElemQty(), an exception\n * will be thrown. For sparse vector spaces, the algorithm may \"hash\"\n * several elements together by summing up their values.\n *\n * Non-vector spaces don't have to support this function, they may\n * throw an exception.\n */\n virtual void CreateDenseVectFromObj(const Object* obj, dist_t* pVect,\n size_t nElem) const = 0;\n protected:\n void SetIndexPhase() const { bIndexPhase = true; }\n void SetQueryPhase() const { bIndexPhase = false; }\n\n friend class Query;\n friend class RangeQuery;\n friend class KNNQuery;\n friend class Experiments;\n /* \n * This function is private, but it will be accessible by the friend class Query\n * IndexTimeDistance access can be disable/enabled only by function friends \n */\n virtual dist_t HiddenDistance(const Object* obj1, const Object* obj2) const = 0;\n private:\n bool mutable bIndexPhase = true;\n \n DISABLE_COPY_AND_ASSIGN(Space);\n};\n\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/spacefactory.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef SPACE_FACTORY_H\n#define SPACE_FACTORY_H\n\n#include \n\n#include \"space.h\"\n#include \"space/space_lp.h\"\n#include \"space/space_bregman.h\"\n#include \"params.h\"\n\nnamespace similarity {\n\n#define REGISTER_SPACE_CREATOR(type, name, func)\\\n SpaceFactoryRegistry::Instance().Register(name, func);\n \n\ntemplate \nclass SpaceFactoryRegistry {\npublic:\n typedef Space* (*CreateFuncPtr)(const AnyParams&);\n static SpaceFactoryRegistry& Instance() {\n static SpaceFactoryRegistry elem;\n\n return elem;\n }\n\n void Register(const string& SpaceType, CreateFuncPtr func) {\n LOG(LIB_INFO) << \"Registering at the factory, space: \" << SpaceType << \" distance type: \" << DistTypeName();\n Creators_[SpaceType] = func;\n }\n\n Space* CreateSpace(const string& SpaceType, const AnyParams& SpaceParams) {\n if (Creators_.count(SpaceType)) {\n return Creators_[SpaceType](SpaceParams);\n } else {\n PREPARE_RUNTIME_ERR(err) << \"It looks like the space \" << SpaceType << \n \" is not defined for the distance type : \" << DistTypeName();\n THROW_RUNTIME_ERR(err);\n }\n return NULL;\n }\nprivate:\n map Creators_;\n};\n\n};\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/include/tune.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef TUNE_H\n#define TUNE_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"experimentconf.h\"\n#include \"searchoracle.h\"\n#include \"logging.h\"\n#include \"ztimer.h\"\n#include \"experiments.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n#include \"index.h\"\n\n//#define DETAILED_PRINT_INFO\n\nnamespace similarity {\n\nusing std::vector;\nusing std::map;\nusing std::make_pair;\nusing std::string;\nusing std::cout;\nusing std::end;\nusing std::stringstream;\n\ntemplate \nvoid GetOptimalAlphas(bool bPrintProgres,\n ExperimentConfig& config, \n OptimMetric metric, \n float desiredRecall,\n const string& SpaceType,\n const string& methodName,\n const AnyParams& IndexParams, \n AnyParams QueryTimeParams, \n float StepFactor, \n float alpha_left_base,\n float alpha_right_base,\n vector>>& \n vManagerGS,\n vector>>&\n vIndexForAllSetsPtrs,\n float& recall, float& time_best, float& impr_best,\n float& alpha_left_best, unsigned exp_left,\n float& alpha_right_best, unsigned exp_right,\n unsigned MaxIter,\n unsigned MaxRecDepth,\n int StepN,\n float maxCacheGSRelativeQty,\n unsigned recLevel) {\n if (recLevel >= MaxRecDepth) {\n if (bPrintProgres) {\n cout << \"Reached the maximum recursion level: \" << recLevel << endl;\n }\n return;\n }\n\n size_t ThreadTestQty = thread::hardware_concurrency();\n\n if (bPrintProgres) {\n cout << \"================================================================\" << endl;\n cout << ALPHA_LEFT_PARAM << \": \" << alpha_left_base << \" \" << ALPHA_RIGHT_PARAM << \": \" << alpha_right_base << endl;\n cout << EXP_LEFT_PARAM << \": \" << exp_left << \" \" << EXP_RIGHT_PARAM \": \" << exp_right << endl;\n cout << \"================================================================\" << endl;\n }\n\n QueryTimeParams.AddChangeParam(EXP_LEFT_PARAM, exp_left);\n QueryTimeParams.AddChangeParam(EXP_RIGHT_PARAM, exp_right);\n\n for (unsigned iter = 0; iter < MaxIter; ++iter) {\n if (bPrintProgres) {\n cout << \"Iteration: \" << iter << \" Level: \" << recLevel << \" StepFactor: \" << StepFactor << endl;\n }\n double MinRecall = 1.0;\n double MaxRecall = 0;\n \n for (int left = -StepN; left < StepN; ++left) {\n for (int right = -StepN; right < StepN; ++right) {\n float alphaLeftCurr = alpha_left_base * pow(StepFactor, left);\n float alphaRightCurr = alpha_right_base * pow(StepFactor, right);\n QueryTimeParams.AddChangeParam(ALPHA_LEFT_PARAM, alphaLeftCurr);\n QueryTimeParams.AddChangeParam(ALPHA_RIGHT_PARAM, alphaRightCurr);\n\n#ifdef DETAILED_PRINT_INFO\n if (bPrintProgres) {\n cout << \"left: \" << left << \" right: \" << right << endl;\n }\n#endif\n\n vector> ExpResRange(config.GetRange().size(),\n vector(1));\n vector> ExpResKNN(config.GetKNN().size(),\n vector(1));\n\n MetaAnalysis Stat(config.GetTestSetToRunQty());\n\n // Stat is used exactly only once: for one GetRange() or one GetKNN() (but not both)\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n ExpResRange[i][0] = &Stat;\n }\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n ExpResKNN[i][0] = &Stat;\n }\n for (int TestSetId = 0; TestSetId < config.GetTestSetToRunQty(); ++TestSetId) {\n config.SelectTestSet(TestSetId);\n#ifdef DETAILED_PRINT_INFO\n if (bPrintProgres) {\n cout << \"****************************************************\" << endl;\n cout << \"*** Test set id: \" << TestSetId << \n \" (set qty: \" << config.GetTestSetToRunQty() << \")\" << \" iteration: \" << iter << \"\\t***\" << endl;\n cout << \"****************************************************\" << endl;\n }\n#endif\n if (!vManagerGS[TestSetId].get()) {\n vManagerGS[TestSetId].reset(new GoldStandardManager(config));\n vManagerGS[TestSetId]->Compute(ThreadTestQty, maxCacheGSRelativeQty);\n } else {\n#ifdef DETAILED_PRINT_INFO\n if (bPrintProgres) {\n cout << \"Using existing GS for test set id: \" << TestSetId << endl;\n }\n#endif\n }\n\n std::shared_ptr> MethodPtr = vIndexForAllSetsPtrs[TestSetId];\n \n if (!MethodPtr.get()) {\n if (bPrintProgres) {\n cout << \"Creating a new index, params: \" << IndexParams.ToString() << endl;\n }\n MethodPtr.reset(MethodFactoryRegistry::Instance().\n CreateMethod(false, /* don't print the progress info, it's gonna be quick */\n methodName,\n SpaceType, config.GetSpace(), \n config.GetDataObjects()));\n MethodPtr->CreateIndex(IndexParams);\n vIndexForAllSetsPtrs[TestSetId] = MethodPtr;\n } else {\n#ifdef DETAILED_PRINT_INFO\n if (bPrintProgres) {\n cout << \"Reusing an existing index, only reseting params\" << endl;\n }\n#endif\n }\n\n vector> vQueryTimeParams;\n vQueryTimeParams.push_back(shared_ptr(new AnyParams(QueryTimeParams)));\n\n Experiments::RunAll(false /* don't print info */, \n ThreadTestQty,\n TestSetId,\n *vManagerGS[TestSetId],\n ExpResRange, ExpResKNN,\n config, \n *MethodPtr,\n vQueryTimeParams);\n }\n Stat.ComputeAll();\n double impr_val = (metric == IMPR_DIST_COMP) ? Stat.GetImprDistCompAvg() : Stat.GetImprEfficiencyAvg();\n if (Stat.GetRecallAvg() >= desiredRecall &&\n impr_val > impr_best) {\n recall = Stat.GetRecallAvg();\n time_best = Stat.GetQueryTimeAvg();\n impr_best = impr_val;\n alpha_left_best = alphaLeftCurr; \n alpha_right_best = alphaRightCurr; \n\n if (bPrintProgres) {\n cout << \" ************* BETTER EFFICIENCY POINT ******************* \" << endl;\n cout << \"iteration: \" << iter << \" out of \" << MaxIter << endl;\n cout << ALPHA_LEFT_PARAM << \"=\" << alpha_left_best << \" \" << EXP_LEFT_PARAM << \"=\" << exp_left << \" \"\n ALPHA_RIGHT_PARAM << \"=\" << alpha_right_best << \" \" << EXP_RIGHT_PARAM << \"=\" << exp_right << endl;\n cout << \"Recall: \" << Stat.GetRecallAvg() << endl;\n cout << \"Query time: \" << Stat.GetQueryTimeAvg() << endl;\n cout << \"Improvement metric value: \" << impr_val << \" (\" << getOptimMetricName(metric) << \")\" << endl;\n cout << \"Impr. in efficiency : \" << Stat.GetImprEfficiencyAvg() << endl;\n cout << \"Impr. in dist comp : \" << Stat.GetImprDistCompAvg() << endl;\n cout << \" ********************************************************** \" << endl;\n }\n }\n MinRecall = std::min(MinRecall, Stat.GetRecallAvg());\n MaxRecall = std::max(MaxRecall, Stat.GetRecallAvg());\n }\n }\n\n if (bPrintProgres) {\n cout << \" ********** After iteration statistics ******************** \" << endl;\n cout << \" Local: MinRecall: \" << MinRecall << \" MaxRecall: \" << MaxRecall << \" Desired: \" << desiredRecall << endl;\n\n cout << \" Global: best improvement. \" << impr_best << \" Corresp. time: \"<< time_best << \" Corresp. Recall: \" << recall << endl;\n cout << \" Using: \" << ALPHA_LEFT_PARAM << \"=\" << alpha_left_best << \" \" << EXP_LEFT_PARAM << \"=\" << exp_left << \" \"\n << ALPHA_RIGHT_PARAM << \"=\" << alpha_right_best << \" \" << EXP_RIGHT_PARAM << \"=\" << exp_right << endl;\n }\n\n // Now let's see, if we need to increase/decrease base alpha levels\n if (MaxRecall < desiredRecall) {\n // Two situations are possible\n if (recall < desiredRecall) { \n // we never got a required recall (including previous iterations), let's decrease existing alphas\n // so that recall will go up.\n alpha_left_base = alpha_left_base / StepFactor;\n alpha_right_base = alpha_right_base / StepFactor;\n\n if (bPrintProgres) {\n cout << \"[CHANGE] max recall < desired recall, setting alpha_left_base=\" << alpha_left_base << \" alpha_right_base=\" << alpha_right_base << endl;\n }\n } else {\n // we encountered the required recall before, but we somehow managed to select\n // large alphas, so that in THIS ITERATION even the maximum recall is below desiredRecall.\n // If this is the case, let's return to the previously know good point,\n // with a decreased step factor.\n if (bPrintProgres) {\n cout << \"[CHANGE] max recall < desired recall, setting alpha_left_base=\" << alpha_left_base << \" alpha_right_base=\" << alpha_right_base << endl;\n }\n\n GetOptimalAlphas(bPrintProgres,\n config, \n metric, desiredRecall,\n SpaceType, methodName,\n IndexParams, QueryTimeParams,\n sqrt(StepFactor), alpha_left_best, alpha_right_best,\n vManagerGS, vIndexForAllSetsPtrs,\n recall, time_best, impr_best,\n alpha_left_best, exp_left,\n alpha_right_best, exp_right,\n MaxIter, MaxRecDepth, StepN, maxCacheGSRelativeQty,\n recLevel + 1);\n return;\n }\n } else if (MinRecall > desiredRecall) {\n /* \n * If we are above the minimum recall, this means we chose alphas two pessimistically.\n * Let's multiply our best values so far by StepFactor.\n */\n alpha_left_base = alpha_left_best * StepFactor;\n alpha_right_base = alpha_right_best * StepFactor;\n if (bPrintProgres) {\n cout << \"[CHANGE] max recall > desired recall, setting alpha_left_base=\" << alpha_left_base << \" alpha_right_base=\" << alpha_right_base << endl;\n }\n } else {\n CHECK(MaxRecall >= desiredRecall && MinRecall <= desiredRecall); \n // Let's return to the previously know good point,\n // with a decreased step factor.\n\n if (bPrintProgres) {\n cout << \"[CHANGE] desired recall is between min and max recall, decreasing factor, setting alpha_left_base=\" << alpha_left_best << \" alpha_right_base=\" << alpha_right_best << endl;\n }\n\n GetOptimalAlphas(bPrintProgres,\n config, \n metric, desiredRecall,\n SpaceType, methodName,\n IndexParams, QueryTimeParams,\n sqrt(StepFactor), alpha_left_best, alpha_right_best,\n vManagerGS, vIndexForAllSetsPtrs,\n recall, time_best, impr_best,\n alpha_left_best, exp_right,\n alpha_right_best, exp_right,\n MaxIter, MaxRecDepth, StepN, maxCacheGSRelativeQty,\n recLevel + 1);\n return;\n }\n }\n\n if (bPrintProgres) {\n cout << \"Exhausted \" << MaxIter << \" iterations\" << endl;\n }\n}\n\ntemplate \nvoid GetOptimalAlphas(bool bPrintProgres,\n ExperimentConfig& config, \n OptimMetric metric, \n float desiredRecall,\n const string& SpaceType,\n const string& methodName,\n const AnyParams IndexParams, \n AnyParams QueryTimeParams, \n float& recall, float& time_best, float& impr_best,\n float& alpha_left_best, unsigned exp_left,\n float& alpha_right_best, unsigned exp_right,\n unsigned MaxIter,\n unsigned MaxRecDepth,\n unsigned StepN,\n float FullFactor,\n float maxCacheGSRelativeQty) {\n time_best = std::numeric_limits::max();\n impr_best = 0;\n recall = 0;\n\n if (bPrintProgres) {\n cout << EXP_LEFT_PARAM << \": \" << exp_left << \" \" << EXP_RIGHT_PARAM \": \" << exp_right << endl;\n }\n\n if (bPrintProgres) {\n cout << \"Method index parameters: \" << IndexParams.ToString() << endl;\n cout << \"Method query-time parameters: \" << QueryTimeParams.ToString() << endl;\n }\n\n vector>> vManagerGS(config.GetTestSetToRunQty());\n vector>> vIndexForAllSetsPtrs(config.GetTestSetToRunQty());\n\n GetOptimalAlphas(bPrintProgres, \n config, \n metric, desiredRecall,\n SpaceType, methodName,\n IndexParams, QueryTimeParams,\n pow(FullFactor, 1.0/StepN), alpha_left_best, alpha_right_best,\n vManagerGS, vIndexForAllSetsPtrs,\n recall, time_best, impr_best,\n alpha_left_best, exp_right,\n alpha_right_best, exp_right,\n MaxIter, MaxRecDepth, StepN, maxCacheGSRelativeQty,\n 0 /* recLevel */);\n}\n\n}\n\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/include/utils.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _UTILS_H_\n#define _UTILS_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"idtype.h\"\n\n// compiler_warning.h\n#define STRINGISE_IMPL(x) #x\n#define STRINGISE(x) STRINGISE_IMPL(x)\n\n/*\n * This solution for generating\n * cross-platform warnings\n * is taken from http://stackoverflow.com/a/1911632/2120401\n * Use: #pragma message WARN(\"My message\")\n *\n * Note: We may need other other definitions for other compilers,\n * but so far it worked for MSVS, GCC, CLang, and Intel.\n */\n# define FILE_LINE_LINK __FILE__ \"(\" STRINGISE(__LINE__) \") : \"\n# define WARN(exp) (FILE_LINE_LINK \"WARNING: \" exp)\n\n#ifdef _MSC_VER\n#define PATH_SEPARATOR \"\\\\\"\n#else \n#define PATH_SEPARATOR \"/\"\n#endif\n\n\n#ifdef _MSC_VER\n#define ISNAN _isnan\n#define __func__ __FUNCTION__ \n#else\n#define ISNAN std::isnan\n#endif\n\n#if defined(_MSC_VER)\n#include \ntypedef SSIZE_T ssize_t;\n#endif\n\n#define FIELD_DELIMITER ':'\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\nusing std::stringstream;\nusing std::random_device;\nusing std::mt19937;\n\nusing namespace std;\n\n\nconst char* GetFileName(const char* fullpath);\n\nbool CreateDir(const char* name, int mode = 0777);\n\nbool DoesFileExist(const char *filename);\n\ninline bool DoesFileExist(const string &filename) { return DoesFileExist(filename.c_str()); }\n\ninline int RandomInt() {\n // Static is thread-safe in C++ 11\n static random_device rdev;\n static mt19937 gen(rdev());\n static std::uniform_int_distribution distr(0, std::numeric_limits::max());\n \n return distr(gen); \n}\n\ntemplate \ninline T RandomReal() {\n // Static is thread-safe in C++ 11\n static random_device rdev;\n static mt19937 gen(rdev());\n static std::uniform_real_distribution distr(0, 1);\n\n return distr(gen); \n}\n\nvoid RStrip(char* str);\n\ndouble Mean(const double* array, const unsigned size);\n\ndouble Variance(const double* array, const unsigned size);\n\ndouble Variance(const double* array, const unsigned size, const double mean);\n\ndouble StdDev(const double* array, const unsigned size);\n\n/*\n * A maximum number of random operations (e.g. while searching\n * for a pivot with given properties) before giving up and firing\n * an exception.\n */\n#define MAX_RAND_ITER_BEFORE_GIVE_UP 100000\n\n/*\n * We want to avoid an overflow in the case when the distance is an integer type.\n */\ntemplate \ninline dist_t DistMax() {\n return std::numeric_limits::max() / 2;\n}\n\n/* \n * 1. For floating-point numbers let's consider numbers to be \n * equal if they are within 4 units in the last place (ULPs)\n * Or, if the numbers are smaller than 2*numeric_limits::epsilon()\n * 2. For integral types an approximate equality is the same as an exact equality.\n */\n#define MAX_ULPS 4\n\ntemplate \nbool ApproxEqual(const T& x, const T& y, unsigned maxUlps = MAX_ULPS);\n\ninline double round1(double x) { return round(x*10.0)/10.0; }\ninline double round2(double x) { return round(x*100.0)/100.0; }\ninline double round3(double x) { return round(x*1000.0)/1000.0; }\ninline double round6(double x) { return round(x*1000000.0)/1000000.0; }\n/*\n * This function will only work for strings without spaces and commas\n * TODO(@leo) replace, perhaps, it with a more generic version.\n * In particular, we want to be able to escape both spaces and commas.\n */\ntemplate \ninline bool SplitStr(const std::string& str_, vector& res, const char SplitChar) {\n res.clear();\n\n if (str_.empty()) return true;\n\n std::string str = str_;\n\n for (auto it = str.begin(); it != str.end(); ++it) {\n if (*it == SplitChar) *it = ' ';\n }\n std::stringstream inp(str);\n\n while (!inp.eof()) {\n ElemType token;\n if (!(inp >> token)) {\n return false;\n }\n res.push_back(token);\n }\n\n return true;\n}\n\ntemplate \ninline std::string MergeIntoStr(const std::vector& ve, char MergeChar) {\n std::stringstream res;\n\n for (size_t i = 0; i < ve.size(); ++i) {\n if (i) res << MergeChar;\n res << ve[i];\n }\n\n return res.str();\n}\n\ntemplate \ninline string ConvertToString(const obj_type& o) {\n std::stringstream str;\n str << o;\n return str.str();\n}\n\ntemplate <>\ninline string ConvertToString(const string& o) {\n return o;\n}\n\ntemplate \ninline void ConvertFromString(const string& s, obj_type& o) {\n std::stringstream str(s);\n if (!(str >> o) || !str.eof()) {\n throw runtime_error(\"Cannot convert '\" + s +\n \"' to the type:\" + string(typeid(obj_type).name()));\n }\n}\n\ntemplate <>\ninline void ConvertFromString(const string& s, string& o) {\n o = s;\n}\n\n/*\n * \"fields\" each occupy a single line, they are in the format:\n * fieldName:fieldValue.\n */\n\ntemplate \ninline void ReadField(istream &in, const string& fieldName, FieldType& fieldValue) {\n string s;\n if (!getline(in, s)) throw runtime_error(\"Error reading a field value\");\n if (s.empty()) {\n throw runtime_error(\"Empty field!\");\n }\n string::size_type p = s.find(FIELD_DELIMITER);\n if (string::npos == p)\n throw runtime_error(\"Wrong field format, no delimiter: '\" + s + \"'\");\n string gotFieldName = s.substr(0, p);\n if (gotFieldName != fieldName) {\n throw runtime_error(\"Expected field '\" + fieldName + \"' but got: '\"\n + gotFieldName + \"'\");\n }\n ConvertFromString(s.substr(p + 1), fieldValue);\n}\n\ntemplate \ninline void WriteField(ostream& out, const string& fieldName, const FieldType& fieldValue) {\n if (!(out << fieldName << \":\" << fieldValue << std::endl)) {\n throw\n runtime_error(\"Error writing to an output stream, field name: \" + fieldName);\n }\n}\n\ninline void ToLower(string &s) {\n for (size_t i = 0; i < s.size(); ++i) s[i] = std::tolower(s[i]);\n}\n\ninline bool StartsWith(const std::string& str, const std::string& prefix) {\n return str.length() >= prefix.length() &&\n str.substr(0, prefix.length()) == prefix;\n}\n\ninline bool HasWhiteSpace(const string& s) {\n for (char c: s)\n if (std::isspace(c)) return true;\n return false;\n}\n\n// Don't remove period here\ninline void ReplaceSomePunct(string &s) {\n for (size_t i = 0; i < s.size(); ++i)\n if (s[i] == ',' || s[i] == FIELD_DELIMITER) s[i] = ' ';\n}\n\ntemplate \nstruct AutoVectDel {\n AutoVectDel(typename std::vector& Vector) : mVector(Vector) {}\n ~AutoVectDel() {\n for (auto i: mVector) { delete i; }\n }\n std::vector& mVector;\n};\n\ntemplate \nstruct AutoMapDel {\n AutoMapDel(typename std::map& Map) : mMap(Map) {}\n ~AutoMapDel() {\n for (auto i: mMap) { delete i.second; }\n }\n typename std::map& mMap;\n};\n\ntemplate \nstruct AutoMultiMapDel {\n AutoMultiMapDel(typename std::multimap& Map) : mMap(Map) {}\n ~AutoMultiMapDel() {\n for (auto i: mMap) { delete i.second; }\n }\n typename std::multimap& mMap;\n};\n\n} // namespace similarity\n\n#endif // _UTILS_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/utils.h~", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _UTILS_H_\n#define _UTILS_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"idtype.h\"\n\n// compiler_warning.h\n#define STRINGISE_IMPL(x) #x\n#define STRINGISE(x) STRINGISE_IMPL(x)\n\n/*\n * This solution for generating\n * cross-platform warnings\n * is taken from http://stackoverflow.com/a/1911632/2120401\n * Use: #pragma message WARN(\"My message\")\n *\n * Note: We may need other other definitions for other compilers,\n * but so far it worked for MSVS, GCC, CLang, and Intel.\n */\n# define FILE_LINE_LINK __FILE__ \"(\" STRINGISE(__LINE__) \") : \"\n# define WARN(exp) (FILE_LINE_LINK \"WARNING: \" exp)\n\n#ifdef _MSC_VER\n#define PATH_SEPARATOR \"\\\\\"\n#else \n#define PATH_SEPARATOR \"/\"\n#endif\n\n\n#ifdef _MSC_VER\n#define ISNAN _isnan\n#define __func__ __FUNCTION__ \n#else\n#define ISNAN std::isnan\n#endif\n\n#if defined(_MSC_VER)\n#include \ntypedef SSIZE_T ssize_t;\n#endif\n\n#define FIELD_DELIMITER ':'\n\nnamespace similarity {\n\nusing std::string;\nusing std::vector;\nusing std::stringstream;\nusing std::random_device;\nusing std::mt19937;\n\nusing namespace std;\n\n\nconst char* GetFileName(const char* fullpath);\n\nbool CreateDir(const char* name, int mode = 0777);\n\nbool DoesFileExist(const char *filename);\n\ninline bool DoesFileExist(const string &filename) { return DoesFileExist(filename.c_str()); }\n\ninline int RandomInt() {\n // Static is thread-safe in C++ 11\n static random_device rdev;\n static mt19937 gen(rdev());\n static std::uniform_int_distribution distr(0, std::numeric_limits::max());\n \n return distr(gen); \n}\n\ntemplate \ninline T RandomReal() {\n // Static is thread-safe in C++ 11\n static random_device rdev;\n static mt19937 gen(rdev());\n static std::uniform_real_distribution distr(0, 1);\n\n return distr(gen); \n}\n\nvoid RStrip(char* str);\n\ndouble Mean(const double* array, const unsigned size);\n\ndouble Variance(const double* array, const unsigned size);\n\ndouble Variance(const double* array, const unsigned size, const double mean);\n\ndouble StdDev(const double* array, const unsigned size);\n\n/*\n * A maximum number of random operations (e.g. while searching\n * for a pivot with given properties) before giving up and firing\n * an exception.\n */\n#define MAX_RAND_ITER_BEFORE_GIVE_UP 100000\n\n/*\n * We want to avoid an overflow in the case when the distance is an integer type.\n */\ntemplate \ninline dist_t DistMax() {\n return std::numeric_limits::max() / 2;\n}\n\n/* \n * 1. For floating-point numbers let's consider numbers to be \n * equal if they are within 4 units in the last place (ULPs)\n * Or, if the numbers are smaller than 2*numeric_limits::epsilon()\n * 2. For integral types an approximate equality is the same as an exact equality.\n */\n#define MAX_ULPS 4\n\ntemplate \nbool ApproxEqual(const T& x, const T& y, unsigned maxUlps = MAX_ULPS);\n\ninline double round1(double x) { return round(x*10.0)/10.0; }\ninline double round2(double x) { return round(x*100.0)/100.0; }\ninline double round3(double x) { return round(x*1000.0)/1000.0; }\n\n/*\n * This function will only work for strings without spaces and commas\n * TODO(@leo) replace, perhaps, it with a more generic version.\n * In particular, we want to be able to escape both spaces and commas.\n */\ntemplate \ninline bool SplitStr(const std::string& str_, vector& res, const char SplitChar) {\n res.clear();\n\n if (str_.empty()) return true;\n\n std::string str = str_;\n\n for (auto it = str.begin(); it != str.end(); ++it) {\n if (*it == SplitChar) *it = ' ';\n }\n std::stringstream inp(str);\n\n while (!inp.eof()) {\n ElemType token;\n if (!(inp >> token)) {\n return false;\n }\n res.push_back(token);\n }\n\n return true;\n}\n\ntemplate \ninline std::string MergeIntoStr(const std::vector& ve, char MergeChar) {\n std::stringstream res;\n\n for (size_t i = 0; i < ve.size(); ++i) {\n if (i) res << MergeChar;\n res << ve[i];\n }\n\n return res.str();\n}\n\ntemplate \ninline string ConvertToString(const obj_type& o) {\n std::stringstream str;\n str << o;\n return str.str();\n}\n\ntemplate <>\ninline string ConvertToString(const string& o) {\n return o;\n}\n\ntemplate \ninline void ConvertFromString(const string& s, obj_type& o) {\n std::stringstream str(s);\n if (!(str >> o) || !str.eof()) {\n throw runtime_error(\"Cannot convert '\" + s +\n \"' to the type:\" + string(typeid(obj_type).name()));\n }\n}\n\ntemplate <>\ninline void ConvertFromString(const string& s, string& o) {\n o = s;\n}\n\n/*\n * \"fields\" each occupy a single line, they are in the format:\n * fieldName:fieldValue.\n */\n\ntemplate \ninline void ReadField(istream &in, const string& fieldName, FieldType& fieldValue) {\n string s;\n if (!getline(in, s)) throw runtime_error(\"Error reading a field value\");\n if (s.empty()) {\n throw runtime_error(\"Empty field!\");\n }\n string::size_type p = s.find(FIELD_DELIMITER);\n if (string::npos == p)\n throw runtime_error(\"Wrong field format, no delimiter: '\" + s + \"'\");\n string gotFieldName = s.substr(0, p);\n if (gotFieldName != fieldName) {\n throw runtime_error(\"Expected field '\" + fieldName + \"' but got: '\"\n + gotFieldName + \"'\");\n }\n ConvertFromString(s.substr(p + 1), fieldValue);\n}\n\ntemplate \ninline void WriteField(ostream& out, const string& fieldName, const FieldType& fieldValue) {\n if (!(out << fieldName << \":\" << fieldValue << std::endl)) {\n throw\n runtime_error(\"Error writing to an output stream, field name: \" + fieldName);\n }\n}\n\ninline void ToLower(string &s) {\n for (size_t i = 0; i < s.size(); ++i) s[i] = std::tolower(s[i]);\n}\n\ninline bool StartsWith(const std::string& str, const std::string& prefix) {\n return str.length() >= prefix.length() &&\n str.substr(0, prefix.length()) == prefix;\n}\n\ninline bool HasWhiteSpace(const string& s) {\n for (char c: s)\n if (std::isspace(c)) return true;\n return false;\n}\n\n// Don't remove period here\ninline void ReplaceSomePunct(string &s) {\n for (size_t i = 0; i < s.size(); ++i)\n if (s[i] == ',' || s[i] == FIELD_DELIMITER) s[i] = ' ';\n}\n\ntemplate \nstruct AutoVectDel {\n AutoVectDel(typename std::vector& Vector) : mVector(Vector) {}\n ~AutoVectDel() {\n for (auto i: mVector) { delete i; }\n }\n std::vector& mVector;\n};\n\ntemplate \nstruct AutoMapDel {\n AutoMapDel(typename std::map& Map) : mMap(Map) {}\n ~AutoMapDel() {\n for (auto i: mMap) { delete i.second; }\n }\n typename std::map& mMap;\n};\n\ntemplate \nstruct AutoMultiMapDel {\n AutoMultiMapDel(typename std::multimap& Map) : mMap(Map) {}\n ~AutoMultiMapDel() {\n for (auto i: mMap) { delete i.second; }\n }\n typename std::multimap& mMap;\n};\n\n} // namespace similarity\n\n#endif // _UTILS_H_\n\n" }, { "path": "algorithms/NMSLIB/code/include/ztimer.h", "content": "/**\n* Non-metric Space Library\n*\n* Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n* With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n*\n* For the complete list of contributors and further details see:\n* https://github.com/searchivarius/NonMetricSpaceLib\n*\n* Copyright (c) 2014\n*\n* This code is released under the\n* Apache License Version 2.0 http://www.apache.org/licenses/.\n*\n*/\n\n#ifndef _CHRONO_TIMER_H_\n#define _CHRONO_TIMER_H_\n\n#include \n#include \n#include \n\nusing namespace std::chrono;\nusing std::round;\n\nclass WallClockTimer {\npublic:\n high_resolution_clock::time_point t1, t2;\n\n WallClockTimer() {\n reset();\n }\n\n void reset() {\n t1 = high_resolution_clock::now();\n t2 = t1;\n }\n\n uint64_t elapsed() {\n duration elapsed_seconds = t2 - t1;\n return static_cast(round(1e6 * elapsed_seconds.count()));\n }\n\n uint64_t split() {\n t2 = high_resolution_clock::now();\n return elapsed();\n }\n};\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/launches/sample.launch", "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/CMakeFiles/CMakeDirectoryInformation.cmake", "content": "# CMAKE generated file: DO NOT EDIT!\n# Generated by \"Unix Makefiles\" Generator, CMake Version 3.2\n\n# Relative path conversion top directories.\nset(CMAKE_RELATIVE_PATH_TOP_SOURCE \"/home/yzhang4/HD/release/algorithms/NMSLIB/code\")\nset(CMAKE_RELATIVE_PATH_TOP_BINARY \"/home/yzhang4/HD/release/algorithms/NMSLIB/code\")\n\n# Force unix paths in dependencies.\nset(CMAKE_FORCE_UNIX_PATHS 1)\n\n\n# The C and CXX include file regular expressions for this directory.\nset(CMAKE_C_INCLUDE_REGEX_SCAN \"^.*$\")\nset(CMAKE_C_INCLUDE_REGEX_COMPLAIN \"^$\")\nset(CMAKE_CXX_INCLUDE_REGEX_SCAN ${CMAKE_C_INCLUDE_REGEX_SCAN})\nset(CMAKE_CXX_INCLUDE_REGEX_COMPLAIN ${CMAKE_C_INCLUDE_REGEX_COMPLAIN})\n" }, { "path": "algorithms/NMSLIB/code/lshkit/CMakeFiles/lshkit.dir/CXX.includecache", "content": "#IncludeRegexLine: ^[ \t]*#[ \t]*(include|import)[ \t]*[<\"]([^\">]+)([\">])\n\n#IncludeRegexScan: ^.*$\n\n#IncludeRegexComplain: ^$\n\n#IncludeRegexTransform: 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provided)\"\n\t@echo \"... clean\"\n\t@echo \"... depend\"\n\t@echo \"... edit_cache\"\n\t@echo \"... rebuild_cache\"\n\t@echo \"... install\"\n\t@echo \"... list_install_components\"\n\t@echo \"... lshkit\"\n\t@echo \"... install/local\"\n\t@echo \"... src/apost.o\"\n\t@echo \"... src/apost.i\"\n\t@echo \"... src/apost.s\"\n\t@echo \"... src/char_bit_cnt.o\"\n\t@echo \"... src/char_bit_cnt.i\"\n\t@echo \"... src/char_bit_cnt.s\"\n\t@echo \"... src/kdtree.o\"\n\t@echo \"... src/kdtree.i\"\n\t@echo \"... src/kdtree.s\"\n\t@echo \"... src/mplsh-model.o\"\n\t@echo \"... src/mplsh-model.i\"\n\t@echo \"... src/mplsh-model.s\"\n\t@echo \"... src/mplsh.o\"\n\t@echo \"... src/mplsh.i\"\n\t@echo \"... src/mplsh.s\"\n\t@echo \"... src/vq.o\"\n\t@echo \"... src/vq.i\"\n\t@echo \"... src/vq.s\"\n.PHONY : help\n\n\n\n#=============================================================================\n# Special targets to cleanup operation of make.\n\n# Special rule to run CMake to check the build system integrity.\n# No rule that depends on this can have commands that come from listfiles\n# because they might be regenerated.\ncmake_check_build_system:\n\tcd /home/yzhang4/HD/release/algorithms/NMSLIB/code && $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0\n.PHONY : cmake_check_build_system\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/cmake_install.cmake", "content": "# Install script for directory: /home/yzhang4/HD/release/algorithms/NMSLIB/code/lshkit\n\n# Set the install prefix\nif(NOT DEFINED CMAKE_INSTALL_PREFIX)\n set(CMAKE_INSTALL_PREFIX \"/usr/local\")\nendif()\nstring(REGEX REPLACE \"/$\" \"\" CMAKE_INSTALL_PREFIX \"${CMAKE_INSTALL_PREFIX}\")\n\n# Set the install configuration name.\nif(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)\n if(BUILD_TYPE)\n string(REGEX REPLACE \"^[^A-Za-z0-9_]+\" \"\"\n CMAKE_INSTALL_CONFIG_NAME \"${BUILD_TYPE}\")\n else()\n set(CMAKE_INSTALL_CONFIG_NAME \"Release\")\n endif()\n message(STATUS \"Install configuration: \\\"${CMAKE_INSTALL_CONFIG_NAME}\\\"\")\nendif()\n\n# Set the component getting installed.\nif(NOT CMAKE_INSTALL_COMPONENT)\n if(COMPONENT)\n message(STATUS \"Install component: \\\"${COMPONENT}\\\"\")\n set(CMAKE_INSTALL_COMPONENT \"${COMPONENT}\")\n else()\n set(CMAKE_INSTALL_COMPONENT)\n endif()\nendif()\n\n# Install shared libraries without execute permission?\nif(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)\n set(CMAKE_INSTALL_SO_NO_EXE \"0\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/lib\" TYPE STATIC_LIBRARY FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/release/liblshkit.a\")\nendif()\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/apost.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file apost.h\n * \\brief A Posteriori Multi-Probe LSH indexing.\n *\n * Reference:\n *\n * Alexis Joly, Olivier Buisson. A posteriori multi-probe locality sensitive\n * hashing. In Proceedings of the 16th ACM International Conference on\n * Multimedia. Vancouver, Canada. October 2008.\n *\n * \n */\n\n#ifndef __LSHKIT_APOST__\n#define __LSHKIT_APOST__\n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace lshkit\n{\n\n\nstruct APostLsh\n{\n unsigned dim;\n unsigned M;\n float W;\n unsigned H;\n std::vector > a;\n std::vector b;\n std::vector c;\n\n std::vector umin;\n std::vector umax;\n\n typedef const float *Domain;\n\n /**\n * Parameter to MPLSH. \n */\n struct Parameter {\n\n unsigned dim;\n unsigned repeat;\n unsigned range;\n float W;\n };\n\n APostLsh () {}\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n dim = param.dim;\n M = param.repeat;\n W = param.W;\n H = param.range;\n\n boost::variate_generator gaussian(rng, Gaussian());\n boost::variate_generator uniform(rng, Uniform(0,W));\n\n a.resize(M);\n b.resize(M);\n c.resize(M);\n umin.resize(M);\n umax.resize(M);\n\n for (unsigned i = 0; i < M; i++) {\n a[i].resize(dim);\n umin[i] = std::numeric_limits::max();\n umax[i] = std::numeric_limits::min();\n for (unsigned j = 0; j < dim; j++) {\n a[i][j] = gaussian();\n }\n b[i] = uniform();\n c[i] = rng();\n }\n }\n\n template \n APostLsh(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n unsigned getRange () const\n {\n return H;\n }\n\n float apply1 (Domain obj, unsigned m) const {\n float r = b[m];\n for (unsigned j = 0; j < dim; ++j)\n {\n r += a[m][j] * obj[j];\n }\n r /= W;\n return r;\n }\n\n void apply1 (Domain obj, std::vector *h) const {\n for (unsigned i = 0; i < M; ++i) {\n h->at(i) = apply1(obj, i);\n }\n }\n\n unsigned operator () (Domain obj)\n {\n unsigned r2 = 0;\n for (unsigned i = 0; i < M; ++i)\n {\n float r1 = apply1(obj, i);\n if (r1 < umin[i]) umin[i] = r1;\n else if (r1 > umax[i]) umax[i] = r1;\n r2 += c[i] * unsigned(int(std::floor(r1)));\n }\n return r2 % H;\n }\n\n unsigned apply_const (Domain obj) const\n {\n unsigned r2 = 0;\n for (unsigned i = 0; i < M; ++i)\n {\n float r1 = apply1(obj, i);\n r2 += c[i] * unsigned(int(std::floor(r1)));\n }\n return r2 % H;\n }\n\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & dim;\n ar & M;\n ar & W;\n ar & H;\n ar & a;\n ar & b;\n ar & c;\n ar & umin;\n ar & umax;\n }\n};\n\nstruct APostExample {\n const float *query;\n std::vector results;\n};\n\nstruct PrH { // Pr[h] of certain compnent\n int h;\n float pr;\n template\n\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & h;\n ar & pr;\n }\n\n friend bool operator < (const PrH &p1, const PrH &p2) {\n // we need descending order\n return p1.pr > p2.pr;\n }\n};\n\nclass APostModel\n{\n unsigned Nz;\n float ex;\n // lookup[M][h_query][h]\n std::vector > > lookup;\n std::vector > means;\n std::vector > stds;\n std::vector umin;\n std::vector umax;\npublic:\n APostModel () {\n };\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & Nz;\n ar & ex;\n ar & lookup;\n ar & means;\n ar & stds;\n ar & umin;\n ar & umax;\n }\n\n void train (const APostLsh &lsh, const std::vector &examples,\n unsigned N, float k_sigma = 1.0/5, float expand = 0.1);\n\n void genProbeSequence (const APostLsh &lsh, const float *query,\n float recall, unsigned T, std::vector *probe) const;\n};\n\n\n/// Multi-Probe LSH index.\ntemplate \nclass APostLshIndex: public LshIndex\n{\npublic:\n typedef LshIndex Super;\n /**\n * Super::Parameter is the same as MultiProbeLsh::Parameter\n */\n typedef typename Super::Parameter Parameter;\n\n typedef typename Super::Domain Domain;\n typedef KEY Key;\n\nprivate:\n\n std::vector model;\n\n /// Query for K-NNs.\n /**\n * @param obj the query object.\n * @param scanner \n */\n template \n void query_helper (Domain obj, float recall, unsigned T, SCANNER &scanner) const\n {\n std::vector seq;\n BOOST_VERIFY(recall <= 1.0);\n recall = 1.0 - exp(1.0/Super::lshs_.size() * log(1.0 - recall));\n for (unsigned i = 0; i < Super::lshs_.size(); ++i) {\n model[i].genProbeSequence(Super::lshs_[i], obj, recall, T, &seq);\n BOOST_FOREACH(unsigned j, seq) {\n typename Super::Bin const &bin = Super::tables_[i][j];\n BOOST_FOREACH(Key key, bin) {\n scanner(key);\n }\n }\n }\n }\n\npublic:\n\n /// Constructor.\n APostLshIndex() {\n } \n\n /// Initialize MPLSH.\n /**\n * @param param parameters.\n * @param engine random number generator (if you are not sure about what to\n * use, then pass DefaultRng.\n * @param accessor object accessor (same as in LshIndex).\n * @param L number of hash tables maintained.\n */\n template \n void init (const Parameter ¶m, Engine &engine, unsigned L) {\n Super::init(param, engine, L);\n model.resize(L);\n // we are going to normalize the distance by window size, so here we pass W = 1.0.\n // We tune adaptive probing for KNN distance range [0.0001W, 20W].\n }\n\n void train (const std::vector &examples, unsigned Nz, float k_sigma, float expand) {\n for (unsigned i = 0; i < model.size(); ++i) {\n model[i].train(Super::lshs_[i], examples, Nz, k_sigma, expand);\n }\n }\n\n /// Load the index from stream.\n void load (std::istream &ar) \n {\n Super::load(ar);\n ar & model;\n BOOST_VERIFY(ar);\n }\n\n /// Save to the index to stream.\n void save (std::ostream &ar)\n {\n Super::save(ar);\n ar & model;\n BOOST_VERIFY(ar);\n }\n\n /// Query for K-NNs.\n /**\n * @param obj the query object.\n * @param scanner \n */\n template \n void query (Domain obj, unsigned T, SCANNER &scanner) const\n {\n query_helper(obj, 1.0, T, scanner);\n }\n\n /// Query for K-NNs, try to achieve the given recall by adaptive probing.\n /**\n * There's a special requirement for the scanner type used in adaptive query.\n * It should support the following method to return the current K-NNs:\n *\n * const Topk &topk () const;\n */\n template \n void query_recall (Domain obj, float recall, SCANNER &scanner) const\n {\n query_helper(obj, recall, std::numeric_limits::max(), scanner);\n }\n};\n\n}\n\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/archive.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_ARCHIVE__\n#define __LSHKIT_ARCHIVE__\n\n#include \n#include \n\n/**\n * \\file archive.h\n * \\brief A set of binary I/O routines.\n *\n * The LSH library was originally designed to use boost serialization,\n * but it turned out that boost serialization is too generalized and too slow for us.\n * So we wrote our own minimal replacement.\n *\n * You are not expected to use these routines unless you are implementing your own\n * LSH class.\n * \n * Given a variable V and a stream S, \"S & V\" will output V to S if S is derived from\n * std::ostream, or load V from S if S is derived from std::istream. The\n * operator is defined for the following types:\n * - classes where a \"serialize\" method is defined (required by\n * the LSH concept);\n * - std::vector of such classes;\n * - unsigned, float, std::vector, std::vector.\n *\n * Note that binary I/O is architecture dependent and not portable.\n *\n*/\n\nnamespace lshkit {\n\nstatic inline std::ostream &operator & (std::ostream &os, int i) {\n return os.write((const char *)&i, sizeof(i));\n}\n\nstatic inline std::ostream &operator & (std::ostream &os, unsigned i) {\n return os.write((const char *)&i, sizeof(i));\n}\n\nstatic inline std::ostream &operator & (std::ostream &os, float i) {\n return os.write((const char *)&i, sizeof(i));\n}\n\nstatic inline std::istream &operator & (std::istream &is, int &i) {\n return is.read((char *)&i, sizeof(i));\n}\n\nstatic inline std::istream &operator & (std::istream &is, unsigned &i) {\n return is.read((char *)&i, sizeof(i));\n}\n\nstatic inline std::istream &operator & (std::istream &is, float &i) {\n return is.read((char *)&i, sizeof(i));\n}\n\nstatic inline std::ostream &operator & (std::ostream &os, std::vector &v) {\n unsigned L = v.size();\n os & L;\n os.write((const char *)&v[0], v.size() * sizeof(float));\n return os;\n}\n\nstatic inline std::istream &operator & (std::istream &is, std::vector &v) {\n unsigned L;\n is & L;\n v.resize(L);\n is.read((char *)&v[0], v.size() * sizeof(float));\n return is;\n}\n\nstatic inline std::ostream &operator & (std::ostream &os, std::vector &v) {\n unsigned L = v.size();\n os & L;\n os.write((const char *)&v[0], v.size() * sizeof(unsigned));\n return os;\n}\n\nstatic inline std::istream &operator & (std::istream &is, std::vector &v) {\n unsigned L;\n is & L;\n v.resize(L);\n is.read((char *)&v[0], v.size() * sizeof(unsigned));\n return is;\n}\n\ntemplate \nstd::ostream &operator & (std::ostream &s, D &t) {\n t.serialize(s, 0);\n return s;\n}\n\ntemplate \nstd::istream &operator & (std::istream &s, D &t) {\n t.serialize(s, 0);\n return s;\n}\n\ntemplate \nstd::ostream &operator & (std::ostream &os, std::vector &v) {\n unsigned L = v.size();\n os & L;\n for (unsigned i = 0; i < L; ++i) {\n os & v[i];\n }\n return os;\n}\n\ntemplate \nstd::istream &operator & (std::istream &is, std::vector &v) {\n unsigned L;\n is & L;\n v.resize(L);\n for (unsigned i = 0; i < L; ++i) {\n is & v[i];\n }\n return is;\n}\n\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/common.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_COMMON__\n#define __LSHKIT_COMMON__\n\n/**\n * \\file common.h\n * \\brief Common type and function definitions used by all components.\n */\n\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n/// Enable concept checking.\n/** Concept checking requires boost > 1.35.\n * Remove this definition to disable concept checking.\n */\n#define CONCEPT_CHECK 0\n\n#ifdef CONCEPT_CHECK\n#include \n#else\n#define BOOST_CONCEPT_ASSERT(A)\n#endif\n\nnamespace lshkit {\n\n/// The default random number generator.\ntypedef boost::mt19937 DefaultRng;\n\n// Some of the frequently used distributions.\n\n/// Gaussian distribution.\ntypedef boost::normal_distribution Gaussian;\n/// Cauchy distribution.\ntypedef boost::cauchy_distribution Cauchy;\n/// Uniform distribution.\ntypedef boost::uniform_real Uniform;\n/// Uniform distribution with int values.\ntypedef boost::uniform_int UniformInt;\n/// Uniform distribution with unsigned values.\ntypedef boost::uniform_int UniformUnsigned;\n\n/// Take the minimum of two values.\n/* We define this so that doesn't have to be included solely for\n this simple function.\n */\ntemplate \nT min (T a, T b) { return a < b ? a : b; }\n\n/// Take the maximum of two values.\ntemplate \nT max (T a, T b) { return a < b ? b : a; }\n\n/// Square function.\ntemplate T sqr (const T &x) { return x * x; }\n\nvoid panic_intern(const char *fmt, ...);\n\n}\n\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/composite.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_COMPOSITE__\n#define __LSHKIT_COMPOSITE__\n\n/**\n * \\file composite.h\n * \\brief Defining a bunch of LSH compositions.\n *\n * LSH composition is to use an existing LSH class as building\n * block to generate a new class of LSH.\n *\n * All template classes in this header are defined in a very similar fashion:\n *\n * \\code\n * template // base LSH is given as a template parameter\n * class SomeDerivedLSH\n * {\n * public:\n * typedef LSH Super; // the base LSH is defined to be Super.\n * typedef ... Domain;\n *\n * struct Parameter: public Super::Parameter {\n * ... // all parameters to the base LSH are inherited.\n * };\n * ...\n * };\n * \\endcode\n *\n * \n */\n\nnamespace lshkit {\n\n/// The modulo operation on hash values.\n/**\n * The mod of an LSH function by some value N is usually still locality\n * sensitive. This can be used to limit the hash value of certain LSH,\n * so that the hash value can be used to index a fixed-sized hash table.\n *\n * Let LSH be the original class, and N be the divisor, then the parameter type\n * is defined as\n * \\code\n * struct Parameter {\n * unsigned range; // the divisor.\n * ... // the original parameters are inherited.\n * };\n * \\endcode\n * The domain is the same as the original LSH and the range is always N.\n *\n */\ntemplate \nclass Tail\n{\n BOOST_CONCEPT_ASSERT((LshConcept));\nprotected:\n LSH lsh_;\n unsigned range_;\npublic:\n /// The base LSH class.\n typedef LSH Super;\n /**\n * Parameter to Tail.\n */\n struct Parameter: public Super::Parameter {\n /// Desired range of the LSH.\n unsigned range;\n\n Parameter() : Super::Parameter(), range(0) {}\n };\n\n typedef typename Super::Domain Domain;\n\n Tail() {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng) {\n range_ = param.range;\n lsh_.reset(param, rng);\n }\n\n template \n Tail(const Parameter ¶m, RNG &rng) {\n reset(param, rng);\n }\n\n unsigned getRange () const {\n return range_;\n }\n\n unsigned operator () (Domain obj) const {\n return lsh_(obj) % range_;\n }\n\n template\n void serialize(Archive & ar, const unsigned int version) {\n ar & lsh_;\n ar & range_;\n }\n};\n\n/// The modulo operation with fixed divisor.\n/**\n * This is the same as Tail, except the divisor is determined at compile time\n * and passed in as a template parameter. Both the Domain and Parameter of the\n * original LSH are kept the same.\n */\ntemplate \nclass FixedTail\n{\n BOOST_CONCEPT_ASSERT((LshConcept));\nprotected:\n LSH lsh_;\npublic:\n typedef LSH Super;\n\n /**\n * Parameter is exactly the same as the base class.\n */\n struct Parameter: public Super::Parameter {\n };\n\n typedef typename Super::Domain Domain;\n\n FixedTail()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n lsh_.reset(param, rng);\n }\n\n template \n FixedTail(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n unsigned getRange () const\n {\n return range;\n }\n\n unsigned operator () (Domain obj) const\n {\n return lsh_(obj) % range;\n }\n\n const LSH &getLsh () const\n {\n return lsh_;\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & lsh_;\n }\n};\n\n/// Take the LSB of the hash value.\n/**\n * This is a special case of FixedTail, with the divisor being 2, with the effect\n * of taking only the least significant bit of the hash value. This is mainly used\n * to generate sketches.\n */\ntemplate \nclass LSB: public FixedTail\n{\npublic:\n typedef FixedTail Super;\n /**\n * Parameter is exactly the same as the base class.\n */\n struct Parameter: public Super::Parameter {\n };\n\n typedef typename Super::Domain Domain;\n\n LSB () {}\n\n template \n LSB(const Parameter ¶m, RNG &rng)\n : FixedTail(param, rng)\n {\n }\n};\n\n/// The DeltaLSH version of LSB..\n/**\n * The original LSH must be a DeltaLSH.\n */\ntemplate \nclass DeltaLSB: public FixedTail\n{\n BOOST_CONCEPT_ASSERT((DeltaLshConcept));\n\npublic:\n typedef FixedTail Super;\n /**\n * Parameter is exactly the same as the base class.\n */\n struct Parameter: public Super::Parameter {\n };\n\n typedef typename Super::Domain Domain;\n\n DeltaLSB() {}\n\n template \n DeltaLSB(const Parameter ¶m, RNG &rng)\n : Super(param, rng)\n {\n }\n\n unsigned operator () (Domain obj) const\n {\n return Super::operator () (obj);\n }\n\n unsigned operator () (Domain obj, float *delta) const\n {\n float d;\n unsigned r = Super::getLsh()(obj, &d);\n *delta = min(d, 1.0F - d);\n return r % Super::getRange();\n }\n};\n\n/// Concatenation of a number of LSHes.\n/**\n * The concatenation of a number of LSHes of the same class is usually used as a\n * new LSH to augment the locality sensitivity. The Repeat class is to\n * concatenate N independent LSH instances.\n *\n * The domain of LSH remains the same. The new parameter is \n * defined as:\n * \\code\n * struct Parameter {\n * unsigned repeat; // # of LSHes to concatenate.\n * ... // all parameters of the base LSH are inherited.\n * };\n * \\endcode\n *\n * Because the hash value is represented as unsigned int, which has only 32\n * bits, the range of the original LSH need to be small enough so that the\n * concatenated value does not overflow. Specifically, we require that\n * \\f[\n * LSH::getRange()^N <= 2^{32}.\n * \\f]\n * We also require that the range of the base LSH only depends on the\n * parameter, so an array of such LSHes initialized with the same parameter but\n * independent random numbers have the same range.\n */\ntemplate \nclass Repeat\n{\n BOOST_CONCEPT_ASSERT((LshConcept));\n\npublic:\n typedef LSH Super;\n /**\n * Parameter to Repeat.\n */\n struct Parameter: public Super::Parameter {\n /// The number of instances of the base LSH concatenated.\n unsigned repeat;\n\n Parameter() : Super::Parameter(), repeat(0) {}\n };\n\n typedef typename Super::Domain Domain;\n\n Repeat ()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n dup_ = param.repeat;\n assert(dup_ > 0);\n lsh_.resize(dup_);\n lsh_[0].reset(param, rng);\n range_ = unit_ = lsh_[0].getRange();\n assert(unit_ > 0);\n assert( (unsigned)(1 << (sizeof(unsigned) * 8 / dup_)) >= unit_);\n for (unsigned i = 1; i < dup_; ++i)\n {\n lsh_[i].reset(param, rng);\n assert(unit_ == lsh_[i].getRange());\n range_ *= unit_;\n }\n }\n\n template \n Repeat(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n unsigned getRange () const\n {\n return range_;\n }\n\n unsigned operator () (Domain obj) const\n {\n unsigned ret = 0;\n for (unsigned i = 0; i < dup_; ++i)\n {\n ret *= unit_;\n ret += lsh_[i](obj);\n }\n return ret;\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & dup_;\n ar & range_;\n ar & unit_;\n ar & lsh_;\n }\n\nprotected:\n std::vector lsh_;\n unsigned dup_;\n unsigned range_;\n unsigned unit_;\n\n};\n\n\n/// Apply a random hash to the concatenation a number of hash values.\n/**\n * This composition is to workaround the case where the range of individual\n * LSHes are so large that the concatenation can not be held in a single\n * unsgined int. The method is to further hash the concatenated value.\n * Specifically, if are the original values, this composition\n * produces (a1*h1 + a2*h2 + aN*hN), with a1~aN being random unsigned integers.\n * The range of the produced LSH is 0 (the whole range of unsigned).\n *\n * The domain of the LSH remains the same. The new parameter is \n * defined as:\n * \\code\n * struct Parameter {\n * unsigned repeat; // # of LSHes to concatenate\n * ... // all parameters of the base LSH are inherited.\n * };\n * \\endcode\n */\ntemplate \nclass RepeatHash\n{\n BOOST_CONCEPT_ASSERT((LshConcept));\n\npublic:\n typedef LSH Super;\n /**\n * Parameter to Repeat.\n */\n struct Parameter: public Super::Parameter {\n /// The number of instances of the base LSH concatenated.\n unsigned repeat;\n\n Parameter() : Super::Parameter(), repeat(0) {}\n };\n\n typedef typename LSH::Domain Domain;\n\n RepeatHash ()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n assert(param.repeat > 0);\n lsh_.resize(param.repeat);\n for (unsigned i = 0; i < param.repeat; ++i)\n {\n lsh_[i].reset(param, rng);\n }\n a_.resize(param.repeat);\n\n for (unsigned i = 0; i < param.repeat; ++i) a_[i] = rng();\n }\n\n template \n RepeatHash(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n unsigned getRange () const\n {\n return 0;\n }\n\n unsigned operator () (Domain obj) const\n {\n unsigned ret = 0;\n for (unsigned i = 0; i < lsh_.size(); ++i)\n {\n ret += lsh_[i](obj) * a_[i];\n }\n return ret;\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & lsh_;\n ar & a_;\n assert(a_.size() == lsh_.size());\n }\nprotected:\n std::vector lsh_;\n std::vector a_;\n\n};\n\n\n/// XOR a number of 1-bit LSHes.\n/*\n * The XOR of a number of 1-bit LSHes has higher locality sensitivity than\n * the original LSH. This serves a similar purpose as RepeatHash.\n *\n * The new parameter is defined as:\n * \\code\n * struct Parameter {\n * unsigned xor_; // # of LSHes to XOR\n * ... // all parameters of the base LSH are inherited.\n * };\n * \\endcode\n */\ntemplate \nclass Xor\n{\n BOOST_CONCEPT_ASSERT((LshConcept));\npublic:\n\n typedef LSH Super;\n /**\n * Parameter to Xor\n */\n struct Parameter: public Super::Parameter {\n unsigned xor_; // xor is a keyword\n\n Parameter() : Super::Parameter(), xor_(0) {}\n };\n\n typedef typename Super::Domain Domain;\n\n Xor ()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n lsh_.resize(param.xor_);\n for (unsigned i = 0; i < lsh_.size(); ++i)\n {\n lsh_[i].reset(param, rng);\n assert(lsh_[i].getRange() == 2);\n }\n }\n\n template \n Xor(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n unsigned getRange () const\n {\n return 2;\n }\n\n unsigned operator () (Domain obj) const\n {\n unsigned ret = 0;\n for (unsigned i = 0; i < lsh_.size(); ++i)\n {\n ret = ret ^ lsh_[i](obj);\n }\n return ret;\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & lsh_;\n }\nprotected:\n std::vector lsh_;\n};\n\n/// DeltaLSH version of XOR.\n/*\n * This is essentially the same as XOR. The delta of XOR is the\n * minimum of the deltas of all the original LSHes.\n */\ntemplate \nclass DeltaXor: public Xor\n{\npublic:\n typedef Xor Super;\n /**\n * Parameter to DeltaXor is exactly the same as Xor.\n */\n struct Parameter: public Super::Parameter {\n };\n\n typedef typename Super::Domain Domain;\n\n DeltaXor ()\n {\n }\n\n template \n DeltaXor(const Parameter ¶m, RNG &rng): Xor(param, rng)\n {\n }\n\n unsigned operator () (Domain obj) const {\n return Super::operator () (obj);\n }\n\n unsigned operator () (Domain obj, float *delta) const\n {\n unsigned ret = 0;\n float m = std::numeric_limits::max(), d;\n for (unsigned i = 0; i < Xor::lsh_.size(); ++i)\n {\n ret = ret ^ Xor::lsh_[i](obj, &d);\n m = min(m, d);\n }\n *delta = m;\n return ret;\n }\n};\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/concept.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file concept.h\n * \\brief Check the LSH concept.\n *\n * An LSH class should define the following items to be used in the \n * LSHKIT framework:\n *\n * - The parameter type:\n * \\code\n * typedef ... Parameter;\n * \\endcode\n * - The domain type (type that the LSH applies on):\n * \\code\n * typedef ... Domain;\n * \\endcode\n * - Default constructor:\n * \\code\n * LSH()\n * \\endcode\n * - Initialization method (same as LSH() immediately followed by reset()):\n * \\code\n * void reset(const Parameter &, RNG &);\n * \\endcode\n * The reset() function and on of the constructors take a random number\n * generator. This random number generator should not be further used after reset() or the constructor has returned.\n * - Initializing constructor:\n * \\code\n * LSH(const Parameter &, RNG &); // equivalent to LSH() followed by reset()\n * \\endcode\n * - A method that returns the range of hash value:\n * \\code\n * unsigned getRange () const;\n * \\endcode\n * If getRange() returns 0, then the hash value (returned by\n * operator()) can be anything. Otherwise, it can be from 0 to getRange() - 1.\n * - An operator to apply the hash function:\n * \\code\n * unsigned operator () (const Domain) const;\n * \\endcode\n * - A serialization method following Boost serialization interface.\n * \\code\n * template\n * void serialize(Archive & ar, const unsigned int version)\n * \\endcode\n *\n * Some of the LSH functions are created by rouding a real number to an integer,\n * and the part rounded off (delta) usually carries useful information.\n * Such LSH functions are modeled by the DeltaLSH concept,\n * which should implement the following extra method:\n *\n * - An operator to apply the LSH and also return the delta.\n * \\code\n * unsigned operator () const (Domain, float *delta); \n * \\endcode\n*/\n\n#ifndef __LSHKIT_CONCEPT__\n#define __LSHKIT_CONCEPT__\n\n#include \n#include \n\nnamespace lshkit {\n\n/// LSH concept checker.\ntemplate \nstruct LshConcept\n{\n typedef typename LSH::Domain Domain;\n typedef typename LSH::Parameter Parameter;\n\n BOOST_CONCEPT_USAGE(LshConcept)\n {\n LSH lsh1(param, rng1);\n LSH lsh2(param, rng2);\n LSH lsh3;\n lsh.reset(param, rng1);\n lsh.reset(param, rng2);\n same_type(lsh.getRange(), u);\n same_type(lsh(object), u);\n // current we do not check serialization.\n }\nprotected:\n boost::mt11213b rng1;\n boost::mt19937 rng2;\n LSH lsh;\n Domain object;\n const Parameter param;\n\n unsigned u;\n template \n void same_type(T const &, T const&);\n};\n\n/// DeltaLSH concept checker.\ntemplate \nstruct DeltaLshConcept: public LshConcept\n{\n typedef LshConcept Super;\n BOOST_CONCEPT_USAGE(DeltaLshConcept)\n {\n float delta;\n Super::same_type(Super::lsh(Super::object, &delta), Super::u);\n }\n};\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/eval.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_EVAL__\n#define __LSHKIT_EVAL__\n\n/**\n * \\file eval.h\n * \\brief A set of classes for evaluation.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\nnamespace lshkit {\n\n/// Generate non-duplicate random numbers in a given range as query IDs.\n/**\n * @param qry the vector to hold the generated IDs.\n * @param max The maximum ID(excluded).\n * @param rng Random number generator, use a variable of type DefaultRng.\n *\n */\ntemplate \nvoid SampleQueries (std::vector *qry, unsigned max, RNG\n &rng)\n{\n boost::variate_generator gen(rng, UniformUnsigned(0,\n max-1));\n for (unsigned i = 0; i < qry->size(); ++i)\n {\n for (;;)\n {\n qry->at(i) = gen();\n unsigned j;\n for (j = 0; j < i; j++) if (qry->at(i) == qry->at(j)) break;\n if (j >= i) break;\n }\n }\n}\n\n\n/// Access a benchmark file.\n/**\n * We assume the feature vectors in the benchmark database are\n * numbered from 0 to N. We sample Q queries as test examples\n * and run K-NN search against the database with linear scan. The results are\n * saved in a benchmark file for evaluation purpose. \n * A benchmark file is made up of Q lines, each line represents a\n * test query and is of the following format:\n *\n * <1st NN's ID> <2nd NN's ID> ... \n *\n * For all queries in the benchmark file, the K value should be the same.\n *\n * Because the query points are also sampled from the database, they should be excluded from\n * scanning when running this particular query.\n */\ntemplate \nclass Benchmark\n{\n unsigned Q_;\n\n std::vector queries_;\n std::vector > topks_;\npublic:\n Benchmark(): Q_(0) {}\n\n void resize(unsigned Q, unsigned K = 0)\n {\n Q_ = Q;\n queries_.resize(Q);\n topks_.resize(Q);\n if (K > 0) {\n BOOST_FOREACH(Topk &knn, topks_) {\n if (knn.size() < K) throw std::runtime_error(\"BENCHMARK NOT LARGE ENOUGH\");\n knn.resize(K);\n }\n }\n }\n\n // Random initialization\n void init(unsigned Q, unsigned maxID, unsigned seed = 0) {\n Q_ = Q;\n queries_.resize(Q);\n topks_.resize(Q);\n\n DefaultRng rng;\n if (seed != 0) rng.seed(seed);\n SampleQueries(&queries_, maxID, rng);\n }\n\n ~Benchmark() {}\n\n void load (std::istream &is)\n {\n std::string line;\n queries_.clear();\n topks_.clear();\n for (;;) {\n unsigned q, k;\n is >> q;\n if (!is) break;\n queries_.push_back(q);\n is >> k;\n topks_.push_back(Topk());\n topks_.back().resize(k);\n for (unsigned j = 0; j < k; j++) {\n is >> topks_.back()[j].key;\n is >> topks_.back()[j].dist;\n }\n }\n Q_ = queries_.size();\n }\n\n void save (std::ostream &os) const\n {\n for (unsigned i = 0; i < Q_; i++) {\n os << queries_[i] << '\\t' << topks_[i].size() << '\\t';\n for (unsigned j = 0; j < topks_[i].size(); j++) {\n os << '\\t' << topks_[i][j].key;\n os << '\\t' << topks_[i][j].dist;\n }\n os << std::endl;\n }\n }\n\n void load (const std::string &path)\n {\n std::ifstream is(path.c_str());\n load(is);\n is.close();\n }\n\n void save (const std::string &path) const\n {\n std::ofstream os(path.c_str());\n save(os);\n os.close();\n }\n\n unsigned getQ () const { return Q_; }\n\n /// Get the ID of the nth query.\n unsigned getQuery (unsigned n) const { return queries_[n]; }\n\n /// Get the nearest neighbors of the nth query.\n const Topk &getAnswer (unsigned n) const { return topks_[n]; }\n\n /// Get the KNNs for modification.\n Topk &getAnswer (unsigned n) { return topks_[n]; }\n};\n\n\n/// Basic statistics.\n/**\n * The interface is self-evident.\n * Usage:\n *\n * \\code\n * Stat stat;\n *\n * stat << 1.0 << 2.0 << 3.0;\n *\n * Stat stat2;\n * stat2 << 3.0 << 5.0 << 6.0;\n *\n * stat.merge(stat2);\n *\n * stat.getCount();\n * stat.getSum();\n * stat.getMax();\n * stat.getMin();\n * stat.getStd();\n * \\endcode\n *\n */\nclass Stat\n{\n int count;\n float sum;\n float sum2;\n float min;\n float max;\npublic:\n Stat () : count(0), sum(0), sum2(0), min(std::numeric_limits::max()), max(-std::numeric_limits::max()) {\n }\n\n ~Stat () {\n }\n\n void reset () {\n count = 0;\n sum = sum2 = 0;\n min = std::numeric_limits::max();\n max = -std::numeric_limits::max();\n }\n\n void append (float r)\n {\n count++;\n sum += r;\n sum2 += r*r;\n if (r > max) max = r;\n if (r < min) min = r;\n }\n\n Stat & operator<< (float r) { append(r); return *this; }\n\n int getCount() const { return count; }\n float getSum() const { return sum; }\n float getAvg() const { return sum/count; }\n float getMax() const { return max; }\n float getMin() const { return min; }\n float getStd() const\n {\n if (count > 1) return std::sqrt((sum2 - (sum/count) * sum)/(count - 1)); \n else return 0; \n }\n\n void merge (const Stat &stat)\n {\n count += stat.count;\n sum += stat.sum;\n sum2 += stat.sum2;\n if (stat.min < min) min = stat.min;\n if (stat.max > max) max = stat.max;\n }\n};\n\n/*\nclass Timer\n{\n struct timeval start; \npublic:\n Timer () {}\n /// Start timing.\n void tick ()\n {\n gettimeofday(&start, 0); \n }\n /// Stop timing, return the time passed (in second).\n float tuck (const char *msg) const\n {\n struct timeval end;\n float diff; \n gettimeofday(&end, 0); \n\n diff = (end.tv_sec - start.tv_sec) \n + (end.tv_usec - start.tv_usec) * 0.000001; \n if (msg != 0) {\n std::cout << msg << ':' << diff << std::endl;\n }\n return diff;\n }\n};\n*/\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/forest.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n\n#ifndef __LSHKIT_FOREST__\n#define __LSHKIT_FOREST__\n\n/**\n * \\file forest.h\n * \\brief A preliminary implementation of LSH Forest index.\n *\n * This is a preliminary implementation of main memory LSH Forest index.\n * The implementation is largely based on the WWW'05 LSH Forest paper.\n * The descend and synchascend algorithms are implemented in a \n * different but equivalent way, so the candidate set do not have to\n * be explicitly generated. I also made a simplification to the synchascending\n * algorithm, so that the original line\n \\code\n while (x > 0 and (|P| < cl or |distinct(P)| < m)) {\n \\endcode\n * is simplified to\n \\code\n while (x > 0 and |P| < M) {\n \\endcode\n * the deduplication is left to the scanning process.\n *\n * The implementation is not efficient. The initial goal of this implementation\n * is to study the selectivity of the algorithm. I'll further optimize the\n * implementation if selectivity is proved competitive.\n * \n * Reference \n * \n * Mayank Bawa , Tyson Condie , Prasanna Ganesan, LSH forest: self-tuning\n * indexes for similarity search, Proceedings of the 14th international\n * conference on World Wide Web, May 10-14, 2005, Chiba, Japan.\n * \n */\n\n#include \n#include \n#include \n\nnamespace lshkit {\n\n/// LSH Forest index\n/**\n * @param LSH LSH class.\n * @param KEY key type.\n */\ntemplate \nclass ForestIndex\n{\n BOOST_CONCEPT_ASSERT((LshConcept));\npublic:\n typedef typename LSH::Parameter Parameter;\n typedef typename LSH::Domain Domain;\n typedef KEY Key;\n\nprivate:\n\n struct Tree\n {\n std::vector lsh; // the hash functions\n\n struct Node\n {\n size_t size; // total # points in subtree\n std::vector children;\n std::vector data;\n\n Node () : size(0) {\n }\n\n ~Node () {\n BOOST_FOREACH(Node *n, children) {\n if (n != 0) delete n;\n }\n }\n\n bool empty () const {\n return size == 0;\n }\n\n template \n void insert (Tree *tree, unsigned depth, Key key, ACCESSOR &acc) {\n ++size;\n if (children.empty()) {\n data.push_back(key);\n if (depth < tree->lsh.size() && data.size() > 1) {\n // split\n LSH &lsh = tree->lsh[depth];\n if (lsh.getRange() == 0) throw std::logic_error(\"LSH WITH UNLIMITED HASH VALUE CANNOT BE USED IN LSH FOREST.\");\n children.resize(lsh.getRange());\n BOOST_FOREACH(Key key, data) {\n unsigned h = lsh(acc(key));\n if (children[h] == 0) {\n children[h] = new Node();\n }\n children[h]->insert(tree, depth+1, key, acc);\n }\n data.clear();\n }\n }\n else {\n unsigned h = tree->lsh[depth](acc(key));\n if (children[h] == 0) {\n children[h] = new Node();\n }\n children[h]->insert(tree, depth+1, key, acc);\n }\n }\n\n template \n void scan (Domain val, SCANNER &scanner) const {\n if (!children.empty()) {\n BOOST_FOREACH(const Node *n, children) {\n if (n != 0) {\n n->scan(val, scanner);\n }\n }\n }\n if (!data.empty()) {\n BOOST_FOREACH(Key key, data) {\n scanner(key);\n }\n }\n }\n } *root;\n\n public:\n\n Tree (): root(0) {\n }\n\n template \n void reset (const Parameter ¶m, ENGINE &engine, unsigned depth)\n {\n lsh.resize(depth);\n BOOST_FOREACH(LSH &h, lsh) {\n h.reset(param, engine);\n }\n root = new Node();\n }\n\n ~Tree ()\n {\n if (root != 0) delete root;\n }\n\n template \n void insert (Key key, ACCESSOR &acc)\n {\n root->insert(this, 0, key, acc);\n }\n\n void lookup (Domain val, std::vector *nodes) const {\n const Node *cur = root;\n unsigned depth = 0;\n nodes->clear();\n for (;;) {\n nodes->push_back(cur);\n if (cur->children.empty()) break;\n unsigned h = lsh[depth](val);\n cur = cur->children[h];\n if (cur == 0) break;\n ++depth;\n }\n }\n };\n\n friend struct Tree;\n\n std::vector trees;\n \n\npublic:\n ForestIndex() \n {\n }\n\n /// Initialize the LSH Forest index.\n /**\n * @param param LSH parameters.\n * @param engine random number generator.\n * @param L number of trees in the forest.\n * @param depth maximal depth of the forest.\n */\n template \n void init(const Parameter ¶m, Engine &engine, unsigned L, unsigned depth) \n {\n trees.resize(L);\n BOOST_FOREACH(Tree &t, trees) {\n t.reset(param, engine, depth);\n }\n }\n\n /// Insert a point to the forest.\n /**\n * @param key The key to be inserted.\n * @param acc The accessor to retrieve the data corresponding to keys.\n *\n * We might need to rebalance the trees, so an accessor is\n * needed to retrieve data points.\n */\n template \n void insert (Key key, ACCESSOR &acc)\n {\n BOOST_FOREACH(Tree &t, trees) {\n t.insert(key, acc);\n }\n }\n\n /// Query for K-NNs.\n /**\n * @param val the query object.\n * @param M lower bound of the total number of points to scan.\n * @param scanner the functional object to passed keys to.\n */\n template \n void query (Domain val, unsigned M, SCANNER &scanner) const\n {\n std::vector > list(trees.size());\n for (unsigned i = 0; i < trees.size(); ++i) {\n trees[i].lookup(val, &list[i]);\n }\n // Find the minimal depth covering at least M points\n unsigned d = 0;\n for (;;) {\n unsigned s = 0;\n for (unsigned i = 0; i < list.size(); ++i) {\n if (d < list[i].size()) {\n s += list[i][d]->size;\n }\n }\n if (s < M) break;\n ++d;\n }\n\n if (d > 0) --d;\n\n // recursively scan the nodes\n for (unsigned i = 0; i < list.size(); ++i) {\n if (d < list[i].size()) {\n list[i][d]->scan(val, scanner);\n }\n }\n }\n};\n\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/histogram.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_HISTOGRAM__\n#define __LSHKIT_HISTOGRAM__\n\n/** \\file histogram.h\n * \\brief LSH-based random histogram construction.\n *\n * Random histograms are used to match sets of feature vectors.\n * A random histogram is a simplified version of LSH hash table, except that\n * for each bin, only a count, instead of a list of features, is maintained.\n * If two sets are similar, then the counts in the corresponding bins would be\n * similar. Thus, set similarity is mapped to vector similarity.\n *\n *\n * Following is an example to embed a set of sift features into a random histogram.\n * We use the ThresholdingLsh to approximate L1 distance between SIFT features.\n *\n * \\code\n * #include \n * using namespace lshkit;\n *\n * // One ThresholdingLsh only produces 1 bit. We use the Repeat composition\n * // to produce a hash value of 8 bits, which can be used to produce one\n * // histogram of 2^8 = 256 dimensions.\n * typedef Repeat MyLSH;\n *\n * typedef Histogram MyHistogram;\n *\n * TresholdingLsh::Parameter param;\n *\n * param.repeat = 8; // repeat 8 times, produce 8 bits.\n * param.dim = 128;\n * param.min = 0.0;\n * param.max = 255.0; // Parameters are set for SIFT features.\n *\n * DefaultRng rng;\n * unsigned N = 10, M = 10;\n * MyHistogram hist;\n * hist.reset(M, N, param, rng);\n * // The histogram is constructed by concatenating N histograms of 2^8 dimensions,\n * // So there will be 2560 dimensions in all.\n *\n * float *output = new float[hist.getDim()]; // should be 2560.\n * hist.zero(output); // init the output histogram\n *\n * // IMAGE is a container of SIFT features.\n * BOOST_FOREACH(const float *sift, IMAGE) {\n * hist.add(output, sift);\n * }\n *\n * // now the output should contain the desired histogram.\n * // The histogram can be fed into SVM for classification.\n *\n * \\endcode\n *\n *\n *\n * See the following reference for details.\n *\n * Wei Dong, Zhe Wang, Moses Charikar, Kai Li. Efficiently Matching Sets of\n * Features with Random Histograms. In Proceedings of the 16th ACM\n * International Conference on Multimedia. Vancouver, Canada. October 2008.\n */\n\nnamespace lshkit {\n\n/// Random histogram construction.\ntemplate \nclass Histogram\n{\n BOOST_CONCEPT_ASSERT((LshConcept));\n\n std::vector lsh_;\n\n unsigned M_, N_;\n unsigned dim_;\n unsigned unit_;\npublic:\n typedef typename LSH::Parameter Parameter;\n typedef typename LSH::Domain Domain;\n\n /** Default constructor.\n */\n Histogram () {\n }\n\n /** Reset the sketcher.\n * @param M Number repeated to take average.\n * @param N Number of concatenated histograms.\n * @param param Parameter to LSH.\n * @param rng Random number generator (usually a variable of type lshkit::DefaultRng.\n */\n template \n void reset(unsigned M, unsigned N, Parameter param, RNG &rng)\n {\n lsh_.resize(M * N);\n M_ = M;\n N_ = N;\n for (unsigned i = 0; i < lsh_.size(); ++i)\n {\n lsh_[i].reset(param, rng);\n }\n unit_ = lsh_[0].getRange();\n dim_ = N_ * unit_;\n }\n\n /** Constructor.\n * @param M Number repeated to take average.\n * @param N Number of concatenated histograms.\n * @param param Parameter to LSH.\n * @param rng Random number generator (usually a variable of type lshkit::DefaultRng.\n */\n template \n Histogram(unsigned M, unsigned N, Parameter parameter, RNG &rng)\n : lsh_(M * N), M_(M), N_(N)\n {\n for (unsigned i = 0; i < lsh_.size(); ++i)\n {\n lsh_[i].reset(parameter, rng);\n }\n unit_ = lsh_[0].getRange();\n dim_ = N_ * unit_;\n }\n\n /// Get the total dimension of the histogram.\n unsigned dim () const\n {\n return dim_;\n }\n\n /// Initialize an output histogram.\n /**\n * @param out The array to hold the output histogram.\n */\n void zero (float *out) const\n {\n std::fill(out, out + dim_, 0);\n }\n \n /// Add the information of a vector to the output histogram.\n /**\n * @param out The array to hold the output histogram.\n * @param in The input vector.\n * @param weight Weight of the input vector.\n */\n void add (float *out, Domain in, float weight = 1.0) const\n {\n unsigned k = 0;\n unsigned base = 0;\n for (unsigned i = 0; i < N_; ++i)\n {\n for (unsigned j = 0; j < M_; ++j)\n {\n unsigned index = base + (lsh_[k])(in);\n out[index] += weight;\n ++k;\n }\n base += unit_;\n }\n }\n};\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/kernel.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file kernel.h\n * \\brief Common kernel definitions.\n */\n\n#ifndef __LSHKIT_KERNEL__\n#define __LSHKIT_KERNEL__\n\n// This file implements some common kernel functions.\n\n#include \n\nnamespace lshkit { namespace kernel {\n\n/// inner product\ntemplate \nclass dot : public std::binary_function\n{\n unsigned dim_;\npublic:\n dot (unsigned dim) : dim_(dim) {}\n float operator () (const T *first1, const T *first2) const\n {\n float r = 0.0;\n for (unsigned i = 0; i < dim_; ++i)\n {\n r += first1[i] * first2[i];\n }\n return r;\n }\n};\n\n\n}}\n\n#endif\n\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/lsh-index.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file lsh-index.h\n * \\brief Flat (non-multi-probe) LSH index.\n */\n\n#ifndef __LSHKIT_FLAT__\n#define __LSHKIT_FLAT__\n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace lshkit {\n\n/// Flat LSH index.\n/** Flat LSH index is implemented as L hash tables using mutually independent\n * LSH functions. Given a query point q, the points in the bins to which q is\n * hashed to are scanned for the nearest neighbors of q.\n *\n * @param LSH The LSH class.\n * @param KEY The key type.\n */\ntemplate \nclass LshIndex\n{\n\n BOOST_CONCEPT_ASSERT((LshConcept));\npublic:\n typedef typename LSH::Parameter Parameter;\n typedef typename LSH::Domain Domain;\n typedef KEY Key;\n\nprotected:\n typedef std::vector Bin;\n\n std::vector lshs_;\n std::vector > tables_;\n\npublic:\n /// Constructor.\n LshIndex() {\n }\n\n /// Initialize the hash tables.\n /**\n * @param param parameter of LSH function.\n * @param engine random number generator.\n * @param L number of hash table maintained.\n *\n */\n template \n void init (const Parameter ¶m, Engine &engine, unsigned L)\n {\n BOOST_VERIFY(lshs_.size() == 0);\n BOOST_VERIFY(tables_.size() == 0);\n lshs_.resize(L);\n tables_.resize(L);\n\n for (unsigned i = 0; i < L; ++i) {\n lshs_[i].reset(param, engine);\n if (lshs_[i].getRange() == 0) {\n throw std::logic_error(\"LSH with unlimited range should not be used to construct an LSH index. Use lshkit::Tail<> to wrapp the LSH.\");\n }\n tables_[i].resize(lshs_[i].getRange());\n }\n }\n\n /// Constructor\n /** Load the LSH index from a stream. */\n void load (std::istream &ar)\n {\n unsigned L;\n ar & L;\n lshs_.resize(L);\n tables_.resize(L);\n for (unsigned i = 0; i < L; ++i) {\n lshs_[i].serialize(ar, 0);\n unsigned l;\n ar & l;\n std::vector &table = tables_[i];\n table.resize(l);\n for (;;) {\n unsigned idx, ll;\n ar & idx;\n ar & ll;\n if (ll == 0) break;\n table[idx].resize(ll);\n ar.read((char *)&table[idx][0], ll * sizeof(Key));\n }\n }\n }\n\n /// Save the LSH index to a stream.\n void save (std::ostream &ar)\n {\n unsigned L;\n L = lshs_.size();\n ar & L;\n for (unsigned i = 0; i < L; ++i) {\n lshs_[i].serialize(ar, 0);\n std::vector &table = tables_[i];\n unsigned l = table.size();\n ar & l;\n unsigned idx, ll;\n for (unsigned j = 0; j < l; ++j) {\n if (table[j].empty()) continue;\n idx = j;\n ll = table[j].size();\n ar & idx;\n ar & ll;\n ar.write((char *)&table[j][0], ll * sizeof(Key));\n }\n idx = ll = 0;\n ar & idx;\n ar & ll;\n }\n }\n\n /// Insert an item to the index.\n /**\n * @param key the key to the item.\n * @param value the value of the key.\n *\n * The inserted object is not explicitly given, but is obtained by\n * accessor(key).\n */\n void insert (Key key, Domain value)\n {\n #if DEBUG_LEVEL > 50\n std::cerr << \"key \" << key << std::endl;\n #endif\n\n for (unsigned i = 0; i < lshs_.size(); ++i) {\n unsigned index = lshs_[i](value);\n\n #if DEBUG_LEVEL > 50\n std::cerr << \"data lshs_ index = \" << index << std::endl;\n #endif\n\n tables_[i][index].push_back(key);\n }\n }\n\n /// Query for K-NNs.\n /**\n * @param obj the query object.\n * @param scanner the scanner object.\n *\n * The scanner is an invokable object. LSH index will pass every candidate key\n * to scanner by invoking scanner(key).\n */\n template \n void query (Domain obj, SCANNER &scanner) const\n {\n for (unsigned i = 0; i < lshs_.size(); ++i) {\n unsigned index = lshs_[i](obj);\n\n #if DEBUG_LEVEL > 50\n std::cerr << \"query lshs_ index \" << index << std::endl;\n #endif\n\n BOOST_FOREACH(Key key, tables_[i][index]) {\n\n #if DEBUG_LEVEL > 50\n std::cout << \"key \" << key << std::endl; // object ID\n #endif\n\n scanner(key);\n }\n }\n }\n};\n\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/lsh.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_LSH__\n#define __LSHKIT_LSH__\n\n/**\n * \\file lsh.h\n * \\brief This file defines a set of basic LSH families.\n *\n * Be aware that the LSH classes defined here are only atomic hash functions.\n * You need to compose them with the templates found in composite.h in order to\n * reproduce the well-known published LSHes. For example, the Stable distribution\n * based LSH in M. Datar's paper (and also Q. Lv's Multi-Probe LSH paper) is\n * really RepeatHash.\n */\n\nnamespace lshkit {\n\nclass TrivialLsh\n{\npublic:\n // Nothing\n struct Parameter\n {\n };\n\n typedef const float * Domain;\n\n TrivialLsh ()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n }\n\n template \n TrivialLsh(const Parameter ¶m, RNG &rng)\n {\n }\n\n unsigned getRange () const\n {\n return 1;\n }\n\n unsigned operator () (Domain obj) const\n {\n return 0;\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n }\n};\n\n/// Stable distribution based LSH.\n/**\n * This LSH is defined on the D-dimensional vector space. For a vector X, the\n * hash value is defined as\n * \\f[\n * h(X) = [b + a_1*X_1 + a_2*X_2 + ... + a_D*X_D] / W\n * \\f]\n * where W is a positive value called the window size; b is sampled uniformly from\n * [0, W); a1 ~ aD are N random variables independently sampled from the so-called stable\n * distribution, which is specifiled by the template parameter DIST.\n *\n * The domain of the LSH is (const float *), and the parameter is defined as\n * \\code\n * struct Parameter {\n * unsigned dim;\n * float W;\n * };\n * \\endcode\n * The range of this LSH is 0.\n *\n * Following are two spacial cases:\n * \\code\n * typedef StableDistLsh CauchyLsh;\n * typedef StableDistLsh GaussianLsh;\n * \\endcode\n * Cauchy distribution is 1-stable and Gaussian distribution is 2-stable. These\n * two LSHes can be used to approximate L1 and L2 distances respectively.\n * \n * For more information on stable distribution based LSH, see the following reference.\n *\n * Mayur Datar , Nicole Immorlica , Piotr Indyk , Vahab S. Mirrokni,\n * Locality-sensitive hashing scheme based on p-stable distributions,\n * Proceedings of the twentieth annual symposium on Computational geometry, June\n * 08-11, 2004, Brooklyn, New York, USA.\n*/\ntemplate \nclass StableDistLsh\n{\n std::vector a_;\n float b_;\n float W_;\n unsigned dim_;\npublic:\n /**\n * Parameter to StableDistLsh.\n */\n struct Parameter\n {\n /// Dimension of domain.\n unsigned dim; \n /// Window size.\n float W;\n\n Parameter() : dim(0), W(0.0) {}\n };\n\n typedef const float *Domain;\n\n StableDistLsh ()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n a_.resize(param.dim);\n W_ = param.W;\n dim_ = param.dim;\n\n boost::variate_generator gen(rng, DIST());\n\n for (unsigned i = 0; i < dim_; ++i) a_[i] = gen();\n\n b_ = boost::variate_generator(rng, Uniform(0,W_))();\n }\n\n template \n StableDistLsh(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n\n unsigned getRange () const\n {\n return 0;\n }\n\n unsigned operator () (Domain obj) const\n {\n float ret = b_;\n for (unsigned i = 0; i < dim_; ++i)\n {\n ret += a_[i] * obj[i];\n }\n return unsigned(int(std::floor(ret / W_)));\n }\n\n unsigned operator () (Domain obj, float *delta) const\n {\n float ret = b_;\n for (unsigned i = 0; i < dim_; ++i)\n {\n ret += a_[i] * obj[i];\n }\n ret /= W_;\n\n float flr = std::floor(ret);\n *delta = ret - flr;\n return unsigned(int(flr));\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & a_;\n ar & b_;\n ar & W_;\n ar & dim_;\n assert(a_.size() == dim_);\n }\n\n};\n\n/// LSH for L1 distance.\ntypedef StableDistLsh CauchyLsh;\n/// LSH for L2 distance.\ntypedef StableDistLsh GaussianLsh;\n\n/// Random hyperplane based LSH for cosine similarity.\n/**\n * Random hyperplane based LSH can be used to approximate cosine similarity.\n * This LSH is defined on the D-dimensional vector space. For a vector X, the\n * hash value is defined as\n * \\f[\n * h(X) = [a_1*X_1 + a_2*X_2 + ... + a_D*X_D] > 0 ? 1 : 0\n * \\f]\n * where is a random vector sampled from the unit hypersphere.\n *\n * The domain of the LSH is (const float *), and the parameter is defined as\n * \\code\n * struct Parameter {\n * unsigned dim;\n * };\n * \\endcode\n * The range of this LSH is 0.\n *\n * For more information on stable distribution based LSH, see the following reference.\n *\n * Charikar, M. S. 2002. Similarity estimation techniques from rounding\n * algorithms. In Proceedings of the Thiry-Fourth Annual ACM Symposium on\n * theory of Computing (Montreal, Quebec, Canada, May 19 - 21, 2002). STOC '02.\n * ACM, New York, NY, 380-388. DOI= http://doi.acm.org/10.1145/509907.509965 \n *\n */\nclass HyperPlaneLsh\n{\n std::vector a_;\n\npublic:\n /**\n * Parameter to StableDistLsh.\n */\n struct Parameter\n {\n /// Dimension of domain.\n unsigned dim;\n\n Parameter() : dim(0) {}\n };\n typedef const float *Domain;\n\n HyperPlaneLsh ()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n a_ = boost::variate_generator >\n (rng, boost::uniform_on_sphere(param.dim))();\n }\n\n template \n HyperPlaneLsh(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n\n unsigned getRange () const\n {\n return 2;\n }\n\n unsigned operator () (Domain obj) const\n {\n float ret = 0;\n for (unsigned i = 0; i < a_.size(); ++i)\n {\n ret += a_[i] * obj[i];\n }\n return ret >= 0 ? 1 : 0;\n }\n\n unsigned operator () (Domain obj, float *delta) const\n {\n float ret = 0;\n for (unsigned i = 0; i < a_.size(); ++i)\n {\n ret += a_[i] * obj[i];\n }\n if (ret >= 0)\n {\n *delta = ret;\n return 1;\n }\n else\n {\n *delta = -ret;\n return 0;\n }\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & a_;\n }\n};\n\n\n/// Random hyperplane based LSH for L1 distance.\n/**\n * This LSH can be used to approximate L1 distance for closed D-dimensional\n * space [min, max]^D. It hashes each input vector into a 0-1 value, so its\n * range is 2. The hash function is the following: a random dimension is\n * chosen, and a threshold T is sampled uniformly in [min, max]. For each input\n * vector, the value at that dimension is check. If the value is larger than T,\n * 1 is returned and otherwise 0 is returned. Following is the parameter.\n *\n * \\code\n * struct Parameter\n * {\n * unsigned dim; // Dimension of domain.\n * float min; // Each dimension is limited in [min, max].\n * float max;\n * };\n * \\endcode\n *\n * The method is discussed in the following papers:\n *\n * Zhe Wang, Wei Dong, William Josephson, Qin Lv, Moses Charikar, Kai Li.\n * Sizing Sketches: A Rank-Based Analysis for Similarity Search. In\n * Proceedings of the 2007 ACM SIGMETRICS International Conference on\n * Measurement and Modeling of Computer Systems . San Diego, CA, USA. June\n * 2007.\n *\n * Qin Lv, Moses Charikar, Kai Li. Image Similarity Search with Compact\n * Data Structures. In Proceedings of ACM 13th Conference on Information\n * and Knowledge Management (CIKM), Washington D.C., USA. November 2004.\n *\n * Note that the original method allows the range of each dimension to be\n * different and also allow each dimensioin to carry a weight. The\n * implementation here is simplified.\n */\nclass ThresholdingLsh\n{\n unsigned dim_;\n float threshold_;\npublic:\n /**\n * Parameter to ThresholdingLsh.\n */\n struct Parameter\n {\n /// Dimension of domain.\n unsigned dim;\n /// Lower bound of each dimension.\n float min;\n /// Upper bound of each dimension.\n float max;\n\n Parameter() : dim(0), min(0), max(0) {}\n };\n typedef const float *Domain;\n\n ThresholdingLsh ()\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n dim_ = boost::variate_generator(rng, UniformUnsigned(0,param.dim - 1))();\n threshold_ = boost::variate_generator(rng, Uniform(param.min,param.max))();\n }\n\n template \n ThresholdingLsh(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n unsigned getRange () const\n {\n return 2;\n }\n\n unsigned operator () (Domain obj) const\n {\n return obj[dim_] >= threshold_ ? 1 : 0;\n }\n\n unsigned operator () (Domain obj, float *delta) const\n {\n float ret = obj[dim_] - threshold_;\n if (ret >= 0)\n {\n *delta = ret;\n return 1;\n }\n else\n {\n *delta = -ret;\n return 0;\n }\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & dim_;\n ar & threshold_;\n }\n};\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/matrix-io.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file matrix-io.h\n * \\brief Matrix I/O routines, inlined implementation (Matrix::load & Matrix::save).\n */\n\n#ifndef LSHKIT_MATRIX_IO\n#define LSHKIT_MATRIX_IO\n\n#include \n#include \n#include \n\n#ifdef MATRIX_MMAP\n#include \n#include \n#include \n#include \n#include \n#include \n#endif\n\nnamespace lshkit{\n\ntemplate \nvoid Matrix::peek (const std::string &path, int *elem_size, int *size, int *dim)\n{\n unsigned header[3]; /* entry size, row, col */\n assert(sizeof header == 3*4);\n std::ifstream is(path.c_str(), std::ios::binary);\n BOOST_VERIFY(is);\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n *elem_size = header[0];\n *size = header[1];\n *dim = header[2];\n}\n\ntemplate \nvoid Matrix::load (std::istream &is)\n{\n unsigned header[3]; /* entry size, row, col */\n assert(sizeof header == 3*4);\n is.read((char *)header, sizeof header);\n BOOST_VERIFY(is);\n BOOST_VERIFY(header[0] == sizeof(T));\n reset(header[2], header[1]);\n size_t sz = sizeof(T) * dim * N;\n is.read((char *)dims, sz);\n BOOST_VERIFY(is);\n}\n\ntemplate \nvoid Matrix::save (std::ostream &os)\n{\n unsigned header[3];\n header[0] = sizeof(T);\n header[1] = N;\n header[2] = dim;\n os.write((char *)header, sizeof header);\n BOOST_VERIFY(os);\n size_t sz = sizeof(T) * dim * N;\n os.write((char *)dims, sz);\n BOOST_VERIFY(os);\n}\n\ntemplate \nvoid Matrix::load (const std::string &path)\n{\n std::ifstream is(path.c_str(), std::ios::binary);\n load(is);\n}\n\ntemplate \nvoid Matrix::save (const std::string &path)\n{\n std::ofstream os(path.c_str(), std::ios::binary);\n save(os);\n}\n\n#ifdef MATRIX_MMAP\ntemplate \nvoid Matrix::map (const std::string &path) {\n\n if (dims != NULL) delete[] dims;\n\n int size = 0;\n\n fd = open(path.c_str(), O_RDONLY);\n BOOST_VERIFY(fd >= 0);\n\n if (read(fd, &size, 4) != sizeof(size)) BOOST_VERIFY(0);\n if (read(fd, &N, 4) != sizeof(N)) BOOST_VERIFY(0);\n if (read(fd, &dim, 4) != sizeof(dim)) BOOST_VERIFY(0);\n\n BOOST_VERIFY(size == sizeof(T));\n\n size_t sz = sizeof(T) * size_t(dim) * size_t(N);\n \n char *start = (char *)mmap(NULL, sz, PROT_READ, MAP_PRIVATE, fd, 0);\n if (start == MAP_FAILED) { BOOST_VERIFY(0); }\n dims = (T *)(start + 3 * 4);\n\n}\n\ntemplate \nvoid Matrix::unmap () {\n char *start = (char *)dims - 3 * 4;\n if (munmap(start, sizeof(T) * size_t(dim) * size_t(N)) != 0) BOOST_VERIFY(0);\n close(fd);\n\n dims = 0;\n dim = 0;\n N = 0;\n}\n\n#endif\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/matrix.h", "content": "#ifndef LSHKIT_MATRIX\n#define LSHKIT_MATRIX\n/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \n#include \n\n/**\n * \\file matrix.h\n * \\brief Dataset management. A dataset is maintained as a matrix in memory.\n *\n * LSHKIT is a main memory indexing data structure and doesn't assume any\n * particular file format. However, the standalone tools come with the LSHKIT\n * package use the following simple binary file format.\n *\n * The file contains N D-dimensional vectors of single precision floating point numbers.\n *\n * In the beginning of the file are three 32-bit unsigned integers: ELEM-SIZE,\n * SIZE, DIM. ELEM-SIZE is the size of the element, and currently the only\n * valid value is 4, which is the size of float. SIZE is the number of vectors\n * in the file and DIM is the dimension.\n *\n * The rest of the file is SIZE vectors stored consecutively, and the total\n * size is SIZE * DIM * 4 bytes.\n *\n * Such binary files can be accessed using lshkit::Matrix.\n */\n\nnamespace lshkit {\n\n/// Matrix\n/**\n * An matrix of size (NxD) is used to store an array of N D-dimensional\n * vectors.\n */\ntemplate \nclass Matrix\n{\n int dim, N;\n T *dims;\n\n void load (const char *);\n void save (const char *);\n\n#ifdef MATRIX_MMAP\n int fd;\n#endif\npublic:\n /// Reset the size of matrix.\n /**\n * @param dim Dimension of each feature vector.\n * @param N Number of feature vectors.\n */\n void reset (int _dim, int _N)\n {\n dim = _dim;\n N = _N;\n if (dims != NULL) delete[] dims;\n dims = new T[dim * N];\n }\n\n /// Release memory.\n void free (void) {\n dim = N = 0;\n if (dims != NULL) delete[] dims;\n dims = NULL;\n }\n \n /// Default constructor.\n /** Allocates an empty matrix. Should invoke reset or load before using it.*/\n Matrix () :dim(0), N(0), dims(NULL) {}\n\n /// Constructor, same as Matrix() followed immediately by reset().\n Matrix (int _dim, int _N) : dims(NULL) { reset(_dim, _N); }\n\n /// Destructor.\n ~Matrix () { if (dims != NULL) delete[] dims; }\n\n /// Access the ith vector.\n const T *operator [] (int i) const {\n return dims + i * dim;\n }\n\n /// Access the ith vector.\n T *operator [] (int i) {\n return dims + i * dim;\n }\n\n int getDim () const {return dim; }\n int getSize () const {return N; }\n\n\n /// Peek into a file to determine the size and dimension of the dataset.\n /**\n * @param path File to peek.\n * @param elem_size Size of the vector element.\n * @param size Number of vectors in the file.\n * @param dim Dimension of the vectors.\n *\n * This function doesn't read the whole matrix into memory, so it is fast.\n */\n static void peek (const std::string &path, int *elem_size, int *size, int *dim);\n\n void load (const std::string &path);\n void save (const std::string &path);\n void load (std::istream &is);\n void save (std::ostream &os);\n\n#ifdef MATRIX_MMAP\n void map (const std::string &path);\n void unmap ();\n#endif\n\n /// Construct from a file.\n Matrix (const std::string &path): dims(NULL) { load(path); }\n\n /// An accessor class to be used with LSH index.\n class Accessor\n {\n const Matrix &matrix_;\n boost::dynamic_bitset<> flags_;\n public:\n typedef unsigned Key;\n typedef const float *Value;\n\n Accessor(const Matrix &matrix)\n : matrix_(matrix), flags_(matrix.getSize()) {}\n\n void reset () {\n flags_.reset();\n }\n\n bool mark (unsigned key) {\n if (flags_[key]) return false;\n flags_.set(key);\n return true;\n }\n\n const float *operator () (unsigned key) {\n return matrix_[key];\n }\n };\n\n /*\n class View\n {\n const Matrix *ref_;\n int off_, len_;\n public:\n View (const Matrix *ref, int off, int len)\n : ref_(ref), off_(off), len_(len) {}\n const T *operator [] (int i) const { return ref_->operator[](off_ + i); }\n int getDim () const {return ref_->getDim(); }\n int getSize () const {return len_; }\n };\n\n View getView (int off, int len) const\n {\n return View(this, off, len);\n }\n\n View getView () const\n {\n return View(this, 0, getSize());\n }\n */\n};\n\ntypedef Matrix FloatMatrix;\n\n}\n\n#include \n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/metric.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file metric.h\n * \\brief Common distance measures.\n */\n\n#ifndef __FERRET_METRIC__\n#define __FERRET_METRIC__\n\n// This file implements some common distance functions.\n\n#include \n#include \n\nnamespace lshkit { namespace metric {\n\n/// L1 distance\ntemplate \nclass l1 : public std::binary_function\n{\n unsigned dim_;\npublic:\n l1 (unsigned dim) : dim_(dim) {}\n float operator () (const T *first1, const T *first2) const\n {\n #if DEBUG_LEVEL > 50\n std::cout << \"dim \" << dim_ << std::endl;\n for (unsigned i = 0; i < dim_; ++i) std::cout << first1[i] << \" \"; std::cout << std::endl;\n for (unsigned i = 0; i < dim_; ++i) std::cout << first2[i] << \" \"; std::cout << std::endl;\n #endif\n\n float r = 0.0;\n for (unsigned i = 0; i < dim_; ++i)\n {\n r += std::fabs(first1[i] - first2[i]);\n }\n return r;\n }\n};\n\n/// L2 distance\ntemplate \nclass l2 : public std::binary_function\n{\n unsigned dim_;\npublic:\n l2 (unsigned dim) : dim_(dim) {}\n float operator () (const T *first1, const T *first2) const\n {\n float r = 0.0;\n for (unsigned i = 0; i < dim_; ++i)\n {\n r += sqr(first1[i] - first2[i]);\n }\n return std::sqrt(r);\n }\n};\n\n/// Squred L2 distance\n/**\n * The square root operation is costly. For K-NN search, l2sqr\n * gives the same ranking as l2.\n */\ntemplate \nclass l2sqr : public std::binary_function\n{\n unsigned dim_;\npublic:\n l2sqr (unsigned dim) : dim_(dim) {}\n unsigned dim() const {\n return dim_;\n }\n float operator () (const T *first1, const T *first2) const\n {\n float r = 0.0;\n for (unsigned i = 0; i < dim_; ++i)\n {\n r += sqr(first1[i] - first2[i]);\n }\n return r;\n }\n};\n\n/// Max-norm distance.\ntemplate \nclass max : public std::binary_function\n{\n unsigned dim_;\npublic:\n max (unsigned dim) : dim_(dim) {}\n float operator () (const T *first1, const T *first2) const\n {\n double r = std::numeric_limits::min();\n for (unsigned i = 0; i < dim_; ++i)\n {\n r += max(r, std::fabs(first1[i] - first2[i]));\n }\n return (float)std::sqrt(r);\n }\n};\n\n/// (Basic) hamming distance\n/** Take the hamming distance between two values of type T as bit-vectors.\n * Normally you should use hamming instead of basic_hamming.\n */\nstruct basic_hamming\n{\n static unsigned char_bit_cnt[];\n\n template \n unsigned __hamming (B a, B b)\n {\n B c = a ^ b;\n unsigned char *p = reinterpret_cast(&c);\n unsigned r = 0;\n for (unsigned i = 0; i < sizeof(B); i++)\n {\n r += char_bit_cnt[*p++];\n }\n return r;\n }\n\n unsigned __hamming (unsigned char c1, unsigned char c2) const\n {\n return char_bit_cnt[c1 ^ c2];\n }\n\n};\n\n/// Hamming distance.\n/**\n * Take the hamming distance between two bit-vectors, represented as arrays\n * of some basic type. The parameter dim is the size of the bit-vectors in\n * terms of the number of basic types. For example, if we use uchar_t as basic\n * type, then the dim of a 256-bit vector is 256/8 = 32.\n */\ntemplate \nstruct hamming : public std::binary_function, public basic_hamming\n{\n unsigned dim_;\npublic:\n\n hamming(unsigned dim): dim_(dim) {}\n float operator () (const T *first1, const T *first2) const\n {\n unsigned r = 0;\n for (unsigned i = 0; i < dim_; ++i)\n {\n r += __hamming(first1[i], first2[i]);\n }\n return r;\n }\n};\n\n\n}}\n\n#endif\n\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/mplsh-model.h", "content": "/*\n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n\n This file is part of LSHKIT.\n\n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n#ifndef __LSHKIT_MPLSH_MODEL__\n#define __LSHKIT_MPLSH_MODEL__\n\n#include \n#include \n#include \n#include \n#include \n\n/**\n * \\file mplsh-model.h\n * \\brief Model of Multi-probe LSH.\n *\n * The modeling code is not well documented because I find it hard to do so.\n * The code is essentially a translation of our CIKM'08 paper. If you do wish\n * to look into the code, beware that when the distance is used, sometime I\n * mean L2 distance (when Gaussian distribution of the distance between\n * the random projection of two points is considered)\n * and sometime I mean L2sqr distance because L2sqr distance between two points\n * in the database follows Gamma distribution.\n */\n\nnamespace lshkit {\n\ntypedef boost::math::normal_distribution GaussianDouble;\ntypedef boost::math::gamma_distribution GammaDouble;\n\n/// Maximum likelihood estimation of gamma distribution.\n/**\n * @param M sample mean.\n * @param G geometric mean of sample.\n */\nGammaDouble GammaDoubleMLE (double M, double G);\n\n/// Data parameter.\n/**\n * This class represents the parameters extracted from the dataset.\n */\n// We use l2sqr here.\nclass DataParam\n{\n double M, G;\n double a_M, b_M, c_M;\n double a_G, b_G, c_G;\n\n /*\n DataParam () {}\n DataParam (const DataParam &dp)\n : M(dp.M), G(dp.G),\n a_M(dp.a_M), b_M(dp.b_M), c_M(dp.c_M),\n a_G(dp.a_G), b_G(dp.b_G), c_G(dp.c_G) {}\n */\npublic:\n /// Constructor.\n /**\n * For now, parameters can only be loaded from a file.\n */\n DataParam (const std::string &path)\n {\n std::ifstream is(path.c_str());\n is >> M >> G >> a_M >> b_M >> c_M\n >> a_G >> b_G >> c_G;\n }\n\n // read from string\n DataParam(const std::string& fit_data, int dummy) {\n std::stringstream ss(fit_data);\n ss >> M >> G >> a_M >> b_M >> c_M\n >> a_G >> b_G >> c_G;\n }\n\n /// Estimate the global distance distribution.\n GammaDouble globalDist () const\n {\n return GammaDoubleMLE(M, G);\n }\n\n /// Estimate the distance distribution of the K-th NN.\n /**\n * @param N size of the (extraplolated) dataset.\n */\n GammaDouble topkDist (unsigned N, unsigned K) const\n {\n double m, g;\n m = std::exp(a_M) * std::pow(double(N), b_M) * std::pow(double(K), c_M);\n g = std::exp(a_G) * std::pow(double(N), b_G) * std::pow(double(K), c_G);\n return GammaDoubleMLE(m,g);\n }\n\n void scale (double s) {\n M /= s;\n G /= s;\n a_M -= std::log(s);\n a_G -= std::log(s);\n }\n\n double scale () {\n double s = M;\n scale(s);\n return s;\n }\n\n};\n\n/// Multi-probe LSH parameters.\n// we use l2 here.\nclass MultiProbeLshModel\n{\n unsigned L_;\n double W_;\n unsigned M_, T_;\npublic:\n MultiProbeLshModel(unsigned L, double W, unsigned M, unsigned T)\n : L_(L), W_(W), M_(M), T_(T)\n {}\n\n double recall (double l2) const;\n\n void setL (unsigned L) { L_ = L; }\n void setW (double W) { W_ = W; }\n void setT (unsigned T) { T_ = T; }\n void setM (unsigned M) { M_ = M; }\n\n unsigned getT () const {return T_; }\n\n};\n\n// we use l2sqr here.\nclass MultiProbeLshDataModel: public MultiProbeLshModel\n{\n// temporary, not thread safe\n GammaDouble globalDist_;\n std::vector topkDists_;\n\npublic:\n MultiProbeLshDataModel(const DataParam ¶m, unsigned N, unsigned K)\n : MultiProbeLshModel(0,0,0,0), globalDist_(1.0),\n topkDists_(K, globalDist_)\n {\n globalDist_ = param.globalDist();\n for (unsigned k = 0; k < K; k++)\n {\n topkDists_[k] = param.topkDist(N, k + 1);\n }\n }\n\n double avgRecall () const;\n double cost () const;\n};\n\n// we use l2 here.\nclass MultiProbeLshRecallTable\n{\n unsigned step_;\n double min_, max_;\n double lmin_, lmax_;\n Matrix table_;\n\npublic:\n\n void load (std::istream &is) {\n is.read((char *)&min_, sizeof(min_));\n is.read((char *)&max_, sizeof(max_));\n table_.load(is);\n\n step_ = table_.getDim();\n lmin_ = log(min_);\n lmax_ = log(max_);\n }\n\n void save (std::ostream &os) {\n os.write((const char *)&min_, sizeof(min_));\n os.write((const char *)&max_, sizeof(max_));\n table_.save(os);\n }\n\n void reset (MultiProbeLshModel model, unsigned d_step, double d_min, double d_max)\n {\n if (d_min <= 0 || d_max <= 0) {\n throw std::logic_error(\"Make sure a distance is positive.\");\n }\n\n step_ = d_step;\n\n min_ = d_min;\n max_ = d_max;\n lmin_ = log(d_min);\n lmax_ = log(d_max);\n table_.reset(d_step, model.getT());\n\n unsigned T = model.getT();\n double delta = (lmax_ - lmin_) / step_;\n for (unsigned t = 0; t < T; ++t)\n {\n model.setT(t+1);\n for (unsigned d = 0; d < step_; ++d)\n {\n table_[t][d] = model.recall(exp(lmin_ + delta * d));\n }\n }\n }\n\n float lookup (float dist, int T) const\n {\n unsigned d;\n if (dist < min_) return 1.0;\n if (!(dist < max_)) return 0.0;\n d = std::floor((log(dist) - lmin_) * step_ / (lmax_ - lmin_) + 0.5);\n return table_[T-1][d];\n }\n\n};\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/mplsh.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file mplsh.h\n * \\brief Multi-Probe LSH indexing.\n *\n * Multi-Probe LSH (MPLSH) uses the same data structure as LshIndex, except that it\n * probes more than one buckets in each hash table to generate more accurate\n * results. Equivalently, less hash tables are needed to achieve the same\n * accuracy. The limitation is that the current implementation only works for\n * L2 distance.\n * \n * Follow the following 4 steps to use the MPLSH API.\n * \n * \\section mplsh-1 1. Implement a scanner class which scan the candidate keys.\n * \n * The MPLSH data structure doesn't manage the feature vectors, but only keeps\n * the keys to retrieve them. You need to provide a scanner class to MPLSH, and\n * for each query, MPLSH will pass the candidate keys to the scanner.\n * The scanner usually keeps an K-NN data structure internally and updates\n * it when receives candidate keys.\n *\n * MPLSH uses the scanner as a unary function taking a key as argument.\n *\n * The default scanner implementation is TopkScanner.\n * \n * \\code\n * class Scanner\n * {\n * ...\n * void operator () (unsigned key);\n * };\n * \\endcode\n *\n * \\section mplsh-2 2. Construct the MPLSH data structure.\n *\n * Assume we use key type KEY.\n *\n * \\code\n *\n * typedef MultiProbeLshIndex Index;\n *\n * Index index;\n * \\endcode\n *\n * The index can not be used yet.\n *\n * \\section mplsh-3 3. Populate the index / Load the index from a previously saved file.\n *\n * When the index is initially built, use the following to populate the index:\n * \\code\n * Index::Parameter param;\n *\n * //Setup the parameters. Note that L is not provided here.\n * param.W = W;\n * param.H = H; // See H in the program parameters. You can just use the default value.\n * param.M = M;\n * param.dim = DIMENSION_OF_THE_DATA\n * DefaultRng rng; // random number generator.\n * \n * index.init(param, rng, L);\n * \n * for (each possible key, value pair) {\n * index.insert(key, value);\n * }\n * \n * // You can now save the index for future use.\n * ofstream os(index_file.c_str(), std::ios::binary);\n * index.save(os);\n * \\endcode\n * \n * Or you can load from a previously saved file\n * \n * \\code\n * ifstream is(index_file.c_str(), std::ios::binary);\n * index.load(is);\n * \\endcode\n * \n * \\section mplsh-4 4. Query the MPLSH. \n * \n * \\code\n * \n * float *query;\n * ...\n * index.query(query, T, scanner); cnt is the number of points actually scanned.\n * \n * \\endcode\n *\n * See the source file lshkit/tools/mplsh-run.cpp for a full example of using MPLSH.\n *\n * For adaptive probing, I hard coded the sensitive range of KNN distance to\n * [0.0001W, 100W] and logarithmically quantized the range to 200 levels.\n * If you find that your KNN distances fall outside this range, or want more refined\n * quantization, you'll have to modify the code in lshkit::MultiProbeLshIndex::init().\n *\n * \\section ref Reference\n *\n * Wei Dong, Zhe Wang, William Josephson, Moses Charikar, Kai Li. Modeling LSH\n * for Performance Tuning.. To appear in In Proceedings of ACM 17th Conference\n * on Information and Knowledge Management (CIKM). Napa Valley, CA, USA.\n * October 2008.\n *\n * Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li. Multi-Probe LSH:\n * Efficient Indexing for High-Dimensional Similarity Search. Proceedings of the\n * 33rd International Conference on Very Large Data Bases (VLDB). Vienna,\n * Austria. September 2007.\n *\n */\n\n#ifndef __LSHKIT_PROBE__\n#define __LSHKIT_PROBE__\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace lshkit\n{\n\nstatic inline unsigned long long leftshift (unsigned N) {\n return (unsigned long long)1 << N;\n}\n\n/// Probe vector.\nstruct Probe\n{\n unsigned long long mask;\n unsigned long long shift;\n float score;\n unsigned reserve;\n bool operator < (const Probe &p) const { return score < p.score; }\n Probe operator + (const Probe &m) const\n {\n Probe ret;\n ret.mask = mask | m.mask;\n ret.shift = shift | m.shift;\n ret.score = score + m.score;\n return ret;\n }\n bool conflict (const Probe &m)\n {\n return (mask & m.mask) != 0;\n }\n static const unsigned MAX_M = 64;\n static const unsigned MAX_T = 200;\n}; \n\n/// Probe sequence.\ntypedef std::vector ProbeSequence;\n\n/// Generate a template probe sequence.\nvoid GenProbeSequenceTemplate (ProbeSequence &seq, unsigned M, unsigned T);\n\n/// Probe sequence template.\nclass ProbeSequenceTemplates: public std::vector\n{\npublic:\n ProbeSequenceTemplates(unsigned max_M, unsigned max_T)\n : std::vector(max_M + 1)\n {\n for (unsigned i = 1; i <= max_M; ++i)\n {\n GenProbeSequenceTemplate(at(i), i, max_T);\n }\n }\n};\n\nextern ProbeSequenceTemplates __probeSequenceTemplates;\n\n/// Multi-Probe LSH class.\nclass MultiProbeLsh: public RepeatHash \n{\n unsigned H_;\npublic:\n typedef RepeatHash Super;\n typedef Super::Domain Domain;\n\n /**\n * Parameter to MPLSH. \n *\n * The following parameters are inherited from the ancestors\n * \\code\n * unsigned repeat; // the same as M in the paper\n * unsigned dim;\n * float W;\n * \\endcode\n */\n struct Parameter : public Super::Parameter {\n\n unsigned range;\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & range;\n ar & repeat;\n ar & dim;\n ar & W;\n }\n };\n\n MultiProbeLsh () {}\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n H_ = param.range;\n Super::reset(param, rng);\n }\n\n template \n MultiProbeLsh(const Parameter ¶m, RNG &rng)\n {\n H_ = param.range;\n Super::reset(param, rng);\n }\n\n unsigned getRange () const\n {\n return H_;\n }\n\n unsigned operator () (Domain obj) const\n {\n return Super::operator ()(obj) % H_;\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n Super::serialize(ar, version);\n ar & H_;\n }\n\n void genProbeSequence (Domain obj, std::vector &seq, unsigned T) const;\n};\n\n\n/// Multi-Probe LSH index.\ntemplate \nclass MultiProbeLshIndex: public LshIndex\n{\npublic:\n typedef LshIndex Super;\n /**\n * Super::Parameter is the same as MultiProbeLsh::Parameter\n */\n typedef typename Super::Parameter Parameter;\n\nprivate:\n\n Parameter param_;\n MultiProbeLshRecallTable recall_;\n\npublic: \n typedef typename Super::Domain Domain;\n typedef KEY Key;\n\n /// Constructor.\n MultiProbeLshIndex() {\n } \n\n /// Initialize MPLSH.\n /**\n * @param param parameters.\n * @param engine random number generator (if you are not sure about what to\n * use, then pass DefaultRng.\n * @param accessor object accessor (same as in LshIndex).\n * @param L number of hash tables maintained.\n */\n template \n void init (const Parameter ¶m, Engine &engine, unsigned L) {\n Super::init(param, engine, L);\n param_ = param;\n // we are going to normalize the distance by window size, so here we pass W = 1.0.\n // We tune adaptive probing for KNN distance range [0.0001W, 20W].\n recall_.reset(MultiProbeLshModel(Super::lshs_.size(), 1.0, param_.repeat, Probe::MAX_T), 200, 0.0001, 20.0);\n }\n\n /// Load the index from stream.\n void load (std::istream &ar) \n {\n Super::load(ar);\n param_.serialize(ar, 0);\n recall_.load(ar);\n BOOST_VERIFY(ar);\n }\n\n /// Save to the index to stream.\n void save (std::ostream &ar)\n {\n Super::save(ar);\n param_.serialize(ar, 0);\n recall_.save(ar);\n BOOST_VERIFY(ar);\n }\n\n /// Query for K-NNs.\n /**\n * @param obj the query object.\n * @param scanner \n */\n template \n void query (Domain obj, unsigned T, SCANNER &scanner)\n {\n std::vector seq;\n for (unsigned i = 0; i < Super::lshs_.size(); ++i) {\n Super::lshs_[i].genProbeSequence(obj, seq, T);\n for (unsigned j = 0; j < seq.size(); ++j) {\n typename Super::Bin &bin = Super::tables_[i][seq[j]];\n BOOST_FOREACH(Key key, bin) {\n scanner(key);\n }\n }\n }\n }\n\n /// Query for K-NNs, try to achieve the given recall by adaptive probing.\n /**\n * There's a special requirement for the scanner type used in adaptive query.\n * It should support the following method to return the current K-NNs:\n *\n * const Topk &topk () const;\n */\n template \n void query_recall (Domain obj, float recall, SCANNER &scanner) const\n {\n unsigned K = scanner.topk().getK();\n if (K == 0) throw std::logic_error(\"CANNOT ACCEPT R-NN QUERY\");\n if (scanner.topk().size() < K) throw std::logic_error(\"ERROR\");\n unsigned L = Super::lshs_.size();\n std::vector > seqs(L);\n for (unsigned i = 0; i < L; ++i) {\n Super::lshs_[i].genProbeSequence(obj, seqs[i], Probe::MAX_T);\n }\n\n for (unsigned j = 0; j < Probe::MAX_T; ++j) {\n if (j >= seqs[0].size()) break;\n for (unsigned i = 0; i < L; ++i) {\n BOOST_FOREACH(Key key, Super::tables_[i][seqs[i][j]]) {\n scanner(key);\n }\n }\n float r = 0.0;\n for (unsigned i = 0; i < K; ++i) {\n r += recall_.lookup(std::sqrt(scanner.topk()[i].dist) / param_.W, j + 1);\n }\n r /= K;\n if (r >= recall) break;\n }\n }\n};\n\n}\n\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/multiprobelsh-fitdata.h", "content": "/*\n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n\n This file is part of LSHKIT.\n\n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file fitdata.cpp\n * \\brief Gather statistics from dataset for MPLSH tuning.\n *\n * This program gahters statistical data from a small sample dataset\n * for automatic MPLSH parameter tuning. It carries out the following\n * steps:\n * -# Sample N points from the dataset. Only those N points will be used for future computation.\n * -# Sample P pairs of points from the sample, calculate the distance for each pair.\n * -# Sample Q points from the sample as queries points.\n * -# Divide the sample into F folds.\n * -# For i = 1 to F, take i folds and run K-NN search, so the query points\n * will be searched against sample datasets of N/F, 2N/F, ..., N/F points.\n *\n * The statistical data is printed to standard output after the progress display.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -N [ -- ] arg (=0) number of points to use\n -P [ -- ] arg (=50000) number of pairs to sample\n -Q [ -- ] arg (=1000) number of queries to sample\n -K [ -- ] arg (=100) search for K nearest neighbors\n -F [ -- ] arg (=10) divide the sample to F folds\n -D [ --data ] arg data file\n\\endverbatim\n */\n\n#ifndef _MPLSH_FITDATA_H_\n#define _MPLSH_FITDATA_H_\n\n#include \"logging.h\"\n\n#include \n#include \n#include \n\nnamespace lshkit {\n\ninline\nbool is_good_value (double v) {\n return ((v > -std::numeric_limits::max()) &&\n (v < std::numeric_limits::max()));\n}\n\ninline\nstd::string FitData(const FloatMatrix& data,\n unsigned N, // number of points to use\n unsigned P, // number of pairs to sample\n unsigned Q, // number of queries to sample\n unsigned K, // search for K neighbors neighbors\n unsigned F // divide the sample to F folds\n )\n{\n LOG(LIB_INFO) << \"started running FitData\" << std::endl;\n\n std::vector idx(data.getSize());\n for (size_t i = 0; i < idx.size(); ++i) idx[i] = i;\n std::random_shuffle(idx.begin(), idx.end());\n\n if (N > 0 && N < static_cast(data.getSize())) idx.resize(N);\n\n metric::l2sqr l2sqr(data.getDim());\n\n DefaultRng rng;\n boost::variate_generator gen(rng,\n UniformUnsigned(0, idx.size()-1));\n\n double gM = 0.0;\n double gG = 0.0;\n {\n // sample P pairs of points\n for (unsigned k = 0; k < P; ++k)\n {\n double dist, logdist;\n for (;;)\n {\n unsigned i = gen();\n unsigned j = gen();\n if (i == j) continue;\n dist = l2sqr(data[idx[i]], data[idx[j]]);\n logdist = log(dist);\n if (is_good_value(logdist)) break;\n }\n gM += dist;\n gG += logdist;\n }\n gM /= P;\n gG /= P;\n gG = exp(gG);\n }\n\n if (Q > idx.size()) Q = idx.size();\n if (K > idx.size() - Q) K = idx.size() - Q;\n /* sample query */\n std::vector qry(Q);\n\n SampleQueries(&qry, idx.size(), rng);\n\n /* do the queries */\n std::vector > topks(Q);\n for (unsigned i = 0; i < Q; ++i) topks[i].reset(K);\n\n /* ... */\n gsl_matrix *X = gsl_matrix_alloc(F * K, 3);\n gsl_vector *yM = gsl_vector_alloc(F * K);\n gsl_vector *yG = gsl_vector_alloc(F * K);\n gsl_vector *pM = gsl_vector_alloc(3);\n gsl_vector *pG = gsl_vector_alloc(3);\n gsl_matrix *cov = gsl_matrix_alloc(3,3);\n\n std::vector M(K);\n std::vector G(K);\n\n unsigned m = 0;\n for (unsigned l = 0; l < F; l++)\n {\n // Scan\n for (unsigned i = l; i< idx.size(); i += F)\n {\n for (unsigned j = 0; j < Q; j++)\n {\n unsigned id = qry[j];\n if (i != id)\n {\n float d = l2sqr(data[idx[id]], data[idx[i]]);\n if (is_good_value(log(double(d))))\n topks[j] << Topk::Element(i, d);\n }\n }\n }\n\n fill(M.begin(), M.end(), 0.0);\n fill(G.begin(), G.end(), 0.0);\n\n for (unsigned i = 0; i < Q; i++)\n {\n for (unsigned k = 0; k < K; k++)\n {\n M[k] += topks[i][k].dist;\n G[k] += log(topks[i][k].dist);\n }\n }\n\n for (unsigned k = 0; k < K; k++)\n {\n M[k] = log(M[k]/Q);\n G[k] /= Q;\n gsl_matrix_set(X, m, 0, 1.0);\n gsl_matrix_set(X, m, 1, log(double(data.getSize() * (l + 1)) / double(F)));\n gsl_matrix_set(X, m, 2, log(double(k + 1)));\n gsl_vector_set(yM, m, M[k]);\n gsl_vector_set(yG, m, G[k]);\n ++m;\n }\n\n //++progress;\n }\n\n gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc(F * K, 3);\n\n double chisq;\n\n gsl_multifit_linear(X, yM, pM, cov, &chisq, work);\n gsl_multifit_linear(X, yG, pG, cov, &chisq, work);\n\n std::stringstream ss;\n ss << gM << \" \" << gG << std::endl;\n ss << gsl_vector_get(pM, 0) << \" \"\n << gsl_vector_get(pM, 1) << \" \"\n << gsl_vector_get(pM, 2) << std::endl;\n ss << gsl_vector_get(pG, 0) << \" \"\n << gsl_vector_get(pG, 1) << \" \"\n << gsl_vector_get(pG, 2) << std::endl;\n\n gsl_matrix_free(X);\n gsl_matrix_free(cov);\n gsl_vector_free(pM);\n gsl_vector_free(pG);\n gsl_vector_free(yM);\n gsl_vector_free(yG);\n\n LOG(LIB_INFO) << ss.str(); \n LOG(LIB_INFO) << \"finished FitData\";\n\n return ss.str();\n}\n\n} // namespace lshkit\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/multiprobelsh-tune.h", "content": "/*\n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n\n This file is part of LSHKIT.\n\n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file mplsh-tune.cpp\n * \\brief Automatic parameter tuning for MPLSH.\n *\n * Assume you have a sample datafile sample.data. You need to take the following two steps for parameter tuning:\n * \\section tune_1 1. Create a model of data distribution.\n *\n * Use the following command\n *\n \\verbatim\n fitdata -D sample.data\n \\endverbatim\n *\n * The following options are acceptable:\n \\verbatim\n Allowed options:\n -h [ --help ] produce help message.\n -N [ -- ] arg (=0) # of points to use. if N = 0, then the whole sample dataset is used.\n -P [ -- ] arg (=50000) # pairs of points to sample\n -Q [ -- ] arg (=1000) # queries to sample\n -K [ -- ] arg (=100) Top K\n -F [ -- ] arg (=10)\n -D [ --data ] arg data file\n \\endverbatim\n *\n * For example, we use the following command to generate statistics from audio.data:\n *\n \\verbatim\n fitdata -D audio.data\n \\endverbatim\n *\n * We get the following output\n *\n \\verbatim\n 0% 10 20 30 40 50 60 70 80 90 100%\n |----|----|----|----|----|----|----|----|----|----|\n ***************************************************\n 3.28031 2.90649\n 0.831193 -0.160539 0.150609\n 0.825921 -0.186676 0.177095\n \\endverbatim\n *\n * Cut and paste the last three lines into a file named audio.param.\n *\n * \\section tune_2 2. Use mplsh-tune to tune parameters.\n *\n * There are four parameters in MPLSH: L, T, M, W. You'll have to choose L and\n * T and let mplsh-tune to find the optimal M and W. For example, we use the\n * following command to tune M and W for the audio dataset.\n *\n * \\verbatim\n mplsh-tune -P audio.param -N 54387 -L 8 -T 20 -R 0.8 -K 50\n\n -P is the parameter file we generated in the 1st step;\n -N is the whole dataset size. Note that in the first step we only use a subset of the data to generate the model.\n -L # hash tables to use.\n -T # of bins probed in each hash table\n -R required recall.\n -r radius\n -K K-NNs to find.\n \\endverbatim\n *\n * mplsh-tune will output best W and M that will meet the required recall. For\n * the audio data, we get the following output:\n *\n \\verbatim\n L = 8 T = 20 M = 20 W = 4.32 recall = 0.805276 cost = 0.0968405\n \\endverbatim\n *\n * We can then run the benchmark and see how close is the prediction to the real number:\n *\n \\verbatim\n mplsh-run -D audio.data -B audio.query -L 8 -T 20 -W 4.32 -M 20 -Q 100 -K 50\n \\endverbatim\n *\n \\verbatim\n Loading data...done.\n Loading benchmark...done.\n Initializing index...\n done.\n Populating index...\n\n 0% 10 20 30 40 50 60 70 80 90 100%\n |----|----|----|----|----|----|----|----|----|----|\n ***************************************************\n CREATE TIME: 3.19s\n Running queries...\n\n 0% 10 20 30 40 50 60 70 80 90 100%\n |----|----|----|----|----|----|----|----|----|----|\n ***************************************************\n QUERY TIME: 0.57s\n [RECALL] 0.8192 +/- 0.234635\n [COST] 0.115853 +/- 0.0980032\n\\endverbatim\n *\n * (Note that the audio dataset is a pretty hard one.)\n *\n * \\section tune_LT How to choose L and T?\n *\n * L is the number of hash tables maintained in the memory and generally larger\n * L results in better performance (smaller cost to reach certain recall).\n * Hash tables only stores pointers to the feature vectors. So on a 64-bit\n * machine, N points will take 8N bytes plus some overhead. For our audio\n * dataset, where there are about 55K points, one hash table takes about 500KB\n * of memory and 8 hash tables will take 4MB.\n *\n * T is the number of buckets to probe in each hash table. A number from 10 ~\n * 100 would be fine. Larger T results in lower cost. Yes, more buckets are\n * probed in each hash table, but that would allow us to make each hash bucket\n * smaller, and the overall effect is less points are scanned to reach the\n * required recall. However, our model doesn't consider the cost of generating\n * the probe sequence and when T is very large, that cost can be significant.\n * So in practice T should not really be much larger than 100.\n *\n *\n */\n\n#ifndef _MPLSH_TUNE_H_\n#define _MPLSH_TUNE_H_\n\n#include \"logging.h\"\n\n#include \n#include \n#include \n\nnamespace lshkit {\n\nstatic const int MIN_L = 1;\nstatic const int MAX_L = 20;\n\nstatic const int MIN_T = 1;\nstatic const int MAX_T = Probe::MAX_T;\n\nstatic const int MIN_M = 1;\nstatic const int MAX_M = Probe::MAX_M;\n\nstatic const double MIN_W = 0.01;\nstatic const double MAX_W = 10;\nstatic const double NUM_W = 400;\nstatic const double DELTA_W = (MAX_W - MIN_W) / NUM_W;\n\n// L, T, M, W\nstatic tune::Interval intervals[] = {\n {MIN_L, MAX_L + 1},\n {MIN_T, MAX_T + 1},\n {0, MAX_M - MIN_M + 1},\n {0, static_cast(NUM_W) + 1}};\n\nstatic double target_recall;\n\nstatic MultiProbeLshDataModel *model;\nstatic double R;\n\ninline\ndouble recall_K (const tune::Input &x) {\n model->setL(x[0]);\n model->setT(x[1]);\n model->setM(MAX_M - x[2]);\n model->setW(MIN_W + DELTA_W * x[3]);\n return model->avgRecall();\n}\n\ninline\ndouble recall_R (const tune::Input &x) {\n model->setL(x[0]);\n model->setT(x[1]);\n model->setM(MAX_M - x[2]);\n model->setW(MIN_W + DELTA_W * x[3]);\n return model->recall(R);\n}\n\ninline\ndouble cost (const tune::Input &x) {\n model->setL(x[0]);\n model->setT(x[1]);\n model->setM(MAX_M - x[2]);\n model->setW(MIN_W + DELTA_W * x[3]);\n return model->cost();\n}\n\ninline\nbool constraint_K (const tune::Input &x) {\n return recall_K(x) > target_recall;\n}\n\ninline\nbool constraint_R (const tune::Input &x) {\n return recall_R(x) > target_recall;\n}\n\ninline\nvoid MPLSHTune(unsigned N, // dataset size\n std::string data_param, // data parameter\n int T,\n int L,\n double desired_recall, // desired recall\n unsigned K, // topk\n int& M,\n float& W\n //unsigned r // radius\n )\n{\n LOG(LIB_INFO) << \"started running MPLSHTune\" ;\n gsl_set_error_handler_off();\n\n // restore initial values\n intervals[0].begin = MIN_L; intervals[0].end = MAX_L + 1;\n intervals[1].begin = MIN_T; intervals[1].end = MAX_T + 1;\n intervals[2].begin = 0; intervals[2].end = MAX_M - MIN_M + 1;\n intervals[3].begin = 0; intervals[3].end = NUM_W + 1;\n\n target_recall = desired_recall;\n model = NULL;\n R = 0.0; // radius\n\n /*\n int T, L, M;\n double W;\n unsigned N, K;\n bool do_K = true;\n string data_param;\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",T\", po::value(&T)->default_value(-1), \"\")\n (\",L\", po::value(&L)->default_value(-1), \"\")\n (\",M\", po::value(&M)->default_value(-1), \"set to -1 for tuning.\")\n (\",W\", po::value(&W)->default_value(-1), \"set to -1 for tuning.\")\n (\"size,N\", po::value(&N), \"dataset size.\")\n (\"param,P\", po::value(&data_param), \"data parameter file.\")\n (\"recall,R\", po::value(&target_recall)->default_value(0.9), \"desired recall.\")\n (\"topk,K\", po::value(&K)->default_value(20), \"\")\n (\"radius,r\", po::value(&R), \"\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm);\n\n if (vm.count(\"help\") || (vm.count(\"param\") < 1) || (vm.count(\"size\") < 1) || T < 1 || L < 1)\n {\n cout << desc;\n return 0;\n }\n\n if (vm.count(\"radius\")) {\n do_K = false;\n }\n */\n\n if (L <= 0 || T <= 0) {\n LOG(LIB_INFO) << \"You need to specify L and T.\" ;\n exit(1);\n }\n\n if (L > 0) {\n intervals[0].begin = L;\n intervals[0].end = L + 1;\n }\n\n if (T > 0) {\n intervals[1].begin = T;\n intervals[1].end = T + 1;\n }\n\n /*\n if (M > 0) {\n intervals[2].begin = MAX_M - M;\n intervals[2].end = MAX_M - M + 1;\n }\n\n if (W > 0) {\n intervals[3].begin = (W - MIN_W) / DELTA_W;\n intervals[3].end = intervals[3].begin + 1;\n }\n */\n\n DataParam param(data_param, 0);\n double scale = param.scale();\n\n MultiProbeLshDataModel local_model(param, N, K);\n model = &local_model;\n\n int begin_M = intervals[2].begin;\n int end_M = intervals[2].end;\n\n LOG(LIB_INFO) << \"iter limits: begin_M \" << begin_M << \" end_M \" << end_M ;\n\n double best_recall = 0.0;\n double best_cost = 1.0;\n M = -1;\n W = -1.0;\n bool found = false;\n\n for (int m = begin_M; m < end_M; ++m) {\n intervals[2].begin = m;\n intervals[2].end = m + 1;\n\n tune::Range range(intervals, intervals + sizeof intervals /sizeof intervals[0]);\n tune::Input input;\n //bool ok = do_K ? tune::Tune(range, constraint_K, &input)\n // : tune::Tune(range, constraint_R, &input);\n\n bool ok = tune::Tune(range, constraint_K, &input);\n\n if (ok) {\n double CurrCost = cost(input);\n LOG(LIB_INFO) << \"iter \" << m << \" \"\n << boost::format(\"L = %d\\tT = %d\\tM = %d\\tW = %g\\trecall = %g\\tcost = %g\")\n % input[0] % input[1] % (MAX_M - input[2]) % ((MIN_W + DELTA_W * input[3]) * sqrt(scale))\n % ( recall_K(input)) % CurrCost \n //% (do_K ? recall_K(input) : recall_R(input)) % cost(input)\n ;\n\n\n double recall = recall_K(input);\n if (//best_recall < recall && \n recall >= target_recall\n && CurrCost < best_cost) {\n best_recall = recall;\n best_cost = CurrCost;\n found = true;\n M = MAX_M - input[2];\n W = (MIN_W + DELTA_W * input[3]) * sqrt(scale);\n }\n\n } else {\n LOG(LIB_INFO) << \"Failed. iter \" << m ;\n }\n }\n\n if (found) {\n LOG(LIB_INFO) << \"best_recall = \" << best_recall << \" \";\n LOG(LIB_INFO) << \"best_cost = \" << best_cost << \" \";\n LOG(LIB_INFO) << \"M = \" << M << \" W = \" << W ;\n } else {\n LOG(LIB_INFO) << \"could not tune M & W\" ;\n exit(1);\n }\n LOG(LIB_INFO) << \"MPLSHTune finished.\" ;\n}\n\n} // namespace lshkit\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/sketch.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n\n#ifndef __LSHKIT_SKETCH__\n#define __LSHKIT_SKETCH__\n\n/**\n * \\file sketch.h\n * \\brief Implementation of LSH based sketches.\n *\n * Sketches are compact representations (as bit-vectors) of large objects. They\n * are simply constructed by concatenating a bunch of 1-bit LSH hash values.\n * The distance between the original objects can be approximated by the hamming\n * distance between sketches. The generated bit-vectors are stored in\n * arrays of type CHUNK (unsigned char by default, so each chunk has 8 bits).\n *\n * Following is an example of distance\n * estimation using sketches:\n *\n * \\code\n * \n * #include \n * using namespace lshkit;\n *\n * typedef Sketch MySketch; // approximate L1 distance\n * TresholdingLsh::Parameter param;\n *\n * param.dim = 128;\n * param.min = 0.0;\n * param.max = 255.0; // Parameters are set for, maybe, SIFT features.\n *\n * DefaultRng rng;\n *\n * const static SKETCH_BITS = 256;\n * const static SKETCH_BYTES = 256 / 8;\n *\n * MySketch sketch(SKETCH_BYTES, param, rng); // from 128 floats to 256 bits.\n * // Note that in practice you are probably not going to simply construct a sketcher like this.\n * // The sketcher used to sketch the query point should be the exact one used to\n * // sketch the data points. So by the time a database has been constructed,\n * // the sketcher is most probably already constructed and saved in an archive.\n * // You should use the serialize method to load the sketcher from the archive (first default construct\n * // a sketcher, than apply sketch.serialize(some_input_stream, 0).)\n *\n *\n * \n * // Allocate space for sketches. Using \"new\" is probably not a good idea.\n * char *query_sketch = new char[SKETCH_BYTES];\n * float *asym_info = new float[SKETCH_BITS]; // to hold asymmetric information.\n *\n * float *query = ...;\n *\n * sketch.apply(query, query_sketch, asym_info);\n *\n * WeightedHammingHelper asym_helper(SKETCH_BYTES);\n * asym_helper.update(query_sketch, asym_info);\n *\n * metric::hamming hamming(SKETCH_BYTES);\n *\n * // Scan the database.\n * // DATABASE is a container of previously constructed sketches.\n * BOOST_FOREACH (const char* data_sketch, DATABASE) {\n *\n * // evaluate the symmetric sketch distance\n * float sym_dist = hamming(query_sketch, data_sketch);\n *\n * // evaluate the asymmetric sketch distance\n * float asym_dist = asym_helper.distTo(data_sketch);\n *\n * // asym_dist should be more reliable than sym_dist for ranking.\n * }\n *\n * \\endcode\n *\n * See src/sketch-run.cpp for a full example.\n *\n *\n * For more information on sketches and asymmetric distance estimators, see\n *\n * Wei Dong, Moses Charikar, Kai Li. Asymmetric Distance Estimation with\n * Sketches for Similarity Search in High-Dimensional Spaces. In\n * Proceedings of the 31st Annual International ACM SIGIR Conference on Research\n * & Development on Information Retrieval. Singapore. July 2008.\n *\n*/\n\n#include \n\n\nnamespace lshkit {\n\n/// LSH-based sketcher.\ntemplate \nclass Sketch\n{\n BOOST_CONCEPT_ASSERT((DeltaLshConcept));\n\n unsigned dim_;\n std::vector lsh_;\n\npublic:\n /// LSH parameter\n typedef typename LSH::Parameter Parameter;\n /// Domain of LSH & Sketcheter\n typedef typename LSH::Domain Domain; \n /// Number of bits in each CHUNK.\n static const unsigned CHUNK_BIT = sizeof(CHUNK) * 8; // #bits in CHUNK\n\n /** Constructor, without initialization.\n */\n Sketch() {\n }\n\n /** Reset the sketcher.\n * @param chunks Number of chunks in the sketch.\n * @param param Parameter to LSH.\n * @param engine Random number generator (usually a variable of type lshkit::DefaultRng.\n */\n template \n void reset (unsigned chunks, Parameter param, Engine &engine) {\n dim_ = chunks;\n lsh_.resize(dim_ * CHUNK_BIT);\n for (unsigned i = 0; i < lsh_.size(); ++i) lsh_[i].reset(param, engine);\n }\n\n /** Constructor with initialation (same as Sketch() immediately followed by reset()).\n * @param chunks Number of chunks in the sketch.\n * @param param Parameter to LSH.\n * @param engine Random number generator (usually a variable of type lshkit::DefaultRng.\n */\n template \n Sketch(unsigned chunks, Parameter param, Engine &engine) : \n dim_(chunks), lsh_(dim_ * CHUNK_BIT)\n {\n for (unsigned i = 0; i < lsh_.size(); ++i) lsh_[i].reset(param, engine);\n }\n\n /// Sketch construction\n void apply (Domain in, CHUNK *out) const\n {\n unsigned l = 0;\n for (unsigned i = 0; i < dim_; ++i) {\n out[i] = 0;\n for (unsigned j = 0; j < CHUNK_BIT; ++j) {\n unsigned k = lsh_[l++](in);\n out[i] = out[i] | (k << j);\n }\n }\n }\n \n /// Asymmetric sketch construction\n /** @param asym The values used for asymmetric distance estimation.\n *\n * For sketch of N bits, the array asym should have enough space to\n * hold N floats.\n */\n void apply (Domain in, CHUNK *out, float *asym) const\n {\n unsigned l = 0;\n for (unsigned i = 0; i < dim_; ++i) {\n out[i] = 0;\n for (unsigned j = 0; j < CHUNK_BIT; ++j) {\n unsigned k = lsh_[l](in, &asym[l]);\n out[i] = out[i] | (k << j);\n l++;\n }\n }\n }\n\n /// Serialize the sketcher.\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & dim_;\n ar & lsh_;\n }\n\n /// Load from stream.\n void load (std::istream &is) {\n serialize(is, 0);\n }\n\n /// Save to stream.\n void save (std::ostream &os) {\n serialize(os, 0);\n }\n\n /// Return the number of bits in the sketch.\n unsigned getBits () const {\n return dim_ * CHUNK_BIT;\n }\n\n /// Return the number of chunks in the sketch.\n unsigned getChunks () const {\n return dim_;\n }\n};\n\n/// Weighted hamming distance calculator.\n/**\n * A helper class to accelerate weighted hamming distance calculation by using\n * a lookup table. Weighted hamming distance is used in\n * asymmetric distance evaluation between a sketch and a query point.\n * A instance of this class holds the information of a single query point, and\n * can be used to estimate the distance from the query point to different sketches\n * in the database. The construction of such a helper instance is costly, so\n * once constructed, you need to use it as many times as possible to lower the\n * amortized cost.\n * \n */\ntemplate \nclass WeightedHammingHelper\n{\npublic:\n static const unsigned CHUNK_BIT = sizeof(CHUNK) * 8;\n /// Constructor.\n /**\n * @param chunks number of chunks in sketch.\n */\n WeightedHammingHelper(unsigned chunks)\n : nchunk_(chunks), lookup_(1 << CHUNK_BIT, chunks)\n {\n }\n\n /// Update the information of the query point.\n /**\n * @param in sketch of the query point.\n * @param asym weights, the output of Sketch::apply.\n */\n void update (const CHUNK *in, const float *asym)\n {\n unsigned l = 0;\n for (unsigned i = 0; i < nchunk_; ++i) {\n CHUNK q = in[i];\n CHUNK p = 0;\n for (;;) {\n CHUNK c = q ^ p;\n float v = 0;\n for (unsigned j = 0; j < CHUNK_BIT; ++j) {\n if (c & (1 << j)) {\n v += asym[l + j];\n }\n }\n lookup_[i][p] = v;\n ++p;\n if (p == 0) break;\n }\n l += CHUNK_BIT;\n }\n }\n\n /// Calculate the distance between the saved query point to an incoming point.\n float distTo (const CHUNK *in)\n {\n float dist = 0;\n for (unsigned i = 0; i < nchunk_; ++i) {\n dist += lookup_[i][in[i]];\n }\n return dist;\n }\n\nprivate:\n unsigned nchunk_;\n Matrix lookup_;\n};\n\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/spectral-hash.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_SPECTRAL_HASH__\n#define __LSHKIT_SPECTRAL_HASH__\n\n// This define is needed for Micrisoft VS\n#define _USE_MATH_DEFINES\n#include \n#include \n#include \n#include \n\n/**\n * \\file spectral-hash.h\n * \\brief LSH based on spectral hashing.\n */\n\nnamespace lshkit {\n\n/// Spectral hashing.\n/**\n * This class only supports loading externally learned hash functions.\n * To learn a hash function from a sample dataset, use the matlab\n * script at matlab/lshkitTrainSH, which is a wrapper of Y. Weiss'\n * spectral hashing code. The learned function can then be loaded\n * by the serialize method.\n *\n * LSHKIT currently doesn't provide native support of learning\n * spectral hash functions.\n *\n * This class can both produce LSH hash values or sketches. When\n * used for hashing and more than 32 bits are produced by the\n * hash function, only the first 32 bits are used.\n *\n * \n * For more information on spectral hashing, see the following reference:\n * Y. Weiss, A. Torralba, R. Fergus. Spectral Hashing.\n * Advances in Neural Information Processing Systems, 2008.\n *\n*/\nclass SpectralHash\n{\n std::vector > pc;\n std::vector mn;\n std::vector > omegas;\npublic:\n typedef unsigned char CHUNK;\n static const unsigned CHUNK_BIT = sizeof(CHUNK) * 8; // #bits in CHUNK\n struct Parameter\n {\n // NO PARAMETER IS DEFINED.\n };\n\n typedef const float *Domain;\n\n SpectralHash ()\n {\n }\n\n ///This is a placeholder and should not be invoked.\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n BOOST_VERIFY(0);\n }\n\n ///This is a placeholder and should not be invoked.\n template \n SpectralHash(const Parameter ¶m, RNG &rng)\n {\n reset(param, rng);\n }\n\n unsigned getRange () const\n {\n if (pc.size() <= sizeof(unsigned) * 8) {\n return 1 << pc.size();\n }\n return 0;\n }\n\n /// Return the number of bits in the sketch.\n unsigned getBits () const {\n return pc.size();\n }\n\n /// Return the number of chunks in the sketch.\n unsigned getChunks () const {\n return (pc.size() + CHUNK_BIT - 1) / CHUNK_BIT;\n }\n\n unsigned operator () (Domain obj) const\n {\n std::vector X(pc.size());\n for (unsigned i = 0; i < pc.size(); ++i) {\n X[i] = 0;\n for (unsigned j = 0; j < pc[i].size(); ++j) {\n X[i] += pc[i][j] * obj[j];\n }\n X[i] -= mn[i];\n }\n\n unsigned ret = 0, mm = std::min(sizeof(unsigned) * 8, omegas.size());\n for (unsigned i = 0; i < mm; ++i) {\n float y = 1;\n for (unsigned j = 0; j < omegas[i].size(); ++j) {\n y *= sin(X[j] * omegas[i][j] + M_PI/2);\n }\n if (y > 0) ret += (1 << i);\n }\n\n return ret;\n }\n\n /// Sketch construction\n void apply (Domain in, CHUNK *out) const\n {\n std::vector X(pc.size());\n for (unsigned i = 0; i < pc.size(); ++i) {\n X[i] = 0;\n for (unsigned j = 0; j < pc[i].size(); ++j) {\n X[i] += pc[i][j] * in[j];\n }\n X[i] -= mn[i];\n }\n\n unsigned l = 0, dim = getChunks();\n for (unsigned i = 0; i < dim; ++i)\n {\n out[i] = 0;\n for (unsigned j = 0; j < CHUNK_BIT; ++j) {\n if (l >= omegas.size()) break;\n float y = 1;\n for (unsigned k = 0; k < omegas[l].size(); ++k) {\n y *= sin(X[k] * omegas[l][k] + M_PI/2);\n }\n if (y > 0) out[i] += (1 << j);\n ++l;\n }\n }\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & pc;\n ar & mn;\n ar & omegas;\n }\n\n};\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/topk.h", "content": "/*\n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n\n This file is part of LSHKIT.\n\n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file topk.h\n * \\brief Top-K data structures.\n */\n\n#ifndef __LSHKIT_TOPK__\n#define __LSHKIT_TOPK__\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n/**\n * \\file topk.h\n * \\brief Top-K data structure for K-NN search.\n *\n * Usage:\n * \\code\n *\n * Topk knn(100); // search for 100-NNs.\n * knn.reset();\n *\n * BOOST_FOREACH(data_point, DATABASE) {\n * Topk::Element e;\n * e.key = key of data_point\n * e.dist = distance(query, data_point);\n * knn << e;\n * }\n *\n * for (unsigned i = 0; i < knn.size(); ++i) {\n * cout << knn[i].key << ':' << knn[i].dist << endl;\n * }\n *\n * \\endcode\n */\n\nnamespace lshkit {\n\n/// Top-K entry.\n/**\n * The entry stored in the top-k data structure. The class Topk is implemented\n * as a heap of TopkEntry.\n */\ntemplate \nstruct TopkEntry\n{\n KEY key;\n float dist;\n bool match (const TopkEntry &e) const { return key == e.key; }\n bool match (KEY e) const { return key == e; }\n\n TopkEntry (KEY key_, float dist_) : key(key_), dist(dist_) {}\n\n // XXX original\n //TopkEntry () : dist(std::numeric_limits::max()) { }\n\n // XXX changed!\n TopkEntry () : key(std::numeric_limits::max()), dist(std::numeric_limits::max()) { }\n\n void reset () { dist = std::numeric_limits::max(); }\n\n friend bool operator < (const TopkEntry &e1, const TopkEntry &e2)\n {\n return e1.dist < e2.dist;\n }\n};\n\n/// Top-K heap.\n/**\n * Following is an example of using the Topk class:\n *\n * Topk topk;\n * topk.reset(k);\n *\n * for each candidate key {\n * topk << key;\n * }\n *\n * At this point topk should contain the best k keys.\n */\ntemplate \nclass Topk: public std::vector >\n{\n unsigned K;\n float R;\n float th;\npublic:\n typedef TopkEntry Element;\n typedef typename std::vector > Base;\n\n Topk () {}\n\n ~Topk () {}\n\n /// Reset the heap.\n void reset (unsigned k, float r = std::numeric_limits::max()) {\n // if (k == 0) throw std::invalid_argument(\"K MUST BE POSITIVE\");\n R = th = r;\n K = k;\n this->resize(k);\n for (typename Base::iterator it = this->begin(); it != this->end(); ++it)\n it->reset();\n }\n\n void reset (unsigned k, KEY key, float r = std::numeric_limits::max()) {\n // if (k == 0) throw std::invalid_argument(\"K MUST BE POSITIVE\");\n R = th = r;\n K = k;\n this->resize(k);\n for (typename Base::iterator it = this->begin(); it != this->end(); ++it) {\n it->reset();\n it->key = key;\n }\n }\n\n void reset (float r) {\n K = 0;\n R = th = r;\n this->clear();\n }\n\n float threshold () const {\n return th;\n }\n\n /// Insert a new element, update the heap.\n Topk &operator << (Element t)\n {\n if (!(t.dist < th)) return *this;\n if (K == 0) { // R-NN\n this->push_back(t);\n return *this;\n }\n // K-NN\n unsigned i = this->size() - 1;\n unsigned j;\n for (;;)\n {\n if (i == 0) break;\n j = i - 1;\n\n #if DEBUG_LEVEL > 90\n std::cout << \"size \" << this->size() << \" j \" << j;\n std::cout << \" t \" << t.key;\n //std::cout << \" this->at(j) \" << this->at(j);\n std::cout << \" this->at(j).dist \" << this->at(j).dist;\n std::cout << \" this->at(j).key \" << this->at(j).key; //XXX\n std::cout << std::endl;\n #endif\n\n if (this->at(j).match(t)) return *this;\n if (this->at(j) < t) break;\n i = j;\n }\n /* i is the place to insert to */\n\n j = this->size() - 1;\n for (;;)\n {\n if (j == i) break;\n this->at(j) = this->at(j-1);\n --j;\n }\n this->at(i) = t;\n th = this->back().dist;\n return *this;\n }\n\n /// Calculate recall.\n /** Recall = size(this /\\ topk) / size(this). */\n float recall (const Topk &topk /* to be evaluated */) const\n {\n unsigned matched = 0;\n //if (this->size() == 0) return 1.0;\n for (typename Base::const_iterator ii = this->begin(); ii != this->end(); ++ii)\n {\n for (typename Base::const_iterator jj = topk.begin(); jj != topk.end(); ++jj)\n {\n if (ii->match(*jj))\n {\n matched++;\n break;\n }\n }\n }\n return float(matched + 1)/float(this->size() + 1);\n }\n\n unsigned getK () const {\n return K;\n }\n};\n\n/// Top-K scanner.\n/**\n * Scans keys for top-K query. This is the object passed into the LSH query interface.\n */\ntemplate \nclass TopkScanner {\npublic:\n /// Key type.\n typedef typename ACCESSOR::Key Key;\n /// Value type.\n typedef typename ACCESSOR::Value Value;\n\n /// Constructor.\n /**\n * @param accessor The scanner use accessor to retrieva values from keys.\n * @param metric The distance metric.\n * @param K value used to reset internal Topk class.\n * @param R value used to reset internal Topk class.\n *\n * (ACCESSOR)accessor is used as a function. Given a key, accessor(key)\n * returns an object (or reference) of the type LSH::Domain. That object is\n * used to calculate a hash value. The key is saved in the hash table.\n * When the associated object is needed, e.g. when scanning a bin,\n * accessor(key) is called to access the object.\n *\n * Metric metric is also used as a function. It accepts two parameters of\n * the type LSH::Domain and returns the distance between the two.\n */\n TopkScanner(const ACCESSOR &accessor, const METRIC &metric, unsigned K, float R = std::numeric_limits::max())\n : accessor_(accessor), metric_(metric), K_(K), R_(R) {\n }\n\n /// Reset the query.\n /**\n * This function should be invoked before each query.\n */\n void reset (Value query) {\n query_ = query;\n accessor_.reset();\n topk_.reset(K_, R_);\n cnt_ = 0;\n }\n\n /// Number of points scanned for the current query.\n unsigned cnt () const {\n return cnt_;\n }\n\n /// TopK results.\n const Topk &topk () const {\n return topk_;\n }\n\n /// TopK results.\n Topk &topk () {\n return topk_;\n }\n\n /// Update the current query by scanning key.\n /**\n * This is normally invoked by the LSH index structure.\n */\n void operator () (Key key) {\n #if DEBUG_LEVEL > 50\n std::cerr << \"topkscanner operator() \" << key << std::endl;\n #endif\n\n if (accessor_.mark(key)) {\n ++cnt_;\n topk_ << typename Topk::Element(key, metric_(query_, accessor_(key)));\n }\n }\nprivate:\n ACCESSOR accessor_;\n METRIC metric_;\n unsigned K_;\n float R_;\n Topk topk_;\n Value query_;\n unsigned cnt_;\n};\n\n/**\n * Specialized for l2sqr.\n */\ntemplate \nclass TopkScanner >{\npublic:\n typedef typename ACCESSOR::Key Key;\n typedef const float *Value;\n\n TopkScanner(const ACCESSOR &accessor, const metric::l2sqr &metric, unsigned K, float R = std::numeric_limits::max())\n : accessor_(accessor), dim_(metric.dim()), K_(K), R_(R) {\n }\n\n void reset (const float *query) {\n query_ = query;\n accessor_.reset();\n topk_.reset(K_, R_);\n cnt_ = 0;\n }\n\n unsigned cnt () const {\n return cnt_;\n }\n\n const Topk &topk () const {\n return topk_;\n }\n\n Topk &topk () {\n return topk_;\n }\n\n void operator () (unsigned key) {\n if (accessor_.mark(key)) {\n ++cnt_;\n unsigned d = dim_ & ~unsigned(7);\n const float *aa = query_, *end_a = aa + d;\n const float *bb = accessor_(key), *end_b = bb + d;\n#ifdef __GNUC__\n __builtin_prefetch(aa, 0, 3);\n __builtin_prefetch(bb, 0, 0);\n#endif\n double th = topk_.threshold();\n float r = 0.0;\n float r0, r1, r2, r3, r4, r5, r6, r7;\n\n const float *a = end_a, *b = end_b;\n\n r0 = r1 = r2 = r3 = r4 = r5 = r6 = r7 = 0.0;\n\n switch (dim_ & 7) {\n case 7: r6 = sqr(a[6] - b[6]);\n case 6: r5 = sqr(a[5] - b[5]);\n case 5: r4 = sqr(a[4] - b[4]);\n case 4: r3 = sqr(a[3] - b[3]);\n case 3: r2 = sqr(a[2] - b[2]);\n case 2: r1 = sqr(a[1] - b[1]);\n case 1: r0 = sqr(a[0] - b[0]);\n }\n\n a = aa; b = bb;\n\n for (; a < end_a; a += 8, b += 8) {\n#ifdef __GNUC__\n __builtin_prefetch(a + 32, 0, 3);\n __builtin_prefetch(b + 32, 0, 0);\n#endif\n r += r0 + r1 + r2 + r3 + r4 + r5 + r6 + r7;\n if (r > th) return;\n r0 = sqr(a[0] - b[0]);\n r1 = sqr(a[1] - b[1]);\n r2 = sqr(a[2] - b[2]);\n r3 = sqr(a[3] - b[3]);\n r4 = sqr(a[4] - b[4]);\n r5 = sqr(a[5] - b[5]);\n r6 = sqr(a[6] - b[6]);\n r7 = sqr(a[7] - b[7]);\n }\n\n r += r0 + r1 + r2 + r3 + r4 + r5 + r6 + r7;\n topk_ << typename Topk::Element(key, r);\n }\n }\n\nprivate:\n ACCESSOR accessor_;\n unsigned dim_;\n unsigned K_;\n float R_;\n Topk topk_;\n const float* query_;\n unsigned cnt_;\n};\n\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/tune.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n#ifndef WDONG_LSHKIT_TUNE\n#define WDONG_LSHKIT_TUNE\n\n/**\n * \\file tune.h\n * \\brief Constrained multi-variate monotone function optimizatioin.\n */\n\n#include \n#include \n#include \n\nnamespace lshkit { namespace tune {\n\nstruct Interval { // [begin, end)\n unsigned begin;\n unsigned end; // max should always work, minimize the working value\n};\n\ntypedef std::vector Range;\ntypedef std::vector Input;\n\ntemplate \nclass TuneHelper\n{\n const Range ⦥\n const F &constraint;\npublic:\n TuneHelper (const Range &ran, const F &con)\n : range(ran), constraint(con)\n {\n }\n\n bool search (Input *v, unsigned depth)\n {\n /*\n std::cout << depth << ':';\n for (unsigned i = 0; i < depth; ++i) {\n std::cout << ' ' << v->at(i);\n }\n std::cout << std::endl;\n */\n\n if (depth >= range.size())\n {\n return constraint(*v);\n }\n\n assert(range[depth].begin < range[depth].end); // range should not be empty\n\n Input left(*v);\n Input right(*v);\n Input middle(*v);\n\n left[depth] = range[depth].begin;\n right[depth] = range[depth].end - 1;\n\n // make sure left doesn't work\n if (search(&left, depth+1)) {\n // if left works, then that's the answer\n *v = left;\n return true;\n }\n\n // make sure right works\n if (!search(&right, depth+1)) { // best doesn't work\n return false;\n }\n\n for (;;) {\n unsigned m = (left[depth] + right[depth]) / 2;\n if (m == left[depth] || m == right[depth]) {\n *v = right;\n return true;\n }\n \n middle[depth] = m;\n if (search(&middle, depth+1)) {\n right = middle;\n }\n else {\n left = middle;\n }\n }\n }\n};\n\ntemplate \nbool Tune (const Range &ran, const F &con, Input *v)\n{\n TuneHelper helper(ran, con);\n v->resize(ran.size());\n return helper.search(v, 0);\n}\n\n} }\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit/vq.h", "content": "/* \n Copyright (C) 2009 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#ifndef __LSHKIT_VQ__\n#define __LSHKIT_VQ__\n\n\n#include \n\nnamespace lshkit {\n\nclass VQ\n{\n unsigned dim;\n unsigned K;\n std::vector means;\n\n void *tree;\n\n void init ();\n unsigned search (const float *obj) const;\n void free ();\npublic:\n // Nothing\n struct Parameter\n {\n unsigned K;\n unsigned dim;\n std::string path;\n };\n\n typedef const float * Domain;\n\n VQ (): tree(0)\n {\n }\n\n template \n void reset(const Parameter ¶m, RNG &rng)\n {\n dim = param.dim;\n K = param.K;\n means.resize(dim * K);\n std::ifstream is(param.path.c_str(), std::ios::binary);\n is.read((char *)&means[0], means.size() * sizeof(float));\n BOOST_VERIFY(is);\n init();\n }\n\n template \n VQ(const Parameter ¶m, RNG &rng) : tree(0)\n {\n reset(param, rng);\n }\n\n ~VQ () {\n free();\n }\n\n unsigned getRange () const\n {\n return K;\n }\n\n unsigned operator () (Domain obj) const\n {\n return search(obj);\n }\n\n template\n void serialize(Archive & ar, const unsigned int version)\n {\n ar & dim;\n ar & K;\n ar & means;\n }\n};\n\n}\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/lshkit/include/lshkit.h", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\mainpage LSHKIT: A C++ Locality Sensitive Hashing Library\n *\n * \\author Wei Dong wdong [@] cs.princeton.edu \n * \\author 2008-2009\n * \\section update_sec UPDATES\n * \\include ../NEWS\n * - Download the CVS snapshot at http://lshkit.sourceforge.net/release/lshkit-snapshot.tar.gz\n * \\section download_sec 1. Getting LSH\n *\n * - Sourceforge projet page: http://sourceforge.net/projects/lshkit/\n * - Browser source code: http://lshkit.cvs.sourceforge.net/lshkit/\n * - Download a release: http://sourceforge.net/project/showfiles.php?group_id=253020\n * - Mailing list: http://sourceforge.net/mailarchive/forum.php?forum_name=lshkit-users\n * - Sample dataset: http://www.cs.princeton.edu/~wdong/lshkit/audio.tar.gz\n *\n * The sample dataset include the data file audio.data and a benchmark file\n * audio.query. Audio.data contains 54,387 192 dimensional vectors and the\n * benchmark contains 10,000 queries of 200-NNs.\n *\n * \\section build_sec 2. Building LSHKIT\n *\n * NOTE: You don't have to build LSHKIT separatly if you only want to use the\n * library but not the tool programs. Instead, you can directly merge the LSHKIT\n * source files to your project. You can avoid installing CMake by doing this.\n * See Section 3.2 for details.\n *\n * LSHKIT requires the following libraries:\n * - Gnu Scientific Library\n * - [OPTIONAL] Boost Library > 1.36, the following packages are used:\n * - program_options\n *\n * LSHKIT uses the CMake cross platform building system. \n *\n * \\subsection build_windows 2.1 Microsoft Visual C++ Express 2008\n *\n * The method would probably work for other versions of visual C++, but I haven't tested.\n *\n * You'll have to install GSL and CMake first if you haven't.\n * Refer to the following pages:\n * - GSL: http://www.quantcode.com/modules/smartfaq/faq.php?faqid=33\n * - CMake: http://www.cmake.org/\n * \n * After installing everything, you still need the following configurations so\n * that CMake can find GSL:\n * - Add an environment variable GSL_ROOT_DIR, so that \\%GSL_ROOT_DIR\\%\\\\include\n * contains the GSL header files and \\%GSL_ROOT_DIR\\%\\\\lib contains the GSL libraries.\n * - Copy the file lshkit\\FindGSL.cmake to \\\\share\\\\cmake-xxx\\\\Modules, where\n * is where you install CMake.\n *\n * If you have Boost installed in your system, you can choose to use your own Boost installation\n * instead of the minimal one comes with LSHKIT. To do that, add an environment\n * variable BOOST_ROOT, so that \\%BOOST_ROOT\\%\\\\include contains the Boost\n * header files and \\%BOOST_ROOT\\%\\\\lib contains the Boost libraries.\n *\n * We are now ready to build LSHKIT. Open the Visual C++ command prompt (by clicking\n * \"Start/All Programs/Microsoft Visual C++.../Visual Studio Tools/Visual\n * Studio 2008 Command Prompt\") and take the following steps:\n * -# Create a building directory, which we call BULID_DIR, change to the directory.\n * -# Run \"cmakesetup \", where is the LSHKIT root directory.\n * -# The CMake setup window will show up. Click the \"Configure\" button in the bottom of the window.\n * -# Choose \"NMake Makefiles\" in the popup window and confirm.\n * -# CMake will run for a while.\n * -# After a list of red cached values show up, click the \"Configure\" button again. You might\n * want to change the value of some variables before that, for example, change CMAKE_BUILD_TYPE\n * from \"Debug\" to \"Release\" to enable optimization.\n * -# The cached variables becomes gray now, click the \"OK\" button in the bottom.\n * -# A Makefile has been generated, run \"nmake\" to make.\n * -# The making process will last for a while. After that, the LSHKIT library will be in the \"lib\"\n * directory and the executables will be in the \"bin\" directory.\n *\n * In Step 4, you can also choose \"Visual Studio 9 2008\" (or other versions) to create a project\n * file, you that you can build using the Visual C++ IDE.\n *\n * \\subsection build_linux 2.2 Linux\n *\n * -# Copy the file lshkit\\FindGSL.cmake to /share/cmake-xxx/Modules, where\n * is where you install CMake, which is usually /usr.\n * -# make a temporary building directory, change to it.\n * -# Run \"cmake LSHKIT_DIR\", where LSHKIT_DIR is the LSHKIT root directory.\n * You can also use \"cmake -DCMAKE_BUILD_TYPE=RELEASE LSHKIT_DIR\" or \"cmake -DCMAKE_BUILD_TYPE=DEBUG LSHKIT_DIR\" to specify the build type.\n * -# A makefile should have been generated. Run make.\n * -# The making process will last for a while. After that, the LSHKIT library will be in the \"lib\"\n * directory and the executables will be in the \"bin\" directory.\n *\n * \\section using_sec 3. Using LSHKIT in Your Project\n *\n * Although the LSHKIT tool programs depends on boost_program_option to parse command line options,\n * the main LSHKIT library only depends on the Boost headers. If you use LSHKIT library in your project,\n * you don't need to link to any of the boost library (unless you use them in other parts of your project). \n *\n * \\subsection using_sec_1 3.1 Use LSHKIT as a library\n * -# Build LSHKIT.\n * -# Configure your building environment so that \"LSHKIT_DIR/include\" and the LSHKIT library can be found\n * by your C++ compiler. If you don't have Boost installed in your system, you'll also need to add\n * \"LSHKIT/3rd-party/boost\" to your compiler's include file search path.\n * -# Add \"#include \" to your C++ source code and you can use most of the LSHKIT functionalities.\n * -# Link libgsl and libgslcblas (they may be named differently depending on your system) to your program.\n *\n * \\subsection using_sec_2 3.2 Directly add LSHKIT source to your project\n * By directly using the LSHKIT source, you can avoid installing CMake.\n\n * -# Configure your building environment so that \"LSHKIT_DIR/include\" and the LSHKIT library can be found\n * by your C++ compiler. If you don't have Boost installed in your system, you'll also need to add\n * \"LSHKIT/3rd-party/boost\" to your compiler's include file search path.\n * -# Make sure that GSL is properly installed in your system and your compiler can find the GSL header files.\n * -# Add all C++ source files in \"LSHKIT/src\" to your project.\n * -# Add \"#include \" to your C++ source code and you can use most of the LSHKIT functionalities.\n * -# Link libgsl and libgslcblas (they may be named differently depending on your system) to your program.\n * \n * \\section usecase_sec 4. Some Specific Use Cases\n * (See the documentation of source files pointed to.)\n * - The file format used by the tool programs: matrix.h\n * - (Semi-)Automatic parameter tuning for Multi-Probe LSH and run benchmarks: mplsh-tune.cpp\n * - Building a Multi-Probe LSH index: mplsh.h\n * - Using LSH to construct sketches: sketch.h\n * - Using LSH to construct random histograms to match sets of features: histogram.h\n * - The supported LSH classes: lsh.h\n * - How to build complex LSH classses out of the basic ones: composite.h\n * - How to add your own LSH classes: concept.h\n *\n * \\section demo_sec 5. Demo Program Documentation\n * - scan.cpp\n * - lsh-run.cpp\n * - mplsh-run.cpp\n * - fitdata.cpp\n * - mplsh-predict.cpp\n * - mplsh-tune.cpp\n * - sketch-run.cpp\n *\n * \\section license_sec 6. License\n *\n * LSHKIT is distributed under the terms of the GNU General Public License (GPL).\n *\n * I do not intend to use the strict GPL license. However, the performance modeling\n * part of the package depends on the GPLed Gnu Scientific Library for regression,\n * root finding and numerical integration. If you want to use LSHKIT in your closed source\n * software and are willing to get rid of the performance modeling part (in principle,\n * you'll still be able to use it for performance tuning so long as you don't\n * distribute it; the tuning results can be hard coded into your non-GPLed\n * software), contact me and maybe we'll be able to work out a way. \n * \n * \\section citing_sec 7. Publications\n * \n * Wei Dong, Zhe Wang, William Josephson, Moses Charikar, Kai Li. Modeling LSH for Performance Tuning. In Proceedings of the 17th Conference on Information and Knowledge Management. Napa Valley, CA, USA. October 2008.\n *\n * Wei Dong, Zhe Wang, Moses Charikar, Kai Li. Efficiently Matching Sets of Features with Random Histograms. In Proceedings of the 16th ACM International Conference on Multimedia. Vancouver, Canada. October 2008.\n *\n * Wei Dong, Moses Charikar, Kai Li. Asymmetric Distance Estimation with Sketches for Similarity Search in High-Dimensional Spaces. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research & Development on Information Retrieval. Singapore. July 2008.\n *\n */\n\n/**\n * \\file lshkit.h\n * \\brief The LSHKIT master header file.\n *\n * You only need to include this file to use the most functionalities of LSHKIT.\n */\n\n#ifndef __LSHKIT_WDONG__\n#define __LSHKIT_WDONG__\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/src/apost.cpp", "content": "/* \n Copyright (C) 2009 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace lshkit\n{\n\n// Gaussian model of nearest neighbor hash components\n// this is the gaussian model for one training query point\nstruct ExampleModel {\n std::vector H;\n std::vector mean;\n std::vector var;\n\n void estimate (const APostLsh &lsh, const APostExample &example)\n {\n#if 0\n // calculate H values\n const float *query = example.query;\n H.resize(lsh.M);\n lsh.apply1(query, &H);\n\n // mean & cov matrix of examples\n const std::vector &results = example.results;\n std::vector M;\n std::vector > cov; // lower matrix\n unsigned dim = lsh.dim;\n\n BOOST_VERIFY(results.size() > 1);\n // calculate mean of NNs\n M.resize(dim);\n std::fill(M.begin(), M.end(), 0.0);\n BOOST_FOREACH(const float *v, results) {\n for (unsigned i = 0; i < dim; ++i) {\n M[i] += v[i];\n }\n }\n for (unsigned i = 0; i < dim; ++i) {\n M[i] /= results.size();\n }\n\n // calculate cov of NNs\n cov.resize(dim);\n for (unsigned i = 0; i < dim; i++) {\n cov[i].resize(i+1);\n std::fill(cov[i].begin(), cov[i].end(), 0.0);\n }\n\n BOOST_FOREACH(const float *v, results) {\n for (unsigned i = 0; i < dim; i++) {\n for (unsigned j = 0; j <= i; j++) {\n cov[i][j] += (v[i] - M[i]) * (v[j] - M[j]);\n }\n }\n }\n\n for (unsigned i = 0; i < dim; i++) {\n for (unsigned j = 0; j <= i; j++) {\n cov[i][j] /= (results.size() - 1);\n }\n }\n\n // mean & std of hash values\n mean.resize(lsh.M);\n lsh.apply1(&M[0], &mean);\n\n var.resize(lsh.M);\n for (unsigned i = 0; i < lsh.M; i++) { // for each hash component\n var[i] = 0;\n for (unsigned ii = 0; ii < lsh.dim; ii++) {\n unsigned jj;\n for (jj = 0; jj <= ii; jj++) {\n var[i] += lsh.a[i][ii] * cov[ii][jj] * lsh.a[i][jj];\n }\n for (; jj < lsh.dim; jj++) {\n var[i] += lsh.a[i][ii] * cov[jj][ii] * lsh.a[i][jj];\n }\n }\n var[i] /= sqr(lsh.W);\n }\n#else // equivalent but simpler method: directly calculate the mean & var of h values\n \n // calculate H values\n const float *query = example.query;\n H.resize(lsh.M);\n lsh.apply1(query, &H);\n\n // mean & cov matrix of examples\n mean.resize(lsh.M);\n var.resize(lsh.M);\n const std::vector &results = example.results;\n\n for (unsigned i = 0; i < lsh.M; ++i) {\n float sum = 0; \n float sum2 = 0;\n BOOST_FOREACH(const float *v, results) {\n float h = lsh.apply1(v, i);\n sum += h;\n sum2 += sqr(h);\n }\n mean[i] = sum / results.size();\n var[i] = (sum2 - mean[i] * sum)/(results.size() - 1);\n }\n#endif\n#ifdef DEBUGGING\n for (unsigned i = 0; i < lsh.M; ++i) {\n std::cout << H[i] << ':' << mean[i] << ':' << var[i] << std::endl;\n }\n#endif\n }\n};\n\nclass GaussianHashModel\n{\n std::vector M;\n float sigma;\npublic:\n GaussianHashModel (const APostLsh &lsh, const std::vector &examples, float s = 0.0) {\n if (s == 0.0) {\n s = 1.0 / 5;\n // I believe there's a bug in paper.\n // Right below equation 16, the paper says the default\n // sigma is W/5. However,\n // the h values are already divided by W (h = (a v + b) / W)\n // and it makes no sense to divide it by W again.\n }\n sigma = s;\n\n M.resize(examples.size());\n for (unsigned i = 0; i < examples.size(); ++i) {\n M[i].estimate(lsh, examples[i]);\n }\n }\n\n void estimate (unsigned m, float h, float *mean, float *std) { // mth hash component\n float mm = 0, vv = 0, ss = 0;\n for (unsigned i = 0; i < M.size(); i++) { // for each example\n float k = gsl_ran_gaussian_pdf(M[i].H[m] - h, sigma);\n mm += k * M[i].mean[m];\n vv += k * M[i].var[m];\n ss += k;\n }\n mm /= ss;\n vv /= ss;\n *mean = mm;\n *std = std::sqrt(vv);\n }\n};\n\nstatic inline float GaussianInterval (float mean, float std, float l, float u) {\n return gsl_cdf_gaussian_P(u - mean, std)\n - gsl_cdf_gaussian_P(l - mean, std);\n}\n\nvoid APostModel::train (const APostLsh &lsh,\n const std::vector &examples,\n unsigned N, float k_sigma, float expand) {\n Nz = N;\n ex = expand;\n GaussianHashModel parzen(lsh, examples, k_sigma);\n\n umin.resize(lsh.M);\n umax.resize(lsh.M);\n lookup.resize(lsh.M);\n means.resize(lsh.M);\n stds.resize(lsh.M);\n // for each component h\n std::cout << \"Training LSH table.\" << std::endl;\n boost::progress_display progress(lsh.M * Nz);\n for (unsigned m = 0; m < lsh.M; ++m) {\n {\n float ex = expand * (lsh.umax[m] - lsh.umin[m]);\n umin[m] = lsh.umin[m] - ex;\n umax[m] = lsh.umax[m] + ex;\n }\n\n float delta = (umax[m] - umin[m]) / Nz;\n\n lookup[m].resize(Nz);\n means[m].resize(Nz);\n stds[m].resize(Nz);\n // for each quantum of h(q)\n for (unsigned n = 0; n < Nz; ++n) {\n float mean, std;\n parzen.estimate(m, umin[m] + (n + 0.5) * delta, &mean, &std);\n means[m][n] = mean;\n stds[m][n] = std;\n std::vector &lmn = lookup[m][n];\n#if 0 // this is the original method\n // a losts of h values will have very small probability and\n // those are essentially not useful\n int minh(std::floor(umin[m]));\n int maxh(std::floor(umax[m])); // min & max inclusive\n unsigned size = maxh - minh + 1;\n BOOST_VERIFY(size >= 1);\n lmn.resize(size);\n for (int h = minh; h <= maxh; ++h) {\n lmn[h].h = h;\n lmn[h].pr = GaussianInterval(mean, std, float(h), float(h + 1));\n }\n#else // here we only generate those h values with reasonably large probability\n {\n const static float THRESHOLD = 1e-7;\n int h0(std::floor(mean));\n for (int h = h0; ; h++) {\n PrH prh;\n prh.h = h;\n prh.pr = GaussianInterval(mean, std, float(h), float(h+1));\n if (prh.pr < THRESHOLD) break;\n lmn.push_back(prh);\n\n }\n for (int h = h0 - 1; ; h--) {\n PrH prh;\n prh.h = h;\n prh.pr = GaussianInterval(mean, std, float(h), float(h+1));\n if (prh.pr < THRESHOLD) break;\n lmn.push_back(prh);\n\n }\n BOOST_VERIFY(lmn.size() > 0);\n }\n\n#endif\n std::sort(lmn.begin(), lmn.end());\n ++progress;\n }\n }\n}\n\nstruct PrC {\n unsigned m;\n const std::vector *prh;\n friend bool operator < (const PrC &c1, const PrC &c2) {\n if (c1.prh->size() <= 1) return false;\n if (c2.prh->size() <= 1) return true;\n return c1.prh->at(1).pr > c2.prh->at(1).pr;\n }\n};\n\nstruct Probe {\n std::vector off;\n unsigned last;\n float pr;\n const std::vector *range;\n\n Probe () {\n BOOST_VERIFY(0);\n }\n\n Probe (const std::vector *range_):\n off(range_->size()),\n last(0),\n range(range_)\n {\n std::fill(off.begin(), off.end(), 0);\n }\n\n bool canShift () const {\n if (off[last] != 1) return false;\n if (last + 1 >= off.size()) return false;\n if (range->at(last + 1) <= 1) return false;\n return true;\n }\n\n void shift () {\n off[last] = 0;\n last++;\n off[last] = 1;\n }\n\n bool canExpand () const {\n if (last + 1 >= off.size()) return false;\n if (range->at(last + 1) <= 1) return false;\n return true;\n }\n\n void expand () {\n last++;\n off[last] = 1;\n }\n\n bool canExtend () const {\n if (off[last] + 1 >= range->at(last)) return false;\n return true;\n }\n\n void extend () {\n off[last]++;\n }\n\n friend bool operator < (const Probe &p1, const Probe &p2) {\n return p1.pr < p2.pr;\n }\n\n void setPr (const std::vector &pl) {\n pr = 1.0;\n for (unsigned i = 0; i < off.size(); ++i) {\n pr *= pl[i].prh->at(off[i]).pr;\n }\n// std::cerr << \"PR = \" << pr << std::endl;\n }\n\n unsigned hash (const APostLsh &lsh,\n const std::vector &pl) {\n unsigned r = 0;\n for (unsigned i = 0; i < lsh.M; ++i) {\n r += lsh.c[pl[i].m] * unsigned(pl[i].prh->at(off[i]).h);\n }\n return r % lsh.H;\n }\n\n#ifdef DEBUGGING\n unsigned print (const std::vector &pl) {\n BOOST_FOREACH(unsigned v, off) {\n std::cout << ' ' << v;\n }\n std::cout << std::endl;\n }\n#endif\n};\n\nvoid APostModel::genProbeSequence (const APostLsh &lsh,\n APostLsh::Domain query,\n float recall, unsigned T,\n std::vector *seq) const\n{\n#ifdef DEBUGGING\n std::cout << \"Range of each hash component:\" << std::endl;\n for (unsigned i = 0; i < lsh.M; ++i) {\n std::cout << boost::format(\"m = %1%: %2%, %3%\") % i % umin[i] % umax[i] << std::endl;\n }\n std::cout << \"Data distribution on each hash component:\" << std::endl;\n#endif\n std::vector pl(lsh.M);\n for (unsigned i = 0; i < lsh.M; ++i) {\n pl[i].m = i;\n\n float h = lsh.apply1(query, i);\n\n if (h < umin[i]) {\n std::cerr << \"hash[\" << i << \"] out of range \" << h << \" < umin = \" << umin[i] << std::endl;\n h = umin[i];\n }\n else if (h > umax[i]) {\n std::cerr << \"hash[\" << i << \"] out of range \" << h << \" > umax = \" << umax[i] << std::endl;\n h = umax[i];\n }\n\n unsigned qh = (h - umin[i]) * Nz / ( umax[i] - umin[i]);\n BOOST_VERIFY(qh < lookup[i].size());\n\n pl[i].prh = &lookup[i][qh];\n\n#ifdef DEBUGGING\n\n std::cout << boost::format(\"m = %1%: h = %2% mean = %3% std= %4%\")\n % i % h % means[i][qh] % stds[i][qh] << std::endl;\n#endif\n }\n\n std::sort(pl.begin(), pl.end());\n#ifdef DEBUGGING\n std::cout << \"Sorted probability of windows on each component:\" << std::endl;\n\n BOOST_FOREACH(const PrC &prc, pl) {\n std::cout << boost::format(\"m = %1%:\") % prc.m;\n BOOST_FOREACH(PrH prh, *prc.prh) {\n if (prh.pr == 0) break;\n std::cout << boost::format(\" %1%@h=%2%\") % prh.pr % prh.h;\n }\n std::cout << std::endl;\n }\n\n std::cout << \"Probe sequence\" << std::endl;\n#endif\n // generate probe sequence\n seq->clear();\n\n std::vector range(pl.size());\n for (unsigned i = 0; i < range.size(); ++i) {\n range[i] = pl[i].prh->size();\n }\n\n\n std::vector heap;\n heap.push_back(Probe(&range));\n\n heap.back().setPr(pl);\n float pr = heap.back().pr;\n seq->push_back(heap.back().hash(lsh, pl));\n\n if (pr >= recall) return;\n if (seq->size() >= T) return;\n\n heap.back().off[0] = 1;\n heap.back().setPr(pl);\n\n for (;;) {\n if (pr >= recall) break;\n if (seq->size() >= T) break;\n if (heap.empty()) break;\n\n pop_heap(heap.begin(), heap.end());\n Probe p = heap.back();\n heap.pop_back();\n\n seq->push_back(p.hash(lsh, pl));\n pr += p.pr;\n#ifdef DEBUGGING\n std::cout << pr << \", \" << p.pr << \":\";\n p.print(pl);\n#endif\n\n if (p.canShift()) {\n heap.push_back(p);\n heap.back().shift();\n heap.back().setPr(pl);\n push_heap(heap.begin(), heap.end());\n }\n\n if (p.canExpand()) {\n heap.push_back(p);\n heap.back().expand();\n heap.back().setPr(pl);\n push_heap(heap.begin(), heap.end());\n }\n\n if (p.canExtend()) {\n heap.push_back(p);\n heap.back().extend();\n heap.back().setPr(pl);\n push_heap(heap.begin(), heap.end());\n }\n }\n#ifdef DEBUGGING\n std::cout << \"DONE\" << std::endl;\n#endif\n}\n\n}\n" }, { "path": "algorithms/NMSLIB/code/lshkit/src/char_bit_cnt.cpp", "content": "#include \n\nunsigned lshkit::metric::basic_hamming::char_bit_cnt [] = {\n0x0,\n0x1,\n0x1,\n0x2,\n0x1,\n0x2,\n0x2,\n0x3,\n0x1,\n0x2,\n0x2,\n0x3,\n0x2,\n0x3,\n0x3,\n0x4,\n0x1,\n0x2,\n0x2,\n0x3,\n0x2,\n0x3,\n0x3,\n0x4,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x1,\n0x2,\n0x2,\n0x3,\n0x2,\n0x3,\n0x3,\n0x4,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x1,\n0x2,\n0x2,\n0x3,\n0x2,\n0x3,\n0x3,\n0x4,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x4,\n0x5,\n0x5,\n0x6,\n0x5,\n0x6,\n0x6,\n0x7,\n0x1,\n0x2,\n0x2,\n0x3,\n0x2,\n0x3,\n0x3,\n0x4,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x4,\n0x5,\n0x5,\n0x6,\n0x5,\n0x6,\n0x6,\n0x7,\n0x2,\n0x3,\n0x3,\n0x4,\n0x3,\n0x4,\n0x4,\n0x5,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x4,\n0x5,\n0x5,\n0x6,\n0x5,\n0x6,\n0x6,\n0x7,\n0x3,\n0x4,\n0x4,\n0x5,\n0x4,\n0x5,\n0x5,\n0x6,\n0x4,\n0x5,\n0x5,\n0x6,\n0x5,\n0x6,\n0x6,\n0x7,\n0x4,\n0x5,\n0x5,\n0x6,\n0x5,\n0x6,\n0x6,\n0x7,\n0x5,\n0x6,\n0x6,\n0x7,\n0x6,\n0x7,\n0x7,\n0x8,\n0};\n" }, { "path": "algorithms/NMSLIB/code/lshkit/src/kdtree.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \"kdtree.h\"\n\n#define verify(_x) \\\n do { \\\n if (!(_x)) { \\\n fprintf(stderr, \"Assertion failure in %s line %d of %s: %s\\n\", \\\n __FUNCTION__, __LINE__, __FILE__, #_x); \\\n } \\\n } while (0)\n\n// l2 distance\nstatic inline float sqr (float x) {\n return x * x;\n}\n\nstatic inline float l2sqr (const float *p1, const float *p2, unsigned D) {\n float l = 0;\n unsigned d;\n // we hard coded DIM so this loop can be unrolled better by the compiler\n for (d = 0; d < D; d++) {\n l += sqr(p1[d] - p2[d]);\n }\n return l;\n}\n\n// very compact simplementation of KD-tree\n// mainly based on D. Mount's ANN library.\n// the data structure is statically allocated\n// and dynamically constructed.\n\ntypedef struct kd_node kd_node_t;\n // node really means internal nodes\nunion kd_leaf_or_node {\n unsigned long long leaf; // leaf, a single index into the means array\n const kd_node_t *node;\n};\n// because all the nodes are statically allocated, the range of\n// the address is known, and the range of leaf is from 0 to K-1.\n// usually the first node address should be larger than K,\n// so we can tell whether it's a leaf or a node simply by looking at the values.\n// we check in the \n\nstatic inline int kd_is_leaf (union kd_leaf_or_node u, unsigned K) {\n // we have to test with node because node is 64-bit\n // and leaf is 32-bit\n // the lower 32-bit of any 64-bit value can be smaller than K\n //return u.node < (const struct kd_node *)(unsigned long long)K;\n return u.leaf < K;\n}\n\n// KD-tree internal node\nstruct kd_node { \n unsigned cut_dim;\n float cut_val;\n float lower, upper; // range of the dimension of this space partition\n union kd_leaf_or_node left, right;\n};\n\n// lower and upper bound of each dimension with all the cluster centers\ntypedef struct {\n float *lo;\n float *hi;\n} bnds_t;\n\nstruct kd_tree {\n unsigned K;\n unsigned dim;\n const float *means;\n kd_node_t *nodes; // statically allocate all the nodes\n unsigned next_node; // keep the number of node used\n bnds_t bnds;\n};\n\nstatic inline kd_node_t *kd_alloc_node (kd_tree_t *tree) {\n return &tree->nodes[tree->next_node++];\n}\n\n\nkd_tree_t *kd_tree_alloc (unsigned K, unsigned dim) {\n kd_tree_t *tree = (kd_tree_t *)malloc(sizeof(kd_tree_t));\n verify(tree);\n tree->K = K;\n tree->dim = dim;\n tree->means = NULL;\n tree->nodes = (kd_node_t*)malloc((K-1) * sizeof(kd_node_t));\n verify(tree->nodes);\n tree->bnds.lo = (float *)malloc(dim * sizeof(float));\n verify(tree->bnds.lo);\n tree->bnds.hi = (float *)malloc(dim * sizeof(float));\n verify(tree->bnds.hi);\n return tree;\n}\n\nvoid kd_tree_free (kd_tree_t *tree) {\n free(tree->nodes);\n free(tree->bnds.lo);\n free(tree->bnds.hi);\n free(tree);\n}\n\n\n#define ERR 0.001\nstatic inline void bisec (\n kd_tree_t *tree,\n unsigned *idx, unsigned n, // item ids to be divided\n // will be shuffled\n const bnds_t *bnds, // bounds of the box to be divided\n unsigned *cut_dim, float *cut_val,\n unsigned *n_lo // # items on the lower side\n ) {\n\n unsigned dim = tree->dim;\n const float *means = tree->means;\n unsigned cd;\n unsigned d, i, t;\n unsigned br1, br2;\n int l, r;\n float cv, ideal_cv;\n float max_len, len;\n float max_spr, spr;\n float cd_min, cd_max;\n cd_min = cd_max = 0;\n\n // find the longest side of the bounding box\n max_len = bnds->hi[0] - bnds->lo[0];\n for (d = 1; d < dim; d++) {\n len = bnds->hi[d] - bnds->lo[d];\n if (len > max_len) max_len = len;\n }\n\n // find the dimension with maximal spread as the cutting dimension\n max_spr = -1;\n cd = 0; // just to avoid warning\n for (d = 0; d < dim; d++) {\n len = bnds->hi[d] - bnds->lo[d];\n\n // if this side is among the longest\n if (len > (1-ERR) * max_len) {\n\n // take the spread\n float min, max;\n min = max = (means + dim * idx[0])[d];\n for (i = 1; i < n; ++i) {\n float v = (means + dim * idx[i])[d];\n if (v < min) min = v;\n if (v > max) max = v;\n }\n spr = max - min;\n if (spr > max_spr) {\n max_spr = spr;\n cd_min = min;\n cd_max = max;\n cd = d;\n }\n }\n }\n\n ideal_cv = cv = (bnds->lo[cd] + bnds->hi[cd]) / 2;\n \n if (cv < cd_min) cv = cd_min;\n else if (cv > cd_max) cv = cd_max;\n\n l = 0;\n r = n - 1;\n for (;;) {\n while (l < n && (means + dim * idx[l])[cd] < cv) l++;\n while (r >= 0 && (means + dim * idx[r])[cd] >= cv) r--;\n if (l > r) break;\n t = idx[l], idx[l] = idx[r], idx[r] = t;\n l++;\n r--;\n }\n br1 = l; // means[0..br1-1][d] < cv <= means[br1..n-1][d]\n r = n - 1;\n for (;;) {\n while (l < n && (means + dim * idx[l])[cd] <= cv) l++;\n while (r >= 0 && (means + dim * idx[r])[cd] > cv) r--;\n if (l > r) break;\n t = idx[l], idx[l] = idx[r], idx[r] = t;\n l++;\n r--;\n }\n br2 = l; // means[br1..br2-1][d] = cv < means[br2..n-1][d]\n\n *cut_dim = cd;\n *cut_val = cv;\n\n if (ideal_cv < cd_min) *n_lo = 1;\n else if (ideal_cv > cd_max) *n_lo = n - 1;\n else if (br1 > n/2) *n_lo = br1;\n else if (br2 < n/2) *n_lo = br2;\n else *n_lo = n/2;\n}\n\nstatic const kd_node_t *kd_index_hlp (kd_tree_t *tree, unsigned *idx, unsigned n,\n bnds_t *bnds // bounds of the subtree\n ) {\n\n kd_node_t *node;\n unsigned n_lo, n_hi, cut_dim;\n float cut_val;\n float lo, hi; // save value for the cutted dimension\n assert(n > 1);\n node = kd_alloc_node(tree);\n\n bisec(tree, idx, n, bnds, &cut_dim, &cut_val, &n_lo);\n\n node->cut_dim = cut_dim;\n node->cut_val = cut_val;\n\n lo = node->lower = bnds->lo[cut_dim];\n hi = node->upper = bnds->hi[cut_dim];\n\n // construct the left subtree\n bnds->hi[cut_dim] = cut_val;\n assert(n_lo > 0);\n if (n_lo == 1) {\n node->left.leaf = idx[0];\n }\n else {\n node->left.node = kd_index_hlp(tree, idx, n_lo, bnds);\n }\n bnds->hi[cut_dim] = hi;\n\n // construct the right subtree\n bnds->lo[cut_dim] = cut_val;\n n_hi = n - n_lo;\n if (n_hi == 1) {\n node->right.leaf = idx[n-1];\n }\n else {\n node->right.node = kd_index_hlp(tree, idx + n_lo, n_hi, bnds);\n }\n bnds->lo[cut_dim] = lo;\n\n return node;\n}\n\nvoid kd_tree_index (kd_tree_t *tree, const float *means) {\n unsigned *kd_idx;\n unsigned d, i;\n\n tree->means = means;\n\n kd_idx = (unsigned *)malloc(tree->K * sizeof(unsigned));\n verify(kd_idx);\n tree->next_node = 0;\n // fprintf(stderr, \"XXX: %llu.\\n\", tree->nodes);\n verify(tree->nodes > (kd_node_t *)(unsigned long long)tree->K);\n\n // init idx to an arbitrary permutation\n for (i = 0; i < tree->K; i++) {\n kd_idx[i] = i;\n }\n\n // bounding box\n for (d = 0; d < tree->dim; d++) {\n const float *cm = means + d;\n tree->bnds.lo[d] = tree->bnds.hi[d] = *cm;\n cm += tree->dim;\n for (i = 1; i < tree->K; i++, cm += tree->dim) {\n if (*cm < tree->bnds.lo[d]) {\n tree->bnds.lo[d] = *cm;\n }\n else if (*cm > tree->bnds.hi[d]) {\n tree->bnds.hi[d] = *cm;\n }\n }\n }\n\n // recursively construct the tree\n // the first node automatically becomes the root\n kd_index_hlp(tree, kd_idx, tree->K, &tree->bnds);\n\n verify(tree->next_node == tree->K - 1);\n\n free(kd_idx);\n}\n\nstruct kd_search_stat {\n const float *means;\n unsigned dim;\n unsigned K;\n const float *pt; // query\n unsigned cnt;\n unsigned nn;\n float nn_dist;\n};\n\nstatic void kd_search_node (const kd_node_t *node, struct kd_search_stat *stat, float d2b);\n\nstatic inline void kd_search_leaf_or_node (union kd_leaf_or_node lon,\n struct kd_search_stat *stat,\n float d2b) {\n\n if (kd_is_leaf(lon, stat->K)) {\n float l = l2sqr(stat->pt, stat->means + stat->dim * lon.leaf, stat->dim);\n stat->cnt++;\n if (l < stat->nn_dist) {\n stat->nn = lon.leaf;\n stat->nn_dist = l;\n }\n }\n else { \n kd_search_node(lon.node, stat, d2b);\n }\n}\n\nstatic void kd_search_node (const kd_node_t *node, struct kd_search_stat *stat, float d2b) {\n\n unsigned cd = node->cut_dim;\n float cv = node->cut_val;\n const float *pt = stat->pt;\n float cut_diff = pt[cd] - cv;\n float box_diff;\n\n if (cut_diff < 0) {\n kd_search_leaf_or_node(node->left, stat, d2b);\n\n box_diff = node->lower - pt[cd];\n if (box_diff < 0) {\n box_diff = 0;\n }\n d2b = d2b + sqr(cut_diff) - sqr(box_diff);\n\n if (d2b < stat->nn_dist) {\n kd_search_leaf_or_node(node->right, stat, d2b);\n }\n }\n else {\n kd_search_leaf_or_node(node->right, stat, d2b);\n\n float box_diff = pt[cd] - node->upper;\n if (box_diff < 0) {\n box_diff = 0;\n }\n\n d2b = d2b + sqr(cut_diff) - sqr(box_diff);\n\n if (d2b < stat->nn_dist) {\n kd_search_leaf_or_node(node->left, stat, d2b);\n }\n }\n}\n\n// lookup is readonly\nunsigned kd_tree_search (kd_tree_t *tree, const float *pt, unsigned *cnt) {\n\n unsigned d;\n float d2b = 0;\n for (d = 0; d < tree->dim; d++) {\n if (pt[d] < tree->bnds.lo[d]) d2b += sqr(tree->bnds.lo[d] - pt[d]);\n else if (pt[d] > tree->bnds.hi[d]) d2b += sqr(pt[d] - tree->bnds.hi[d]);\n }\n\n struct kd_search_stat stat;\n stat.dim = tree->dim;\n stat.K = tree->K;\n stat.means = tree->means;\n stat.pt = pt;\n stat.cnt = 0;\n stat.nn = 0;\n stat.nn_dist = FLT_MAX;\n\n kd_search_node(tree->nodes, &stat, d2b);\n\n *cnt = stat.cnt;\n\n /*\n {\n // fprintf(stderr, \"C\");\n unsigned r = kd_tree_ln_search(tree, pt, cnt);\n if (r != stat.nn) {\n float l = l2sqr(pt, tree->means + tree->dim * r, tree->dim);\n fprintf(stderr, \"KDTREE: %u %g %u %g\\n\", stat.nn, stat.nn_dist, r, l);\n }\n }\n */\n\n return stat.nn;\n}\n\nunsigned kd_tree_ln_search (kd_tree_t *tree, const float *pt, unsigned *cnt)\n{\n unsigned k, c = 0;\n const float *cm = tree->means;\n float d, min = l2sqr(cm, pt, tree->dim);\n cm += tree->dim;\n for (k = 1; k < tree->K; k++, cm += tree->dim) {\n d = l2sqr(cm, pt, tree->dim);\n if (d < min) {\n c = k;\n min = d;\n }\n }\n *cnt = tree->K;\n return c;\n}\n" }, { "path": "algorithms/NMSLIB/code/lshkit/src/kdtree.h", "content": "#ifndef WDONG_KD_TREE\n#define WDONG_KD_TREE\n\ntypedef struct kd_tree kd_tree_t;\n\nkd_tree_t *kd_tree_alloc (unsigned K, unsigned dim);\n\nvoid kd_tree_free (kd_tree_t *tree);\n\nvoid kd_tree_index (kd_tree_t *tree, const float *means);\n\nunsigned kd_tree_search (kd_tree_t *tree, const float *pt, unsigned *cnt);\n\nunsigned kd_tree_ln_search (kd_tree_t *tree, const float *pt, unsigned *cnt);\n\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/src/mplsh-model.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \"logging.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#define ABS_ERROR 1e-5l\n#define REL_ERROR 1e-5l\n#define MAX_SHAPE 1000.0l\n#define LIMIT 40000\n\nnamespace lshkit {\n\nstatic GaussianDouble normal;\n\n/* Maximum Likelihood Estimation of gamma distribution */\nclass GammaDoubleMleHelper\n{\n double rate_; // G / M\npublic:\n GammaDoubleMleHelper(double rate): rate_(rate) {}\n\n double operator () (double k)\n {\n return std::log(k) - boost::math::digamma(k) + std::log(rate_);\n }\n};\n\nbool GammaDoubleMleTol (double first, double second)\n{\n return (second - first) < ABS_ERROR;\n}\n\nGammaDouble GammaDoubleMLE (double M, double G)\n{\n GammaDoubleMleHelper hlp(G/M);\n // Conversion to double is due to an Intel's bug with __builtin_signbit being undefined for float\n std::pair pair = boost::math::tools::bisect(hlp, (double)ABS_ERROR, (double)MAX_SHAPE, GammaDoubleMleTol);\n double k = (pair.first + pair.second) / 2.0;\n return GammaDouble(k, M/k);\n}\n\n/* estimate the cost and miss of LSH */\nstatic inline double col_helper (double x)\n{\n double result;\n result = 2.0*boost::math::cdf(normal, x) - 1.0;\n result += std::sqrt(2.0/M_PI) * (std::exp(-x*x/2.0)-1.0)/x;\n return result; \n}\n\nstatic inline double p_col_helper (double x, double k)\n{\n return boost::math::cdf(normal,(1.0 + k) * x)\n - boost::math::cdf(normal, k*x);\n}\n\ndouble MultiProbeLshModel::recall (double x) const\n{\n double x2 = W_ / x;\n double p = col_helper(x2);\n\n\n unsigned MT = __probeSequenceTemplates[M_].size();\n if (MT > T_) MT = T_;\n \n double result = 0;\n for (unsigned i = 0; i < MT; i++)\n {\n double r = 1.0;\n for (unsigned j = 0; j < M_; j++)\n {\n Probe &probe = __probeSequenceTemplates[M_][i];\n if (probe.mask & leftshift(j))\n {\n double delta = (j + 1.0) / (M_ + 1.0) * 0.5; // expected value\n if (probe.shift & leftshift(j))\n {\n r *= p_col_helper(x2, 1.0 - delta);\n }\n else\n {\n r *= p_col_helper(x2, delta);\n }\n }\n else r *= p;\n }\n result += r;\n }\n return 1.0 - std::exp(std::log(1.0 - result) * L_);\n}\n\nstruct __MpLshMdlHlpr\n{\n const MultiProbeLshModel *model;\n const GammaDouble *gamma;\n};\n\nstatic double recall_helper (double xsqr, void *_param)\n{\n __MpLshMdlHlpr *param = reinterpret_cast<__MpLshMdlHlpr\n *>(_param);\n return boost::math::pdf(*param->gamma, xsqr) * param->model->recall(std::sqrt(xsqr));\n}\n\nstatic double recall (__MpLshMdlHlpr *param)\n{\n static gsl_integration_workspace *workspace = NULL;\n double f, error;\n gsl_function I;\n if (workspace == NULL)\n {\n workspace = gsl_integration_workspace_alloc(LIMIT);\n BOOST_VERIFY(workspace != NULL);\n }\n I.params = param;\n I.function = recall_helper;\n //if (gsl_integration_qagiu(&I, 0.0, ABS_ERROR, REL_ERROR, LIMIT, workspace, &f, &error) != 0) f = 1.0;\n if (gsl_integration_qagiu(&I, 0.0, ABS_ERROR, REL_ERROR, LIMIT, workspace, &f, &error) != 0) {\n LOG(LIB_INFO) << \"The integral didn't converge, assuming the recall is zero.\";\n f = 0.0;\n }\n return f;\n}\n\ndouble MultiProbeLshDataModel::avgRecall () const\n{\n double f = 0.0;\n __MpLshMdlHlpr param;\n param.model = this;\n for (unsigned k = 0; k < topkDists_.size(); k++)\n {\n param.gamma = &topkDists_[k];\n f += lshkit::recall(¶m);\n }\n f /= topkDists_.size();\n return f;\n}\n\ndouble MultiProbeLshDataModel::cost () const\n{\n __MpLshMdlHlpr param;\n param.model = this;\n param.gamma = &globalDist_;\n return lshkit::recall(¶m);\n}\n\n}\n" }, { "path": "algorithms/NMSLIB/code/lshkit/src/mplsh.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \n#include \n#include \n\nnamespace lshkit\n{\n ProbeSequenceTemplates __probeSequenceTemplates(Probe::MAX_M, Probe::MAX_T);\n\n void GenExpectScores (ProbeSequence &seq, unsigned M)\n {\n assert(M <= sizeof(seq[0].mask)* 8);\n seq.resize(2 * M);\n for (unsigned l = 0; l < M; ++l)\n {\n unsigned r = 2 * M - l - 1;\n seq[l].mask = seq[r].mask = seq[r].shift = leftshift(l);\n seq[l].shift = 0;\n seq[l].reserve = seq[r].reserve = 0;\n float delta = (l + 1.0) / (M + 1.0) * 0.5;\n seq[l].score = (l + 1.0) * (l + 2.0) / (M + 1.0) / (M + 2.0) * 0.25;\n seq[r].score = 1.0 - 2.0 * delta + seq[l].score;\n }\n }\n\n class ProbeGT\n {\n public:\n bool operator () (const Probe &p1, const Probe &p2) const\n {\n return p2 < p1;\n }\n };\n\n void GenProbeSequenceTemplate (ProbeSequence &seq, unsigned M, unsigned T)\n {\n ProbeSequence scores;\n GenExpectScores(scores, M);\n assert(T > 0);\n\n std::priority_queue, ProbeGT> heap;\n Probe init;\n init.mask = init.shift = 0;\n init.score = 0;\n init.reserve = 0;\n heap.push(init);\n\n seq.clear();\n\n for (;;)\n {\n if (heap.empty()) break;\n seq.push_back(heap.top());\n if (seq.size() == T) break;\n\n Probe shift = heap.top();\n heap.pop();\n\n for (unsigned next = shift.reserve; next < 2 * M; ++next)\n {\n if (!shift.conflict(scores[next]))\n {\n Probe tmp = shift + scores[next];\n tmp.reserve = next + 1;\n heap.push(tmp);\n }\n }\n }\n }\n\n void MultiProbeLsh::genProbeSequence (Domain obj, std::vector\n &seq, unsigned T) const\n {\n ProbeSequence scores;\n std::vector base;\n scores.resize(2 * lsh_.size());\n base.resize(lsh_.size());\n for (unsigned i = 0; i < lsh_.size(); ++i)\n {\n float delta;\n base[i] = Super::lsh_[i](obj, &delta);\n scores[2*i].mask = i;\n scores[2*i].reserve = 1; // direction\n scores[2*i].score = delta;\n scores[2*i+1].mask = i;\n scores[2*i+1].reserve = unsigned(-1);\n scores[2*i+1].score = 1.0 - delta;\n }\n std::sort(scores.begin(), scores.end());\n\n ProbeSequence &tmpl = __probeSequenceTemplates[lsh_.size()];\n\n seq.clear();\n for (ProbeSequence::const_iterator it = tmpl.begin();\n it != tmpl.end(); ++it)\n {\n if (seq.size() == T) break;\n const Probe &probe = *it;\n unsigned hash = 0;\n for (unsigned i = 0; i < lsh_.size(); ++i)\n {\n unsigned h = base[scores[i].mask];\n if (probe.mask & leftshift(i))\n {\n if (probe.shift & leftshift(i))\n {\n h += scores[i].reserve;\n }\n else\n {\n h += unsigned(-1) * scores[i].reserve;\n }\n }\n hash += h * a_[scores[i].mask];\n }\n seq.push_back(hash % H_);\n }\n }\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/src/vq.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \n#include \n#include \n#include \n#include \"kdtree.h\"\n\nnamespace lshkit\n{\n void VQ::init () {\n if (tree != 0) free();\n tree = kd_tree_alloc(K, dim);\n kd_tree_index((kd_tree_t *)tree, &means[0]);\n }\n\n void VQ::free () {\n kd_tree_free((kd_tree_t *)tree);\n }\n\n unsigned VQ::search (Domain obj) const {\n unsigned cnt;\n return kd_tree_search((kd_tree_t *)tree, obj, &cnt);\n }\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/CMakeLists.txt", "content": "SET(TOOLS fitdata lsh-run mplsh-run apost-run mplsh-predict mplsh-tune sketch-run forest-run scan embed txt2bin dump-query run-spectral)\r\nFOREACH(TOOL ${TOOLS})\r\nADD_EXECUTABLE(${TOOL} ${TOOL}.cpp)\r\nTARGET_LINK_LIBRARIES(${TOOL} lshkit ${Boost_LIBRARIES} ${GSL_LIBRARIES})\r\nENDFOREACH(TOOL)\r\n\r\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/apost-run.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \n#include \n#include \n#include \n#include \n\n/**\n * \\file apost-run.cpp\n * \\brief Example of using A Posteriori MPLSH.\n *\n * This program is an example of using A Posteriori MPLSH index.\n *\n * You need to run this program twice: once for index building\n * and training, once for testing.\n * For index building (with --build), you need to specify the following parameters:\n * -W -M -L -Q -K|-R -D -B --index --build\n * -N --expand --k-sigma\n * The parameter -N specify the quantization granularity (Nz in equation 17).\n * For now, do not specify the expand parameter (just use the default).\n * k-sigma specifies the sigma in Gaussian kernel used in equation 14 & 15.\n * Here the benchmark is used to train the a posteriori model. The index\n * will be written to the --index parameter.\n *\n * For testing (without --build), you need to specify the following:\n * -Q -K|-R -D -B --index -T|--recall\n *\n * The benchmark provided for training and benchmarking should be different.\n * To generate the benchmark file, use the scan program with different --seed\n * parameters.\n *\n * Following is a example run on the audio dataset:\n * Training:\n * apost-run -D audio.data -B audio.train -L 10 -W 4.32 -M 20 -Q 1000 -K 50 --build --index audio.apost\n * Testig:\n * apost-run -D audio.data -B audio.query -T 20 -Q 100 -K 50 --index audio.apost\n *\n\\verbatim\n -h [ --help ] produce help message.\n -W [ -- ] arg (=1)\n -M [ -- ] arg (=1)\n -N [ -- ] arg (=2500)\n -T [ -- ] arg (=1) # probes\n -L [ -- ] arg (=1) # hash tables\n -Q [ -- ] arg (=100) # queries\n -K [ -- ] arg (=0) # nearest neighbor to retrieve\n -R [ --radius ] arg (=3.40282347e+38) R-NN distance range (L2)\n --recall arg desired recall\n -D [ --data ] arg data file\n -B [ --benchmark ] arg benchmark file\n --index arg index file\n --build build index, using benchmark as\n training examples\n -H [ -- ] arg (=1017881) hash table size, use the default value.\n --expand arg (=0)\n --k-sigmal arg (=0.2)\n\\endverbatim\n */\n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\nint main (int argc, char *argv[])\n{\n string data_file;\n string benchmark;\n string index_file;\n\n float W, R, desired_recall = 1.0, expand, k_sigma;\n unsigned M, L, H, Nz;\n unsigned Q, K, T;\n bool do_recall = false;\n bool do_build = false;\n\n boost::timer timer;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",W\", po::value(&W)->default_value(1.0), \"\")\n (\",M\", po::value(&M)->default_value(1), \"\")\n (\",N\", po::value(&Nz)->default_value(2500), \"\")\n (\",T\", po::value(&T)->default_value(1), \"# probes\")\n (\",L\", po::value(&L)->default_value(1), \"# hash tables\")\n (\",Q\", po::value(&Q)->default_value(100), \"# queries\")\n (\",K\", po::value(&K)->default_value(0), \"# nearest neighbor to retrieve\")\n (\"radius,R\", po::value(&R)->default_value(numeric_limits::max()), \"R-NN distance range (L2)\")\n (\"recall\", po::value(&desired_recall), \"desired recall\")\n (\"data,D\", po::value(&data_file), \"data file\")\n (\"benchmark,B\", po::value(&benchmark), \"benchmark file\")\n (\"index\", po::value(&index_file), \"index file\")\n (\"build\", \"build index, using benchmark as training examples\")\n (\",H\", po::value(&H)->default_value(1017881), \"hash table size, use the default value.\")\n (\"k-sigma\", po::value(&k_sigma)->default_value(1.0/5), \"\")\n (\"expand\", po::value(&expand)->default_value(0), \"\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"data\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n if (vm.count(\"build\") >= 1) {\n do_build = true;\n }\n\n if (vm.count(\"radius\") >= 1) {\n R *= R; // we use L2sqr in the program.\n }\n\n if (vm.count(\"recall\") >= 1)\n {\n do_recall = true;\n }\n\n if ((Q == 0) || (vm.count(\"benchmark\") == 0)) {\n cerr << \"No benchmark data.\" << endl;\n return -1;\n }\n\n if (vm.count(\"index\") != 1) {\n cerr << \"No index file specified.\" << endl;\n return -1;\n }\n\n cout << \"LOADING DATA...\" << endl;\n timer.restart();\n FloatMatrix data(data_file);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n\n typedef APostLshIndex Index;\n\n FloatMatrix::Accessor accessor(data);\n Index index;\n\n if (do_build) {\n // We define a short name for the MPLSH index.\n Index::Parameter param;\n\n // Setup the parameters. Note that L is not provided here.\n param.W = W;\n param.range = H; // See H in the program parameters. You can just use the default value.\n param.repeat = M;\n param.dim = data.getDim();\n DefaultRng rng;\n\n index.init(param, rng, L);\n // The accessor.\n\n // Initialize the index structure. Note L is passed here.\n cout << \"CONSTRUCTING INDEX...\" << endl;\n\n timer.restart();\n {\n boost::progress_display progress(data.getSize());\n for (int i = 0; i < data.getSize(); ++i)\n {\n // Insert an item to the hash table.\n // Note that only the key is passed in here.\n // MPLSH will get the feature from the accessor.\n index.insert(i, data[i]);\n ++progress;\n }\n }\n cout << boost::format(\"CONSTRUCTION TIME: %1%s.\") % timer.elapsed() << endl;\n\n Benchmark<> bench;\n cout << \"LOADING BENCHMARK...\" << endl;\n bench.load(benchmark);\n bench.resize(Q, K);\n cout << \"DONE.\" << endl;\n\n cout << \"TRAINING INDEX...\" << endl;\n timer.restart();\n\n vector examples(Q);\n for (unsigned i = 0; i < Q; ++i) {\n examples[i].query = data[bench.getQuery(i)];\n const Topk &topk = bench.getAnswer(i);\n examples[i].results.resize(topk.size());\n for (unsigned j = 0; j < topk.size(); j++) {\n examples[i].results[j] = data[topk[j].key];\n }\n }\n\n index.train(examples, Nz, k_sigma, expand);\n cout << boost::format(\"TRAINING TIME: %1%s.\") % timer.elapsed() << endl;\n\n timer.restart();\n cout << \"SAVING INDEX...\" << endl;\n {\n ofstream os(index_file.c_str(), ios_base::binary);\n os.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n index.save(os);\n }\n cout << boost::format(\"SAVING TIME: %1%s\") % timer.elapsed() << endl;\n }\n else {\n\n // try loading index\n {\n ifstream is(index_file.c_str(), ios_base::binary);\n BOOST_VERIFY(is);\n is.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n cout << \"LOADING INDEX...\" << endl;\n timer.restart();\n index.load(is);\n BOOST_VERIFY(is);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n }\n\n Benchmark<> bench;\n cout << \"LOADING BENCHMARK...\" << endl;\n bench.load(benchmark);\n bench.resize(Q, K);\n cout << \"DONE.\" << endl;\n\n for (unsigned i = 0; i < Q; ++i)\n {\n for (unsigned j = 0; j < K; ++j)\n {\n assert(bench.getAnswer(i)[j].key < data.getSize());\n }\n }\n\n cout << \"RUNNING QUERIES...\" << endl;\n\n Stat recall;\n Stat cost;\n metric::l2sqr l2sqr(data.getDim());\n TopkScanner > query(accessor, l2sqr, K, R);\n vector > topks(Q);\n\n timer.restart();\n if (do_recall)\n // Specify the required recall\n // and let MPLSH to guess how many bins to probe.\n {\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n // Query for one point.\n query.reset(data[bench.getQuery(i)]);\n index.query_recall(data[bench.getQuery(i)], desired_recall, query);\n cost << double(query.cnt())/double(data.getSize());\n topks[i].swap(query.topk());\n ++progress;\n }\n }\n else\n // specify how many bins to probe.\n {\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n query.reset(data[bench.getQuery(i)]);\n index.query(data[bench.getQuery(i)], T, query);\n cost << double(query.cnt())/double(data.getSize());\n topks[i].swap(query.topk());\n ++progress;\n }\n }\n\n for (unsigned i = 0; i < Q; ++i) {\n recall << bench.getAnswer(i).recall(topks[i]);\n }\n\n cout << boost::format(\"QUERY TIME: %1%s.\") % timer.elapsed() << endl;\n\n cout << \"[RECALL] \" << recall.getAvg() << \" +/- \" << recall.getStd() << endl;\n cout << \"[COST] \" << cost.getAvg() << \" +/- \" << cost.getStd() << endl;\n\n }\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/dump-query.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \n#include \n#include \n#include \n#include \n\n/**\n * \\file mplsh-run.cpp\n * \\brief Example of using MPLSH.\n *\n * This program is an example of using MPLSH index.\n *\n * The program reconstruct the LSH index by default. You can give the\n * --index option to make the program save the LSH index. The next\n * time you run the program with the same --index option, the program\n * will try to load the previously saved index. When a saved index is\n * used, you need to make sure that the dataset and other parameters match\n * the previous run. However, the benchmark file, Q and K can be different.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -W [ -- ] arg (=1)\n -M [ -- ] arg (=1)\n -T [ -- ] arg (=1) # probes\n -L [ -- ] arg (=1) # hash tables\n -Q [ -- ] arg (=100) # queries\n -K [ -- ] arg (=50) # nearest neighbor to retrieve\n -R [ -- ] arg (=3.40282347e+38) R-NN distance range\n --recall arg desired recall\n -D [ --data ] arg data file\n -B [ --benchmark ] arg benchmark file\n --index arg index file\n -H [ -- ] arg (=1017881) hash table size, use the default value.\n\\endverbatim\n */\n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\n/*\n You must provide an access class to query the MPLSH.\n MPLSH only saves keys (pointers to the real feature vectors) in the\n hash tables and it will rely on the accessor class to retrieve\n the feature vector.\n\n An accessor must provide three methods:\n\n bool mark (unsigned key);\n\n mark that key has been accessed. If key has already been marked, return false,\n otherwise return true. MPLSH will use this to avoid scanning the data more than\n one time per query.\n\n void reset ();\n\n to clear all the marks.\n \n const float *operator () (unsigned key);\n\n given a key, return the pointer to a feature vector.\n\n The MatrixAccessor class is used to access feature vectors stored in a Matrix.\n*/\n\n/* This class has been merged to include/lshkit/matrix.h */\n/*\nclass MatrixAccessor\n{\n const Matrix &matrix_;\n boost::dynamic_bitset<> flags_;\npublic:\n typedef unsigned Key;\n MatrixAccessor(const Matrix &matrix)\n : matrix_(matrix), flags_(matrix.getSize()) {}\n\n MatrixAccessor(const MatrixAccessor &accessor)\n : matrix_(accessor.matrix_), flags_(matrix_.getSize()) {}\n\n void reset ()\n {\n flags_.reset();\n }\n\n bool mark (unsigned key)\n {\n if (flags_[key]) return false;\n flags_.set(key);\n return true;\n }\n\n const float *operator () (unsigned key)\n {\n return matrix_[key];\n }\n};\n*/\n\nint main (int argc, char *argv[])\n{\n string data_file;\n string benchmark;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\"data,D\", po::value(&data_file), \"data file\")\n (\"benchmark,B\", po::value(&benchmark), \"benchmark file\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n FloatMatrix data(data_file);\n\n Benchmark<> bench;\n bench.load(benchmark);\n\n for (unsigned i = 0; i < bench.getQ(); ++i) {\n const float *p = data[bench.getQuery(i)];\n cout << p[0];\n for (int d = 1; d < data.getDim(); ++d) {\n cout << ' ' << p[d];\n }\n cout << endl;\n }\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/embed.cpp", "content": "/* \n Copyright (C) 2009 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file embed.cpp\n * \\brief Example program of set embedding with random histograms.\n *\n * This program implements the two random histogram embedding methods\n * proposed in the random histogram paper by W. Dong et al.\n *\n * The program reads feature sets from an input text file. The file\n * is of the following format\n *\n \\verbatim\n ID N // ID is the identifier of the set, N is the number of features in the set\n weight D1 D2 ... // a weight followed by D dimensions, 1st feature\n weight D1 D2 ... // 2nd feature\n ...\n weight D1 D2 ... // Nth feature\n ID N // another set\n weight D1 D2 ...\n \\endverbatim\n * ID is string which cannot contain space characters; N is positive integer; weight and the dimension\n * values are floats.\n *\n * The program embedds the input sets into single feature vectors and output\n * them in the following format\n *\n \\verbatim\n ID D1 D2 ... // The input IDs are copied to the output\n ID D1 D2 ... // Following is the histogram, whose dimensionality is determined by the\n ... // input parameters.\n \\endverbatim\n *\n * The user is encouraged to modify this program to customize the input and output format.\n *\n * Usage:\n *\n \\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -t [ --type ] arg (=1) embedding algorithm:\n 1 - stripe embedding [-B, -M, -N, -W],\n 2 - random hyperplane [-B, -M, -N].\n\n --norm normalize the output vector to unit length.\n -I [ --input ] arg (=-) input file.\n -O [ --output ] arg (=-) output file.\n -D [ --dim ] arg input dimension.\n -B [ -- ] arg (=8) #bits per projection.\n -M [ -- ] arg (=1) take the sum of M.\n -N [ -- ] arg (=10) repeat N times.\n -W [ -- ] arg (=1) for type 1 only, LSH window size.\n \\endverbatim\n */\n\n\n#include \n#include \n#include \n\n/*\n The Histogram<> class in LSHKIT doesn't support polymorphism.\n Following we are going to wrap up Histogram<> with Embedder classes\n to support polymorphism, so the user can choose different\n embedder classes in command line.\n*/\n\n/// The abstract embedder.\n/**\n * An Embedder class only have to implement two virtual functions: dim() and add().\n */\nclass Embedder {\npublic:\n /// The dimension of the output histogram.\n virtual unsigned dim () const = 0;\n /// Add a point to the output histogram with weight.\n virtual void add (float *out, const float *in, float weight) const = 0;\n /// Add a point to the output histogram with weight = 1.\n void add (float *out, const float *in) const {\n add(out, in , 1.0);\n }\n /// Initialize the output histogram to zeros.\n void zero (float *out) const {\n std::fill(out, out + dim(), 0);\n }\n /// Scale the output histogram by *s.\n void scale (float *out, float s) const\n {\n for (unsigned i = 0; i < dim(); i++) out[i] *= s;\n }\n /// Normalize the output histogram to a unit vector.\n void norm (float *out) const\n {\n float s = 0.0;\n for (unsigned i = 0; i < dim(); i++) s += out[i] * out[i];\n s = 1.0/sqrtf(s);\n scale(out, s);\n }\n};\n\n/// The Stripe LSH. See Section 4.1 of MM08 paper.\ntypedef lshkit::Repeat > StripeLsh;\ntypedef lshkit::Histogram StripeEmbedderBase;\n\n/// Wrapper of the histogram embedder.\nclass StripeEmbedder: public Embedder, public StripeEmbedderBase {\n typedef StripeEmbedderBase Base;\npublic:\n struct Parameter: public Base::Parameter {\n unsigned M, N;\n /**\n Inheritec parameters:\n unsigned dim;\n float W;\n unsigned repeat\n */\n\n };\n\n StripeEmbedder (const Parameter ¶m, lshkit::DefaultRng &rng)\n : Base(param.M, param.N, param, rng) {\n }\n\n virtual unsigned dim () const {\n return Base::dim();\n }\n\n virtual void add (float *out, const float *in, float weight) const {\n Base::add(out, const_cast(in), weight);\n }\n};\n\n/// Random hyperplane LSH. See Section 4.2 of MM08 paper.\ntypedef lshkit::Repeat HyperPlaneLsh;\ntypedef lshkit::Histogram HyperPlaneEmbedderBase;\n\n/// Wrapper of the histogram embedder.\nclass HyperPlaneEmbedder: public Embedder, public HyperPlaneEmbedderBase {\n typedef HyperPlaneEmbedderBase Base;\npublic:\n struct Parameter: public Base::Parameter {\n unsigned M, N;\n /**\n Inheritec parameters:\n unsigned dim;\n unsigned repeat\n */\n };\n\n HyperPlaneEmbedder (const Parameter ¶m, lshkit::DefaultRng &rng)\n : Base(param.M, param.N, param, rng) {\n }\n\n virtual unsigned dim () const {\n return Base::dim();\n }\n\n virtual void add (float *out, const float *in, float weight) const {\n Base::add(out, const_cast(in), weight);\n }\n};\n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\n#define EMBEDDER_STRIP 1\n#define EMBEDDER_RP 2\n\nint main (int argc, char *argv[])\n{\n int type;\n\n string input;\n string output;\n\n unsigned D, B, M, N;\n float W;\n bool norm = false;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\"type,t\", po::value(&type)->default_value(1), \"embedding algorithm:\\n\"\n \"\\t1 - stripe embedding [-B, -M, -N, -W],\\n\"\n \"\\t2 - random hyperplane [-B, -M, -N].\\n\"\n )\n (\"norm\", \"normalize the output vector to unit length.\")\n (\"input,I\", po::value(&input)->default_value(\"-\"), \"input file.\")\n (\"output,O\", po::value(&output)->default_value(\"-\"), \"output file.\")\n (\"dim,D\", po::value(&D), \"input dimension.\")\n (\",B\", po::value(&B)->default_value(8), \"#bits per projection.\")\n (\",M\", po::value(&M)->default_value(1), \"take the sum of M.\")\n (\",N\", po::value(&N)->default_value(10), \"repeat N times.\")\n (\",W\", po::value(&W)->default_value(1.0), \"for type 1 only, LSH window size.\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"dim\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n if (vm.count(\"norm\")) norm = true;\n\n DefaultRng rng;\n Embedder *emb;\n\n switch (type)\n {\n case 1: {\n StripeEmbedder::Parameter param;\n param.dim = D;\n param.W = W;\n param.repeat = B;\n param.M = M;\n param.N = N;\n emb = new StripeEmbedder(param, rng);\n break;\n }\n case 2: {\n\n HyperPlaneEmbedder::Parameter param;\n param.dim = D;\n param.repeat = B;\n param.M = M;\n param.N = N;\n emb = new HyperPlaneEmbedder(param, rng);\n break;\n }\n default:\n throw invalid_argument(\"INVALID EMBEDDER TYPE.\");\n }\n \n ifstream is(input.c_str());\n ofstream os(output.c_str());\n\n float *in = new float[D];\n float *out = new float[emb->dim()];\n\n for (;;)\n {\n/* !!!!!!!!!!!!!!!!! Modify here for different input format !!!!!!!!!!!!!! */\n unsigned n; /* number of feature vectors for the next object */\n string id;\n is >> id >> n;\n if (!is) break;\n emb->zero(out);\n // process the n vectors\n for (unsigned i = 0; i < n; ++i)\n {\n float weight;\n is >> weight;\n /* read a feature vector */\n for (unsigned j = 0; j < D; ++j) {\n is >> in[j];\n }\n emb->add(out, in, weight);\n }\n\n/* !!!!!!!!!!!!!!!!! Modify here for different output format !!!!!!!!!!!!!! */\n if (norm) emb->norm(out);\n os << id;\n for (unsigned j = 0; j < emb->dim(); ++j) {\n os << '\\t' << out[j];\n }\n os << endl;\n }\n\n delete emb;\n delete []in;\n delete []out;\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/fitdata.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file fitdata.cpp\n * \\brief Gather statistics from dataset for MPLSH tuning.\n *\n * This program gahters statistical data from a small sample dataset\n * for automatic MPLSH parameter tuning. It carries out the following\n * steps:\n * -# Sample N points from the dataset. Only those N points will be used for future computation.\n * -# Sample P pairs of points from the sample, calculate the distance for each pair.\n * -# Sample Q points from the sample as queries points.\n * -# Divide the sample into F folds.\n * -# For i = 1 to F, take i folds and run K-NN search, so the query points\n * will be searched against sample datasets of N/F, 2N/F, ..., N/F points.\n *\n * The statistical data is printed to standard output after the progress display.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -N [ -- ] arg (=0) number of points to use\n -P [ -- ] arg (=50000) number of pairs to sample\n -Q [ -- ] arg (=1000) number of queries to sample\n -K [ -- ] arg (=100) search for K nearest neighbors\n -F [ -- ] arg (=10) divide the sample to F folds\n -D [ --data ] arg data file\n\\endverbatim\n */\n\n\n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace lshkit;\n//using namespace tr1;\nnamespace po = boost::program_options; \n\n/*\nbool is_good_value (double v) {\n return ((v > -std::numeric_limits::max())\n && (v < std::numeric_limits::max()));\n}\n*/\n\nint main (int argc, char *argv[])\n{\n string data_file;\n unsigned N, P, Q, K, F;\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",N\", po::value(&N)->default_value(0), \"number of points to use\")\n (\",P\", po::value(&P)->default_value(50000), \"number of pairs to sample\")\n (\",Q\", po::value(&Q)->default_value(1000), \"number of queries to sample\")\n (\",K\", po::value(&K)->default_value(100), \"search for K nearest neighbors\")\n (\",F\", po::value(&F)->default_value(10), \"divide the sample to F folds\")\n (\"data,D\", po::value(&data_file), \"data file\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"data\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n /* load matrix */\n Matrix data(data_file);\n\n vector idx(data.getSize());\n for (unsigned i = 0; i < idx.size(); ++i) idx[i] = i;\n random_shuffle(idx.begin(), idx.end());\n\n if (N > 0 && static_cast(N) < data.getSize()) idx.resize(N);\n\n metric::l2sqr l2sqr(data.getDim());\n\n DefaultRng rng;\n boost::variate_generator gen(rng,\n UniformUnsigned(0, idx.size()-1));\n\n double gM = 0.0;\n double gG = 0.0;\n {\n // sample P pairs of points\n for (unsigned k = 0; k < P; ++k)\n {\n double dist, logdist;\n for (;;)\n {\n unsigned i = gen();\n unsigned j = gen();\n if (i == j) continue;\n dist = l2sqr(data[idx[i]], data[idx[j]]);\n logdist = log(dist);\n if (is_good_value(logdist)) break;\n }\n gM += dist;\n gG += logdist;\n }\n gM /= P;\n gG /= P;\n gG = exp(gG);\n }\n\n if (Q > idx.size()) Q = idx.size();\n if (K > idx.size() - Q) K = idx.size() - Q;\n /* sample query */\n vector qry(Q);\n\n SampleQueries(&qry, idx.size(), rng);\n\n /* do the queries */\n vector > topks(Q);\n for (unsigned i = 0; i < Q; ++i) topks[i].reset(K);\n\n /* ... */\n gsl_matrix *X = gsl_matrix_alloc(F * K, 3);\n gsl_vector *yM = gsl_vector_alloc(F * K);\n gsl_vector *yG = gsl_vector_alloc(F * K);\n gsl_vector *pM = gsl_vector_alloc(3);\n gsl_vector *pG = gsl_vector_alloc(3);\n gsl_matrix *cov = gsl_matrix_alloc(3,3);\n\n vector M(K);\n vector G(K);\n\n boost::progress_display progress(F, cerr);\n unsigned m = 0;\n for (unsigned l = 0; l < F; l++)\n {\n // Scan\n for (unsigned i = l; i< idx.size(); i += F)\n {\n for (unsigned j = 0; j < Q; j++) \n {\n unsigned id = qry[j];\n if (i != id) \n {\n float d = l2sqr(data[idx[id]], data[idx[i]]);\n if (is_good_value(log(double(d)))) topks[j] << Topk::Element(i, d);\n }\n }\n }\n\n fill(M.begin(), M.end(), 0.0);\n fill(G.begin(), G.end(), 0.0);\n\n for (unsigned i = 0; i < Q; i++)\n {\n for (unsigned k = 0; k < K; k++)\n {\n M[k] += topks[i][k].dist;\n G[k] += log(topks[i][k].dist);\n }\n }\n\n for (unsigned k = 0; k < K; k++)\n {\n M[k] = log(M[k]/Q);\n G[k] /= Q;\n gsl_matrix_set(X, m, 0, 1.0);\n gsl_matrix_set(X, m, 1, log(double(data.getSize() * (l + 1)) / double(F)));\n gsl_matrix_set(X, m, 2, log(double(k + 1)));\n gsl_vector_set(yM, m, M[k]);\n gsl_vector_set(yG, m, G[k]);\n ++m;\n }\n\n ++progress;\n }\n\n gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc(F * K, 3);\n\n double chisq;\n\n gsl_multifit_linear(X, yM, pM, cov, &chisq, work);\n gsl_multifit_linear(X, yG, pG, cov, &chisq, work);\n\n cout << gM << '\\t' << gG << endl;\n cout << gsl_vector_get(pM, 0) << '\\t'\n << gsl_vector_get(pM, 1) << '\\t'\n << gsl_vector_get(pM, 2) << endl;\n cout << gsl_vector_get(pG, 0) << '\\t'\n << gsl_vector_get(pG, 1) << '\\t'\n << gsl_vector_get(pG, 2) << endl;\n\n gsl_matrix_free(X);\n gsl_matrix_free(cov);\n gsl_vector_free(pM);\n gsl_vector_free(pG);\n gsl_vector_free(yM);\n gsl_vector_free(yG);\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/forest-run.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file lsh-run.cpp\n * \\brief Example of using LSH Forest index for L2 distance.\n *\n * This program is an example of using LSH Forest index. The LSH used is\n * LSB, so the trees in the forest are binary trees. \n *\n * The current implementation of LSH Forest doesn't allow read/write\n * index files.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -c [ -- ] arg (=20) # points to scan from each tree\n -L [ -- ] arg (=1) number of trees\n -D [ -- ] arg (=10) maximal depth of tree\n -W [ -- ] arg (=1) hash function window size\n -Q [ -- ] arg (=100) number of queries to use\n -K [ -- ] arg (=50) number of nearest neighbors to retrieve\n -R [ -- ] arg (=3.40282347e+38) R-NN distance range\n -D [ --data ] arg dataset path\n -B [ --benchmark ] arg \n\\endverbatim\n * \n */\n\n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\nint main (int argc, char *argv[])\n{\n string data_file;\n string benchmark;\n\n float R, W;\n unsigned c, L, H;\n unsigned Q, K;\n bool do_benchmark = true;\n // bool use_index = false; // load the index from a file\n\n boost::timer timer;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",c\", po::value(&c)->default_value(20), \"# points to scan from each tree\")\n (\",L\", po::value(&L)->default_value(1), \"number of trees\")\n (\",H\", po::value(&H)->default_value(10), \"maximal depth of tree\")\n (\",W\", po::value(&W)->default_value(1.0), \"hash function window size\")\n (\",Q\", po::value(&Q)->default_value(100), \"number of queries to use\")\n (\",K\", po::value(&K)->default_value(50), \"number of nearest neighbors to retrieve\")\n (\",R\", po::value(&R)->default_value(numeric_limits::max()), \"R-NN distance range\")\n (\"data,D\", po::value(&data_file), \"dataset path\")\n (\"benchmark,B\", po::value(&benchmark), \"benchmark path\")\n // (\"index\", po::value(&index_file), \"index file\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"data\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n if ((Q == 0) || (vm.count(\"benchmark\") == 0)) {\n do_benchmark = false;\n }\n\n /*\n if (vm.count(\"index\") == 1) {\n use_index = true;\n }\n */\n\n cout << \"LOADING DATA...\" << endl;\n timer.restart();\n FloatMatrix data(data_file);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n\n //typedef Tail > MyLsh;\n\n typedef LSB MyLsh;\n typedef ForestIndex Index;\n\n FloatMatrix::Accessor accessor(data);\n metric::l2 l2(data.getDim());\n Index index;\n\n // bool index_loaded = false;\n\n /*\n if (use_index) {\n ifstream is(index_file.c_str(), ios_base::binary);\n if (is) {\n is.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n cout << \"LOADING INDEX...\" << endl;\n timer.restart();\n index.load(is);\n verify(is);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n index_loaded = true;\n }\n }\n\n if (!index_loaded) {\n // We define a short name for the MPLSH index.\n float min = numeric_limits::max();\n float max = -numeric_limits::max();\n\n for (unsigned i = 0; i < data.getSize(); ++i) {\n for (unsigned j = 0; j < data.getDim(); ++j) {\n if (data[i][j] > max) max = data[i][j];\n if (data[i][j] < min) min = data[i][j];\n }\n }\n\n */\n\n Index::Parameter param;\n\n // Setup the parameters. Note that L is not provided here.\n param.W = W;\n param.dim = data.getDim();\n DefaultRng rng;\n\n index.init(param, rng, L, H);\n // The accessor.\n\n // Initialize the index structure. Note L is passed here.\n cout << \"CONSTRUCTING INDEX...\" << endl;\n\n timer.restart();\n {\n boost::progress_display progress(data.getSize());\n for (int i = 0; i < data.getSize(); ++i)\n {\n // Insert an item to the hash table.\n // Note that only the key is passed in here.\n // MPLSH will get the feature from the accessor.\n index.insert(i, accessor);\n ++progress;\n }\n }\n cout << boost::format(\"CONSTRUCTION TIME: %1%s.\") % timer.elapsed() << endl;\n\n /*\n if (use_index) {\n timer.restart();\n cout << \"SAVING INDEX...\" << endl;\n {\n ofstream os(index_file.c_str(), ios_base::binary);\n os.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n index.save(os);\n verify(os);\n }\n cout << boost::format(\"SAVING TIME: %1%s\") % timer.elapsed() << endl;\n }\n }\n */\n\n if (do_benchmark) {\n\n Benchmark<> bench;\n cout << \"LOADING BENCHMARK...\" << endl;\n bench.load(benchmark);\n bench.resize(Q, K);\n cout << \"DONE.\" << endl;\n\n for (unsigned i = 0; i < Q; ++i)\n {\n for (unsigned j = 0; j < K; ++j)\n {\n assert(bench.getAnswer(i)[j].key < data.getSize());\n }\n }\n\n cout << \"RUNNING QUERIES...\" << endl;\n\n Stat recall;\n Stat cost;\n\n timer.restart();\n {\n TopkScanner > query(accessor, l2, K, R);\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n query.reset(data[bench.getQuery(i)]);\n index.query(data[bench.getQuery(i)], c * L, query);\n recall << bench.getAnswer(i).recall(query.topk());\n cost << double(query.cnt())/double(data.getSize());\n ++progress;\n }\n }\n cout << boost::format(\"QUERY TIME: %1%s.\") % timer.elapsed() << endl;\n\n cout << \"[RECALL] \" << recall.getAvg() << \" +/- \" << recall.getStd() << endl;\n cout << \"[COST] \" << cost.getAvg() << \" +/- \" << cost.getStd() << endl;\n\n }\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/lsh-run.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file lsh-run.cpp\n * \\brief Example of using RandomThresholding LSH index for L1 distance.\n *\n * This program is an example of using LSH index. The LSH used is\n * Tail >. The ThresholdingLSH is repeated\n * M times and then randomly hashed to a integer within the range of\n * [0, H). The size of one hash table is H. The LSH family approximates\n * L1 distance.\n *\n * The program reconstruct the LSH index by default. You can give the\n * --index option to make the program save the LSH index. The next\n * time you run the program with the same --index option, the program\n * will try to load the previously saved index. When a saved index is\n * used, you need to make sure that the dataset and other parameters match\n * the previous run. However, the benchmark file, Q and K can be different.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -M [ -- ] arg (=20)\n -L [ -- ] arg (=1) number of hash tables\n -Q [ -- ] arg (=100) number of queries to use\n -K [ -- ] arg (=50) number of nearest neighbors to retrieve\n -D [ --data ] arg dataset path\n -B [ --benchmark ] arg benchmark path\n --index arg index file\n -H [ -- ] arg (=1017881) hash table size, use the default.\n\\endverbatim\n * \n */\n\n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\nint main (int argc, char *argv[])\n{\n string data_file;\n string benchmark;\n string index_file;\n\n float R;\n unsigned M, L, H;\n unsigned Q, K;\n bool do_benchmark = true;\n bool use_index = false; // load the index from a file\n\n boost::timer timer;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",M\", po::value(&M)->default_value(20), \"\")\n (\",L\", po::value(&L)->default_value(1), \"number of hash tables\")\n (\",Q\", po::value(&Q)->default_value(100), \"number of queries to use\")\n (\",K\", po::value(&K)->default_value(50), \"number of nearest neighbors to retrieve\")\n (\",R\", po::value(&R)->default_value(numeric_limits::max()), \"R-NN distance range\")\n (\"data,D\", po::value(&data_file), \"dataset path\")\n (\"benchmark,B\", po::value(&benchmark), \"benchmark path\")\n (\"index\", po::value(&index_file), \"index file\")\n (\",H\", po::value(&H)->default_value(1017881), \"hash table size, use the default.\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"data\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n if ((Q == 0) || (vm.count(\"benchmark\") == 0)) {\n do_benchmark = false;\n }\n\n if (vm.count(\"index\") == 1) {\n use_index = true;\n }\n\n cout << \"LOADING DATA...\" << endl;\n timer.restart();\n FloatMatrix data(data_file);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n\n //typedef Tail > MyLsh;\n\n typedef Tail > MyLsh;\n typedef LshIndex Index;\n\n metric::l1 l1(data.getDim());\n FloatMatrix::Accessor accessor(data);\n Index index;\n\n bool index_loaded = false;\n\n if (use_index) {\n ifstream is(index_file.c_str(), ios_base::binary);\n if (is) {\n is.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n cout << \"LOADING INDEX...\" << endl;\n timer.restart();\n index.load(is);\n BOOST_VERIFY(is);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n index_loaded = true;\n }\n }\n\n if (!index_loaded) {\n // We define a short name for the MPLSH index.\n float min = numeric_limits::max();\n float max = -numeric_limits::max();\n\n for (int i = 0; i < data.getSize(); ++i) {\n for (int j = 0; j < data.getDim(); ++j) {\n if (data[i][j] > max) max = data[i][j];\n if (data[i][j] < min) min = data[i][j];\n }\n }\n\n Index::Parameter param;\n\n // Setup the parameters. Note that L is not provided here.\n param.range = H;\n param.repeat = M;\n param.min = min;\n param.max = max;\n param.dim = data.getDim();\n DefaultRng rng;\n\n index.init(param, rng, L);\n // The accessor.\n\n // Initialize the index structure. Note L is passed here.\n cout << \"CONSTRUCTING INDEX...\" << endl;\n\n timer.restart();\n {\n boost::progress_display progress(data.getSize());\n for (int i = 0; i < data.getSize(); ++i)\n {\n // Insert an item to the hash table.\n // Note that only the key is passed in here.\n // MPLSH will get the feature from the accessor.\n index.insert(i, data[i]);\n ++progress;\n }\n }\n cout << boost::format(\"CONSTRUCTION TIME: %1%s.\") % timer.elapsed() << endl;\n\n if (use_index) {\n timer.restart();\n cout << \"SAVING INDEX...\" << endl;\n {\n ofstream os(index_file.c_str(), ios_base::binary);\n os.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n index.save(os);\n BOOST_VERIFY(os);\n }\n cout << boost::format(\"SAVING TIME: %1%s\") % timer.elapsed() << endl;\n }\n }\n\n if (do_benchmark) {\n\n Benchmark<> bench;\n cout << \"LOADING BENCHMARK...\" << endl;\n bench.load(benchmark);\n bench.resize(Q, K);\n cout << \"DONE.\" << endl;\n\n for (unsigned i = 0; i < Q; ++i)\n {\n for (unsigned j = 0; j < K; ++j)\n {\n assert(bench.getAnswer(i)[j].key < data.getSize());\n }\n }\n\n cout << \"RUNNING QUERIES...\" << endl;\n\n Stat recall;\n Stat cost;\n\n timer.restart();\n {\n TopkScanner > query(accessor, l1, K, R);\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n query.reset(data[bench.getQuery(i)]);\n index.query(data[bench.getQuery(i)], query);\n recall << bench.getAnswer(i).recall(query.topk());\n cost << double(query.cnt())/double(data.getSize());\n ++progress;\n }\n }\n cout << boost::format(\"QUERY TIME: %1%s.\") % timer.elapsed() << endl;\n\n cout << \"[RECALL] \" << recall.getAvg() << \" +/- \" << recall.getStd() << endl;\n cout << \"[COST] \" << cost.getAvg() << \" +/- \" << cost.getStd() << endl;\n\n }\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/mplsh-predict.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n#include \n#include \n\n/**\n * \\file mplsh-predict.cpp\n * \\brief Predict MPLSH performance.\n *\n * This program predicts MPLSH performance based on the statistical data\n * gathered by fitdata and the MPLSH parameters. It extrapolates\n * the performance to a dataset of N points.\n *\n * Either -K or -r, but not both should be used.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -T [ -- ] arg (=1)\n -L [ -- ] arg (=1)\n -M [ -- ] arg (=1)\n -W [ -- ] arg (=1)\n -K [ --topk ] arg (=50)\n -r [ --radius ] arg radius\n -N [ --size ] arg size of dataset\n -P [ --param ] arg data parameter file\n\\endverbatim\n */\n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\nint main (int argc, char *argv[])\n{\n double w;\n unsigned N, K, T, L, M;\n double R;\n string data_param;\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",T\", po::value(&T)->default_value(1), \"\")\n (\",L\", po::value(&L)->default_value(1), \"\")\n (\",M\", po::value(&M)->default_value(1), \"\")\n (\",W\", po::value(&w)->default_value(1), \"\")\n (\"topk,K\", po::value(&K)->default_value(50), \"\")\n (\"radius,r\", po::value(&R)->default_value(1.0), \"\")\n (\"size,N\", po::value(&N), \"size of dataset\")\n (\"param,P\", po::value(&data_param), \"data parameter file\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"param\") < 1) || \n (vm.count(\"topk\") < 1) || (vm.count(\"size\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n DataParam param(data_param);\n param.scale(w * w);\n\n MultiProbeLshDataModel model(param, N, K);\n model.setT(T);\n model.setL(L);\n model.setM(M);\n model.setW(1.0);\n\n double recall = vm.count(\"radius\") > 0 ? model.recall(R) : model.avgRecall();\n\n cout << recall << '\\t' << model.cost() << endl;\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/mplsh-run.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n#include \n#include \n#include \n#include \n#include \n\n/**\n * \\file mplsh-run.cpp\n * \\brief Example of using MPLSH.\n *\n * This program is an example of using MPLSH index.\n *\n * The program reconstruct the LSH index by default. You can give the\n * --index option to make the program save the LSH index. The next\n * time you run the program with the same --index option, the program\n * will try to load the previously saved index. When a saved index is\n * used, you need to make sure that the dataset and other parameters match\n * the previous run. However, the benchmark file, Q and K can be different.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -W [ -- ] arg (=1)\n -M [ -- ] arg (=1)\n -T [ -- ] arg (=1) # probes\n -L [ -- ] arg (=1) # hash tables\n -Q [ -- ] arg (=100) # queries\n -K [ -- ] arg (=50) # nearest neighbor to retrieve\n -R [ -- ] arg (=3.40282347e+38) R-NN distance range\n --recall arg desired recall\n -D [ --data ] arg data file\n -B [ --benchmark ] arg benchmark file\n --index arg index file\n -H [ -- ] arg (=1017881) hash table size, use the default value.\n\\endverbatim\n */\n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\n/*\n You must provide an access class to query the MPLSH.\n MPLSH only saves keys (pointers to the real feature vectors) in the\n hash tables and it will rely on the accessor class to retrieve\n the feature vector.\n\n An accessor must provide three methods:\n\n bool mark (unsigned key);\n\n mark that key has been accessed. If key has already been marked, return false,\n otherwise return true. MPLSH will use this to avoid scanning the data more than\n one time per query.\n\n void reset ();\n\n to clear all the marks.\n \n const float *operator () (unsigned key);\n\n given a key, return the pointer to a feature vector.\n\n The MatrixAccessor class is used to access feature vectors stored in a Matrix.\n*/\n\n/* This class has been merged to include/lshkit/matrix.h */\n/*\nclass MatrixAccessor\n{\n const Matrix &matrix_;\n boost::dynamic_bitset<> flags_;\npublic:\n typedef unsigned Key;\n MatrixAccessor(const Matrix &matrix)\n : matrix_(matrix), flags_(matrix.getSize()) {}\n\n MatrixAccessor(const MatrixAccessor &accessor)\n : matrix_(accessor.matrix_), flags_(matrix_.getSize()) {}\n\n void reset ()\n {\n flags_.reset();\n }\n\n bool mark (unsigned key)\n {\n if (flags_[key]) return false;\n flags_.set(key);\n return true;\n }\n\n const float *operator () (unsigned key)\n {\n return matrix_[key];\n }\n};\n*/\n\nint main (int argc, char *argv[])\n{\n string data_file;\n string benchmark;\n string index_file;\n\n float W, R, desired_recall = 1.0;\n unsigned M, L, H;\n unsigned Q, K, T;\n bool do_recall = false;\n bool do_benchmark = true;\n bool use_index = false; // load the index from a file\n\n boost::timer timer;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",W\", po::value(&W)->default_value(1.0), \"\")\n (\",M\", po::value(&M)->default_value(1), \"\")\n (\",T\", po::value(&T)->default_value(1), \"# probes\")\n (\",L\", po::value(&L)->default_value(1), \"# hash tables\")\n (\",Q\", po::value(&Q)->default_value(100), \"# queries\")\n (\",K\", po::value(&K)->default_value(0), \"# nearest neighbor to retrieve\")\n (\"radius,R\", po::value(&R)->default_value(numeric_limits::max()), \"R-NN distance range (L2)\")\n (\"recall\", po::value(&desired_recall), \"desired recall\")\n (\"data,D\", po::value(&data_file), \"data file\")\n (\"benchmark,B\", po::value(&benchmark), \"benchmark file\")\n (\"index\", po::value(&index_file), \"index file\")\n (\",H\", po::value(&H)->default_value(1017881), \"hash table size, use the default value.\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"data\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n if (vm.count(\"radius\") >= 1) {\n R *= R; // we use L2sqr in the program.\n }\n\n if (vm.count(\"recall\") >= 1)\n {\n do_recall = true;\n if (K == 0) {\n cerr << \"Automatic probing does not support R-NN query.\" << endl;\n }\n }\n\n if ((Q == 0) || (vm.count(\"benchmark\") == 0)) {\n do_benchmark = false;\n }\n\n if (vm.count(\"index\") == 1) {\n use_index = true;\n }\n\n cout << \"LOADING DATA...\" << endl;\n timer.restart();\n FloatMatrix data(data_file);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n\n typedef MultiProbeLshIndex Index;\n\n FloatMatrix::Accessor accessor(data);\n Index index;\n\n // try loading index\n bool index_loaded = false;\n\n if (use_index) {\n ifstream is(index_file.c_str(), ios_base::binary);\n if (is) {\n is.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n cout << \"LOADING INDEX...\" << endl;\n timer.restart();\n index.load(is);\n BOOST_VERIFY(is);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n index_loaded = true;\n }\n }\n\n if (!index_loaded) {\n // We define a short name for the MPLSH index.\n Index::Parameter param;\n\n // Setup the parameters. Note that L is not provided here.\n param.W = W;\n param.range = H; // See H in the program parameters. You can just use the default value.\n param.repeat = M;\n param.dim = data.getDim();\n DefaultRng rng;\n\n index.init(param, rng, L);\n // The accessor.\n\n // Initialize the index structure. Note L is passed here.\n cout << \"CONSTRUCTING INDEX...\" << endl;\n\n timer.restart();\n {\n boost::progress_display progress(data.getSize());\n for (int i = 0; i < data.getSize(); ++i)\n {\n // Insert an item to the hash table.\n // Note that only the key is passed in here.\n // MPLSH will get the feature from the accessor.\n index.insert(i, data[i]);\n ++progress;\n }\n }\n cout << boost::format(\"CONSTRUCTION TIME: %1%s.\") % timer.elapsed() << endl;\n\n if (use_index) {\n timer.restart();\n cout << \"SAVING INDEX...\" << endl;\n {\n ofstream os(index_file.c_str(), ios_base::binary);\n os.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n index.save(os);\n }\n cout << boost::format(\"SAVING TIME: %1%s\") % timer.elapsed() << endl;\n }\n }\n\n if (do_benchmark) {\n\n Benchmark<> bench;\n cout << \"LOADING BENCHMARK...\" << endl;\n bench.load(benchmark);\n bench.resize(Q, K);\n cout << \"DONE.\" << endl;\n\n for (unsigned i = 0; i < Q; ++i)\n {\n for (unsigned j = 0; j < K; ++j)\n {\n assert(bench.getAnswer(i)[j].key < data.getSize());\n }\n }\n\n cout << \"RUNNING QUERIES...\" << endl;\n\n Stat recall;\n Stat cost;\n metric::l2sqr l2sqr(data.getDim());\n TopkScanner > query(accessor, l2sqr, K, R);\n vector > topks(Q);\n\n timer.restart();\n if (do_recall)\n // Specify the required recall\n // and let MPLSH to guess how many bins to probe.\n {\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n // Query for one point.\n query.reset(data[bench.getQuery(i)]);\n index.query_recall(data[bench.getQuery(i)], desired_recall, query);\n cost << double(query.cnt())/double(data.getSize());\n topks[i].swap(query.topk());\n ++progress;\n }\n }\n else\n // specify how many bins to probe.\n {\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n query.reset(data[bench.getQuery(i)]);\n index.query(data[bench.getQuery(i)], T, query);\n cost << double(query.cnt())/double(data.getSize());\n topks[i].swap(query.topk());\n ++progress;\n }\n }\n\n for (unsigned i = 0; i < Q; ++i) {\n recall << bench.getAnswer(i).recall(topks[i]);\n }\n\n cout << boost::format(\"QUERY TIME: %1%s.\") % timer.elapsed() << endl;\n\n cout << \"[RECALL] \" << recall.getAvg() << \" +/- \" << recall.getStd() << endl;\n cout << \"[COST] \" << cost.getAvg() << \" +/- \" << cost.getStd() << endl;\n\n }\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/mplsh-tune.cpp", "content": "/*\n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n\n This file is part of LSHKIT.\n\n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file mplsh-tune.cpp\n * \\brief Automatic parameter tuning for MPLSH.\n *\n * Assume you have a sample datafile sample.data. You need to take the following two steps for parameter tuning:\n * \\section tune_1 1. Create a model of data distribution.\n *\n * Use the following command\n *\n \\verbatim\n fitdata -D sample.data\n \\endverbatim\n *\n * The following options are acceptable:\n \\verbatim\n Allowed options:\n -h [ --help ] produce help message.\n -N [ -- ] arg (=0) # of points to use. if N = 0, then the whole sample dataset is used.\n -P [ -- ] arg (=50000) # pairs of points to sample\n -Q [ -- ] arg (=1000) # queries to sample\n -K [ -- ] arg (=100) Top K\n -F [ -- ] arg (=10)\n -D [ --data ] arg data file\n \\endverbatim\n *\n * For example, we use the following command to generate statistics from audio.data:\n *\n \\verbatim\n fitdata -D audio.data\n \\endverbatim\n *\n * We get the following output\n *\n \\verbatim\n 0% 10 20 30 40 50 60 70 80 90 100%\n |----|----|----|----|----|----|----|----|----|----|\n ***************************************************\n 3.28031 2.90649\n 0.831193 -0.160539 0.150609\n 0.825921 -0.186676 0.177095\n \\endverbatim\n *\n * Cut and paste the last three lines into a file named audio.param.\n *\n * \\section tune_2 2. Use mplsh-tune to tune parameters.\n *\n * There are four parameters in MPLSH: L, T, M, W. You'll have to choose L and\n * T and let mplsh-tune to find the optimal M and W. For example, we use the\n * following command to tune M and W for the audio dataset.\n *\n * \\verbatim\n mplsh-tune -P audio.param -N 54387 -L 8 -T 20 -R 0.8 -K 50\n\n -P is the parameter file we generated in the 1st step;\n -N is the whole dataset size. Note that in the first step we only use a subset of the data to generate the model.\n -L # hash tables to use.\n -T # of bins probed in each hash table\n -R required recall.\n -r radius\n -K K-NNs to find.\n \\endverbatim\n *\n * mplsh-tune will output best W and M that will meet the required recall. For\n * the audio data, we get the following output:\n *\n \\verbatim\n L = 8 T = 20 M = 20 W = 4.32 recall = 0.805276 cost = 0.0968405\n \\endverbatim\n *\n * We can then run the benchmark and see how close is the prediction to the real number:\n *\n \\verbatim\n mplsh-run -D audio.data -B audio.query -L 8 -T 20 -W 4.32 -M 20 -Q 100 -K 50\n \\endverbatim\n *\n \\verbatim\n Loading data...done.\n Loading benchmark...done.\n Initializing index...\n done.\n Populating index...\n\n 0% 10 20 30 40 50 60 70 80 90 100%\n |----|----|----|----|----|----|----|----|----|----|\n ***************************************************\n CREATE TIME: 3.19s\n Running queries...\n\n 0% 10 20 30 40 50 60 70 80 90 100%\n |----|----|----|----|----|----|----|----|----|----|\n ***************************************************\n QUERY TIME: 0.57s\n [RECALL] 0.8192 +/- 0.234635\n [COST] 0.115853 +/- 0.0980032\n\\endverbatim\n *\n * (Note that the audio dataset is a pretty hard one.)\n *\n * \\section tune_LT How to choose L and T?\n *\n * L is the number of hash tables maintained in the memory and generally larger\n * L results in better performance (smaller cost to reach certain recall).\n * Hash tables only stores pointers to the feature vectors. So on a 64-bit\n * machine, N points will take 8N bytes plus some overhead. For our audio\n * dataset, where there are about 55K points, one hash table takes about 500KB\n * of memory and 8 hash tables will take 4MB.\n *\n * T is the number of buckets to probe in each hash table. A number from 10 ~\n * 100 would be fine. Larger T results in lower cost. Yes, more buckets are\n * probed in each hash table, but that would allow us to make each hash bucket\n * smaller, and the overall effect is less points are scanned to reach the\n * required recall. However, our model doesn't consider the cost of generating\n * the probe sequence and when T is very large, that cost can be significant.\n * So in practice T should not really be much larger than 100.\n *\n *\n */\n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options;\n\n/*\nstatic const int MIN_L = 1;\nstatic const int MAX_L = 20;\n\nstatic const int MIN_T = 1;\nstatic const int MAX_T = Probe::MAX_T;\n\nstatic const int MIN_M = 1;\nstatic const int MAX_M = Probe::MAX_M;\n\nstatic const double MIN_W = 0.01;\nstatic const double MAX_W = 10;\nstatic const double NUM_W = 400;\nstatic const double DELTA_W = (MAX_W - MIN_W) / NUM_W;\n\n// L, T, M, W\ntune::Interval intervals[]= {{MIN_L, MAX_L + 1},\n {MIN_T, MAX_T + 1},\n {0, MAX_M - MIN_M + 1},\n {0, NUM_W + 1}};\n\ndouble target_recall;\n\nMultiProbeLshDataModel *model;\ndouble R;\n\ndouble recall_K (const tune::Input &x) {\n model->setL(x[0]);\n model->setT(x[1]);\n model->setM(MAX_M - x[2]);\n model->setW(MIN_W + DELTA_W * x[3]);\n return model->avgRecall();\n}\n\ndouble recall_R (const tune::Input &x) {\n model->setL(x[0]);\n model->setT(x[1]);\n model->setM(MAX_M - x[2]);\n model->setW(MIN_W + DELTA_W * x[3]);\n return model->recall(R);\n}\n\ndouble cost (const tune::Input &x) {\n model->setL(x[0]);\n model->setT(x[1]);\n model->setM(MAX_M - x[2]);\n model->setW(MIN_W + DELTA_W * x[3]);\n return model->cost();\n}\n\nbool constraint_K (const tune::Input &x) {\n return recall_K(x) > target_recall;\n}\n\nbool constraint_R (const tune::Input &x) {\n return recall_R(x) > target_recall;\n}\n*/\n\nint main (int argc, char *argv[])\n{\n // restore initial values\n intervals[0].begin = MIN_L; intervals[0].end = MAX_L + 1;\n intervals[1].begin = MIN_T; intervals[1].end = MAX_T + 1;\n intervals[2].begin = 0; intervals[2].end = MAX_M - MIN_M + 1;\n intervals[3].begin = 0; intervals[3].end = NUM_W + 1;\n\n target_recall = 0.0;\n model = NULL;\n R = 0.0;\n\n int T, L, M;\n double W;\n unsigned N, K;\n bool do_K = true;\n string data_param;\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",T\", po::value(&T)->default_value(-1), \"\")\n (\",L\", po::value(&L)->default_value(-1), \"\")\n (\",M\", po::value(&M)->default_value(-1), \"set to -1 for tuning.\")\n (\",W\", po::value(&W)->default_value(-1), \"set to -1 for tuning.\")\n (\"size,N\", po::value(&N), \"dataset size.\")\n (\"param,P\", po::value(&data_param), \"data parameter file.\")\n (\"recall,R\", po::value(&target_recall)->default_value(0.9), \"desired recall.\")\n (\"topk,K\", po::value(&K)->default_value(20), \"\")\n (\"radius,r\", po::value(&R), \"\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm);\n\n if (vm.count(\"help\") || (vm.count(\"param\") < 1) || (vm.count(\"size\") < 1) || T < 1 || L < 1)\n {\n cout << desc;\n return 0;\n }\n\n if (vm.count(\"radius\")) {\n do_K = false;\n }\n\n if (L <= 0 || T <= 0) {\n cout << \"You need to specify L and T.\" << endl;\n return 0;\n }\n\n if (L > 0) {\n intervals[0].begin = L;\n intervals[0].end = L + 1;\n }\n\n if (T > 0) {\n intervals[1].begin = T;\n intervals[1].end = T + 1;\n }\n\n if (M > 0) {\n intervals[2].begin = MAX_M - M;\n intervals[2].end = MAX_M - M + 1;\n }\n\n if (W > 0) {\n intervals[3].begin = (W - MIN_W) / DELTA_W;\n intervals[3].end = intervals[3].begin + 1;\n }\n\n DataParam param(data_param);\n double scale = param.scale();\n\n MultiProbeLshDataModel local_model(param, N, K);\n model = &local_model;\n\n\n int begin_M = intervals[2].begin;\n int end_M = intervals[2].end;\n\n for (int m = begin_M; m < end_M; ++m) {\n\n intervals[2].begin = m;\n intervals[2].end = m + 1;\n\n tune::Range range(intervals, intervals + sizeof intervals /sizeof intervals[0]);\n tune::Input input;\n bool ok = do_K ? tune::Tune(range, constraint_K, &input)\n : tune::Tune(range, constraint_R, &input);\n\n if (ok) {\n cout << boost::format(\"L = %d\\tT = %d\\tM = %d\\tW = %g\\trecall = %g\\tcost = %g\")\n % input[0] % input[1] % (MAX_M - input[2]) % ((MIN_W + DELTA_W * input[3]) * sqrt(scale))\n % (do_K ? recall_K(input) : recall_R(input)) % cost(input) << endl;\n } else {\n cout << \"Failed.\" << endl;\n }\n\n }\n\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/run-spectral.cpp", "content": "#include \n\nusing namespace std;\nusing namespace lshkit;\n\nint main (int argc, char *argv[])\n{\n FloatMatrix data(argv[1]);\n SpectralHash hash;\n {\n ifstream is(argv[2], ios::binary);\n hash.serialize(is, 0);\n }\n\n for (int i = 0; i < data.getSize(); ++i) {\n cout << hash(data[i]) << endl;\n }\n\n return 0;\n}\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/scan.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file scan.cpp\n * \\brief Linear scan dataset and construct benchmark.\n *\n * This program randomly picks Q points from a dataset as queries, and\n * then linear-scan the database to find K-NN/R-NN for each query to produce\n * a benchmark file. For each query, the query point itself is excluded\n * from the K-NN/R-NN list.\n *\n * You can specify both K and R and the prorgram will search for the K\n * points closest to queries which are within distance range of R.\n * If K = 0, then all points within distance range of R are returned.\n * The default value of R is the maximal value of float.\n *\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -Q [ -- ] arg (=1) number of queries to sample.\n -K [ -- ] arg (=1) number of nearest neighbors.\n --metric arg (=2) 1: L1; 2: L2\n -D [ --data ] arg dataset path\n -B [ --benchmark ] arg output benchmark file path\n\\endverbatim\n *\n */\nconst char *help = \"This program searches for K-NNs by linear scan and generate a benchmark file.\";\n\n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\n\nint main (int argc, char *argv[])\n{\n string data_file;\n string query_file;\n\n unsigned K, Q, metric, seed;\n float R;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",Q\", po::value(&Q)->default_value(1), \"number of queries to sample.\")\n (\",K\", po::value(&K)->default_value(0), \"number of nearest neighbors.\")\n (\",R\", po::value(&R)->default_value(numeric_limits::max()), \"distance range to search for\")\n (\"seed\", po::value(&seed)->default_value(0), \"random number seed, 0 to use default.\")\n (\"metric\", po::value(&metric)->default_value(2), \"1: L1; 2: L2\")\n (\"data,D\", po::value(&data_file), \"dataset path\")\n (\"benchmark,B\", po::value(&query_file), \"output benchmark file path\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"data\") < 1) || (vm.count(\"benchmark\") < 1))\n {\n cout << help << endl;\n cout << desc;\n return 0;\n }\n\n Matrix data(data_file);\n\n Benchmark bench;\n bench.init(Q, data.getSize(), seed);\n boost::timer timer;\n\n timer.restart();\n if (metric == 1) {\n\n metric::l1 l1(data.getDim());\n\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n int q = static_cast(bench.getQuery(i));\n Topk &topk = bench.getAnswer(i);\n topk.reset(K, R);\n for (int j = 0; j < data.getSize(); ++j)\n {\n if (q == j) continue;\n topk << Topk::Element(j, l1(data[q],\n data[j]));\n }\n ++progress;\n }\n }\n else if (metric == 2) {\n\n metric::l2 l2(data.getDim());\n\n boost::progress_display progress(Q);\n for (unsigned i = 0; i < Q; ++i)\n {\n int q = static_cast(bench.getQuery(i));\n Topk &topk = bench.getAnswer(i);\n topk.reset(K, R);\n for (int j = 0; j < data.getSize(); ++j)\n {\n if (q == j) continue;\n topk << Topk::Element(j, l2(data[q],\n data[j]));\n }\n ++progress;\n }\n }\n else {\n cout << \"METRIC NOT SUPPORTED.\" << endl;\n }\n\n cout << boost::format(\"QUERY TIME: %1%s.\") % timer.elapsed() << endl;\n bench.save(query_file);\n\n //timer.tuck(\"QUERY\");\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/sketch-run.cpp", "content": "/* \n Copyright (C) 2008 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file sketch-run.cpp\n * \\brief Example of sketch construction with Gaussian LSH.\n * \n * This program uses sketch filtering to accelerate K-NN search.\n * The idea is to first search against a dataset of sketches and keep the\n * top C*K points as candidates. The candidates are than ranked using\n * the raw feature vectors. The sketch database can be viewed as a index.\n * \n * In this program, we use 2-stable LSH based sketch. Each sketch is a bit-vector\n * of M bits, and each bit is produced by a independent hash function from the\n * family DeltaLSB.\n *\n * The program reconstruct the sketches by default. You can give the\n * --index option to make the program save the sketches. The next\n * time you run the program with the same --index option, the program\n * will try to load the previously saved sketches. When a saved sketch database is\n * used, you need to make sure that the dataset and other parameters match\n * the previous run. However, the benchmark file, Q and K can be different.\n\\verbatim\nAllowed options:\n -h [ --help ] produce help message.\n -W [ -- ] arg (=1)\n -M [ -- ] arg (=1) skech size / byte\n -C [ -- ] arg (=10) # candidates = C x K\n -Q [ -- ] arg (=100) # queries to use\n -K [ -- ] arg (=50) K-NNs retrieved\n -D [ --data ] arg data file\n -B [ --benchmark ] arg benchmark file\n --index arg sketch file\n --asym Asymmetric distance estimation\n\\endverbatim\n */\n\n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace lshkit;\nnamespace po = boost::program_options; \n\nint main (int argc, char *argv[])\n{\n string data_file;\n string index_file;\n string benchmark;\n\n float W, R;\n unsigned M;\n unsigned Q, K, C;\n bool do_benchmark = true;\n bool use_index = false; // load the index from a file\n bool do_asym = false;\n\n boost::timer timer;\n\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help,h\", \"produce help message.\")\n (\",W\", po::value(&W)->default_value(1.0), \"\")\n (\",M\", po::value(&M)->default_value(1), \"skech size / byte\")\n (\",C\", po::value(&C)->default_value(10), \"# candidates = C x K\")\n (\",Q\", po::value(&Q)->default_value(100), \"# queries to use\")\n (\",K\", po::value(&K)->default_value(50), \"K-NNs retrieved\")\n (\",R\", po::value(&R)->default_value(numeric_limits::max()), \"R-NN distnace range\")\n (\"data,D\", po::value(&data_file), \"data file\")\n (\"benchmark,B\", po::value(&benchmark), \"benchmark file\")\n (\"index\", po::value(&index_file), \"sketch file\")\n (\"asym\", \"Asymmetric distance estimation\")\n ;\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.count(\"help\") || (vm.count(\"data\") < 1))\n {\n cout << desc;\n return 0;\n }\n\n if ((Q == 0) || (vm.count(\"benchmark\") == 0)) {\n do_benchmark = false;\n }\n\n if (vm.count(\"asym\") == 1) {\n do_asym = true;\n }\n\n if (vm.count(\"index\") == 1) {\n use_index = true;\n }\n\n cout << \"LOADING DATA...\" << endl;\n timer.restart();\n FloatMatrix data(data_file);\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n\n typedef Sketch > MySketch;\n\n MySketch sketcher;\n Matrix index;\n\n bool index_loaded = false;\n\n if (use_index) {\n ifstream is(index_file.c_str(), ios_base::binary);\n if (is) {\n is.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n cout << \"LOADING INDEX...\" << endl;\n timer.restart();\n sketcher.load(is);\n index.load(is);\n BOOST_VERIFY(is);\n BOOST_VERIFY(M <= sketcher.getChunks());\n cout << boost::format(\"LOAD TIME: %1%s.\") % timer.elapsed() << endl;\n index_loaded = true;\n }\n }\n\n if (!index_loaded) {\n\n // We define a short name for the MPLSH index.\n MySketch::Parameter param;\n\n // Setup the parameters. Note that L is not provided here.\n param.W = W;\n param.dim = data.getDim();\n DefaultRng rng;\n\n sketcher.reset(M, param, rng);\n // The accessor.\n\n // Initialize the index structure. Note L is passed here.\n cout << \"CONSTRUCTING INDEX...\" << endl;\n\n index.reset(M, data.getSize());\n\n // CONSTRUCT SKETCHES FOR DATABASE\n timer.restart();\n {\n boost::progress_display progress(data.getSize());\n for (int i = 0; i < data.getSize(); ++i)\n {\n sketcher.apply(data[i], index[i]);\n ++progress;\n }\n }\n cout << boost::format(\"CONSTRUCTION TIME: %1%s.\") % timer.elapsed() << endl;\n\n if (use_index) {\n timer.restart();\n cout << \"SAVING INDEX...\" << endl;\n {\n ofstream os(index_file.c_str(), ios_base::binary);\n os.exceptions(ios_base::eofbit | ios_base::failbit | ios_base::badbit);\n sketcher.save(os);\n index.save(os);\n }\n cout << boost::format(\"SAVING TIME: %1%s\") % timer.elapsed() << endl;\n }\n }\n\n BOOST_VERIFY(index.getSize() == data.getSize());\n\n if (do_benchmark) {\n\n Benchmark<> bench;\n cout << \"LOADING BENCHMARK...\" << endl;\n bench.load(benchmark);\n bench.resize(Q, K);\n cout << \"DONE.\" << endl;\n\n for (unsigned i = 0; i < Q; ++i)\n {\n for (unsigned j = 0; j < K; ++j)\n {\n assert(bench.getAnswer(i)[j].key < data.getSize());\n }\n }\n\n cout << \"RUNNING QUERIES...\" << endl;\n\n Stat recall;\n Topk candidate;\n Topk topk;\n unsigned char *query_sketch = new unsigned char[sketcher.getChunks()];\n boost::progress_display progress(Q);\n metric::l2 l2(data.getDim());\n\n timer.restart();\n if (do_asym)\n {\n float *asym = new float[sketcher.getBits()];\n WeightedHammingHelper<> helper(M);\n for (unsigned i = 0; i < Q; ++i)\n {\n // Query for one point.\n int query = static_cast(bench.getQuery(i));\n candidate.reset(C * K, R);\n topk.reset(K, R);\n\n sketcher.apply(data[query], query_sketch, asym);\n helper.update(query_sketch, asym);\n // Scan the sketches for candidates.\n for (int j = 0; j < index.getSize(); ++j) {\n if (j == query) continue;\n Topk::Element e;\n e.key = j;\n e.dist = helper.distTo(index[j]);\n candidate << e;\n }\n // Rank with raw feature.\n for (unsigned j = 0; j < candidate.size(); ++j) {\n Topk::Element e;\n e.key = candidate[j].key;\n e.dist = l2(data[query], data[e.key]);\n topk << e;\n }\n recall << bench.getAnswer(i).recall(topk);\n ++progress;\n }\n delete []asym;\n }\n else\n // symmetric sketch\n {\n metric::hamming hamming(M);\n for (unsigned i = 0; i < Q; ++i)\n {\n // Query for one point.\n int query = static_cast(bench.getQuery(i));\n candidate.reset(C * K, R);\n topk.reset(K, R);\n\n sketcher.apply(data[query], query_sketch);\n for (int j = 0; j < index.getSize(); ++j) {\n if (j == query) continue;\n Topk::Element e;\n e.key = j;\n e.dist = hamming(index[j], query_sketch);\n candidate << e;\n }\n for (unsigned j = 0; j < candidate.size(); ++j) {\n Topk::Element e;\n e.key = candidate[j].key;\n e.dist = l2(data[query], data[e.key]);\n topk << e;\n }\n recall << bench.getAnswer(i).recall(topk);\n ++progress;\n }\n }\n\n delete []query_sketch;\n cout << boost::format(\"QUERY TIME: %1%s.\") % timer.elapsed() << endl;\n cout << \"[RECALL] \" << recall.getAvg() << \" +/- \" << recall.getStd() << endl;\n\n }\n\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/lshkit/tools/txt2bin.cpp", "content": "/* \n Copyright (C) 2009 Wei Dong . All Rights Reserved.\n \n This file is part of LSHKIT.\n \n LSHKIT is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n LSHKIT is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with LSHKIT. If not, see .\n*/\n\n/**\n * \\file txt2bin.cpp\n * \\brief Convert a dataset file from text to binary.\n *\n * Usage: txt2bin \n *\n * Example input file:\n \\verbatim\n 1 0 1 2\n 2 3 4 5\n 0.5 0.4 0.6 0.1\n \\endverbatim\n *\n * Each row of the text file must contain the same number of columns. Columns are\n * separated by single spaces or tabs.\n *\n */\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace boost;\n\nint main (int argc, char *argv[])\n{\n unsigned size = sizeof(float), col, row, cnt;\n\n if (argc < 3)\n {\n cout << format(\"usage:\\n\\t%1% \") % argv[0] << endl;\n return 0;\n }\n\n col = row = cnt = 0;\n\n ifstream is(argv[1]);\n // determine dimension (# columns)\n {\n string line;\n getline(is, line);\n is.seekg(0, ios::beg);\n\n bool is_blank = true;\n BOOST_FOREACH(char c, line) {\n if (isspace(c)) {\n is_blank = true;\n }\n else {\n if (is_blank) {\n col++;\n }\n is_blank = false;\n }\n }\n }\n\n ofstream os(argv[2], ios::binary);\n os.seekp(sizeof(unsigned) * 3, ios::beg);\n for (;;)\n {\n float f;\n is >> f;\n if (!is) break;\n os.write((const char*)&f, sizeof(f));\n cnt++;\n }\n row = cnt / col;\n if (cnt % col != 0) throw runtime_error(\"FILE FORMAT ERROR\");\n os.seekp(0, ios::beg);\n os.write((const char *)&size, sizeof(unsigned));\n os.write((const char *)&row, sizeof(unsigned));\n os.write((const char *)&col, sizeof(unsigned));\n return 0;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/CMakeFiles/CMakeDirectoryInformation.cmake", "content": "# CMAKE generated file: DO NOT EDIT!\n# Generated by \"Unix Makefiles\" Generator, CMake Version 3.2\n\n# Relative path conversion top directories.\nset(CMAKE_RELATIVE_PATH_TOP_SOURCE \"/home/yzhang4/HD/release/algorithms/NMSLIB/code\")\nset(CMAKE_RELATIVE_PATH_TOP_BINARY \"/home/yzhang4/HD/release/algorithms/NMSLIB/code\")\n\n# Force unix paths in dependencies.\nset(CMAKE_FORCE_UNIX_PATHS 1)\n\n\n# The C and CXX include file regular expressions for this directory.\nset(CMAKE_C_INCLUDE_REGEX_SCAN \"^.*$\")\nset(CMAKE_C_INCLUDE_REGEX_COMPLAIN \"^$\")\nset(CMAKE_CXX_INCLUDE_REGEX_SCAN ${CMAKE_C_INCLUDE_REGEX_SCAN})\nset(CMAKE_CXX_INCLUDE_REGEX_COMPLAIN ${CMAKE_C_INCLUDE_REGEX_COMPLAIN})\n" }, { "path": 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ition.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/././HessenbergDecomposition.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/./HessenbergDecomposition.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/./RealQZ.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/./RealSchur.h\n./HessenbergDecomposition.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/././HessenbergDecomposition.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/./Tridiagonalization.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h\n./ComplexSchur.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/./ComplexSchur.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h\n./HessenbergDecomposition.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/./HessenbergDecomposition.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_MKL.h\nEigen/src/Core/util/MKL_support.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/Eigen/src/Core/util/MKL_support.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h\n./RealSchur.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/./RealSchur.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h\n./RealQZ.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/./RealQZ.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h\n./Tridiagonalization.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/./Tridiagonalization.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h\n./HessenbergDecomposition.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/./HessenbergDecomposition.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/RealSchur_MKL.h\nEigen/src/Core/util/MKL_support.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/Eigen/src/Core/util/MKL_support.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h\n./Tridiagonalization.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/./Tridiagonalization.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h\nEigen/src/Core/util/MKL_support.h\n/usr/include/eigen3/Eigen/src/Eigenvalues/Eigen/src/Core/util/MKL_support.h\n\n/usr/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h\n\n/usr/include/eigen3/Eigen/src/Geometry/AlignedBox.h\n\n/usr/include/eigen3/Eigen/src/Geometry/AngleAxis.h\n\n/usr/include/eigen3/Eigen/src/Geometry/EulerAngles.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Homogeneous.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Hyperplane.h\n\n/usr/include/eigen3/Eigen/src/Geometry/OrthoMethods.h\n\n/usr/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Quaternion.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Rotation2D.h\n\n/usr/include/eigen3/Eigen/src/Geometry/RotationBase.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Scaling.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Transform.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Translation.h\n\n/usr/include/eigen3/Eigen/src/Geometry/Umeyama.h\n\n/usr/include/eigen3/Eigen/src/Geometry/arch/Geometry_SSE.h\n\n/usr/include/eigen3/Eigen/src/Householder/BlockHouseholder.h\n\n/usr/include/eigen3/Eigen/src/Householder/Householder.h\n\n/usr/include/eigen3/Eigen/src/Householder/HouseholderSequence.h\n\n/usr/include/eigen3/Eigen/src/Jacobi/Jacobi.h\n\n/usr/include/eigen3/Eigen/src/LU/Determinant.h\n\n/usr/include/eigen3/Eigen/src/LU/FullPivLU.h\n\n/usr/include/eigen3/Eigen/src/LU/Inverse.h\n\n/usr/include/eigen3/Eigen/src/LU/PartialPivLU.h\n\n/usr/include/eigen3/Eigen/src/LU/PartialPivLU_MKL.h\nEigen/src/Core/util/MKL_support.h\n/usr/include/eigen3/Eigen/src/LU/Eigen/src/Core/util/MKL_support.h\n\n/usr/include/eigen3/Eigen/src/LU/arch/Inverse_SSE.h\n\n/usr/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h\n\n/usr/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_MKL.h\nEigen/src/Core/util/MKL_support.h\n/usr/include/eigen3/Eigen/src/QR/Eigen/src/Core/util/MKL_support.h\n\n/usr/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h\n\n/usr/include/eigen3/Eigen/src/QR/HouseholderQR.h\n\n/usr/include/eigen3/Eigen/src/QR/HouseholderQR_MKL.h\n../Core/util/MKL_support.h\n/usr/include/eigen3/Eigen/src/Core/util/MKL_support.h\n\n/usr/include/eigen3/Eigen/src/SVD/JacobiSVD.h\n\n/usr/include/eigen3/Eigen/src/SVD/JacobiSVD_MKL.h\nEigen/src/Core/util/MKL_support.h\n/usr/include/eigen3/Eigen/src/SVD/Eigen/src/Core/util/MKL_support.h\n\n/usr/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h\n\n/usr/include/eigen3/Eigen/src/misc/Image.h\n\n/usr/include/eigen3/Eigen/src/misc/Kernel.h\n\n/usr/include/eigen3/Eigen/src/misc/Solve.h\n\n/usr/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h\n\n/usr/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h\n\n/usr/include/eigen3/Eigen/src/plugins/BlockMethods.h\n\n/usr/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h\n\n/usr/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h\n\n/usr/include/eigen3/Eigen/src/pl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\n#include \n#include \n#include \n\n#define PARANOID_CHECK\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nstruct DataWrapper {\n DataWrapper(IdTypeUnsign dataId, IdType centerIndex, const Object* pCenter, dist_t dist) :\n dataId_(dataId), centerIndex_(centerIndex), pCenter_(pCenter), dist_(dist) { }\n DataWrapper(){}\n\n IdTypeUnsign dataId_ = 0;\n IdType centerIndex_ = -1;\n const Object* pCenter_ = NULL;\n dist_t dist_ = 0;\n};\n\nusing namespace std;\nconst size_t MIN_ITER_PROGRESS_QTY = 1000; // A minimum # of assigned items that we expect to see in a single iteration\n\nconst float MAX_UNASSIGN_FRACT = 0.02;\n\nconst bool PRINT_FIRMAL_DEBUG = false;\nconst bool PRINT_CLARANS_DEBUG = false;\n\n\ntemplate \nvoid ClusterUtils::doFIRMAL(bool PrintProgress,\n const Space& space, // A space object\n ObjectVector data, // A list of objects, it is copied\n float ExpCenterQty, // A hint for the number of clusters, the acutal # fo clusters can be much longer\n ObjectVector& vCenters, // Centers\n vector>>& vClustAssign, // Cluster assignment\n ObjectVector& vUnassigned,\n size_t SearchCloseIterQty,// A # of search iterations to find a point that is close to already selected centers\n // For good performance it should be in the order of sqrt(data.size())\n size_t SampleDistQty, // A # of samples to determine the distribution of distances\n bool bUseAllClustersInIter // If true the point is compared against all previously created clusters\n) {\n std::mt19937 randGen;\n std::shuffle(data.begin(), data.end(), randGen);\n\n vUnassigned.clear();\n\n LOG(LIB_INFO) << \"ExpCenterQty = \" << ExpCenterQty;\n LOG(LIB_INFO) << \"SearchCloseIterQty = \" << SearchCloseIterQty;\n LOG(LIB_INFO) << \"SampleDistQty = \" << SampleDistQty;\n\n CHECK_MSG(SampleDistQty > 0, \"The number of samples shouldn't be zero!\");\n\n if (data.empty()) return;\n CHECK_MSG(SampleDistQty > 0, \"The number of samples should be > 0!\");\n CHECK_MSG(ExpCenterQty >= 2, \"There should be at least two centers!\");\n\n vCenters.clear();\n vClustAssign.clear();\n\n unique_ptr progress_bar;\n\n if (PrintProgress) {\n LOG(LIB_INFO) << \"Sampling progress: \";\n progress_bar.reset(new ProgressDisplay(SampleDistQty, cerr));\n }\n\n vector dists;\n\n for (size_t i = 0; i < SampleDistQty; ++i) {\n IdType id1 = RandomInt() % data.size();\n IdType id2 = RandomInt() % data.size();\n dist_t dist = space.IndexTimeDistance(data[id1], data[id2]);\n dists.push_back(dist);\n if (progress_bar.get()) (*progress_bar) += 1;\n }\n sort (dists.begin(), dists.end());\n\n float ExpClustSize = round(min(max(1, SampleDistQty / ExpCenterQty), SampleDistQty - 1));\n size_t pctPos = size_t(ExpClustSize);\n CHECK(pctPos < dists.size());\n float R = dists[pctPos];\n\n LOG(LIB_INFO) << \"Expected center #: \" << ExpCenterQty << \" Expected in sample pos (for percentiles only): \" << ExpClustSize;\n LOG(LIB_INFO) << \"Sampled distances: [\" << dists[0] << \",\" << dists.back() << \"] \";\n CHECK(!dists.empty());\n LOG(LIB_INFO) << \"5%/50%/100% percentiles: [\"\n << dists[min(dists.size()-1,static_cast(dists.size()*0.05))] << \",\"\n << dists[min(dists.size()-1,static_cast(dists.size()*0.5))] << \",\"\n << dists[min(dists.size()-1,static_cast(dists.size()*0.95))] << \"]\";\n LOG(LIB_INFO) << \"R =\" << R << \", this represents \" << pctPos << \"th entry\";\n\n if (PrintProgress) {\n cerr << \"Center-selection progress: \";\n progress_bar.reset(new ProgressDisplay(data.size(), cerr));\n }\n\n if (data.empty()) return;\n vector> dataArr(data.size());\n for (IdTypeUnsign did = 0; did < data.size(); ++did) {\n dataArr[did] = DataWrapper({did, -1, NULL, 0});\n }\n /*\n * In the loop we move assigned objects, including new cluster centers to the beginning of the data array\n * and the temporarily unassigned objects (center candidates) to the end of the array. Clusters, will be\n * collected in the end of the array.\n *\n * The structure of the array:\n * [ assigned ] [ not yet processed ] [not assigned in the current iteration ] [ current iteration clusters ] [ previous iteration clusters ]\n * The number of assigned points is: assignedQty\n * The last index of not yet processed is lastUnprocInIterPlus1 - 1\n * The last index of the not yet assigned in the current iteration is lastCluster - 1\n * The last index of the current iteration clusters is data.size() - iterStartCent - 1\n */\n CHECK(!data.empty());\n IdTypeUnsign lastCluster = data.size();\n\n IdTypeUnsign stopQty = (IdTypeUnsign)data.size() * 0.01;\n IdTypeUnsign assignedQty = 0, iterNum = 0;\n\n vector vTmpClustCenter;\n vector vTmpClustQty;\n\n IdTypeUnsign lastUnprocInIterPlus1 = data.size();\n\n CHECK(stopQty > 0);\n\n size_t prevAssignQty = 0;\n\n while (lastCluster > assignedQty && assignedQty < (1-MAX_UNASSIGN_FRACT)*data.size()) {\n ++iterNum;\n\n IdTypeUnsign trueCenterQty = vTmpClustCenter.size();\n if (assignedQty - prevAssignQty < MIN_ITER_PROGRESS_QTY) {\n if (PRINT_FIRMAL_DEBUG) {\n LOG(LIB_INFO) << \"The progress in the previous iteration is too small, let's try to increase the radius\";\n }\n if (pctPos >= dists.size()/2) {\n LOG(LIB_INFO) << \"Cannot further increase radius, exiting (pctPos == dists.size())\";\n goto fin;\n }\n\n dist_t oldR = R;\n pctPos = min(pctPos*2, min(dists.size()/2, dists.size()- 1));\n R = dists[pctPos];\n if (PRINT_FIRMAL_DEBUG) {\n LOG(LIB_INFO) << \"Increasing radius from \" << oldR << \" to \" << R;\n }\n }\n prevAssignQty = assignedQty;\n\n if (PRINT_FIRMAL_DEBUG)\n LOG(LIB_INFO) << \"Iteration: \" << iterNum << \" assignedQty=\" << assignedQty <<\n \" centerQty=\" << trueCenterQty << \" R=\" << R << \" total centerQty=\" << vTmpClustCenter.size();\n\n IdTypeUnsign iterStartCent = vTmpClustCenter.size();\n IdTypeUnsign lastClusterIterStart = lastCluster;\n\n CHECK(lastCluster > 1);\n {\n // Select a random cluster in the range [assignedQty, lastCluster)\n IdTypeUnsign cid = assignedQty + RandomInt() % (lastCluster - assignedQty);\n\n dataArr[cid].pCenter_ = data[dataArr[cid].dataId_];\n dataArr[cid].centerIndex_ = vTmpClustCenter.size();\n vTmpClustCenter.push_back(dataArr[cid].pCenter_);\n if (PRINT_FIRMAL_DEBUG)\n LOG(LIB_INFO) << \"New cluster candidate: objId=\" << dataArr[cid].pCenter_->id();\n vTmpClustQty.push_back(0);\n\n --lastCluster;\n swap(dataArr[cid], dataArr[lastCluster]); // Becomes a new cluster\n }\n\n lastUnprocInIterPlus1 = lastCluster;\n\n IdTypeUnsign batchUnassignQty = 0;\n while (assignedQty < lastUnprocInIterPlus1) {\n if (batchUnassignQty < SearchCloseIterQty) {\n IdTypeUnsign randUnAssignId = assignedQty + RandomInt() % (lastUnprocInIterPlus1 - assignedQty); // Sample among yet unprocessed\n // Ok here we found a non-yet-visited node, check if it can be attached to one of the existing clusters\n IdType closestCenterIndex = -1;\n dist_t minDist = numeric_limits::max();\n /*\n * Here, we start from iterStartCent, because all the points \"had a chance\" to be connected to these clusters.\n * In additions, all vTmpClustCenter elements are non-NULL for the current iteration, but this is not true\n * for clusters from previous iterations. Specifically, when we find empty clusters in the end of the iteration,\n * we NULLify respective elements of vTmpClustCenter and return cluster centers into the game (so that they\n * can be linked/clustered in subsequent iterations).\n * An alternative version where we start from zero seems to be too slow (ALSO don't forget that some clusters may be NULLs)\n */\n CHECK(!vTmpClustCenter.empty());\n for (IdTypeUnsign centerId = bUseAllClustersInIter ? 0 : iterStartCent; centerId < vTmpClustCenter.size(); ++centerId) {\n CHECK(vTmpClustCenter[centerId]!=NULL);\n dist_t dist = space.IndexTimeDistance(data[dataArr[randUnAssignId].dataId_] /* object or query is the left argument */,\n vTmpClustCenter[centerId] /* cluster center is the right argument */);\n if (dist <= R && dist <= minDist) {\n minDist = dist;\n closestCenterIndex = centerId;\n // Don't break we may find a better cluster assignment\n // break;\n }\n }\n\n if (closestCenterIndex >= 0) {\n if (progress_bar.get()) *progress_bar += 1;\n vTmpClustQty[closestCenterIndex]++;\n dataArr[randUnAssignId].pCenter_ = vTmpClustCenter[closestCenterIndex];\n dataArr[randUnAssignId].centerIndex_ = -2; // This marks an assigned data point\n dataArr[randUnAssignId].dist_ = minDist;\n if (PRINT_FIRMAL_DEBUG) {\n LOG(LIB_INFO) << \"Excluded cid=\" << randUnAssignId << \" (regular attach) \"\n << \" dataId=\" << dataArr[randUnAssignId].dataId_\n << \" objId=\" << data[dataArr[randUnAssignId].dataId_]->id()\n << \" dist = \" << dataArr[randUnAssignId].dist_\n << \" cluster center objId=\" << dataArr[randUnAssignId].pCenter_->id()\n << \" center qty: \" << vTmpClustCenter.size() << \"# of centers in iteration (\"< 0); // By design lastUnprocInIterPlus1 > assignedQty >= 0, see the above loop conditions\n CHECK(dataArr[randUnAssignId].centerIndex_ == -1);\n CHECK(dataArr[randUnAssignId].pCenter_ == NULL);\n CHECK(lastUnprocInIterPlus1 > assignedQty);\n swap(dataArr[lastUnprocInIterPlus1 - 1], dataArr[randUnAssignId]);\n lastUnprocInIterPlus1--;\n batchUnassignQty++;\n continue;\n }\n } else {\n /*\n * Let's select a random unassigned point to be a new cluster.\n * After doing so, we also have to check, if this point can become a center\n * of previously unassigned points.\n */\n CHECK(lastUnprocInIterPlus1 < lastCluster);\n /*\n * When the new cluster is selected: lastCluster == lastUnprocInIterPlus1.\n * However, afterwards we make batchUnassignQty < 0 unsuccessful attempts to assign\n * a data points, each of which leads to decrementing lastUnprocInIterPlus1\n */\n IdTypeUnsign cid = lastUnprocInIterPlus1 + RandomInt() % (lastCluster - lastUnprocInIterPlus1);\n --lastCluster;\n IdTypeUnsign centerId = vTmpClustCenter.size();\n dataArr[cid].centerIndex_ = centerId;\n dataArr[cid].pCenter_ = data[dataArr[cid].dataId_];\n vTmpClustCenter.push_back(dataArr[cid].pCenter_);\n vTmpClustQty.push_back(0);\n if (PRINT_FIRMAL_DEBUG)\n LOG(LIB_INFO) << \"New cluster candidate: objId=\" << dataArr[cid].pCenter_->id();\n\n // Now swap\n swap(dataArr[cid], dataArr[lastCluster]);\n\n for (IdTypeUnsign cid = lastUnprocInIterPlus1; cid < lastCluster; ++cid) {\n CHECK(!vTmpClustCenter.empty());\n dist_t dist = space.IndexTimeDistance(data[dataArr[cid].dataId_] /* object or query is the left argument */,\n vTmpClustCenter.back() /* cluster center is the right argument */);\n if (dist <= R) {\n if (progress_bar.get()) *progress_bar += 1;\n dataArr[cid].centerIndex_ = -2; // This marks an assigned data point\n dataArr[cid].pCenter_ = vTmpClustCenter[centerId];\n vTmpClustQty[centerId]++;\n dataArr[cid].dist_ = dist;\n if (PRINT_FIRMAL_DEBUG) {\n LOG(LIB_INFO) << \"Excluded cid=\" << cid << \" (late attach) \"\n << \" dataId=\" << dataArr[cid].dataId_\n << \" objId=\" << data[dataArr[cid].dataId_]->id()\n << \" dist = \" << dataArr[cid].dist_\n << \" cluster center objId=\" << dataArr[cid].pCenter_->id()\n << \" center qty: \" << (vTmpClustCenter.size()) << \"# of centers in iteration (\"< vDelIds;\n for (IdTypeUnsign i = lastCluster; i < lastClusterIterStart; ++i) {\n IdType centerIndex = dataArr[i].centerIndex_;\n CHECK(centerIndex >= 0);\n CHECK(centerIndex >= iterStartCent);\n CHECK(vTmpClustCenter[centerIndex] != NULL);\n if (!vTmpClustQty[centerIndex]) {\n CHECK(lastCluster >= assignedQty);\n if (PRINT_FIRMAL_DEBUG) LOG(LIB_INFO) << \"Reclaiming a cluster of an empty center! dataId=\" <<\n dataArr[i].dataId_ << \" objId=\" << data[dataArr[i].dataId_]->id();\n dataArr[i].pCenter_ = NULL; // No cluster center any more\n dataArr[i].dist_ = 0;\n dataArr[i].centerIndex_ = -1;\n vDelIds.push_back(centerIndex);\n swap(dataArr[i], dataArr[lastCluster]);\n ++lastCluster;\n CHECK(lastCluster > assignedQty);\n }\n }\n // Don't forget to sort IDs (that we have to delete) in the **DESCENDING** order!\n // otherwise the deletion algorithm will fail miserably\n sort(vDelIds.begin(), vDelIds.end(), std::greater());\n CHECK(vDelIds.size() < 2 || (vDelIds[0] > vDelIds[1]));\n\n for (IdType delId: vDelIds) {\n if (PRINT_FIRMAL_DEBUG)\n LOG(LIB_INFO) << \"Actually deleting previously reclaimed cluster, objId=\" << vTmpClustCenter[delId]->id();\n vTmpClustCenter.erase(vTmpClustCenter.begin() + delId);\n vTmpClustQty.erase(vTmpClustQty.begin() + delId);\n }\n CHECK(vTmpClustCenter.size() == vTmpClustQty.size());\n }\n fin:\n if (progress_bar.get()) *progress_bar += data.size() - progress_bar->count();\n\n // Now create the final representation\n unordered_map ptr2pos;\n\n for (IdTypeUnsign pos = 0; pos < vTmpClustCenter.size(); ++pos) {\n CHECK(vTmpClustCenter[pos] != NULL);\n/*\n if (PRINT_FIRMAL_DEBUG)\n LOG(LIB_INFO) << \"Cluster # \" << pos << \" qty: \" << vTmpClustQty[pos] << \" objId=\" << vTmpClustCenter[pos]->id();\n*/\n vCenters.push_back(vTmpClustCenter[pos]);\n vClustAssign.push_back(shared_ptr>(new DistObjectPairVector()));\n ptr2pos.insert(make_pair(vTmpClustCenter[pos], pos));\n }\n\n CHECK(vCenters.size() == vClustAssign.size());\n for (IdTypeUnsign i = 0; i < assignedQty; ++i) {\n const DataWrapper o = dataArr[i];\n CHECK(o.centerIndex_ == -2);\n CHECK(o.pCenter_ != NULL);\n\n CHECK_MSG(o.pCenter_ != NULL, \"Bug: encountered an unassigned points, while expecting an assigned one!\");\n auto it = ptr2pos.find(o.pCenter_);\n CHECK_MSG(it != ptr2pos.end(),\n \"Bug: cannot find an array position of the cluster with objId=\"+ConvertToString(o.pCenter_->id())+\n \" dataId=\"+ConvertToString(o.dataId_));\n IdTypeUnsign pos = it->second;\n CHECK(pos < vClustAssign.size());\n vClustAssign[pos]->push_back(make_pair(o.dist_, data[o.dataId_]));\n }\n\n for (IdTypeUnsign i = assignedQty; i < lastCluster; ++i) {\n const DataWrapper o = dataArr[i];\n CHECK(o.centerIndex_ == -1);\n CHECK(o.pCenter_ == NULL);\n vUnassigned.push_back(data[o.dataId_]);\n }\n\n LOG(LIB_INFO) << \"Created: \" << vCenters.size() << \" centers in \" << iterNum << \" iterations\";\n LOG(LIB_INFO) << \"The number of unassigned data points: \" << (data.size() - assignedQty - vCenters.size());\n LOG(LIB_INFO) << \"The number of unassigned data points collected: \" << vUnassigned.size();\n\n CHECK(data.size() - assignedQty - vCenters.size() == vUnassigned.size());\n\n CHECK(vCenters.size() == vClustAssign.size());\n sortInsideClustersDist(vClustAssign);\n}\n\ntemplate \nvoid ClusterUtils::doCLARANS(bool PrintProgress,\n const Space& space, // A space object\n ObjectVector data, // A list of objects, it is copied\n size_t centerQty,// Number of clusters/centers\n ObjectVector& vCenters, // Centers\n vector>>& vClustAssign, // Cluster assignment\n IdTypeUnsign inClusterSwapAttempts, // Number of random swaps to find a better cluster center\n IdTypeUnsign inClusterSampleQty, // Number of random points to estimate if the swap was beneficial\n size_t randRestQty,// A number of random restarts\n IdTypeUnsign maxIterQty, // A maximum number of iterations\n double errMinDiff // Stop iterating if the total configuration\n // cost doesn't decrease by at least this value\n) {\n static std::mt19937 randGen;\n\n LOG(LIB_INFO) << \"centerQty = \" << centerQty;\n LOG(LIB_INFO) << \"inClusterSwapAttempts = \" << inClusterSwapAttempts;\n LOG(LIB_INFO) << \"inClusterSampleQty = \" << inClusterSampleQty;\n LOG(LIB_INFO) << \"randRestQty = \" << randRestQty;\n LOG(LIB_INFO) << \"maxIterQty = \" << maxIterQty;\n LOG(LIB_INFO) << \"errMinDiff = \" << errMinDiff;\n\n CHECK_MSG(data.size() >= centerQty,\n \"The number of objects is too small, expecting at least the number of centers/clusters! # of clusters/centers: \"\n + ConvertToString(centerQty) + \" # of data points: \" + ConvertToString(data.size()));\n size_t N = data.size();\n\n double bestConfigCost = numeric_limits::max();\n\n#ifdef PARANOID_CHECK\n {\n\n IdType MaxId = 0;\n for (const Object *pObj: data) {\n MaxId = max(MaxId, pObj->id());\n CHECK_MSG(pObj->id()>=0, \"Got negative Id!\");\n }\n\n // Sanity check, is our data correct?\n {\n vector seen(MaxId+1);\n for (const Object *pObj: data) {\n CHECK_MSG(!seen[pObj->id()],\n \"Inconsistent data, repeating id: \" + ConvertToString(pObj->id()));\n seen[pObj->id()] = true;\n }\n }\n }\n#endif\n\n for (size_t randRestId = 0; randRestId < randRestQty; ++randRestId) {\n#ifdef PARANOID_CHECK\n IdType MaxId = 0;\n for (const Object *pObj: data) {\n MaxId = max(MaxId, pObj->id());\n }\n\n // Sanity check, is our data correct?\n {\n vector seen(MaxId+1);\n for (const Object *pObj: data) {\n CHECK_MSG(!seen[pObj->id()],\n \"Inconsistent data (before shuffle), repeating id: \" + ConvertToString(pObj->id()) + \" randRestId=\" + ConvertToString(randRestId));\n seen[pObj->id()] = true;\n }\n }\n#endif\n std::shuffle(data.begin(), data.end(), randGen);\n\n#ifdef PARANOID_CHECK\n // Sanity check, is our data correct?\n {\n vector seen(MaxId+1);\n for (const Object *pObj: data) {\n CHECK_MSG(!seen[pObj->id()],\n \"Inconsistent data (after shuffle), repeating id: \" + ConvertToString(pObj->id()) + \" randRestId=\" + ConvertToString(randRestId));\n seen[pObj->id()] = true;\n }\n }\n#endif\n\n LOG(LIB_INFO) << \"Found \" << centerQty << \" random seeds! Random restart id: \" << randRestId;\n\n vector vClustErr(centerQty);\n\n size_t reserveQty = 4 * float(N) / centerQty;\n\n\n vector>> vClustAssignLoc(centerQty);\n\n double prevConfCost = numeric_limits::max();\n\n for (size_t iter = 0; iter < maxIterQty; ++iter) {\n LOG(LIB_INFO) << \"Iteration: \" << iter << \" let's find closest centers!\";\n size_t swapQty = 0;\n CHECK(data.size() >= centerQty); // See the check in the beginning of the function\n\n for (size_t cid = 0; cid < centerQty; ++cid) {\n vClustErr[cid] = 0;\n vClustAssignLoc[cid].clear();\n vClustAssignLoc[cid].reserve(reserveQty);\n }\n\n double confCost = 0;\n\n for (size_t did = centerQty; did < N; ++did) {\n dist_t minDist = numeric_limits::max();\n size_t bestClust = 0;\n for (size_t cid = 0; cid < centerQty; ++cid) {\n dist_t d = space.IndexTimeDistance(data[did] /* object or query is the left argument */,\n data[cid] /* cluster center is the right argument */);\n if (d < minDist) {\n minDist = d;\n bestClust = cid;\n }\n }\n\n vClustAssignLoc[bestClust].push_back(make_pair(minDist, did));\n // If a point is selected for cluster-quality estimation\n // incorporate its distance to the center into the error (by adding it)\n if (vClustAssignLoc[bestClust].size() <= inClusterSampleQty)\n vClustErr[bestClust] += minDist;\n\n confCost += minDist;\n }\n\n LOG(LIB_INFO) << \"All points are assigned to clusters.\";\n LOG(LIB_INFO) << \"Configuration cost: \" << confCost << \" previous cost: \" << prevConfCost;\n if (iter > 0 && prevConfCost - confCost < errMinDiff) {\n LOG(LIB_ERROR) << \"Change in configuration error < \" << errMinDiff << \" finishing...\";\n break;\n }\n if (iter +1 == maxIterQty) {\n LOG(LIB_INFO) << \"Maximum # of iterations carried out, exiting.\";\n break;\n }\n /*\n * IMPORTANT NOTE: If we have to exit the loop, e.g., because we reached the maximum # of iterations,\n * we must break here, but no further. Subsequent swaps invalidate the distances to centers stored in vClustAsslignLoc[...][...].first\n * Simply because changing the cluster center entails recalculation of all distances to the new center.\n * However, we don't do this until the beginning of the iteration.\n * Hence, if any swaps ocurr, we do not stop until these distances are recalculated. Otherwise, incorrect\n * data will be returned from the function\n */\n if (PRINT_CLARANS_DEBUG) {\n LOG(LIB_INFO) << \"Cluster centers: \";\n vector tmp;\n for (size_t cid = 0; cid < centerQty; ++cid) {\n tmp.push_back(data[cid]->id());\n }\n LOG(LIB_INFO) << MergeIntoStr(tmp, ' ');\n }\n prevConfCost = confCost;\n for (size_t cid = 0; cid < centerQty; ++cid) {\n // Points are already shuffled\n size_t currClustQty = vClustAssignLoc[cid].size();\n if (currClustQty < inClusterSampleQty) continue; // The cluster is too small\n\n size_t swapAttempts = min(\n currClustQty - inClusterSampleQty, // Doesn't make sense to make more than this # of random swaps\n inClusterSwapAttempts);\n for (size_t att = 0; att < swapAttempts; ++att) {\n IdTypeUnsign randCandPos = inClusterSampleQty +\n // This way a new candidate is never the same\n // as the one of \"samplers\"\n RandomInt() % (currClustQty - inClusterSampleQty);\n IdTypeUnsign randCandId = vClustAssignLoc[cid][randCandPos].second;\n swap(data[cid], data[randCandId]); // do a swap\n // Compute a new error.\n double newErr = 0;\n CHECK(inClusterSampleQty < vClustAssignLoc[cid].size());\n for (size_t k = 0; k < inClusterSampleQty; ++k) {\n newErr += space.IndexTimeDistance(data[vClustAssignLoc[cid][k].second]/* object or query is the left argument */,\n data[cid] /* cluster center is the right argument */);\n }\n if (newErr < vClustErr[cid]) {\n //LOG(LIB_INFO) << vClustErr[cid] << \" -> \" << newErr;\n vClustErr[cid] = newErr;\n swapQty++;\n //break;\n } else {\n swap(data[cid], data[randCandId]); // do a reversing swap\n }\n }\n }\n\n /*\n * IMPORTANT NOTE: swaps invalidate the distances to centers stored in vClustAsslignLoc[...][...].first\n * Simply because changing the cluster center entails recalculation of all distances to the new center.\n * However, we don't do this until the beginning of the iteration.\n * Hence, if any swaps ocurr, we do not stop until these distances are recalculated. Otherwise, incorrect\n * data will be returned from the function\n */\n\n LOG(LIB_INFO) << \"The number of swaps in this iteration: \" << swapQty;\n if (swapQty == 0) {\n LOG(LIB_INFO) << \"No changes, process converged\";\n break;\n }\n }\n\n if (0 == randRestId || prevConfCost < bestConfigCost) {\n LOG(LIB_INFO) << \"Found a better configuration: \" << prevConfCost << \" previous best cost: \" << bestConfigCost;\n bestConfigCost = prevConfCost;\n\n#ifdef PARANOID_CHECK\n // Sanity check, if we swapped correctly, all IDs in data should be unique\n {\n vector seen(MaxId+1);\n for (const Object *pObj: data) {\n CHECK_MSG(!seen[pObj->id()],\n \"Bug, repeating id: \" + ConvertToString(pObj->id()));\n seen[pObj->id()] = true;\n }\n }\n#endif\n\n vClustAssign.resize(centerQty);\n for (size_t cid = 0; cid < centerQty; ++cid) {\n vClustAssign[cid].reset(new DistObjectPairVector());\n vClustAssign[cid]->reserve(vClustAssignLoc[cid].size());\n for (auto curr : vClustAssignLoc[cid]) {\n dist_t dist = curr.first;\n IdType id = curr.second;\n vClustAssign[cid]->push_back(make_pair(dist, data[id]));\n }\n\n }\n vCenters.resize(centerQty);\n for (size_t cid = 0; cid < centerQty; ++cid)\n vCenters[cid] = data[cid];\n } else {\n LOG(LIB_INFO) << \"Current configuration's cost: \" << prevConfCost << \" is worse than the previous best cost: \" << bestConfigCost;\n }\n }\n}\n\n\ntemplate \nvoid ClusterUtils::doReductiveCLARANS(bool PrintProgress,\n const Space& space, // A space object\n ObjectVector data, // A list of objects, it is copied\n IdTypeUnsign maxMetaIterQty,\n float keepFrac, // Percentage of assigned points kept after a meta-iteration is finished\n size_t centerQty, // Number of clusters/centers\n ObjectVector& vCentersGlobal, // Centers\n vector>>& vClusterAssignGlobal, // Cluster assignment\n ObjectVector& vUnassigned, // Unassigned points\n IdTypeUnsign inClusterSwapAttempts, // Number of random swaps to find a better cluster center\n IdTypeUnsign inClusterSampleQty, // Number of random points to estimate if the swap was beneficial\n IdTypeUnsign maxIterQty, // A maximum number of iterations in one meta-iteration\n double errMinDiff // Stop iterating if the total configuration\n // cost doesn't decrease by at least this value\n) {\n size_t origDataSize = data.size();\n\n unique_ptr progress_bar;\n\n const size_t SampleDistQty = SAMPLE_LIST_CLUST_DEFAULT_SAMPLE_QTY;\n if (PrintProgress) {\n cerr << \"Sampling progress: \";\n progress_bar.reset(new ProgressDisplay(SampleDistQty, cerr));\n }\n\n vector dists;\n\n for (size_t i = 0; i < SampleDistQty; ++i) {\n IdType id1 = RandomInt() % data.size();\n IdType id2 = RandomInt() % data.size();\n dist_t dist = space.IndexTimeDistance(data[id1], data[id2]);\n dists.push_back(dist);\n if (progress_bar.get()) (*progress_bar) += 1;\n }\n sort (dists.begin(), dists.end());\n\n float ExpClustSize = round(min(max(1, SampleDistQty / centerQty), SampleDistQty - 1));\n size_t pctPos = size_t(ExpClustSize);\n CHECK(pctPos < dists.size());\n float R = dists[pctPos];\n LOG(LIB_INFO) << \"Sampled distances: [\" << dists[0] << \",\" << dists.back() << \"] \";\n CHECK(!dists.empty());\n LOG(LIB_INFO) << \"5%/50%/100% percentiles: [\"\n << dists[min(dists.size()-1,static_cast(dists.size()*0.05))] << \",\"\n << dists[min(dists.size()-1,static_cast(dists.size()*0.5))] << \",\"\n << dists[min(dists.size()-1,static_cast(dists.size()*0.95))] << \"]\";\n LOG(LIB_INFO) << \"R =\" << R << \", this represents \" << pctPos << \"th entry\";\n\n\n vUnassigned.clear();\n\n vCentersGlobal.clear();\n vClusterAssignGlobal.clear();\n\n LOG(LIB_INFO) << \"maxMetaIterQty = \" << maxMetaIterQty;\n LOG(LIB_INFO) << \"keepFrac = \" << keepFrac;\n\n for (IdTypeUnsign metaIter = 0;\n metaIter < maxMetaIterQty && data.size() >= origDataSize * 0.01 ;\n// data.size() >= centerQty * REDUCTIVE_CLARANS_MIN_CLUSTER_SIZE;\n ++metaIter) {\n\n LOG(LIB_INFO) << \"Meta iteration: \" << metaIter << \" # of data points \" << data.size();\n ObjectVector vCentersMetaIter;\n vector>> vClusterAssignMetaIter;\n\n ClusterUtils::doCLARANS(PrintProgress,\n space,\n data,\n centerQty,\n vCentersMetaIter,\n vClusterAssignMetaIter,\n inClusterSwapAttempts,\n inClusterSampleQty,\n 1 /* randRestQty */,\n maxIterQty,\n errMinDiff);\n\n\n ObjectVector newData;\n\n CHECK(vCentersMetaIter.size() == vClusterAssignMetaIter.size());\n\n\n\n for (IdTypeUnsign cid = 0; cid < vCentersMetaIter.size(); ++cid) {\n vCentersGlobal.push_back(vCentersMetaIter[cid]);\n if (metaIter + 1 >= maxMetaIterQty) {\n vClusterAssignGlobal.push_back(vClusterAssignMetaIter[cid]);\n } else {\n size_t keepSize = (size_t) round(keepFrac * vClusterAssignMetaIter[cid]->size());\n shared_ptr> keepAssign(new DistObjectPairVector());\n\n/* for (IdTypeUnsign i = 0; i < keepSize; ++i) {\n keepAssign->push_back((*vClusterAssignMetaIter[cid])[i]);\n }\n for (IdTypeUnsign i = keepSize; i < vClusterAssignMetaIter[cid]->size(); ++i) {\n newData.push_back((*vClusterAssignMetaIter[cid])[i].second);\n }*/\n for (IdTypeUnsign i = 0; i < vClusterAssignMetaIter[cid]->size(); ++i) {\n auto e = (*vClusterAssignMetaIter[cid])[i];\n if (e.first < R || i < keepSize) keepAssign->push_back(e);\n else {\n IdType id = e.second->id();\n CHECK(id >= 0);\n newData.push_back(e.second);\n } \n }\n\n vClusterAssignGlobal.push_back(keepAssign);\n }\n }\n\n data.swap(newData);\n CHECK(vCentersGlobal.size() == vClusterAssignGlobal.size());\n }\n // Move what's remained to the unassigned array\n for (const Object* o: data) vUnassigned.push_back(o);\n\n // Let's do a quick sanity check\n size_t qty = vUnassigned.size();\n for (IdTypeUnsign cid = 0; cid < vCentersGlobal.size(); ++cid) {\n qty += 1 + vClusterAssignGlobal[cid]->size();\n }\n CHECK(qty == origDataSize);\n}\n\ntemplate \nvoid ClusterUtils::sortInsideClustersDist(const vector>>& vClusterAssign) {\n for (const shared_ptr>& v:vClusterAssign) {\n sort(v->begin(), v->end());\n }\n}\n\ntemplate \nvoid ClusterUtils::printClusterStat(const Space &space,\n const vector>>&vClustAssign,\n IdTypeUnsign sampleQty) {\n static std::mt19937 randGen;\n for (IdTypeUnsign cid = 0; cid < vClustAssign.size(); ++cid) {\n LOG(LIB_INFO) << \"Cluster id: \" << cid ;\n vector dists;\n\n dists.reserve(sampleQty);\n DistObjectPairVector vClustElem(*vClustAssign[cid]);\n sort(vClustElem.begin(), vClustElem.end());\n\n for (IdTypeUnsign i = 0; i < min(sampleQty, vClustElem.size()); ++i) {\n dists.push_back(vClustElem[i].first);\n }\n\n LOG(LIB_INFO) << \"# of elements: \" << vClustElem.size() ;\n if (!vClustElem.empty())\n LOG(LIB_INFO) << \"90% percentile: \" << dists[size_t(floor((dists.size() - 1) * 0.9))] ;\n LOG(LIB_INFO) << \"Distance quantiles:\" ;\n LOG(LIB_INFO) << \"[\" ;\n if (!dists.empty()) {\n for (size_t i = 0; i < 10; ++i) {\n for (size_t k = 0; k < 10; ++k) {\n auto num = i * 10 + k;\n LOG(LIB_INFO) << num << \":\" << dists[size_t(floor((dists.size() - 1) * num / 100.0))] << \" \";\n }\n LOG(LIB_INFO) ;\n }\n }\n LOG(LIB_INFO) << \"]\" ;\n }\n\n}\n\n\ntemplate \nvoid ClusterUtils::printAndVerifyClusterStat(const Space &space, const ObjectVector &vCenters,\n const vector>> &vClustAssign,\n IdTypeUnsign sampleQty) {\n //static std::mt19937 randGen;\n for (IdTypeUnsign cid = 0; cid < vClustAssign.size(); ++cid) {\n LOG(LIB_INFO) << \"Cluster id: \" << cid << \" objId=\" << vCenters[cid]->id() ;\n vector dists;\n\n dists.reserve(sampleQty);\n DistObjectPairVector vClustElem(*vClustAssign[cid]);\n sort(vClustElem.begin(), vClustElem.end());\n\n for (IdTypeUnsign i = 0; i < min(sampleQty, vClustElem.size()); ++i) {\n dist_t dist = space.IndexTimeDistance(vClustElem[i].second /* object or query is the left argument */,\n vCenters[cid] /* cluster center is the right argument */);\n CHECK_MSG(ApproxEqual(vClustElem[i].first, dist),\n \"Bug: the precomputed distance: \" + ConvertToString(vClustElem[i].first) +\n \" is different from the real one: \" + ConvertToString(dist) +\n \" when computing the distance between \" + ConvertToString(vClustElem[i].second->id()) +\n \" and \" +ConvertToString(vCenters[cid]->id()) );\n dists.push_back(vClustElem[i].first);\n }\n\n LOG(LIB_INFO) << \"# of elements: \" << vClustElem.size() ;\n if (!vClustElem.empty())\n LOG(LIB_INFO) << \"90% percentile: \" << dists[size_t(floor((dists.size() - 1) * 0.9))] ;\n LOG(LIB_INFO) << \"Distance quantiles:\" ;\n LOG(LIB_INFO) << \"[\" ;\n if (!dists.empty()) {\n for (size_t i = 0; i < 10; ++i) {\n for (size_t k = 0; k < 10; ++k) {\n auto num = i * 10 + k;\n LOG(LIB_INFO) << num << \":\" << dists[size_t(floor((dists.size() - 1) * num / 100.0))] << \" \";\n }\n LOG(LIB_INFO) ;\n }\n }\n LOG(LIB_INFO) << \"]\" ;\n }\n\n}\n\ntemplate class ClusterUtils;\ntemplate class ClusterUtils;\ntemplate class ClusterUtils;\n\n} // namespace similarity\n\n\n\n" }, { "path": "algorithms/NMSLIB/code/src/cmake_install.cmake", "content": "# Install script for directory: /home/yzhang4/HD/release/algorithms/NMSLIB/code/src\n\n# Set the install prefix\nif(NOT DEFINED CMAKE_INSTALL_PREFIX)\n set(CMAKE_INSTALL_PREFIX \"/usr/local\")\nendif()\nstring(REGEX REPLACE \"/$\" \"\" CMAKE_INSTALL_PREFIX \"${CMAKE_INSTALL_PREFIX}\")\n\n# Set the install configuration name.\nif(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)\n if(BUILD_TYPE)\n string(REGEX REPLACE \"^[^A-Za-z0-9_]+\" \"\"\n CMAKE_INSTALL_CONFIG_NAME \"${BUILD_TYPE}\")\n else()\n set(CMAKE_INSTALL_CONFIG_NAME \"Release\")\n endif()\n message(STATUS \"Install configuration: \\\"${CMAKE_INSTALL_CONFIG_NAME}\\\"\")\nendif()\n\n# Set the component getting installed.\nif(NOT CMAKE_INSTALL_COMPONENT)\n if(COMPONENT)\n message(STATUS \"Install component: \\\"${COMPONENT}\\\"\")\n set(CMAKE_INSTALL_COMPONENT \"${COMPONENT}\")\n else()\n set(CMAKE_INSTALL_COMPONENT)\n endif()\nendif()\n\n# Install shared libraries without execute permission?\nif(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)\n set(CMAKE_INSTALL_SO_NO_EXE \"0\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/cluster_util.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/distcomp.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/distcomp_edist.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/eval_metrics.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/eval_results.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/experimentconf.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/experiments.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/flexbuff.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/floatdiff.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/global.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/gold_standard.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/idtype.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/incremental_quick_select.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/index.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/init.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/knnquery.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/knnqueue.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/logging.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/memory.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/meta_analysis.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/methodfactory.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/object.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/params.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/params_cmdline.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/params_def.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/permutation_type.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/permutation_utils.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/ported_boost_progress.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/pow.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/projection.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/query.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/query_creator.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/randproj_util.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/rangequery.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/report_intr_dim.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/searchoracle.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/simddebug.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/simdutils.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/sort_arr_bi.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/spacefactory.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/tune.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/ztimer.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/utils.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/report.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/bbtree.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/dummy.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/ghtree.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/hnsw.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/lcstrategy.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/list_clusters.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/lsh.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/lsh_multiprobe.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/lsh_space.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/multi_index.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/multi_vantage_point_tree.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/multi_vantage_point_tree_utils.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/nndes.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/nonmetr_list_clust.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/omedrank.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/perm_bin_vptree.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/perm_index_incr_bin.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/perm_lsh_bin.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/permutation_inverted_index.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/permutation_prefix_index.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/pivot_neighb_common.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/pivot_neighb_invindx.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/proj_vptree.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/projection_index_incremental.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/seqsearch.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/small_world_rand.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/small_world_rand_split.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/spatial_approx_tree.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/vptree.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/method\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/method/vptree_utils.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_bit_hamming.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_bregman.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_dummy.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_js.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_leven.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_lp.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_rank_correl.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_scalar.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_sparse_lp.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_sparse_scalar.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_sparse_scalar_fast.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_sparse_vector.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_sparse_vector_inter.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_sqfd.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_string.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_vector.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_vector_gen.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/space\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/space/space_word_embed.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/factory\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/factory/init_methods.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include/factory\" TYPE FILE FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/include/factory/init_spaces.h\")\nendif()\n\nif(NOT CMAKE_INSTALL_COMPONENT OR \"${CMAKE_INSTALL_COMPONENT}\" STREQUAL \"Unspecified\")\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/lib\" TYPE STATIC_LIBRARY FILES \"/home/yzhang4/HD/release/algorithms/NMSLIB/code/release/libNonMetricSpaceLib.a\")\nendif()\n\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_bregman.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \"distcomp.h\"\n#include \"string.h\"\n#include \"utils.h\"\n#include \"simdutils.h\"\n\n#include \n#include \n#include \n\n#ifdef PORTABLE_SSE2\n#include \n#endif\n\nnamespace similarity {\n\nusing namespace std;\n\n\n/*\n * Itakura-Saito distance\n */\n\ntemplate \nT ItakuraSaito(const T* pVect1, const T* pVect2, size_t qty) {\n T sum = 0;\n\n for (size_t i = 0; i < qty; i++) { \n sum += pVect1[i]/pVect2[i] - log(pVect1[i]/pVect2[i]) - 1;\n }\n\n return sum;\n}\n\ntemplate float ItakuraSaito(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double ItakuraSaito(const double* pVect1, const double* pVect2, size_t qty);\n\ntemplate \nT ItakuraSaitoPrecomp(const T* pVect1, const T* pVect2, size_t qty) {\n T sum = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n const T* pVectLog1 = pVect1 + qty;\n const T* pVectLog2 = pVect2 + qty;\n\n while (pVect1 < pEnd1) {\n sum += (*pVect1)/(*pVect2) - ((*pVectLog1) - (*pVectLog2)); ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n sum += (*pVect1)/(*pVect2) - ((*pVectLog1) - (*pVectLog2)); ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n sum += (*pVect1)/(*pVect2) - ((*pVectLog1) - (*pVectLog2)); ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n sum += (*pVect1)/(*pVect2) - ((*pVectLog1) - (*pVectLog2)); ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n }\n\n while (pVect1 < pEnd2) {\n sum += (*pVect1)/(*pVect2) - ((*pVectLog1) - (*pVectLog2)); ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n }\n\n return sum - qty;\n}\n\ntemplate float ItakuraSaitoPrecomp(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double ItakuraSaitoPrecomp(const double* pVect1, const double* pVect2, size_t qty);\n\n\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\ntemplate <>\nfloat ItakuraSaitoPrecompSIMD(const float* pVect1, const float* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"ItakuraSaitoPrecompSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return ItakuraSaitoPrecomp(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n const float* pVectLog1 = pVect1 + qty;\n const float* pVectLog2 = pVect2 + qty;\n\n __m128 v1, v2, vLog1, vLog2;\n __m128 sum = _mm_set1_ps(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_sub_ps(_mm_div_ps(v1, v2), _mm_sub_ps(vLog1, vLog2)));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_sub_ps(_mm_div_ps(v1, v2), _mm_sub_ps(vLog1, vLog2)));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_sub_ps(_mm_div_ps(v1, v2), _mm_sub_ps(vLog1, vLog2)));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_sub_ps(_mm_div_ps(v1, v2), _mm_sub_ps(vLog1, vLog2)));\n\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_sub_ps(_mm_div_ps(v1, v2), _mm_sub_ps(vLog1, vLog2)));\n\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n float res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n res += (*pVect1)/(*pVect2) - ((*pVectLog1) - (*pVectLog2)); ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n }\n\n return res - qty;\n#endif\n}\n\ntemplate <>\ndouble ItakuraSaitoPrecompSIMD(const double* pVect1, const double* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"ItakuraSaitoPrecompSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return ItakuraSaitoPrecomp(pVect1, pVect2, qty);\n#else\n size_t qty8 = qty/8;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + qty;\n\n const double* pVectLog1 = pVect1 + qty;\n const double* pVectLog2 = pVect2 + qty;\n\n __m128d v1, v2, vLog1, vLog2;\n __m128d sum = _mm_set1_pd(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n vLog1 = _mm_loadu_pd(pVectLog1); pVectLog1 += 2;\n vLog2 = _mm_loadu_pd(pVectLog2); pVectLog2 += 2;\n sum = _mm_add_pd(sum, _mm_sub_pd(_mm_div_pd(v1, v2), _mm_sub_pd(vLog1, vLog2)));\n \n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n vLog1 = _mm_loadu_pd(pVectLog1); pVectLog1 += 2;\n vLog2 = _mm_loadu_pd(pVectLog2); pVectLog2 += 2;\n sum = _mm_add_pd(sum, _mm_sub_pd(_mm_div_pd(v1, v2), _mm_sub_pd(vLog1, vLog2)));\n \n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1];\n\n while (pVect1 < pEnd2) {\n res += (*pVect1)/(*pVect2) - ((*pVectLog1) - (*pVectLog2)); ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n }\n\n return res - qty;\n#endif\n}\n\ntemplate float ItakuraSaitoPrecompSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double ItakuraSaitoPrecompSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n\n/*\n * KL-divergence\n */\n\ntemplate T KLStandard(const T *pVect1, const T *pVect2, size_t qty) \n{ \n T sum = 0;\n\n for (size_t i = 0; i < qty; i++) { \n sum += pVect1[i] * log(pVect1[i]/pVect2[i]);\n }\n\n return sum;\n}\n\ntemplate float KLStandard(const float *pVect1, const float *pVect2, size_t qty); \ntemplate double KLStandard(const double *pVect1, const double *pVect2, size_t qty);\n\ntemplate T KLStandardLogDiff(const T *p1, const T *p2, size_t qty) \n{ \n T sum = 0;\n\n for (size_t i = 0; i < qty; i++) { \n sum += p1[i] * (log(p1[i])-log(p2[i]));\n }\n\n return sum;\n}\n\ntemplate float KLStandardLogDiff(const float *p1, const float *p2, size_t qty);\ntemplate double KLStandardLogDiff(const double *p1, const double *p2, size_t qty);\n\n\ntemplate \nT KLPrecomp(const T* pVect1, const T* pVect2, size_t qty) {\n T sum = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n const T* pVectLog1 = pVect1 + qty;\n const T* pVectLog2 = pVect2 + qty;\n\n while (pVect1 < pEnd1) {\n sum += (*pVect1++) * ((*pVectLog1++) - (*pVectLog2++));\n sum += (*pVect1++) * ((*pVectLog1++) - (*pVectLog2++));\n sum += (*pVect1++) * ((*pVectLog1++) - (*pVectLog2++));\n sum += (*pVect1++) * ((*pVectLog1++) - (*pVectLog2++));\n }\n\n while (pVect1 < pEnd2) {\n sum += (*pVect1++) * ((*pVectLog1++) - (*pVectLog2++));\n }\n\n return sum;\n}\n\ntemplate float KLPrecomp(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double KLPrecomp(const double* pVect1, const double* pVect2, size_t qty);\n\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\ntemplate <>\nfloat KLPrecompSIMD(const float* pVect1, const float* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"KLPrecompSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return KLPrecomp(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n const float* pVectLog1 = pVect1 + qty;\n const float* pVectLog2 = pVect2 + qty;\n\n __m128 v1, vLog1, vLog2;\n __m128 sum = _mm_set1_ps(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2)));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2)));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2)));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2)));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2)));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n float res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n res += (*pVect1++) * ((*pVectLog1++) - (*pVectLog2++));\n }\n\n return res;\n#endif\n}\n\ntemplate <>\ndouble KLPrecompSIMD(const double* pVect1, const double* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"KLPrecompSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return KLPrecomp(pVect1, pVect2, qty);\n#else\n size_t qty8 = qty/8;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + qty;\n\n const double* pVectLog1 = pVect1 + qty;\n const double* pVectLog2 = pVect2 + qty;\n\n __m128d v1, vLog1, vLog2;\n __m128d sum = _mm_set1_pd(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n vLog1 = _mm_loadu_pd(pVectLog1); pVectLog1 += 2;\n vLog2 = _mm_loadu_pd(pVectLog2); pVectLog2 += 2;\n sum = _mm_add_pd(sum, _mm_mul_pd(v1, _mm_sub_pd(vLog1, vLog2)));\n \n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n vLog1 = _mm_loadu_pd(pVectLog1); pVectLog1 += 2;\n vLog2 = _mm_loadu_pd(pVectLog2); pVectLog2 += 2;\n sum = _mm_add_pd(sum, _mm_mul_pd(v1, _mm_sub_pd(vLog1, vLog2)));\n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1];\n\n while (pVect1 < pEnd2) {\n res += (*pVect1++) * ((*pVectLog1++) - (*pVectLog2++));\n }\n\n return res;\n#endif\n}\n\ntemplate float KLPrecompSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double KLPrecompSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\ntemplate T KLGeneralStandard(const T *pVect1, const T *pVect2, size_t qty) \n{ \n T sum = 0;\n\n for (size_t i = 0; i < qty; i++) { \n sum += pVect1[i] * log(pVect1[i]/pVect2[i]) + pVect2[i] - pVect1[i];\n }\n\n return sum;\n}\n\n/*\n * Generalized KL-divergence\n */\n\ntemplate float KLGeneralStandard(const float *pVect1, const float *pVect2, size_t qty); \ntemplate double KLGeneralStandard(const double *pVect1, const double *pVect2, size_t qty);\n\ntemplate \nT KLGeneralPrecomp(const T* pVect1, const T* pVect2, size_t qty) {\n T sum = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n const T* pVectLog1 = pVect1 + qty;\n const T* pVectLog2 = pVect2 + qty;\n\n while (pVect1 < pEnd1) {\n sum += (*pVect1) * (*pVectLog1 - *pVectLog2) + *pVect2 - *pVect1; ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n sum += (*pVect1) * (*pVectLog1 - *pVectLog2) + *pVect2 - *pVect1; ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n sum += (*pVect1) * (*pVectLog1 - *pVectLog2) + *pVect2 - *pVect1; ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n sum += (*pVect1) * (*pVectLog1 - *pVectLog2) + *pVect2 - *pVect1; ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n }\n\n while (pVect1 < pEnd2) {\n sum += (*pVect1) * (*pVectLog1 - *pVectLog2) + *pVect2 - *pVect1; ++pVect1; ++pVect2; ++pVectLog1; ++pVectLog2;\n }\n\n return sum;\n}\n\ntemplate float KLGeneralPrecomp(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double KLGeneralPrecomp(const double* pVect1, const double* pVect2, size_t qty);\n\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\ntemplate <>\nfloat KLGeneralPrecompSIMD(const float* pVect1, const float* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"KLGeneralPrecompSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return KLGeneralPrecomp(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n const float* pVectLog1 = pVect1 + qty;\n const float* pVectLog2 = pVect2 + qty;\n\n __m128 v1, v2, vLog1, vLog2;\n __m128 sum = _mm_set1_ps(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(_mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2))), _mm_sub_ps(v2, v1));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(_mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2))), _mm_sub_ps(v2, v1));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(_mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2))), _mm_sub_ps(v2, v1));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(_mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2))), _mm_sub_ps(v2, v1));\n\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n sum = _mm_add_ps(_mm_add_ps(sum, _mm_mul_ps(v1, _mm_sub_ps(vLog1, vLog2))), _mm_sub_ps(v2, v1));\n\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n float res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n res += (*pVect1) * ((*pVectLog1++) - (*pVectLog2++)) + (*pVect2) - (*pVect1);\n ++pVect1; ++pVect2;\n }\n\n return res;\n#endif\n}\n\ntemplate <>\ndouble KLGeneralPrecompSIMD(const double* pVect1, const double* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"KLGeneralPrecompSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return KLGeneralPrecomp(pVect1, pVect2, qty);\n#else\n size_t qty8 = qty/8;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + qty;\n\n const double* pVectLog1 = pVect1 + qty;\n const double* pVectLog2 = pVect2 + qty;\n\n __m128d v1, v2, vLog1, vLog2;\n __m128d sum = _mm_set1_pd(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n vLog1 = _mm_loadu_pd(pVectLog1); pVectLog1 += 2;\n vLog2 = _mm_loadu_pd(pVectLog2); pVectLog2 += 2;\n sum = _mm_add_pd(_mm_add_pd(sum, _mm_mul_pd(v1, _mm_sub_pd(vLog1, vLog2))), _mm_sub_pd(v2, v1));\n \n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n vLog1 = _mm_loadu_pd(pVectLog1); pVectLog1 += 2;\n vLog2 = _mm_loadu_pd(pVectLog2); pVectLog2 += 2;\n sum = _mm_add_pd(_mm_add_pd(sum, _mm_mul_pd(v1, _mm_sub_pd(vLog1, vLog2))), _mm_sub_pd(v2, v1));\n \n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1];\n\n while (pVect1 < pEnd2) {\n res += (*pVect1) * ((*pVectLog1++) - (*pVectLog2++)) + (*pVect2) - (*pVect1);\n ++pVect1; ++pVect2;\n }\n\n return res;\n#endif\n}\n\ntemplate float KLGeneralPrecompSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double KLGeneralPrecompSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_edist.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n\n#include \"distcomp.h\"\n#include \"string.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate int levenshtein(const T* p1, size_t len1, const T* p2, size_t len2) {\n int aStackBuf[2 * MAX_LEVEN_BUFFER_QTY];\n int *pMemBuf = NULL;\n int *pBuff;\n\n if (len1 > len2) {\n swap(p1, p2);\n swap(len1, len2);\n }\n\n size_t nr = len1, nc = len2;\n\n if (!nr || !nc) return nr + nc;\n\n if (nr + 1 > MAX_LEVEN_BUFFER_QTY) {\n pBuff = pMemBuf = new int[2 * (nr + 1)];\n } else {\n pBuff = aStackBuf;\n }\n\n int* pColPtr[2] = {pBuff, pBuff + nr + 1};\n\n for (size_t k = 0; k <= nr; ++k) pColPtr[0][k] = k;\n\n size_t nCurr = 1;\n\n for (size_t i = 0; i < nc; ++i) {\n int* pPrevCol = pColPtr[1 - nCurr];\n int* pCurrCol = pColPtr[nCurr];\n\n pCurrCol[0] = i + 1;\n\n for (size_t k = 1; k <= nr; ++k) {\n int nextVal = min(1 + min(pPrevCol[k], pCurrCol[k-1]), // insertion or deletion\n pPrevCol[k-1] + (p1[k-1]==p2[i] ? 0 : 1)); // substitution\n pCurrCol[k] = nextVal;\n }\n\n nCurr = 1 - nCurr;\n }\n\n // Memorize this value before removing memory\n int res = pColPtr[1 - nCurr][nr];\n\n if (pMemBuf) delete [] pMemBuf;\n\n return res;\n}\n\ntemplate int levenshtein(const char* p1, size_t len1, const char* p2, size_t len2);\ntemplate int levenshtein(const char32_t* p1, size_t len1, const char32_t* p2, size_t len2);\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_js.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \"distcomp.h\"\n#include \"string.h\"\n#include \"utils.h\"\n#include \"simdutils.h\"\n\n#include \n#include \n#include \n#include \n\n#ifdef PORTABLE_SSE2\n#include \n#endif\n\nnamespace similarity {\n\nusing namespace std;\n\n\n/*\n * Jensen-Shannon divergence.\n * \n * The square root of JS is a metric: \n *\n * Österreicher, Ferdinand, and Igor Vajda. \n * \"A new class of metric divergences on probability spaces and its applicability in statistics.\" \n * Annals of the Institute of Statistical Mathematics 55.3 (2003): 639-653.\n *\n *\n * Endres, Dominik Maria, and Johannes E. Schindelin. \n * \"A new metric for probability distributions.\" \n * Information Theory, IEEE Transactions on 49.7 (2003): 1858-1860.\n *\n */\ntemplate T JSStandard(const T *pVect1, const T *pVect2, size_t qty)\n{\n T sum1 = 0, sum2 = 0;\n\n for (size_t i = 0; i < qty; i++) {\n T m = (pVect1[i] + pVect2[i])*T(0.5);\n T lg1 = pVect1[i] < numeric_limits::min() ? T(0):log(pVect1[i]);\n T lg2 = pVect2[i] < numeric_limits::min() ? T(0):log(pVect2[i]);\n sum1 += pVect1[i] * lg1 + pVect2[i] * lg2;\n if (m >= numeric_limits::min()) {\n sum2 += m * log(m);\n }\n }\n\n // Due to computation/rounding errors, we may get a small-magnitude negative number\n return std::max(T(0.5)*sum1 - sum2, T(0));\n}\n\ntemplate float JSStandard(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double JSStandard(const double* pVect1, const double* pVect2, size_t qty);\n\ntemplate \nT JSPrecomp(const T* pVect1, const T* pVect2, size_t qty) {\n T sum1 = 0, sum2 = 0;\n\n const T* pEnd2 = pVect1 + qty;\n\n const T* pVectLog1 = pVect1 + qty;\n const T* pVectLog2 = pVect2 + qty;\n\n while (pVect1 < pEnd2) {\n T m = T(0.5)*(*pVect1 + *pVect2);\n sum1 += (*pVect1) * (*pVectLog1) + (*pVect2)*(*pVectLog2);\n if (m >= numeric_limits::min()) {\n sum2 += m * log(m);\n }\n pVect1++; pVect2++; pVectLog1++; pVectLog2++;\n }\n\n // Due to computation/rounding errors, we may get a small-magnitude negative number\n return std::max(T(0.5)*sum1 - sum2, T(0));\n}\n\ntemplate float JSPrecomp(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double JSPrecomp(const double* pVect1, const double* pVect2, size_t qty);\n\nconst unsigned LogQty = 65536;\n\ntemplate \ninline unsigned lapprox(T f) {\n return static_cast(floor(LogQty*f));\n};\n\ntemplate \nclass ApproxLogs {\npublic:\n ApproxLogs() {\n for (unsigned i = 0; i <= LogQty; ++i) {\n T v = i * T(1.0) / LogQty;\n unsigned k = lapprox(v);\n LogTable[k] = log(1 + v);\n }\n }\n T LogTable[LogQty + 2];\n};\n\ntemplate T \nJSPrecompApproxLog(const T *pVect1, const T *pVect2, size_t qty) {\n static ApproxLogs ApproxLogs; // Thread-safe in C++11 \n static T* ltbl = ApproxLogs.LogTable;\n static T clog2 = log(T(2)); // Thread-safe in C++11 \n T sum = 0;\n\n const T* pEnd2 = pVect1 + qty;\n\n const T* pVectLog1 = pVect1 + qty;\n const T* pVectLog2 = pVect2 + qty;\n\n while (pVect1 < pEnd2) {\n T v1 = *pVect1;\n T v2 = *pVect2;\n T lv1 = *pVectLog1;\n T lv2 = *pVectLog2;\n\n sum += v1 * lv1 + v2 * lv2;\n\n if (v1 > v2) {\n swap(v1, v2);\n swap(lv1, lv2);\n }\n if (v2 >= numeric_limits::min()) {\n sum -= (v1 + v2)*(lv2 + ltbl[lapprox(v1/v2)] - clog2);\n }\n pVect1++; pVect2++; pVectLog1++; pVectLog2++;\n }\n\n return std::max(T(0.5)*sum, T(0));\n}\n\ntemplate float JSPrecompApproxLog(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double JSPrecompApproxLog(const double* pVect1, const double* pVect2, size_t qty);\n\ntemplate <>\nfloat JSPrecompSIMDApproxLog(const float* pVect1, const float* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"JSPrecompSIMDApproxLog: SSE2 is not available, defaulting to pure C++ implementation!\")\n return JSPrecompApproxLog(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n\n static ApproxLogs ApproxLogs; // Thread-safe in C++11\n static float clog2 = log(2.0f); // Thread-safe in C++11 \n static __m128 clog2simd = _mm_set1_ps(clog2); // Thread-safe in C++11\n static float* ltbl = ApproxLogs.LogTable;\n __m128 cmult = _mm_set1_ps(LogQty);\n\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n const float* pVectLog1 = pVect1 + qty;\n const float* pVectLog2 = pVect2 + qty;\n\n __m128 v1, v2, vLog1, vLog2, maxv, minv, max_mod_logv, ltmp;\n __m128 sum = _mm_set1_ps(0), s1, s2;\n __m128 minVal = _mm_set1_ps(numeric_limits::min());\n __m128i tmpi;\n\n while (pVect1 < pEnd2) {\n int PORTABLE_ALIGN16 TmpRes[4];\n\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n vLog1 = _mm_loadu_ps(pVectLog1); pVectLog1 += 4;\n s1 = _mm_mul_ps(v1, vLog1);\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n vLog2 = _mm_loadu_ps(pVectLog2); pVectLog2 += 4;\n s2 = _mm_mul_ps(v2, vLog2);\n /* \n * If want to prevent division by zero, if v1 == v2 == 0,\n * then, we don't have to compute the second factor in\n * (v1 + v2)*(lv2 + ltbl[lapprox(v1/v2)] - clog2);\n * correctly, it will be multiplied by zero anyway.\n */\n maxv = _mm_max_ps(_mm_max_ps(v1, v2), minVal);\n // This is the log with the largest modulo (recall that logs are < 0 here)\n max_mod_logv = _mm_max_ps(vLog1, vLog2);\n minv = _mm_min_ps(v1, v2);\n tmpi = _mm_cvttps_epi32(_mm_mul_ps(cmult, _mm_div_ps(minv, maxv)));\n sum = _mm_add_ps(sum, _mm_add_ps(s1,s2));\n\n _mm_store_si128(reinterpret_cast<__m128i*>(TmpRes), tmpi);\n\n ltmp = _mm_set_ps(ltbl[TmpRes[3]], ltbl[TmpRes[2]], ltbl[TmpRes[1]], ltbl[TmpRes[0]]);\n __m128 d = _mm_sub_ps(_mm_add_ps(max_mod_logv, ltmp), clog2simd);\n sum = _mm_sub_ps(sum, _mm_mul_ps(_mm_add_ps(v1, v2), d));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n float res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n float v1 = *pVect1;\n float v2 = *pVect2;\n float lv1 = *pVectLog1;\n float lv2 = *pVectLog2;\n\n res += v1 * lv1 + v2 * lv2;\n\n if (v1 > v2) {\n swap(v1, v2);\n swap(lv1, lv2);\n }\n if (v2 >= numeric_limits::min()) {\n res -= (v1 + v2)*(lv2 + ltbl[lapprox(v1/v2)] - clog2);\n }\n pVect1++; pVect2++; pVectLog1++; pVectLog2++;\n }\n\n // Due to computation/rounding errors, we may get a small-magnitude negative number\n return 0.5*max(0.f, res);\n#endif\n}\n \n\ntemplate <>\ndouble JSPrecompSIMDApproxLog(const double* pVect1, const double* pVect2, size_t qty)\n{\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"JSPrecompSIMDApproxLog: SSE2 is not available, defaulting to pure C++ implementation!\")\n return JSPrecompApproxLog(pVect1, pVect2, qty);\n#else\n size_t qty2 = qty/2;\n\n static ApproxLogs ApproxLogs; // Thread-safe in C++11\n static double clog2 = log((double)2.0f); // Thread-safe in C++11 \n static __m128d clog2simd = _mm_set1_pd(clog2); // Thread-safe in C++11\n static double* ltbl = ApproxLogs.LogTable;\n __m128d cmult = _mm_set1_pd(LogQty);\n\n const double* pEnd2 = pVect1 + 2 * qty2;\n const double* pEnd3 = pVect1 + qty;\n\n const double* pVectLog1 = pVect1 + qty;\n const double* pVectLog2 = pVect2 + qty;\n\n __m128d v1, v2, vLog1, vLog2, maxv, minv, max_mod_logv, ltmp;\n __m128d sum = _mm_set1_pd(0), s1, s2;\n __m128d minVal = _mm_set1_pd(numeric_limits::min());\n __m128i tmpi;\n\n while (pVect1 < pEnd2) {\n uint32_t PORTABLE_ALIGN16 TmpRes[4];\n\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n vLog1 = _mm_loadu_pd(pVectLog1); pVectLog1 += 2;\n s1 = _mm_mul_pd(v1, vLog1);\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n vLog2 = _mm_loadu_pd(pVectLog2); pVectLog2 += 2;\n s2 = _mm_mul_pd(v2, vLog2);\n /* \n * If want to prevent division by zero, if v1 == v2 == 0,\n * then, we don't have to compute the second factor in\n * (v1 + v2)*(lv2 + ltbl[lapprox(v1/v2)] - clog2);\n * correctly, it will be multiplied by zero anyway.\n */\n maxv = _mm_max_pd(_mm_max_pd(v1, v2), minVal);\n // This is the log with the largest modulo (recall that logs are < 0 here)\n max_mod_logv = _mm_max_pd(vLog1, vLog2); \n minv = _mm_min_pd(v1, v2);\n sum = _mm_add_pd(sum, _mm_add_pd(s1, s2));\n tmpi = _mm_cvttpd_epi32(_mm_mul_pd(cmult, _mm_div_pd(minv, maxv)));\n\n _mm_store_si128(reinterpret_cast<__m128i*>(TmpRes), tmpi);\n ltmp = _mm_set_pd(ltbl[TmpRes[1]], ltbl[TmpRes[0]]);\n __m128d d = _mm_sub_pd(_mm_add_pd(max_mod_logv, ltmp), clog2simd);\n\n sum = _mm_sub_pd(sum, _mm_mul_pd(_mm_add_pd(v1, v2), d));\n\n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1];\n\n while (pVect1 < pEnd3) {\n double v1 = *pVect1;\n double v2 = *pVect2;\n double lv1 = *pVectLog1;\n double lv2 = *pVectLog2;\n\n res += v1 * lv1 + v2 * lv2;\n\n if (v1 > v2) {\n swap(v1, v2);\n swap(lv1, lv2);\n }\n if (v2 >= numeric_limits::min()) {\n res -= (v1 + v2)*(lv2 + ltbl[lapprox(v1/v2)] - clog2);\n }\n pVect1++; pVect2++; pVectLog1++; pVectLog2++;\n }\n\n // Due to computation errors, we may get a small-magnitude negative number\n return max(0.5*res, double(0));\n#endif\n}\n\ntemplate float JSPrecompSIMDApproxLog(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double JSPrecompSIMDApproxLog(const double* pVect1, const double* pVect2, size_t qty);\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_lp.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \"distcomp.h\"\n#include \"simdutils.h\"\n#include \"string.h\"\n#include \"logging.h\"\n#include \"utils.h\"\n#include \"pow.h\"\n\n#include \n#include \n#include \n#include \n\n#define PORTABLE_SSE2 0\n#ifdef PORTABLE_SSE2\n#include \n#endif\n\nnamespace similarity {\n\nusing namespace std;\n\n/*\n * LInf-norm.\n */\n\ntemplate \nT LInfNormStandard(const T *p1, const T *p2, size_t qty) \n{ \n T res = 0;\n\n for (size_t i = 0; i < qty; i++) { \n res = max(res, fabs(p1[i]-p2[i]));\n }\n\n return res;\n}\n\ntemplate float LInfNormStandard(const float *p1, const float *p2, size_t qty);\ntemplate double LInfNormStandard(const double *p1, const double *p2, size_t qty);\n\n\ntemplate \nT LInfNorm(const T* pVect1, const T* pVect2, size_t qty) {\n T res = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n while (pVect1 < pEnd1) {\n res = max(res, fabs(*pVect1++ - *pVect2++));\n res = max(res, fabs(*pVect1++ - *pVect2++));\n res = max(res, fabs(*pVect1++ - *pVect2++));\n res = max(res, fabs(*pVect1++ - *pVect2++));\n }\n\n while (pVect1 < pEnd2) {\n res = max(res, fabs(*pVect1++ - *pVect2++));\n }\n\n return res;\n}\n\ntemplate float LInfNorm(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double LInfNorm(const double* pVect1, const double* pVect2, size_t qty);\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\n\ntemplate <> \nfloat LInfNormSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"LInfNormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return LInfNormStandard(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 diff, v1, v2; \n\n /* \n * A hack to quickly unset the sign flag.\n */\n __m128 mask_sign = _mm_castsi128_ps(_mm_set1_epi32(0x7fffffffu));\n\n __m128 MAX = _mm_setzero_ps();\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, MAX);\n float res= max(max(TmpRes[0], TmpRes[1]), max(TmpRes[2], TmpRes[3]));\n\n while (pVect1 < pEnd3) {\n res = max(res, fabsf(*pVect1++ - *pVect2++));\n }\n\n return res;\n#endif\n}\n\ntemplate <> \ndouble LInfNormSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"LInfNormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return LInfNormStandard(pVect1, pVect2, qty);\n#else\n size_t qty8 = qty/8;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + qty;\n\n __m128d diff, v1, v2; \n __m128d MAX = _mm_set1_pd(0); \n\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n MAX = _mm_max_pd(MAX, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n MAX = _mm_max_pd(MAX, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, MAX);\n double res= max(TmpRes[0], TmpRes[1]);\n\n while (pVect1 < pEnd2) {\n double diff = fabs(*pVect1++ - *pVect2++);\n res = max(res, (double)diff);\n }\n\n return res;\n#endif\n}\n\ntemplate float LInfNormSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double LInfNormSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * L1-norm.\n */\n\ntemplate \nT L1NormStandard(const T *p1, const T *p2, size_t qty) \n{ \n T sum = 0;\n\n for (size_t i = 0; i < qty; i++) { \n sum += fabs(p1[i]-p2[i]);\n }\n\n return sum;\n}\n\ntemplate float L1NormStandard(const float *p1, const float *p2, size_t qty);\ntemplate double L1NormStandard(const double *p1, const double *p2, size_t qty);\n\n\ntemplate \nT L1Norm(const T* pVect1, const T* pVect2, size_t qty) {\n T res = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n while (pVect1 < pEnd1) {\n res += fabs(*pVect1++ - *pVect2++);\n res += fabs(*pVect1++ - *pVect2++);\n res += fabs(*pVect1++ - *pVect2++);\n res += fabs(*pVect1++ - *pVect2++);\n }\n\n while (pVect1 < pEnd2) {\n res += fabs(*pVect1++ - *pVect2++);\n }\n\n return res;\n}\n\ntemplate float L1Norm(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L1Norm(const double* pVect1, const double* pVect2, size_t qty);\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\n\ntemplate <> \nfloat L1NormSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"L1NormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return L1NormStandard(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 diff, v1, v2; \n __m128 sum = _mm_setzero_ps();\n\n /* \n * A hack to quickly unset the sign flag.\n */\n __m128 mask_sign = _mm_castsi128_ps(_mm_set1_epi32(0x7fffffffu));\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n res += fabs(*pVect1++ - *pVect2++);\n }\n\n return res;\n#endif\n}\n\ntemplate <> \ndouble L1NormSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"L1NormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n\treturn L1NormStandard(pVect1, pVect2, qty);\n#else\n\tsize_t qty8 = qty/8;\n\n\tconst double* pEnd1 = pVect1 + 8 * qty8;\n\tconst double* pEnd2 = pVect1 + qty;\n\n\t__m128d diff, v1, v2; \n\t__m128d sum = _mm_set1_pd(0); \n\n\n\twhile (pVect1 < pEnd1) {\n\t\tv1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n\t\tv2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n\t\tdiff = _mm_sub_pd(v1, v2);\n\t\tsum = _mm_add_pd(sum, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n\t\tv1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n\t\tv2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n\t\tdiff = _mm_sub_pd(v1, v2);\n\t\tsum = _mm_add_pd(sum, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n\t}\n\n\tdouble PORTABLE_ALIGN16 TmpRes[2];\n\n\t_mm_store_pd(TmpRes, sum);\n\tdouble res= TmpRes[0] + TmpRes[1];\n\n\twhile (pVect1 < pEnd2) {\n\t\t// Leonid (@TODO) sometimes seg-faults in the unit test if float is replaced with double\n\t\tfloat diff = *pVect1++ - *pVect2++;\n\t\tres += fabs(diff);\n\t}\n\n\treturn res;\n#endif\n}\n\ntemplate float L1NormSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L1NormSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * L2-norm.\n */\n\ntemplate \nT L2NormStandard(const T *p1, const T *p2, size_t qty) \n{ \n T sum = 0;\n for (size_t i = 0; i < qty; i++) { \n T diff = (p1[i]-p2[i]);\n sum += diff * diff;\n }\n return sqrt(sum);\n}\n\ntemplate float L2NormStandard(const float *p1, const float *p2, size_t qty);\ntemplate double L2NormStandard(const double *p1, const double *p2, size_t qty);\n\n\ntemplate \nT L2Norm(const T* pVect1, const T* pVect2, size_t qty) {\n\n\tT sum = 0;\n for (size_t i = 0; i < qty; i++) { \n T diff = (pVect1[i]-pVect2[i]);\n sum += diff * diff;\n }\n return sqrt(sum);\n\t/*\n T res = 0, diff = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n while (pVect1 < pEnd1) {\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n }\n\n while (pVect1 < pEnd2) {\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n }\n\n return sqrt(res);*/\n}\n\ntemplate float L2Norm(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L2Norm(const double* pVect1, const double* pVect2, size_t qty);\n\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\n\nfloat L2SqrSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n/*#ifndef PORTABLE_SSE2PORTABLE_SSE2\n#pragma message WARN(\"L2SqrSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")*/\n float res = 0, diff;\n for (int i = 0; i < qty; ++i) {\n diff = pVect1[i] - pVect2[i];\n res += diff * diff;\n }\n return res;\n/*#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 diff, v1, v2; \n __m128 sum = _mm_set1_ps(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n float res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n float diff = *pVect1++ - *pVect2++; \n res += diff * diff;\n }\n\n return res;\n#endif*/\n}\n\ntemplate <> \nfloat L2NormSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n float res = L2SqrSIMD(pVect1, pVect2, qty);\n return sqrt(res);\n}\n\ntemplate <> \ndouble L2NormSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n/*#ifndef PORTABLE_SSE2\n#pragma message WARN(\"L2NormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")*/\n return L2NormStandard(pVect1, pVect2, qty);\n/*#else\n size_t qty8 = qty/8;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + qty;\n\n __m128d diff, v1, v2; \n __m128d sum = _mm_set1_pd(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n sum = _mm_add_pd(sum, _mm_mul_pd(diff, diff));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n sum = _mm_add_pd(sum, _mm_mul_pd(diff, diff));\n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1];\n\n while (pVect1 < pEnd2) {\n double diff = *pVect1++ - *pVect2++; \n res += diff * diff;\n }\n\n return sqrt(res);\n#endif*/\n}\n\ntemplate float L2NormSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L2NormSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * Slower versions of LP-distance\n */\n\ntemplate \nT LPGenericDistance(const T* x, const T* y, const int length, const T p) {\n T result = 0;\n T temp;\n\n CHECK(p > 0);\n\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += pow(temp, p);\n }\n\n return std::pow(result, T(1.0) / p);\n}\n\ntemplate float LPGenericDistance(const float* x, const float* y, const int length, const float p);\ntemplate double LPGenericDistance(const double* x, const double* y, const int length, const double p);\n\n/*\n * Exponentiation by square rooting and squaring:\n * a hacky implementation that works faster than std::pow()\n * if p * 2^maxDig is an integer.\n */\ntemplate \nT LPGenericDistanceOptim(const T* x, const T* y, const int length, const T p) {\n T result = 0;\n T temp;\n\n\n CHECK(p > 0);\n\n const unsigned maxDig = 18;\n const unsigned maxK = 1 << maxDig;\n\n unsigned pfm = static_cast(floor(maxK * p));\n\n if (fabs(maxK*p - pfm) <= std::numeric_limits::min()) {\n unsigned intPow = pfm >> maxDig;\n unsigned fractPow = pfm - (intPow << maxDig);\n\n if (!intPow) {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += EfficientFractPowUtil(temp, fractPow, maxK);;\n }\n } else if (!fractPow) {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += EfficientPow(temp, intPow);\n }\n } else {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += EfficientPow(temp, intPow) * EfficientFractPowUtil(temp, fractPow, maxK);;\n }\n }\n } else {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += pow(temp, p);\n }\n }\n\n return std::pow(result, T(1.0) / p);\n}\n\ntemplate float LPGenericDistanceOptim(const float* x, const float* y, const int length, const float p);\ntemplate double LPGenericDistanceOptim(const double* x, const double* y, const int length, const double p);\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_lp.cc~", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \"distcomp.h\"\n#include \"simdutils.h\"\n#include \"string.h\"\n#include \"logging.h\"\n#include \"utils.h\"\n#include \"pow.h\"\n\n#include \n#include \n#include \n#include \n\n#define PORTABLE_SSE2 0\n#ifdef PORTABLE_SSE2\n#include \n#endif\n\nnamespace similarity {\n\nusing namespace std;\n\n/*\n * LInf-norm.\n */\n\ntemplate \nT LInfNormStandard(const T *p1, const T *p2, size_t qty) \n{ \n T res = 0;\n\n for (size_t i = 0; i < qty; i++) { \n res = max(res, fabs(p1[i]-p2[i]));\n }\n\n return res;\n}\n\ntemplate float LInfNormStandard(const float *p1, const float *p2, size_t qty);\ntemplate double LInfNormStandard(const double *p1, const double *p2, size_t qty);\n\n\ntemplate \nT LInfNorm(const T* pVect1, const T* pVect2, size_t qty) {\n T res = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n while (pVect1 < pEnd1) {\n res = max(res, fabs(*pVect1++ - *pVect2++));\n res = max(res, fabs(*pVect1++ - *pVect2++));\n res = max(res, fabs(*pVect1++ - *pVect2++));\n res = max(res, fabs(*pVect1++ - *pVect2++));\n }\n\n while (pVect1 < pEnd2) {\n res = max(res, fabs(*pVect1++ - *pVect2++));\n }\n\n return res;\n}\n\ntemplate float LInfNorm(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double LInfNorm(const double* pVect1, const double* pVect2, size_t qty);\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\n\ntemplate <> \nfloat LInfNormSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"LInfNormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return LInfNormStandard(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 diff, v1, v2; \n\n /* \n * A hack to quickly unset the sign flag.\n */\n __m128 mask_sign = _mm_castsi128_ps(_mm_set1_epi32(0x7fffffffu));\n\n __m128 MAX = _mm_setzero_ps();\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n MAX = _mm_max_ps(MAX, _mm_and_ps(diff, mask_sign ));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, MAX);\n float res= max(max(TmpRes[0], TmpRes[1]), max(TmpRes[2], TmpRes[3]));\n\n while (pVect1 < pEnd3) {\n res = max(res, fabsf(*pVect1++ - *pVect2++));\n }\n\n return res;\n#endif\n}\n\ntemplate <> \ndouble LInfNormSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"LInfNormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return LInfNormStandard(pVect1, pVect2, qty);\n#else\n size_t qty8 = qty/8;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + qty;\n\n __m128d diff, v1, v2; \n __m128d MAX = _mm_set1_pd(0); \n\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n MAX = _mm_max_pd(MAX, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n MAX = _mm_max_pd(MAX, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, MAX);\n double res= max(TmpRes[0], TmpRes[1]);\n\n while (pVect1 < pEnd2) {\n double diff = fabs(*pVect1++ - *pVect2++);\n res = max(res, (double)diff);\n }\n\n return res;\n#endif\n}\n\ntemplate float LInfNormSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double LInfNormSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * L1-norm.\n */\n\ntemplate \nT L1NormStandard(const T *p1, const T *p2, size_t qty) \n{ \n T sum = 0;\n\n for (size_t i = 0; i < qty; i++) { \n sum += fabs(p1[i]-p2[i]);\n }\n\n return sum;\n}\n\ntemplate float L1NormStandard(const float *p1, const float *p2, size_t qty);\ntemplate double L1NormStandard(const double *p1, const double *p2, size_t qty);\n\n\ntemplate \nT L1Norm(const T* pVect1, const T* pVect2, size_t qty) {\n T res = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n while (pVect1 < pEnd1) {\n res += fabs(*pVect1++ - *pVect2++);\n res += fabs(*pVect1++ - *pVect2++);\n res += fabs(*pVect1++ - *pVect2++);\n res += fabs(*pVect1++ - *pVect2++);\n }\n\n while (pVect1 < pEnd2) {\n res += fabs(*pVect1++ - *pVect2++);\n }\n\n return res;\n}\n\ntemplate float L1Norm(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L1Norm(const double* pVect1, const double* pVect2, size_t qty);\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\n\ntemplate <> \nfloat L1NormSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"L1NormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return L1NormStandard(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 diff, v1, v2; \n __m128 sum = _mm_setzero_ps();\n\n /* \n * A hack to quickly unset the sign flag.\n */\n __m128 mask_sign = _mm_castsi128_ps(_mm_set1_epi32(0x7fffffffu));\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_and_ps(diff, mask_sign));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n res += fabs(*pVect1++ - *pVect2++);\n }\n\n return res;\n#endif\n}\n\ntemplate <> \ndouble L1NormSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"L1NormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n\treturn L1NormStandard(pVect1, pVect2, qty);\n#else\n\tsize_t qty8 = qty/8;\n\n\tconst double* pEnd1 = pVect1 + 8 * qty8;\n\tconst double* pEnd2 = pVect1 + qty;\n\n\t__m128d diff, v1, v2; \n\t__m128d sum = _mm_set1_pd(0); \n\n\n\twhile (pVect1 < pEnd1) {\n\t\tv1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n\t\tv2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n\t\tdiff = _mm_sub_pd(v1, v2);\n\t\tsum = _mm_add_pd(sum, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n\t\tv1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n\t\tv2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n\t\tdiff = _mm_sub_pd(v1, v2);\n\t\tsum = _mm_add_pd(sum, _mm_max_pd(_mm_sub_pd(_mm_setzero_pd(), diff), diff));\n\n\t}\n\n\tdouble PORTABLE_ALIGN16 TmpRes[2];\n\n\t_mm_store_pd(TmpRes, sum);\n\tdouble res= TmpRes[0] + TmpRes[1];\n\n\twhile (pVect1 < pEnd2) {\n\t\t// Leonid (@TODO) sometimes seg-faults in the unit test if float is replaced with double\n\t\tfloat diff = *pVect1++ - *pVect2++;\n\t\tres += fabs(diff);\n\t}\n\n\treturn res;\n#endif\n}\n\ntemplate float L1NormSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L1NormSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * L2-norm.\n */\n\ntemplate \nT L2NormStandard(const T *p1, const T *p2, size_t qty) \n{ \n T sum = 0;\n for (size_t i = 0; i < qty; i++) { \n T diff = (p1[i]-p2[i]);\n sum += diff * diff;\n }\n return sqrt(sum);\n}\n\ntemplate float L2NormStandard(const float *p1, const float *p2, size_t qty);\ntemplate double L2NormStandard(const double *p1, const double *p2, size_t qty);\n\n\ntemplate \nT L2Norm(const T* pVect1, const T* pVect2, size_t qty) {\n\n\tT sum = 0;\n for (size_t i = 0; i < qty; i++) { \n T diff = (pVect1[i]-pVect2[i]);\n sum += diff * diff;\n }\n return sqrt(sum);\n\t/*\n T res = 0, diff = 0;\n\n size_t qty4 = qty/4;\n const T* pEnd1 = pVect1 + qty4 * 4;\n const T* pEnd2 = pVect1 + qty;\n\n while (pVect1 < pEnd1) {\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n }\n\n while (pVect1 < pEnd2) {\n diff = *pVect1++ - *pVect2++; res += diff * diff;\n }\n\n return sqrt(res);*/\n}\n\ntemplate float L2Norm(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L2Norm(const double* pVect1, const double* pVect2, size_t qty);\n\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\n\nfloat L2SqrSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n/*#ifndef PORTABLE_SSE2PORTABLE_SSE2\n#pragma message WARN(\"L2SqrSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")*/\n float res = 0, diff;\n for (int i = 0; i < qty; ++i) {\n diff = pVect1[i] - pVect2[i];\n res += diff * diff;\n }\n return res;\n/*#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 diff, v1, v2; \n __m128 sum = _mm_set1_ps(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n diff = _mm_sub_ps(v1, v2);\n sum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n float res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n float diff = *pVect1++ - *pVect2++; \n res += diff * diff;\n }\n\n return res;\n#endif*/\n}\n\ntemplate <> \nfloat L2NormSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n float res = L2SqrSIMD(pVect1, pVect2, qty);\n return sqrt(res);\n}\n\ntemplate <> \ndouble L2NormSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n/*#ifndef PORTABLE_SSE2\n#pragma message WARN(\"L2NormSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")*/\n return L2NormStandard(pVect1, pVect2, qty);\n/*#else\n size_t qty8 = qty/8;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + qty;\n\n __m128d diff, v1, v2; \n __m128d sum = _mm_set1_pd(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n sum = _mm_add_pd(sum, _mm_mul_pd(diff, diff));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n diff = _mm_sub_pd(v1, v2);\n sum = _mm_add_pd(sum, _mm_mul_pd(diff, diff));\n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1];\n\n while (pVect1 < pEnd2) {\n double diff = *pVect1++ - *pVect2++; \n res += diff * diff;\n }\n\n return sqrt(res);\n#endif*/\n}\n\ntemplate float L2NormSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double L2NormSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * Slower versions of LP-distance\n */\n\ntemplate \nT LPGenericDistance(const T* x, const T* y, const int length, const T p) {\n T result = 0;\n T temp;\n\n CHECK(p > 0);\n\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += pow(temp, p);\n }\n\n return std::pow(result, T(1.0) / p);\n}\n\ntemplate float LPGenericDistance(const float* x, const float* y, const int length, const float p);\ntemplate double LPGenericDistance(const double* x, const double* y, const int length, const double p);\n\n/*\n * Exponentiation by square rooting and squaring:\n * a hacky implementation that works faster than std::pow()\n * if p * 2^maxDig is an integer.\n */\ntemplate \nT LPGenericDistanceOptim(const T* x, const T* y, const int length, const T p) {\n T result = 0;\n T temp;\n\n\n CHECK(p > 0);\n\n const unsigned maxDig = 18;\n const unsigned maxK = 1 << maxDig;\n\n unsigned pfm = static_cast(floor(maxK * p));\n\n if (fabs(maxK*p - pfm) <= std::numeric_limits::min()) {\n unsigned intPow = pfm >> maxDig;\n unsigned fractPow = pfm - (intPow << maxDig);\n\n if (!intPow) {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += EfficientFractPowUtil(temp, fractPow, maxK);;\n }\n } else if (!fractPow) {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += EfficientPow(temp, intPow);\n }\n } else {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += EfficientPow(temp, intPow) * EfficientFractPowUtil(temp, fractPow, maxK);;\n }\n }\n } else {\n for (int i = 0; i < length; ++i) {\n // In C++ 11, std::abs is also defined for floating-point numbers\n temp = std::abs(x[i] - y[i]);\n result += pow(temp, p);\n }\n }\n\n return std::pow(result, T(1.0) / p);\n}\n\ntemplate float LPGenericDistanceOptim(const float* x, const float* y, const int length, const float p);\ntemplate double LPGenericDistanceOptim(const double* x, const double* y, const int length, const double p);\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_rankcorr.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"distcomp.h\"\n#include \"simdutils.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\n#ifdef PORTABLE_SSE4\n#include \n#include \n#include \n#endif\n\nint SpearmanFootrule(const PivotIdType* x, const PivotIdType* y, size_t qty) {\n int res = 0;\n int diff;\n\n for (size_t i = 0; i < qty; ++i) {\n diff = x[i] >= y[i] ? x[i] - y[i] : y[i] - x[i];\n res += diff;\n }\n\n return res;\n}\n\nint SpearmanFootruleSIMD(const int32_t* pVect1, const int32_t* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE4\n#pragma message WARN(\"SpearmanFootruleSIMD: SSE4.2 is not available, defaulting to pure C++ implementation!\")\n\n return SpearmanFootrule(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const int32_t* pEnd1 = pVect1 + 16 * qty16;\n const int32_t* pEnd2 = pVect1 + 4 * qty4;\n const int32_t* pEnd3 = pVect1 + qty;\n\n __m128i diff, v1, v2;\n __m128i sum = _mm_set1_epi32(0);\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_max_epi32(_mm_sub_epi32(_mm_setzero_si128(), diff), diff));\n\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_max_epi32(_mm_sub_epi32(_mm_setzero_si128(), diff), diff));\n\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_max_epi32(_mm_sub_epi32(_mm_setzero_si128(), diff), diff));\n\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_max_epi32(_mm_sub_epi32(_mm_setzero_si128(), diff), diff));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_max_epi32(_mm_sub_epi32(_mm_setzero_si128(), diff), diff));\n\n }\n\n int32_t PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_si128(reinterpret_cast<__m128i*>(TmpRes), sum);\n int res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n res += std::abs(*pVect1++ - *pVect2++);\n }\n\n return res;\n#endif\n}\n\n\nint SpearmanRho(const PivotIdType* x, const PivotIdType* y, size_t qty) {\n int res = 0;\n PivotIdType diff;\n size_t i = 0;\n\n for (; i < qty; ++i) {\n diff = y[i] - x[i];\n res += diff * diff;\n }\n return res;\n}\n\nint SpearmanRhoSIMD(const PivotIdType* pVect1, const PivotIdType* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE4\n#pragma message WARN(\"SpearmanRhoSIMD: SSE4.2 is not available, defaulting to pure C++ implementation!\")\n return SpearmanRho(pVect1, pVect2, qty);\n#else\n size_t qty4 = qty/4;\n size_t qty16 = qty/16;\n\n const int32_t* pEnd1 = pVect1 + 16 * qty16;\n const int32_t* pEnd2 = pVect1 + 4 * qty4;\n const int32_t* pEnd3 = pVect1 + qty;\n\n __m128i diff, v1, v2;\n __m128i sum = _mm_set1_epi16(0);\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_mullo_epi32(diff, diff));\n\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_mullo_epi32(diff, diff));\n\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_mullo_epi32(diff, diff));\n\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_mullo_epi32(diff, diff));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_si128(reinterpret_cast(pVect1)); pVect1 += 4;\n v2 = _mm_loadu_si128(reinterpret_cast(pVect2)); pVect2 += 4;\n diff = _mm_sub_epi32(v1, v2);\n sum = _mm_add_epi32(sum, _mm_mullo_epi32(diff, diff));\n }\n\n int32_t PORTABLE_ALIGN16 TmpRes[8];\n\n _mm_store_si128(reinterpret_cast<__m128i*>(TmpRes), sum);\n int32_t res= TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n int diff = abs(*pVect1++ - *pVect2++);\n res += diff * diff;\n }\n\n return res;\n#endif\n}\n\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_scalar.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \"distcomp.h\"\n#include \"string.h\"\n#include \"utils.h\"\n\n#include \n#include \n#include \n\nnamespace similarity {\n\nusing namespace std;\n/*\n * Scalar-product (divided by Euclidean vector norms)\n *\n * TODO: @leo implement a more efficient version\n */\n\ntemplate \nT NormScalarProduct(const T *p1, const T *p2, size_t qty) \n{ \n const T eps = numeric_limits::min() * 2;\n\n T sum = 0;\n T norm1 = 0;\n T norm2 = 0;\n\n for (size_t i = 0; i < qty; i++) {\n norm1 += p1[i] * p1[i];\n norm2 += p2[i] * p2[i];\n sum += p1[i] * p2[i];\n }\n\n if (norm1 < eps) { /* \n * This shouldn't normally happen for this space, but \n * if it does, we don't want to get NANs \n */\n if (norm2 < eps) return 1;\n return 0;\n } \n /* \n * Sometimes due to rounding errors, we get values > 1 or < -1.\n * This throws off other functions that use scalar product, e.g., acos\n */\n return max(T(-1), min(T(1), sum / sqrt(norm1) / sqrt(norm2)));\n}\n\ntemplate float NormScalarProduct(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double NormScalarProduct(const double* pVect1, const double* pVect2, size_t qty);\n\ntemplate <> \nfloat NormScalarProductSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"ScalarProductSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return NormScalarProduct(pVect1, pVect2, qty);\n#else\n size_t qty16 = qty/16;\n size_t qty4 = qty/4;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 v1, v2; \n __m128 sum_prod = _mm_set1_ps(0); \n __m128 sum_square1 = sum_prod;\n __m128 sum_square2 = sum_prod;\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n sum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n sum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n sum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n sum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n sum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n sum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n sum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n sum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n sum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n sum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n }\n\n float PORTABLE_ALIGN16 TmpResProd[4];\n float PORTABLE_ALIGN16 TmpResSquare1[4];\n float PORTABLE_ALIGN16 TmpResSquare2[4];\n\n _mm_store_ps(TmpResProd, sum_prod);\n float sum = TmpResProd[0] + TmpResProd[1] + TmpResProd[2] + TmpResProd[3];\n _mm_store_ps(TmpResSquare1, sum_square1);\n float norm1 = TmpResSquare1[0] + TmpResSquare1[1] + TmpResSquare1[2] + TmpResSquare1[3];\n _mm_store_ps(TmpResSquare2, sum_square2);\n float norm2 = TmpResSquare2[0] + TmpResSquare2[1] + TmpResSquare2[2] + TmpResSquare2[3];\n\n while (pVect1 < pEnd3) {\n sum += (*pVect1) * (*pVect2); \n norm1 += (*pVect1) * (*pVect1); \n norm2 += (*pVect2) * (*pVect2); \n\n ++pVect1; ++pVect2;\n }\n\n const float eps = numeric_limits::min() * 2;\n\n if (norm1 < eps) { /* \n * This shouldn't normally happen for this space, but \n * if it does, we don't want to get NANs \n */\n if (norm2 < eps) return 1;\n return 0;\n } \n /* \n * Sometimes due to rounding errors, we get values > 1 or < -1.\n * This throws off other functions that use scalar product, e.g., acos\n */\n return max(float(-1), min(float(1), sum / sqrt(norm1) / sqrt(norm2)));\n#endif\n}\n\ntemplate <> \ndouble NormScalarProductSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"NormScalarProductSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return NormScalarProduct(pVect1, pVect2, qty);\n#else\n size_t qty8 = qty/8;\n size_t qty2 = qty/2;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + 2 * qty2;\n const double* pEnd3 = pVect1 + qty;\n\n __m128d v1, v2; \n __m128d sum_prod = _mm_set1_pd(0); \n __m128d sum_square1 = sum_prod;\n __m128d sum_square2 = sum_prod;\n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum_prod = _mm_add_pd(sum_prod, _mm_mul_pd(v1, v2));\n sum_square1 = _mm_add_pd(sum_square1, _mm_mul_pd(v1, v1));\n sum_square2 = _mm_add_pd(sum_square2, _mm_mul_pd(v2, v2));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum_prod = _mm_add_pd(sum_prod, _mm_mul_pd(v1, v2));\n sum_square1 = _mm_add_pd(sum_square1, _mm_mul_pd(v1, v1));\n sum_square2 = _mm_add_pd(sum_square2, _mm_mul_pd(v2, v2));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum_prod = _mm_add_pd(sum_prod, _mm_mul_pd(v1, v2));\n sum_square1 = _mm_add_pd(sum_square1, _mm_mul_pd(v1, v1));\n sum_square2 = _mm_add_pd(sum_square2, _mm_mul_pd(v2, v2));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum_prod = _mm_add_pd(sum_prod, _mm_mul_pd(v1, v2));\n sum_square1 = _mm_add_pd(sum_square1, _mm_mul_pd(v1, v1));\n sum_square2 = _mm_add_pd(sum_square2, _mm_mul_pd(v2, v2));\n\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum_prod = _mm_add_pd(sum_prod, _mm_mul_pd(v1, v2));\n sum_square1 = _mm_add_pd(sum_square1, _mm_mul_pd(v1, v1));\n sum_square2 = _mm_add_pd(sum_square2, _mm_mul_pd(v2, v2));\n\n }\n\n double PORTABLE_ALIGN16 TmpResProd[2];\n double PORTABLE_ALIGN16 TmpResSquare1[2];\n double PORTABLE_ALIGN16 TmpResSquare2[2];\n\n _mm_store_pd(TmpResProd, sum_prod);\n double sum = TmpResProd[0] + TmpResProd[1];\n\n _mm_store_pd(TmpResSquare1, sum_square1);\n double norm1 = TmpResSquare1[0] + TmpResSquare1[1];\n\n _mm_store_pd(TmpResSquare2, sum_square2);\n double norm2 = TmpResSquare2[0] + TmpResSquare2[1];\n\n while (pVect1 < pEnd3) {\n sum += (*pVect1) * (*pVect2); \n norm1 += (*pVect1) * (*pVect1); \n norm2 += (*pVect2) * (*pVect2); \n\n ++pVect1; ++pVect2;\n }\n\n const double eps = numeric_limits::min() * 2;\n\n if (norm1 < eps) { /* \n * This shouldn't normally happen for this space, but \n * if it does, we don't want to get NANs \n */\n if (norm2 < eps) return 1;\n return 0;\n } \n /* \n * Sometimes due to rounding errors, we get values > 1 or < -1.\n * This throws off other functions that use scalar product, e.g., acos\n */\n return max(double(-1), min(double(1), sum / sqrt(norm1) / sqrt(norm2)));\n#endif\n}\n\ntemplate float NormScalarProductSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double NormScalarProductSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * Scalar products that are not normalized.\n */\n\ntemplate \nT ScalarProduct(const T *p1, const T *p2, size_t qty) { \n T sum = 0;\n\n for (size_t i = 0; i < qty; i++) { \n sum += p1[i] * p2[i];\n }\n return sum;\n}\n\ntemplate float ScalarProduct(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double ScalarProduct(const double* pVect1, const double* pVect2, size_t qty);\n\n/* \n * On new architectures unaligned loads are almost as fast as aligned ones. \n * Ensuring that both pVect1 and pVect2 are similarly aligned could be hard.\n */\n\ntemplate <> \nfloat ScalarProductSIMD(const float* pVect1, const float* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"ScalarProductSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return ScalarProduct(pVect1, pVect2, qty);\n#else\n size_t qty16 = qty/16;\n size_t qty4 = qty/4;\n\n const float* pEnd1 = pVect1 + 16 * qty16;\n const float* pEnd2 = pVect1 + 4 * qty4;\n const float* pEnd3 = pVect1 + qty;\n\n __m128 v1, v2; \n __m128 sum = _mm_set1_ps(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, v2));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, v2));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, v2));\n\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, v2));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n v2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n sum = _mm_add_ps(sum, _mm_mul_ps(v1, v2));\n }\n\n float PORTABLE_ALIGN16 TmpRes[4];\n\n _mm_store_ps(TmpRes, sum);\n float res = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n while (pVect1 < pEnd3) {\n res += (*pVect1) * (*pVect2); \n ++pVect1; ++pVect2;\n }\n\n return res;\n#endif\n}\n\ntemplate <> \ndouble ScalarProductSIMD(const double* pVect1, const double* pVect2, size_t qty) {\n#ifndef PORTABLE_SSE2\n#pragma message WARN(\"ScalarProductSIMD: SSE2 is not available, defaulting to pure C++ implementation!\")\n return ScalarProduct(pVect1, pVect2, qty);\n#else\n size_t qty8 = qty/8;\n size_t qty2 = qty/2;\n\n const double* pEnd1 = pVect1 + 8 * qty8;\n const double* pEnd2 = pVect1 + 2 * qty2;\n const double* pEnd3 = pVect1 + qty;\n\n __m128d v1, v2; \n __m128d sum = _mm_set1_pd(0); \n\n while (pVect1 < pEnd1) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum = _mm_add_pd(sum, _mm_mul_pd(v1, v2));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum = _mm_add_pd(sum, _mm_mul_pd(v1, v2));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum = _mm_add_pd(sum, _mm_mul_pd(v1, v2));\n\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum = _mm_add_pd(sum, _mm_mul_pd(v1, v2));\n }\n\n while (pVect1 < pEnd2) {\n v1 = _mm_loadu_pd(pVect1); pVect1 += 2;\n v2 = _mm_loadu_pd(pVect2); pVect2 += 2;\n sum = _mm_add_pd(sum, _mm_mul_pd(v1, v2));\n }\n\n double PORTABLE_ALIGN16 TmpRes[2];\n\n _mm_store_pd(TmpRes, sum);\n double res= TmpRes[0] + TmpRes[1];\n\n while (pVect1 < pEnd3) {\n res += (*pVect1) * (*pVect2); \n ++pVect1; ++pVect2;\n }\n\n return res;\n#endif\n}\n\ntemplate float ScalarProductSIMD(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double ScalarProductSIMD(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * Angular distance (a proper metric)\n *\n */\n\ntemplate \nT AngularDistance(const T *p1, const T *p2, size_t qty) \n{ \n return acos(NormScalarProductSIMD(p1, p2, qty));\n}\n\ntemplate float AngularDistance(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double AngularDistance(const double* pVect1, const double* pVect2, size_t qty);\n\n/*\n * Cosine similarity (not exactly a metric)\n *\n */\n\ntemplate \nT CosineSimilarity(const T *p1, const T *p2, size_t qty) \n{ \n return std::max(T(0), 1 - NormScalarProductSIMD(p1, p2, qty));\n}\n\ntemplate float CosineSimilarity(const float* pVect1, const float* pVect2, size_t qty);\ntemplate double CosineSimilarity(const double* pVect1, const double* pVect2, size_t qty);\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/distcomp_sparse_scalar_fast.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"utils.h\"\n#include \"logging.h\"\n#include \"simdutils.h\"\n\n#include \"space/space_sparse_vector_inter.h\"\n\nnamespace similarity {\n\n#ifdef PORTABLE_SSE4\nconst static __m128i shuffle_mask16[16] = {\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,3,2,1,0),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,7,6,5,4),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,7,6,5,4,3,2,1,0),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,11,10,9,8),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,11,10,9,8,3,2,1,0),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,11,10,9,8,7,6,5,4),\n _mm_set_epi8(-127,-127,-127,-127,11,10,9,8,7,6,5,4,3,2,1,0),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,-127,15,14,13,12),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,15,14,13,12,3,2,1,0),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,15,14,13,12,7,6,5,4),\n _mm_set_epi8(-127,-127,-127,-127,15,14,13,12,7,6,5,4,3,2,1,0),\n _mm_set_epi8(-127,-127,-127,-127,-127,-127,-127,-127,15,14,13,12,11,10,9,8),\n _mm_set_epi8(-127,-127,-127,-127,15,14,13,12,11,10,9,8,3,2,1,0),\n _mm_set_epi8(-127,-127,-127,-127,15,14,13,12,11,10,9,8,7,6,5,4),\n _mm_set_epi8(15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,0),\n};\n#endif\n\n/*\n* The maximum number of sparse elements that will be kept on the stack\n* by the function ComputeDistanceHelper.\n*\n* TODO:@leo If there are too many threads, we might run out stack memory.\n* but it is probably extremely unlikely with the buffer of this size.\n*\n*/\n#define MAX_BUFFER_QTY 8192\n\n/*\n * The efficient SIMD intersection is based on the code of Daniel Lemire (lemire.me). \n *\n * Lemire's code implemented an algorithm similar to one described in:\n *\n * Schlegel, Benjamin, Thomas Willhalm, and Wolfgang Lehner. \n * \"Fast sorted-set intersection using simd instructions.\" ADMS Workshop, Seattle, WA, USA. 2011.\n *\n * Daniel improved the code of Schlegel et al by replacing a slow function\n * _mm_cmpistri with a faster analog _mm_cmpistrm. The _mm_cmpistrm is \n * fast, but it cannot deal with IDs that are multiples of 65536.\n * Thus, some extra checking is need to handle the case of such IDs.\n * In this version, however, Leo works around this problem by\n * transforming IDs in advance.\n *\n * More details about Leo's changes:\n *\n * 1) The original algorithm was used to obtain only IDs.\n * to extract both IDs and respective floating-point values,\n * we need to call _mm_cmpistrm twice. \n * 2) During partitioning we transform ids so that \n * none of them is a multiple of 65536.\n * The latter trick allows us to employ a slightly faster\n * version of the _mm_cmpistrm without worrying about\n * zero values (handling those requires a check).\n *\n */\nfloat ScalarProductFast(const char* pData1, size_t len1,\n const char* pData2, size_t len2) {\n float norm1 = 0, norm2 = 0;\n size_t blockQty1 = 0, blockQty2 = 0;\n const size_t* pBlockQtys1 = NULL; \n const size_t* pBlockQtys2 = NULL; \n const size_t* pBlockOffs1 = NULL; \n const size_t* pBlockOffs2 = NULL; \n const char* pBlockBeg1 = NULL; \n const char* pBlockBeg2 = NULL;\n\n float buf1[MAX_BUFFER_QTY];\n float buf2[MAX_BUFFER_QTY];\n unique_ptr mem1;\n unique_ptr mem2;\n size_t allocQty = 0;\n\n ParseSparseElementHeader(pData1, blockQty1, norm1, pBlockQtys1, pBlockOffs1, pBlockBeg1);\n ParseSparseElementHeader(pData2, blockQty2, norm2, pBlockQtys2, pBlockOffs2, pBlockBeg2);\n\n float sum = 0;\n\n size_t bid1 = 0, bid2 = 0; // block ids\n\n size_t elemSize = 2 + sizeof(float);\n\n while (bid1 < blockQty1 && bid2 < blockQty2) {\n if (pBlockOffs1[bid1] == pBlockOffs2[bid2]) {\n size_t qty1 = pBlockQtys1[bid1];\n const uint16_t* pBlockIds1 = reinterpret_cast(pBlockBeg1);\n const float* pBlockVals1 = reinterpret_cast(pBlockIds1 + qty1);\n\n size_t qty2 = pBlockQtys2[bid2];\n const uint16_t* pBlockIds2 = reinterpret_cast(pBlockBeg2);\n const float* pBlockVals2 = reinterpret_cast(pBlockIds2 + qty2);\n\n size_t mx = max(qty1, qty2);\n\n float* val1 = buf1;\n float* val2 = buf2;\n\n /* \n * Let's do some flexible memory allocation. \n * If there is enough space on stack, use the stack,\n * otherwise allocate a large chunk of memory \n */\n if (mx > MAX_BUFFER_QTY) {\n if (allocQty < mx) {\n mem1.reset(new float[mx]);\n mem2.reset(new float[mx]);\n allocQty = mx;\n }\n val1 = mem1.get();\n val2 = mem2.get();\n }\n \n float* pVal1 = val1;\n float* pVal2 = val2;\n\n size_t i1 = 0, i2 = 0;\n size_t iEnd1 = qty1 / 8 * 8; \n size_t iEnd2 = qty2 / 8 * 8; \n\n#ifdef PORTABLE_SSE4\n if (i1 < iEnd1 && i2 < iEnd2) {\n while (pBlockIds1[i1 + 7] < pBlockIds2[i2]) {\n i1 += 8;\n if (i1 >= iEnd1) goto scalar_inter; \n }\n while (pBlockIds2[i2 + 7] < pBlockIds1[i1]) {\n i2 += 8;\n if (i2 >= iEnd2) goto scalar_inter; \n }\n __m128i id1 = _mm_loadu_si128(reinterpret_cast(&pBlockIds1[i1]));\n __m128i id2 = _mm_loadu_si128(reinterpret_cast(&pBlockIds2[i2]));\n\n while (true) {\n __m128i cmpRes = _mm_cmpistrm(id2, id1,\n _SIDD_UWORD_OPS | \n _SIDD_CMP_EQUAL_ANY | \n _SIDD_BIT_MASK);\n\n int r = _mm_extract_epi32(cmpRes, 0);\n\n if (r) {\n\n int r1 = r & 15;\n __m128i v = _mm_loadu_si128(reinterpret_cast(&pBlockVals1[i1]));\n __m128 vs = _mm_castsi128_ps(_mm_shuffle_epi8(v, shuffle_mask16[r1]));\n _mm_storeu_ps(pVal1, vs);\n pVal1 += _mm_popcnt_u32(r1);\n\n int r2 = (r >> 4) & 15;\n v = _mm_loadu_si128(reinterpret_cast(&pBlockVals1[i1+4]));\n vs = _mm_castsi128_ps(_mm_shuffle_epi8(v, shuffle_mask16[r2]));\n _mm_storeu_ps(pVal1, vs);\n pVal1 += _mm_popcnt_u32(r2);\n\n cmpRes = _mm_cmpistrm(id1, id2,\n _SIDD_UWORD_OPS | \n _SIDD_CMP_EQUAL_ANY | \n _SIDD_BIT_MASK);\n r = _mm_extract_epi32(cmpRes, 0);\n\n r1 = r & 15;\n\n v = _mm_loadu_si128(reinterpret_cast(&pBlockVals2[i2]));\n vs = _mm_castsi128_ps(_mm_shuffle_epi8(v, shuffle_mask16[r1]));\n _mm_storeu_ps(pVal2, vs);\n pVal2 += _mm_popcnt_u32(r1);\n\n r2 = (r >> 4) & 15;\n v = _mm_loadu_si128(reinterpret_cast(&pBlockVals2[i2+4]));\n vs = _mm_castsi128_ps(_mm_shuffle_epi8(v, shuffle_mask16[r2]));\n _mm_storeu_ps(pVal2, vs);\n pVal2 += _mm_popcnt_u32(r2);\n }\n\n const uint16_t id1max = pBlockIds1[i1 + 7];\n if (id1max <= pBlockIds2[i2 + 7]) {\n i1 += 8;\n if (i1 >= iEnd1) goto scalar_inter; \n id1 = _mm_loadu_si128(reinterpret_cast(&pBlockIds1[i1]));\n }\n if (id1max >= pBlockIds2[i2 + 7]) {\n i2 += 8;\n if (i2 >= iEnd2) goto scalar_inter; \n id2 = _mm_loadu_si128(reinterpret_cast(&pBlockIds2[i2]));\n }\n }\n }\n scalar_inter:\n#else\n #pragma message WARN(\"No SSE 4.2, defaulting to scalar implementation!\")\n#endif\n\n while (i1 < qty1 && i2 < qty2) {\n if (pBlockIds1[i1] == pBlockIds2[i2]) {\n *pVal1++ = pBlockVals1[i1]; \n *pVal2++ = pBlockVals2[i2];\n ++i1;\n ++i2;\n } else if (pBlockIds1[i1] < pBlockIds2[i2]) {\n ++i1;\n } else {\n ++i2;\n } \n }\n\n pBlockBeg1 += elemSize * pBlockQtys1[bid1++];\n pBlockBeg2 += elemSize * pBlockQtys2[bid2++];\n\n ssize_t resQty = pVal1 - val1;\n\n CHECK(resQty == pVal2 - val2);\n\n \n\n#ifdef PORTABLE_SSE4\n ssize_t resQty4 = resQty / 4 * 4;\n\n if (resQty4) {\n __m128 sum128 = _mm_set1_ps(0);\n\n for (ssize_t k = 0; k < resQty4; k += 4) {\n sum128 = _mm_add_ps(sum128,\n _mm_mul_ps(_mm_loadu_ps(val1 + k),\n _mm_loadu_ps(val2 + k)));\n }\n\n sum += MM_EXTRACT_FLOAT(sum128, 0);\n sum += MM_EXTRACT_FLOAT(sum128, 1);\n sum += MM_EXTRACT_FLOAT(sum128, 2);\n sum += MM_EXTRACT_FLOAT(sum128, 3);\n }\n\n for (ssize_t k = resQty4; k < resQty; ++k)\n sum += val1[k] * val2[k];\n#else\n for (ssize_t k = 0; k < resQty; ++k)\n sum += val1[k] * val2[k];\n#endif\n\n } else if (pBlockOffs1[bid1] < pBlockOffs2[bid2]) {\n pBlockBeg1 += elemSize * pBlockQtys1[bid1++];\n } else {\n pBlockBeg2 += elemSize * pBlockQtys2[bid2++];\n }\n }\n\n // These two loops are necessary to carry out size checks below\n while (bid1 < blockQty1) {\n pBlockBeg1 += elemSize * pBlockQtys1[bid1++];\n }\n\n while (bid2 < blockQty2) {\n pBlockBeg2 += elemSize * pBlockQtys2[bid2++];\n }\n\n CHECK(pBlockBeg1 - pData1 == (ssize_t)len1);\n CHECK(pBlockBeg2 - pData2 == (ssize_t)len2);\n\n /* \n * Sometimes due to rounding errors, we get values > 1 or < -1.\n * This throws off other functions that use scalar product, e.g., acos\n */\n\n return max(float(-1), min(float(1), sum / sqrt(norm1) / sqrt(norm2)));\n}\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/dummy_app.cc", "content": "#include \n\nusing namespace std;\nint main(void) {\n cout << \"Hello world!\" << endl;\n}\n" }, { "path": "algorithms/NMSLIB/code/src/dummy_app.vcxproj", "content": "\n\n \n \n Debug\n x64\n \n \n Release\n x64\n \n \n RelWithDebInfo\n x64\n \n \n \n {46643623-A4FA-4185-8DB2-61D4FAAF6A53}\n Win32Proj\n Win32\n dummy_app\n \n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n \n \n \n \n \n \n \n <_ProjectFileVersion>10.0.20506.1\n dummy_app\n .exe\n true\n true\n dummy_app\n .exe\n false\n true\n dummy_app\n .exe\n true\n true\n \n \n 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$(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n $(OutDir)experiment_$(TargetName).pgd\n \n \n false\n \n \n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)include;%(AdditionalIncludeDirectories)\n Release/\n CompileAsCpp\n Sync\n AnySuitable\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"Release\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Speed\n Fast\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"Release\\\";%(PreprocessorDefinitions)\n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n c:/local/boost_1_59_0/lib64-msvc-14.0;$(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n false\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)include;%(AdditionalIncludeDirectories)\n RelWithDebInfo/\n CompileAsCpp\n ProgramDatabase\n Sync\n OnlyExplicitInline\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"RelWithDebInfo\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n Speed\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"RelWithDebInfo\\\";%(PreprocessorDefinitions)\n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n c:/local/boost_1_59_0/lib64-msvc-14.0;$(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n \n \n \n \n 08B5B5CC-2938-4C1C-B835-A3868076D791\n \n \n \n \n \n" }, { "path": "algorithms/NMSLIB/code/src/experiment.vcxproj.filters", "content": "\n\n \n \n Source Files\n \n \n Source Files\n \n \n \n \n {3EF538C7-CC7F-4CC0-AA2E-461C0B950388}\n \n \n" }, { "path": "algorithms/NMSLIB/code/src/experimentconf.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"logging.h\"\n#include \"experimentconf.h\"\n\n\n#define SPACE \"Space\"\n\n#define DATA_FILE \"DataFile\"\n#define DATA_FILE_QTY \"DataFileQty\"\n\n// The query file can be empty\n#define QUERY_FILE \"QueryFile\"\n#define TEST_SET_QTY \"TestSetQty\"\n\n#define RANGE_QTY \"RangeQty\"\n#define KNN_QTY \"KNNQty\"\n// This is either\n#define QUERY_QTY \"QueryQty\"\n\n//#define PARANOID_SPLIT_CHECK\n\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nvoid ExperimentConfig::Read(istream& controlStream,\n istream& binaryStream,\n size_t& dataFileQty) {\n if (dataSetWasRead_) {\n throw runtime_error(\"Bug: the function to read cache shouldn't be called after the data set is read!\"); \n }\n string s;\n size_t i;\n\n ReadField(controlStream, SPACE, s);\n\n if (s != space_.StrDesc()) {\n stringstream err;\n err << \"The specified space ('\" << space_.StrDesc() << \"' \"\n << \" doesn't match the space ('\"\n << s << \") in the gold standard cache (must be char-by-char equal).\";\n throw runtime_error(err.str());\n }\n\n ReadField(controlStream, DATA_FILE, s);\n\n if (s != datafile_) {\n stringstream err;\n err << \"The specified data file ('\" << datafile_ << \"' \"\n << \" doesn't match the data file ('\"\n << s << \") in the gold standard cache (must be char-by-char equal).\";\n throw runtime_error(err.str());\n }\n\n ReadField(controlStream, DATA_FILE_QTY, s);\n ConvertFromString(s, dataFileQty);\n\n ReadField(controlStream, QUERY_FILE, s);\n\n if (s != queryfile_) {\n stringstream err;\n err << \"The specified query file ('\" << queryfile_ << \"' \"\n << \" doesn't match the query file ('\"\n << s << \") in the gold standard cache (must be char-by-char equal).\";\n throw runtime_error(err.str());\n }\n\n ReadField(controlStream, TEST_SET_QTY, s);\n ConvertFromString(s, testSetQty_);\n\n if (testSetQty_ < testSetToRunQty_) {\n stringstream err;\n err << \"The specified # of test sets (\" << testSetToRunQty_ << \") \"\n << \" is larger than the value (\" << testSetQty_ << \") \"\n << \" in the gold standard cache.\";\n throw runtime_error(err.str());\n }\n\n ReadField(controlStream, RANGE_QTY, s);\n ConvertFromString(s, i);\n\n if (i != range_.size()) {\n stringstream err;\n err << \"The specified # of range searches (\" << range_.size() << \") \"\n << \" doesn't match the value (\" << i << \") \"\n << \" in the gold standard cache.\";\n throw runtime_error(err.str());\n }\n\n ReadField(controlStream, KNN_QTY, s);\n ConvertFromString(s, i);\n\n if (i != knn_.size()) {\n stringstream err;\n err << \"The specified # of KNN searches (\" << knn_.size() << \") \"\n << \" doesn't match the value (\" << i << \") \"\n << \" in the gold standard cache.\";\n throw runtime_error(err.str());\n }\n\n dist_t val;\n for (size_t i = 0; i < range_.size(); ++i) {\n binaryStream.read(reinterpret_cast(&val), sizeof val);\n if (!ApproxEqual(range_[i], val)) {\n stringstream err;\n err << \"The specified range value #\" << (i+1) << \" (\" << range_[i] << \") \"\n << \" isn't equal to the value (\" << val << \") \"\n << \" in the gold standard cache.\";\n throw runtime_error(err.str());\n }\n }\n\n // Note that the type of eps_ is not necessarily dist_t!!!\n decltype(eps_) epsVal;\n\n binaryStream.read(reinterpret_cast(&epsVal), sizeof epsVal);\n if (!ApproxEqual(eps_, epsVal)) {\n stringstream err;\n err << \"The specified eps (\" << eps_ << \") \"\n << \" isn't equal to the value (\" << epsVal << \") \"\n << \" in the gold standard cache.\";\n throw runtime_error(err.str());\n }\n\n unsigned kVal;\n\n if (sizeof(kVal)!=sizeof(knn_[0])) {\n stringstream err;\n err << \"Bug: types apparently changed, sizeof(kVal) = \" << sizeof(kVal) <<\n \" which is different from sizeof(knn_[0]) = \" << sizeof(knn_[0]);\n throw runtime_error(err.str());\n }\n\n for (size_t i = 0; i < knn_.size(); ++i) {\n binaryStream.read(reinterpret_cast(&kVal), sizeof kVal);\n // We can use a smaller KNN value, but not the other way around!\n if (kVal < knn_[i]) {\n stringstream err;\n err << \"The specified KNN value #\" << (i+1) << \" (\" << knn_[i] << \") \"\n << \" is greater than the value (\" << kVal << \") \"\n << \" in the gold standard cache.\";\n throw runtime_error(err.str());\n }\n }\n\n ReadField(controlStream, QUERY_QTY, s);\n ConvertFromString(s, maxNumQuery_);\n\n /*\n * The number of queries specified by the user can be smaller than\n * the number of GS entries in the file, but not the other\n * way around.\n */\n\n if (maxNumQuery_ < maxNumQueryToRun_) {\n stringstream err;\n err << \"The specified # queries (\" << maxNumQueryToRun_ << \") \"\n << \" exceeds the value (\" << maxNumQuery_ << \") \"\n << \" in the gold standard cache.\";\n throw runtime_error(err.str());\n }\n\n if (noQueryData_) {\n for (size_t TestSetId = 0; TestSetId < testSetQty_; ++TestSetId) {\n vector vTmp;\n if (!getline(controlStream, s)) {\n throw runtime_error(\"Error reading from the control/text cache file!\");\n }\n\n SplitStr(s, vTmp, ' ');\n for (int id : vTmp) {\n cachedDataAssignment_.insert(make_pair(id, TestSetId));\n }\n }\n }\n}\n\ntemplate \nvoid ExperimentConfig::Write(ostream& controlStream, ostream& binaryStream) {\n WriteField(controlStream, SPACE, space_.StrDesc());\n WriteField(controlStream, DATA_FILE, datafile_);\n WriteField(controlStream, DATA_FILE_QTY, ConvertToString(origData_.size()));\n WriteField(controlStream, QUERY_FILE, queryfile_);\n WriteField(controlStream, TEST_SET_QTY, ConvertToString(testSetQty_));\n WriteField(controlStream, RANGE_QTY, ConvertToString(range_.size()));\n WriteField(controlStream, KNN_QTY, ConvertToString(knn_.size()));\n // Let's write range and knn-query parameters in the binary format\n for (size_t i = 0; i < range_.size(); ++i)\n binaryStream.write(reinterpret_cast(&range_[i]), sizeof range_[i]);\n\n binaryStream.write(reinterpret_cast(&eps_), sizeof eps_);\n\n for (size_t i = 0; i < knn_.size(); ++i) {\n binaryStream.write(reinterpret_cast(&knn_[i]), sizeof knn_[i]);\n }\n unsigned queryQty = origQuery_.size();\n\n if (noQueryData_) {\n if (!testSetToRunQty_) {\n throw runtime_error(\"Bug: zero number of test sets!\");\n }\n /*\n * Let's obtain the number of queries + double-check\n * that each subset has the same number of queries\n */\n vector qtys(testSetQty_);\n for (size_t i = 0; i < origDataAssignment_.size(); ++i) {\n int dst = origDataAssignment_[i];\n if (dst >= 0) {\n if (dst >= testSetQty_) {\n stringstream err;\n err << \"Bug: an assignment id (\" << dst <<\n \") is > # of sets (\" << testSetQty_ << \")\";\n throw runtime_error(err.str());\n }\n qtys[dst]++;\n }\n }\n queryQty = qtys[0]; // see the check above queryQty should be > 0\n if (!queryQty) {\n throw runtime_error(\"Bug: zero number of queries!\");\n }\n for (size_t i = 1; i < testSetToRunQty_; ++i)\n if (qtys[i] != queryQty) {\n stringstream err;\n err << \"Bug, different # of queries in the subsets, \" <<\n \"id=0, qty=\" << queryQty <<\n \"id=\" << i <<\", qty=\" << qtys[i];\n throw runtime_error(err.str());\n }\n\n }\n\n WriteField(controlStream, QUERY_QTY, ConvertToString(queryQty));\n\n if (noQueryData_) {\n // Let's save test set assignments\n size_t OrigQty = origData_.size();\n for (size_t SetNum = 0; SetNum < testSetQty_; ++SetNum) {\n stringstream line;\n bool bFirst = true;\n for (size_t i = 0; i < OrigQty; ++i) {\n int dst = origDataAssignment_[i];\n\n if (dst == SetNum) {\n if (!bFirst) line << \" \";\n bFirst = false;\n line << i;\n }\n }\n controlStream << line.str() << endl;\n }\n }\n}\n\ntemplate \nvoid ExperimentConfig::SelectTestSet(int SetNum) {\n if (!noQueryData_) return;\n if (SetNum <0 || static_cast(SetNum) >= testSetToRunQty_) {\n stringstream err;\n err << \"Invalid test set #: \" << SetNum;\n throw runtime_error(err.str());\n }\n dataobjects_.clear();\n queryobjects_.clear();\n\n size_t OrigQty = origData_.size();\n for (size_t i = 0; i < OrigQty; ++i) {\n int dst = origDataAssignment_[i];\n\n /*\n * dst == -1 => the data point is always put to dataobjects\n * if dst >= 0 and SetNum == dst, add this\n * data point to the current query set (with the ID=SetNum).\n * Otherwise, it goes to the data set.\n */\n if (dst == SetNum) {\n /*\n * There can be more queries than we need to run.\n * One typical scenario: the user saves cache for 1000 queries,\n * next time she runs a test using the saved cache she asks\n * to evaluate using only 100 queries.\n */\n if (queryobjects_.size() < maxNumQueryToRun_)\n queryobjects_.push_back(origData_[i]);\n }\n else dataobjects_.push_back(origData_[i]);\n }\n#ifdef PARANOID_SPLIT_CHECK\n {\n\n IdType MaxId = 0;\n for (const Object *pObj: dataobjects_) {\n MaxId = max(MaxId, pObj->id());\n CHECK_MSG(pObj->id()>=0, \"Got negative Id!\");\n }\n\n // Sanity check, is our data correct?\n {\n vector seen(MaxId);\n for (const Object *pObj: dataobjects_) {\n CHECK_MSG(!seen[pObj->id()],\n \"Bug in splitting data, repeating id: \" + ConvertToString(pObj->id()) + \" testSetId: \" +ConvertToString(SetNum));\n seen[pObj->id()] = true;\n }\n }\n }\n#endif\n}\n\ntemplate \nvoid ExperimentConfig::CopyExternal(const ObjectVector& src, ObjectVector& dst, size_t maxQty) {\n for (size_t i = 0; i < src.size() && i < maxQty; ++i) {\n dst.push_back(src[i]->Clone());\n }\n}\n\ntemplate \nvoid ExperimentConfig::ReadDataset() {\n vector tmp;\n\n if (!dataobjects_.empty())\n throw runtime_error(\n \"The set of data objects in non-empty, did you read the data set already?\");\n\n if (!queryobjects_.empty())\n throw runtime_error(\n \"The set of query objects in non-empty, did you read the data set already?\");\n\n if (pExternalData_) CopyExternal(*pExternalData_, origData_, maxNumData_);\n else {\n unique_ptr inpState(space_.ReadDataset(origData_, tmp, datafile_, maxNumData_));\n space_.UpdateParamsFromFile(*inpState);\n }\n\n /*\n * Note!!! \n * The destructor of this class will delete pointers stored in OrigData & OrigQuery.\n * Applications should not delete objects whose pointers are stored in arrays:\n * 1) dataobjects \n * 2) queryobjects.\n */\n if (!noQueryData_) {\n dataobjects_ = origData_;\n if (pExternalQuery_) \n CopyExternal(*pExternalQuery_, queryobjects_, maxNumQuery_);\n else \n space_.ReadDataset(queryobjects_, tmp, queryfile_, maxNumQueryToRun_);\n\n origQuery_ = queryobjects_;\n } else {\n size_t OrigQty = origData_.size();\n size_t MinOrigQty = (testSetQty_ + 1) * maxNumQuery_;\n if (OrigQty < MinOrigQty) {\n stringstream err;\n err << \"The data set is too small, add \" << (MinOrigQty - OrigQty) << \" MORE data points. \" <<\n \"Try to either increase the number of data points, or to decrease parameters: \" <<\n \"testSetQty and/or maxNumQuery \";\n throw runtime_error(err.str());\n }\n origDataAssignment_.resize(OrigQty);\n fill(origDataAssignment_.begin(), origDataAssignment_.end(), -1);\n\n if (!cachedDataAssignment_.empty()) {\n for (auto elem: cachedDataAssignment_)\n origDataAssignment_[elem.first] = elem.second;\n } else {\n /*\n * Test queries are selected randomly without resampling.\n * The algorithm of efficient sampling without replacement\n * was based on conclusions of D. Lemire's:\n * https://github.com/lemire/Code-used-on-Daniel-Lemire-s-blog/blob/master/2013/08/14/cpp/synthetic.h\n */\n\n for (unsigned i = 0; i < testSetQty_; ++i) {\n size_t card = 0;\n\n while (card < maxNumQuery_) {\n size_t Id = RandomInt() % OrigQty;\n\n if (-1 == origDataAssignment_[Id]) {\n origDataAssignment_[Id] = i;\n ++card;\n }\n }\n }\n }\n }\n\n dataSetWasRead_ = true;\n LOG(LIB_INFO) << \"data & query .... ok!\\n\";\n}\n\ntemplate \nvoid ExperimentConfig::PrintInfo() const {\n LOG(LIB_INFO) << space_.StrDesc();\n LOG(LIB_INFO) << \"distance type = \" << DistTypeName();\n LOG(LIB_INFO) << \"data file = \" << datafile_;\n LOG(LIB_INFO) << \"# of test sets = \" << GetTestSetTotalQty();\n LOG(LIB_INFO) << \"# of test sets to run = \" << GetTestSetToRunQty();\n LOG(LIB_INFO) << \"Use held-out queries = \" << !noQueryData_;\n LOG(LIB_INFO) << \"# of data points = \" << origData_.size() - (noQueryData_ ? GetQueryToRunQty():0);\n LOG(LIB_INFO) << \"# of query points = \" << GetQueryToRunQty();\n}\n\ntemplate class ExperimentConfig;\ntemplate class ExperimentConfig;\ntemplate class ExperimentConfig;\n\n}\n\n" }, { "path": "algorithms/NMSLIB/code/src/global.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"global.h\"\n\nnamespace similarity {\n\n}\n\n" }, { "path": "algorithms/NMSLIB/code/src/init.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"init.h\"\n\n/*\n * Important note: there is some issue when compling using Intel.\n * It involves header inclusion order. If we include spaces first\n * it then somehow handicaps ability of \n * boost/math/special_functions/detail/lanczos_sse2.hpp\n * to properly use SSE.\n */\n#include \"factory/init_methods.h\"\n#include \"factory/init_spaces.h\"\n\n#include \"logging.h\"\n\nnamespace similarity {\n\nvoid initLibrary(LogChoice choice, const char* pLogFile) {\n InitializeLogger(choice, pLogFile);\n initSpaces();\n initMethods();\n}\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/knnquery.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"knnqueue.h\"\n#include \"utils.h\"\n#include \"space.h\"\n#include \"index.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n\nnamespace similarity {\n\nusing std::unique_ptr;\n\ntemplate \nKNNQuery::KNNQuery(const Space& space, const Object* query_object, const unsigned K, float eps)\n : Query(space, query_object),\n K_(K), eps_(eps),\n result_(new KNNQueue(K)) {\n}\n\ntemplate \nKNNQuery::~KNNQuery() {\n delete result_;\n}\n\ntemplate \nvoid KNNQuery::Reset() {\n this->ResetStats();\n result_->Reset();\n}\n\ntemplate \nconst KNNQueue* KNNQuery::Result() const {\n return result_;\n}\n\ntemplate \ndist_t KNNQuery::Radius() const {\n return result_->Size() < static_cast(K_)\n ? DistMax()\n : static_cast(result_->TopDistance() / (static_cast(1) + eps_));\n}\n\ntemplate \nunsigned KNNQuery::ResultSize() const {\n return result_->Size();\n}\n\ntemplate \nbool KNNQuery::CheckAndAddToResult(const dist_t distance,\n const Object* object) {\n if (result_->Size() < static_cast(K_) ||\n distance < result_->TopDistance()) {\n result_->Push(distance, object);\n return true;\n }\n return false;\n}\n\ntemplate \nbool KNNQuery::CheckAndAddToResult(const Object* object) {\n return this->CheckAndAddToResult(this->DistanceObjLeft(object), object);\n}\n\ntemplate \nsize_t KNNQuery::CheckAndAddToResult(const ObjectVector& bucket) {\n size_t res = 0;\n for (size_t i = 0; i < bucket.size(); ++i) {\n if (CheckAndAddToResult(bucket[i])) {\n ++res;\n }\n }\n return res;\n}\n\ntemplate \nbool KNNQuery::Equals(const KNNQuery* other) const {\n bool equal = true;\n unique_ptr> first(result_->Clone());\n unique_ptr> second(other->result_->Clone());\n while (equal && !first->Empty() && !second->Empty()) {\n equal = equal && ApproxEqual(first->TopDistance(), second->TopDistance());\n if (!equal) {\n std::cerr << \"Equality check failed: \"\n << first->TopDistance() << \" != \"\n << second->TopDistance() << std::endl;\n }\n first->Pop();\n second->Pop();\n }\n equal = equal && first->Empty() && second->Empty(); // both should be empty\n return equal;\n}\n\ntemplate \nvoid KNNQuery::Print() const {\n unique_ptr> clone(Result()->Clone());\n std::cerr << \"queryID = \" << this->query_object_->id()\n << \" size = \" << ResultSize()\n << \" (k=\" << GetK()\n << \" dc=\" << this->DistanceComputations()\n << \") \";\n while (!clone->Empty()) {\n if (clone->TopObject() == NULL) {\n std::cerr << \"null (\" << clone->TopDistance() << \")\";\n } else {\n const Object* object = reinterpret_cast(clone->TopObject());\n std::cerr << object->id() << \"(\"\n << clone->TopDistance() << \" \"\n << this->space_.IndexTimeDistance(object, this->QueryObject()) << \") \";\n }\n clone->Pop();\n }\n std::cerr << std::endl;\n}\n\ntemplate class KNNQuery;\ntemplate class KNNQuery;\ntemplate class KNNQuery;\ntemplate class KNNQuery;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/logging.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n// To shutup Microsoft Compiler who complains about localtime being unsafe\n#ifdef _MSC_VER\n#define _CRT_SECURE_NO_WARNINGS\n#endif\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"logging.h\"\n\nconst char* log_severity[] = {\"INFO\", \"WARNING\", \"ERROR\", \"FATAL\"};\n\nusing namespace std;\n\n// allocate the static member\nstd::ofstream Logger::logfile_;\n\nstatic struct voidstream : public ostream {\n template ostream& operator<< (T) { return *this; }\n template ostream& operator<< (T*) { return *this; }\n template ostream& operator<< (T&) { return *this; }\n voidstream() : ostream(0) {}\n} voidstream_;\n\nstd::string LibGetCurrentTime() {\n time_t now;\n time(&now);\n struct tm* timeinfo = localtime(&now);\n char time_string[50];\n strftime(time_string, sizeof(time_string), \"%Y-%m-%d %H:%M:%S\", timeinfo);\n return std::string(time_string);\n}\n\nostream* Logger::currstrm_ = &cerr;\n\nLogger::Logger(LogSeverity severity, const std::string& _file, int line, const char* function)\n : severity_(severity) {\n std::string file = _file;\n size_t n = file.rfind('/');\n if (n != std::string::npos) {\n file.erase(file.begin(), file.begin() + n + 1);\n }\n stream() << LibGetCurrentTime() << \" \" << file << \":\" << line\n << \" (\" << function << \") [\" << log_severity[severity] << \"] \";\n}\n\nLogger::~Logger() {\n stream() << '\\n';\n if (severity_ == LIB_FATAL) {\n stream().flush();\n if (logfile_.is_open())\n logfile_.close();\n // TODO(@leo/@bileg) do we want to abort here?\n //abort();\n exit(1); \n }\n}\n\nvoid InitializeLogger(LogChoice choice, const char* logfile) {\n if (choice == LIB_LOGNONE) {\n Logger::currstrm_ = &voidstream_;\n }\n if (choice == LIB_LOGFILE) {\n Logger::logfile_.open(logfile);\n if (!Logger::logfile_) {\n LOG(LIB_FATAL) << \"Can't open the logfile: '\" << logfile << \"'\";\n }\n Logger::currstrm_ = &Logger::logfile_;\n }\n if (choice == LIB_LOGSTDERR) {\n Logger::currstrm_ = &cerr;\n }\n}\n\n\nRuntimeErrorWrapper::RuntimeErrorWrapper(const std::string& _file, int line, const char* function) {\n std::string file = _file;\n size_t n = file.rfind('/');\n if (n != std::string::npos) {\n file.erase(file.begin(), file.begin() + n + 1);\n }\n stream() << LibGetCurrentTime() << \" \" << file << \":\" << line\n << \" (\" << function << \") \";\n}\n" }, { "path": "algorithms/NMSLIB/code/src/main.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"init.h\"\n#include \"global.h\"\n#include \"utils.h\"\n#include \"memory.h\"\n#include \"ztimer.h\"\n#include \"experiments.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n#include \"spacefactory.h\"\n#include \"logging.h\"\n#include \"report.h\"\n#include \"methodfactory.h\"\n\n#include \"meta_analysis.h\"\n#include \"params.h\"\n#include \"params_cmdline.h\"\n\nusing namespace similarity;\n\nusing std::multimap;\nusing std::vector;\nusing std::string;\nusing std::stringstream;\n\nvoid OutData(bool DoAppend, const string& FilePrefix,\n const string& Print, const string& Header, const string& Data) {\n \n string FileNameRep = FilePrefix ;//+ \".rep\";\n /*string FileNameData = FilePrefix+ \".dat\";\n LOG(LIB_INFO) << \"DoAppend? \" << DoAppend;\n\n std::ofstream OutFileData(FileNameData.c_str(),\n (DoAppend ? std::ios::app : (std::ios::trunc | std::ios::out)));\n\n if (!OutFileData) {\n LOG(LIB_FATAL) << \"Cannot create output file: '\" << FileNameData << \"'\";\n }\n OutFileData.exceptions(std::ios::badbit);\n if (!DoAppend) {\n OutFileData << Header;\n }\n OutFileData<< Data;\n OutFileData.close();\n */\n\n std::ofstream OutFileRep(FileNameRep.c_str(),\n (DoAppend ? std::ios::app : (std::ios::trunc | std::ios::out)));\n\n if (!OutFileRep) {\n LOG(LIB_FATAL) << \"Cannot create output file: '\" << FileNameRep << \"'\";\n }\n OutFileRep.exceptions(std::ios::badbit);\n\n OutFileRep<< Print;\n OutFileRep.close();\n \n}\n\ntemplate \nvoid ProcessResults(const ExperimentConfig& config,\n MetaAnalysis& ExpRes,\n const string& MethodName,\n const string& IndexParamStr,\n const string& QueryTimeParamStr,\n string& PrintStr, // For display\n string& HeaderStr,\n string& DataStr /* to be processed by a script */) {\n std::stringstream Print, Data, Header;\n\n ExpRes.ComputeAll();\n\n Header << \"MethodName\\tRecall\\tRecall@1\\tPrecisionOfApprox\\tRelPosError\\tNumCloser\\tClassAccuracy\\tQueryTime\\tDistComp\\tImprEfficiency\\tImprDistComp\\tMem\\tIndexTime\\tIndexLoadTime\\tIndexSaveTime\\tQueryPerSec\\tIndexParams\\tQueryTimeParams\\tNumData\" << std::endl;\n\n Data << \"\\\"\" << MethodName << \"\\\"\\t\";\n Data << ExpRes.GetRecallAvg() << \"\\t\";\n Data << ExpRes.GetRecallAt1Avg() << \"\\t\";\n Data << ExpRes.GetPrecisionOfApproxAvg() << \"\\t\";\n Data << ExpRes.GetRelPosErrorAvg() << \"\\t\";\n Data << ExpRes.GetNumCloserAvg() << \"\\t\";\n Data << ExpRes.GetClassAccuracyAvg() << \"\\t\";\n Data << ExpRes.GetQueryTimeAvg() << \"\\t\";\n Data << ExpRes.GetDistCompAvg() << \"\\t\";\n Data << ExpRes.GetImprEfficiencyAvg() << \"\\t\";\n Data << ExpRes.GetImprDistCompAvg() << \"\\t\";\n Data << size_t(ExpRes.GetMemAvg()) << \"\\t\";\n Data << ExpRes.GetIndexTimeAvg() << \"\\t\";\n Data << ExpRes.GetLoadTimeAvg() << \"\\t\";\n Data << ExpRes.GetSaveTimeAvg() << \"\\t\";\n Data << ExpRes.GetQueryPerSecAvg() << \"\\t\";\n Data << \"\\\"\" << IndexParamStr << \"\\\"\" << \"\\t\";\n Data << \"\\\"\" << QueryTimeParamStr << \"\\\"\" << \"\\t\";\n Data << config.GetDataObjects().size();\n Data << std::endl;\n\n PrintStr = produceHumanReadableReport(config, ExpRes, MethodName, IndexParamStr, QueryTimeParamStr);\n\n DataStr = Data.str();\n HeaderStr = Header.str();\n};\n\ntemplate \nvoid RunExper(bool bPrintProgress,\n const string& LoadIndexLoc,\n const string& SaveIndexLoc, \n const string& MethodName,\n const shared_ptr& IndexTimeParams,\n const vector>& QueryTimeParams,\n const string SpaceType,\n const shared_ptr& SpaceParams,\n unsigned ThreadTestQty,\n bool DoAppend, \n const string& ResFilePrefix,\n unsigned TestSetQty,\n const string& DataFile,\n const string& QueryFile,\n const string& CacheGSFilePrefix,\n float maxCacheGSRelativeQty,\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQuery,\n const vector& knn,\n const float eps,\n const string& RangeArg\n)\n{\n LOG(LIB_INFO) << \"### Append? : \" << DoAppend;\n LOG(LIB_INFO) << \"### OutFilePrefix : \" << ResFilePrefix;\n vector range;\n\n bool bWriteGSCache = false;\n bool bReadGSCache = false;\n bool bFail = false;\n bool bCacheGS = !CacheGSFilePrefix.empty();\n\n unique_ptr cacheGSControl;\n unique_ptr cacheGSBinary;\n\n if (!RangeArg.empty()) {\n if (!SplitStr(RangeArg, range, ',')) {\n LOG(LIB_FATAL) << \"Wrong format of the range argument: '\" << RangeArg << \"' Should be a list of coma-separated values.\";\n }\n }\n\n \n unique_ptr> space (SpaceFactoryRegistry::\n Instance().CreateSpace(SpaceType, *SpaceParams));\n\n ExperimentConfig config(*space,\n DataFile, QueryFile, TestSetQty,\n MaxNumData, MaxNumQuery,\n knn, eps, range);\n\n size_t cacheDataSetQty = 0;\n\tfloat index_time =0;\n\n const string& cacheGSIncompleteFlagName = CacheGSFilePrefix + \"_incomplete.flag\";\n\n if (bCacheGS) {\n const string& cacheGSControlName = CacheGSFilePrefix + \"_ctrl.txt\";\n const string& cacheGSBinaryName = CacheGSFilePrefix + \"_data.bin\";\n\n if (DoesFileExist(cacheGSIncompleteFlagName) ||\n (DoesFileExist(cacheGSControlName) != DoesFileExist(cacheGSBinaryName))\n ) {\n LOG(LIB_INFO) << \"Incomplete cache file detected! Removing incomplete entries...\";\n if (DoesFileExist(cacheGSBinaryName))\n CHECK_MSG(std::remove(cacheGSBinaryName.c_str())==0, \"Error removing the file: \" + cacheGSBinaryName);\n if (DoesFileExist(cacheGSControlName))\n CHECK_MSG(std::remove(cacheGSControlName.c_str())==0, \"Error removing the file: \" + cacheGSControlName);\n }\n\n if (DoesFileExist(cacheGSControlName)) {\n // Cache exists => reuse it\n if (!DoesFileExist(cacheGSBinaryName)) {\n throw runtime_error(\"Inconsistent cache state, there is a text control file: '\" +\n cacheGSControlName + \"' but no binary data file: '\" +\n cacheGSBinaryName + \"'\");\n }\n cacheGSControl.reset(new fstream(cacheGSControlName.c_str(),\n std::ios::in));\n cacheGSBinary.reset(new fstream(cacheGSBinaryName.c_str(),\n std::ios::in \n // On Windows you don't get a proper binary stream without ios::binary!\n | ios::binary));\n\n bReadGSCache = true;\n } else {\n if (DoesFileExist(cacheGSBinaryName)) {\n throw runtime_error(\"Inconsistent cache state, there is no text control file: '\" +\n cacheGSControlName + \"' but there is binary data file: '\" +\n cacheGSBinaryName + \"'\");\n }\n // No cache => create new file, but first mark cache as incomplete!\n ofstream flag_file(cacheGSIncompleteFlagName.c_str());\n CHECK_MSG(flag_file, \"Error creating file: \" + cacheGSIncompleteFlagName);\n flag_file.close();\n\n cacheGSControl.reset(new fstream(cacheGSControlName.c_str(),\n std::ios::trunc | std::ios::out));\n cacheGSBinary.reset(new fstream(cacheGSBinaryName.c_str(),\n std::ios::trunc | std::ios::out |\n // On Windows you don't get a proper binary stream without ios::binary!\n ios::binary));\n\n bWriteGSCache = true;\n }\n\n cacheGSControl->exceptions(std::ios::badbit);\n cacheGSBinary->exceptions(std::ios::badbit);\n\n /*\n * If the cache exists, it should be read before ReadData() is called.\n */\n if (!bWriteGSCache) {\n config.Read(*cacheGSControl, *cacheGSBinary, cacheDataSetQty);\n }\n }\n\n config.ReadDataset();\n\n if (bReadGSCache) {\n // Let's check the number of data entries, must exactly coincide with\n // what was used to create the cache!\n if (config.GetOrigDataQty() != cacheDataSetQty) {\n stringstream err;\n err << \"The number of entries in the file, or the maximum number \"\n << \"of data elements don't match the value in the cache file: \"\n << cacheDataSetQty;\n throw runtime_error(err.str());\n }\n }\n\n /*\n * Yet, if we need to create a new cache file, we must write the cache\n * after reading the data set.\n */\n if (bWriteGSCache) {\n\n config.Write(*cacheGSControl, *cacheGSBinary);\n }\n\n\n MemUsage mem_usage_measure;\n\n\n vector MemUsage;\n\n CHECK_MSG(!QueryTimeParams.empty(), \"The array of query-time parameters shouldn't be empty!\");\n\n vector> ExpResRange(config.GetRange().size(),\n vector(QueryTimeParams.size()));\n vector> ExpResKNN(config.GetKNN().size(),\n vector(QueryTimeParams.size()));\n\n GoldStandardManager managerGS(config);\n\n\n string MethodDescStr;\n\n // qtmParamId is the ID of the query time parameter set\n for (size_t qtmParamId = 0; qtmParamId < QueryTimeParams.size(); ++qtmParamId) {\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n ExpResRange[i][qtmParamId] = new MetaAnalysis(config.GetTestSetToRunQty());\n }\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n ExpResKNN[i][qtmParamId] = new MetaAnalysis(config.GetTestSetToRunQty());\n }\n }\n\n for (int TestSetId = 0; TestSetId < config.GetTestSetToRunQty(); ++TestSetId) {\n config.SelectTestSet(TestSetId);\n\n string indexLocAdd = \"\";\n\n if (QueryFile.empty() && config.GetTestSetToRunQty() > 0) {\n indexLocAdd = \"_\" + ConvertToString(TestSetId);\n }\n\n // SelectTestSet must go before managerGS.Compute()!!!\n\n if (bReadGSCache) {\n size_t cacheTestId = 0, savedThreadQty = 0;\n managerGS.Read(*cacheGSControl, *cacheGSBinary,\n config.GetTotalQueryQty(),\n cacheTestId, savedThreadQty);\n if (cacheTestId != TestSetId) {\n stringstream err;\n err << \"Perhaps, the input file is corrput (or is incompatible with \"\n << \"program parameters), expect test set id=\" << TestSetId\n << \"but obtained \" << cacheTestId;\n throw runtime_error(err.str());\n }\n CHECK_MSG(savedThreadQty == ThreadTestQty,\n \"Error: the gold standard was computed using \" +ConvertToString(savedThreadQty) + \" threads, but the current test will use \" +\n ConvertToString(ThreadTestQty) + \" threads. You have to use the same number of threads while compute gold standard data and testing!\");\n } else {\n managerGS.Compute(ThreadTestQty, maxCacheGSRelativeQty);\n if (bWriteGSCache) {\n LOG(LIB_INFO) << \"Saving the cache\";\n managerGS.Write(*cacheGSControl, *cacheGSBinary, TestSetId, ThreadTestQty);\n\n // Remove the incomplete-file flag, but only if this is the last test set \n if (TestSetId + 1 == config.GetTestSetToRunQty())\n CHECK_MSG(std::remove(cacheGSIncompleteFlagName.c_str())==0, \n \"Error removing the file: \" + cacheGSIncompleteFlagName);\n }\n }\n\n LOG(LIB_INFO) << \">>>> Test set id: \" << TestSetId << \" (set qty: \" << config.GetTestSetToRunQty() << \")\";\n\n //ReportIntrinsicDimensionality(\"Main data set\" , *config.GetSpace(), config.GetDataObjects());\n\n if (MethodName.empty()) {\n LOG(LIB_INFO) << \"No method is specified, so we will not run any tests...\";\n } else {\n unique_ptr> IndexPtr;\n\n try {\n LOG(LIB_INFO) << \">>>> Index type : \" << MethodName;\n LOG(LIB_INFO) << \">>>> Index Time Parameters: \" << IndexTimeParams->ToString();\n\n const double vmsize_before = mem_usage_measure.get_vmsize();\n\n WallClockTimer wtm;\n\n wtm.reset();\n \n IndexPtr.reset(MethodFactoryRegistry::Instance().\n CreateMethod(bPrintProgress,\n MethodName, \n SpaceType, config.GetSpace(), \n config.GetDataObjects()));\n\n string adjLoadLoc = LoadIndexLoc + indexLocAdd;\n\n bool bCreate = LoadIndexLoc.empty() || !DoesFileExist(adjLoadLoc);\n\n if (bCreate) {\n LOG(LIB_INFO) << \"Creating an index from scratch\";\n\n IndexPtr->CreateIndex(*IndexTimeParams);\n } else {\n LOG(LIB_INFO) << \"Loading an index for test set id \" << TestSetId << \" using location: \" << adjLoadLoc;\n IndexPtr->LoadIndex(adjLoadLoc);\n }\n\n if (!TestSetId) MethodDescStr = IndexPtr->StrDesc();\n\n LOG(LIB_INFO) << \"==============================================\";\n\n const double vmsize_after = mem_usage_measure.get_vmsize();\n\n wtm.split();\n\n const double IndexTime = bCreate ? double(wtm.elapsed())/1e6 : 0;\n const double LoadTime = !bCreate ? double(wtm.elapsed())/1e6 : 0;\n\n const double data_size = DataSpaceUsed(config.GetDataObjects()) / 1024.0 / 1024.0;\n const double TotalMemByMethod = vmsize_after - vmsize_before + data_size;\n double AdjustedMemByMethod = TotalMemByMethod;\n\n if (IndexPtr->DuplicateData()) AdjustedMemByMethod -= data_size;\n\n wtm.reset();\n // We won't save the index if it is already created\n string adjSaveLoc = SaveIndexLoc + indexLocAdd;\n if (!SaveIndexLoc.empty() && !DoesFileExist(adjSaveLoc)) {\n LOG(LIB_INFO) << \"Saving an index for test set id \" << TestSetId << \" using location: \" << adjSaveLoc;\n IndexPtr->SaveIndex(adjSaveLoc);\n }\n wtm.split();\n const double SaveTime = double(wtm.elapsed())/1e6;\n \n\n LOG(LIB_INFO) << \">>>> Process memory usage: \" << vmsize_after << \" MBs\";\n LOG(LIB_INFO) << \">>>> Virtual memory usage: \" << TotalMemByMethod << \" MBs\";\n LOG(LIB_INFO) << \">>>> Adjusted memory usage: \" << AdjustedMemByMethod << \" MBs\";\n LOG(LIB_INFO) << \">>>> Data size: \" << data_size << \" MBs\";\n LOG(LIB_INFO) << \">>>> Indexing time: \" << IndexTime << \" sec\";\n LOG(LIB_INFO) << \">>>> Index loading time: \" << LoadTime << \" sec\";\n LOG(LIB_INFO) << \">>>> Index saving time: \" << SaveTime << \" sec\";\n\n for (size_t qtmParamId = 0; qtmParamId < QueryTimeParams.size(); ++qtmParamId) {\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n MetaAnalysis* res = ExpResRange[i][qtmParamId];\n res->SetMem(TestSetId, AdjustedMemByMethod);\n res->SetIndexTime(TestSetId, IndexTime);\n res->SetLoadTime(TestSetId, LoadTime);\n res->SetSaveTime(TestSetId, SaveTime);\n }\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n MetaAnalysis* res = ExpResKNN[i][qtmParamId];\n res->SetMem(TestSetId, AdjustedMemByMethod);\n res->SetIndexTime(TestSetId, IndexTime);\n res->SetLoadTime(TestSetId, LoadTime);\n res->SetSaveTime(TestSetId, SaveTime);\n }\n }\n \t//report indextime\n\n\t\tstd::ofstream OutFileRep(ResFilePrefix.c_str(),std::ios::app);\n \t\tOutFileRep<< IndexTime <<\" #index_time \\n\" ;\n \t\tOutFileRep.close();\n\t\tindex_time=IndexTime;\n\n Experiments::RunAll(bPrintProgress,\n ThreadTestQty, \n TestSetId,\n managerGS,\n ExpResRange, ExpResKNN,\n config, \n *IndexPtr, \n QueryTimeParams);\n\n\n } catch (const std::exception& e) {\n LOG(LIB_ERROR) << \"Exception: \" << e.what();\n bFail = true;\n } catch (...) {\n LOG(LIB_ERROR) << \"Unknown exception\";\n bFail = true;\n }\n\n if (bFail) {\n LOG(LIB_FATAL) << \"Failure due to an exception!\";\n }\n }\n }\n\n // Don't save results, if the method wasn't specified\n if (!MethodName.empty()) {\n for (size_t MethNum = 0; MethNum < QueryTimeParams.size(); ++MethNum) {\n // Don't overwrite file after we output data at least for one method!\n bool DoAppendHere = DoAppend || MethNum;\n\n string Print, Data, Header;\n\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n MetaAnalysis* res = ExpResRange[i][MethNum];\n\n ProcessResults(config, *res, MethodDescStr,\n IndexTimeParams->ToString(), QueryTimeParams[MethNum]->ToString(), \n Print, Header, Data);\n LOG(LIB_INFO) << \"Range: \" << config.GetRange()[i];\n LOG(LIB_INFO) << Print;\n LOG(LIB_INFO) << \"Data: \" << Header << Data;\n\n if (!ResFilePrefix.empty()) {\n stringstream str;\n str << ResFilePrefix << \"_r=\" << config.GetRange()[i];\n OutData(DoAppendHere, str.str(), Print, Header, Data);\n }\n\n delete res;\n }\n\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n MetaAnalysis* res = ExpResKNN[i][MethNum];\n\n ProcessResults(config, *res, MethodDescStr,\n IndexTimeParams->ToString(), QueryTimeParams[MethNum]->ToString(), \n Print, Header, Data);\n LOG(LIB_INFO) << \"KNN: \" << config.GetKNN()[i];\n LOG(LIB_INFO) << Print;\n LOG(LIB_INFO) << \"Data: \" << Header << Data;\n\n if (!ResFilePrefix.empty()) {\n stringstream str;\n str << ResFilePrefix ;//<< \"_K=\" << config.GetKNN()[i];\n OutData(DoAppendHere, str.str(), Print, Header, Data);\n }\n\n\t\t//std::ofstream OutFileRep(ResFilePrefix.c_str(),std::ios::app);\n \t\t//OutFileRep<ToString()<<\" \"<< index_time <<\" #index_time \\n\" ;\n \t\t//OutFileRep.close();\n\t\t//cerr << index_time <<\" #index_time \\n\" ;\n delete res;\n }\n }\n }\n}\n\nint main(int ac, char* av[]) {\n // This should be the first function called before\n\n WallClockTimer timer;\n timer.reset();\n\n\n bool bPrintProgress;\n string LogFile;\n string DistType;\n string LoadIndexLoc;\n string SaveIndexLoc;\n string SpaceType;\n shared_ptr SpaceParams;\n bool DoAppend;\n string ResFilePrefix;\n unsigned TestSetQty;\n string DataFile;\n string QueryFile;\n string CacheGSFilePrefix;\n float maxCacheGSRelativeQty;\n IdTypeUnsign MaxNumData;\n IdTypeUnsign MaxNumQuery;\n vector knn;\n string RangeArg;\n float eps = 0.0;\n unsigned ThreadTestQty;\n\n shared_ptr IndexTimeParams;\n vector> QueryTimeParams;\n\n try {\n string MethodName;\n\n ParseCommandLine(ac, av, bPrintProgress,\n LogFile,\n LoadIndexLoc,\n SaveIndexLoc, \n DistType,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg,\n MethodName,\n IndexTimeParams,\n QueryTimeParams);\n\n if ((!LoadIndexLoc.empty() || !SaveIndexLoc.empty()) &&\n CacheGSFilePrefix.empty() &&\n MaxNumQuery != 0 &&\n QueryFile.empty()) {\n throw\n runtime_error(string(\"If there is i) no query file ii) # of queries > 0 iii) you ask to save/load the index,\")+\n \"then you have to specify the gold-standard cache file!\");\n }\n\n initLibrary(LogFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, LogFile.c_str());\n\n LOG(LIB_INFO) << \"Program arguments are processed\";\n\n ToLower(DistType);\n\n if (DIST_TYPE_INT == DistType) {\n RunExper(bPrintProgress,\n LoadIndexLoc,\n SaveIndexLoc,\n MethodName,\n IndexTimeParams,\n QueryTimeParams,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg\n );\n } else if (DIST_TYPE_FLOAT == DistType) {\n RunExper(bPrintProgress,\n LoadIndexLoc,\n SaveIndexLoc,\n MethodName,\n IndexTimeParams,\n QueryTimeParams,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg\n );\n } else if (DIST_TYPE_DOUBLE == DistType) {\n RunExper(bPrintProgress,\n LoadIndexLoc,\n SaveIndexLoc,\n MethodName,\n IndexTimeParams,\n QueryTimeParams,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg\n );\n } else {\n LOG(LIB_FATAL) << \"Unknown distance value type: \" << DistType;\n }\n\n timer.split();\n LOG(LIB_INFO) << \"Time elapsed = \" << timer.elapsed() / 1e6;\n LOG(LIB_INFO) << \"Finished at \" << LibGetCurrentTime();\n } catch (const std::exception& e) {\n LOG(LIB_FATAL) << \"Exception: \" << e.what();\n } catch (...) {\n LOG(LIB_FATAL) << \"Unknown exception\";\n }\n\n return 0;\n}\n" }, { "path": "algorithms/NMSLIB/code/src/main.cc~", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"init.h\"\n#include \"global.h\"\n#include \"utils.h\"\n#include \"memory.h\"\n#include \"ztimer.h\"\n#include \"experiments.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n#include \"spacefactory.h\"\n#include \"logging.h\"\n#include \"report.h\"\n#include \"methodfactory.h\"\n\n#include \"meta_analysis.h\"\n#include \"params.h\"\n#include \"params_cmdline.h\"\n\nusing namespace similarity;\n\nusing std::multimap;\nusing std::vector;\nusing std::string;\nusing std::stringstream;\n\nvoid OutData(bool DoAppend, const string& FilePrefix,\n const string& Print, const string& Header, const string& Data) {\n \n string FileNameRep = FilePrefix ;//+ \".rep\";\n /*string FileNameData = FilePrefix+ \".dat\";\n LOG(LIB_INFO) << \"DoAppend? \" << DoAppend;\n\n std::ofstream OutFileData(FileNameData.c_str(),\n (DoAppend ? std::ios::app : (std::ios::trunc | std::ios::out)));\n\n if (!OutFileData) {\n LOG(LIB_FATAL) << \"Cannot create output file: '\" << FileNameData << \"'\";\n }\n OutFileData.exceptions(std::ios::badbit);\n if (!DoAppend) {\n OutFileData << Header;\n }\n OutFileData<< Data;\n OutFileData.close();\n */\n\n std::ofstream OutFileRep(FileNameRep.c_str(),\n (DoAppend ? std::ios::app : (std::ios::trunc | std::ios::out)));\n\n if (!OutFileRep) {\n LOG(LIB_FATAL) << \"Cannot create output file: '\" << FileNameRep << \"'\";\n }\n OutFileRep.exceptions(std::ios::badbit);\n\n OutFileRep<< Print;\n OutFileRep.close();\n \n}\n\ntemplate \nvoid ProcessResults(const ExperimentConfig& config,\n MetaAnalysis& ExpRes,\n const string& MethodName,\n const string& IndexParamStr,\n const string& QueryTimeParamStr,\n string& PrintStr, // For display\n string& HeaderStr,\n string& DataStr /* to be processed by a script */) {\n std::stringstream Print, Data, Header;\n\n ExpRes.ComputeAll();\n\n Header << \"MethodName\\tRecall\\tRecall@1\\tPrecisionOfApprox\\tRelPosError\\tNumCloser\\tClassAccuracy\\tQueryTime\\tDistComp\\tImprEfficiency\\tImprDistComp\\tMem\\tIndexTime\\tIndexLoadTime\\tIndexSaveTime\\tQueryPerSec\\tIndexParams\\tQueryTimeParams\\tNumData\" << std::endl;\n\n Data << \"\\\"\" << MethodName << \"\\\"\\t\";\n Data << ExpRes.GetRecallAvg() << \"\\t\";\n Data << ExpRes.GetRecallAt1Avg() << \"\\t\";\n Data << ExpRes.GetPrecisionOfApproxAvg() << \"\\t\";\n Data << ExpRes.GetRelPosErrorAvg() << \"\\t\";\n Data << ExpRes.GetNumCloserAvg() << \"\\t\";\n Data << ExpRes.GetClassAccuracyAvg() << \"\\t\";\n Data << ExpRes.GetQueryTimeAvg() << \"\\t\";\n Data << ExpRes.GetDistCompAvg() << \"\\t\";\n Data << ExpRes.GetImprEfficiencyAvg() << \"\\t\";\n Data << ExpRes.GetImprDistCompAvg() << \"\\t\";\n Data << size_t(ExpRes.GetMemAvg()) << \"\\t\";\n Data << ExpRes.GetIndexTimeAvg() << \"\\t\";\n Data << ExpRes.GetLoadTimeAvg() << \"\\t\";\n Data << ExpRes.GetSaveTimeAvg() << \"\\t\";\n Data << ExpRes.GetQueryPerSecAvg() << \"\\t\";\n Data << \"\\\"\" << IndexParamStr << \"\\\"\" << \"\\t\";\n Data << \"\\\"\" << QueryTimeParamStr << \"\\\"\" << \"\\t\";\n Data << config.GetDataObjects().size();\n Data << std::endl;\n\n PrintStr = produceHumanReadableReport(config, ExpRes, MethodName, IndexParamStr, QueryTimeParamStr);\n\n DataStr = Data.str();\n HeaderStr = Header.str();\n};\n\ntemplate \nvoid RunExper(bool bPrintProgress,\n const string& LoadIndexLoc,\n const string& SaveIndexLoc, \n const string& MethodName,\n const shared_ptr& IndexTimeParams,\n const vector>& QueryTimeParams,\n const string SpaceType,\n const shared_ptr& SpaceParams,\n unsigned ThreadTestQty,\n bool DoAppend, \n const string& ResFilePrefix,\n unsigned TestSetQty,\n const string& DataFile,\n const string& QueryFile,\n const string& CacheGSFilePrefix,\n float maxCacheGSRelativeQty,\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQuery,\n const vector& knn,\n const float eps,\n const string& RangeArg\n)\n{\n LOG(LIB_INFO) << \"### Append? : \" << DoAppend;\n LOG(LIB_INFO) << \"### OutFilePrefix : \" << ResFilePrefix;\n vector range;\n\n bool bWriteGSCache = false;\n bool bReadGSCache = false;\n bool bFail = false;\n bool bCacheGS = !CacheGSFilePrefix.empty();\n\n unique_ptr cacheGSControl;\n unique_ptr cacheGSBinary;\n\n if (!RangeArg.empty()) {\n if (!SplitStr(RangeArg, range, ',')) {\n LOG(LIB_FATAL) << \"Wrong format of the range argument: '\" << RangeArg << \"' Should be a list of coma-separated values.\";\n }\n }\n\n \n unique_ptr> space (SpaceFactoryRegistry::\n Instance().CreateSpace(SpaceType, *SpaceParams));\n\n ExperimentConfig config(*space,\n DataFile, QueryFile, TestSetQty,\n MaxNumData, MaxNumQuery,\n knn, eps, range);\n\n size_t cacheDataSetQty = 0;\n\tfloat index_time =0;\n\n const string& cacheGSIncompleteFlagName = CacheGSFilePrefix + \"_incomplete.flag\";\n\n if (bCacheGS) {\n const string& cacheGSControlName = CacheGSFilePrefix + \"_ctrl.txt\";\n const string& cacheGSBinaryName = CacheGSFilePrefix + \"_data.bin\";\n\n if (DoesFileExist(cacheGSIncompleteFlagName) ||\n (DoesFileExist(cacheGSControlName) != DoesFileExist(cacheGSBinaryName))\n ) {\n LOG(LIB_INFO) << \"Incomplete cache file detected! Removing incomplete entries...\";\n if (DoesFileExist(cacheGSBinaryName))\n CHECK_MSG(std::remove(cacheGSBinaryName.c_str())==0, \"Error removing the file: \" + cacheGSBinaryName);\n if (DoesFileExist(cacheGSControlName))\n CHECK_MSG(std::remove(cacheGSControlName.c_str())==0, \"Error removing the file: \" + cacheGSControlName);\n }\n\n if (DoesFileExist(cacheGSControlName)) {\n // Cache exists => reuse it\n if (!DoesFileExist(cacheGSBinaryName)) {\n throw runtime_error(\"Inconsistent cache state, there is a text control file: '\" +\n cacheGSControlName + \"' but no binary data file: '\" +\n cacheGSBinaryName + \"'\");\n }\n cacheGSControl.reset(new fstream(cacheGSControlName.c_str(),\n std::ios::in));\n cacheGSBinary.reset(new fstream(cacheGSBinaryName.c_str(),\n std::ios::in \n // On Windows you don't get a proper binary stream without ios::binary!\n | ios::binary));\n\n bReadGSCache = true;\n } else {\n if (DoesFileExist(cacheGSBinaryName)) {\n throw runtime_error(\"Inconsistent cache state, there is no text control file: '\" +\n cacheGSControlName + \"' but there is binary data file: '\" +\n cacheGSBinaryName + \"'\");\n }\n // No cache => create new file, but first mark cache as incomplete!\n ofstream flag_file(cacheGSIncompleteFlagName.c_str());\n CHECK_MSG(flag_file, \"Error creating file: \" + cacheGSIncompleteFlagName);\n flag_file.close();\n\n cacheGSControl.reset(new fstream(cacheGSControlName.c_str(),\n std::ios::trunc | std::ios::out));\n cacheGSBinary.reset(new fstream(cacheGSBinaryName.c_str(),\n std::ios::trunc | std::ios::out |\n // On Windows you don't get a proper binary stream without ios::binary!\n ios::binary));\n\n bWriteGSCache = true;\n }\n\n cacheGSControl->exceptions(std::ios::badbit);\n cacheGSBinary->exceptions(std::ios::badbit);\n\n /*\n * If the cache exists, it should be read before ReadData() is called.\n */\n if (!bWriteGSCache) {\n config.Read(*cacheGSControl, *cacheGSBinary, cacheDataSetQty);\n }\n }\n\n config.ReadDataset();\n\n if (bReadGSCache) {\n // Let's check the number of data entries, must exactly coincide with\n // what was used to create the cache!\n if (config.GetOrigDataQty() != cacheDataSetQty) {\n stringstream err;\n err << \"The number of entries in the file, or the maximum number \"\n << \"of data elements don't match the value in the cache file: \"\n << cacheDataSetQty;\n throw runtime_error(err.str());\n }\n }\n\n /*\n * Yet, if we need to create a new cache file, we must write the cache\n * after reading the data set.\n */\n if (bWriteGSCache) {\n\n config.Write(*cacheGSControl, *cacheGSBinary);\n }\n\n\n MemUsage mem_usage_measure;\n\n\n vector MemUsage;\n\n CHECK_MSG(!QueryTimeParams.empty(), \"The array of query-time parameters shouldn't be empty!\");\n\n vector> ExpResRange(config.GetRange().size(),\n vector(QueryTimeParams.size()));\n vector> ExpResKNN(config.GetKNN().size(),\n vector(QueryTimeParams.size()));\n\n GoldStandardManager managerGS(config);\n\n\n string MethodDescStr;\n\n // qtmParamId is the ID of the query time parameter set\n for (size_t qtmParamId = 0; qtmParamId < QueryTimeParams.size(); ++qtmParamId) {\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n ExpResRange[i][qtmParamId] = new MetaAnalysis(config.GetTestSetToRunQty());\n }\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n ExpResKNN[i][qtmParamId] = new MetaAnalysis(config.GetTestSetToRunQty());\n }\n }\n\n for (int TestSetId = 0; TestSetId < config.GetTestSetToRunQty(); ++TestSetId) {\n config.SelectTestSet(TestSetId);\n\n string indexLocAdd = \"\";\n\n if (QueryFile.empty() && config.GetTestSetToRunQty() > 0) {\n indexLocAdd = \"_\" + ConvertToString(TestSetId);\n }\n\n // SelectTestSet must go before managerGS.Compute()!!!\n\n if (bReadGSCache) {\n size_t cacheTestId = 0, savedThreadQty = 0;\n managerGS.Read(*cacheGSControl, *cacheGSBinary,\n config.GetTotalQueryQty(),\n cacheTestId, savedThreadQty);\n if (cacheTestId != TestSetId) {\n stringstream err;\n err << \"Perhaps, the input file is corrput (or is incompatible with \"\n << \"program parameters), expect test set id=\" << TestSetId\n << \"but obtained \" << cacheTestId;\n throw runtime_error(err.str());\n }\n CHECK_MSG(savedThreadQty == ThreadTestQty,\n \"Error: the gold standard was computed using \" +ConvertToString(savedThreadQty) + \" threads, but the current test will use \" +\n ConvertToString(ThreadTestQty) + \" threads. You have to use the same number of threads while compute gold standard data and testing!\");\n } else {\n managerGS.Compute(ThreadTestQty, maxCacheGSRelativeQty);\n if (bWriteGSCache) {\n LOG(LIB_INFO) << \"Saving the cache\";\n managerGS.Write(*cacheGSControl, *cacheGSBinary, TestSetId, ThreadTestQty);\n\n // Remove the incomplete-file flag, but only if this is the last test set \n if (TestSetId + 1 == config.GetTestSetToRunQty())\n CHECK_MSG(std::remove(cacheGSIncompleteFlagName.c_str())==0, \n \"Error removing the file: \" + cacheGSIncompleteFlagName);\n }\n }\n\n LOG(LIB_INFO) << \">>>> Test set id: \" << TestSetId << \" (set qty: \" << config.GetTestSetToRunQty() << \")\";\n\n //ReportIntrinsicDimensionality(\"Main data set\" , *config.GetSpace(), config.GetDataObjects());\n\n if (MethodName.empty()) {\n LOG(LIB_INFO) << \"No method is specified, so we will not run any tests...\";\n } else {\n unique_ptr> IndexPtr;\n\n try {\n LOG(LIB_INFO) << \">>>> Index type : \" << MethodName;\n LOG(LIB_INFO) << \">>>> Index Time Parameters: \" << IndexTimeParams->ToString();\n\n const double vmsize_before = mem_usage_measure.get_vmsize();\n\n WallClockTimer wtm;\n\n wtm.reset();\n \n IndexPtr.reset(MethodFactoryRegistry::Instance().\n CreateMethod(bPrintProgress,\n MethodName, \n SpaceType, config.GetSpace(), \n config.GetDataObjects()));\n\n string adjLoadLoc = LoadIndexLoc + indexLocAdd;\n\n bool bCreate = LoadIndexLoc.empty() || !DoesFileExist(adjLoadLoc);\n\n if (bCreate) {\n LOG(LIB_INFO) << \"Creating an index from scratch\";\n\n IndexPtr->CreateIndex(*IndexTimeParams);\n } else {\n LOG(LIB_INFO) << \"Loading an index for test set id \" << TestSetId << \" using location: \" << adjLoadLoc;\n IndexPtr->LoadIndex(adjLoadLoc);\n }\n\n if (!TestSetId) MethodDescStr = IndexPtr->StrDesc();\n\n LOG(LIB_INFO) << \"==============================================\";\n\n const double vmsize_after = mem_usage_measure.get_vmsize();\n\n wtm.split();\n\n const double IndexTime = bCreate ? double(wtm.elapsed())/1e6 : 0;\n const double LoadTime = !bCreate ? double(wtm.elapsed())/1e6 : 0;\n\n const double data_size = DataSpaceUsed(config.GetDataObjects()) / 1024.0 / 1024.0;\n const double TotalMemByMethod = vmsize_after - vmsize_before + data_size;\n double AdjustedMemByMethod = TotalMemByMethod;\n\n if (IndexPtr->DuplicateData()) AdjustedMemByMethod -= data_size;\n\n wtm.reset();\n // We won't save the index if it is already created\n string adjSaveLoc = SaveIndexLoc + indexLocAdd;\n if (!SaveIndexLoc.empty() && !DoesFileExist(adjSaveLoc)) {\n LOG(LIB_INFO) << \"Saving an index for test set id \" << TestSetId << \" using location: \" << adjSaveLoc;\n IndexPtr->SaveIndex(adjSaveLoc);\n }\n wtm.split();\n const double SaveTime = double(wtm.elapsed())/1e6;\n \n\n LOG(LIB_INFO) << \">>>> Process memory usage: \" << vmsize_after << \" MBs\";\n LOG(LIB_INFO) << \">>>> Virtual memory usage: \" << TotalMemByMethod << \" MBs\";\n LOG(LIB_INFO) << \">>>> Adjusted memory usage: \" << AdjustedMemByMethod << \" MBs\";\n LOG(LIB_INFO) << \">>>> Data size: \" << data_size << \" MBs\";\n LOG(LIB_INFO) << \">>>> Indexing time: \" << IndexTime << \" sec\";\n LOG(LIB_INFO) << \">>>> Index loading time: \" << LoadTime << \" sec\";\n LOG(LIB_INFO) << \">>>> Index saving time: \" << SaveTime << \" sec\";\n\n for (size_t qtmParamId = 0; qtmParamId < QueryTimeParams.size(); ++qtmParamId) {\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n MetaAnalysis* res = ExpResRange[i][qtmParamId];\n res->SetMem(TestSetId, AdjustedMemByMethod);\n res->SetIndexTime(TestSetId, IndexTime);\n res->SetLoadTime(TestSetId, LoadTime);\n res->SetSaveTime(TestSetId, SaveTime);\n }\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n MetaAnalysis* res = ExpResKNN[i][qtmParamId];\n res->SetMem(TestSetId, AdjustedMemByMethod);\n res->SetIndexTime(TestSetId, IndexTime);\n res->SetLoadTime(TestSetId, LoadTime);\n res->SetSaveTime(TestSetId, SaveTime);\n }\n }\n \t//report indextime\n\n\t\tstd::ofstream OutFileRep(ResFilePrefix.c_str(),std::ios::app);\n \t\tOutFileRep<< IndexTime <<\" #index_time \\n\" ;\n \t\tOutFileRep.close();\n\t\tindex_time=IndexTime;\n\n Experiments::RunAll(bPrintProgress,\n ThreadTestQty, \n TestSetId,\n managerGS,\n ExpResRange, ExpResKNN,\n config, \n *IndexPtr, \n QueryTimeParams);\n\n\n } catch (const std::exception& e) {\n LOG(LIB_ERROR) << \"Exception: \" << e.what();\n bFail = true;\n } catch (...) {\n LOG(LIB_ERROR) << \"Unknown exception\";\n bFail = true;\n }\n\n if (bFail) {\n LOG(LIB_FATAL) << \"Failure due to an exception!\";\n }\n }\n }\n\n // Don't save results, if the method wasn't specified\n if (!MethodName.empty()) {\n for (size_t MethNum = 0; MethNum < QueryTimeParams.size(); ++MethNum) {\n // Don't overwrite file after we output data at least for one method!\n bool DoAppendHere = DoAppend || MethNum;\n\n string Print, Data, Header;\n\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n MetaAnalysis* res = ExpResRange[i][MethNum];\n\n ProcessResults(config, *res, MethodDescStr,\n IndexTimeParams->ToString(), QueryTimeParams[MethNum]->ToString(), \n Print, Header, Data);\n LOG(LIB_INFO) << \"Range: \" << config.GetRange()[i];\n LOG(LIB_INFO) << Print;\n LOG(LIB_INFO) << \"Data: \" << Header << Data;\n\n if (!ResFilePrefix.empty()) {\n stringstream str;\n str << ResFilePrefix << \"_r=\" << config.GetRange()[i];\n OutData(DoAppendHere, str.str(), Print, Header, Data);\n }\n\n delete res;\n }\n\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n MetaAnalysis* res = ExpResKNN[i][MethNum];\n\n ProcessResults(config, *res, MethodDescStr,\n IndexTimeParams->ToString(), QueryTimeParams[MethNum]->ToString(), \n Print, Header, Data);\n LOG(LIB_INFO) << \"KNN: \" << config.GetKNN()[i];\n LOG(LIB_INFO) << Print;\n LOG(LIB_INFO) << \"Data: \" << Header << Data;\n\n if (!ResFilePrefix.empty()) {\n stringstream str;\n str << ResFilePrefix ;//<< \"_K=\" << config.GetKNN()[i];\n OutData(DoAppendHere, str.str(), Print, Header, Data);\n }\n\n\t\t//std::ofstream OutFileRep(ResFilePrefix.c_str(),std::ios::app);\n \t\t//OutFileRep<ToString()<<\" \"<< index_time <<\" #index_time \\n\" ;\n \t\t//OutFileRep.close();\n\t\t//cerr << index_time <<\" #index_time \\n\" ;\n delete res;\n }\n }\n }\n}\n\nint main(int ac, char* av[]) {\n // This should be the first function called before\n\n WallClockTimer timer;\n timer.reset();\n\n\n bool bPrintProgress;\n string LogFile;\n string DistType;\n string LoadIndexLoc;\n string SaveIndexLoc;\n string SpaceType;\n shared_ptr SpaceParams;\n bool DoAppend;\n string ResFilePrefix;\n unsigned TestSetQty;\n string DataFile;\n string QueryFile;\n string CacheGSFilePrefix;\n float maxCacheGSRelativeQty;\n IdTypeUnsign MaxNumData;\n IdTypeUnsign MaxNumQuery;\n vector knn;\n string RangeArg;\n float eps = 0.0;\n unsigned ThreadTestQty;\n\n shared_ptr IndexTimeParams;\n vector> QueryTimeParams;\n\n try {\n string MethodName;\n\n ParseCommandLine(ac, av, bPrintProgress,\n LogFile,\n LoadIndexLoc,\n SaveIndexLoc, \n DistType,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg,\n MethodName,\n IndexTimeParams,\n QueryTimeParams);\n\n if ((!LoadIndexLoc.empty() || !SaveIndexLoc.empty()) &&\n CacheGSFilePrefix.empty() &&\n MaxNumQuery != 0 &&\n QueryFile.empty()) {\n throw\n runtime_error(string(\"If there is i) no query file ii) # of queries > 0 iii) you ask to save/load the index,\")+\n \"then you have to specify the gold-standard cache file!\");\n }\n\n initLibrary(LogFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, LogFile.c_str());\n\n LOG(LIB_INFO) << \"Program arguments are processed\";\n\n ToLower(DistType);\n\n if (DIST_TYPE_INT == DistType) {\n RunExper(bPrintProgress,\n LoadIndexLoc,\n SaveIndexLoc,\n MethodName,\n IndexTimeParams,\n QueryTimeParams,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg\n );\n } else if (DIST_TYPE_FLOAT == DistType) {\n RunExper(bPrintProgress,\n LoadIndexLoc,\n SaveIndexLoc,\n MethodName,\n IndexTimeParams,\n QueryTimeParams,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg\n );\n } else if (DIST_TYPE_DOUBLE == DistType) {\n RunExper(bPrintProgress,\n LoadIndexLoc,\n SaveIndexLoc,\n MethodName,\n IndexTimeParams,\n QueryTimeParams,\n SpaceType,\n SpaceParams,\n ThreadTestQty,\n DoAppend, \n ResFilePrefix,\n TestSetQty,\n DataFile,\n QueryFile,\n CacheGSFilePrefix,\n maxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg\n );\n } else {\n LOG(LIB_FATAL) << \"Unknown distance value type: \" << DistType;\n }\n\n timer.split();\n LOG(LIB_INFO) << \"Time elapsed = \" << timer.elapsed() / 1e6;\n LOG(LIB_INFO) << \"Finished at \" << LibGetCurrentTime();\n } catch (const std::exception& e) {\n LOG(LIB_FATAL) << \"Exception: \" << e.what();\n } catch (...) {\n LOG(LIB_FATAL) << \"Unknown exception\";\n }\n\n return 0;\n}\n" }, { "path": "algorithms/NMSLIB/code/src/memory.cc", "content": "/**\n* Non-metric Space Library\n*\n* Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n* With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n*\n* For the complete list of contributors and further details see:\n* https://github.com/searchivarius/NonMetricSpaceLib \n*\n* Copyright (c) 2014\n*\n* This code is released under the\n* Apache License Version 2.0 http://www.apache.org/licenses/.\n*\n*/\n\n#include \"memory.h\"\n#include \"logging.h\"\n\n\n#ifdef __linux\n\n#include \n#include \n#include \n#include \n\n#endif\n\n#ifdef _MSC_VER\n\n#include \n#include \n\n#endif\n\nnamespace similarity {\n\nMemUsage::MemUsage() {\n#ifdef __linux\n int pid = getpid();\n snprintf(status_file_, sizeof(status_file_),\n \"/proc/%d/status\", pid);\n#endif\n}\n\ndouble \nMemUsage::get_vmsize() {\n#ifdef __linux\n FILE* f = fopen(status_file_, \"rt\");\n if (!f) {\n return -1.0;\n }\n char buf[100];\n int vmsize = -1024;\n while (fgets(buf, sizeof(buf), f)) {\n if (strncmp(buf, \"VmSize:\", 7) == 0) {\n sscanf(buf + 7, \"%d\", &vmsize);\n break;\n }\n }\n fclose(f);\n return vmsize / 1024.0;\n#endif\n#ifdef _MSC_VER\n PROCESS_MEMORY_COUNTERS memCounter;\n bool result = GetProcessMemoryInfo(GetCurrentProcess(),\n &memCounter,\n sizeof(memCounter));\n\n /*\n * This seems to be a decent estimates of currently used \n * RESIDENT memory size\n * see, e.g., http://social.msdn.microsoft.com/Forums/vstudio/en-US/bda6a289-42e7-41f7-a42b-6a89b623c89c/process-ram-usage-in-visual-c?forum=vcgeneral\n */\n return memCounter.WorkingSetSize / 1024.0 / 1024.0;\n#endif\n return -1.0;\n}\n\n\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/bbtree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is based on the BBTREE implementation written by Lawrence Cayton\n *\n * The algorithms were originally published in the papers:\n * 1) L. Cayton. Fast nearest neighbor retrieval for bregman divergences. \n * Twenty-Fifth International Conference on Machine Learning (ICML), 2008. \n *\n * 2) L. Cayton. Efficient bregman range search. \n * Advances in Neural Information Processing Systems 22 (NIPS), 2009. \n *\n * See https://github.com/lcayton/bbtree and http://lcayton.com/code.html\n *\n * Even though the original code was originally released \n * under the terms of the GNU General Public License,\n *\n * Lawrence Cayton (the author) allowed us to release this code under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n\n#include \"space/space_bregman.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"method/bbtree.h\"\n\nnamespace similarity {\n\nusing std::unique_ptr;\n\ntemplate \nBBTree::BBTree(\n const Space& space, \n const ObjectVector& data) : data_(data) {\n BregmanDivSpace_ = BregmanDiv::ConvertFrom(&space); // Should be the special space!\n}\n\n\ntemplate \nvoid BBTree::CreateIndex(const AnyParams& MethParams) {\n AnyParamManager pmgr(MethParams);\n\n pmgr.GetParamOptional(\"bucketSize\", BucketSize_, 50);\n pmgr.GetParamOptional(\"chunkBucket\", ChunkBucket_, true);\n\n LOG(LIB_INFO) << \"bucketSize = \" << BucketSize_;\n LOG(LIB_INFO) << \"ChunkBucket = \" << ChunkBucket_;\n\n\n pmgr.CheckUnused();\n\n root_node_.reset(new BBNode(BregmanDivSpace_, data_, BucketSize_, ChunkBucket_));\n}\n\ntemplate \nBBTree::~BBTree() {\n}\n\ntemplate \nconst std::string BBTree::StrDesc() const {\n return \"bbtree\";\n}\n\n\ntemplate \nvoid BBTree::Search(RangeQuery* query, IdType) const {\n unique_ptr query_gradient(BregmanDivSpace_->GradientFunction(query->QueryObject()));\n\n /*\n * This is a basic version of the range search that is almost identical to NN search.\n * It is possible to do better though. See for details:\n * L. Cayton. Efficient bregman range search. \n * Advances in Neural Information Processing Systems 22 (NIPS), 2009. \n */\n\n int mx = MaxLeavesToVisit_;\n root_node_->LeftSearch(BregmanDivSpace_, query_gradient.get(), query, mx);\n}\n\ntemplate \nvoid BBTree::Search(KNNQuery* query, IdType) const {\n unique_ptr query_gradient(BregmanDivSpace_->GradientFunction(query->QueryObject()));\n\n int mx = MaxLeavesToVisit_;\n root_node_->LeftSearch(BregmanDivSpace_, query_gradient.get(), query, mx);\n}\n\ntemplate \nBBTree::BBNode::BBNode(\n const BregmanDiv* div, const ObjectVector& data, size_t bucket_size, bool use_optim)\n : center_(div->Mean(data)),\n center_gradf_(div->GradientFunction(center_)),\n covering_radius_(0.0),\n is_leaf_(false),\n bucket_(NULL),\n CacheOptimizedBucket_(NULL),\n left_child_(NULL),\n right_child_(NULL) {\n dist_t dist;\n for (size_t i = 0; i < data.size(); ++i) {\n dist = div->IndexTimeDistance(data[i], center_);\n if (dist > covering_radius_) {\n covering_radius_ = dist;\n }\n }\n\n if (data.size() <= bucket_size) {\n is_leaf_ = true;\n if (use_optim) {\n CreateCacheOptimizedBucket(data, CacheOptimizedBucket_, bucket_);\n } else {\n bucket_ = new ObjectVector(data);\n }\n } else {\n ObjectVector bucket_left;\n ObjectVector bucket_right;\n int retry = 0;\n while (retry < kMaxRetry && (bucket_left.empty() || bucket_right.empty())) {\n FindSplitKMeans(div, data, bucket_left, bucket_right);\n retry++;\n }\n if (retry < kMaxRetry) {\n if (!bucket_left.empty()) {\n left_child_ = new BBNode(div, bucket_left, bucket_size, use_optim);\n }\n if (!bucket_right.empty()) {\n right_child_ = new BBNode(div, bucket_right, bucket_size, use_optim);\n }\n } else {\n is_leaf_ = true;\n if (use_optim) {\n CreateCacheOptimizedBucket(data, CacheOptimizedBucket_, bucket_);\n } else {\n bucket_ = new ObjectVector(data);\n }\n }\n }\n}\n\ntemplate \nBBTree::BBNode::~BBNode() {\n delete left_child_;\n delete right_child_;\n delete center_;\n delete center_gradf_;\n ClearBucket(CacheOptimizedBucket_, bucket_);\n}\n\ntemplate \nbool BBTree::BBNode::IsLeaf() const {\n return is_leaf_;\n}\n\ntemplate \nvoid BBTree::BBNode::SelectCenters(\n const ObjectVector& data, ObjectVector& centers) {\n std::vector center_idx(centers.size());\n for (size_t j, i = 0; i < centers.size(); ) {\n while (true) {\n int r = RandomInt() % data.size();\n for (j = 0; j < i; ++j) {\n if (center_idx[j] == r) break;\n }\n if (j == i) {\n center_idx[i++] = r;\n break;\n }\n }\n }\n\n for (size_t i = 0; i < centers.size(); ++i) {\n centers[i] = data[center_idx[i]]->Clone();\n }\n}\n\ntemplate \nvoid BBTree::BBNode::FindSplitKMeans(\n const BregmanDiv* div, const ObjectVector& data,\n ObjectVector& bucket_left, ObjectVector& bucket_right) {\n ObjectVector centers(2);\n SelectCenters(data, centers);\n\n for (int retry = 0; retry < kMaxRetry; ++retry) {\n bucket_left.clear();\n bucket_right.clear();\n\n for (size_t i = 0; i < data.size(); ++i) {\n const dist_t div_left = div->IndexTimeDistance(data[i], centers[0]);\n const dist_t div_right = div->IndexTimeDistance(data[i], centers[1]);\n if (div_left < div_right) {\n bucket_left.push_back(data[i]);\n } else {\n bucket_right.push_back(data[i]);\n }\n }\n\n for (size_t i = 0; i < centers.size(); ++i) {\n delete centers[i];\n }\n\n if (bucket_left.empty() || bucket_right.empty()) {\n SelectCenters(data, centers);\n } else {\n centers[0] = div->Mean(bucket_left);\n centers[1] = div->Mean(bucket_right);\n }\n }\n\n for (size_t i = 0; i < centers.size(); ++i) {\n delete centers[i];\n }\n}\n\ntemplate \ntemplate \nvoid BBTree::BBNode::LeftSearch(const BregmanDiv* div, \n Object* query_gradient, \n QueryType* query,\n int& MaxLeavesToVisit) const {\n if (MaxLeavesToVisit <= 0) return; // early termination\n if (IsLeaf()) {\n --MaxLeavesToVisit;\n dist_t dist;\n for (size_t j = 0; j < bucket_->size(); ++j) {\n dist = query->DistanceObjLeft((*bucket_)[j]);\n query->CheckAndAddToResult(dist, (*bucket_)[j]);\n }\n } else {\n const dist_t div_left = query->DistanceObjRight(left_child_->center_);\n const dist_t div_right = query->DistanceObjRight(right_child_->center_);\n\n if (div_left < div_right) {\n left_child_->LeftSearch(div, query_gradient, query, MaxLeavesToVisit);\n if (right_child_->NeedToSearch(div, query_gradient, query, query->Radius(), div_right)) {\n right_child_->LeftSearch(div, query_gradient, query, MaxLeavesToVisit);\n }\n } else {\n right_child_->LeftSearch(div, query_gradient, query, MaxLeavesToVisit);\n if (left_child_->NeedToSearch(div, query_gradient, query, query->Radius(), div_left)) {\n left_child_->LeftSearch(div, query_gradient, query, MaxLeavesToVisit);\n }\n }\n }\n}\n\ntemplate \ntemplate \nbool BBTree::BBNode::NeedToSearch(\n const BregmanDiv* div,\n Object* query_gradient, \n QueryType* query,\n dist_t mindist_est,\n dist_t div_query_to_center) const {\n if (div_query_to_center < covering_radius_ ||\n div_query_to_center < mindist_est) {\n return true;\n }\n return RecBinSearch(div, query_gradient, query, mindist_est);\n}\n\ntemplate \ntemplate \nbool BBTree::BBNode::RecBinSearch(\n const BregmanDiv* div,\n Object* query_gradient, \n QueryType* query, dist_t mindist_est,\n dist_t l, dist_t r, int depth) const {\n // sanity checks\n if (depth >= 1e6) { // something is wrong!\n PREPARE_RUNTIME_ERR(err) << \"Recursive depth exceeds \" << depth << std::endl;\n THROW_RUNTIME_ERR(err);\n }\n CHECK(query->QueryObject()->datalength() == center_gradf_->datalength());\n\n const dist_t *qp = reinterpret_cast(query_gradient->data());\n const dist_t *cp = reinterpret_cast(center_gradf_->data());\n\n Object* tmp = Object::CreateNewEmptyObject(query->QueryObject()->datalength());\n dist_t *tmp_value = reinterpret_cast(const_cast(tmp->data()));\n\n const size_t length = div->GetElemQty(tmp);\n\n const dist_t theta = (l + r) / 2.0;\n for (size_t i = 0; i < length; ++i) {\n tmp_value[i] = theta * qp[i] + (1.0 - theta) * cp[i];\n }\n\n Object *x = div->InverseGradientFunction(tmp);\n delete tmp;\n\n dist_t div_to_center = query->Distance(x, center_); // d(x, center)\n dist_t div_to_query = query->DistanceObjLeft(x); // d(x, query)\n delete x;\n\n dist_t lower_bound = div_to_query +\n (1.0/theta - 1.0) * (div_to_center - covering_radius_);\n\n if (lower_bound >= mindist_est) {\n return false;\n }\n\n static dist_t kCloseEnough = 1e-3;\n\n // In C++ 11, std::abs is also defined for floating-point numbers\n if (std::abs(div_to_center - covering_radius_) < covering_radius_ * kCloseEnough) {\n return true;\n }\n\n if (div_to_center > covering_radius_) {\n return RecBinSearch(div, query_gradient, query, mindist_est, l, theta, depth+1);\n } else {\n if (div_to_query < mindist_est) {\n return true;\n }\n return RecBinSearch(div, query_gradient, query, mindist_est, theta, r, depth+1);\n }\n}\n\ntemplate class BBTree;\ntemplate class BBTree;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/dummy.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"method/dummy.h\"\n\nnamespace similarity {\n\ntemplate \nvoid DummyMethod::Search(RangeQuery* query, IdType) const {\n if (bDoSeqSearch_) {\n for (size_t i = 0; i < data_.size(); ++i) {\n query->CheckAndAddToResult(data_[i]);\n }\n } else {\n for (int i =0; i < 100000; ++i);\n }\n}\n\ntemplate \nvoid DummyMethod::Search(KNNQuery* query, IdType) const {\n if (bDoSeqSearch_) {\n for (size_t i = 0; i < data_.size(); ++i) {\n query->CheckAndAddToResult(data_[i]);\n }\n } else {\n for (int i =0; i < 100000; ++i);\n }\n}\n\ntemplate \nvoid \nDummyMethod::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n // Check if a user specified extra parameters, which can be also misspelled variants of existing ones\n AnyParamManager pmgr(QueryTimeParams);\n int dummy;\n // Note that GetParamOptional() should always have a default value\n pmgr.GetParamOptional(\"dummyParam\", dummy, -1);\n LOG(LIB_INFO) << \"Set dummy = \" << dummy;\n pmgr.CheckUnused();\n}\n\n\ntemplate class DummyMethod;\ntemplate class DummyMethod;\ntemplate class DummyMethod;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/ghtree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"method/ghtree.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\ntemplate \nGHTree::GHTree(const Space& space,\n const ObjectVector& data,\n bool use_random_center) : \n space_(space), \n data_(data),\n use_random_center_(use_random_center) {\n}\n\ntemplate \nvoid GHTree::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"bucketSize\", BucketSize_, 50);\n pmgr.GetParamOptional(\"chunkBucket\", ChunkBucket_, true);\n\n LOG(LIB_INFO) << \"bucketSize = \" << BucketSize_;\n LOG(LIB_INFO) << \"chunkBucket = \" << ChunkBucket_;\n\n pmgr.CheckUnused();\n this->ResetQueryTimeParams();\n\n root_.reset(new GHNode(space_, data_,\n BucketSize_, ChunkBucket_,\n use_random_center_ /* random center */));\n}\n\ntemplate \nGHTree::~GHTree() { }\n\ntemplate \nconst std::string GHTree::StrDesc() const {\n return \"ghtree\";\n}\n\ntemplate \nvoid GHTree::Search(RangeQuery* query, IdType) const {\n int mx = MaxLeavesToVisit_;\n root_->GenericSearch(query, mx);\n}\n\ntemplate \nvoid GHTree::Search(KNNQuery* query, IdType) const {\n int mx = MaxLeavesToVisit_;\n root_->GenericSearch(query, mx);\n}\n\ntemplate \nGHTree::GHNode::GHNode(\n const Space& space, const ObjectVector& data,\n size_t bucket_size, bool chunk_bucket,\n const bool use_random_center)\n : pivot1_(NULL), pivot2_(NULL), left_child_(NULL), right_child_(NULL),\n bucket_(NULL), CacheOptimizedBucket_(NULL) {\n CHECK(!data.empty());\n\n if (data.size() <= bucket_size) {\n if (chunk_bucket) {\n CreateCacheOptimizedBucket(data, CacheOptimizedBucket_, bucket_);\n } else {\n bucket_ = new ObjectVector(data);\n }\n return;\n }\n\n int data_size = static_cast(data.size());\n int pivot1_id = use_random_center ? RandomInt() % data_size : data_size - 1;\n int pivot2_id = -1;\n pivot1_ = data[pivot1_id];\n\n if (data.size() >= 2) { // check if there exists at least 1 except pivot1\n dist_t maxd = 0;\n int id;\n for (int t = 0; t < 100; ++t) {\n id = RandomInt() % data_size;\n if (id != pivot1_id) {\n dist_t curd = space.IndexTimeDistance(pivot1_, data[id]);\n if (curd >= maxd) {\n maxd = curd;\n pivot2_id = id;\n }\n }\n }\n CHECK(pivot2_id >= 0);\n CHECK(pivot2_id != pivot1_id);\n pivot2_ = data[pivot2_id];\n }\n\n if (data.size() >= 3) { // at least 1 object except pivot1 & 2\n ObjectVector left_subset, right_subset;\n dist_t dist_to_pivot1, dist_to_pivot2;\n for (int i = 0; i < data_size; ++i) {\n if (i == pivot1_id || i == pivot2_id)\n continue;\n dist_to_pivot1 = space.IndexTimeDistance(pivot1_, data[i]);\n dist_to_pivot2 = space.IndexTimeDistance(pivot2_, data[i]);\n if (dist_to_pivot1 < dist_to_pivot2) { // close to pivot1\n left_subset.push_back(data[i]);\n } else {\n right_subset.push_back(data[i]);\n }\n }\n\n if (!left_subset.empty()) {\n left_child_ = new GHNode(space, left_subset, bucket_size, chunk_bucket, use_random_center);\n }\n\n if (!right_subset.empty()) {\n right_child_ = new GHNode(space, right_subset, bucket_size, chunk_bucket, use_random_center);\n }\n }\n}\n\ntemplate \nGHTree::GHNode::~GHNode() {\n delete left_child_;\n delete right_child_;\n ClearBucket(CacheOptimizedBucket_, bucket_);\n}\n\ntemplate \ntemplate \nvoid GHTree::GHNode::GenericSearch(QueryType* query, int& MaxLeavesToVisit) {\n if (MaxLeavesToVisit <= 0) return; // early termination\n if (bucket_) {\n --MaxLeavesToVisit;\n\n for (unsigned i = 0; i < bucket_->size(); ++i) {\n const Object* Obj = (*bucket_)[i];\n dist_t distQC = query->DistanceObjLeft(Obj);\n query->CheckAndAddToResult(distQC, Obj);\n }\n return;\n }\n\n dist_t dist_to_pivot1 = DistMax();\n dist_t dist_to_pivot2 = DistMax();\n bool exists_pivot1 = false;\n bool exists_pivot2 = false;\n if (pivot1_ != NULL) {\n exists_pivot1 = true;\n // Distance function is a symmetric metric, so it can be DistanceObjRight as well\n dist_to_pivot1 = query->DistanceObjLeft(pivot1_);\n query->CheckAndAddToResult(dist_to_pivot1, pivot1_);\n }\n if (pivot2_ != NULL) {\n exists_pivot2 = true;\n // Distance function is a symmetric metric, so it can be DistanceObjRight as well\n dist_to_pivot2 = query->DistanceObjLeft(pivot2_);\n query->CheckAndAddToResult(dist_to_pivot2, pivot2_);\n }\n if (exists_pivot1 && exists_pivot2) {\n if (dist_to_pivot1 < dist_to_pivot2) {\n // check left first\n if (left_child_ != NULL &&\n (dist_to_pivot1 - dist_to_pivot2) / 2 <= query->Radius()) {\n left_child_->GenericSearch(query, MaxLeavesToVisit);\n }\n // then right\n if (right_child_ != NULL &&\n (dist_to_pivot2 - dist_to_pivot1) / 2 <= query->Radius()) {\n right_child_->GenericSearch(query, MaxLeavesToVisit);\n }\n } else {\n // check right first\n if (right_child_ != NULL &&\n (dist_to_pivot2 - dist_to_pivot1) / 2 <= query->Radius()) {\n right_child_->GenericSearch(query, MaxLeavesToVisit);\n }\n // then left\n if (left_child_ != NULL &&\n (dist_to_pivot1 - dist_to_pivot2) / 2 <= query->Radius()) {\n left_child_->GenericSearch(query, MaxLeavesToVisit);\n }\n }\n } else {\n if (left_child_ != NULL) left_child_->GenericSearch(query, MaxLeavesToVisit);\n if (right_child_ != NULL) right_child_->GenericSearch(query, MaxLeavesToVisit);\n }\n}\n\ntemplate class GHTree;\ntemplate class GHTree;\ntemplate class GHTree;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/hnsw.cc", "content": "/**\n* Non-metric Space Library\n*\n* Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n* With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n*\n* For the complete list of contributors and further details see:\n* https://github.com/searchivarius/NonMetricSpaceLib\n*\n* Copyright (c) 2014\n*\n* This code is released under the\n* Apache License Version 2.0 http://www.apache.org/licenses/.\n*\n*/\n\n/*\n*\n* A Hierarchical Navigable Small World (HNSW) approach.\n*\n* The main publication is (available on arxiv: http://arxiv.org/abs/1603.09320):\n* \"Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs\" by Yu. A. Malkov, D. A. Yashunin\n* This code was contributed by Yu. A. Malkov. It also was used in tests from the paper.\n*\n*\n*/\n\n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/hnsw.h\"\n#include \"space/space_lp.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n#ifdef _OPENMP\n#include \n#endif\n\n#define USE_BITSET_FOR_INDEXING 1\n#define EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR (0) // 0 is faster build, 1 is faster search on clustered data\n\n#if defined(__GNUC__)\n#define PORTABLE_ALIGN16 __attribute__((aligned(16)))\n#else\n#define PORTABLE_ALIGN16 __declspec(align(16))\n#endif\n\n\nnamespace similarity {\n\n using namespace std;\n /*Functions from hnsw_distfunc_opt.cc:*/\n float L2SqrSIMDExt(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes);\n float L2SqrSIMD16Ext(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes);\n float NormScalarProductSIMD(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes);\n\n template Hnsw::Hnsw(bool PrintProgress, const Space& space, const ObjectVector& data) :\n space_(space), PrintProgress_(PrintProgress), data_(data) {}\n\n template void Hnsw::CreateIndex(const AnyParams& IndexParams)\n {\n\n AnyParamManager pmgr(IndexParams);\n\n generator = new std::default_random_engine(100);\n\n pmgr.GetParamOptional(\"M\", M_, 16);\n\n // Let's use a generic algorithm by default!\n pmgr.GetParamOptional(\"searchMethod\", searchMethod_, 0); // this is just to prevent terminating the program when searchMethod is specified\n searchMethod_ = 0;\n\n#ifdef _OPENMP\n indexThreadQty_ = omp_get_max_threads();\n#endif\n pmgr.GetParamOptional(\"indexThreadQty\", indexThreadQty_, indexThreadQty_);\n pmgr.GetParamOptional(\"efConstruction\", efConstruction_, 200);\n pmgr.GetParamOptional(\"maxM\", maxM_, M_);\n pmgr.GetParamOptional(\"maxM0\", maxM0_, M_ * 2);\n pmgr.GetParamOptional(\"mult\", mult_, 1 / log(1.0*M_));\n pmgr.GetParamOptional(\"delaunay_type\", delaunay_type_, 1);\n int skip_optimized_index = 0;\n pmgr.GetParamOptional(\"skip_optimized_index\", skip_optimized_index, 0);\n \n LOG(LIB_INFO) << \"M = \" << M_;\n LOG(LIB_INFO) << \"indexThreadQty = \" << indexThreadQty_;\n LOG(LIB_INFO) << \"efConstruction = \" << efConstruction_;\n LOG(LIB_INFO) << \"maxM\t\t\t = \" << maxM_;\n LOG(LIB_INFO) << \"maxM0\t\t\t = \" << maxM0_;\n\n LOG(LIB_INFO) << \"mult = \" << mult_;\n LOG(LIB_INFO) << \"skip_optimized_index= \" << skip_optimized_index;\n LOG(LIB_INFO) << \"delaunay_type = \" << delaunay_type_;\n \n\t\tSetQueryTimeParams(getEmptyParams());\n if (data_.empty()) {\n pmgr.CheckUnused();\n return;\n }\n\n // One entry should be added before all the threads are started, or else add() will not work properly\n HnswNode *first = new HnswNode(data_[0], 0 /* id == 0 */); \n first->init(getRandomLevel(mult_), maxM_, maxM0_); \n maxlevel_ = first->level;\n enterpoint_ = first;\n addToElementListSynchronized(first);\n\n visitedlistpool = new VisitedListPool(indexThreadQty_, data_.size());\n\n unique_ptr progress_bar(PrintProgress_ ? new ProgressDisplay(data_.size(), cerr) : NULL);\n\n//#pragma omp parallel for schedule(dynamic,128) num_threads(indexThreadQty_)\n for (int id = 1; id < data_.size(); ++id) {\n HnswNode* node = new HnswNode(data_[id], id);\n add(&space_, node); \n\n if (progress_bar) ++(*progress_bar);\n\n }\n\n\n data_level0_memory_ = NULL;\n linkLists_ = NULL;\n\t\t\n if (skip_optimized_index) {\n LOG(LIB_INFO) << \"searchMethod\t\t\t = \" << searchMethod_;\n pmgr.CheckUnused();\n return;\n }\n\n int friendsSectionSize = (maxM0_ + 1)*sizeof(int);\n //Checking for maximum size of the datasection:\n int dataSectionSize = 1;\n for (int i = 0; i < ElList_.size(); i++) {\n ElList_[i]->id_ = i;\n if (ElList_[i]->getData()->bufferlength()>dataSectionSize)\n dataSectionSize = ElList_[i]->getData()->bufferlength();\n }\n\n // Selecting custom made functions \n if (space_.StrDesc().compare(\"SpaceLp: p = 2 do we have a special implementation for this p? : 1\") == 0 && sizeof(dist_t) == 4)\n {\n LOG(LIB_INFO) << \"\\nThe space is Euclidean\";\n vectorlength_ = ((dataSectionSize - 16) >> 2);\n LOG(LIB_INFO) << \"Vector length=\" << vectorlength_;\n if (vectorlength_ % 16 == 0) {\n LOG(LIB_INFO) << \"Thus using an optimised function for base 16\";\n fstdistfunc_ = L2SqrSIMD16Ext;\n dist_func_type_ = 1;\n searchMethod_ = 3;\n }\n else {\n LOG(LIB_INFO) << \"Thus using function with any base\";\n fstdistfunc_ = L2SqrSIMDExt;\n dist_func_type_ = 2;\n searchMethod_ = 3;\n }\n }\n else if (space_.StrDesc().compare(\"CosineSimilarity\") == 0 && sizeof(dist_t) == 4)\n {\n LOG(LIB_INFO) << \"\\nThe vectorspace is Cosine Similarity\";\n vectorlength_ = ((dataSectionSize - 16) >> 2);\n LOG(LIB_INFO) << \"Vector length=\" << vectorlength_;\n iscosine_ = true;\n if (vectorlength_ % 4 == 0) {\n LOG(LIB_INFO) << \"Thus using an optimised function for base 4\";\n fstdistfunc_ = NormScalarProductSIMD;\n dist_func_type_ = 3;\n searchMethod_ = 4;\n }\n else {\n LOG(LIB_INFO) << \"Thus using function with any base\";\n LOG(LIB_INFO) << \"Search method 4 is not allowed in this case\";\n fstdistfunc_ = NormScalarProductSIMD;\n dist_func_type_ = 3;\n searchMethod_ = 3;\n }\n }\n else {\n LOG(LIB_INFO) << \"No appropriate custom distance function for \" << space_.StrDesc();\n //if (searchMethod_ != 0 && searchMethod_ != 1)\n searchMethod_ = 0;\n LOG(LIB_INFO) << \"searchMethod\t\t\t = \" << searchMethod_;\n pmgr.CheckUnused();\n return; // No optimized index\n }\n pmgr.CheckUnused();\n\t\t\n LOG(LIB_INFO) << \"searchMethod\t\t\t = \" << searchMethod_;\n memoryPerObject_ = dataSectionSize + friendsSectionSize;\n\n int total_memory_allocated = (memoryPerObject_*ElList_.size());\n data_level0_memory_ = (char*)malloc(memoryPerObject_*ElList_.size()); \n\n offsetLevel0_ = dataSectionSize;\n offsetData_ = 0;\n \n\n memset(data_level0_memory_, 1, memoryPerObject_*ElList_.size());\n LOG(LIB_INFO) << \"Making optimized index\";\n for (long i = 0; i < ElList_.size(); i++) { \n ElList_[i]->copyDataAndLevel0LinksToOptIndex(data_level0_memory_ + (size_t)i*memoryPerObject_, offsetLevel0_, offsetData_);\n };\n ////////////////////////////////////////////////////////////////////////\n //\n // The next step is needed only fos cosine similarity space\n // All vectors are normalized, so we don't have to normalize them later\n //\n ////////////////////////////////////////////////////////////////////////\n if (iscosine_)\n {\n for (long i = 0; i < ElList_.size(); i++) {\n float *v = (float *)(data_level0_memory_ + (size_t)i*memoryPerObject_ + offsetData_ + 16);\n float sum = 0;\n for (int i = 0; i < vectorlength_; i++) {\n sum += v[i] * v[i];\n } \n if (sum != 0.0) {\n sum = 1 / sqrt(sum);\n for (int i = 0; i < vectorlength_; i++) {\n v[i] *= sum;\n }\n }\n };\n } \n \n /////////////////////////////////////////////////////////\n ////////////////////////////////////////////////////////\n linkLists_ = (char**)malloc(sizeof(void*)*ElList_.size());\n for (long i = 0; i < ElList_.size(); i++) {\n if (ElList_[i]->level < 1) {\n linkLists_[i] = nullptr;\n continue;\n }\n int sizemass = ((ElList_[i]->level)*(maxM_ + 1))*sizeof(int);\n total_memory_allocated += sizemass;\n char *linkList = (char*)malloc(sizemass);\n linkLists_[i] = linkList;\n ElList_[i]->copyHigherLevelLinksToOptIndex(linkList, 0);\n };\n enterpointId_ = enterpoint_->getId();\n LOG(LIB_INFO) << \"Finished making optimized index\";\n LOG(LIB_INFO) << \"Maximum level = \" << enterpoint_->level;\n LOG(LIB_INFO) << \"Total memory allocated for optimized index+data: \" << (total_memory_allocated >> 20) << \" Mb\";\n\n }\n\n template \n void Hnsw::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n\n if (pmgr.hasParam(\"ef\") && pmgr.hasParam(\"efSearch\")) {\n throw new runtime_error(\"The user shouldn't specify parameters ef and efSearch at the same time (they are synonyms)\");\n }\n\n // ef and efSearch are going to be parameter-synonyms with the default value 20\n pmgr.GetParamOptional(\"ef\", ef_, 20);\n pmgr.GetParamOptional(\"efSearch\", ef_, ef_);\n\n int tmp;\n pmgr.GetParamOptional(\"searchMethod\", tmp, 0); // this is just to prevent terminating the program when searchMethod is specified\n\n pmgr.CheckUnused();\n LOG(LIB_INFO) << \"Set HNSW query-time parameters:\";\n LOG(LIB_INFO) << \"ef(Search) =\" << ef_;\n }\n\n template \n const std::string Hnsw::StrDesc() const {\n return METH_HNSW;\n }\n\n template \n Hnsw::~Hnsw() {\n \n delete visitedlistpool;\n if (data_level0_memory_)\n free(data_level0_memory_);\n if (linkLists_) {\n for (int i = 0; i < ElList_.size(); i++) {\n if (linkLists_[i])\n free(linkLists_[i]);\n }\n free(linkLists_);\n }\n for (int i = 0; i < ElList_.size(); i++)\n delete ElList_[i];\n }\n \n template \n void Hnsw::add(const Space* space, HnswNode *NewElement) {\n int curlevel = getRandomLevel(mult_);\n unique_lock *lock = nullptr;\n if (curlevel > maxlevel_)\n lock = new unique_lock(MaxLevelGuard_);\n\n NewElement->init(curlevel, maxM_, maxM0_); \n\n \n\n\n int maxlevelcopy = maxlevel_;\n HnswNode *ep = enterpoint_;\n if (curlevel < maxlevelcopy) {\n const Object* currObj = ep->getData();\n\n dist_t d = space->IndexTimeDistance(NewElement->getData(), currObj);\n dist_t curdist = d;\n HnswNode *curNode = ep;\n for (int level = maxlevelcopy; level > curlevel; level--) {\n bool changed = true;\n while (changed) {\n changed = false;\n unique_lock lock(curNode->accessGuard_);\n\n\n const vector& neighbor = curNode->getAllFriends(level);\n int size = neighbor.size();\n /*\n for (int i = 0; i < size; i++) {\n HnswNode *node = neighbor[i];\n _mm_prefetch((char *)(node)->getData(), _MM_HINT_T0);\n }*/\n for (int i = 0; i < size; i++)\n {\n currObj = (neighbor[i])->getData();\n d = space->IndexTimeDistance(NewElement->getData(), currObj);\n if (d < curdist) {\n curdist = d;\n curNode = neighbor[i];\n changed = true;\n }\n }\n\n }\n\n }\n ep = curNode;\n }\n\n \n for (int level = min(curlevel, maxlevelcopy); level >= 0; level--) {\n\t\t\tpriority_queue> resultSet;\n kSearchElementsWithAttemptsLevel(space, NewElement->getData(), efConstruction_, resultSet, ep, level);//DOTO: make level\n \n switch (delaunay_type_) {\n case 0:\n while (resultSet.size() > M_)\n resultSet.pop();\n break;\n case 1:\n NewElement->getNeighborsByDistanceHeuristic(resultSet, M_, space);\n break;\n case 2:\n NewElement->getNeighborsByMiniGreedySearches(resultSet, M_, space, level);\n break;\n }\n while (!resultSet.empty()) {\n link(resultSet.top().getMSWNodeHier(), NewElement, level, space, delaunay_type_);\n resultSet.pop();\n }\n\n }\n if (curlevel > enterpoint_->level)\n {\n enterpoint_ = NewElement;\n maxlevel_ = curlevel;\n }\n if (lock != nullptr)\n delete lock;\n addToElementListSynchronized(NewElement);\n }\n\n\n\n\n template \n void\n Hnsw::kSearchElementsWithAttemptsLevel(const Space* space,\n const Object* queryObj, size_t efConstruction, priority_queue>& resultSet, HnswNode* ep, int level) const\n {\n\n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n priority_queue> fullResultSet;\n#endif\n \n#if USE_BITSET_FOR_INDEXING\n VisitedList * vl = visitedlistpool->getFreeVisitedList();\n unsigned int *mass = vl->mass;\n unsigned int curV = vl->curV;\n#else\n unordered_set visited;\n#endif\n HnswNode* provider = ep;\n priority_queue > candidateSet;\n dist_t d = space->IndexTimeDistance(queryObj, provider->getData());\n HnswNodeDistFarther ev(d, provider);\n\n candidateSet.push(ev);\n resultSet.emplace(d, provider);\n \n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n fullResultSet.emplace(d, provider);\n#endif\n \n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = provider->getId();\n mass[nodeId] = curV;\n#else\n visited.insert(provider);\n#endif\n\n\n while (!candidateSet.empty()) {\n const HnswNodeDistFarther& currEv = candidateSet.top();\n dist_t lowerBound = resultSet.top().getDistance();\n\n /*\n * Check if we reached a local minimum.\n */\n if (currEv.getDistance() > lowerBound) {\n break;\n }\n HnswNode* currNode = currEv.getMSWNodeHier();\n\n /*\n * This lock protects currNode from being modified\n * while we are accessing elements of currNode.\n */\n unique_lock lock(currNode->accessGuard_);\n const vector& neighbor = currNode->getAllFriends(level);\n\n // Can't access curEv anymore! The reference would become invalid\n candidateSet.pop();\n\t \n // calculate distance to each neighbor\n /*\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n _mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n }*/\n\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = (*iter)->getId();\n if (mass[nodeId] != curV) {\n mass[nodeId] = curV;\n#else\n if (visited.find((*iter)) == visited.end()) {\n visited.insert(*iter);\n#endif\n d = space->IndexTimeDistance(queryObj, (*iter)->getData());\n HnswNodeDistFarther evE1(d, *iter);\n \n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n fullResultSet.emplace(d, *iter);\n#endif\n \n if (resultSet.size() < efConstruction || resultSet.top().getDistance() > d) {\n resultSet.emplace(d, *iter);\n candidateSet.push(evE1);\n if (resultSet.size() > efConstruction) {\n resultSet.pop();\n }\n }\n }\n }\n }\n \n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n resultSet.swap(fullResultSet);\n#endif\n \n#if USE_BITSET_FOR_INDEXING\n visitedlistpool->releaseVisitedList(vl);\n#endif\n }\n\n template \n void Hnsw::addToElementListSynchronized(HnswNode *HierElement) {\n unique_lock lock(ElListGuard_);\n ElList_.push_back(HierElement);\n }\n template \n void Hnsw::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search is not supported!\");\n }\n\n\n template \n void Hnsw::Search(KNNQuery* query, IdType) const {\n\t\t\n switch (searchMethod_) {\n default:\n throw runtime_error(\"Invalid searchMethod: \" + ConvertToString(searchMethod_));\n break;\n \t\tcase 0:\n /// Basic search using Nmslib data structure:\n const_cast(this)->baseSearchAlgorithm(query);\n \t\t\tbreak;\n \t\tcase 1:\n /// Experimental search using Nmslib data structure (should not be used):\n const_cast(this)->listPassingModifiedAlgorithm(query);\n \t\t\tbreak;\n \t\tcase 3:\n /// Basic search using optimized index(cosine+L2)\n const_cast(this)->SearchL2Custom(query);\n \t\t\tbreak;\n \t\tcase 4:\n /// Basic search using optimized index with one-time normalized cosine similarity\n /// Only for cosine similarity!\n const_cast(this)->SearchCosineNormalized(query);\n \t\t\tbreak;\n \t\t};\n }\n\n template \n void Hnsw::SaveIndex(const string &location) {\n if (!data_level0_memory_)\n throw runtime_error(\"Storing non-optimized index is not supported yet!\");\n\n std::ofstream output(location, std::ios::binary);\n streampos position;\n totalElementsStored_ = ElList_.size(); \n output.write((char*)&totalElementsStored_, sizeof(size_t));\n output.write((char*)&memoryPerObject_, sizeof(size_t));\n output.write((char*)&offsetLevel0_, sizeof(size_t));\n output.write((char*)&offsetData_, sizeof(size_t));\n output.write((char*)&maxlevel_, sizeof(size_t));\n output.write((char*)&enterpointId_, sizeof(size_t));\n output.write((char*)&maxM_, sizeof(size_t));\n output.write((char*)&maxM0_, sizeof(size_t));\n output.write((char*)&dist_func_type_, sizeof(size_t)); \n output.write((char*)&searchMethod_, sizeof(searchMethod_));\n \n size_t data_plus_links0_size = memoryPerObject_*totalElementsStored_;\n LOG(LIB_INFO) << \"writing \" << data_plus_links0_size << \" bytes\";\n output.write(data_level0_memory_, data_plus_links0_size);\n \n //output.write(data_level0_memory_, memoryPerObject_*totalElementsStored_);\n\n //size_t total_memory_allocated = 0;\n\n for (size_t i = 0; i < totalElementsStored_; i++) { \n unsigned int sizemass = ((ElList_[i]->level)*(maxM_ + 1))*sizeof(int); \n output.write((char*)&sizemass, sizeof(unsigned int));\n if((sizemass))\n output.write(linkLists_[i], sizemass);\n }; \n output.close();\n }\n\n template \n void Hnsw::LoadIndex(const string &location) {\n LOG(LIB_INFO) << \"Loading index from \"<\n \tvoid Hnsw::baseSearchAlgorithm(KNNQuery* query) {\n\t\t\tint checks =0 ;\n \t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n \t\tunsigned int *massVisited = vl->mass;\n \t\tunsigned int currentV = vl->curV;\n\n \t\tHnswNode* provider;\n \t\tint maxlevel1 = enterpoint_->level;\n \t\tprovider = enterpoint_;\n \n \t\tconst Object* currObj = provider->getData();\n \n \t\tdist_t d = query->DistanceObjLeft(currObj);\n\t\t\tchecks ++ ;\n \t\tdist_t curdist = d;\n \t\tHnswNode *curNode = provider;\n \t\tfor (int i = maxlevel1; i > 0; i--) {\n \t\t\tbool changed = true;\n \t\t\twhile (changed) {\n \t\t\t\tchanged = false;\n \n \t\t\t\tconst vector& neighbor = curNode->getAllFriends(i);\n \t\t\t\t/*\n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t}*/\n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\td = query->DistanceObjLeft(currObj);\n\t\t\t\t\t\tchecks++;\n \t\t\t\t\tif (d < curdist) {\n \t\t\t\t\t\tcurdist = d;\n \t\t\t\t\t\tcurNode = *iter;\n \t\t\t\t\t\tchanged = true;\n \t\t\t\t\t}\n \t\t\t\t}\n \t\t\t}\n \t\t}\n \t\n \n \t\tpriority_queue > candidateQueue; //the set of elements which we can use to evaluate \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \n \t\tpriority_queue > closestDistQueue1; //The set of closest found elements \n \n \t\tHnswNodeDistFarther ev(curdist, curNode);\n \t\tcandidateQueue.emplace(curdist, curNode);\n \t\tclosestDistQueue1.emplace(curdist, curNode);\n \n \t\tquery->CheckAndAddToResult(curdist, curNode->getData());\n \t\tmassVisited[curNode->getId()] = currentV;\n \t\t//visitedQueue.insert(curNode->getId());\n \n \t\t////////////////////////////////////////////////////////////////////////////////\n \t\t// PHASE TWO OF THE SEARCH\n \t\t// Extraction of the neighborhood to find k nearest neighbors.\n \t\t////////////////////////////////////////////////////////////////////////////////\n \t\t\n \t\twhile (!candidateQueue.empty()) {\n \t\t\t\n \t\t\tauto iter = candidateQueue.top(); // This one was already compared to the query\n \t\t\tconst HnswNodeDistFarther& currEv = iter;\n \t\t\t//Check condtion to end the search\n \t\t\tdist_t lowerBound = closestDistQueue1.top().getDistance();\n \t\t\tif (currEv.getDistance() > lowerBound) {\n \t\t\t\tbreak;\n \t\t\t}\n \n \t\t\tHnswNode *initNode = currEv.getMSWNodeHier();\n \t\t\tcandidateQueue.pop();\n \n \t\t\tconst vector& neighbor = (initNode)->getAllFriends(0);\n \n \t\t\tsize_t curId;\n \t\t\t/*\n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t_mm_prefetch((char *)(massVisited + (*iter)->getId()), _MM_HINT_T0);\n \t\t\t}*/\n \t\t\t//calculate distance to each neighbor\n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \n \t\t\t\tcurId = (*iter)->getId();\n \t\t\t\t\n \t\t\t\tif (!(massVisited[curId] == currentV))\n \t\t\t\t{\n \t\t\t\t\tmassVisited[curId] = currentV;\n \t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\td = query->DistanceObjLeft(currObj);\n\t\t\t\t\t\tchecks ++ ;\n \t\t\t\t\tif (closestDistQueue1.top().getDistance() > d || closestDistQueue1.size() < ef_) {\n \t\t\t\t\t\t{\n \t\t\t\t\t\t\tquery->CheckAndAddToResult(d, currObj); \n \t\t\t\t\t\t\tcandidateQueue.emplace(d, *iter);\n \t\t\t\t\t\t\tclosestDistQueue1.emplace(d, *iter);\n \t\t\t\t\t\t\tif (closestDistQueue1.size() > ef_) {\n \t\t\t\t\t\t\t\tclosestDistQueue1.pop();\n \t\t\t\t\t\t\t}\n \t\t\t\t\t\t}\n \t\t\t\t\t}\n \t\t\t\t}\n \t\t\t}\n \n \t\t}\n \t\tvisitedlistpool->releaseVisitedList(vl);\n\t\t\t//cout << checks << endl;\n \t}\n // Experimental search algorithm\n \ttemplate \n \tvoid Hnsw::listPassingModifiedAlgorithm(KNNQuery* query) {\n\n \t\tint efSearchL = 4; // This parameters defines the confidence of searches at level higher than zero \n // for zero level it is set to ef\n //Getting the visitedlist\n \t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n \t\tunsigned int *massVisited = vl->mass;\n \t\tunsigned int currentV = vl->curV;\n \n \t\tint maxlevel1 = enterpoint_->level; \n \n \t\tconst Object* currObj = enterpoint_->getData();\n \n \t\tdist_t d = query->DistanceObjLeft(currObj);\n \t\tdist_t curdist = d;\n \t\tHnswNode *curNode = enterpoint_;\n \n \n \t\tpriority_queue > candidateQueue; //the set of elements which we can use to evaluate \t\t\t\t\t\t\t\t\t\t\t\t\t\t\n \t\tpriority_queue > closestDistQueue= priority_queue >(); //The set of closest found elements \n \t\tpriority_queue > closestDistQueueCpy = priority_queue >();\n\n \t\tHnswNodeDistFarther ev(curdist, curNode);\n \t\tcandidateQueue.emplace(curdist, curNode);\n \t\tclosestDistQueue.emplace(curdist, curNode);\n\n \t\tmassVisited[curNode->getId()] = currentV;\n\n \t\tfor (int i = maxlevel1; i > 0; i--) {\n \t\t\t\n \t\t\twhile (!candidateQueue.empty()) {\n \t\n \t\t\t\tauto iter = candidateQueue.top(); \n \t\t\t\tconst HnswNodeDistFarther& currEv = iter;\n \t\t\t\t//Check condtion to end the search\n \t\t\t\tdist_t lowerBound = closestDistQueue.top().getDistance();\n \t\t\t\tif (currEv.getDistance() > lowerBound) {\n \t\t\t\t\tbreak;\n \t\t\t\t}\n \n \t\t\t\tHnswNode *initNode = currEv.getMSWNodeHier();\n \t\t\t\tcandidateQueue.pop();\n \n \t\t\t\tconst vector& neighbor = (initNode)->getAllFriends(i);\n \n \t\t\t\tsize_t curId;\n \t\t\t\t/*\n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t\t_mm_prefetch((char *)(massVisited + (*iter)->getId()), _MM_HINT_T0);\n \t\t\t\t}*/\n \t\t\t\t//calculate distance to each neighbor\n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) { \n \t\t\t\t\tcurId = (*iter)->getId();\n \t\t\t\t\tif (!(massVisited[curId] == currentV))\n \t\t\t\t\t{\n \t\t\t\t\t\tmassVisited[curId] = currentV;\n \t\t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\t\td = query->DistanceObjLeft(currObj);\n if (closestDistQueue.top().getDistance() > d || closestDistQueue.size() < efSearchL) {\n candidateQueue.emplace(d, *iter);\n closestDistQueue.emplace(d, *iter);\n if (closestDistQueue.size() > efSearchL) {\n closestDistQueue.pop();\n }\n }\n \t\t\t\t\t}\n \t\t\t\t}\n \n \t\t\t}\n //Updating the bitset key:\n currentV++;\n vl->curV++;// not to forget updating in the pool\n \t\t\tif (currentV == 0) {\n \t\t\t\tmemset(massVisited, 0, ElList_.size()*sizeof(int));\n \t\t\t\tcurrentV++;\n vl->curV++;// not to forget updating in the pool\n \t\t\t}\n candidateQueue = priority_queue >();\n closestDistQueueCpy = priority_queue >(closestDistQueue);\n \t\t\tif (i > 1) { // Passing the closest neighbors to layers higher than zero:\n while (closestDistQueueCpy.size() > 0) {\n massVisited[closestDistQueueCpy.top().getMSWNodeHier()->getId()] = currentV;\n candidateQueue.emplace(closestDistQueueCpy.top().getDistance(), closestDistQueueCpy.top().getMSWNodeHier()); \n closestDistQueueCpy.pop();\n } \t\t\t\t\n \t\t\t}\n else { // Passing the closest neighbors to the 0 zero layer(one has to add also to query): \n while (closestDistQueueCpy.size() > 0) {\n massVisited[closestDistQueueCpy.top().getMSWNodeHier()->getId()] = currentV;\n candidateQueue.emplace(closestDistQueueCpy.top().getDistance(), closestDistQueueCpy.top().getMSWNodeHier());\n query->CheckAndAddToResult(closestDistQueueCpy.top().getDistance(), closestDistQueueCpy.top().getMSWNodeHier()->getData());\n closestDistQueueCpy.pop();\n }\n }\n \t\t}\n \t\t\n \t\t////////////////////////////////////////////////////////////////////////////////\n \t\t// PHASE TWO OF THE SEARCH\n \t\t// Extraction of the neighborhood to find k nearest neighbors.\n \t\t////////////////////////////////////////////////////////////////////////////////\n \n \n \t\twhile (!candidateQueue.empty()) {\n \n \t\t\tauto iter = candidateQueue.top();\n \t\t\tconst HnswNodeDistFarther& currEv = iter;\n \t\t\t//Check condtion to end the search\n \t\t\tdist_t lowerBound = closestDistQueue.top().getDistance();\n \t\t\tif (currEv.getDistance() > lowerBound) {\n \t\t\t\tbreak;\n \t\t\t}\n \n \t\t\tHnswNode *initNode = currEv.getMSWNodeHier();\n \t\t\tcandidateQueue.pop();\n \n \t\t\tconst vector& neighbor = (initNode)->getAllFriends(0);\n \n \t\t\tsize_t curId;\n \t\t\t/*\n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t_mm_prefetch((char *)(massVisited + (*iter)->getId()), _MM_HINT_T0);\n \t\t\t}*/\n \t\t\t//calculate distance to each neighbor\n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \n \t\t\t\tcurId = (*iter)->getId();\n \t\t\t\tif (!(massVisited[curId] == currentV))\n \t\t\t\t{\n \t\t\t\t\tmassVisited[curId] = currentV;\n \t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\td = query->DistanceObjLeft(currObj);\n \t\t\t\t\tif (closestDistQueue.top().getDistance() > d || closestDistQueue.size() < ef_) {\n \t\t\t\t\t\t{\n \t\t\t\t\t\t\tquery->CheckAndAddToResult(d, currObj);\n \t\t\t\t\t\t\tcandidateQueue.emplace(d, *iter);\n \t\t\t\t\t\t\tclosestDistQueue.emplace(d, *iter);\n \t\t\t\t\t\t\tif (closestDistQueue.size() > ef_) {\n \t\t\t\t\t\t\t\tclosestDistQueue.pop();\n \t\t\t\t\t\t\t}\n \t\t\t\t\t\t}\n \t\t\t\t\t}\n \t\t\t\t}\n \t\t\t}\n \n \t\t}\n \t\tvisitedlistpool->releaseVisitedList(vl);\n \n \t}\n\n\n template class Hnsw;\n template class Hnsw;\n template class Hnsw;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/hnsw.cc~", "content": "/**\n* Non-metric Space Library\n*\n* Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n* With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n*\n* For the complete list of contributors and further details see:\n* https://github.com/searchivarius/NonMetricSpaceLib\n*\n* Copyright (c) 2014\n*\n* This code is released under the\n* Apache License Version 2.0 http://www.apache.org/licenses/.\n*\n*/\n\n/*\n*\n* A Hierarchical Navigable Small World (HNSW) approach.\n*\n* The main publication is (available on arxiv: http://arxiv.org/abs/1603.09320):\n* \"Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs\" by Yu. A. Malkov, D. A. Yashunin\n* This code was contributed by Yu. A. Malkov. It also was used in tests from the paper.\n*\n*\n*/\n\n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/hnsw.h\"\n#include \"space/space_lp.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n#ifdef _OPENMP\n#include \n#endif\n\n#define USE_BITSET_FOR_INDEXING 1\n#define EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR (0) // 0 is faster build, 1 is faster search on clustered data\n\n#if defined(__GNUC__)\n#define PORTABLE_ALIGN16 __attribute__((aligned(16)))\n#else\n#define PORTABLE_ALIGN16 __declspec(align(16))\n#endif\n\n\nnamespace similarity {\n\n using namespace std;\n /*Functions from hnsw_distfunc_opt.cc:*/\n float L2SqrSIMDExt(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes);\n float L2SqrSIMD16Ext(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes);\n float NormScalarProductSIMD(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes);\n\n template Hnsw::Hnsw(bool PrintProgress, const Space& space, const ObjectVector& data) :\n space_(space), PrintProgress_(PrintProgress), data_(data) {}\n\n template void Hnsw::CreateIndex(const AnyParams& IndexParams)\n {\n\n AnyParamManager pmgr(IndexParams);\n\n generator = new std::default_random_engine(100);\n\n pmgr.GetParamOptional(\"M\", M_, 16);\n\n // Let's use a generic algorithm by default!\n pmgr.GetParamOptional(\"searchMethod\", searchMethod_, 0); // this is just to prevent terminating the program when searchMethod is specified\n searchMethod_ = 0;\n\n#ifdef _OPENMP\n indexThreadQty_ = omp_get_max_threads();\n#endif\n pmgr.GetParamOptional(\"indexThreadQty\", indexThreadQty_, indexThreadQty_);\n pmgr.GetParamOptional(\"efConstruction\", efConstruction_, 200);\n pmgr.GetParamOptional(\"maxM\", maxM_, M_);\n pmgr.GetParamOptional(\"maxM0\", maxM0_, M_ * 2);\n pmgr.GetParamOptional(\"mult\", mult_, 1 / log(1.0*M_));\n pmgr.GetParamOptional(\"delaunay_type\", delaunay_type_, 1);\n int skip_optimized_index = 0;\n pmgr.GetParamOptional(\"skip_optimized_index\", skip_optimized_index, 0);\n \n LOG(LIB_INFO) << \"M = \" << M_;\n LOG(LIB_INFO) << \"indexThreadQty = \" << indexThreadQty_;\n LOG(LIB_INFO) << \"efConstruction = \" << efConstruction_;\n LOG(LIB_INFO) << \"maxM\t\t\t = \" << maxM_;\n LOG(LIB_INFO) << \"maxM0\t\t\t = \" << maxM0_;\n\n LOG(LIB_INFO) << \"mult = \" << mult_;\n LOG(LIB_INFO) << \"skip_optimized_index= \" << skip_optimized_index;\n LOG(LIB_INFO) << \"delaunay_type = \" << delaunay_type_;\n \n\t\tSetQueryTimeParams(getEmptyParams());\n if (data_.empty()) {\n pmgr.CheckUnused();\n return;\n }\n\n // One entry should be added before all the threads are started, or else add() will not work properly\n HnswNode *first = new HnswNode(data_[0], 0 /* id == 0 */); \n first->init(getRandomLevel(mult_), maxM_, maxM0_); \n maxlevel_ = first->level;\n enterpoint_ = first;\n addToElementListSynchronized(first);\n\n visitedlistpool = new VisitedListPool(indexThreadQty_, data_.size());\n\n unique_ptr progress_bar(PrintProgress_ ? new ProgressDisplay(data_.size(), cerr) : NULL);\n\n//#pragma omp parallel for schedule(dynamic,128) num_threads(indexThreadQty_)\n for (int id = 1; id < data_.size(); ++id) {\n HnswNode* node = new HnswNode(data_[id], id);\n add(&space_, node); \n\n if (progress_bar) ++(*progress_bar);\n\n }\n\n\n data_level0_memory_ = NULL;\n linkLists_ = NULL;\n\t\t\n if (skip_optimized_index) {\n LOG(LIB_INFO) << \"searchMethod\t\t\t = \" << searchMethod_;\n pmgr.CheckUnused();\n return;\n }\n\n int friendsSectionSize = (maxM0_ + 1)*sizeof(int);\n //Checking for maximum size of the datasection:\n int dataSectionSize = 1;\n for (int i = 0; i < ElList_.size(); i++) {\n ElList_[i]->id_ = i;\n if (ElList_[i]->getData()->bufferlength()>dataSectionSize)\n dataSectionSize = ElList_[i]->getData()->bufferlength();\n }\n\n // Selecting custom made functions \n if (space_.StrDesc().compare(\"SpaceLp: p = 2 do we have a special implementation for this p? : 1\") == 0 && sizeof(dist_t) == 4)\n {\n LOG(LIB_INFO) << \"\\nThe space is Euclidean\";\n vectorlength_ = ((dataSectionSize - 16) >> 2);\n LOG(LIB_INFO) << \"Vector length=\" << vectorlength_;\n if (vectorlength_ % 16 == 0) {\n LOG(LIB_INFO) << \"Thus using an optimised function for base 16\";\n fstdistfunc_ = L2SqrSIMD16Ext;\n dist_func_type_ = 1;\n searchMethod_ = 3;\n }\n else {\n LOG(LIB_INFO) << \"Thus using function with any base\";\n fstdistfunc_ = L2SqrSIMDExt;\n dist_func_type_ = 2;\n searchMethod_ = 3;\n }\n }\n else if (space_.StrDesc().compare(\"CosineSimilarity\") == 0 && sizeof(dist_t) == 4)\n {\n LOG(LIB_INFO) << \"\\nThe vectorspace is Cosine Similarity\";\n vectorlength_ = ((dataSectionSize - 16) >> 2);\n LOG(LIB_INFO) << \"Vector length=\" << vectorlength_;\n iscosine_ = true;\n if (vectorlength_ % 4 == 0) {\n LOG(LIB_INFO) << \"Thus using an optimised function for base 4\";\n fstdistfunc_ = NormScalarProductSIMD;\n dist_func_type_ = 3;\n searchMethod_ = 4;\n }\n else {\n LOG(LIB_INFO) << \"Thus using function with any base\";\n LOG(LIB_INFO) << \"Search method 4 is not allowed in this case\";\n fstdistfunc_ = NormScalarProductSIMD;\n dist_func_type_ = 3;\n searchMethod_ = 3;\n }\n }\n else {\n LOG(LIB_INFO) << \"No appropriate custom distance function for \" << space_.StrDesc();\n //if (searchMethod_ != 0 && searchMethod_ != 1)\n searchMethod_ = 0;\n LOG(LIB_INFO) << \"searchMethod\t\t\t = \" << searchMethod_;\n pmgr.CheckUnused();\n return; // No optimized index\n //}\n pmgr.CheckUnused();\n\t\t\n LOG(LIB_INFO) << \"searchMethod\t\t\t = \" << searchMethod_;\n memoryPerObject_ = dataSectionSize + friendsSectionSize;\n\n int total_memory_allocated = (memoryPerObject_*ElList_.size());\n data_level0_memory_ = (char*)malloc(memoryPerObject_*ElList_.size()); \n\n offsetLevel0_ = dataSectionSize;\n offsetData_ = 0;\n \n\n memset(data_level0_memory_, 1, memoryPerObject_*ElList_.size());\n LOG(LIB_INFO) << \"Making optimized index\";\n for (long i = 0; i < ElList_.size(); i++) { \n ElList_[i]->copyDataAndLevel0LinksToOptIndex(data_level0_memory_ + (size_t)i*memoryPerObject_, offsetLevel0_, offsetData_);\n };\n ////////////////////////////////////////////////////////////////////////\n //\n // The next step is needed only fos cosine similarity space\n // All vectors are normalized, so we don't have to normalize them later\n //\n ////////////////////////////////////////////////////////////////////////\n if (iscosine_)\n {\n for (long i = 0; i < ElList_.size(); i++) {\n float *v = (float *)(data_level0_memory_ + (size_t)i*memoryPerObject_ + offsetData_ + 16);\n float sum = 0;\n for (int i = 0; i < vectorlength_; i++) {\n sum += v[i] * v[i];\n } \n if (sum != 0.0) {\n sum = 1 / sqrt(sum);\n for (int i = 0; i < vectorlength_; i++) {\n v[i] *= sum;\n }\n }\n };\n } \n \n /////////////////////////////////////////////////////////\n ////////////////////////////////////////////////////////\n linkLists_ = (char**)malloc(sizeof(void*)*ElList_.size());\n for (long i = 0; i < ElList_.size(); i++) {\n if (ElList_[i]->level < 1) {\n linkLists_[i] = nullptr;\n continue;\n }\n int sizemass = ((ElList_[i]->level)*(maxM_ + 1))*sizeof(int);\n total_memory_allocated += sizemass;\n char *linkList = (char*)malloc(sizemass);\n linkLists_[i] = linkList;\n ElList_[i]->copyHigherLevelLinksToOptIndex(linkList, 0);\n };\n enterpointId_ = enterpoint_->getId();\n LOG(LIB_INFO) << \"Finished making optimized index\";\n LOG(LIB_INFO) << \"Maximum level = \" << enterpoint_->level;\n LOG(LIB_INFO) << \"Total memory allocated for optimized index+data: \" << (total_memory_allocated >> 20) << \" Mb\";\n\n }\n\n template \n void Hnsw::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n\n if (pmgr.hasParam(\"ef\") && pmgr.hasParam(\"efSearch\")) {\n throw new runtime_error(\"The user shouldn't specify parameters ef and efSearch at the same time (they are synonyms)\");\n }\n\n // ef and efSearch are going to be parameter-synonyms with the default value 20\n pmgr.GetParamOptional(\"ef\", ef_, 20);\n pmgr.GetParamOptional(\"efSearch\", ef_, ef_);\n\n int tmp;\n pmgr.GetParamOptional(\"searchMethod\", tmp, 0); // this is just to prevent terminating the program when searchMethod is specified\n\n pmgr.CheckUnused();\n LOG(LIB_INFO) << \"Set HNSW query-time parameters:\";\n LOG(LIB_INFO) << \"ef(Search) =\" << ef_;\n }\n\n template \n const std::string Hnsw::StrDesc() const {\n return METH_HNSW;\n }\n\n template \n Hnsw::~Hnsw() {\n \n delete visitedlistpool;\n if (data_level0_memory_)\n free(data_level0_memory_);\n if (linkLists_) {\n for (int i = 0; i < ElList_.size(); i++) {\n if (linkLists_[i])\n free(linkLists_[i]);\n }\n free(linkLists_);\n }\n for (int i = 0; i < ElList_.size(); i++)\n delete ElList_[i];\n }\n \n template \n void Hnsw::add(const Space* space, HnswNode *NewElement) {\n int curlevel = getRandomLevel(mult_);\n unique_lock *lock = nullptr;\n if (curlevel > maxlevel_)\n lock = new unique_lock(MaxLevelGuard_);\n\n NewElement->init(curlevel, maxM_, maxM0_); \n\n \n\n\n int maxlevelcopy = maxlevel_;\n HnswNode *ep = enterpoint_;\n if (curlevel < maxlevelcopy) {\n const Object* currObj = ep->getData();\n\n dist_t d = space->IndexTimeDistance(NewElement->getData(), currObj);\n dist_t curdist = d;\n HnswNode *curNode = ep;\n for (int level = maxlevelcopy; level > curlevel; level--) {\n bool changed = true;\n while (changed) {\n changed = false;\n unique_lock lock(curNode->accessGuard_);\n\n\n const vector& neighbor = curNode->getAllFriends(level);\n int size = neighbor.size();\n for (int i = 0; i < size; i++) {\n HnswNode *node = neighbor[i];\n _mm_prefetch((char *)(node)->getData(), _MM_HINT_T0);\n }\n for (int i = 0; i < size; i++)\n {\n currObj = (neighbor[i])->getData();\n d = space->IndexTimeDistance(NewElement->getData(), currObj);\n if (d < curdist) {\n curdist = d;\n curNode = neighbor[i];\n changed = true;\n }\n }\n\n }\n\n }\n ep = curNode;\n }\n\n \n for (int level = min(curlevel, maxlevelcopy); level >= 0; level--) {\n\t\t\tpriority_queue> resultSet;\n kSearchElementsWithAttemptsLevel(space, NewElement->getData(), efConstruction_, resultSet, ep, level);//DOTO: make level\n \n switch (delaunay_type_) {\n case 0:\n while (resultSet.size() > M_)\n resultSet.pop();\n break;\n case 1:\n NewElement->getNeighborsByDistanceHeuristic(resultSet, M_, space);\n break;\n case 2:\n NewElement->getNeighborsByMiniGreedySearches(resultSet, M_, space, level);\n break;\n }\n while (!resultSet.empty()) {\n link(resultSet.top().getMSWNodeHier(), NewElement, level, space, delaunay_type_);\n resultSet.pop();\n }\n\n }\n if (curlevel > enterpoint_->level)\n {\n enterpoint_ = NewElement;\n maxlevel_ = curlevel;\n }\n if (lock != nullptr)\n delete lock;\n addToElementListSynchronized(NewElement);\n }\n\n\n\n\n template \n void\n Hnsw::kSearchElementsWithAttemptsLevel(const Space* space,\n const Object* queryObj, size_t efConstruction, priority_queue>& resultSet, HnswNode* ep, int level) const\n {\n\n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n priority_queue> fullResultSet;\n#endif\n \n#if USE_BITSET_FOR_INDEXING\n VisitedList * vl = visitedlistpool->getFreeVisitedList();\n unsigned int *mass = vl->mass;\n unsigned int curV = vl->curV;\n#else\n unordered_set visited;\n#endif\n HnswNode* provider = ep;\n priority_queue > candidateSet;\n dist_t d = space->IndexTimeDistance(queryObj, provider->getData());\n HnswNodeDistFarther ev(d, provider);\n\n candidateSet.push(ev);\n resultSet.emplace(d, provider);\n \n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n fullResultSet.emplace(d, provider);\n#endif\n \n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = provider->getId();\n mass[nodeId] = curV;\n#else\n visited.insert(provider);\n#endif\n\n\n while (!candidateSet.empty()) {\n const HnswNodeDistFarther& currEv = candidateSet.top();\n dist_t lowerBound = resultSet.top().getDistance();\n\n /*\n * Check if we reached a local minimum.\n */\n if (currEv.getDistance() > lowerBound) {\n break;\n }\n HnswNode* currNode = currEv.getMSWNodeHier();\n\n /*\n * This lock protects currNode from being modified\n * while we are accessing elements of currNode.\n */\n unique_lock lock(currNode->accessGuard_);\n const vector& neighbor = currNode->getAllFriends(level);\n\n // Can't access curEv anymore! The reference would become invalid\n candidateSet.pop();\n\n // calculate distance to each neighbor\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n _mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n }\n\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = (*iter)->getId();\n if (mass[nodeId] != curV) {\n mass[nodeId] = curV;\n#else\n if (visited.find((*iter)) == visited.end()) {\n visited.insert(*iter);\n#endif\n d = space->IndexTimeDistance(queryObj, (*iter)->getData());\n HnswNodeDistFarther evE1(d, *iter);\n \n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n fullResultSet.emplace(d, *iter);\n#endif\n \n if (resultSet.size() < efConstruction || resultSet.top().getDistance() > d) {\n resultSet.emplace(d, *iter);\n candidateSet.push(evE1);\n if (resultSet.size() > efConstruction) {\n resultSet.pop();\n }\n }\n }\n }\n }\n \n#if EXTEND_USE_EXTENDED_NEIGHB_AT_CONSTR!=0\n resultSet.swap(fullResultSet);\n#endif\n \n#if USE_BITSET_FOR_INDEXING\n visitedlistpool->releaseVisitedList(vl);\n#endif\n }\n\n template \n void Hnsw::addToElementListSynchronized(HnswNode *HierElement) {\n unique_lock lock(ElListGuard_);\n ElList_.push_back(HierElement);\n }\n template \n void Hnsw::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search is not supported!\");\n }\n\n\n template \n void Hnsw::Search(KNNQuery* query, IdType) const {\n\t\t\n switch (searchMethod_) {\n default:\n throw runtime_error(\"Invalid searchMethod: \" + ConvertToString(searchMethod_));\n break;\n \t\tcase 0:\n /// Basic search using Nmslib data structure:\n const_cast(this)->baseSearchAlgorithm(query);\n \t\t\tbreak;\n \t\tcase 1:\n /// Experimental search using Nmslib data structure (should not be used):\n const_cast(this)->listPassingModifiedAlgorithm(query);\n \t\t\tbreak;\n \t\tcase 3:\n /// Basic search using optimized index(cosine+L2)\n const_cast(this)->SearchL2Custom(query);\n \t\t\tbreak;\n \t\tcase 4:\n /// Basic search using optimized index with one-time normalized cosine similarity\n /// Only for cosine similarity!\n const_cast(this)->SearchCosineNormalized(query);\n \t\t\tbreak;\n \t\t};\n }\n\n template \n void Hnsw::SaveIndex(const string &location) {\n if (!data_level0_memory_)\n throw runtime_error(\"Storing non-optimized index is not supported yet!\");\n\n std::ofstream output(location, std::ios::binary);\n streampos position;\n totalElementsStored_ = ElList_.size(); \n output.write((char*)&totalElementsStored_, sizeof(size_t));\n output.write((char*)&memoryPerObject_, sizeof(size_t));\n output.write((char*)&offsetLevel0_, sizeof(size_t));\n output.write((char*)&offsetData_, sizeof(size_t));\n output.write((char*)&maxlevel_, sizeof(size_t));\n output.write((char*)&enterpointId_, sizeof(size_t));\n output.write((char*)&maxM_, sizeof(size_t));\n output.write((char*)&maxM0_, sizeof(size_t));\n output.write((char*)&dist_func_type_, sizeof(size_t)); \n output.write((char*)&searchMethod_, sizeof(searchMethod_));\n \n size_t data_plus_links0_size = memoryPerObject_*totalElementsStored_;\n LOG(LIB_INFO) << \"writing \" << data_plus_links0_size << \" bytes\";\n output.write(data_level0_memory_, data_plus_links0_size);\n \n //output.write(data_level0_memory_, memoryPerObject_*totalElementsStored_);\n\n //size_t total_memory_allocated = 0;\n\n for (size_t i = 0; i < totalElementsStored_; i++) { \n unsigned int sizemass = ((ElList_[i]->level)*(maxM_ + 1))*sizeof(int); \n output.write((char*)&sizemass, sizeof(unsigned int));\n if((sizemass))\n output.write(linkLists_[i], sizemass);\n }; \n output.close();\n }\n\n template \n void Hnsw::LoadIndex(const string &location) {\n LOG(LIB_INFO) << \"Loading index from \"<\n \tvoid Hnsw::baseSearchAlgorithm(KNNQuery* query) {\n\t\t\tint checks =0 ;\n \t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n \t\tunsigned int *massVisited = vl->mass;\n \t\tunsigned int currentV = vl->curV;\n\n \t\tHnswNode* provider;\n \t\tint maxlevel1 = enterpoint_->level;\n \t\tprovider = enterpoint_;\n \n \t\tconst Object* currObj = provider->getData();\n \n \t\tdist_t d = query->DistanceObjLeft(currObj);\n\t\t\tchecks ++ ;\n \t\tdist_t curdist = d;\n \t\tHnswNode *curNode = provider;\n \t\tfor (int i = maxlevel1; i > 0; i--) {\n \t\t\tbool changed = true;\n \t\t\twhile (changed) {\n \t\t\t\tchanged = false;\n \n \t\t\t\tconst vector& neighbor = curNode->getAllFriends(i);\n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t}\n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\td = query->DistanceObjLeft(currObj);\n\t\t\t\t\t\tchecks++;\n \t\t\t\t\tif (d < curdist) {\n \t\t\t\t\t\tcurdist = d;\n \t\t\t\t\t\tcurNode = *iter;\n \t\t\t\t\t\tchanged = true;\n \t\t\t\t\t}\n \t\t\t\t}\n \t\t\t}\n \t\t}\n \t\n \n \t\tpriority_queue > candidateQueue; //the set of elements which we can use to evaluate \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \n \t\tpriority_queue > closestDistQueue1; //The set of closest found elements \n \n \t\tHnswNodeDistFarther ev(curdist, curNode);\n \t\tcandidateQueue.emplace(curdist, curNode);\n \t\tclosestDistQueue1.emplace(curdist, curNode);\n \n \t\tquery->CheckAndAddToResult(curdist, curNode->getData());\n \t\tmassVisited[curNode->getId()] = currentV;\n \t\t//visitedQueue.insert(curNode->getId());\n \n \t\t////////////////////////////////////////////////////////////////////////////////\n \t\t// PHASE TWO OF THE SEARCH\n \t\t// Extraction of the neighborhood to find k nearest neighbors.\n \t\t////////////////////////////////////////////////////////////////////////////////\n \t\t\n \t\twhile (!candidateQueue.empty()) {\n \t\t\t\n \t\t\tauto iter = candidateQueue.top(); // This one was already compared to the query\n \t\t\tconst HnswNodeDistFarther& currEv = iter;\n \t\t\t//Check condtion to end the search\n \t\t\tdist_t lowerBound = closestDistQueue1.top().getDistance();\n \t\t\tif (currEv.getDistance() > lowerBound) {\n \t\t\t\tbreak;\n \t\t\t}\n \n \t\t\tHnswNode *initNode = currEv.getMSWNodeHier();\n \t\t\tcandidateQueue.pop();\n \n \t\t\tconst vector& neighbor = (initNode)->getAllFriends(0);\n \n \t\t\tsize_t curId;\n \n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t_mm_prefetch((char *)(massVisited + (*iter)->getId()), _MM_HINT_T0);\n \t\t\t}\n \t\t\t//calculate distance to each neighbor\n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \n \t\t\t\tcurId = (*iter)->getId();\n \t\t\t\t\n \t\t\t\tif (!(massVisited[curId] == currentV))\n \t\t\t\t{\n \t\t\t\t\tmassVisited[curId] = currentV;\n \t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\td = query->DistanceObjLeft(currObj);\n\t\t\t\t\t\tchecks ++ ;\n \t\t\t\t\tif (closestDistQueue1.top().getDistance() > d || closestDistQueue1.size() < ef_) {\n \t\t\t\t\t\t{\n \t\t\t\t\t\t\tquery->CheckAndAddToResult(d, currObj); \n \t\t\t\t\t\t\tcandidateQueue.emplace(d, *iter);\n \t\t\t\t\t\t\tclosestDistQueue1.emplace(d, *iter);\n \t\t\t\t\t\t\tif (closestDistQueue1.size() > ef_) {\n \t\t\t\t\t\t\t\tclosestDistQueue1.pop();\n \t\t\t\t\t\t\t}\n \t\t\t\t\t\t}\n \t\t\t\t\t}\n \t\t\t\t}\n \t\t\t}\n \n \t\t}\n \t\tvisitedlistpool->releaseVisitedList(vl);\n\t\t\t//cout << checks << endl;\n \t}\n // Experimental search algorithm\n \ttemplate \n \tvoid Hnsw::listPassingModifiedAlgorithm(KNNQuery* query) {\n\n \t\tint efSearchL = 4; // This parameters defines the confidence of searches at level higher than zero \n // for zero level it is set to ef\n //Getting the visitedlist\n \t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n \t\tunsigned int *massVisited = vl->mass;\n \t\tunsigned int currentV = vl->curV;\n \n \t\tint maxlevel1 = enterpoint_->level; \n \n \t\tconst Object* currObj = enterpoint_->getData();\n \n \t\tdist_t d = query->DistanceObjLeft(currObj);\n \t\tdist_t curdist = d;\n \t\tHnswNode *curNode = enterpoint_;\n \n \n \t\tpriority_queue > candidateQueue; //the set of elements which we can use to evaluate \t\t\t\t\t\t\t\t\t\t\t\t\t\t\n \t\tpriority_queue > closestDistQueue= priority_queue >(); //The set of closest found elements \n \t\tpriority_queue > closestDistQueueCpy = priority_queue >();\n\n \t\tHnswNodeDistFarther ev(curdist, curNode);\n \t\tcandidateQueue.emplace(curdist, curNode);\n \t\tclosestDistQueue.emplace(curdist, curNode);\n\n \t\tmassVisited[curNode->getId()] = currentV;\n\n \t\tfor (int i = maxlevel1; i > 0; i--) {\n \t\t\t\n \t\t\twhile (!candidateQueue.empty()) {\n \t\n \t\t\t\tauto iter = candidateQueue.top(); \n \t\t\t\tconst HnswNodeDistFarther& currEv = iter;\n \t\t\t\t//Check condtion to end the search\n \t\t\t\tdist_t lowerBound = closestDistQueue.top().getDistance();\n \t\t\t\tif (currEv.getDistance() > lowerBound) {\n \t\t\t\t\tbreak;\n \t\t\t\t}\n \n \t\t\t\tHnswNode *initNode = currEv.getMSWNodeHier();\n \t\t\t\tcandidateQueue.pop();\n \n \t\t\t\tconst vector& neighbor = (initNode)->getAllFriends(i);\n \n \t\t\t\tsize_t curId;\n \n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t\t_mm_prefetch((char *)(massVisited + (*iter)->getId()), _MM_HINT_T0);\n \t\t\t\t}\n \t\t\t\t//calculate distance to each neighbor\n \t\t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) { \n \t\t\t\t\tcurId = (*iter)->getId();\n \t\t\t\t\tif (!(massVisited[curId] == currentV))\n \t\t\t\t\t{\n \t\t\t\t\t\tmassVisited[curId] = currentV;\n \t\t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\t\td = query->DistanceObjLeft(currObj);\n if (closestDistQueue.top().getDistance() > d || closestDistQueue.size() < efSearchL) {\n candidateQueue.emplace(d, *iter);\n closestDistQueue.emplace(d, *iter);\n if (closestDistQueue.size() > efSearchL) {\n closestDistQueue.pop();\n }\n }\n \t\t\t\t\t}\n \t\t\t\t}\n \n \t\t\t}\n //Updating the bitset key:\n currentV++;\n vl->curV++;// not to forget updating in the pool\n \t\t\tif (currentV == 0) {\n \t\t\t\tmemset(massVisited, 0, ElList_.size()*sizeof(int));\n \t\t\t\tcurrentV++;\n vl->curV++;// not to forget updating in the pool\n \t\t\t}\n candidateQueue = priority_queue >();\n closestDistQueueCpy = priority_queue >(closestDistQueue);\n \t\t\tif (i > 1) { // Passing the closest neighbors to layers higher than zero:\n while (closestDistQueueCpy.size() > 0) {\n massVisited[closestDistQueueCpy.top().getMSWNodeHier()->getId()] = currentV;\n candidateQueue.emplace(closestDistQueueCpy.top().getDistance(), closestDistQueueCpy.top().getMSWNodeHier()); \n closestDistQueueCpy.pop();\n } \t\t\t\t\n \t\t\t}\n else { // Passing the closest neighbors to the 0 zero layer(one has to add also to query): \n while (closestDistQueueCpy.size() > 0) {\n massVisited[closestDistQueueCpy.top().getMSWNodeHier()->getId()] = currentV;\n candidateQueue.emplace(closestDistQueueCpy.top().getDistance(), closestDistQueueCpy.top().getMSWNodeHier());\n query->CheckAndAddToResult(closestDistQueueCpy.top().getDistance(), closestDistQueueCpy.top().getMSWNodeHier()->getData());\n closestDistQueueCpy.pop();\n }\n }\n \t\t}\n \t\t\n \t\t////////////////////////////////////////////////////////////////////////////////\n \t\t// PHASE TWO OF THE SEARCH\n \t\t// Extraction of the neighborhood to find k nearest neighbors.\n \t\t////////////////////////////////////////////////////////////////////////////////\n \n \n \t\twhile (!candidateQueue.empty()) {\n \n \t\t\tauto iter = candidateQueue.top();\n \t\t\tconst HnswNodeDistFarther& currEv = iter;\n \t\t\t//Check condtion to end the search\n \t\t\tdist_t lowerBound = closestDistQueue.top().getDistance();\n \t\t\tif (currEv.getDistance() > lowerBound) {\n \t\t\t\tbreak;\n \t\t\t}\n \n \t\t\tHnswNode *initNode = currEv.getMSWNodeHier();\n \t\t\tcandidateQueue.pop();\n \n \t\t\tconst vector& neighbor = (initNode)->getAllFriends(0);\n \n \t\t\tsize_t curId;\n \n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \t\t\t\t_mm_prefetch((char *)(*iter)->getData(), _MM_HINT_T0);\n \t\t\t\t_mm_prefetch((char *)(massVisited + (*iter)->getId()), _MM_HINT_T0);\n \t\t\t}\n \t\t\t//calculate distance to each neighbor\n \t\t\tfor (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter) {\n \n \t\t\t\tcurId = (*iter)->getId();\n \t\t\t\tif (!(massVisited[curId] == currentV))\n \t\t\t\t{\n \t\t\t\t\tmassVisited[curId] = currentV;\n \t\t\t\t\tcurrObj = (*iter)->getData();\n \t\t\t\t\td = query->DistanceObjLeft(currObj);\n \t\t\t\t\tif (closestDistQueue.top().getDistance() > d || closestDistQueue.size() < ef_) {\n \t\t\t\t\t\t{\n \t\t\t\t\t\t\tquery->CheckAndAddToResult(d, currObj);\n \t\t\t\t\t\t\tcandidateQueue.emplace(d, *iter);\n \t\t\t\t\t\t\tclosestDistQueue.emplace(d, *iter);\n \t\t\t\t\t\t\tif (closestDistQueue.size() > ef_) {\n \t\t\t\t\t\t\t\tclosestDistQueue.pop();\n \t\t\t\t\t\t\t}\n \t\t\t\t\t\t}\n \t\t\t\t\t}\n \t\t\t\t}\n \t\t\t}\n \n \t\t}\n \t\tvisitedlistpool->releaseVisitedList(vl);\n \n \t}\n\n\n template class Hnsw;\n template class Hnsw;\n template class Hnsw;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/hnsw_distfunc_opt.cc", "content": "/**\n* Non-metric Space Library\n*\n* Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n* With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n*\n* For the complete list of contributors and further details see:\n* https://github.com/searchivarius/NonMetricSpaceLib\n*\n* Copyright (c) 2014\n*\n* This code is released under the\n* Apache License Version 2.0 http://www.apache.org/licenses/.\n*\n*/\n\n/*\n*\n* A Hierarchical Navigable Small World (HNSW) approach.\n*\n* The main publication is (available on arxiv: http://arxiv.org/abs/1603.09320):\n* \"Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs\" by Yu. A. Malkov, D. A. Yashunin\n* This code was contributed by Yu. A. Malkov. It also was used in tests from the paper.\n*\n*\n*/\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/hnsw.h\"\n\n#include \n#include \n#include // std::min\n#if defined(__GNUC__)\n#define PORTABLE_ALIGN16 __attribute__((aligned(16)))\n#else\n#define PORTABLE_ALIGN16 __declspec(align(16))\n#endif\n//#define DIST_CALC \nnamespace similarity {\n\tfloat L2SqrSIMD16Ext(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes) {\n\n\t\t//size_t qty4 = qty >> 2;\n\t\tsize_t qty16 = qty >> 4;\n\n\t\tconst float* pEnd1 = pVect1 + (qty16 << 4);\n\t\t//const float* pEnd2 = pVect1 + (qty4 << 2);\n\t\t//const float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 diff, v1, v2;\n\t\t__m128 sum = _mm_set1_ps(0);\n\n\t\twhile (pVect1 < pEnd1) {\n\t\t\t_mm_prefetch((char*)(pVect2 + 16), _MM_HINT_T0);\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}\n\n\t\t/*while (pVect1 < pEnd2) {\n\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\tdiff = _mm_sub_ps(v1, v2);\n\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}*/\n\n\t\t//float PORTABLE_ALIGN16 TmpRes[4];\n\n\t\t_mm_store_ps(TmpRes, sum);\n\t\tfloat res = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n\t\t/*while (pVect1 < pEnd3) {\n\t\tfloat diff = *pVect1++ - *pVect2++;\n\t\tres += diff * diff;\n\t\t}*/\n\n\t\treturn sqrt(res);\n\t};\n\tfloat L2SqrSIMDExt(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes) {\n\n\t\tsize_t qty4 = qty >> 2;\n\t\tsize_t qty16 = qty >> 4;\n\n\t\tconst float* pEnd1 = pVect1 + (qty16 << 4);\n\t\tconst float* pEnd2 = pVect1 + (qty4 << 2);\n\t\tconst float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 diff, v1, v2;\n\t\t__m128 sum = _mm_set1_ps(0);\n\n\t\twhile (pVect1 < pEnd1) {\n\t\t\t_mm_prefetch((char*)(pVect2 + 16), _MM_HINT_T0);\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}\n\n\t\twhile (pVect1 < pEnd2) {\n\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\tdiff = _mm_sub_ps(v1, v2);\n\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}\n\n\t\t//float PORTABLE_ALIGN16 TmpRes[4];\n\n\t\t_mm_store_ps(TmpRes, sum);\n\t\tfloat res = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n\t\twhile (pVect1 < pEnd3) {\n\t\tfloat diff = *pVect1++ - *pVect2++;\n\t\tres += diff * diff;\n\t\t}\n\n\t\treturn sqrt(res);\n\t};\n\tfloat ScalarProductSIMD(const float* __restrict pVect1, const float* __restrict pVect2, size_t qty, float * __restrict TmpRes) {\n\t\tsize_t qty16 = qty / 16;\n\t\tsize_t qty4 = qty / 4;\n\n\t\tconst float* pEnd1 = pVect1 + 16 * qty16;\n\t\tconst float* pEnd2 = pVect1 + 4 * qty4;\n\t\t//const float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 v1, v2;\n\t\t__m128 sum_prod = _mm_set1_ps(0);\n\t\t//__m128 sum_square1 = sum_prod;\n\t\t//__m128 sum_square2 = sum_prod;\n\n\t\twhile (pVect1 < pEnd1) {\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\t\t}\n\n\t\twhile (pVect1 < pEnd2) {\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\t\t}\n\n\n\t\t_mm_store_ps(TmpRes, sum_prod);\n\t\tfloat sum = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\t/*while (pVect1 < pEnd3) {\n\t\t\t//cout << \"!!!\";\n\t\t\tsum += (*pVect1) * (*pVect2);\n\n\t\t\t++pVect1; ++pVect2;\n\t\t}*/\n\t\t//return -sum;\n\n\t\treturn std::max(0.0f, 1 - std::max(float(-1), std::min(float(1), sum)));\n\t};\n\n\tfloat NormScalarProductSIMD(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes) {\n \n\t\tsize_t qty16 = qty / 16;\n\t\tsize_t qty4 = qty / 4;\n\t\t\n\t\tconst float* pEnd1 = pVect1 + 16 * qty16;\n\t\tconst float* pEnd2 = pVect1 + 4 * qty4;\n\t\tconst float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 v1, v2;\n\t\t__m128 sum_prod = _mm_set1_ps(0);\n\t\t__m128 sum_square1 = sum_prod;\n\t\t__m128 sum_square2 = sum_prod;\n\n\t\twhile (pVect1 < pEnd1) {\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\t\t}\n\n\t\twhile (pVect1 < pEnd2) {\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\t\t}\n\t\t\n\n\t\t_mm_store_ps(TmpRes, sum_prod);\n\t\tfloat sum = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\t_mm_store_ps(TmpRes, sum_square1);\n\t\tfloat norm1 = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\t_mm_store_ps(TmpRes, sum_square2);\n\t\tfloat norm2 = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\twhile (pVect1 < pEnd3) {\n\t\t\tsum += (*pVect1) * (*pVect2);\n\t\t\tnorm1 += (*pVect1) * (*pVect1);\n\t\t\tnorm2 += (*pVect2) * (*pVect2);\n\n\t\t\t++pVect1; ++pVect2;\n\t\t}\n\n\t\t const float eps = numeric_limits::min() * 2;\n\n if (norm1 < eps) { /* \n * This shouldn't normally happen for this space, but \n * if it does, we don't want to get NANs \n */\n if (norm2 < eps) return 1;\n return 0;\n } \n\t\treturn std::max(0.0f, 1-std::max(float(-1), std::min(float(1), sum / sqrt(norm1*norm2))));\n\t};\n\t\n\n\t/****************************************************************\n\n\tUNIVERSAL FUNCTION FOR CUSTOM MADE\n\n\t****************************************************************/\n\ttemplate \n\tvoid Hnsw::SearchL2Custom(KNNQuery* query) {\n\n\t\tfloat *pVectq = (float *)((char *)query->QueryObject()->data());\n\t\tfloat PORTABLE_ALIGN16 TmpRes[4];\n\t\tsize_t qty = query->QueryObject()->datalength() >> 2;\n\n\t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n\t\tunsigned int *massVisited = vl->mass;\n\t\tunsigned int currentV = vl->curV;\n\n\n\n\t\tint maxlevel1 = maxlevel_;\n int curNodeNum = enterpointId_;\t\t\n\t\tdist_t curdist = (fstdistfunc_(pVectq, (float *)(data_level0_memory_ + enterpointId_*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n\t\t\n\n\t\tfor (int i = maxlevel1; i > 0; i--) {\n\t\t\tbool changed = true;\n\t\t\twhile (changed) {\n\t\t\t\tchanged = false;\t\t\t\t\n\t\t\t\tint *data = (int *)(linkLists_[curNodeNum] + (maxM_ + 1)*(i - 1)*sizeof(int));\n\t\t\t\tint size = *data;\n\t\t\t\tfor (int j = 1; j <= size; j++) {\n\t\t\t\t\t_mm_prefetch(data_level0_memory_ + (*(data + j))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n\t\t\t\t}\n#ifdef DIST_CALC\n\t\t\t\tquery->distance_computations_ += size;\n#endif\n\n\t\t\t\tfor (int j = 1; j <= size; j++) {\n\t\t\t\t\tint tnum = *(data + j);\n\n dist_t d = (fstdistfunc_(pVectq, (float *)(data_level0_memory_ + tnum*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n\t\t\t\t\tif (d < curdist) {\n\t\t\t\t\t\tcurdist = d;\n\t\t\t\t\t\tcurNodeNum = tnum;\n\t\t\t\t\t\tchanged = true;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\n\t\t\t}\n\t\t}\n\t\t\n\t\tpriority_queue > candidateQueuei; //the set of elements which we can use to evaluate\t\t\t\t\t\t\t\t\t\t\t\t\t\n\n\t\tpriority_queue > closestDistQueuei; //The set of closest found elements \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t//EvaluatedMSWNodeInt evi(curdist, curNodeNum);\n\t\tcandidateQueuei.emplace(-curdist, curNodeNum);\n\n\t\tclosestDistQueuei.emplace(curdist, curNodeNum);\n\n\t\tquery->CheckAndAddToResult(curdist, new Object(data_level0_memory_ + (curNodeNum)*memoryPerObject_ + offsetData_));\n\t\tmassVisited[curNodeNum] = currentV;\n\n\t\twhile (!candidateQueuei.empty()) {\n\n\t\t\tEvaluatedMSWNodeInt currEv = candidateQueuei.top(); // This one was already compared to the query\n\n\t\t\tdist_t lowerBound = closestDistQueuei.top().getDistance();\n\t\t\tif ((-currEv.getDistance()) > lowerBound) {\n\t\t\t\tbreak;\n\t\t\t}\n\n\t\t\tcandidateQueuei.pop();\n\t\t\tcurNodeNum = currEv.element;\n\t\t\tint *data = (int *)(data_level0_memory_ + curNodeNum*memoryPerObject_ + offsetLevel0_);\n\t\t\tint size = *data;\n\t\t\t_mm_prefetch((char *)(massVisited + *(data + 1)), _MM_HINT_T0);\n\t\t\t_mm_prefetch((char *)(massVisited + *(data + 1) + 64), _MM_HINT_T0);\n\t\t\t_mm_prefetch(data_level0_memory_ + (*(data + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n\t\t\t_mm_prefetch((char *)(data + 2), _MM_HINT_T0);\n\n\t\t\tfor (int j = 1; j <= size; j++) {\n\t\t\t\tint tnum = *(data + j);\n\t\t\t\t_mm_prefetch((char *)(massVisited + *(data + j + 1)), _MM_HINT_T0);\n\t\t\t\t_mm_prefetch(data_level0_memory_ + (*(data + j + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n\t\t\t\tif (!(massVisited[tnum] == currentV))\n\t\t\t\t{\n\n#ifdef DIST_CALC\n\t\t\t\t\tquery->distance_computations_++;\n#endif\n\t\t\t\t\tmassVisited[tnum] = currentV;\n\t\t\t\t\tchar *currObj1 = (data_level0_memory_ + tnum*memoryPerObject_ + offsetData_);\n dist_t d = (fstdistfunc_(pVectq, (float *)(currObj1 + 16), qty, TmpRes));\n\t\t\t\t\tif (closestDistQueuei.top().getDistance() > d || closestDistQueuei.size() < ef_) {\n\t\t\t\t\t\tcandidateQueuei.emplace(-d, tnum);\n\t\t\t\t\t\t_mm_prefetch(data_level0_memory_ + candidateQueuei.top().element*memoryPerObject_ + offsetLevel0_, _MM_HINT_T0);\n\t\t\t\t\t\tquery->CheckAndAddToResult(d, new Object(currObj1));\n\t\t\t\t\t\tclosestDistQueuei.emplace(d, tnum);\n\n\t\t\t\t\t\tif (closestDistQueuei.size() > ef_) {\n\t\t\t\t\t\t\tclosestDistQueuei.pop();\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tvisitedlistpool->releaseVisitedList(vl);\n\n\t}\n\t/****************************************************************\n\n\tSearch function for cosine\n\n\t****************************************************************/\n\ttemplate \n\tvoid Hnsw::SearchCosineNormalized(KNNQuery* query) {\n\t\t\n\t\tfloat *pVectq = (float *)((char *)query->QueryObject()->data());\n float PORTABLE_ALIGN16 TmpRes[4];\n size_t qty = query->QueryObject()->datalength() >> 2;\n\n\n float *v = pVectq;\n float sum = 0;\n for (int i = 0; i < qty; i++) {\n sum += v[i] * v[i];\n }\n if (sum != 0.0) {\n sum = 1 / sqrt(sum);\n for (int i = 0; i < qty; i++) {\n v[i] *= sum;\n }\n }\n\n\n\n\n\t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n\t\tunsigned int *massVisited = vl->mass;\n\t\tunsigned int currentV = vl->curV;\n\n\n int maxlevel1 = maxlevel_;\n int curNodeNum = enterpointId_;\n dist_t curdist = (ScalarProductSIMD(pVectq, (float *)(data_level0_memory_ + enterpointId_*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n\n\n for (int i = maxlevel1; i > 0; i--) {\n bool changed = true;\n while (changed) {\n changed = false;\n int *data = (int *)(linkLists_[curNodeNum] + (maxM_ + 1)*(i - 1)*sizeof(int));\n int size = *data;\n for (int j = 1; j <= size; j++) {\n _mm_prefetch(data_level0_memory_ + (*(data + j))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n }\n#ifdef DIST_CALC\n query->distance_computations_ += size;\n#endif\n\n for (int j = 1; j <= size; j++) {\n int tnum = *(data + j);\n\n dist_t d = (ScalarProductSIMD(pVectq, (float *)(data_level0_memory_ + tnum*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n if (d < curdist) {\n curdist = d;\n curNodeNum = tnum;\n changed = true;\n }\n }\n\n\n }\n }\n\n priority_queue > candidateQueuei; //the set of elements which we can use to evaluate\t\t\t\t\t\t\t\t\t\t\t\t\t\n\n priority_queue > closestDistQueuei; //The set of closest found elements \n //EvaluatedMSWNodeInt evi(curdist, curNodeNum);\n candidateQueuei.emplace(-curdist, curNodeNum);\n\n closestDistQueuei.emplace(curdist, curNodeNum);\n\n query->CheckAndAddToResult(curdist, new Object(data_level0_memory_ + (curNodeNum)*memoryPerObject_ + offsetData_));\n massVisited[curNodeNum] = currentV;\n\n while (!candidateQueuei.empty()) {\n\n EvaluatedMSWNodeInt currEv = candidateQueuei.top(); // This one was already compared to the query\n\n dist_t lowerBound = closestDistQueuei.top().getDistance();\n if ((-currEv.getDistance()) > lowerBound) {\n break;\n }\n\n candidateQueuei.pop();\n curNodeNum = currEv.element;\n int *data = (int *)(data_level0_memory_ + curNodeNum*memoryPerObject_ + offsetLevel0_);\n int size = *data;\n _mm_prefetch((char *)(massVisited + *(data + 1)), _MM_HINT_T0);\n _mm_prefetch((char *)(massVisited + *(data + 1) + 64), _MM_HINT_T0);\n _mm_prefetch(data_level0_memory_ + (*(data + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n _mm_prefetch((char *)(data + 2), _MM_HINT_T0);\n\n for (int j = 1; j <= size; j++) {\n int tnum = *(data + j);\n _mm_prefetch((char *)(massVisited + *(data + j + 1)), _MM_HINT_T0);\n _mm_prefetch(data_level0_memory_ + (*(data + j + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n if (!(massVisited[tnum] == currentV))\n {\n\n#ifdef DIST_CALC\n query->distance_computations_++;\n#endif\n massVisited[tnum] = currentV;\n char *currObj1 = (data_level0_memory_ + tnum*memoryPerObject_ + offsetData_);\n dist_t d = (ScalarProductSIMD(pVectq, (float *)(currObj1 + 16), qty, TmpRes));\n if (closestDistQueuei.top().getDistance() > d || closestDistQueuei.size() < ef_) {\n candidateQueuei.emplace(-d, tnum);\n _mm_prefetch(data_level0_memory_ + candidateQueuei.top().element*memoryPerObject_ + offsetLevel0_, _MM_HINT_T0);\n query->CheckAndAddToResult(d, new Object(currObj1));\n closestDistQueuei.emplace(d, tnum);\n\n if (closestDistQueuei.size() > ef_) {\n closestDistQueuei.pop();\n }\n\n }\n\n }\n }\n }\n visitedlistpool->releaseVisitedList(vl);\n\n\n\t}\n\t\n\ttemplate class Hnsw;\n\ttemplate class Hnsw;\n\ttemplate class Hnsw;\n\t\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/hnsw_distfunc_opt.cc~", "content": "/**\n* Non-metric Space Library\n*\n* Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n* With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n*\n* For the complete list of contributors and further details see:\n* https://github.com/searchivarius/NonMetricSpaceLib\n*\n* Copyright (c) 2014\n*\n* This code is released under the\n* Apache License Version 2.0 http://www.apache.org/licenses/.\n*\n*/\n\n/*\n*\n* A Hierarchical Navigable Small World (HNSW) approach.\n*\n* The main publication is (available on arxiv: http://arxiv.org/abs/1603.09320):\n* \"Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs\" by Yu. A. Malkov, D. A. Yashunin\n* This code was contributed by Yu. A. Malkov. It also was used in tests from the paper.\n*\n*\n*/\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/hnsw.h\"\n\n#include \n#include \n#include // std::min\n#if defined(__GNUC__)\n#define PORTABLE_ALIGN16 __attribute__((aligned(16)))\n#else\n#define PORTABLE_ALIGN16 __declspec(align(16))\n#endif\n//#define DIST_CALC \nnamespace similarity {\n\tfloat L2SqrSIMD16Ext(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes) {\n\n\t\t//size_t qty4 = qty >> 2;\n\t\tsize_t qty16 = qty >> 4;\n\n\t\tconst float* pEnd1 = pVect1 + (qty16 << 4);\n\t\t//const float* pEnd2 = pVect1 + (qty4 << 2);\n\t\t//const float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 diff, v1, v2;\n\t\t__m128 sum = _mm_set1_ps(0);\n\n\t\twhile (pVect1 < pEnd1) {\n\t\t\t_mm_prefetch((char*)(pVect2 + 16), _MM_HINT_T0);\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}\n\n\t\t/*while (pVect1 < pEnd2) {\n\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\tdiff = _mm_sub_ps(v1, v2);\n\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}*/\n\n\t\t//float PORTABLE_ALIGN16 TmpRes[4];\n\n\t\t_mm_store_ps(TmpRes, sum);\n\t\tfloat res = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n\t\t/*while (pVect1 < pEnd3) {\n\t\tfloat diff = *pVect1++ - *pVect2++;\n\t\tres += diff * diff;\n\t\t}*/\n\n\t\treturn sqrt(res);\n\t};\n\tfloat L2SqrSIMDExt(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes) {\n\n\t\tsize_t qty4 = qty >> 2;\n\t\tsize_t qty16 = qty >> 4;\n\n\t\tconst float* pEnd1 = pVect1 + (qty16 << 4);\n\t\tconst float* pEnd2 = pVect1 + (qty4 << 2);\n\t\tconst float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 diff, v1, v2;\n\t\t__m128 sum = _mm_set1_ps(0);\n\n\t\twhile (pVect1 < pEnd1) {\n\t\t\t_mm_prefetch((char*)(pVect2 + 16), _MM_HINT_T0);\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tdiff = _mm_sub_ps(v1, v2);\n\t\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}\n\n\t\twhile (pVect1 < pEnd2) {\n\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\tdiff = _mm_sub_ps(v1, v2);\n\t\tsum = _mm_add_ps(sum, _mm_mul_ps(diff, diff));\n\t\t}\n\n\t\t//float PORTABLE_ALIGN16 TmpRes[4];\n\n\t\t_mm_store_ps(TmpRes, sum);\n\t\tfloat res = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\n\t\twhile (pVect1 < pEnd3) {\n\t\tfloat diff = *pVect1++ - *pVect2++;\n\t\tres += diff * diff;\n\t\t}\n\n\t\treturn sqrt(res);\n\t};\n\tfloat ScalarProductSIMD(const float* __restrict pVect1, const float* __restrict pVect2, size_t qty, float * __restrict TmpRes) {\n\t\tsize_t qty16 = qty / 16;\n\t\tsize_t qty4 = qty / 4;\n\n\t\tconst float* pEnd1 = pVect1 + 16 * qty16;\n\t\tconst float* pEnd2 = pVect1 + 4 * qty4;\n\t\t//const float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 v1, v2;\n\t\t__m128 sum_prod = _mm_set1_ps(0);\n\t\t//__m128 sum_square1 = sum_prod;\n\t\t//__m128 sum_square2 = sum_prod;\n\n\t\twhile (pVect1 < pEnd1) {\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\t\t}\n\n\t\twhile (pVect1 < pEnd2) {\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\n\t\t}\n\n\n\t\t_mm_store_ps(TmpRes, sum_prod);\n\t\tfloat sum = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\t/*while (pVect1 < pEnd3) {\n\t\t\t//cout << \"!!!\";\n\t\t\tsum += (*pVect1) * (*pVect2);\n\n\t\t\t++pVect1; ++pVect2;\n\t\t}*/\n\t\t//return -sum;\n\n\t\treturn std::max(0.0f, 1 - std::max(float(-1), std::min(float(1), sum)));\n\t};\n\n\tfloat NormScalarProductSIMD(const float* pVect1, const float* pVect2, size_t &qty, float *TmpRes) {\n \n\t\tsize_t qty16 = qty / 16;\n\t\tsize_t qty4 = qty / 4;\n\t\t\n\t\tconst float* pEnd1 = pVect1 + 16 * qty16;\n\t\tconst float* pEnd2 = pVect1 + 4 * qty4;\n\t\tconst float* pEnd3 = pVect1 + qty;\n\n\t\t__m128 v1, v2;\n\t\t__m128 sum_prod = _mm_set1_ps(0);\n\t\t__m128 sum_square1 = sum_prod;\n\t\t__m128 sum_square2 = sum_prod;\n\n\t\twhile (pVect1 < pEnd1) {\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\t\t}\n\n\t\twhile (pVect1 < pEnd2) {\n\t\t\tv1 = _mm_loadu_ps(pVect1); pVect1 += 4;\n\t\t\tv2 = _mm_loadu_ps(pVect2); pVect2 += 4;\n\t\t\tsum_prod = _mm_add_ps(sum_prod, _mm_mul_ps(v1, v2));\n\t\t\tsum_square1 = _mm_add_ps(sum_square1, _mm_mul_ps(v1, v1));\n\t\t\tsum_square2 = _mm_add_ps(sum_square2, _mm_mul_ps(v2, v2));\n\t\t}\n\t\t\n\n\t\t_mm_store_ps(TmpRes, sum_prod);\n\t\tfloat sum = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\t_mm_store_ps(TmpRes, sum_square1);\n\t\tfloat norm1 = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\t_mm_store_ps(TmpRes, sum_square2);\n\t\tfloat norm2 = TmpRes[0] + TmpRes[1] + TmpRes[2] + TmpRes[3];\n\t\twhile (pVect1 < pEnd3) {\n\t\t\tsum += (*pVect1) * (*pVect2);\n\t\t\tnorm1 += (*pVect1) * (*pVect1);\n\t\t\tnorm2 += (*pVect2) * (*pVect2);\n\n\t\t\t++pVect1; ++pVect2;\n\t\t}\n\n\t\t const float eps = numeric_limits::min() * 2;\n\n if (norm1 < eps) { /* \n * This shouldn't normally happen for this space, but \n * if it does, we don't want to get NANs \n */\n if (norm2 < eps) return 1;\n return 0;\n } \n\t\treturn std::max(0.0f, 1-std::max(float(-1), std::min(float(1), sum / sqrt(norm1*norm2))));\n\t};\n\t\n\n\t/****************************************************************\n\n\tUNIVERSAL FUNCTION FOR CUSTOM MADE\n\n\t****************************************************************/\n\ttemplate \n\tvoid Hnsw::SearchL2Custom(KNNQuery* query) {\n\n\t\tcerr << \"search L2 custom \" <QueryObject()->data());\n\t\tfloat PORTABLE_ALIGN16 TmpRes[4];\n\t\tsize_t qty = query->QueryObject()->datalength() >> 2;\n\n\t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n\t\tunsigned int *massVisited = vl->mass;\n\t\tunsigned int currentV = vl->curV;\n\n\n\n\t\tint maxlevel1 = maxlevel_;\n int curNodeNum = enterpointId_;\t\t\n\t\tdist_t curdist = (fstdistfunc_(pVectq, (float *)(data_level0_memory_ + enterpointId_*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n\t\t\n\n\t\tfor (int i = maxlevel1; i > 0; i--) {\n\t\t\tbool changed = true;\n\t\t\twhile (changed) {\n\t\t\t\tchanged = false;\t\t\t\t\n\t\t\t\tint *data = (int *)(linkLists_[curNodeNum] + (maxM_ + 1)*(i - 1)*sizeof(int));\n\t\t\t\tint size = *data;\n\t\t\t\tfor (int j = 1; j <= size; j++) {\n\t\t\t\t\t_mm_prefetch(data_level0_memory_ + (*(data + j))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n\t\t\t\t}\n#ifdef DIST_CALC\n\t\t\t\tquery->distance_computations_ += size;\n#endif\n\n\t\t\t\tfor (int j = 1; j <= size; j++) {\n\t\t\t\t\tint tnum = *(data + j);\n\n dist_t d = (fstdistfunc_(pVectq, (float *)(data_level0_memory_ + tnum*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n\t\t\t\t\tif (d < curdist) {\n\t\t\t\t\t\tcurdist = d;\n\t\t\t\t\t\tcurNodeNum = tnum;\n\t\t\t\t\t\tchanged = true;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\n\t\t\t}\n\t\t}\n\t\t\n\t\tpriority_queue > candidateQueuei; //the set of elements which we can use to evaluate\t\t\t\t\t\t\t\t\t\t\t\t\t\n\n\t\tpriority_queue > closestDistQueuei; //The set of closest found elements \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t//EvaluatedMSWNodeInt evi(curdist, curNodeNum);\n\t\tcandidateQueuei.emplace(-curdist, curNodeNum);\n\n\t\tclosestDistQueuei.emplace(curdist, curNodeNum);\n\n\t\tquery->CheckAndAddToResult(curdist, new Object(data_level0_memory_ + (curNodeNum)*memoryPerObject_ + offsetData_));\n\t\tmassVisited[curNodeNum] = currentV;\n\n\t\twhile (!candidateQueuei.empty()) {\n\n\t\t\tEvaluatedMSWNodeInt currEv = candidateQueuei.top(); // This one was already compared to the query\n\n\t\t\tdist_t lowerBound = closestDistQueuei.top().getDistance();\n\t\t\tif ((-currEv.getDistance()) > lowerBound) {\n\t\t\t\tbreak;\n\t\t\t}\n\n\t\t\tcandidateQueuei.pop();\n\t\t\tcurNodeNum = currEv.element;\n\t\t\tint *data = (int *)(data_level0_memory_ + curNodeNum*memoryPerObject_ + offsetLevel0_);\n\t\t\tint size = *data;\n\t\t\t_mm_prefetch((char *)(massVisited + *(data + 1)), _MM_HINT_T0);\n\t\t\t_mm_prefetch((char *)(massVisited + *(data + 1) + 64), _MM_HINT_T0);\n\t\t\t_mm_prefetch(data_level0_memory_ + (*(data + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n\t\t\t_mm_prefetch((char *)(data + 2), _MM_HINT_T0);\n\n\t\t\tfor (int j = 1; j <= size; j++) {\n\t\t\t\tint tnum = *(data + j);\n\t\t\t\t_mm_prefetch((char *)(massVisited + *(data + j + 1)), _MM_HINT_T0);\n\t\t\t\t_mm_prefetch(data_level0_memory_ + (*(data + j + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n\t\t\t\tif (!(massVisited[tnum] == currentV))\n\t\t\t\t{\n\n#ifdef DIST_CALC\n\t\t\t\t\tquery->distance_computations_++;\n#endif\n\t\t\t\t\tmassVisited[tnum] = currentV;\n\t\t\t\t\tchar *currObj1 = (data_level0_memory_ + tnum*memoryPerObject_ + offsetData_);\n dist_t d = (fstdistfunc_(pVectq, (float *)(currObj1 + 16), qty, TmpRes));\n\t\t\t\t\tif (closestDistQueuei.top().getDistance() > d || closestDistQueuei.size() < ef_) {\n\t\t\t\t\t\tcandidateQueuei.emplace(-d, tnum);\n\t\t\t\t\t\t_mm_prefetch(data_level0_memory_ + candidateQueuei.top().element*memoryPerObject_ + offsetLevel0_, _MM_HINT_T0);\n\t\t\t\t\t\tquery->CheckAndAddToResult(d, new Object(currObj1));\n\t\t\t\t\t\tclosestDistQueuei.emplace(d, tnum);\n\n\t\t\t\t\t\tif (closestDistQueuei.size() > ef_) {\n\t\t\t\t\t\t\tclosestDistQueuei.pop();\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tvisitedlistpool->releaseVisitedList(vl);\n\n\t}\n\t/****************************************************************\n\n\tSearch function for cosine\n\n\t****************************************************************/\n\ttemplate \n\tvoid Hnsw::SearchCosineNormalized(KNNQuery* query) {\n\t\t\n\t\tfloat *pVectq = (float *)((char *)query->QueryObject()->data());\n float PORTABLE_ALIGN16 TmpRes[4];\n size_t qty = query->QueryObject()->datalength() >> 2;\n\n\n float *v = pVectq;\n float sum = 0;\n for (int i = 0; i < qty; i++) {\n sum += v[i] * v[i];\n }\n if (sum != 0.0) {\n sum = 1 / sqrt(sum);\n for (int i = 0; i < qty; i++) {\n v[i] *= sum;\n }\n }\n\n\n\n\n\t\tVisitedList * vl = visitedlistpool->getFreeVisitedList();\n\t\tunsigned int *massVisited = vl->mass;\n\t\tunsigned int currentV = vl->curV;\n\n\n int maxlevel1 = maxlevel_;\n int curNodeNum = enterpointId_;\n dist_t curdist = (ScalarProductSIMD(pVectq, (float *)(data_level0_memory_ + enterpointId_*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n\n\n for (int i = maxlevel1; i > 0; i--) {\n bool changed = true;\n while (changed) {\n changed = false;\n int *data = (int *)(linkLists_[curNodeNum] + (maxM_ + 1)*(i - 1)*sizeof(int));\n int size = *data;\n for (int j = 1; j <= size; j++) {\n _mm_prefetch(data_level0_memory_ + (*(data + j))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n }\n#ifdef DIST_CALC\n query->distance_computations_ += size;\n#endif\n\n for (int j = 1; j <= size; j++) {\n int tnum = *(data + j);\n\n dist_t d = (ScalarProductSIMD(pVectq, (float *)(data_level0_memory_ + tnum*memoryPerObject_ + offsetData_ + 16), qty, TmpRes));\n if (d < curdist) {\n curdist = d;\n curNodeNum = tnum;\n changed = true;\n }\n }\n\n\n }\n }\n\n priority_queue > candidateQueuei; //the set of elements which we can use to evaluate\t\t\t\t\t\t\t\t\t\t\t\t\t\n\n priority_queue > closestDistQueuei; //The set of closest found elements \n //EvaluatedMSWNodeInt evi(curdist, curNodeNum);\n candidateQueuei.emplace(-curdist, curNodeNum);\n\n closestDistQueuei.emplace(curdist, curNodeNum);\n\n query->CheckAndAddToResult(curdist, new Object(data_level0_memory_ + (curNodeNum)*memoryPerObject_ + offsetData_));\n massVisited[curNodeNum] = currentV;\n\n while (!candidateQueuei.empty()) {\n\n EvaluatedMSWNodeInt currEv = candidateQueuei.top(); // This one was already compared to the query\n\n dist_t lowerBound = closestDistQueuei.top().getDistance();\n if ((-currEv.getDistance()) > lowerBound) {\n break;\n }\n\n candidateQueuei.pop();\n curNodeNum = currEv.element;\n int *data = (int *)(data_level0_memory_ + curNodeNum*memoryPerObject_ + offsetLevel0_);\n int size = *data;\n _mm_prefetch((char *)(massVisited + *(data + 1)), _MM_HINT_T0);\n _mm_prefetch((char *)(massVisited + *(data + 1) + 64), _MM_HINT_T0);\n _mm_prefetch(data_level0_memory_ + (*(data + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n _mm_prefetch((char *)(data + 2), _MM_HINT_T0);\n\n for (int j = 1; j <= size; j++) {\n int tnum = *(data + j);\n _mm_prefetch((char *)(massVisited + *(data + j + 1)), _MM_HINT_T0);\n _mm_prefetch(data_level0_memory_ + (*(data + j + 1))*memoryPerObject_ + offsetData_, _MM_HINT_T0);\n if (!(massVisited[tnum] == currentV))\n {\n\n#ifdef DIST_CALC\n query->distance_computations_++;\n#endif\n massVisited[tnum] = currentV;\n char *currObj1 = (data_level0_memory_ + tnum*memoryPerObject_ + offsetData_);\n dist_t d = (ScalarProductSIMD(pVectq, (float *)(currObj1 + 16), qty, TmpRes));\n if (closestDistQueuei.top().getDistance() > d || closestDistQueuei.size() < ef_) {\n candidateQueuei.emplace(-d, tnum);\n _mm_prefetch(data_level0_memory_ + candidateQueuei.top().element*memoryPerObject_ + offsetLevel0_, _MM_HINT_T0);\n query->CheckAndAddToResult(d, new Object(currObj1));\n closestDistQueuei.emplace(d, tnum);\n\n if (closestDistQueuei.size() > ef_) {\n closestDistQueuei.pop();\n }\n\n }\n\n }\n }\n }\n visitedlistpool->releaseVisitedList(vl);\n\n\n\t}\n\t\n\ttemplate class Hnsw;\n\ttemplate class Hnsw;\n\ttemplate class Hnsw;\n\t\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/list_clusters.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"method/list_clusters.h\"\n#include \"utils.h\"\n#include \"logging.h\"\n\n#include \n#include \n\nnamespace similarity {\n\nusing std::priority_queue;\nusing std::pair;\nusing std::make_pair;\n\ntemplate \nusing DistDistObjectTuple = std::tuple;\n\ntemplate \nstruct DistDistObjectTupleAscComparator {\n bool operator()(const DistDistObjectTuple& x,\n const DistDistObjectTuple& y) const {\n return std::get<0>(x) < std::get<0>(y);\n }\n};\n \ntemplate \nvoid \nListClusters::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n pmgr.GetParamOptional(\"maxLeavesToVisit\", MaxLeavesToVisit_, FAKE_MAX_LEAVES_TO_VISIT);\n LOG(LIB_INFO) << \"Set list of clusters query-time parameters:\";\n LOG(LIB_INFO) << \"maxLeavesToVisit=\" << MaxLeavesToVisit_;\n pmgr.CheckUnused();\n}\n\n\ntemplate \nListClusters::ListClusters(\n const Space& space,\n const ObjectVector& data) : space_(space), data_(data) { }\n\ntemplate \nvoid ListClusters::CreateIndex(const AnyParams& IndexParams) \n{\n AnyParamManager pmgr(IndexParams);\n\n string sVal; \n\n pmgr.GetParamOptional(\"strategy\", sVal, \"random\");\n\n if (sVal == \"random\") {\n Strategy_ = ListClustersStrategy::kRandom;\n } else if (sVal == \"closestPrevCenter\") {\n Strategy_ = ListClustersStrategy::kClosestPrevCenter;\n } else if (sVal == \"farthestPrevCenter\") {\n Strategy_ = ListClustersStrategy::kFarthestPrevCenter;\n } else if (sVal == \"minSumDistPrevCenters\") {\n Strategy_ = ListClustersStrategy::kMinSumDistPrevCenters;\n } else if (sVal == \"maxSumDistPrevCenters\") {\n Strategy_ = ListClustersStrategy::kMaxSumDistPrevCenters;\n } else {\n PREPARE_RUNTIME_ERR(err) << \"Incorrect value :'\" << sVal << \"' for parameter strategy \";\n THROW_RUNTIME_ERR(err);\n }\n\n\n pmgr.GetParamOptional(\"useBucketSize\", UseBucketSize_, true);\n pmgr.GetParamOptional(\"bucketSize\", BucketSize_, 50);\n pmgr.GetParamOptional(\"radius\", Radius_, 1);\n pmgr.GetParamOptional(\"chunkBucket\", ChunkBucket_, true);\n\n pmgr.CheckUnused();\n\n this->ResetQueryTimeParams();\n\n // \n DistObjectPairVector remaining;\n for (const auto& object : data_) {\n remaining.push_back(std::make_pair(0, object));\n }\n\n while (!remaining.empty()) {\n const Object* center = SelectNextCenter(remaining, Strategy_);\n Cluster* new_cluster = new Cluster(center);\n cluster_list_.push_back(new_cluster);\n\n if (remaining.size() == 1) {\n break;\n }\n\n DistObjectPairVector outside;\n if (UseBucketSize_) { // use bucket size\n // \n std::vector> dp;\n bool center_skipped = false;\n for (const auto& p : remaining) {\n if (p.second == center) {\n // sanity check\n if (center_skipped) {\n PREPARE_RUNTIME_ERR(err) << \"found skipped center again\" << std::endl;\n THROW_RUNTIME_ERR(err);\n }\n center_skipped = true;\n } else {\n dp.push_back(std::make_tuple(\n space_.IndexTimeDistance(p.second, center), p.first, p.second));\n }\n }\n std::sort(dp.begin(), dp.end(),\n DistDistObjectTupleAscComparator());\n for (const auto& p : dp) {\n if (new_cluster->GetBucket().size() < BucketSize_) {\n new_cluster->AddObject(std::get<2>(p), std::get<0>(p));\n } else {\n outside.push_back(std::make_pair(\n std::get<0>(p) + std::get<1>(p), std::get<2>(p)));\n }\n }\n } else { // use radius\n bool center_skipped = false;\n for (const auto& p : remaining) {\n if (p.second == center) {\n // sanity check\n if (center_skipped) {\n PREPARE_RUNTIME_ERR(err) << \"found skipped center again\" << std::endl;\n THROW_RUNTIME_ERR(err);\n }\n center_skipped = true;\n } else {\n const dist_t dist = space_.IndexTimeDistance(p.second, center);\n if (dist < Radius_) {\n new_cluster->AddObject(p.second, dist);\n } else {\n outside.push_back(std::make_pair(p.first + dist, p.second));\n }\n }\n }\n }\n\n remaining.swap(outside);\n }\n\n if (ChunkBucket_) {\n for (auto i: cluster_list_) {\n i->OptimizeBucket();\n }\n }\n}\n\ntemplate \nListClusters::~ListClusters() {\n for (auto& cluster : cluster_list_) {\n delete cluster;\n }\n}\n\ntemplate \nconst std::string ListClusters::StrDesc() const {\n return \"list of clusters\";\n}\n\ntemplate \nvoid ListClusters::Search(RangeQuery* query, IdType) const {\n GenSearch(query);\n}\n\ntemplate \nvoid ListClusters::Search(KNNQuery* query, IdType) const {\n GenSearch(query);\n}\n\ntemplate \ntemplate \nvoid ListClusters::GenSearch(QueryType* query) const {\n if (MaxLeavesToVisit_ == FAKE_MAX_LEAVES_TO_VISIT) {\n for (const auto& cluster : cluster_list_) {\n const dist_t dist_qc = query->DistanceObjLeft(cluster->GetCenter());\n query->CheckAndAddToResult(dist_qc, cluster->GetCenter());\n\n if (dist_qc - query->Radius() < cluster->GetCoveringRadius()) {\n cluster->Search(query);\n if (dist_qc + query->Radius() < cluster->GetCoveringRadius()) {\n /* \n * All the query points are inside the current cluster,\n * they have all been compared to the query already.\n */\n break;\n }\n }\n }\n } else {\n /* \n * NOTE that the below code is a naive early termination algorithm\n * that WAS NOT proposed by Chavez and Navarro!!!! \n *\n * TODO (@leo) Ideally, if the MaxLeavesToVisit_ is very large, \n * the code should work as an exact method. Yet, it does not\n * due to specifics of the index construction algorithm.\n * Think about possible improvements for the early termination strategy.\n */\n struct Elem {\n Cluster* cluster;\n dist_t dist_qc;\n bool operator<(const Elem& o) const { return dist_qc > o.dist_qc; }\n Elem(Cluster* c, dist_t d) : cluster(c), dist_qc(d) {}\n };\n\n priority_queue queue;\n\n for (const auto& cluster : cluster_list_) {\n const dist_t dist_qc = query->DistanceObjLeft(cluster->GetCenter());\n query->CheckAndAddToResult(dist_qc, cluster->GetCenter());\n\n if (dist_qc - query->Radius() < cluster->GetCoveringRadius()) {\n queue.push(Elem(cluster, dist_qc));\n }\n }\n\n dist_t PrevDist = 0;\n\n int lproc = 0;\n\n while (!queue.empty() && lproc < MaxLeavesToVisit_) {\n Cluster* cluster = queue.top().cluster;\n const dist_t dist_qc = queue.top().dist_qc;\n\n CHECK(dist_qc >= PrevDist);\n PrevDist = dist_qc;\n\n cluster->Search(query);\n ++lproc;\n\n if (dist_qc + query->Radius() < cluster->GetCoveringRadius()) {\n return;\n }\n queue.pop();\n }\n }\n}\n\ntemplate \nconst Object* ListClusters::SelectNextCenter(\n DistObjectPairVector& remaining,\n ListClustersStrategy strategy) {\n CHECK(!remaining.empty());\n size_t idx;\n switch (strategy) {\n case ListClustersStrategy::kRandom:\n return remaining[RandomInt() % remaining.size()].second;\n\n case ListClustersStrategy::kClosestPrevCenter:\n return remaining.front().second;\n\n case ListClustersStrategy::kFarthestPrevCenter:\n return remaining.back().second;\n\n case ListClustersStrategy::kMinSumDistPrevCenters:\n idx = RandomInt() % remaining.size();\n for (size_t i = 0; i < remaining.size(); ++i) {\n if (remaining[i].first < remaining[idx].first) {\n idx = i;\n }\n }\n return remaining[idx].second;\n\n case ListClustersStrategy::kMaxSumDistPrevCenters:\n idx = RandomInt() % remaining.size();\n for (size_t i = 0; i < remaining.size(); ++i) {\n if (remaining[i].first > remaining[idx].first) {\n idx = i;\n }\n }\n return remaining[idx].second;\n }\n\n throw runtime_error(\"Unknown CenterStrategy\");\n return remaining[0].second;\n}\n\ntemplate \nListClusters::Cluster::Cluster(const Object* center)\n : center_(center), covering_radius_(0), \n CacheOptimizedBucket_(NULL), bucket_(new(ObjectVector)) {\n}\n\ntemplate \nListClusters::Cluster::~Cluster() {\n ClearBucket(CacheOptimizedBucket_, bucket_);\n}\n\ntemplate \nvoid ListClusters::Cluster::OptimizeBucket() {\n ObjectVector* OldBucket = bucket_;\n\n CreateCacheOptimizedBucket(*OldBucket, CacheOptimizedBucket_, bucket_);\n \n delete OldBucket;\n}\n\ntemplate \nvoid ListClusters::Cluster::AddObject(\n const Object* object,\n const dist_t dist) {\n bucket_->push_back(object);\n if (covering_radius_ < dist) {\n covering_radius_ = dist;\n }\n}\n\ntemplate \nconst Object* ListClusters::Cluster::GetCenter() {\n return center_;\n}\n\ntemplate \nconst dist_t ListClusters::Cluster::GetCoveringRadius() {\n return covering_radius_;\n}\n\ntemplate \nconst ObjectVector& ListClusters::Cluster::GetBucket() {\n return *bucket_;\n}\n\ntemplate \ntemplate \nvoid ListClusters::Cluster::Search(QueryType* query) const {\n for (const auto& object : (*bucket_)) {\n query->CheckAndAddToResult(object);\n }\n}\n\ntemplate class ListClusters;\ntemplate class ListClusters;\ntemplate class ListClusters;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/lsh.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"space.h\"\n#include \"method/lsh_space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"method/lsh.h\"\n\nnamespace similarity {\n\ntemplate \nLSH::LSH(const Space& space,\n const ObjectVector& data,\n int P) : data_(data), p_(P) {\n\n}\n\ntemplate \nvoid LSH::CreateIndex(const AnyParams& IndexParams) {\n float LSH_W;\n unsigned LSH_M;\n unsigned LSH_L;\n unsigned LSH_H;\n\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"W\", LSH_W, 20);\n pmgr.GetParamOptional(\"M\", LSH_M, 20);\n pmgr.GetParamOptional(\"L\", LSH_L, 50);\n pmgr.GetParamOptional(\"H\", LSH_H, data_.size() + 1);\n\n int is_float = std::is_same::value;\n CHECK_MSG(is_float, \"LSH works only for single-precision numbers\");\n CHECK_MSG(sizeof(dist_t) == sizeof(float), \"LSH works only for single-precision numbers\");\n CHECK_MSG(!data_.empty(), \"The data set shouldn't be empty\");\n CHECK_MSG(p_ == 1 || p_ == 2, \"The value of the space selector should be either 1 or 2!\");\n\n const size_t datalength = data_[0]->datalength();\n\n LOG(LIB_INFO) << \"W (window size (used only for LSHCauchy and LSHGaussian)) : \" << LSH_W;\n LOG(LIB_INFO) << \"M (# of hash functions) : \" << LSH_M;\n LOG(LIB_INFO) << \"L (# of hash tables) : \" << LSH_L;\n LOG(LIB_INFO) << \"H (# hash table size) : \" << LSH_H;\n\n const int dim = static_cast(datalength / sizeof(float));\n matrix_ = new lshkit::FloatMatrix(dim, static_cast(data_.size()));\n\n for (size_t i = 0; i < data_.size(); ++i) {\n CHECK(datalength == data_[i]->datalength());\n const float* x = reinterpret_cast(data_[i]->data());\n for (int j = 0; j < dim; ++j) {\n (*matrix_)[i][j] = x[j];\n }\n }\n\n LOG(LIB_INFO) << paramcreator_t::StrDesc();\n lshkit::FloatMatrix::Accessor accessor(*matrix_);\n lshkit::DefaultRng rng;\n index_ = new LshIndexType;\n index_->init(paramcreator_t::GetParameter(*matrix_, LSH_H, LSH_M, LSH_W), rng, LSH_L);\n\n for (int i = 0; i < matrix_->getSize(); ++i) {\n index_->insert(i, (*matrix_)[i]);\n }\n}\n\ntemplate \nLSH::~LSH() {\n delete matrix_;\n delete index_;\n}\n\ntemplate \nconst std::string LSH::StrDesc() const {\n return \"lsh\";\n}\n\ntemplate \nvoid LSH::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search isn't supported by LSH\");\n}\n\ntemplate \nvoid LSH::Search(KNNQuery* query, IdType) const {\n const size_t datalength = query->QueryObject()->datalength();\n const int dim = static_cast(datalength / sizeof(float));\n CHECK(dim == matrix_->getDim());\n\n const float* q = reinterpret_cast(query->QueryObject()->data());\n\n lshkit::FloatMatrix::Accessor accessor(*matrix_);\n LSHLpSpace lp(dim, p_, query);\n lshkit::TopkScanner>\n query_scanner(accessor, lp, query->GetK());\n query_scanner.reset(q);\n\n index_->query(q, query_scanner);\n\n const lshkit::Topk& knn = query_scanner.topk();\n for (size_t i = 0; i < knn.size(); ++i) {\n if (knn[i].key != std::numeric_limits::max()) {\n query->CheckAndAddToResult(knn[i].dist, data_[knn[i].key]);\n }\n }\n}\n\ntemplate class LSH>;\ntemplate class LSH>;\ntemplate class LSH>;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/method/lsh_multiprobe.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"space.h\"\n#include \"method/lsh_space.h\"\n#include \"method/lsh_multiprobe.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n\nnamespace similarity {\n\ntemplate \nMultiProbeLSH::MultiProbeLSH(const Space& space,\n const ObjectVector& data) : data_(data) {\n}\n\ntemplate \nvoid MultiProbeLSH::CreateIndex(const AnyParams& IndexParams) {\n unsigned LSH_M = 20;\n unsigned LSH_L = 50;\n unsigned LSH_H = 1017881;\n float LSH_W = 20;\n unsigned LSH_T = 10;\n unsigned LSH_TuneK = 1;\n float DesiredRecall = 0.9;\n\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"M\", LSH_M, 20);\n pmgr.GetParamOptional(\"L\", LSH_L, 50);\n pmgr.GetParamOptional(\"H\", LSH_H, data_.size() + 1);\n pmgr.GetParamOptional(\"W\", LSH_W, 20);\n pmgr.GetParamOptional(\"T\", LSH_T, 10);\n pmgr.GetParamOptional(\"tuneK\", LSH_TuneK, 1);\n pmgr.GetParamOptional(\"desiredRecall\", DesiredRecall, 0.9);\n\n\n // For FitData():\n // number of points to use\n unsigned N1 = data_.size();\n // number of pairs to sample\n unsigned P = 10000;\n // number of queries to sample\n unsigned Q = 1000;\n // search for K neighbors neighbors\n unsigned K = LSH_TuneK;\n\n pmgr.GetParamOptional(\"numSamplePairs\", P, 10000);\n pmgr.GetParamOptional(\"numSampleQueries\", Q, 1000);\n\n pmgr.CheckUnused();\n\n\n LOG(LIB_INFO) << \"M (# of hash functions) = \" << LSH_M;\n LOG(LIB_INFO) << \"L (# of hash tables) = \" << LSH_L;\n LOG(LIB_INFO) << \"H (# hash table size) = \" << LSH_H;\n LOG(LIB_INFO) << \"W (window size) = \" << LSH_W;\n LOG(LIB_INFO) << \"T (# of probes) = \" << LSH_T;\n LOG(LIB_INFO) << \"desiredRecall (desired recall) = \" << DesiredRecall;\n LOG(LIB_INFO) << \"numSamplePairs (# of sample pairs, P) = \" << P;\n LOG(LIB_INFO) << \"numSampleQueries (# of sample queries, Q) = \" << Q;\n LOG(LIB_INFO) << \"lshTuneK = \" << K;\n\n this->ResetQueryTimeParams(); // set query-time parameters to default values\n\n // divide the sample to F folds\n unsigned F = 10;\n // For MPLSHTune():\n // dataset size\n unsigned N2 = data_.size();\n // desired recall\n\n CreateIndexInternal(\n N1, P, Q, K, F, N2,\n DesiredRecall,\n LSH_L,\n LSH_T,\n LSH_H,\n LSH_M,\n LSH_W\n );\n}\n\ntemplate \nvoid MultiProbeLSH::CreateIndexInternal(\n unsigned N1,\n unsigned P,\n unsigned Q,\n unsigned K,\n unsigned F,\n unsigned N2,\n double R,\n unsigned L,\n unsigned T,\n unsigned H,\n int M,\n float W) {\n int is_float = std::is_same::value;\n CHECK(is_float);\n CHECK(sizeof(dist_t) == sizeof(float));\n CHECK(!data_.empty());\n const size_t datalength = data_[0]->datalength();\n dim_ = static_cast(datalength / sizeof(float));\n matrix_ = new lshkit::FloatMatrix(dim_, static_cast(data_.size()));\n for (size_t i = 0; i < data_.size(); ++i) {\n CHECK(datalength == data_[i]->datalength());\n const float* x = reinterpret_cast(data_[i]->data());\n for (int j = 0; j < dim_; ++j) {\n (*matrix_)[i][j] = x[j];\n }\n }\n T_ = T;\n\n CHECK_MSG(W > 0, \"W must be > 0\");\n\n#define TUNE_MPLSH_PARAMS\n#ifdef TUNE_MPLSH_PARAMS\n R_ = R;\n\n const std::string fit_data = lshkit::FitData(*matrix_, N1, P, Q, K, F);\n\n lshkit::MPLSHTune(N2, fit_data, T_, L, R, K, M, W);\n#endif\n\n lshkit::FloatMatrix::Accessor accessor(*matrix_);\n LshIndexType::Parameter param;\n param.W = W;\n param.range = H;\n param.repeat = M;\n param.dim = dim_;\n lshkit::DefaultRng rng;\n\n index_ = new LshIndexType;\n index_->init(param, rng, L);\n\n for (int i = 0; i < matrix_->getSize(); ++i) {\n index_->insert(i, (*matrix_)[i]);\n }\n}\n\ntemplate \nMultiProbeLSH::~MultiProbeLSH() {\n delete matrix_;\n delete index_;\n}\n\ntemplate \nconst std::string MultiProbeLSH::StrDesc() const {\n return \"multiprobe lsh\";\n}\n\ntemplate \nvoid MultiProbeLSH::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search isn't supported by LSH\");\n}\n\ntemplate \nvoid MultiProbeLSH::Search(KNNQuery* query, IdType) const {\n const size_t datalength = query->QueryObject()->datalength();\n const int dim = static_cast(datalength / sizeof(float));\n CHECK(dim == dim_);\n\n const float* q = reinterpret_cast(query->QueryObject()->data());\n\n lshkit::FloatMatrix::Accessor accessor(*matrix_);\n LSHMultiProbeLpSpace lp(dim_, query);\n lshkit::TopkScanner>\n query_scanner(accessor, lp, query->GetK());\n query_scanner.reset(q);\n\n index_->query(q, T_, query_scanner);\n\n const lshkit::Topk& knn = query_scanner.topk();\n for (size_t i = 0; i < knn.size(); ++i) {\n if (knn[i].key != std::numeric_limits::max()) {\n query->CheckAndAddToResult(sqrt(knn[i].dist), data_[knn[i].key]);\n }\n }\n}\n\ntemplate class MultiProbeLSH;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/method/lsh_space.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"distcomp.h\"\n#include \"method/lsh_space.h\"\n\nnamespace similarity {\n\n/*\n * TODO(@leo/bileg) \n * Do we compute the number of times we invoke these functions during retrieval?\n * Perhaps, through query?\n */ \n\n// l1 & l2 distances for lsh\nfloat LSHLp(const float* x, const float* y, const int dim, const int p) {\n if (p == 1) {\n return L1NormSIMD(x, y, dim);\n } else if (p == 2) {\n return L2NormSIMD(x, y, dim);\n } else {\n throw runtime_error(\"LSH can work only with l_1 and l_2\");\n }\n return -1;\n}\n\n// l2sqr (squared euclidean distance) for multi probe lsh\nfloat LSHMultiProbeLp(const float* x, const float* y, const int dim) {\n#if 1\n return L2SqrSIMD(x, y, dim);\n#else\n\n float r = 0, diff;\n for (int i = 0; i < dim; ++i) {\n diff = x[i] - y[i];\n r += diff * diff;\n }\n return r;\n#endif\n}\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/method/multi_index.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"knnqueue.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"methodfactory.h\"\n#include \"method/multi_index.h\"\n\nnamespace similarity {\n\nusing std::unique_ptr;\n\ntemplate \nMultiIndex::MultiIndex(\n bool PrintProgress,\n const string& SpaceType,\n Space& space, \n const ObjectVector& data) : space_(space), data_(data), SpaceType_(SpaceType), PrintProgress_(PrintProgress) {}\n\n\ntemplate \nvoid MultiIndex::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamRequired(\"indexQty\", IndexQty_);\n pmgr.GetParamRequired(\"methodName\", MethodName_);\n\n AnyParams RemainParams = pmgr.ExtractParametersExcept( {\"indexQty\", \"methodName\"} );\n\n for (size_t i = 0; i < IndexQty_; ++i) {\n LOG(LIB_INFO) << \"Method: \" << MethodName_ << \" index # \" << (i+1) << \" out of \" << IndexQty_;\n indices_.push_back(MethodFactoryRegistry::Instance().CreateMethod(PrintProgress_, \n MethodName_,\n SpaceType_,\n space_,\n data_));\n\n indices_.back()->CreateIndex(RemainParams);\n }\n\n this->ResetQueryTimeParams(); // reset query time parameters\n}\n\ntemplate \nvoid MultiIndex::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n for (size_t i = 0; i < indices_.size(); ++i) {\n AnyParams ParamCopy(QueryTimeParams);\n indices_[i]->SetQueryTimeParams(ParamCopy);\n }\n}\n\ntemplate \nMultiIndex::~MultiIndex() {\n for (size_t i = 0; i < indices_.size(); ++i) \n delete indices_[i];\n}\n\ntemplate \nconst std::string MultiIndex::StrDesc() const {\n std::stringstream str;\n str << \"\" << indices_.size() << \" copies of \" << MethodName_;\n return str.str();\n}\n\ntemplate \nvoid MultiIndex::Search(RangeQuery* query, IdType) const {\n /* \n * There may be duplicates: the same object coming from \n * different indices. The set found is used to filter them out.\n */\n std::unordered_set found;\n\n for (size_t i = 0; i < indices_.size(); ++i) {\n RangeQuery TmpRes(space_, query->QueryObject(), query->Radius());\n indices_[i]->Search(&TmpRes);\n const ObjectVector& res = *TmpRes.Result();\n const std::vector& dists = *TmpRes.ResultDists();\n\n query->AddDistanceComputations(TmpRes.DistanceComputations());\n for (size_t i = 0; i < res.size(); ++i) {\n const Object* obj = res[i];\n if (!found.count(obj)) {\n query->CheckAndAddToResult(dists[i], obj);\n found.insert(obj);\n }\n }\n }\n}\n\ntemplate \nvoid MultiIndex::Search(KNNQuery* query, IdType) const {\n /* \n * There may be duplicates: the same object coming from \n * different indices. The set found is used to filter them out.\n */\n std::unordered_set found;\n\n for (size_t i = 0; i < indices_.size(); ++i) {\n KNNQuery TmpRes(space_, query->QueryObject(), query->GetK(), query->GetEPS());\n\n indices_[i]->Search(&TmpRes);\n unique_ptr> ResQ(TmpRes.Result()->Clone());\n\n query->AddDistanceComputations(TmpRes.DistanceComputations());\n while(!ResQ->Empty()) {\n const Object* obj = reinterpret_cast(ResQ->TopObject());\n\n /* \n * It's essential to check for previously added entries.\n * If we don't do this, the same close entry may be added multiple\n * times. At the same time, other relevant entries will be removed!\n */\n if (!found.count(obj->id())) {\n query->CheckAndAddToResult(ResQ->TopDistance(), obj);\n found.insert(obj->id());\n }\n ResQ->Pop();\n }\n }\n}\n\ntemplate class MultiIndex;\ntemplate class MultiIndex;\ntemplate class MultiIndex;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/method/multi_vantage_point_tree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"utils.h\"\n#include \"method/multi_vantage_point_tree_utils.h\"\n#include \"method/multi_vantage_point_tree.h\"\n\nnamespace similarity {\n\ntemplate \nMultiVantagePointTree::MultiVantagePointTree(\n const Space& space,\n const ObjectVector& data) : space_(space), data_(data) {\n}\n\ntemplate \nvoid MultiVantagePointTree::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"maxPathLen\", MaxPathLength_, 5);\n pmgr.GetParamOptional(\"bucketSize\", BucketSize_, 50);\n pmgr.GetParamOptional(\"chunkBucket\", ChunkBucket_, true);\n\n LOG(LIB_INFO) << \"maxPathLen= \" << MaxPathLength_;\n LOG(LIB_INFO) << \"bucketSize= \" << BucketSize_;\n LOG(LIB_INFO) << \"chunkBucket=\" << ChunkBucket_;\n\n pmgr.CheckUnused();\n\n if (BucketSize_ < 2) {\n throw runtime_error(\"Bug: The bucket size should be at least 2 (multi vantage point tree)\");\n }\n\n Entries entries;\n entries.reserve(data_.size());\n for (size_t i = 0; i < data_.size(); ++i) {\n entries.push_back(Entry(data_[i]));\n }\n root_.reset(BuildTree(&space_, entries));\n}\n\ntemplate \nMultiVantagePointTree::~MultiVantagePointTree() {\n}\n\ntemplate \nconst std::string MultiVantagePointTree::StrDesc() const {\n return \"mvp-tree\";\n}\n\ntemplate \ntypename MultiVantagePointTree::Node*\nMultiVantagePointTree::BuildTree(\n const Space* space,\n typename MultiVantagePointTree::Entries& entries) {\n if (entries.empty()) {\n return NULL;\n }\n\n const Object* pivot1 =\n Remove(entries, RandomInt() % entries.size()).object;\n const Object* pivot2 = NULL;\n\n if (entries.size() + 1 <= BucketSize_ + 2) {\n if (!entries.empty()) {\n size_t pivot2_idx = 0;\n dist_t pivot2_dist = 0;\n for (size_t i = 0; i < entries.size(); ++i) {\n entries[i].d1 = space->IndexTimeDistance(pivot1, entries[i].object);\n if (entries[i].d1 > pivot2_dist) {\n pivot2_idx = i;\n pivot2_dist = entries[i].d1;\n }\n }\n pivot2 = Remove(entries, pivot2_idx).object;\n for (size_t i = 0; i < entries.size(); ++i) {\n entries[i].d2 = space->IndexTimeDistance(pivot2, entries[i].object);\n }\n }\n LeafNode* node = new LeafNode(pivot1, pivot2, entries, ChunkBucket_);\n return node;\n } else {\n dist_t m1 = 0;\n dist_t m21 = 0, m22 = 0;\n MultiVantagePointTree::Entries entry_list11, entry_list12;\n MultiVantagePointTree::Entries entry_list21, entry_list22;\n if (!entries.empty()) {\n for (size_t i = 0; i < entries.size(); ++i) {\n entries[i].d1 = space->IndexTimeDistance(pivot1, entries[i].object);\n if (entries[i].path.size() < MaxPathLength_) {\n entries[i].path.push_back(entries[i].d1);\n }\n }\n std::sort(entries.begin(), entries.end(), Dist1AscComparator());\n CHECK(entries[0].d1 <= entries[entries.size() - 1].d1);\n // entries = elist1+elist2\n MultiVantagePointTree::Entries elist1, elist2;\n m1 = SplitByMedian(entries, elist1, elist2).d1;\n pivot2 = Remove(elist2, RandomInt() % elist2.size()).object;\n for (size_t i = 0; i < elist1.size(); ++i) {\n elist1[i].d2 = space->IndexTimeDistance(pivot2, elist1[i].object);\n if (elist1[i].path.size() < MaxPathLength_) {\n elist1[i].path.push_back(elist1[i].d2);\n }\n }\n for (size_t i = 0; i < elist2.size(); ++i) {\n elist2[i].d2 = space->IndexTimeDistance(pivot2, elist2[i].object);\n if (elist2[i].path.size() < MaxPathLength_) {\n elist2[i].path.push_back(elist2[i].d2);\n }\n }\n std::sort(elist1.begin(), elist1.end(), Dist2AscComparator());\n CHECK(elist1[0].d2 <= elist1[elist1.size() - 1].d2); \n std::sort(elist2.begin(), elist2.end(), Dist2AscComparator());\n CHECK(elist2[0].d2 <= elist2[elist2.size() - 1].d2); \n m21 = SplitByMedian(elist1, entry_list11, entry_list12).d2;\n m22 = SplitByMedian(elist2, entry_list21, entry_list22).d2;\n }\n InternalNode* node = new InternalNode(pivot1, pivot2, m1, m21, m22);\n node->child1_ = BuildTree(space, entry_list11);\n node->child2_ = BuildTree(space, entry_list12);\n node->child3_ = BuildTree(space, entry_list21);\n node->child4_ = BuildTree(space, entry_list22);\n return node;\n }\n}\n\ntemplate \nvoid MultiVantagePointTree::Search(RangeQuery* query, IdType) const {\n int mx = MaxLeavesToVisit_;\n Dists path(MaxPathLength_);\n GenericSearch(root_.get(), query, path, 0, mx);\n}\n\ntemplate \nvoid MultiVantagePointTree::Search(KNNQuery* query, IdType) const {\n int mx = MaxLeavesToVisit_;\n Dists path(MaxPathLength_);\n GenericSearch(root_.get(), query, path, 0, mx);\n}\n\n// Range search algorithm\ntemplate \ntemplate \nvoid MultiVantagePointTree::GenericSearch(\n typename MultiVantagePointTree::Node* node,\n QueryType* query,\n Dists& path,\n size_t query_path_len,\n int& MaxLeavesToVisit) const {\n if (node == NULL) {\n return;\n }\n if (MaxLeavesToVisit <= 0) return; // early termination\n\n const bool exists_p1 = node->pivot1_ != NULL;\n const bool exists_p2 = node->pivot2_ != NULL;\n const dist_t dp1 = exists_p1 ? query->DistanceObjLeft(node->pivot1_) : DistMax();\n const dist_t dp2 = exists_p2 ? query->DistanceObjLeft(node->pivot2_) : DistMax();\n\n if (exists_p1) query->CheckAndAddToResult(dp1, node->pivot1_);\n if (exists_p2) query->CheckAndAddToResult(dp2, node->pivot2_);\n\n if (node->isleaf()) {\n --MaxLeavesToVisit;\n const LeafNode* leaf_node = reinterpret_cast(node);\n const Entries& entries = leaf_node->entries_;\n for (size_t i = 0; i < entries.size(); ++i) {\n if (dp1 - query->Radius() <= entries[i].d1 &&\n dp1 + query->Radius() >= entries[i].d1 &&\n dp2 - query->Radius() <= entries[i].d2 &&\n dp2 + query->Radius() >= entries[i].d2) {\n size_t path_len = std::min(query_path_len, entries[i].path.size());\n bool ok = true;\n for (size_t k = 0; k < path_len; ++k) {\n if (path[k] - query->Radius() > entries[i].path[k] ||\n path[k] + query->Radius() < entries[i].path[k]) {\n ok = false;\n break;\n }\n }\n if (ok) {\n query->CheckAndAddToResult(entries[i].object);\n }\n }\n }\n } else {\n const InternalNode* internal_node =\n reinterpret_cast(node);\n\n if (exists_p1 && query_path_len < MaxPathLength_) {\n path[query_path_len++] = dp1;\n }\n if (exists_p2 && query_path_len < MaxPathLength_) {\n path[query_path_len++] = dp2;\n }\n\n bool FirstRight1 = dp1 > internal_node->m1_; \n\n for (int order1 = 0; order1 < 2; ++ order1) {\n if (order1 == int(FirstRight1) && dp1 - query->Radius() <= internal_node->m1_) { // left\n bool FirstRight2 = dp2 > internal_node->m21_;\n for (int order2 = 0; order2 < 2; ++order2) {\n if (order2 == int(FirstRight2) && dp2 - query->Radius() <= internal_node->m21_) { // left-left\n GenericSearch(internal_node->child1_, query, path, query_path_len, MaxLeavesToVisit);\n }\n\t\t if (order2 != int(FirstRight2) && dp2 + query->Radius() >= internal_node->m21_) { // left-right\n GenericSearch(internal_node->child2_, query, path, query_path_len, MaxLeavesToVisit);\n }\n }\n }\n\t if (order1 != int(FirstRight1) && dp1 + query->Radius() >= internal_node->m1_) { // right\n bool FirstRight2 = dp2 > internal_node->m22_;\n for (int order2 = 0; order2 < 2; ++order2) {\n if (order2 == int(FirstRight2) && dp2 - query->Radius() <= internal_node->m22_) { // right-left\n GenericSearch(internal_node->child3_, query, path, query_path_len, MaxLeavesToVisit);\n }\n\t\t if (order2 != int(FirstRight2) && dp2 + query->Radius() >= internal_node->m22_) { // right-right\n GenericSearch(internal_node->child4_, query, path, query_path_len, MaxLeavesToVisit);\n }\n }\n }\n }\n }\n}\n\ntemplate class MultiVantagePointTree;\ntemplate class MultiVantagePointTree;\ntemplate class MultiVantagePointTree;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/nndes.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * This is a wrapper class for the Wei Dong implementation of https://code.google.com/p/nndes/,\n * which also contains some of the original code from Wei Dong's repository. \n * Wei Dong implemented only the graph construction algorithm [1]. \n * We implemented two search algorithms\n * i) A greedy walk that starts from a random point and always proceeds to the closest neighbor [2].\n * ii) A priority queue based procedure where the queue may contain more distance not necessarily\n * adjacent nodes [3]\n *\n * 1. Wei Dong, Charikar Moses, and Kai Li. 2011. \n * Efficient k-nearest neighbor graph construction for generic similarity measures. \n * In Proceedings of the 20th international conference on World wide web (WWW '11). \n * ACM, New York, NY, USA, 577-586. \n *\n * 2. K. Hajebi, Y. Abbasi-Yadkori, H. Shahbazi, and H. Zhang. \n * Fast approximate nearest-neighbor search with k-nearest neighbor graph. \n * In IJCAI Proceedings-International Joint Conference on Artificial Intelligence, \n * volume 22, page 1312, 2011\n *\n * 3. The main publication is as follows, but the basic algorithm was also presented as SISAP'12: \n * Malkov, Y., Ponomarenko, A., Logvinov, A., & Krylov, V., 2014. \n * Approximate nearest neighbor algorithm based on navigable small world graphs. Information Systems, 45, 61-68. \n * \n * The Wei Dong's code (to construct the knn-graph) can be redistributed given \n * that the license (see below) is retained in the source code.\n */\n/*\nCopyright (C) 2010,2011 Wei Dong \nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"space.h\"\n#include \"utils.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"method/nndes.h\"\n\n#define USE_BITSET_FOR_SEARCHING 1\n\nnamespace similarity {\n\ntemplate \nNNDescentMethod::NNDescentMethod(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) :\n space_(space), data_(data), PrintProgress_(PrintProgress),\n controlQty_(0), // default value from Wei Dong's code\n nndesOracle_(space, data)\n{}\n\ntemplate \nvoid NNDescentMethod::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"NN\", NN_, 20);// default value from Wei Dong's code\n pmgr.GetParamOptional(\"iterationQty\", iterationQty_, 100); // default value from Wei Dong's code\n\n pmgr.GetParamOptional(\"rho\", rho_, 1.0); // Fast rho is 0.5, 1.0 is from Wei Dong's code\n pmgr.GetParamOptional(\"delta\", delta_, 0.001), // default value from Wei Dong's code\n\n LOG(LIB_INFO) << \"NN = \" << NN_;\n LOG(LIB_INFO) << \"iterationQty = \" << iterationQty_;\n LOG(LIB_INFO) << \"rho = \" << rho_;\n LOG(LIB_INFO) << \"delta = \" << delta_;\n\n SetQueryTimeParams(getEmptyParams()); // reset query-time parameter\n\n\n LOG(LIB_INFO) << \"Starting NN-Descent...\";\n\n nndesObj_.reset(new NNDescent(data_.size(), // N\n NN_, //K \n rho_, //S, \n nndesOracle_, GRAPH_BOTH));\n\n float total = float(data_.size()) * (data_.size() - 1) / 2;\n cout.precision(5);\n cout.setf(ios::fixed);\n for (int it = 0; it < iterationQty_; ++it) {\n int t = nndesObj_->iterate(PrintProgress_);\n float rate = float(t) / (NN_ * data_.size());\n\n// TODO @leo computation of recall needs to be re-written, can't use original Wei Dong's code\n/*\n float recall = 0;\n if (controlQty_) { // report accuracy\n const vector &nn = nndes.getNN();\n#pragma omp parallel for default(shared) reduction(+:recall) \n for (int i = 0; i < control_knn.size(); ++i) {\n recall += nndes::recall(control_knn[i], nn[control_index[i]], K);\n }\n recall /= control;\n }\n cout << setw(2) << it << \" update:\" << rate << \" recall:\" << recall << \" cost:\" << float(nndes.getCost())/total << endl;\n*/\n LOG(LIB_INFO) << setw(2) << it << \" update:\" << rate << \" cost:\" << float(nndesObj_->getCost())/total;\n if (rate < delta_) break;\n }\n\n\n LOG(LIB_INFO) << \"NN-Descent finished!\";\n}\n\ntemplate \nvoid NNDescentMethod::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search is not supported!\");\n}\n\ntemplate \nvoid NNDescentMethod::Search(KNNQuery* query, IdType) const {\n greedy_ ? SearchGreedy(query) : SearchSmallWorld(query);\n}\n\ntemplate \nvoid NNDescentMethod::SearchSmallWorld(KNNQuery* query) const {\n const vector &nn = nndesObj_->getNN();\n\n#if USE_BITSET_FOR_SEARCHING\n vector visitedBitset(data_.size());\n#else\n unordered_set visitedNodes;\n#endif\n\n for (size_t i=0; i < initSearchAttempts_; i++) {\n /**\n * Search of most k-closest elements to the query.\n */\n IdType randPoint = RandomInt() % data_.size();\n\n priority_queue closestDistQueue; //The set of all elements which distance was calculated\n priority_queue candidateSet; //the set of elements which we can use to evaluate\n\n const Object* currObj = data_[randPoint];\n dist_t d = query->DistanceObjLeft(currObj);\n query->CheckAndAddToResult(d, currObj);\n \n EvaluatedNode ev(-d, randPoint);\n\n candidateSet.push(ev);\n closestDistQueue.push(d);\n#if USE_BITSET_FOR_SEARCHING\n visitedBitset[randPoint] = true;\n#else\n visitedNodes.insert(randPoint);\n#endif\n\n while(!candidateSet.empty()){\n const EvaluatedNode& currEv = candidateSet.top();\n dist_t lowerBound = closestDistQueue.top();\n\n // Did we reach a local minimum?\n if ((-currEv.first) > lowerBound) {\n break;\n }\n\n IdType currEvId = currEv.second;\n\n // Can't access curEv anymore! The reference would become invalid\n candidateSet.pop();\n\n //calculate distance to each neighbor\n for (const KNNEntry& e: nn[currEvId]) {\n IdType currNew = e.key;\n if (currNew == KNNEntry::BAD) continue;\n\n#if USE_BITSET_FOR_SEARCHING\n if (!visitedBitset[currNew]) {\n visitedBitset[currNew] = true;\n#else\n if (visitedNodes.find(currNew) == visitedNodes.end()){\n visitedNodes.insert(currNew);\n#endif\n\n currObj = data_[currNew];\n d = query->DistanceObjLeft(currObj);\n query->CheckAndAddToResult(d, currObj);\n EvaluatedNode evE1(-d, currNew);\n\n closestDistQueue.push(d);\n if (closestDistQueue.size() > efSearch_) { \n closestDistQueue.pop(); \n }\n candidateSet.push(evE1);\n }\n }\n }\n }\n}\n\n\ntemplate \nvoid NNDescentMethod::SearchGreedy(KNNQuery* query) const {\n const vector &nn = nndesObj_->getNN();\n\n for (size_t i=0; i < initSearchAttempts_; i++) {\n IdType curr = RandomInt() % data_.size();\n\n dist_t currDist = query->DistanceObjLeft(data_[curr]);\n query->CheckAndAddToResult(currDist, data_[curr]);\n\n\n IdType currOld;\n\n do {\n currOld = curr;\n // Iterate over neighbors\n for (const KNNEntry&e: nn[currOld]) {\n IdType currNew = e.key;\n if (currNew != KNNEntry::BAD) {\n dist_t currDistNew = query->DistanceObjLeft(data_[currNew]);\n query->CheckAndAddToResult(currDistNew, data_[currNew]);\n if (currDistNew < currDist) {\n curr = currNew;\n currDist = currDistNew;\n }\n }\n }\n } while (currOld != curr);\n }\n}\n\ntemplate \nvoid \nNNDescentMethod::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n pmgr.GetParamOptional(\"initSearchAttempts\", initSearchAttempts_, 3);\n pmgr.GetParamOptional(\"efSearch\", efSearch_, NN_);\n pmgr.GetParamOptional(\"greedy\", greedy_, false);\n pmgr.CheckUnused();\n LOG(LIB_INFO) << \"Set NNDescentMethod query-time parameters:\";\n LOG(LIB_INFO) << \"initSearchAttempts=\" << initSearchAttempts_;\n LOG(LIB_INFO) << \"efSearch=\" << efSearch_;\n LOG(LIB_INFO) << \"greedy=\" << greedy_;\n}\n\ntemplate class NNDescentMethod;\ntemplate class NNDescentMethod;\ntemplate class NNDescentMethod;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/nonmetr_list_clust.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"cluster_util.h\"\n#include \"knnquery.h\"\n\n#include \"method/nonmetr_list_clust.h\"\n\nnamespace similarity {\n\ntemplate \nvoid NonMetrListClust::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n string clusterType;\n pmgr.GetParamRequired(\"clusterType\", clusterType);\n size_t centerQty;\n pmgr.GetParamRequired(\"centerQty\", centerQty);\n\n LOG(LIB_INFO) << \"clusterType=\" << clusterType;\n LOG(LIB_INFO) << \"centerQty=\" << centerQty;\n if (clusterType == CLUST_TYPE_FIRMAL) {\n size_t searchCloseIterQty = 500;\n pmgr.GetParamOptional(\"searchCloseIterQty\", searchCloseIterQty, searchCloseIterQty);\n size_t sampleDistQty = SAMPLE_LIST_CLUST_DEFAULT_SAMPLE_QTY;\n pmgr.GetParamOptional(\"sampleDistQty\", sampleDistQty, sampleDistQty);\n\n LOG(LIB_INFO) << \"searchCloseIterQty=\" << searchCloseIterQty;\n LOG(LIB_INFO) << \"sampleDistQty=\" << sampleDistQty;\n ClusterUtils::doFIRMAL(printProgress_, space_, data_, centerQty, vCenters_, vClusterAssign_, vUnassigned_,\n searchCloseIterQty, sampleDistQty, true /* do use all previous clusters in each iteration */);\n } else if (clusterType == CLUST_TYPE_CLARAN) {\n size_t randRestQty = CLARANS_RAND_RESTART_QTY;\n pmgr.GetParamOptional(\"randRestQty\", randRestQty, randRestQty);\n size_t inClusterSwapAttempts = CLARANS_SWAP_ATTEMPTS;\n pmgr.GetParamOptional(\"inClusterSwapAttempts\", inClusterSwapAttempts, inClusterSwapAttempts);\n size_t inClusterSampleQty = CLARANS_SAMPLE_QTY;\n pmgr.GetParamOptional(\"inClusterSampleQty\", inClusterSampleQty, inClusterSampleQty);\n\n LOG(LIB_INFO) << \"randRestQty=\" << randRestQty;\n ClusterUtils::doCLARANS(printProgress_, space_, data_, centerQty, vCenters_, vClusterAssign_,\n inClusterSwapAttempts, inClusterSampleQty, randRestQty);\n } else if (clusterType == CLUST_TYPE_REDUCT_CLARAN) {\n size_t inClusterSwapAttempts = CLARANS_SWAP_ATTEMPTS;\n pmgr.GetParamOptional(\"inClusterSwapAttempts\", inClusterSwapAttempts, inClusterSwapAttempts);\n size_t inClusterSampleQty = CLARANS_SAMPLE_QTY;\n pmgr.GetParamOptional(\"inClusterSampleQty\", inClusterSampleQty, inClusterSampleQty);\n\n IdTypeUnsign maxMetaIterQty = MAX_METAITER_CLARANS_ITER_QTY;\n pmgr.GetParamOptional(\"maxMetaIterQty\", maxMetaIterQty, maxMetaIterQty);\n float keepFrac = 0.2;\n pmgr.GetParamOptional(\"keepFrac\", keepFrac, keepFrac);\n LOG(LIB_INFO) << \"maxMetaIterQty = \" << maxMetaIterQty;\n LOG(LIB_INFO) << \"keepFrac = \" << keepFrac;\n ClusterUtils::doReductiveCLARANS(printProgress_, space_, data_,\n maxMetaIterQty, keepFrac,\n centerQty, vCenters_, vClusterAssign_, vUnassigned_,\n inClusterSwapAttempts, inClusterSampleQty);\n } else throw runtime_error(\"Wrong cluster type, expecting: \" + CLUST_TYPE_CLARAN + \" or \" + CLUST_TYPE_FIRMAL);\n\n ClusterUtils::printAndVerifyClusterStat(space_, vCenters_, vClusterAssign_, 1000);\n\n pmgr.CheckUnused();\n}\n\ntemplate \ntemplate \nvoid NonMetrListClust::GenericSearch(QueryType* query, IdType) const {\n size_t dbScan = 0;\n vector vis(maxObjId_ + 1);\n for (const Object* o:vUnassigned_) {\n if (vis[o->id()]) continue;\n query->CheckAndAddToResult(o);\n vis[o->id()] = true;\n dbScan++;\n }\n\n const Object* pQueryObj = query->QueryObject();\n\n if (dbScan < db_scan_) {\n vector> orderedClusters;\n\n for (IdTypeUnsign cid = 0; cid < vCenters_.size(); ++cid) {\n dist_t d = query->Distance(pQueryObj /* object or query is the left argument */,\n vCenters_[cid]/* cluster center is the right argument */);\n orderedClusters.push_back(make_pair(d, cid));\n }\n sort(orderedClusters.begin(), orderedClusters.end());\n\n CHECK(orderedClusters.size() < 2 || (orderedClusters[0].first <= orderedClusters[1].first));\n //cerr << \"######## @@@\";\n for (IdTypeUnsign i = 0; dbScan <= db_scan_ && i < orderedClusters.size(); ++i) {\n IdTypeUnsign cid = orderedClusters[i].second;\n //cerr << \" \" << orderedClusters[i].first;\n for (const DistObjectPair p: (*vClusterAssign_[cid])) {\n const Object* o = p.second;\n if (vis[o->id()]) continue;\n query->CheckAndAddToResult(o);\n vis[o->id()] = true;\n dbScan++;\n }\n }\n //cerr<\nvoid NonMetrListClust::Search(RangeQuery* query, IdType id) const {\n GenericSearch(query, id);\n}\n\ntemplate \nvoid NonMetrListClust::Search(KNNQuery* query, IdType id) const {\n GenericSearch(query, id);\n}\n\ntemplate \nvoid\nNonMetrListClust::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n // Check if a user specified extra parameters, which can be also misspelled variants of existing ones\n AnyParamManager pmgr(QueryTimeParams);\n float dbScanFrac;\n // Note that GetParamOptional() should always have a default value\n pmgr.GetParamOptional(\"dbScanFrac\", dbScanFrac, 0.1);\n CHECK_MSG(dbScanFrac > 0 && dbScanFrac <= 1, \"dbScanFrac should be >0 and <=1\");\n db_scan_ = size_t(ceil(dbScanFrac * data_.size()));\n LOG(LIB_INFO) << \"db_scan=\" << db_scan_;\n pmgr.CheckUnused();\n}\n\n\ntemplate class NonMetrListClust;\ntemplate class NonMetrListClust;\ntemplate class NonMetrListClust;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/omedrank.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * Patent ALERT: even though the code is released under the liberal Apache 2 license,\n * the underlying search method is patented. Therefore, it's free to use in research,\n * but may be problematic in a production setting.\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"permutation_utils.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"method/omedrank.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nOMedRank::OMedRank(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) :\n space_(space), data_(data), PrintProgress_(PrintProgress),\n index_qty_(0) // If ComputeDbScan is called before index_qty_ is computed, it will see this zero\n{ }\n\ntemplate \nvoid OMedRank::CreateIndex(const AnyParams &IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"projType\", proj_type_, PROJ_TYPE_RAND);\n pmgr.GetParamOptional(\"intermDim\", interm_dim_, 0);\n pmgr.GetParamOptional(\"numPivot\", num_pivot_, 8);\n\n pmgr.GetParamOptional(\"chunkIndexSize\", chunk_index_size_, 65536);\n\n ToLower(proj_type_);\n if (PROJ_TYPE_PERM_BIN == proj_type_)\n throw runtime_error(\"This method cannot be used with binarized permutations!\");\n\n projection_.reset(Projection::createProjection(\n space_,\n data_,\n proj_type_,\n interm_dim_,\n num_pivot_,\n 0 /* can't be used with binarized permutations */));\n\n if (projection_.get() == NULL) {\n throw runtime_error(\"Cannot create projection class '\" + proj_type_ + \"'\" +\n \" for the space: '\" + space_.StrDesc() +\"' \" +\n \" distance value type: '\" + DistTypeName() + \"'\");\n }\n\n index_qty_ = (data_.size() + chunk_index_size_ - 1) / chunk_index_size_;\n \n pmgr.CheckUnused();\n\n LOG(LIB_INFO) << \"# of entries in an index chunk = \" << chunk_index_size_;\n LOG(LIB_INFO) << \"# of index chunks = \" << index_qty_;\n LOG(LIB_INFO) << \"projection type: \" << proj_type_;\n LOG(LIB_INFO) << \"intermediate dim: \" << interm_dim_;\n LOG(LIB_INFO) << \"# pivots/target dim \" << num_pivot_;\n\n posting_lists_.resize(index_qty_);\n\n for (size_t chunkId = 0; chunkId < index_qty_; ++chunkId) {\n posting_lists_[chunkId] = shared_ptr>(new vector());\n }\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n for (size_t chunkId = 0; chunkId < index_qty_; ++chunkId) {\n IndexChunk(chunkId, progress_bar.get());\n }\n}\n\ntemplate \ntemplate \nvoid OMedRank::GenSearch(QueryType* query, size_t K) const {\n // Let's make this check here. Otherwise, if you misspell dbScanFrac, you will get \n // a strange error message that says: dbScanFrac should be in the range [0,1].\n if (!knn_amp_) {\n if (db_scan_frac_ < 0.0 || db_scan_frac_ > 1.0) {\n stringstream err;\n err << METH_OMEDRANK << \" requires that dbScanFrac is in the range [0,1]\";\n throw runtime_error(err.str());\n }\n }\n\n size_t db_scan = computeDbScan(K);\n\n /* \n * Let these guys have num_pivot_ elements despite\n * only num_pivot_search_ (potentially << num_pivot_) are used.\n * This simplifies the code: element arrays can be addressed\n * using the pivotId that can be > num_pivot_search_\n */\n vector lowIndx(num_pivot_);\n vector highIndx(num_pivot_);\n\n ObjectInvEntry e(IdType(0), 0); \n\n vector counter(chunk_index_size_);\n vector projDists(num_pivot_);\n\n projection_->compProj(query, NULL, &projDists[0]);\n\n typedef pair DIPair;\n\n // Let's use the closest pivots\n priority_queue closePivots; \n\n for (size_t i = 0 ; i < num_pivot_; ++i) {\n dist_t posDist = projDists[i] > 0 ? projDists[i] : - projDists[i]; \n /*\n * minus means that the closest pivots are selected. This works\n * better for random projections. However, it appears to work\n * worse for type = rand (at least for CoPhIr)\n */\n closePivots.push(make_pair(-posDist, i));\n if (closePivots.size() > num_pivot_search_) closePivots.pop();\n }\n\n if (closePivots.size() != num_pivot_search_) {\n stringstream err;\n err << \"Bug: the number of close pivots '\" << closePivots.size() << \"' isn't equal to \" << num_pivot_search_;\n throw runtime_error(err.str());\n }\n\n vector closePivotIds(num_pivot_search_);\n\n // Note we should have exactly num_pivot_search_ elements in the queue\n for (size_t kk = 0; kk < num_pivot_search_; ++kk) {\n closePivotIds[kk] = closePivots.top().second; \n closePivots.pop();\n } \n\n for (size_t chunkId = 0; chunkId < posting_lists_.size(); ++chunkId) {\n const auto & chunkPostLists = *posting_lists_[chunkId];\n\n if (chunkId != 0)\n memset(&counter[0], 0, sizeof(counter[0])*counter.size());\n\n //for (size_t i = 0 ; i < num_pivot_; ++i) {\n for (size_t kk = 0; kk < num_pivot_search_; ++kk) {\n size_t pivotId = closePivotIds[kk];\n // Again, pivot is the left argument, see the comment in the constructor\n e.pivot_dist_ = projDists[pivotId];\n lowIndx[pivotId] = (lower_bound(chunkPostLists[pivotId].begin(), chunkPostLists[pivotId].end(), e) - chunkPostLists[pivotId].begin());;\n --lowIndx[pivotId]; // Can become less than zero\n highIndx[pivotId] = lowIndx[pivotId] + 1;\n CHECK(chunkPostLists[pivotId].size() > 0);\n CHECK(lowIndx[pivotId] < 0 || static_cast(lowIndx[pivotId]) < chunkPostLists[pivotId].size());\n CHECK(highIndx[pivotId] >= 0);\n }\n\n bool eof = false;\n size_t totOp = 0;\n size_t scannedQty = 0;\n size_t minMatchPivotQty = max(size_t(1), static_cast(round(min_freq_ * num_pivot_search_)));\n\n size_t minId = chunkId * chunk_index_size_;\n size_t maxId = min(data_.size(), minId + chunk_index_size_);\n size_t chunkQty = (maxId - minId);\n\n CHECK(chunkQty <= chunk_index_size_);\n\n while (scannedQty < min(db_scan, chunkQty) && !eof) {\n eof = true;\n //for (size_t i = 0 ; i < num_pivot_; ++i) {\n for (size_t kk = 0; kk < num_pivot_search_; ++kk) {\n size_t pivotId = closePivotIds[kk];\n IdType indx[2];\n unsigned iQty = 0;\n\n if (lowIndx[pivotId] >= 0) {\n indx[iQty++]=lowIndx[pivotId];\n --lowIndx[pivotId];\n }\n if (static_cast(highIndx[pivotId]) < chunkPostLists[pivotId].size()) {\n indx[iQty++]=highIndx[pivotId];\n ++highIndx[pivotId];\n }\n\n for (unsigned k = 0; k < iQty; ++k) {\n ++totOp;\n IdType objIdDiff = chunkPostLists[pivotId][indx[k]].id_;\n unsigned freq = counter[objIdDiff]++;\n\n if (freq == minMatchPivotQty) { // Add only the first time when we exceeded the threshold!\n ++scannedQty;\n if (!skip_check_) query->CheckAndAddToResult(data_[objIdDiff + minId]);\n } \n eof = false;\n }\n }\n }\n }\n}\n \n\ntemplate \nvoid OMedRank::Search(RangeQuery* query, IdType) const {\n GenSearch(query, 0);\n}\n\ntemplate \nvoid OMedRank::Search(KNNQuery* query, IdType) const {\n GenSearch(query, query->GetK());\n}\n\ntemplate \nvoid OMedRank::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n\n pmgr.GetParamOptional(\"skipChecking\", skip_check_, false);\n\n pmgr.GetParamOptional(\"minFreq\", min_freq_, 0.5);\n pmgr.GetParamOptional(\"numPivotSearch\", num_pivot_search_, num_pivot_);\n\n if (num_pivot_search_ > num_pivot_) \n throw runtime_error(\"numPivotSearch can't be > numPivot\");\n\n if (pmgr.hasParam(\"dbScanFrac\") && pmgr.hasParam(\"knnAmp\")) {\n throw runtime_error(\"One shouldn't specify both parameters dbScanFrac and knnAmp\");\n }\n \n pmgr.GetParamOptional(\"dbScanFrac\", db_scan_frac_, 0.05);\n pmgr.GetParamOptional(\"knnAmp\", knn_amp_, 0);\n\n pmgr.CheckUnused();\n \n LOG(LIB_INFO) << \"Set query-time parameters for OMedRank:\";\n LOG(LIB_INFO) << \"# dbScanFrac = \" << db_scan_frac_;\n LOG(LIB_INFO) << \"# knnAmp = \" << knn_amp_;\n LOG(LIB_INFO) << \"# minFreq = \" << min_freq_;\n LOG(LIB_INFO) << \"# numPivotSearch = \" << num_pivot_search_;\n LOG(LIB_INFO) << \"# skipChecking = \" << skip_check_;\n}\n\ntemplate \nvoid OMedRank::IndexChunk(size_t chunkId, ProgressDisplay* displayBar) {\n size_t minId = chunkId * chunk_index_size_;\n size_t maxId = min(data_.size(), minId + chunk_index_size_);\n\n auto & chunkPostLists = *posting_lists_[chunkId];\n chunkPostLists.resize(num_pivot_);\n\n vector projDists(num_pivot_);\n\n\n for (size_t i = 0; i < maxId - minId; ++i) {\n IdType id = minId + i;\n\n projection_->compProj(NULL, data_[id], &projDists[0]);\n\n for (size_t j = 0; j < num_pivot_; ++j) {\n /* \n * Object (in this case pivot) is the left argument.\n * At search time, the right argument of the distance will be the query point\n * and pivot again will be the left argument.\n */\n dist_t leftObjDst = projDists[j];\n chunkPostLists[j].push_back(ObjectInvEntry(id - minId, leftObjDst));\n }\n if (displayBar) ++(*displayBar);\n }\n for (size_t j = 0; j < num_pivot_; ++j) {\n sort(chunkPostLists[j].begin(), chunkPostLists[j].end());\n }\n}\n\ntemplate class OMedRank;\ntemplate class OMedRank;\ntemplate class OMedRank;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/perm_bin_vptree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"space/space_bit_hamming.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"knnqueue.h\"\n#include \"method/perm_bin_vptree.h\"\n#include \"utils.h\"\n#include \"distcomp.h\"\n#include \"logging.h\"\n#include \"report_intr_dim.h\"\n\nnamespace similarity {\n\nusing std::unique_ptr;\n\ntemplate \nPermBinVPTree::PermBinVPTree(\n bool PrintProgress,\n Space& space,\n const ObjectVector& data) : \n space_(space), data_(data), \n PrintProgress_(PrintProgress),\n VPTreeSpace_(new SpaceBitHamming())\n{}\n\ntemplate \nvoid PermBinVPTree::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n size_t NumPivot;\n\n pmgr.GetParamOptional(\"numPivot\", NumPivot, 16);\n pmgr.GetParamOptional(\"binThreshold\", bin_threshold_, NumPivot / 2);\n\n bin_perm_word_qty_ = (NumPivot + 31)/32;\n\n\n LOG(LIB_INFO) << \"NumPivot = \" << NumPivot;\n LOG(LIB_INFO) << \"binThreshold = \" << bin_threshold_;\n LOG(LIB_INFO) << \"# binary entry size (words) = \" << bin_perm_word_qty_;\n\n AnyParams RemainParams = pmgr.ExtractParametersExcept({ \"numPivot\", \"binThreshold\"});\n\n\n GetPermutationPivot(data_, space_, NumPivot, &pivots_);\n BinPermData_.resize(data_.size());\n\n for (size_t i = 0; i < data_.size(); ++i) {\n Permutation TmpPerm;\n GetPermutation(pivots_, space_, data_[i], &TmpPerm);\n vector binPivot;\n Binarize(TmpPerm, bin_threshold_, binPivot);\n CHECK(binPivot.size() == bin_perm_word_qty_);\n BinPermData_[i] = VPTreeSpace_->CreateObjFromVect(i, -1, binPivot);\n }\n\n ReportIntrinsicDimensionality(\"Set of permutations\" , *VPTreeSpace_, BinPermData_);\n VPTreeIndex_.reset(new VPTree>(\n PrintProgress_,\n *VPTreeSpace_,\n BinPermData_));\n\n VPTreeIndex_->CreateIndex(RemainParams);\n // Reset parameters only after the VP-tree index is created!\n this->ResetQueryTimeParams();\n}\n\ntemplate \nvoid PermBinVPTree::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n\n AnyParams vptreeQueryParams = pmgr.ExtractParameters(VPTreeIndex_->getQueryTimeParams());\n VPTreeIndex_->SetQueryTimeParams(vptreeQueryParams);\n\n float dbScanFrac = 0;\n\n pmgr.GetParamOptional(\"dbScanFrac\", dbScanFrac, 0.05);\n\n if (dbScanFrac < 0.0 || dbScanFrac > 1.0) {\n PREPARE_RUNTIME_ERR(err) << METH_PERM_BIN_VPTREE << \" requires that dbScanFrac is in the range [0,1]\";\n THROW_RUNTIME_ERR(err);\n }\n\n\n LOG(LIB_INFO) << \"Set query-time parameters for PermBinVPTree:\";\n LOG(LIB_INFO) << \"dbScanFrac=\" << dbScanFrac;\n\n db_scan_qty_ = max(size_t(1), static_cast(dbScanFrac * data_.size()));\n\n LOG(LIB_INFO) << \"db_scan_qty_=\" << db_scan_qty_;\n\n pmgr.CheckUnused();\n}\n\n\ntemplate \nPermBinVPTree::~PermBinVPTree() {\n for (size_t i = 0; i < data_.size(); ++i) {\n delete BinPermData_[i];\n }\n}\n\ntemplate \nconst std::string PermBinVPTree::StrDesc() const {\n std::stringstream str;\n str << \"binarized permutation (vptree)\";\n return str.str();\n}\n\ntemplate \nvoid PermBinVPTree::Search(RangeQuery* query, IdType) const {\n Permutation perm_q;\n GetPermutation(pivots_, query, &perm_q);\n\n vector binPivot;\n Binarize(perm_q, bin_threshold_, binPivot);\n CHECK(binPivot.size() == bin_perm_word_qty_);\n\n unique_ptr QueryObject(VPTreeSpace_->CreateObjFromVect(0, -1, binPivot));\n\n unique_ptr> VPTreeQuery(new KNNQuery(*VPTreeSpace_, QueryObject.get(), db_scan_qty_, 0.0));\n\n VPTreeIndex_->Search(VPTreeQuery.get(), -1);\n\n unique_ptr> ResQueue(VPTreeQuery->Result()->Clone());\n\n while (!ResQueue->Empty()) {\n size_t id = reinterpret_cast(ResQueue->TopObject())->id();\n query->CheckAndAddToResult(data_[id]);\n ResQueue->Pop();\n }\n}\n\ntemplate \nvoid PermBinVPTree::Search(KNNQuery* query, IdType) const {\n Permutation perm_q;\n GetPermutation(pivots_, query, &perm_q);\n\n vector binPivot;\n Binarize(perm_q, bin_threshold_, binPivot);\n CHECK(binPivot.size() == bin_perm_word_qty_);\n\n unique_ptr QueryObject(VPTreeSpace_->CreateObjFromVect(0, -1, binPivot));\n\n unique_ptr> VPTreeQuery(new KNNQuery(*VPTreeSpace_, QueryObject.get(), db_scan_qty_, 0.0));\n\n VPTreeIndex_->Search(VPTreeQuery.get(), -1);\n\n unique_ptr> ResQueue(VPTreeQuery->Result()->Clone());\n\n while (!ResQueue->Empty()) {\n size_t id = reinterpret_cast(ResQueue->TopObject())->id();\n query->CheckAndAddToResult(data_[id]);\n ResQueue->Pop();\n }\n}\n\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\ntemplate class PermBinVPTree;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/perm_index_incr_bin.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"ported_boost_progress.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"incremental_quick_select.h\"\n#include \"method/perm_index_incr_bin.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\ntemplate \nPermutationIndexIncrementalBin::PermutationIndexIncrementalBin(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) \n : space_(space), data_(data), PrintProgress_(PrintProgress) {}\n\ntemplate \nvoid PermutationIndexIncrementalBin::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"numPivot\", num_pivot_, 16);\n pmgr.GetParamOptional(\"binThreshold\", bin_threshold_, num_pivot_ / 2);\n\n bin_perm_word_qty_ = (num_pivot_ + 31)/32,\n\n pmgr.CheckUnused();\n this->ResetQueryTimeParams();\n\n LOG(LIB_INFO) << \"# pivots = \" << num_pivot_;\n LOG(LIB_INFO) << \"# binarization threshold = \" << bin_threshold_;\n LOG(LIB_INFO) << \"# binary entry size (words) = \" << bin_perm_word_qty_;\n\n GetPermutationPivot(data_, space_, num_pivot_, &pivot_);\n\n permtable_.resize(data_.size() * bin_perm_word_qty_);\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n for (size_t i = 0, start = 0; i < data_.size(); ++i, start += bin_perm_word_qty_) {\n Permutation TmpPerm;\n GetPermutation(pivot_, space_, data_[i], &TmpPerm);\n CHECK(TmpPerm.size() == num_pivot_);\n vector binPivot;\n Binarize(TmpPerm, bin_threshold_, binPivot);\n CHECK(binPivot.size() == bin_perm_word_qty_);\n memcpy(&permtable_[start], &binPivot[0], bin_perm_word_qty_ * sizeof(binPivot[0]));\n if (progress_bar) ++(*progress_bar);\n }\n //SavePermTable(permtable_, \"permtab\");\n}\n \ntemplate \nvoid PermutationIndexIncrementalBin::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n\n pmgr.GetParamOptional(\"skipChecking\", skip_checking_, false);\n pmgr.GetParamOptional(\"useSort\", use_sort_, true);\n pmgr.GetParamOptional(\"maxHammingDist\", max_hamming_dist_, num_pivot_);\n\n \n if (pmgr.hasParam(\"dbScanFrac\") && pmgr.hasParam(\"knnAmp\")) {\n throw runtime_error(\"One shouldn't specify both parameters dbScanFrac and knnAmp\");\n }\n \n pmgr.GetParamOptional(\"dbScanFrac\", db_scan_frac_, 0.05);\n pmgr.GetParamOptional(\"knnAmp\", knn_amp_, 0);\n\n pmgr.CheckUnused();\n\n LOG(LIB_INFO) << \"Set query-time parameters fro PermutationIndexIncrementalBin:\";\n LOG(LIB_INFO) << \"use sort = \" << use_sort_;\n if (use_sort_) {\n LOG(LIB_INFO) << \"db scan fraction = \" << db_scan_frac_;\n } else {\n LOG(LIB_INFO) << \"max hamming distance = \" << max_hamming_dist_;\n }\n LOG(LIB_INFO) << \"skip checking = \" << skip_checking_;\n}\n\ntemplate \nPermutationIndexIncrementalBin::~PermutationIndexIncrementalBin() {\n}\n\ntemplate \nconst std::string PermutationIndexIncrementalBin::StrDesc() const {\n std::stringstream str;\n str << \"permutation binarized (incr. sorting)\";\n return str.str();\n}\n\ntemplate \ntemplate \nvoid PermutationIndexIncrementalBin::GenSearch(QueryType* query, size_t K) const {\n // Let's make this check here. Otherwise, if you misspell dbScanFrac, you will get \n // a strange error message that says: dbScanFrac should be in the range [0,1].\n if (!knn_amp_) {\n if (db_scan_frac_ < 0.0 || db_scan_frac_ > 1.0) {\n stringstream err;\n err << METH_PERMUTATION_INC_SORT_BIN << \" requires that dbScanFrac is in the range [0,1]\";\n throw runtime_error(err.str());\n }\n }\n\n size_t db_scan = computeDbScan(K);\n\n\n Permutation perm_q;\n GetPermutation(pivot_, query, &perm_q);\n vector binPivot;\n Binarize(perm_q, bin_threshold_, binPivot);\n\n std::vector perm_dists;\n perm_dists.reserve(data_.size());\n\n if (use_sort_) {\n for (size_t i = 0, start = 0; i < data_.size(); ++i, start += bin_perm_word_qty_) {\n perm_dists.push_back(std::make_pair(BitHamming(&permtable_[start], &binPivot[0], bin_perm_word_qty_), i));\n }\n\n IncrementalQuickSelect quick_select(perm_dists);\n for (size_t i = 0; i < db_scan; ++i) {\n const size_t idx = quick_select.GetNext().second;\n quick_select.Next();\n if (!skip_checking_) query->CheckAndAddToResult(data_[idx]);\n }\n } else {\n for (size_t i = 0, start = 0; i < data_.size(); ++i, start += bin_perm_word_qty_) {\n if (BitHamming(&permtable_[start], &binPivot[0], bin_perm_word_qty_) < max_hamming_dist_) {\n if (!skip_checking_) query->CheckAndAddToResult(data_[i]);\n }\n }\n }\n}\n\ntemplate \nvoid PermutationIndexIncrementalBin::Search(RangeQuery* query, IdType) const {\n GenSearch(query, 0);\n}\n\ntemplate \nvoid PermutationIndexIncrementalBin::Search(KNNQuery* query, IdType) const {\n GenSearch(query, query->GetK());\n}\n\n\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\ntemplate class PermutationIndexIncrementalBin;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/perm_lsh_bin.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n\n#include \"space.h\"\n#include \"params.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"method/perm_lsh_bin.h\"\n#include \"ported_boost_progress.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\ntemplate \nPermutationIndexLSHBin::PermutationIndexLSHBin(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) : \n space_(space), data_(data), printProgress_(PrintProgress) {}\n\ntemplate \nvoid PermutationIndexLSHBin::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"numPivot\", num_pivot_, 32);\n pmgr.GetParamOptional(\"binThreshold\", bin_threshold_, num_pivot_ / 2);\n pmgr.GetParamOptional(\"bitSampleQty\", bit_sample_qty_, 20 /* good for 1m records */);\n pmgr.GetParamOptional(\"L\", num_hash_, 50);\n\n if (!bit_sample_qty_) {\n throw runtime_error(\"bitSampleQty should be non-zero\");\n }\n\n if (bit_sample_qty_ > 63 || bit_sample_qty_ > num_pivot_) {\n stringstream err;\n err << \"bitSampleQty cannot be larger than \" << min(size_t(63), num_pivot_);\n throw runtime_error(err.str());\n }\n\n hash_table_size_ = 1 << bit_sample_qty_;\n \n LOG(LIB_INFO) << \"# of hashes = \" << num_hash_;\n LOG(LIB_INFO) << \"hashe table size = \" << hash_table_size_;\n LOG(LIB_INFO) << \"# pivots = \" << num_pivot_;\n LOG(LIB_INFO) << \"bin threshold = \" << bin_threshold_;\n LOG(LIB_INFO) << \"bit sample qty = \" << bit_sample_qty_;\n\n pmgr.CheckUnused();\n this->ResetQueryTimeParams();\n\n pivots_.resize(num_hash_);\n bit_sample_flags_.resize(num_hash_);\n\n for (size_t i = 0; i < num_hash_; ++i) {\n GetPermutationPivot(data_, space_, num_pivot_, &pivots_[i]);\n bit_sample_flags_[i].resize(num_pivot_);\n\n\n if (bit_sample_qty_ <= num_pivot_) {\n fill(bit_sample_flags_[i].begin(),\n bit_sample_flags_[i].end(), 0);\n // Select bit_sample_qty 1s randomly\n size_t toSelQty = bit_sample_qty_;\n while (toSelQty) {\n while (true) {\n size_t p = RandomInt() % bit_sample_qty_;\n if (!bit_sample_flags_[i][p]) {\n bit_sample_flags_[i][p] = 1;\n --toSelQty;\n break;\n }\n }\n }\n } else {\n // Inversion trick\n fill(bit_sample_flags_[i].begin(),\n bit_sample_flags_[i].end(), 1);\n // Select (num_pivot_ - bit_sample_qty) 0s randomly\n size_t toSelQty = num_pivot_ - bit_sample_qty_;\n while (toSelQty) {\n while (true) {\n size_t p = RandomInt() % bit_sample_qty_;\n if (bit_sample_flags_[i][p]) {\n bit_sample_flags_[i][p] = 0;\n --toSelQty;\n break;\n }\n }\n }\n }\n }\n\n hash_tables_.resize(num_hash_);\n\n for (size_t hashId = 0; hashId < num_hash_; ++hashId) {\n hash_tables_[hashId].resize(hash_table_size_);\n }\n\n unique_ptr progress_bar(printProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n for (size_t id = 0; id < data_.size(); ++id) {\n for (size_t hashId = 0; hashId < num_hash_; ++hashId) {\n size_t val = computeHashValue(hashId, data_[id], NULL); // Already <= hash_table_size_;\n //cout << val << endl;\n if (!hash_tables_[hashId][val]) {\n hash_tables_[hashId][val] = new vector();\n }\n hash_tables_[hashId][val]->push_back(id);\n }\n if (progress_bar) ++(*progress_bar);\n }\n}\n\ntemplate \nPermutationIndexLSHBin::~PermutationIndexLSHBin() { \n for (size_t hashId = 0; hashId < num_hash_; ++hashId) {\n for (auto e: hash_tables_[hashId]) delete e;\n }\n}\n\ntemplate \ntemplate \nvoid PermutationIndexLSHBin::GenSearch(QueryType* query) const {\n std::unordered_set found;\n\n for (size_t hashId = 0; hashId < num_hash_; ++hashId) {\n size_t val = computeHashValue(hashId, NULL, query); // Already <= hash_table_size_;\n\n vector* pObjIds = hash_tables_[hashId][val]; \n if (pObjIds) {\n for (IdType id : *pObjIds)\n /* \n * It's essential to check for previously added entries.\n * If we don't do this, the same close entry may be added multiple\n * times. At the same time, other relevant entries will be removed!\n */\n if (!found.count(id)) {\n query->CheckAndAddToResult(data_[id]);\n found.insert(id);\n }\n }\n }\n}\n\ntemplate class PermutationIndexLSHBin;\ntemplate class PermutationIndexLSHBin;\ntemplate class PermutationIndexLSHBin;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/permutation_inverted_index.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"incremental_quick_select.h\"\n#include \"method/permutation_inverted_index.h\"\n#include \"ported_boost_progress.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\n\ntemplate \nvoid \nPermutationInvertedIndex::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n if (pmgr.hasParam(\"dbScanFrac\") && pmgr.hasParam(\"knnAmp\")) {\n throw runtime_error(\"One shouldn't specify both parameters dbScanFrac and knnAmp\");\n }\n \n pmgr.GetParamOptional(\"dbScanFrac\", db_scan_frac_, 0.05);\n pmgr.GetParamOptional(\"knnAmp\", knn_amp_, 0);\n\n\n pmgr.GetParamOptional(\"numPivotSearch\", num_pivot_search_, max(1, num_pivot_index_ / 2));\n pmgr.GetParamOptional(\"maxPosDiff\", max_pos_diff_, num_pivot_);\n\n if (num_pivot_search_ > num_pivot_index_) {\n stringstream err;\n err << METH_PERM_INVERTED_INDEX << \" requires that numPivotSearch \"\n << \"should be less than or equal to numPivotIndex\";\n throw runtime_error(err.str());\n }\n\n pmgr.CheckUnused();\n\n LOG(LIB_INFO) << \"Set query-time parameters for PermutationInvertedIndex:\";\n LOG(LIB_INFO) << \"dbScanFrac= \" << db_scan_frac_;\n LOG(LIB_INFO) << \"knnAmp= \" << knn_amp_;\n LOG(LIB_INFO) << \"numPivotSearch= \" << num_pivot_search_;\n LOG(LIB_INFO) << \"maxPosDiff= \" << max_pos_diff_;\n}\n \n\ntemplate \nPermutationInvertedIndex::PermutationInvertedIndex(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) : space_(space), data_(data), PrintProgress_(PrintProgress) {\n}\n\n\ntemplate \nvoid PermutationInvertedIndex::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"numPivot\", num_pivot_, 512);\n pmgr.GetParamOptional(\"numPivotIndex\", num_pivot_index_, 16);\n\n pmgr.CheckUnused();\n\n this->ResetQueryTimeParams(); // set query-time parameters to default\n\n if (num_pivot_index_ > num_pivot_) {\n stringstream err;\n err << METH_PERM_INVERTED_INDEX << \" requires that numPivotIndex \"\n << \"should be less than or equal to numPivot\";\n throw runtime_error(err.str());\n }\n\n LOG(LIB_INFO) << \"# pivots = \" << num_pivot_;\n LOG(LIB_INFO) << \"# pivots to index requested (ki) = \" << num_pivot_index_;\n LOG(LIB_INFO) << \"# pivots to search (ks) = \" << num_pivot_search_;\n LOG(LIB_INFO) << \"# max position difference = \" << max_pos_diff_;\n LOG(LIB_INFO) << \"# dbScanFrac = \" << db_scan_frac_;\n LOG(LIB_INFO) << \"# knnAmp = \" << knn_amp_;\n\n unique_ptr progress_bar(PrintProgress_ ? \n new ProgressDisplay(data_.size(), cerr):\n NULL);\n\n\n GetPermutationPivot(data_, space_, num_pivot_, &pivot_);\n\n posting_lists_.resize(num_pivot_);\n\n\n for (size_t id = 0; id < data_.size(); ++id) {\n Permutation perm;\n GetPermutation(pivot_, space_, data_[id], &perm);\n for (size_t j = 0; j < perm.size(); ++j) {\n if (perm[j] < num_pivot_index_) {\n posting_lists_[j].push_back(ObjectInvEntry(id, perm[j]));\n }\n }\n if (progress_bar) ++(*progress_bar);\n }\n for (size_t j = 0; j < posting_lists_.size(); ++j) {\n sort(posting_lists_[j].begin(), posting_lists_[j].end());\n }\n if (progress_bar) { // make it 100%\n (*progress_bar) += (progress_bar->expected_count() - progress_bar->count());\n }\n}\n\ntemplate \nPermutationInvertedIndex::~PermutationInvertedIndex() {\n}\n\ntemplate \nconst string PermutationInvertedIndex::StrDesc() const {\n stringstream str;\n str << \"(permutation) inverted index\";\n return str.str();\n}\n\ntemplate \ntemplate \nvoid PermutationInvertedIndex::GenSearch(QueryType* query, size_t K) const {\n // Let's make this check here. Otherwise, if you misspell dbScanFrac, you will get \n // a strange error message that says: dbScanFrac should be in the range [0,1].\n if (!knn_amp_) {\n if (db_scan_frac_ < 0.0 || db_scan_frac_ > 1.0) {\n stringstream err;\n err << METH_PERM_INVERTED_INDEX << \" requires that dbScanFrac is in the range [0,1]\";\n throw runtime_error(err.str());\n }\n }\n\n\n size_t db_scan = computeDbScan(K);\n\n Permutation perm_q;\n GetPermutation(pivot_, query, &perm_q);\n vector::ObjectInvEntry>::const_iterator> iterBegs;\n vector::ObjectInvEntry>::const_iterator> iterEnds;\n\n size_t maxScanQty = 0;\n\n for (size_t i = 0; i < perm_q.size(); ++i) {\n if (perm_q[i] < num_pivot_search_) {\n ObjectInvEntry o1(0, std::max(perm_q[i] - static_cast(max_pos_diff_), 0));\n ObjectInvEntry o2(0, std::min(perm_q[i] + static_cast(max_pos_diff_) + 1, static_cast(num_pivot_index_)));\n\n auto itEnd = lower_bound(posting_lists_[i].begin(), posting_lists_[i].end(), o2);\n auto it = lower_bound(posting_lists_[i].begin(), itEnd, o1);\n\n maxScanQty += itEnd - it;\n iterBegs.push_back(it);\n iterEnds.push_back(itEnd);\n }\n }\n\n bool bUseMap = maxScanQty < USE_MAP_THRESHOLD * data_.size(); // TODO: @leo this is rather adhoc\n\n vector perm_dists;\n\n //LOG(LIB_INFO) << \"bUseMap: \" << bUseMap << \" maxScanQty: \" << maxScanQty << \" data.size() \" << data_.size();\n\n if (bUseMap) {\n unordered_map perm_dists_set;\n\n int MaxDist = (num_pivot_search_ - 1) * num_pivot_index_; \n\n for (size_t i = 0, inum = 0; i < perm_q.size(); ++i) {\n if (perm_q[i] < num_pivot_search_) {\n auto it = iterBegs[inum];\n const auto& itEnd = iterEnds[inum];\n ++inum;\n\n for (; it != itEnd; ++it) {\n int id = it->id_;\n int pos = it->pos_;\n\n CHECK(std::abs(pos - perm_q[i]) <= max_pos_diff_);\n\n // spearman footrule\n int spearman_dist = std::abs(static_cast(pos) - static_cast(perm_q[i]));\n\n auto iter = perm_dists_set.find(id);\n\n if (iter != perm_dists_set.end()) {\n iter->second += spearman_dist - static_cast(num_pivot_index_);\n } else {\n perm_dists_set.insert(make_pair(id, MaxDist + spearman_dist));\n }\n }\n }\n }\n // Copy data from set to array\n perm_dists.reserve(perm_dists_set.size());\n\n for(const auto it: perm_dists_set) {\n // Id goes second\n perm_dists.push_back(make_pair(it.second, it.first));\n }\n } else {\n int MaxDist = num_pivot_search_ * num_pivot_index_; \n\n perm_dists.reserve(data_.size());\n\n for (size_t i = 0; i < data_.size(); ++i)\n perm_dists.push_back(make_pair(MaxDist, i));\n\n for (size_t i = 0, inum = 0; i < perm_q.size(); ++i) {\n if (perm_q[i] < num_pivot_search_) {\n auto it = iterBegs[inum];\n const auto& itEnd = iterEnds[inum];\n ++inum;\n\n for (; it < itEnd; ++it) {\n int id = it->id_;\n int pos = it->pos_;\n\n CHECK(std::abs(pos - perm_q[i]) <= max_pos_diff_);\n\n // spearman footrule\n int spearman_dist = std::abs(static_cast(pos) - static_cast(perm_q[i]));\n perm_dists[id].first += spearman_dist - static_cast(num_pivot_index_);\n }\n }\n }\n }\n size_t scan_qty = min(db_scan, perm_dists.size());\n\n if (!scan_qty) {\n stringstream err;\n err << \"One should specify a proper value for either dbScanFrac or knnAmp\" <<\n \" currently, dbScanFrac=\" << db_scan_frac_ << \" knnAmp=\" << knn_amp_;\n throw runtime_error(err.str());\n }\n\n IncrementalQuickSelect quick_select(perm_dists);\n for (size_t i = 0; i < scan_qty; ++i) {\n const size_t idx = quick_select.GetNext().second;\n quick_select.Next();\n query->CheckAndAddToResult(data_[idx]);\n }\n}\n\ntemplate \nvoid PermutationInvertedIndex::Search (RangeQuery* query, IdType ) const {\n GenSearch(query, 0);\n}\n\ntemplate \nvoid PermutationInvertedIndex::Search (KNNQuery* query, IdType ) const {\n GenSearch(query, query->GetK());\n}\n\ntemplate class PermutationInvertedIndex;\ntemplate class PermutationInvertedIndex;\ntemplate class PermutationInvertedIndex;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/method/permutation_prefix_index.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"permutation_utils.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/permutation_prefix_index.h\"\n\nnamespace similarity {\n\nclass PrefixNode;\nclass PrefixNodeInternal;\nclass PrefixNodeLeaf;\n\nclass PrefixNode {\n public:\n virtual ~PrefixNode() {};\n virtual bool IsLeaf() const = 0;\n virtual void Insert(const Permutation& perm,\n const Object* object,\n const size_t length,\n const size_t cur_depth) = 0;\n virtual size_t GetNumberObjects() const = 0;\n virtual void GetAllObjectsInThisSubTree(\n ObjectVector* objects,\n std::unordered_set* visited) const = 0;\n virtual const PrefixNode* SearchPath(const Permutation& perm,\n const size_t sub_length,\n const size_t cur_depth) const = 0;\n virtual void ChunkBuckets() = 0;\n};\n\nclass PrefixNodeLeaf : public PrefixNode {\n public:\n PrefixNodeLeaf() : bucket_(new ObjectVector()), CacheOptimizedBucket_(NULL) {}\n virtual ~PrefixNodeLeaf() { ClearBucket(CacheOptimizedBucket_, bucket_); }\n\n virtual void ChunkBuckets() {\n ObjectVector* pData = bucket_;\n\n CreateCacheOptimizedBucket(*pData, CacheOptimizedBucket_, bucket_);\n\n delete pData;\n }\n\n bool IsLeaf() const { return true; }\n\n void Insert(const Permutation& perm,\n const Object* object,\n const size_t length,\n const size_t cur_depth) {\n CHECK(cur_depth == length);\n bucket_->push_back(object);\n }\n\n size_t GetNumberObjects() const { return bucket_->size(); }\n\n void GetAllObjectsInThisSubTree(\n ObjectVector* objects,\n std::unordered_set* visited) const {\n if (visited->count(this) == 0) {\n visited->insert(this);\n objects->insert(objects->end(), bucket_->begin(), bucket_->end());\n }\n }\n\n const PrefixNode* SearchPath(const Permutation& perm,\n const size_t sub_length,\n const size_t cur_depth) const {\n return this;\n }\n\n private:\n ObjectVector* bucket_;\n char* CacheOptimizedBucket_;\n};\n\nclass PrefixNodeInternal : public PrefixNode {\n public:\n PrefixNodeInternal() : num_objects_(0) {}\n\n ~PrefixNodeInternal() {\n for (auto& it : children_) {\n delete it.second;\n }\n }\n\n bool IsLeaf() const { return false; }\n\n virtual void ChunkBuckets() {\n for (auto& it: children_) it.second->ChunkBuckets();\n }\n\n void Insert(const Permutation& perm,\n const Object* object,\n const size_t length,\n const size_t cur_depth) {\n CHECK(cur_depth < length);\n ++num_objects_;\n PrefixNode* node = NULL;\n auto it = children_.find(perm[cur_depth]);\n if (it != children_.end()) {\n node = it->second;\n } else {\n if (cur_depth + 1 < length) {\n node = new PrefixNodeInternal;\n } else {\n node = new PrefixNodeLeaf;\n }\n children_[perm[cur_depth]] = node;\n }\n node->Insert(perm, object, length, cur_depth + 1);\n }\n\n size_t GetNumberObjects() const { return num_objects_; }\n\n void GetAllObjectsInThisSubTree(\n ObjectVector* objects,\n std::unordered_set* visited) const {\n if (visited->count(this) == 0) {\n visited->insert(this);\n for (const auto& it : children_) {\n it.second->GetAllObjectsInThisSubTree(objects, visited);\n }\n }\n }\n\n const PrefixNode* SearchPath(const Permutation& perm,\n const size_t sub_length,\n const size_t cur_depth) const {\n CHECK(cur_depth <= sub_length);\n if (cur_depth == sub_length) {\n return this;\n } else {\n const auto& it = children_.find(perm[cur_depth]);\n return it != children_.end()\n ? it->second->SearchPath(perm, sub_length, cur_depth + 1)\n : NULL;\n }\n }\n\n private:\n typedef std::unordered_map PrefixMap;\n PrefixMap children_;\n size_t num_objects_;\n};\n\nclass PrefixTree {\n public:\n PrefixTree() : root_(new PrefixNodeInternal) {}\n void ChunkBuckets() { root_->ChunkBuckets(); }\n\n ~PrefixTree() { delete root_; }\n\n void Insert(const Permutation& perm,\n const Object* object,\n const size_t length) {\n root_->Insert(perm, object, length, 0);\n }\n\n void FindCandidates(const Permutation& perm_q,\n const size_t prefix_length,\n const size_t min_candidate,\n ObjectVector* candidates) {\n std::unordered_set visited;\n visited.reserve(min_candidate * 2);\n for (int i = static_cast(prefix_length); i >= 0; --i) {\n const PrefixNode* node = root_->SearchPath(perm_q, i, 0);\n if (node != NULL &&\n (node->GetNumberObjects() >= min_candidate || i == 0)) {\n node->GetAllObjectsInThisSubTree(candidates, &visited);\n CHECK(node->GetNumberObjects() == candidates->size());\n return;\n }\n }\n }\n\n private:\n PrefixNode* root_;\n};\n\ntemplate \nvoid PermutationPrefixIndex::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n\n if (pmgr.hasParam(\"minCandidate\") && pmgr.hasParam(\"knnAmp\")) {\n throw runtime_error(\"One shouldn't specify both parameters minCandidate and knnAmp, b/c they are synonyms!\");\n }\n\n pmgr.GetParamOptional(\"minCandidate\", min_candidate_, 0);\n pmgr.GetParamOptional(\"knnAmp\", knn_amp_, 0);\n\n LOG(LIB_INFO) << \"Set query-time parameters for PermutationPrefixIndex:\";\n LOG(LIB_INFO) << \"knnAmp= \" << knn_amp_;\n LOG(LIB_INFO) << \"minCandidate= \" << min_candidate_;\n}\n\ntemplate \nPermutationPrefixIndex::PermutationPrefixIndex(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) : space_(space), data_(data), PrintProgress_(PrintProgress) {\n}\n\ntemplate \nvoid PermutationPrefixIndex::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"numPivot\", num_pivot_, 16);\n pmgr.GetParamOptional(\"chunkBucket\", chunkBucket_, true);\n pmgr.GetParamOptional(\"prefixLength\", prefix_length_, max(1, num_pivot_/4));\n\n pmgr.CheckUnused();\n this->ResetQueryTimeParams();\n\n LOG(LIB_INFO) << \"# pivots = \" << num_pivot_;\n LOG(LIB_INFO) << \"prefix length = \" << prefix_length_;\n LOG(LIB_INFO) << \"ChunkBucket = \" << chunkBucket_;\n\n GetPermutationPivot(data_, space_, num_pivot_, &pivot_);\n prefixtree_.reset(new PrefixTree);\n Permutation permutation;\n\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n for (const auto& it : data_) {\n permutation.clear();\n GetPermutationPPIndex(pivot_, space_, it, &permutation);\n prefixtree_->Insert(permutation, it, prefix_length_);\n\n if (progress_bar) ++(*progress_bar);\n }\n // Store elements in leaves/buckets contiguously\n if (chunkBucket_) prefixtree_->ChunkBuckets();\n}\n\ntemplate \nPermutationPrefixIndex::~PermutationPrefixIndex() {\n}\n\ntemplate \nconst std::string PermutationPrefixIndex::StrDesc() const {\n return \"permutation (pref. index)\";\n}\n\ntemplate \ntemplate \nvoid PermutationPrefixIndex::GenSearch(QueryType* query, size_t K) const {\n if (prefix_length_ == 0 || prefix_length_ > num_pivot_) {\n stringstream err;\n err << METH_PERMUTATION_PREFIX_IND\n << \" requires that prefix length should be in the range in [1,\"\n << num_pivot_ << \"]\";\n throw runtime_error(err.str());\n }\n\n Permutation perm_q;\n GetPermutationPPIndex(pivot_, query, &perm_q);\n\n size_t db_scan = computeDbScan(K);\n\n if (!db_scan) {\n if (!db_scan) {\n throw runtime_error(\"One should specify a proper value for either minCandidate or knnAmp\");\n }\n }\n\n ObjectVector candidates;\n candidates.reserve(2 * db_scan);\n prefixtree_->FindCandidates(perm_q, prefix_length_,\n db_scan, &candidates);\n\n for (const auto& it : candidates) {\n query->CheckAndAddToResult(it);\n }\n}\n\ntemplate \nvoid PermutationPrefixIndex::Search(RangeQuery* query, IdType ) const {\n GenSearch(query, 0);\n}\n\ntemplate \nvoid PermutationPrefixIndex::Search(KNNQuery* query, IdType ) const {\n GenSearch(query, query->GetK());\n}\n\ntemplate class PermutationPrefixIndex;\ntemplate class PermutationPrefixIndex;\ntemplate class PermutationPrefixIndex;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/pivot_neighb_invindx.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2010--2013\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"incremental_quick_select.h\"\n#include \"method/pivot_neighb_invindx.h\"\n#include \"utils.h\"\n\nnamespace similarity {\n\nusing std::vector;\nusing std::pair;\nusing std::mutex;\n\ntemplate \nstruct IndexThreadParamsPNII {\n PivotNeighbInvertedIndex& index_;\n size_t chunk_qty_;\n size_t index_every_;\n size_t out_of_;\n ProgressDisplay* progress_bar_;\n mutex& display_mutex_;\n\n IndexThreadParamsPNII(\n PivotNeighbInvertedIndex& index,\n size_t chunk_qty,\n size_t index_every,\n size_t out_of,\n ProgressDisplay* progress_bar,\n mutex& display_mutex\n ) :\n index_(index),\n chunk_qty_(chunk_qty),\n index_every_(index_every),\n out_of_(out_of),\n progress_bar_(progress_bar),\n display_mutex_(display_mutex)\n { }\n};\n\ntemplate \nstruct IndexThreadPNII {\n void operator()(IndexThreadParamsPNII& prm) {\n for (size_t i = 0; i < prm.chunk_qty_; ++i) {\n if (prm.index_every_ == i % prm.out_of_) {\n prm.index_.IndexChunk(i, prm.progress_bar_, prm.display_mutex_);\n }\n }\n }\n};\n\ntemplate \nPivotNeighbInvertedIndex::PivotNeighbInvertedIndex(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) \n : data_(data), \n space_(space), \n PrintProgress_(PrintProgress),\n recreate_points_(false) {\n}\n\n\ntemplate \nvoid PivotNeighbInvertedIndex::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n\n pmgr.GetParamOptional(\"numPivot\", num_pivot_, 512);\n\n if (pmgr.hasParam(\"numPivotIndex\") && pmgr.hasParam(\"numPrefix\")) {\n throw runtime_error(\"One shouldn't specify both parameters numPrefix and numPivotIndex, b/c they are synonyms!\");\n }\n pmgr.GetParamOptional(\"numPivotIndex\", num_prefix_, 32);\n pmgr.GetParamOptional(\"numPrefix\", num_prefix_, num_prefix_);\n\n pmgr.GetParamOptional(\"chunkIndexSize\", chunk_index_size_, 65536);\n\n pmgr.GetParamOptional(\"indexThreadQty\", index_thread_qty_, thread::hardware_concurrency());\n pmgr.GetParamOptional(\"recreatePoints\", recreate_points_, false);\n\n if (num_prefix_ > num_pivot_) {\n PREPARE_RUNTIME_ERR(err) << METH_PIVOT_NEIGHB_INVINDEX << \" requires that numPrefix (\" << num_prefix_ << \") \"\n << \"should be less than or equal to numPivot (\" << num_pivot_ << \")\";\n THROW_RUNTIME_ERR(err);\n }\n\n CHECK(num_prefix_ <= num_pivot_);\n\n pmgr.GetParamOptional(\"pivotFile\", pivot_file_, \"\");\n \n size_t indexQty = (data_.size() + chunk_index_size_ - 1) / chunk_index_size_;\n\n pmgr.CheckUnused();\n this->ResetQueryTimeParams();\n\n LOG(LIB_INFO) << \"# of entries in an index chunk = \" << chunk_index_size_;\n LOG(LIB_INFO) << \"# of index chunks = \" << indexQty;\n LOG(LIB_INFO) << \"# of indexing thread = \" << index_thread_qty_;\n LOG(LIB_INFO) << \"# pivotFile = \" << pivot_file_;\n LOG(LIB_INFO) << \"# pivots = \" << num_pivot_;\n LOG(LIB_INFO) << \"# pivots to index (numPrefix) = \" << num_prefix_;\n LOG(LIB_INFO) << \"Do we recreate points during indexing when computing distances to pivots? = \" << recreate_points_;\n\n if (pivot_file_.empty())\n GetPermutationPivot(data_, space_, num_pivot_, &pivot_, &pivot_pos_);\n else {\n vector vExternIds;\n space_.ReadDataset(pivot_, vExternIds, pivot_file_, num_pivot_);\n if (pivot_.size() < num_pivot_) {\n throw runtime_error(\"Not enough pivots in the file '\" + pivot_file_ + \"'\");\n }\n genPivot_ = pivot_;\n }\n\n posting_lists_.resize(indexQty);\n\n /*\n * After we allocated a pointer to each index chunks' vector,\n * it is thread-safe to index each chunk separately.\n */\n for (size_t chunkId = 0; chunkId < indexQty; ++chunkId) {\n posting_lists_[chunkId] = shared_ptr>(new vector());\n }\n\n // Don't need more thread than you have chunks\n index_thread_qty_ = min(index_thread_qty_, indexQty);\n\n mutex progressBarMutex;\n\n if (index_thread_qty_ <= 1) {\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n for (size_t chunkId = 0; chunkId < indexQty; ++chunkId) {\n IndexChunk(chunkId, progress_bar.get(), progressBarMutex);\n }\n\n if (progress_bar) {\n (*progress_bar) += (progress_bar->expected_count() - progress_bar->count());\n }\n } else {\n vector threads(index_thread_qty_);\n vector>> threadParams;\n\n LOG(LIB_INFO) << \"Will create \" << index_thread_qty_ << \" indexing threads\";;\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n for (size_t i = 0; i < index_thread_qty_; ++i) {\n threadParams.push_back(shared_ptr>(\n new IndexThreadParamsPNII(*this, indexQty, i, index_thread_qty_,\n progress_bar.get(), progressBarMutex)));\n }\n\n for (size_t i = 0; i < index_thread_qty_; ++i) {\n threads[i] = thread(IndexThreadPNII(), ref(*threadParams[i]));\n }\n\n for (size_t i = 0; i < index_thread_qty_; ++i) {\n threads[i].join();\n }\n\n if (progress_bar) {\n (*progress_bar) += (progress_bar->expected_count() - progress_bar->count());\n }\n }\n}\n\ntemplate \nvoid \nPivotNeighbInvertedIndex::IndexChunk(size_t chunkId, ProgressDisplay* progress_bar, mutex& display_mutex) {\n size_t minId = chunkId * chunk_index_size_;\n size_t maxId = min(data_.size(), minId + chunk_index_size_);\n\n\n auto & chunkPostLists = *posting_lists_[chunkId];\n chunkPostLists.resize(num_pivot_);\n string externId;\n\n for (size_t id = 0; id < maxId - minId; ++id) {\n Permutation perm;\n const Object* pObj = data_[minId + id];\n\n unique_ptr extObj;\n if (recreate_points_) {\n extObj=space_.CreateObjFromStr(-1, -1, space_.CreateStrFromObj(pObj, externId), NULL);\n pObj=extObj.get();\n }\n\n GetPermutationPPIndex(pivot_, space_, pObj, &perm);\n for (size_t j = 0; j < num_prefix_; ++j) {\n chunkPostLists[perm[j]].push_back(id);\n }\n \n if (id % 1000) {\n unique_lock lock(display_mutex);\n if (progress_bar) ++(*progress_bar);\n }\n }\n\n // Sorting is essential for merging algos\n for (auto & p:chunkPostLists) {\n sort(p.begin(), p.end());\n }\n}\n \n\n \n \ntemplate \nvoid \nPivotNeighbInvertedIndex::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n string inv_proc_alg;\n \n pmgr.GetParamOptional(\"skipChecking\", skip_checking_, false);\n pmgr.GetParamOptional(\"useSort\", use_sort_, false);\n pmgr.GetParamOptional(\"invProcAlg\", inv_proc_alg, PERM_PROC_FAST_SCAN);\n\n if (pmgr.hasParam(\"minTimes\") && pmgr.hasParam(\"numPivotSearch\")) {\n throw runtime_error(\"One shouldn't specify both parameters minTimes and numPivotSearch, b/c they are synonyms!\");\n }\n\n pmgr.GetParamOptional(\"minTimes\", min_times_, 2);\n pmgr.GetParamOptional(\"numPivotSearch\", min_times_, 2);\n \n if (inv_proc_alg == PERM_PROC_FAST_SCAN) {\n inv_proc_alg_ = kScan; \n } else if (inv_proc_alg == PERM_PROC_MAP) {\n inv_proc_alg_ = kMap; \n } else if (inv_proc_alg == PERM_PROC_MERGE) {\n inv_proc_alg_ = kMerge; \n } else {\n stringstream err;\n err << \"Unknown value of parameter for the inverted file processing algorithm: \" << inv_proc_alg_;\n throw runtime_error(err.str());\n } \n \n if (pmgr.hasParam(\"dbScanFrac\") && pmgr.hasParam(\"knnAmp\")) {\n throw runtime_error(\"One shouldn't specify both parameters dbScanFrac and knnAmp\");\n }\n\n // It is important to use a non-zero default here, otherwise the code\n // will through an exception when a) useSort=1 and b) the parameter dbScanFrac is not specified explicitly\n pmgr.GetParamOptional(\"dbScanFrac\", db_scan_frac_, 0.05);\n CHECK_MSG(db_scan_frac_ >=0 && db_scan_frac_ <=1, \"dbScanFrac should be >=0 and <= 1\");\n pmgr.GetParamOptional(\"knnAmp\", knn_amp_, 0);\n\n pmgr.CheckUnused();\n \n LOG(LIB_INFO) << \"Set query-time parameters for PivotNeighbInvertedIndex:\";\n LOG(LIB_INFO) << \"# pivots to search (minTimes) = \" << min_times_;\n LOG(LIB_INFO) << \"# dbScanFrac = \" << db_scan_frac_;\n LOG(LIB_INFO) << \"# knnAmp = \" << knn_amp_;\n LOG(LIB_INFO) << \"# useSort = \" << use_sort_;\n LOG(LIB_INFO) << \"invProcAlg (code) = \" << inv_proc_alg_ << \"(\" << toString(inv_proc_alg_) << \")\";\n LOG(LIB_INFO) << \"# skipChecking = \" << skip_checking_;\n}\n\ntemplate \nPivotNeighbInvertedIndex::~PivotNeighbInvertedIndex() {\n for (const Object* o: genPivot_) delete o;\n}\n\ntemplate \nconst string PivotNeighbInvertedIndex::StrDesc() const {\n stringstream str;\n str << \"permutation (inverted index over neighboring pivots)\";\n return str.str();\n}\n\ntemplate \nvoid PivotNeighbInvertedIndex::SaveIndex(const string &location) {\n ofstream outFile(location);\n CHECK_MSG(outFile, \"Cannot open file '\" + location + \"' for writing\");\n outFile.exceptions(std::ios::badbit);\n\n size_t lineNum = 0;\n // Save main parameters\n WriteField(outFile, METHOD_DESC, StrDesc()); lineNum++;\n WriteField(outFile, \"numPivot\", num_pivot_); lineNum++;\n WriteField(outFile, \"numPivotIndex\", num_prefix_); lineNum++;\n WriteField(outFile, \"chunkIndexSize\", chunk_index_size_); lineNum++;\n WriteField(outFile, \"indexQty\", posting_lists_.size()); lineNum++;\n WriteField(outFile, \"pivotFile\", pivot_file_); lineNum++;\n\n if (pivot_file_.empty()) {\n // Save pivots positions\n outFile << MergeIntoStr(pivot_pos_, ' ') << endl;\n lineNum++;\n vector oIDs;\n for (const Object *pObj: pivot_)\n oIDs.push_back(pObj->id());\n // Save pivot IDs\n outFile << MergeIntoStr(oIDs, ' ') << endl;\n lineNum++;\n }\n\n for(size_t i = 0; i < posting_lists_.size(); ++i) {\n WriteField(outFile, \"chunkId\", i); lineNum++;\n CHECK(posting_lists_[i]->size() == num_pivot_);\n for (size_t pivotId = 0; pivotId < num_pivot_; ++pivotId) {\n outFile << MergeIntoStr((*posting_lists_[i])[pivotId], ' ') << endl; lineNum++;\n }\n }\n\n WriteField(outFile, LINE_QTY, lineNum + 1 /* including this line */);\n outFile.close();\n}\n\ntemplate \nvoid PivotNeighbInvertedIndex::LoadIndex(const string &location) {\n ifstream inFile(location);\n CHECK_MSG(inFile, \"Cannot open file '\" + location + \"' for reading\");\n inFile.exceptions(std::ios::badbit);\n\n size_t lineNum = 1;\n string methDesc;\n ReadField(inFile, METHOD_DESC, methDesc); lineNum++;\n CHECK_MSG(methDesc == StrDesc(),\n \"Looks like you try to use an index created by a different method: \" + methDesc);\n ReadField(inFile, \"numPivot\", num_pivot_); lineNum++;\n ReadField(inFile, \"numPivotIndex\", num_prefix_); lineNum++;\n ReadField(inFile, \"chunkIndexSize\", chunk_index_size_); lineNum++;\n size_t indexQty;\n ReadField(inFile, \"indexQty\", indexQty); lineNum++;\n ReadField(inFile, \"pivotFile\", pivot_file_); lineNum++;\n\n string line;\n if (pivot_file_.empty()) {\n // Read pivot positions\n CHECK_MSG(getline(inFile, line),\n \"Failed to read line #\" + ConvertToString(lineNum) + \" from \" + location);\n pivot_pos_.clear();\n CHECK_MSG(SplitStr(line, pivot_pos_, ' '),\n \"Failed to extract pivot indices from line #\" + ConvertToString(lineNum) + \" from \" + location);\n CHECK_MSG(pivot_pos_.size() == num_pivot_,\n \"# of extracted pivots indices from line #\" + ConvertToString(lineNum) + \" (\" +\n ConvertToString(pivot_pos_.size()) + \")\"\n \" doesn't match the number of pivots (\" + ConvertToString(num_pivot_) +\n \" from the header (location \" + location + \")\");\n pivot_.resize(num_pivot_);\n for (size_t i = 0; i < pivot_pos_.size(); ++i) {\n CHECK_MSG(pivot_pos_[i] < data_.size(),\n DATA_MUTATION_ERROR_MSG + \" (detected an object index >= #of data points\");\n pivot_[i] = data_[pivot_pos_[i]];\n }\n ++lineNum;\n // Read pivot object IDs\n vector oIDs;\n CHECK_MSG(getline(inFile, line),\n \"Failed to read line #\" + ConvertToString(lineNum) + \" from \" + location);\n CHECK_MSG(SplitStr(line, oIDs, ' '),\n \"Failed to extract pivot IDs from line #\" + ConvertToString(lineNum) + \" from \" + location);\n CHECK_MSG(oIDs.size() == num_pivot_,\n \"# of extracted pivots IDs from line #\" + ConvertToString(lineNum) + \" (\" +\n ConvertToString(pivot_pos_.size()) + \")\"\n \" doesn't match the number of pivots (\" + ConvertToString(num_pivot_) +\n \" from the header (location \" + location + \")\");\n /*\n * Now let's make a quick sanity-check to see if the pivot IDs match what was saved previously.\n * If the user used a different data set, or a different test split (and a different gold-standard file),\n * we cannot re-use the index\n */\n for (size_t i = 0; i < num_pivot_; ++i) {\n if (oIDs[i] != pivot_[i]->id()) {\n PREPARE_RUNTIME_ERR(err) << DATA_MUTATION_ERROR_MSG <<\n \" (different pivot IDs detected, old: \" << oIDs[i] << \" new: \" << pivot_[i]->id() <<\n \" pivot index: \" << i << \")\";\n THROW_RUNTIME_ERR(err);\n }\n }\n ++lineNum;\n } else {\n vector vExternIds;\n space_.ReadDataset(pivot_, vExternIds, pivot_file_, num_pivot_);\n if (pivot_.size() < num_pivot_) {\n throw runtime_error(\"Not enough pivots in the file '\" + pivot_file_+ \"'\");\n }\n genPivot_ = pivot_;\n }\n\n\n posting_lists_.resize(indexQty);\n\n for (size_t chunkId = 0; chunkId < indexQty; ++chunkId) {\n size_t tmp;\n ReadField(inFile, \"chunkId\", tmp);\n CHECK_MSG(tmp == chunkId, \"The chunkId (\" + ConvertToString(tmp) + \" read from line \" + ConvertToString(lineNum) +\n \" doesn't match the expected chunk ID \" + ConvertToString(chunkId));\n ++lineNum;\n posting_lists_[chunkId] = shared_ptr>(new vector());\n (*posting_lists_[chunkId]).resize(num_pivot_);\n for (size_t pivotId = 0; pivotId < num_pivot_; ++pivotId) {\n CHECK_MSG(getline(inFile, line),\n \"Failed to read line #\" + ConvertToString(lineNum) + \" from \" + location);\n CHECK_MSG(SplitStr(line, (*posting_lists_[chunkId])[pivotId], ' '),\n \"Failed to extract object IDs from line #\" + ConvertToString(lineNum) +\n \" chunkId \" + ConvertToString(chunkId) + \" location: \" + location);\n ++lineNum;\n }\n }\n size_t ExpLineNum;\n ReadField(inFile, LINE_QTY, ExpLineNum);\n CHECK_MSG(lineNum == ExpLineNum,\n DATA_MUTATION_ERROR_MSG + \" (expected number of lines \" + ConvertToString(ExpLineNum) +\n \" read so far doesn't match the number of read lines: \" + ConvertToString(lineNum));\n inFile.close();\n}\n\ntemplate \ntemplate \nvoid PivotNeighbInvertedIndex::GenSearch(QueryType* query, size_t K) const {\n // Let's make this check here. Otherwise, if you misspell dbScanFrac, you will get \n // a strange error message that says: dbScanFrac should be in the range [0,1].\n if (!knn_amp_) {\n if (db_scan_frac_ < 0.0 || db_scan_frac_ > 1.0) {\n stringstream err;\n err << METH_PIVOT_NEIGHB_INVINDEX << \" requires that dbScanFrac is in the range [0,1]\";\n throw runtime_error(err.str());\n }\n }\n\n size_t db_scan = computeDbScan(K, posting_lists_.size());\n\n\n Permutation perm_q;\n GetPermutationPPIndex(pivot_, query, &perm_q);\n\n vector counter(chunk_index_size_);\n\n\n\n for (size_t chunkId = 0; chunkId < posting_lists_.size(); ++chunkId) {\n const auto & chunkPostLists = *posting_lists_[chunkId];\n size_t minId = chunkId * chunk_index_size_;\n size_t maxId = min(data_.size(), minId + chunk_index_size_);\n size_t chunkQty = maxId - minId;\n\n const auto data_start = &data_[0] + minId;\n\n if (use_sort_) {\n if (!db_scan) {\n stringstream err;\n err << \"Bug: either dbScanFrac or knnAmp are expected to be non-zero \" <<\n \" currently, dbScanFrac=\" << db_scan_frac_ << \" knnAmp=\" << knn_amp_;\n throw runtime_error(err.str());\n }\n vector> candidates;\n\n if (inv_proc_alg_ == kMap) {\n std::unordered_map map_counter;\n for (size_t i = 0; i < num_prefix_; ++i) {\n for (auto& p : chunkPostLists[perm_q[i]]) {\n map_counter[p]++;\n }\n }\n\n candidates.reserve(db_scan);\n\n for (auto& it : map_counter) {\n if (it.second >= min_times_) {\n candidates.push_back(std::make_pair(-static_cast(it.second), it.first));\n }\n }\n } else if (inv_proc_alg_ == kScan) {\n candidates.resize(chunkQty);\n\n for (size_t i = 0; i < candidates.size(); ++i) {\n candidates[i].second = i;\n }\n for (size_t i = 0; i < num_prefix_; ++i) {\n for (auto& p : chunkPostLists[perm_q[i]]) {\n candidates[p].first--;\n }\n }\n } else if (inv_proc_alg_ == kMerge) {\n VectIdCount tmpRes[2];\n unsigned prevRes = 0;\n\n for (size_t i = 0; i < num_prefix_; ++i) {\n postListUnion(tmpRes[prevRes], chunkPostLists[perm_q[i]], tmpRes[1-prevRes]);\n prevRes = 1 - prevRes;\n }\n\n candidates.reserve(db_scan);\n\n for (const auto& it: tmpRes[1-prevRes]) {\n if (it.qty >= min_times_) {\n candidates.push_back(std::make_pair(-static_cast(it.qty), it.id));\n }\n }\n } else {\n PREPARE_RUNTIME_ERR(err) << \"Bug, unknown inv_proc_alg_: \" << inv_proc_alg_;\n THROW_RUNTIME_ERR(err);\n }\n\n IncrementalQuickSelect quick_select(candidates);\n\n size_t scan_qty = min(db_scan, candidates.size());\n\n for (size_t i = 0; i < scan_qty; ++i) {\n auto z = quick_select.GetNext();\n if (static_cast(-z.first) >= min_times_) {\n const size_t idx = z.second;\n quick_select.Next();\n if (!skip_checking_) query->CheckAndAddToResult(data_start[idx]);\n } else {\n break;\n }\n }\n } else {\n if (inv_proc_alg_ == kMap) {\n std::unordered_map map_counter;\n for (size_t i = 0; i < num_prefix_; ++i) {\n for (auto& p : chunkPostLists[perm_q[i]]) {\n map_counter[p]++;\n }\n }\n for (auto& it : map_counter) {\n if (it.second >= min_times_) {\n const size_t idx = it.first;\n if (!skip_checking_) query->CheckAndAddToResult(data_start[idx]);\n }\n }\n } else if (inv_proc_alg_ == kScan) {\n if (chunkId) {\n memset(&counter[0], 0, sizeof(counter[0])*counter.size());\n }\n for (size_t i = 0; i < num_prefix_; ++i) {\n for (auto& p : chunkPostLists[perm_q[i]]) {\n counter[p]++;\n }\n }\n for (size_t i = 0; i < chunkQty; ++i) {\n if (counter[i] >= min_times_) {\n if (!skip_checking_) query->CheckAndAddToResult(data_start[i]);\n }\n }\n } else if (inv_proc_alg_ == kMerge) {\n VectIdCount tmpRes[2];\n unsigned prevRes = 0;\n for (size_t i = 0; i < num_prefix_; ++i) {\n postListUnion(tmpRes[prevRes], chunkPostLists[perm_q[i]], tmpRes[1-prevRes]);\n prevRes = 1 - prevRes;\n }\n\n for (const auto& it: tmpRes[1-prevRes]) {\n if (it.qty >= min_times_) {\n if (!skip_checking_) query->CheckAndAddToResult(data_start[it.id]);\n }\n }\n } else {\n PREPARE_RUNTIME_ERR(err) << \"Bug, unknown inv_proc_alg_: \" << inv_proc_alg_;\n THROW_RUNTIME_ERR(err);\n }\n }\n }\n}\n\ntemplate \nvoid PivotNeighbInvertedIndex::Search(RangeQuery* query, IdType) const {\n GenSearch(query, 0);\n}\n\ntemplate \nvoid PivotNeighbInvertedIndex::Search(KNNQuery* query, IdType) const {\n GenSearch(query, query->GetK());\n}\n\ntemplate class PivotNeighbInvertedIndex;\ntemplate class PivotNeighbInvertedIndex;\ntemplate class PivotNeighbInvertedIndex;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/method/proj_vptree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"knnqueue.h\"\n#include \"method/proj_vptree.h\"\n#include \"utils.h\"\n#include \"distcomp.h\"\n#include \"projection.h\"\n#include \"spacefactory.h\"\n#include \"report_intr_dim.h\"\n\nnamespace similarity {\n\nusing std::unique_ptr;\n\ntemplate \nObject* ProjectionVPTree::ProjectOneVect(size_t targSpaceId,\n const Query* pQuery,\n const Object* pSrcObj) const {\n vector targVect(projDim_);\n\n projObj_->compProj(pQuery, pSrcObj, &targVect[0]);\n\n return VPTreeSpace_->CreateObjFromVect(targSpaceId, -1, targVect);\n}\n\ntemplate \nvoid ProjectionVPTree::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n CHECK_MSG(VPTreeIndex_ != NULL, \"Expecting non-null pointer for the VP-tree index in SetQueryTimeParams\");\n AnyParamManager pmgr(QueryTimeParams);\n\n AnyParams vptreeQueryParams = pmgr.ExtractParameters(VPTreeIndex_->getQueryTimeParams());\n VPTreeIndex_->SetQueryTimeParams(vptreeQueryParams);\n\n if (pmgr.hasParam(\"dbScanFrac\") && pmgr.hasParam(\"knnAmp\")) {\n throw runtime_error(\"One shouldn't specify both parameters dbScanFrac and knnAmp\");\n }\n \n pmgr.GetParamOptional(\"dbScanFrac\", db_scan_frac_, 0);\n pmgr.GetParamOptional(\"knnAmp\", knn_amp_, 0);\n\n LOG(LIB_INFO) << \"Set query-time parameters for ProjectionVPTree:\";\n LOG(LIB_INFO) << \"dbScanFrac=\" << db_scan_frac_;\n LOG(LIB_INFO) << \"knnAmp=\" << knn_amp_;\n}\n\ntemplate \nProjectionVPTree::ProjectionVPTree(\n bool PrintProgress,\n Space& space,\n const ObjectVector& data) :\n space_(space),\n data_(data), \n PrintProgress_(PrintProgress),\n K_(0),\n knn_amp_(0),\n db_scan_frac_(0)\n{\n}\n\n\ntemplate \nvoid ProjectionVPTree::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n string projSpaceType = \"l2\";\n\n size_t intermDim = 0;\n\n size_t binThreshold = 0;\n string projType;\n\n pmgr.GetParamOptional(\"intermDim\", intermDim, 0);\n pmgr.GetParamRequired(\"projDim\", projDim_);\n pmgr.GetParamRequired(\"projType\", projType);\n pmgr.GetParamOptional(\"binThreshold\", binThreshold, 0);\n pmgr.GetParamOptional(\"projSpaceType\", projSpaceType, \"l2\");\n\n AnyParams RemainParams;\n\n RemainParams = pmgr.ExtractParametersExcept(\n {\n \"intermDim\",\n \"projDim\",\n \"projType\",\n \"binThreshold\",\n \"projSpaceType\"\n });\n\n LOG(LIB_INFO) << \"projType = \" << projType;\n LOG(LIB_INFO) << \"projSpaceType= \" << projSpaceType;\n LOG(LIB_INFO) << \"projDim = \" << projDim_;\n LOG(LIB_INFO) << \"intermDim = \" << intermDim;\n LOG(LIB_INFO) << \"binThreshold = \" << binThreshold;\n\n\n /*\n * Let's extract all parameters before doing\n * any heavy lifting. If an exception fires for some reason,\n * e.g., because the user specified a wrong projection\n * type, the destructor of the AnyParams will check\n * for unclaimed parameters. Currently, this destructor\n * terminates the application and prints and apparently unrelated\n * error message (e.g. a wrong value for the parameter projType)\n */\n\n projObj_.reset(Projection::createProjection(\n space_,\n data_,\n projType,\n intermDim,\n projDim_,\n binThreshold));\n\n const string projDescStr = projSpaceType;\n vector projSpaceDesc;\n\n ParseSpaceArg(projDescStr, projSpaceType, projSpaceDesc);\n unique_ptr projSpaceParams =\n unique_ptr(new AnyParams(projSpaceDesc));\n\n unique_ptr> tmpSpace(SpaceFactoryRegistry::\n Instance().CreateSpace(projSpaceType, *projSpaceParams));\n\n if (NULL == tmpSpace.get()) {\n stringstream err;\n err << \"Cannot create the projection space: '\" << projSpaceType\n << \"' (desc: '\" << projDescStr << \"')\";\n throw runtime_error(err.str());\n }\n\n VectorSpaceSimpleStorage* ps =\n dynamic_cast*>(tmpSpace.get());\n\n if (NULL == ps) {\n stringstream err;\n err << \"The target projection space: '\" << projDescStr << \"' \"\n << \" should be a simple-storage dense vector space, e.g., l2\";\n throw runtime_error(err.str());\n }\n VPTreeSpace_.reset(ps);\n tmpSpace.release();\n\n\n\n projData_.resize(data_.size());\n\n for (size_t id = 0; id < data_.size(); ++id) {\n projData_[id] = ProjectOneVect(id, NULL, data_[id]);\n }\n\n ReportIntrinsicDimensionality(\"Set of projections\" , *VPTreeSpace_, projData_);\n\n VPTreeIndex_.reset(new VPTree>(\n PrintProgress_,\n *VPTreeSpace_,\n projData_, true /* use random centers */));\n VPTreeIndex_->CreateIndex(RemainParams);\n\n // Reset parameters only after the VP-tree index is created!\n this->ResetQueryTimeParams();\n}\n\ntemplate \nProjectionVPTree::~ProjectionVPTree() {\n for (size_t i = 0; i < data_.size(); ++i) {\n delete projData_[i];\n }\n}\n\ntemplate \nconst std::string ProjectionVPTree::StrDesc() const {\n std::stringstream str;\n str << \"projection (vptree)\";\n return str.str();\n}\n\ntemplate \nvoid ProjectionVPTree::Search(RangeQuery* query, IdType) const {\n if (db_scan_frac_ < 0.0 || db_scan_frac_ > 1.0) {\n stringstream err;\n err << METH_PROJ_VPTREE << \" requires that dbScanFrac is in the range [0,1]\";\n throw runtime_error(err.str());\n }\n size_t db_scan_qty = computeDbScan(0);\n if (!db_scan_qty) {\n throw runtime_error(\"For the range search you need to specify a sufficiently large dbScanFrac!\");\n }\n unique_ptr QueryObject(ProjectOneVect(0, query, query->QueryObject()));\n unique_ptr> VPTreeQuery(new KNNQuery(*VPTreeSpace_,\n QueryObject.get(),\n db_scan_qty, 0.0));\n\n VPTreeIndex_->Search(VPTreeQuery.get(), -1);\n\n unique_ptr> ResQueue(VPTreeQuery->Result()->Clone());\n\n while (!ResQueue->Empty()) {\n size_t id = reinterpret_cast(ResQueue->TopObject())->id();\n query->CheckAndAddToResult(data_[id]);\n ResQueue->Pop();\n }\n}\n\ntemplate \nvoid ProjectionVPTree::Search(KNNQuery* query, IdType) const {\n size_t db_scan_qty = computeDbScan(query->GetK());\n if (!db_scan_qty) {\n throw runtime_error(\"You need to specify knnAmp > 0 or a sufficiently large dbScanFrac!\");\n }\n\n unique_ptr QueryObject(ProjectOneVect(0, query, query->QueryObject()));\n unique_ptr> VPTreeQuery(new KNNQuery(*VPTreeSpace_,\n QueryObject.get(),\n db_scan_qty, 0.0));\n\n VPTreeIndex_->Search(VPTreeQuery.get(), -1);\n\n unique_ptr> ResQueue(VPTreeQuery->Result()->Clone());\n\n while (!ResQueue->Empty()) {\n size_t id = reinterpret_cast(ResQueue->TopObject())->id();\n query->CheckAndAddToResult(data_[id]);\n ResQueue->Pop();\n }\n}\n\ntemplate class ProjectionVPTree;\ntemplate class ProjectionVPTree;\ntemplate class ProjectionVPTree;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/projection_index_incremental.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"distcomp.h\"\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"knnquery.h\"\n#include \"incremental_quick_select.h\"\n#include \"method/projection_index_incremental.h\"\n#include \"utils.h\"\n\n#define METH_PROJ_INDEX_INCREMENTAL \"proj_incr\"\n\nusing namespace std;\n\ntypedef pair FloatInt; \n\nnamespace similarity {\n\ntemplate \nProjectionIndexIncremental::ProjectionIndexIncremental(\n bool PrintProgress,\n const Space& space,\n const ObjectVector& data) \n : space_(space), data_(data), PrintProgress_(PrintProgress),\n K_(0) {\n}\n\ntemplate \nvoid ProjectionIndexIncremental::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n size_t binThreshold;\n string projType;\n string projSpaceType;\n size_t intermDim;\n\n pmgr.GetParamOptional(\"intermDim\", intermDim, 0);\n pmgr.GetParamRequired(\"projDim\", proj_dim_);\n pmgr.GetParamRequired(\"projType\", proj_descr_);\n pmgr.GetParamOptional(\"binThreshold\", binThreshold, 0);\n\n pmgr.CheckUnused();\n this->ResetQueryTimeParams();\n\n\n LOG(LIB_INFO) << \"projType = \" << proj_descr_;\n LOG(LIB_INFO) << \"projDim = \" << proj_dim_;\n LOG(LIB_INFO) << \"intermDim = \" << intermDim;\n LOG(LIB_INFO) << \"binThreshold = \" << binThreshold;\n\n /*\n * Let's extract all parameters before doing\n * any heavy lifting. If an exception fires for some reason,\n * e.g., because the user specified a wrong projection\n * type, the destructor of the AnyParams will check\n * for unclaimed parameters. Currently, this destructor\n * terminates the application and prints and apparently unrelated\n * error message (e.g. a wrong parameter name)\n */\n\n proj_obj_.reset(Projection::createProjection(\n space_,\n data_,\n proj_descr_,\n intermDim,\n proj_dim_,\n binThreshold));\n\n const string projDescStr = proj_descr_;\n vector projSpaceDesc;\n\n ParseSpaceArg(projDescStr, projSpaceType, projSpaceDesc);\n unique_ptr projSpaceParams =\n unique_ptr(new AnyParams(projSpaceDesc));\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n#ifdef PROJ_CONTIGUOUS_STORAGE\n proj_vects_.resize(data_.size() * proj_dim_);\n vector TmpVect(proj_dim_);\n\n for (size_t i = 0, start = 0; i < data_.size(); ++i, start += proj_dim_) {\n proj_obj_->compProj(NULL, data_[i], &TmpVect[0]);\n memcpy(&proj_vects_[start], &TmpVect[0], sizeof(proj_vects_[0])*proj_dim_); \n if (progress_bar) ++(*progress_bar);\n }\n#else\n proj_vects_.resize(data.size());\n for (size_t i = 0; i < data.size(); ++i) {\n proj_vects_[i].resize(proj_dim_);\n proj_obj_->compProj(NULL, data_[i], &proj_vects_[i][0]);\n if (progress_bar) ++(*progress_bar);\n }\n#endif\n}\n \ntemplate \nvoid \nProjectionIndexIncremental::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n\n pmgr.GetParamOptional(\"useQueue\", use_priority_queue_, false);\n pmgr.GetParamOptional(\"maxProjDist\", max_proj_dist_, numeric_limits::max());\n pmgr.GetParamOptional(\"useCosine\", use_cosine_, false);\n \n if (pmgr.hasParam(\"dbScanFrac\") && pmgr.hasParam(\"knnAmp\")) {\n throw runtime_error(\"One shouldn't specify both parameters dbScanFrac and knnAmp\");\n }\n \n pmgr.GetParamOptional(\"dbScanFrac\", db_scan_frac_, 0.05);\n pmgr.GetParamOptional(\"knnAmp\", knn_amp_, 0);\n pmgr.CheckUnused();\n LOG(LIB_INFO) << \"Set query-time parameters for ProjectionIndexIncremental:\";\n LOG(LIB_INFO) << \"dbDscanFrac = \" << db_scan_frac_;\n LOG(LIB_INFO) << \"knnAmp = \" << knn_amp_;\n LOG(LIB_INFO) << \"maxProjDist = \" << max_proj_dist_;\n LOG(LIB_INFO) << \"useQueue = \" << use_priority_queue_;\n LOG(LIB_INFO) << \"useCosine = \" << use_cosine_;\n}\n\ntemplate \nProjectionIndexIncremental::~ProjectionIndexIncremental() {\n}\n\ntemplate \nconst std::string ProjectionIndexIncremental::StrDesc() const {\n std::stringstream str;\n str << \"projection (\" << proj_descr_ << \") incr. sorting\";\n return str.str();\n}\n\ntemplate \ntemplate \nvoid ProjectionIndexIncremental::GenSearch(QueryType* query, size_t K) const {\n // Let's make this check here. Otherwise, if you misspell dbScanFrac, you will get \n // a strange error message that says: dbScanFrac should be in the range [0,1].\n if (!knn_amp_) {\n if (db_scan_frac_ < 0.0 || db_scan_frac_ > 1.0) {\n stringstream err;\n err << METH_PROJECTION_INC_SORT << \" requires that dbScanFrac is in the range [0,1]\";\n throw runtime_error(err.str());\n }\n }\n\n size_t db_scan = computeDbScan(K);\n\n\n vector QueryVect(proj_dim_);\n proj_obj_->compProj(query, query->QueryObject(), &QueryVect[0]);\n\n if (!use_priority_queue_) {\n std::vector proj_dists;\n proj_dists.reserve(data_.size());\n\n#ifdef PROJ_CONTIGUOUS_STORAGE\n for (size_t i = 0, start = 0; i < data_.size(); ++i, start += proj_dim_) {\n float projDist = use_cosine_ ? \n CosineSimilarity(&proj_vects_[start], &QueryVect[0], proj_dim_)\n :\n L2NormSIMD(&proj_vects_[start], &QueryVect[0], proj_dim_)\n ;\n#else\n for (size_t i = 0; i < proj_vects_.size(); ++i) {\n float projDist = use_cosine_ ? \n CosineSimilarity(&proj_vects_[i][0], &QueryVect[0], proj_dim_)\n :\n L2SqrSIMD(&proj_vects_[i][0], &QueryVect[0], proj_dim_);\n#endif\n if (projDist <= max_proj_dist_)\n proj_dists.push_back(std::make_pair(projDist, i));\n }\n\n IncrementalQuickSelect quick_select(proj_dists);\n\n size_t scan_qty = min(db_scan, proj_dists.size());\n\n for (size_t i = 0; i < scan_qty; ++i) {\n const size_t idx = quick_select.GetNext().second;\n quick_select.Next();\n query->CheckAndAddToResult(data_[idx]);\n }\n } else {\n priority_queue filterQueue;\n\n#ifdef PROJ_CONTIGUOUS_STORAGE\n for (size_t i = 0, start = 0; i < data_.size(); ++i, start += proj_dim_) {\n float projDist = L2NormSIMD(&proj_vects_[start], &QueryVect[0], proj_dim_);\n#else\n for (size_t i = 0; i < proj_vects_.size(); ++i) {\n float projDist = L2NormSIMD(&proj_vects_[i][0], &QueryVect[0], proj_dim_);\n#endif\n if (projDist <= max_proj_dist_) {\n filterQueue.push(std::make_pair(projDist, i));\n if (filterQueue.size() > db_scan) filterQueue.pop();\n }\n }\n\n while (filterQueue.size() > db_scan) filterQueue.pop();\n while (!filterQueue.empty()) {\n const size_t idx = filterQueue.top().second;\n query->CheckAndAddToResult(data_[idx]);\n filterQueue.pop();\n }\n }\n}\n\ntemplate \nvoid ProjectionIndexIncremental::Search(\n RangeQuery* query, IdType) const {\n GenSearch(query, 0);\n}\n\ntemplate \nvoid ProjectionIndexIncremental::Search(\n KNNQuery* query, IdType) const {\n GenSearch(query, query->GetK());\n}\n\n\ntemplate class ProjectionIndexIncremental;\ntemplate class ProjectionIndexIncremental;\ntemplate class ProjectionIndexIncremental;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/seqsearch.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"knnqueue.h\"\n#include \"method/seqsearch.h\"\n\nnamespace similarity {\n\ntemplate \nstruct SearchThreadParamSeqSearch {\n const Space& space_;\n const ObjectVector& data_;\n IdTypeUnsign threadId_;\n QueryType& query_;\n\n SearchThreadParamSeqSearch(\n const Space& space,\n const ObjectVector& data,\n IdTypeUnsign threadId,\n QueryType& query\n ) :\n space_(space),\n data_(data),\n threadId_(threadId),\n query_(query) {}\n};\n\ntemplate \nstruct SearchThreadSeqSearch {\n void operator()(SearchThreadParamSeqSearch &prm) {\n for (const Object* o: prm.data_) {\n prm.query_.CheckAndAddToResult(o);\n }\n }\n};\n\n\ntemplate \nSeqSearch::SeqSearch(Space& space, const ObjectVector& origData) :\n space_(space), origData_(origData), cacheOptimizedBucket_(NULL), pData_(NULL) {\n}\n\ntemplate \nvoid SeqSearch::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n bool bCopyMem;\n pmgr.GetParamOptional(\"copyMem\", bCopyMem, false);\n pmgr.GetParamOptional(\"multiThread\", multiThread_, false);\n pmgr.GetParamOptional(\"threadQty\", threadQty_, thread::hardware_concurrency()/2);\n if (threadQty_ < 2) multiThread_ = false;\n pmgr.CheckUnused();\n\n LOG(LIB_INFO) << \"copyMem = \" << bCopyMem;\n LOG(LIB_INFO) << \"multiThread = \" << multiThread_;\n\n if (multiThread_) {\n CHECK(threadQty_ > 1);\n const ObjectVector& data = getData();\n vvThreadData.resize(threadQty_);\n for (size_t i = 0; i < threadQty_; ++i)\n vvThreadData.reserve((data.size() + threadQty_ - 1)/threadQty_);\n\n size_t D = (data.size() + threadQty_ - 1)/threadQty_;\n\n for (size_t i = 0; i < data.size(); ++i) {\n //vvThreadData[i % threadQty_].push_back(data[i]);\n vvThreadData[i / D].push_back(data[i]); // This version seems to be working much better\n }\n /*\n for (size_t i = 0; i < threadQty_; ++i) {\n cout << \"#######@@@\" << vvThreadData[i].size() << endl;\n }\n */\n LOG(LIB_INFO) << \"threadQty = \" << threadQty_;\n }\n\n SetQueryTimeParams(getEmptyParams());\n\n if (bCopyMem) {\n CreateCacheOptimizedBucket(origData_, cacheOptimizedBucket_, pData_);\n }\n}\n\ntemplate \nSeqSearch::~SeqSearch() {\n if (cacheOptimizedBucket_ != NULL) {\n ClearBucket(cacheOptimizedBucket_, pData_);\n }\n}\n\ntemplate \nvoid SeqSearch::Search(RangeQuery* query, IdType) const {\n const ObjectVector& data = getData();\n\n if (!multiThread_) {\n for (size_t i = 0; i < data.size(); ++i) {\n query->CheckAndAddToResult(data[i]);\n }\n } else {\n vector>> vQueries(threadQty_);\n vector vThreads(threadQty_);\n vector>>> vThreadParams(threadQty_);\n\n for (size_t i = 0; i < threadQty_; ++i) {\n vQueries[i].reset(new RangeQuery(space_, query->QueryObject(), query->Radius()));\n vThreadParams[i].reset(new SearchThreadParamSeqSearch>(space_, vvThreadData[i], i, *vQueries[i]));\n }\n for (size_t i = 0; i < threadQty_; ++i) {\n vThreads[i] = thread(SearchThreadSeqSearch>(), ref(*vThreadParams[i]));\n }\n for (size_t i = 0; i < threadQty_; ++i) {\n vThreads[i].join();\n }\n for (size_t i = 0; i < threadQty_; ++i) {\n RangeQuery& threadQuery = vThreadParams[i]->query_;\n const ObjectVector& res = *threadQuery.Result();\n const std::vector& dists = *threadQuery.ResultDists();\n query->AddDistanceComputations(threadQuery.DistanceComputations());\n for (size_t k = 0; k < res.size(); ++k) {\n query->CheckAndAddToResult(dists[k], res[k]);\n }\n }\n }\n}\n\ntemplate \nvoid SeqSearch::Search(KNNQuery* query, IdType) const {\n const ObjectVector& data = getData();\n\n if (!multiThread_) {\n for (size_t i = 0; i < data.size(); ++i) {\n query->CheckAndAddToResult(data[i]);\n }\n } else {\n vector>> vQueries(threadQty_);\n vector vThreads(threadQty_);\n vector>>> vThreadParams(threadQty_);\n\n for (size_t i = 0; i < threadQty_; ++i) {\n vQueries[i].reset(new KNNQuery(space_, query->QueryObject(), query->GetK(), query->GetEPS()));\n vThreadParams[i].reset(new SearchThreadParamSeqSearch>(space_, vvThreadData[i], i, *vQueries[i]));\n }\n for (size_t i = 0; i < threadQty_; ++i) {\n vThreads[i] = thread(SearchThreadSeqSearch>(), ref(*vThreadParams[i]));\n }\n for (size_t i = 0; i < threadQty_; ++i) {\n vThreads[i].join();\n }\n for (size_t i = 0; i < threadQty_; ++i) {\n KNNQuery& threadQuery = vThreadParams[i]->query_;\n unique_ptr> ResQ(threadQuery.Result()->Clone());\n query->AddDistanceComputations(threadQuery.DistanceComputations());\n while(!ResQ->Empty()) {\n const Object *pObj = reinterpret_cast(ResQ->TopObject());\n query->CheckAndAddToResult(ResQ->TopDistance(), pObj);\n ResQ->Pop();\n }\n }\n }\n}\n\ntemplate class SeqSearch;\ntemplate class SeqSearch;\ntemplate class SeqSearch;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/small_world_rand.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/small_world_rand.h\"\n#include \"sort_arr_bi.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n//#define START_WITH_E0\n#define START_WITH_E0_AT_QUERY_TIME\n\n#define USE_BITSET_FOR_INDEXING 1\n//#define USE_ALTERNATIVE_FOR_INDEXING \n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nstruct IndexThreadParamsSW {\n const Space& space_;\n SmallWorldRand& index_;\n const ObjectVector& data_;\n size_t index_every_;\n size_t out_of_;\n ProgressDisplay* progress_bar_;\n mutex& display_mutex_;\n size_t progress_update_qty_;\n \n IndexThreadParamsSW(\n const Space& space,\n SmallWorldRand& index, \n const ObjectVector& data,\n size_t index_every,\n size_t out_of,\n ProgressDisplay* progress_bar,\n mutex& display_mutex,\n size_t progress_update_qty\n ) : \n space_(space),\n index_(index), \n data_(data),\n index_every_(index_every),\n out_of_(out_of),\n progress_bar_(progress_bar),\n display_mutex_(display_mutex),\n progress_update_qty_(progress_update_qty)\n { }\n};\n\ntemplate \nstruct IndexThreadSW {\n void operator()(IndexThreadParamsSW& prm) {\n ProgressDisplay* progress_bar = prm.progress_bar_;\n mutex& display_mutex(prm.display_mutex_); \n /* \n * Skip the first element, it was added already\n */\n size_t nextQty = prm.progress_update_qty_;\n for (size_t id = 1; id < prm.data_.size(); ++id) {\n if (prm.index_every_ == id % prm.out_of_) {\n MSWNode* node = new MSWNode(prm.data_[id], id);\n prm.index_.add(node);\n \n if ((id + 1 >= min(prm.data_.size(), nextQty)) && progress_bar) {\n unique_lock lock(display_mutex);\n (*progress_bar) += (nextQty - progress_bar->count());\n nextQty += prm.progress_update_qty_;\n }\n }\n }\n if (progress_bar) {\n unique_lock lock(display_mutex);\n (*progress_bar) += (progress_bar->expected_count() - progress_bar->count());\n }\n }\n};\n\ntemplate \nSmallWorldRand::SmallWorldRand(bool PrintProgress,\n const Space& space,\n const ObjectVector& data) : \n space_(space), data_(data), PrintProgress_(PrintProgress) {}\n\ntemplate \nvoid SmallWorldRand::CreateIndex(const AnyParams& IndexParams) \n{\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"NN\", NN_, 10);\n pmgr.GetParamOptional(\"efConstruction\", efConstruction_, NN_);\n efSearch_ = NN_;\n pmgr.GetParamOptional(\"initIndexAttempts\", initIndexAttempts_, 1);\n pmgr.GetParamOptional(\"indexThreadQty\", indexThreadQty_, thread::hardware_concurrency());\n\n LOG(LIB_INFO) << \"NN = \" << NN_;\n LOG(LIB_INFO) << \"efConstruction_ = \" << efConstruction_;\n LOG(LIB_INFO) << \"initIndexAttempts = \" << initIndexAttempts_;\n LOG(LIB_INFO) << \"indexThreadQty = \" << indexThreadQty_;\n\n pmgr.CheckUnused();\n\n SetQueryTimeParams(getEmptyParams());\n\n if (data_.empty()) return;\n\n // 2) One entry should be added before all the threads are started, or else add() will not work properly\n addCriticalSection(new MSWNode(data_[0], 0 /* id == 0 */));\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n if (indexThreadQty_ <= 1) {\n // Skip the first element, one element is already added\n if (progress_bar) ++(*progress_bar);\n for (size_t id = 1; id < data_.size(); ++id) {\n MSWNode* node = new MSWNode(data_[id], id);\n add(node);\n if (progress_bar) ++(*progress_bar);\n }\n } else {\n vector threads(indexThreadQty_);\n vector>> threadParams; \n mutex progressBarMutex;\n\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threadParams.push_back(shared_ptr>(\n new IndexThreadParamsSW(space_, *this, data_, i, indexThreadQty_,\n progress_bar.get(), progressBarMutex, 200)));\n }\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threads[i] = thread(IndexThreadSW(), ref(*threadParams[i]));\n }\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threads[i].join();\n }\n if (ElList_.size() != data_.size()) {\n stringstream err;\n err << \"Bug: ElList_.size() (\" << ElList_.size() << \") isn't equal to data_.size() (\" << data_.size() << \")\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n LOG(LIB_INFO) << indexThreadQty_ << \" indexing threads have finished\";\n }\n}\n\ntemplate \nvoid \nSmallWorldRand::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n pmgr.GetParamOptional(\"initSearchAttempts\", initSearchAttempts_, 1);\n pmgr.GetParamOptional(\"efSearch\", efSearch_, NN_);\n string tmp;\n pmgr.GetParamOptional(\"algoType\", tmp, \"v1merge\"); \n ToLower(tmp);\n if (tmp == \"v1merge\") searchAlgoType_ = kV1Merge;\n else if (tmp == \"old\") searchAlgoType_ = kOld;\n else {\n throw runtime_error(\"algoType should be one of the following: old, v1merge\");\n }\n pmgr.CheckUnused();\n LOG(LIB_INFO) << \"Set SmallWorldRand query-time parameters:\";\n LOG(LIB_INFO) << \"initSearchAttempts =\" << initSearchAttempts_;\n LOG(LIB_INFO) << \"efSearch =\" << efSearch_;\n}\n\ntemplate \nconst std::string SmallWorldRand::StrDesc() const {\n return METH_SMALL_WORLD_RAND;\n}\n\ntemplate \nSmallWorldRand::~SmallWorldRand() {\n}\n\ntemplate \nMSWNode* SmallWorldRand::getRandomEntryPointLocked() const\n{\n unique_lock lock(ElListGuard_);\n MSWNode* res = getRandomEntryPoint();\n return res;\n}\n\ntemplate \nsize_t SmallWorldRand::getEntryQtyLocked() const\n{\n unique_lock lock(ElListGuard_);\n size_t res = ElList_.size();\n return res;\n}\n\ntemplate \nMSWNode* SmallWorldRand::getRandomEntryPoint() const {\n size_t size = ElList_.size();\n\n if(!ElList_.size()) {\n return NULL;\n } else {\n size_t num = RandomInt()%size;\n return ElList_[num];\n }\n}\n\ntemplate \nvoid \nSmallWorldRand::searchForIndexing(const Object *queryObj,\n priority_queue> &resultSet) const\n{\n#if USE_BITSET_FOR_INDEXING\n/*\n * The trick of using large dense bitsets instead of unordered_set was \n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n vector visitedBitset(data_.size()); // seems to be working fine even in a multi-threaded mode.\n#else\n unordered_set visited;\n#endif\n\n vector neighborCopy;\n\n for (size_t i=0; i < initIndexAttempts_; i++){\n\n /**\n * Search for the k most closest elements to the query.\n */\n#ifdef START_WITH_E0\n MSWNode* provider = i ? getRandomEntryPointLocked() : ElList_[0];\n#else\n MSWNode* provider = getRandomEntryPointLocked();\n#endif\n\n priority_queue closestDistQueue; \n priority_queue > candidateSet; \n\n#ifdef USE_ALTERNATIVE_FOR_INDEXING\n dist_t d = space_.ProxyDistance(queryObj, provider->getData());\n #pragma message \"Using an alternative/proxy function for indexing, not the original one!\" \n#else\n dist_t d = space_.IndexTimeDistance(queryObj, provider->getData());\n#endif\n EvaluatedMSWNodeReverse ev(d, provider);\n\n candidateSet.push(ev);\n closestDistQueue.push(d);\n\n if (closestDistQueue.size() > efConstruction_) {\n closestDistQueue.pop();\n }\n \n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = provider->getId();\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n visitedBitset[nodeId] = true;\n#else\n visited.insert(provider);\n#endif\n resultSet.emplace(d, provider);\n \n if (resultSet.size() > NN_) { // TODO check somewhere that NN > 0\n resultSet.pop();\n } \n\n while (!candidateSet.empty()) {\n const EvaluatedMSWNodeReverse& currEv = candidateSet.top();\n dist_t lowerBound = closestDistQueue.top();\n\n /*\n * Check if we reached a local minimum.\n */\n if (currEv.getDistance() > lowerBound) {\n break;\n }\n MSWNode* currNode = currEv.getMSWNode();\n\n /*\n * This lock protects currNode from being modified\n * while we are accessing elements of currNode.\n */\n size_t neighborQty = 0;\n {\n unique_lock lock(currNode->accessGuard_);\n\n //const vector& neighbor = currNode->getAllFriends();\n const vector& neighbor = currNode->getAllFriends();\n neighborQty = neighbor.size();\n if (neighborQty > neighborCopy.size()) neighborCopy.resize(neighborQty);\n for (size_t k = 0; k < neighborQty; ++k)\n neighborCopy[k]=neighbor[k];\n }\n\n // Can't access curEv anymore! The reference would become invalid\n candidateSet.pop();\n\n // calculate distance to each neighbor\n for (size_t neighborId = 0; neighborId < neighborQty; ++neighborId) {\n MSWNode* pNeighbor = neighborCopy[neighborId];\n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = pNeighbor->getId();\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n if (!visitedBitset[nodeId]) {\n visitedBitset[nodeId] = true;\n#else\n if (visited.find(pNeighbor) == visited.end()) {\n visited.insert(pNeighbor);\n#endif\n#ifdef USE_ALTERNATIVE_FOR_INDEXING\n d = space_.ProxyDistance(queryObj, pNeighbor->getData());\n #pragma message \"Using an alternative/proxy function for indexing, not the original one!\" \n#else\n d = space_.IndexTimeDistance(queryObj, pNeighbor->getData());\n#endif\n\n if (closestDistQueue.size() < efConstruction_ || d < closestDistQueue.top()) {\n closestDistQueue.push(d);\n if (closestDistQueue.size() > efConstruction_) {\n closestDistQueue.pop();\n }\n candidateSet.emplace(d, pNeighbor);\n }\n\n if (resultSet.size() < NN_ || resultSet.top().getDistance() > d) {\n resultSet.emplace(d, pNeighbor);\n if (resultSet.size() > NN_) { // TODO check somewhere that NN > 0\n resultSet.pop();\n }\n }\n }\n }\n }\n }\n}\n\n\ntemplate \nvoid SmallWorldRand::add(MSWNode *newElement){\n newElement->removeAllFriends(); \n\n bool isEmpty = false;\n\n {\n unique_lock lock(ElListGuard_);\n isEmpty = ElList_.empty();\n }\n\n if(isEmpty){\n // Before add() is called, the first node should be created!\n LOG(LIB_INFO) << \"Bug: the list of nodes shouldn't be empty!\";\n throw runtime_error(\"Bug: the list of nodes shouldn't be empty!\");\n }\n\n {\n priority_queue> resultSet;\n\n searchForIndexing(newElement->getData(), resultSet);\n\n // TODO actually we might need to add elements in the reverse order in the future.\n // For the current implementation, however, the order doesn't seem to matter\n while (!resultSet.empty()) {\n link(resultSet.top().getMSWNode(), newElement);\n resultSet.pop();\n }\n }\n\n addCriticalSection(newElement);\n}\n\ntemplate \nvoid SmallWorldRand::addCriticalSection(MSWNode *newElement){\n unique_lock lock(ElListGuard_);\n\n ElList_.push_back(newElement);\n}\n\ntemplate \nvoid SmallWorldRand::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search is not supported!\");\n}\n\ntemplate \nvoid SmallWorldRand::Search(KNNQuery* query, IdType) const {\n if (searchAlgoType_ == kV1Merge) SearchV1Merge(query);\n else SearchOld(query);\n}\n\ntemplate \nvoid SmallWorldRand::SearchV1Merge(KNNQuery* query) const {\n if (ElList_.empty()) return;\n CHECK_MSG(efSearch_ > 0, \"efSearch should be > 0\");\n/*\n * The trick of using large dense bitsets instead of unordered_set was\n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n vector visitedBitset(ElList_.size());\n int checks=0;\n for (size_t i=0; i < initSearchAttempts_; i++) {\n /**\n * Search of most k-closest elements to the query.\n */\n#ifdef START_WITH_E0_AT_QUERY_TIME\n MSWNode* currNode = i ? getRandomEntryPoint(): ElList_[0];\n#else\n MSWNode* currNode = getRandomEntryPoint();\n#endif\n SortArrBI sortedArr(efSearch_);\n\n const Object* currObj = currNode->getData();\n dist_t d = query->DistanceObjLeft(currObj);\n\tchecks++;\n sortedArr.push_unsorted_grow(d, currNode); // It won't grow\n\n size_t nodeId = currNode->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n CHECK(nodeId < data_.size());\n\n visitedBitset[nodeId] = true;\n\n int_fast32_t currElem = 0;\n\n typedef typename SortArrBI::Item QueueItem;\n\n vector& queueData = sortedArr.get_data();\n vector itemBuff(8*NN_);\n\n //LOG(LIB_INFO) << \"@@@ start size = \" << queueData.size();\n while(currElem < sortedArr.size()){\n auto& e = queueData[currElem];\n CHECK(!e.used);\n e.used = true;\n currNode = e.data;\n ++currElem;\n/*\n for (MSWNode* neighbor : currNode->getAllFriends()) {\n _mm_prefetch(reinterpret_cast(const_cast(neighbor->getData())), _MM_HINT_T0);\n }\n for (MSWNode* neighbor : currNode->getAllFriends()) {\n _mm_prefetch(const_cast(neighbor->getData()->data()), _MM_HINT_T0);\n }*/\n\n if (currNode->getAllFriends().size() > itemBuff.size())\n itemBuff.resize(currNode->getAllFriends().size());\n\n size_t itemQty = 0;\n dist_t topKey = sortedArr.top_key();\n //calculate distance to each neighbor\n for (MSWNode* neighbor : currNode->getAllFriends()) {\n nodeId = neighbor->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n CHECK(nodeId < data_.size());\n\n if (!visitedBitset[nodeId]) {\n currObj = neighbor->getData();\n d = query->DistanceObjLeft(currObj);\n\t\t checks ++ ;\n visitedBitset[nodeId] = true;\n if (d < topKey) {\n itemBuff[itemQty++]=QueueItem(d, neighbor);\n }\n }\n }\n\n if (itemQty) {\n //_mm_prefetch(const_cast(reinterpret_cast(&itemBuff[0])), _MM_HINT_T0);\n std::sort(itemBuff.begin(), itemBuff.begin() + itemQty);\n size_t insIndex = sortedArr.merge_with_sorted_items(&itemBuff[0], itemQty);\n\n if (insIndex < currElem) {\n //LOG(LIB_INFO) << \"@@@ \" << currElem << \" -> \" << insIndex;\n currElem = insIndex;\n }\n }\n\n //LOG(LIB_INFO) << \"@@@ \" << itemQty << \" out of \" << currNode->getAllFriends().size() << \" queue size: \" << sortedArr.size();\n\n // To ensure that we either reach the end of the unexplored queue or currElem points to the first unused element\n while (currElem < sortedArr.size() && queueData[currElem].used == true)\n ++currElem;\n }\n\n for (int_fast32_t i = 0; i < query->GetK() && i < sortedArr.size(); ++i) {\n query->CheckAndAddToResult(queueData[i].key, queueData[i].data->getData());\n }\n }\n\t//cout << checks <\nvoid SmallWorldRand::SearchOld(KNNQuery* query) const {\n if (ElList_.empty()) return;\n CHECK_MSG(efSearch_ > 0, \"efSearch should be > 0\");\n/*\n * The trick of using large dense bitsets instead of unordered_set was\n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n vector visitedBitset(ElList_.size());\n\n for (size_t i=0; i < initSearchAttempts_; i++) {\n /**\n * Search of most k-closest elements to the query.\n */\n#ifdef START_WITH_E0_AT_QUERY_TIME\n MSWNode* provider = i ? getRandomEntryPoint(): ElList_[0];\n#else\n MSWNode* provider = getRandomEntryPoint();\n#endif\n //MSWNode* provider = getRandomEntryPoint();\n\n priority_queue closestDistQueue; //The set of all elements which distance was calculated\n priority_queue > candidateQueue; //the set of elements which we can use to evaluate\n\n const Object* currObj = provider->getData();\n dist_t d = query->DistanceObjLeft(currObj);\n query->CheckAndAddToResult(d, currObj); // This should be done before the object goes to the queue: otherwise it will not be compared to the query at all!\n\n EvaluatedMSWNodeReverse ev(d, provider);\n candidateQueue.push(ev);\n closestDistQueue.emplace(d);\n\n size_t nodeId = provider->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n visitedBitset[nodeId] = true;\n\n while(!candidateQueue.empty()){\n\n auto iter = candidateQueue.top(); // This one was already compared to the query\n const EvaluatedMSWNodeReverse& currEv = iter;\n\n // Did we reach a local minimum?\n if (currEv.getDistance() > closestDistQueue.top()) {\n break;\n }\n\n for (MSWNode* neighbor : (currEv.getMSWNode())->getAllFriends()) {\n _mm_prefetch(reinterpret_cast(const_cast(neighbor->getData())), _MM_HINT_T0);\n }\n for (MSWNode* neighbor : (currEv.getMSWNode())->getAllFriends()) {\n _mm_prefetch(const_cast(neighbor->getData()->data()), _MM_HINT_T0);\n }\n\n const vector& neighbor = (currEv.getMSWNode())->getAllFriends();\n\n // Can't access curEv anymore! The reference would become invalid\n candidateQueue.pop();\n\n //calculate distance to each neighbor\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter){\n nodeId = (*iter)->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n if (!visitedBitset[nodeId]) {\n currObj = (*iter)->getData();\n d = query->DistanceObjLeft(currObj);\n visitedBitset[nodeId] = true;\n\n if (closestDistQueue.size() < efSearch_ || d < closestDistQueue.top()) {\n closestDistQueue.emplace(d);\n if (closestDistQueue.size() > efSearch_) {\n closestDistQueue.pop();\n }\n candidateQueue.emplace(d, *iter);\n }\n\n query->CheckAndAddToResult(d, currObj);\n }\n }\n }\n }\n\n}\n\ntemplate \nvoid SmallWorldRand::SaveIndex(const string &location) {\n ofstream outFile(location);\n CHECK_MSG(outFile, \"Cannot open file '\" + location + \"' for writing\");\n outFile.exceptions(std::ios::badbit);\n size_t lineNum = 0;\n\n WriteField(outFile, METHOD_DESC, StrDesc()); lineNum++;\n WriteField(outFile, \"NN\", NN_); lineNum++;\n\n for (const MSWNode* pNode: ElList_) {\n IdType nodeID = pNode->getId();\n CHECK_MSG(nodeID >= 0 && nodeID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeID) +\n \" for object ID \" + ConvertToString(pNode->getData()->id()) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n outFile << nodeID << \":\" << pNode->getData()->id() << \":\";\n for (const MSWNode* pNodeFriend: pNode->getAllFriends()) {\n IdType nodeFriendID = pNodeFriend->getId();\n CHECK_MSG(nodeFriendID >= 0 && nodeFriendID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeFriendID) +\n \" for object ID \" + ConvertToString(pNodeFriend->getData()->id()) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n outFile << ' ' << nodeFriendID;\n }\n outFile << endl; lineNum++;\n }\n outFile << endl; lineNum++; // The empty line indicates the end of data entries\n WriteField(outFile, LINE_QTY, lineNum + 1 /* including this line */);\n outFile.close();\n}\n\ntemplate \nvoid SmallWorldRand::LoadIndex(const string &location) {\n vector ptrMapper(data_.size());\n\n for (unsigned pass = 0; pass < 2; ++ pass) {\n ifstream inFile(location);\n CHECK_MSG(inFile, \"Cannot open file '\" + location + \"' for reading\");\n inFile.exceptions(std::ios::badbit);\n\n size_t lineNum = 1;\n string methDesc;\n ReadField(inFile, METHOD_DESC, methDesc);\n lineNum++;\n CHECK_MSG(methDesc == StrDesc(),\n \"Looks like you try to use an index created by a different method: \" + methDesc);\n ReadField(inFile, \"NN\", NN_);\n lineNum++;\n\n string line;\n while (getline(inFile, line)) {\n if (line.empty()) {\n lineNum++; break;\n }\n stringstream str(line);\n str.exceptions(std::ios::badbit);\n char c1, c2;\n IdType nodeID, objID;\n CHECK_MSG((str >> nodeID) && (str >> c1) && (str >> objID) && (str >> c2),\n \"Bug or inconsitent data, wrong format, line: \" + ConvertToString(lineNum)\n );\n CHECK_MSG(c1 == ':' && c2 == ':',\n string(\"Bug or inconsitent data, wrong format, c1=\") + c1 + \",c2=\" + c2 +\n \" line: \" + ConvertToString(lineNum)\n );\n CHECK_MSG(nodeID >= 0 && nodeID < data_.size(),\n DATA_MUTATION_ERROR_MSG + \" (unexpected node ID \" + ConvertToString(nodeID) +\n \" for object ID \" + ConvertToString(objID) +\n \" data_.size() = \" + ConvertToString(data_.size()) + \")\");\n CHECK_MSG(data_[nodeID]->id() == objID,\n DATA_MUTATION_ERROR_MSG + \" (unexpected object ID \" + ConvertToString(data_[nodeID]->id()) +\n \" for data element with ID \" + ConvertToString(nodeID) +\n \" expected object ID: \" + ConvertToString(objID) + \")\"\n );\n if (pass == 0) {\n unique_ptr node(new MSWNode(data_[nodeID], nodeID));\n ptrMapper[nodeID] = node.get();\n ElList_.push_back(node.release());\n } else {\n MSWNode *pNode = ptrMapper[nodeID];\n CHECK_MSG(pNode != NULL,\n \"Bug, got NULL pointer in the second pass for nodeID \" + ConvertToString(nodeID));\n IdType nodeFriendID;\n while (str >> nodeFriendID) {\n CHECK_MSG(nodeFriendID >= 0 && nodeFriendID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeFriendID) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n MSWNode *pFriendNode = ptrMapper[nodeFriendID];\n CHECK_MSG(pFriendNode != NULL,\n \"Bug, got NULL pointer in the second pass for nodeID \" + ConvertToString(nodeFriendID));\n pNode->addFriend(pFriendNode, false /* don't check for duplicates */);\n }\n CHECK_MSG(str.eof(),\n \"It looks like there is some extract erroneous stuff in the end of the line \" +\n ConvertToString(lineNum));\n }\n ++lineNum;\n }\n\n size_t ExpLineNum;\n ReadField(inFile, LINE_QTY, ExpLineNum);\n CHECK_MSG(lineNum == ExpLineNum,\n DATA_MUTATION_ERROR_MSG + \" (expected number of lines \" + ConvertToString(ExpLineNum) +\n \" read so far doesn't match the number of read lines: \" + ConvertToString(lineNum) + \")\");\n inFile.close();\n }\n}\n\ntemplate class SmallWorldRand;\ntemplate class SmallWorldRand;\ntemplate class SmallWorldRand;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/small_world_rand.cc~", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/small_world_rand.h\"\n#include \"sort_arr_bi.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n//#define START_WITH_E0\n#define START_WITH_E0_AT_QUERY_TIME\n\n#define USE_BITSET_FOR_INDEXING 1\n//#define USE_ALTERNATIVE_FOR_INDEXING \n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nstruct IndexThreadParamsSW {\n const Space& space_;\n SmallWorldRand& index_;\n const ObjectVector& data_;\n size_t index_every_;\n size_t out_of_;\n ProgressDisplay* progress_bar_;\n mutex& display_mutex_;\n size_t progress_update_qty_;\n \n IndexThreadParamsSW(\n const Space& space,\n SmallWorldRand& index, \n const ObjectVector& data,\n size_t index_every,\n size_t out_of,\n ProgressDisplay* progress_bar,\n mutex& display_mutex,\n size_t progress_update_qty\n ) : \n space_(space),\n index_(index), \n data_(data),\n index_every_(index_every),\n out_of_(out_of),\n progress_bar_(progress_bar),\n display_mutex_(display_mutex),\n progress_update_qty_(progress_update_qty)\n { }\n};\n\ntemplate \nstruct IndexThreadSW {\n void operator()(IndexThreadParamsSW& prm) {\n ProgressDisplay* progress_bar = prm.progress_bar_;\n mutex& display_mutex(prm.display_mutex_); \n /* \n * Skip the first element, it was added already\n */\n size_t nextQty = prm.progress_update_qty_;\n for (size_t id = 1; id < prm.data_.size(); ++id) {\n if (prm.index_every_ == id % prm.out_of_) {\n MSWNode* node = new MSWNode(prm.data_[id], id);\n prm.index_.add(node);\n \n if ((id + 1 >= min(prm.data_.size(), nextQty)) && progress_bar) {\n unique_lock lock(display_mutex);\n (*progress_bar) += (nextQty - progress_bar->count());\n nextQty += prm.progress_update_qty_;\n }\n }\n }\n if (progress_bar) {\n unique_lock lock(display_mutex);\n (*progress_bar) += (progress_bar->expected_count() - progress_bar->count());\n }\n }\n};\n\ntemplate \nSmallWorldRand::SmallWorldRand(bool PrintProgress,\n const Space& space,\n const ObjectVector& data) : \n space_(space), data_(data), PrintProgress_(PrintProgress) {}\n\ntemplate \nvoid SmallWorldRand::CreateIndex(const AnyParams& IndexParams) \n{\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"NN\", NN_, 10);\n pmgr.GetParamOptional(\"efConstruction\", efConstruction_, NN_);\n efSearch_ = NN_;\n pmgr.GetParamOptional(\"initIndexAttempts\", initIndexAttempts_, 1);\n pmgr.GetParamOptional(\"indexThreadQty\", indexThreadQty_, thread::hardware_concurrency());\n\n LOG(LIB_INFO) << \"NN = \" << NN_;\n LOG(LIB_INFO) << \"efConstruction_ = \" << efConstruction_;\n LOG(LIB_INFO) << \"initIndexAttempts = \" << initIndexAttempts_;\n LOG(LIB_INFO) << \"indexThreadQty = \" << indexThreadQty_;\n\n pmgr.CheckUnused();\n\n SetQueryTimeParams(getEmptyParams());\n\n if (data_.empty()) return;\n\n // 2) One entry should be added before all the threads are started, or else add() will not work properly\n addCriticalSection(new MSWNode(data_[0], 0 /* id == 0 */));\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n if (indexThreadQty_ <= 1) {\n // Skip the first element, one element is already added\n if (progress_bar) ++(*progress_bar);\n for (size_t id = 1; id < data_.size(); ++id) {\n MSWNode* node = new MSWNode(data_[id], id);\n add(node);\n if (progress_bar) ++(*progress_bar);\n }\n } else {\n vector threads(indexThreadQty_);\n vector>> threadParams; \n mutex progressBarMutex;\n\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threadParams.push_back(shared_ptr>(\n new IndexThreadParamsSW(space_, *this, data_, i, indexThreadQty_,\n progress_bar.get(), progressBarMutex, 200)));\n }\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threads[i] = thread(IndexThreadSW(), ref(*threadParams[i]));\n }\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threads[i].join();\n }\n if (ElList_.size() != data_.size()) {\n stringstream err;\n err << \"Bug: ElList_.size() (\" << ElList_.size() << \") isn't equal to data_.size() (\" << data_.size() << \")\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n LOG(LIB_INFO) << indexThreadQty_ << \" indexing threads have finished\";\n }\n}\n\ntemplate \nvoid \nSmallWorldRand::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n pmgr.GetParamOptional(\"initSearchAttempts\", initSearchAttempts_, 1);\n pmgr.GetParamOptional(\"efSearch\", efSearch_, NN_);\n string tmp;\n pmgr.GetParamOptional(\"algoType\", tmp, \"v1merge\"); \n ToLower(tmp);\n if (tmp == \"v1merge\") searchAlgoType_ = kV1Merge;\n else if (tmp == \"old\") searchAlgoType_ = kOld;\n else {\n throw runtime_error(\"algoType should be one of the following: old, v1merge\");\n }\n pmgr.CheckUnused();\n LOG(LIB_INFO) << \"Set SmallWorldRand query-time parameters:\";\n LOG(LIB_INFO) << \"initSearchAttempts =\" << initSearchAttempts_;\n LOG(LIB_INFO) << \"efSearch =\" << efSearch_;\n}\n\ntemplate \nconst std::string SmallWorldRand::StrDesc() const {\n return METH_SMALL_WORLD_RAND;\n}\n\ntemplate \nSmallWorldRand::~SmallWorldRand() {\n}\n\ntemplate \nMSWNode* SmallWorldRand::getRandomEntryPointLocked() const\n{\n unique_lock lock(ElListGuard_);\n MSWNode* res = getRandomEntryPoint();\n return res;\n}\n\ntemplate \nsize_t SmallWorldRand::getEntryQtyLocked() const\n{\n unique_lock lock(ElListGuard_);\n size_t res = ElList_.size();\n return res;\n}\n\ntemplate \nMSWNode* SmallWorldRand::getRandomEntryPoint() const {\n size_t size = ElList_.size();\n\n if(!ElList_.size()) {\n return NULL;\n } else {\n size_t num = RandomInt()%size;\n return ElList_[num];\n }\n}\n\ntemplate \nvoid \nSmallWorldRand::searchForIndexing(const Object *queryObj,\n priority_queue> &resultSet) const\n{\n#if USE_BITSET_FOR_INDEXING\n/*\n * The trick of using large dense bitsets instead of unordered_set was \n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n vector visitedBitset(data_.size()); // seems to be working fine even in a multi-threaded mode.\n#else\n unordered_set visited;\n#endif\n\n vector neighborCopy;\n\n for (size_t i=0; i < initIndexAttempts_; i++){\n\n /**\n * Search for the k most closest elements to the query.\n */\n#ifdef START_WITH_E0\n MSWNode* provider = i ? getRandomEntryPointLocked() : ElList_[0];\n#else\n MSWNode* provider = getRandomEntryPointLocked();\n#endif\n\n priority_queue closestDistQueue; \n priority_queue > candidateSet; \n\n#ifdef USE_ALTERNATIVE_FOR_INDEXING\n dist_t d = space_.ProxyDistance(queryObj, provider->getData());\n #pragma message \"Using an alternative/proxy function for indexing, not the original one!\" \n#else\n dist_t d = space_.IndexTimeDistance(queryObj, provider->getData());\n#endif\n EvaluatedMSWNodeReverse ev(d, provider);\n\n candidateSet.push(ev);\n closestDistQueue.push(d);\n\n if (closestDistQueue.size() > efConstruction_) {\n closestDistQueue.pop();\n }\n \n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = provider->getId();\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n visitedBitset[nodeId] = true;\n#else\n visited.insert(provider);\n#endif\n resultSet.emplace(d, provider);\n \n if (resultSet.size() > NN_) { // TODO check somewhere that NN > 0\n resultSet.pop();\n } \n\n while (!candidateSet.empty()) {\n const EvaluatedMSWNodeReverse& currEv = candidateSet.top();\n dist_t lowerBound = closestDistQueue.top();\n\n /*\n * Check if we reached a local minimum.\n */\n if (currEv.getDistance() > lowerBound) {\n break;\n }\n MSWNode* currNode = currEv.getMSWNode();\n\n /*\n * This lock protects currNode from being modified\n * while we are accessing elements of currNode.\n */\n size_t neighborQty = 0;\n {\n unique_lock lock(currNode->accessGuard_);\n\n //const vector& neighbor = currNode->getAllFriends();\n const vector& neighbor = currNode->getAllFriends();\n neighborQty = neighbor.size();\n if (neighborQty > neighborCopy.size()) neighborCopy.resize(neighborQty);\n for (size_t k = 0; k < neighborQty; ++k)\n neighborCopy[k]=neighbor[k];\n }\n\n // Can't access curEv anymore! The reference would become invalid\n candidateSet.pop();\n\n // calculate distance to each neighbor\n for (size_t neighborId = 0; neighborId < neighborQty; ++neighborId) {\n MSWNode* pNeighbor = neighborCopy[neighborId];\n#if USE_BITSET_FOR_INDEXING\n size_t nodeId = pNeighbor->getId();\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n if (!visitedBitset[nodeId]) {\n visitedBitset[nodeId] = true;\n#else\n if (visited.find(pNeighbor) == visited.end()) {\n visited.insert(pNeighbor);\n#endif\n#ifdef USE_ALTERNATIVE_FOR_INDEXING\n d = space_.ProxyDistance(queryObj, pNeighbor->getData());\n #pragma message \"Using an alternative/proxy function for indexing, not the original one!\" \n#else\n d = space_.IndexTimeDistance(queryObj, pNeighbor->getData());\n#endif\n\n if (closestDistQueue.size() < efConstruction_ || d < closestDistQueue.top()) {\n closestDistQueue.push(d);\n if (closestDistQueue.size() > efConstruction_) {\n closestDistQueue.pop();\n }\n candidateSet.emplace(d, pNeighbor);\n }\n\n if (resultSet.size() < NN_ || resultSet.top().getDistance() > d) {\n resultSet.emplace(d, pNeighbor);\n if (resultSet.size() > NN_) { // TODO check somewhere that NN > 0\n resultSet.pop();\n }\n }\n }\n }\n }\n }\n}\n\n\ntemplate \nvoid SmallWorldRand::add(MSWNode *newElement){\n newElement->removeAllFriends(); \n\n bool isEmpty = false;\n\n {\n unique_lock lock(ElListGuard_);\n isEmpty = ElList_.empty();\n }\n\n if(isEmpty){\n // Before add() is called, the first node should be created!\n LOG(LIB_INFO) << \"Bug: the list of nodes shouldn't be empty!\";\n throw runtime_error(\"Bug: the list of nodes shouldn't be empty!\");\n }\n\n {\n priority_queue> resultSet;\n\n searchForIndexing(newElement->getData(), resultSet);\n\n // TODO actually we might need to add elements in the reverse order in the future.\n // For the current implementation, however, the order doesn't seem to matter\n while (!resultSet.empty()) {\n link(resultSet.top().getMSWNode(), newElement);\n resultSet.pop();\n }\n }\n\n addCriticalSection(newElement);\n}\n\ntemplate \nvoid SmallWorldRand::addCriticalSection(MSWNode *newElement){\n unique_lock lock(ElListGuard_);\n\n ElList_.push_back(newElement);\n}\n\ntemplate \nvoid SmallWorldRand::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search is not supported!\");\n}\n\ntemplate \nvoid SmallWorldRand::Search(KNNQuery* query, IdType) const {\n if (searchAlgoType_ == kV1Merge) SearchV1Merge(query);\n else SearchOld(query);\n}\n\ntemplate \nvoid SmallWorldRand::SearchV1Merge(KNNQuery* query) const {\n if (ElList_.empty()) return;\n CHECK_MSG(efSearch_ > 0, \"efSearch should be > 0\");\n/*\n * The trick of using large dense bitsets instead of unordered_set was\n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n vector visitedBitset(ElList_.size());\n int checks=0;\n for (size_t i=0; i < initSearchAttempts_; i++) {\n /**\n * Search of most k-closest elements to the query.\n */\n#ifdef START_WITH_E0_AT_QUERY_TIME\n MSWNode* currNode = i ? getRandomEntryPoint(): ElList_[0];\n#else\n MSWNode* currNode = getRandomEntryPoint();\n#endif\n SortArrBI sortedArr(efSearch_);\n\n const Object* currObj = currNode->getData();\n dist_t d = query->DistanceObjLeft(currObj);\n\tchecks++;\n sortedArr.push_unsorted_grow(d, currNode); // It won't grow\n\n size_t nodeId = currNode->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n CHECK(nodeId < data_.size());\n\n visitedBitset[nodeId] = true;\n\n int_fast32_t currElem = 0;\n\n typedef typename SortArrBI::Item QueueItem;\n\n vector& queueData = sortedArr.get_data();\n vector itemBuff(8*NN_);\n\n //LOG(LIB_INFO) << \"@@@ start size = \" << queueData.size();\n while(currElem < sortedArr.size()){\n auto& e = queueData[currElem];\n CHECK(!e.used);\n e.used = true;\n currNode = e.data;\n ++currElem;\n\n for (MSWNode* neighbor : currNode->getAllFriends()) {\n _mm_prefetch(reinterpret_cast(const_cast(neighbor->getData())), _MM_HINT_T0);\n }\n for (MSWNode* neighbor : currNode->getAllFriends()) {\n _mm_prefetch(const_cast(neighbor->getData()->data()), _MM_HINT_T0);\n }\n\n if (currNode->getAllFriends().size() > itemBuff.size())\n itemBuff.resize(currNode->getAllFriends().size());\n\n size_t itemQty = 0;\n dist_t topKey = sortedArr.top_key();\n //calculate distance to each neighbor\n for (MSWNode* neighbor : currNode->getAllFriends()) {\n nodeId = neighbor->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n CHECK(nodeId < data_.size());\n\n if (!visitedBitset[nodeId]) {\n currObj = neighbor->getData();\n d = query->DistanceObjLeft(currObj);\n\t\t checks ++ ;\n visitedBitset[nodeId] = true;\n if (d < topKey) {\n itemBuff[itemQty++]=QueueItem(d, neighbor);\n }\n }\n }\n\n if (itemQty) {\n _mm_prefetch(const_cast(reinterpret_cast(&itemBuff[0])), _MM_HINT_T0);\n std::sort(itemBuff.begin(), itemBuff.begin() + itemQty);\n size_t insIndex = sortedArr.merge_with_sorted_items(&itemBuff[0], itemQty);\n\n if (insIndex < currElem) {\n //LOG(LIB_INFO) << \"@@@ \" << currElem << \" -> \" << insIndex;\n currElem = insIndex;\n }\n }\n\n //LOG(LIB_INFO) << \"@@@ \" << itemQty << \" out of \" << currNode->getAllFriends().size() << \" queue size: \" << sortedArr.size();\n\n // To ensure that we either reach the end of the unexplored queue or currElem points to the first unused element\n while (currElem < sortedArr.size() && queueData[currElem].used == true)\n ++currElem;\n }\n\n for (int_fast32_t i = 0; i < query->GetK() && i < sortedArr.size(); ++i) {\n query->CheckAndAddToResult(queueData[i].key, queueData[i].data->getData());\n }\n }\n\tcout << checks <\nvoid SmallWorldRand::SearchOld(KNNQuery* query) const {\n if (ElList_.empty()) return;\n CHECK_MSG(efSearch_ > 0, \"efSearch should be > 0\");\n/*\n * The trick of using large dense bitsets instead of unordered_set was\n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n vector visitedBitset(ElList_.size());\n\n for (size_t i=0; i < initSearchAttempts_; i++) {\n /**\n * Search of most k-closest elements to the query.\n */\n#ifdef START_WITH_E0_AT_QUERY_TIME\n MSWNode* provider = i ? getRandomEntryPoint(): ElList_[0];\n#else\n MSWNode* provider = getRandomEntryPoint();\n#endif\n //MSWNode* provider = getRandomEntryPoint();\n\n priority_queue closestDistQueue; //The set of all elements which distance was calculated\n priority_queue > candidateQueue; //the set of elements which we can use to evaluate\n\n const Object* currObj = provider->getData();\n dist_t d = query->DistanceObjLeft(currObj);\n query->CheckAndAddToResult(d, currObj); // This should be done before the object goes to the queue: otherwise it will not be compared to the query at all!\n\n EvaluatedMSWNodeReverse ev(d, provider);\n candidateQueue.push(ev);\n closestDistQueue.emplace(d);\n\n size_t nodeId = provider->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n visitedBitset[nodeId] = true;\n\n while(!candidateQueue.empty()){\n\n auto iter = candidateQueue.top(); // This one was already compared to the query\n const EvaluatedMSWNodeReverse& currEv = iter;\n\n // Did we reach a local minimum?\n if (currEv.getDistance() > closestDistQueue.top()) {\n break;\n }\n\n for (MSWNode* neighbor : (currEv.getMSWNode())->getAllFriends()) {\n _mm_prefetch(reinterpret_cast(const_cast(neighbor->getData())), _MM_HINT_T0);\n }\n for (MSWNode* neighbor : (currEv.getMSWNode())->getAllFriends()) {\n _mm_prefetch(const_cast(neighbor->getData()->data()), _MM_HINT_T0);\n }\n\n const vector& neighbor = (currEv.getMSWNode())->getAllFriends();\n\n // Can't access curEv anymore! The reference would become invalid\n candidateQueue.pop();\n\n //calculate distance to each neighbor\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter){\n nodeId = (*iter)->getId();\n // data_.size() is guaranteed to be equal to ElList_.size()\n if (nodeId >= data_.size()) {\n stringstream err;\n err << \"Bug: nodeId > data_size()\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n if (!visitedBitset[nodeId]) {\n currObj = (*iter)->getData();\n d = query->DistanceObjLeft(currObj);\n visitedBitset[nodeId] = true;\n\n if (closestDistQueue.size() < efSearch_ || d < closestDistQueue.top()) {\n closestDistQueue.emplace(d);\n if (closestDistQueue.size() > efSearch_) {\n closestDistQueue.pop();\n }\n candidateQueue.emplace(d, *iter);\n }\n\n query->CheckAndAddToResult(d, currObj);\n }\n }\n }\n }\n\n}\n\ntemplate \nvoid SmallWorldRand::SaveIndex(const string &location) {\n ofstream outFile(location);\n CHECK_MSG(outFile, \"Cannot open file '\" + location + \"' for writing\");\n outFile.exceptions(std::ios::badbit);\n size_t lineNum = 0;\n\n WriteField(outFile, METHOD_DESC, StrDesc()); lineNum++;\n WriteField(outFile, \"NN\", NN_); lineNum++;\n\n for (const MSWNode* pNode: ElList_) {\n IdType nodeID = pNode->getId();\n CHECK_MSG(nodeID >= 0 && nodeID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeID) +\n \" for object ID \" + ConvertToString(pNode->getData()->id()) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n outFile << nodeID << \":\" << pNode->getData()->id() << \":\";\n for (const MSWNode* pNodeFriend: pNode->getAllFriends()) {\n IdType nodeFriendID = pNodeFriend->getId();\n CHECK_MSG(nodeFriendID >= 0 && nodeFriendID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeFriendID) +\n \" for object ID \" + ConvertToString(pNodeFriend->getData()->id()) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n outFile << ' ' << nodeFriendID;\n }\n outFile << endl; lineNum++;\n }\n outFile << endl; lineNum++; // The empty line indicates the end of data entries\n WriteField(outFile, LINE_QTY, lineNum + 1 /* including this line */);\n outFile.close();\n}\n\ntemplate \nvoid SmallWorldRand::LoadIndex(const string &location) {\n vector ptrMapper(data_.size());\n\n for (unsigned pass = 0; pass < 2; ++ pass) {\n ifstream inFile(location);\n CHECK_MSG(inFile, \"Cannot open file '\" + location + \"' for reading\");\n inFile.exceptions(std::ios::badbit);\n\n size_t lineNum = 1;\n string methDesc;\n ReadField(inFile, METHOD_DESC, methDesc);\n lineNum++;\n CHECK_MSG(methDesc == StrDesc(),\n \"Looks like you try to use an index created by a different method: \" + methDesc);\n ReadField(inFile, \"NN\", NN_);\n lineNum++;\n\n string line;\n while (getline(inFile, line)) {\n if (line.empty()) {\n lineNum++; break;\n }\n stringstream str(line);\n str.exceptions(std::ios::badbit);\n char c1, c2;\n IdType nodeID, objID;\n CHECK_MSG((str >> nodeID) && (str >> c1) && (str >> objID) && (str >> c2),\n \"Bug or inconsitent data, wrong format, line: \" + ConvertToString(lineNum)\n );\n CHECK_MSG(c1 == ':' && c2 == ':',\n string(\"Bug or inconsitent data, wrong format, c1=\") + c1 + \",c2=\" + c2 +\n \" line: \" + ConvertToString(lineNum)\n );\n CHECK_MSG(nodeID >= 0 && nodeID < data_.size(),\n DATA_MUTATION_ERROR_MSG + \" (unexpected node ID \" + ConvertToString(nodeID) +\n \" for object ID \" + ConvertToString(objID) +\n \" data_.size() = \" + ConvertToString(data_.size()) + \")\");\n CHECK_MSG(data_[nodeID]->id() == objID,\n DATA_MUTATION_ERROR_MSG + \" (unexpected object ID \" + ConvertToString(data_[nodeID]->id()) +\n \" for data element with ID \" + ConvertToString(nodeID) +\n \" expected object ID: \" + ConvertToString(objID) + \")\"\n );\n if (pass == 0) {\n unique_ptr node(new MSWNode(data_[nodeID], nodeID));\n ptrMapper[nodeID] = node.get();\n ElList_.push_back(node.release());\n } else {\n MSWNode *pNode = ptrMapper[nodeID];\n CHECK_MSG(pNode != NULL,\n \"Bug, got NULL pointer in the second pass for nodeID \" + ConvertToString(nodeID));\n IdType nodeFriendID;\n while (str >> nodeFriendID) {\n CHECK_MSG(nodeFriendID >= 0 && nodeFriendID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeFriendID) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n MSWNode *pFriendNode = ptrMapper[nodeFriendID];\n CHECK_MSG(pFriendNode != NULL,\n \"Bug, got NULL pointer in the second pass for nodeID \" + ConvertToString(nodeFriendID));\n pNode->addFriend(pFriendNode, false /* don't check for duplicates */);\n }\n CHECK_MSG(str.eof(),\n \"It looks like there is some extract erroneous stuff in the end of the line \" +\n ConvertToString(lineNum));\n }\n ++lineNum;\n }\n\n size_t ExpLineNum;\n ReadField(inFile, LINE_QTY, ExpLineNum);\n CHECK_MSG(lineNum == ExpLineNum,\n DATA_MUTATION_ERROR_MSG + \" (expected number of lines \" + ConvertToString(ExpLineNum) +\n \" read so far doesn't match the number of read lines: \" + ConvertToString(lineNum) + \")\");\n inFile.close();\n }\n}\n\ntemplate class SmallWorldRand;\ntemplate class SmallWorldRand;\ntemplate class SmallWorldRand;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/small_world_rand_split.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"knnquery.h\"\n#include \"rangequery.h\"\n#include \"ported_boost_progress.h\"\n#include \"method/small_world_rand_split.h\"\n\n#include \n#include \n#include \n#include \n#include \n\n//#define START_WITH_E0\n#define START_WITH_E0_AT_QUERY_TIME\n\n//#define USE_ALTERNATIVE_FOR_INDEXING\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nstruct IndexThreadParamsSplitSW {\n const Space& space_;\n SmallWorldRandSplit& index_;\n const ObjectVector& data_;\n size_t index_every_;\n size_t out_of_;\n size_t start_;\n size_t end_;\n ProgressDisplay* progress_bar_;\n mutex& display_mutex_;\n size_t progress_update_qty_;\n vector visitedBitset_;\n\n IndexThreadParamsSplitSW(\n const Space& space,\n SmallWorldRandSplit& index, \n const ObjectVector& data,\n size_t index_every,\n size_t out_of,\n size_t start,\n size_t end,\n ProgressDisplay* progress_bar,\n mutex& display_mutex,\n size_t progress_update_qty\n ) : \n space_(space),\n index_(index), \n data_(data),\n index_every_(index_every),\n out_of_(out_of),\n start_(start),\n end_(end),\n progress_bar_(progress_bar),\n display_mutex_(display_mutex),\n progress_update_qty_(progress_update_qty),\n visitedBitset_(end - start)\n { }\n};\n\ntemplate \nstruct IndexThreadSplitSW {\n void operator()(IndexThreadParamsSplitSW& prm) {\n ProgressDisplay* progress_bar = prm.progress_bar_;\n mutex& display_mutex(prm.display_mutex_); \n\n size_t nextQty = prm.progress_update_qty_;\n for (size_t id = prm.start_; id < prm.end_; ++id) {\n if (prm.index_every_ == id % prm.out_of_) {\n typename SmallWorldRandSplit::MSWNode* node = new typename SmallWorldRandSplit::MSWNode(prm.data_[id], id);\n prm.index_.add(node, prm.start_, prm.end_, prm.visitedBitset_);\n \n if ((id + 1 >= min(prm.data_.size(), nextQty)) && progress_bar) {\n unique_lock lock(display_mutex);\n (*progress_bar) += (nextQty - progress_bar->count());\n nextQty += prm.progress_update_qty_;\n }\n }\n }\n }\n};\n\ntemplate \nSmallWorldRandSplit::SmallWorldRandSplit(bool PrintProgress,\n const Space& space,\n const ObjectVector& data) : \n space_(space), data_(data), PrintProgress_(PrintProgress) {}\n\ntemplate \nvoid SmallWorldRandSplit::CreateIndex(const AnyParams& IndexParams) \n{\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"NN\", NN_, 10);\n pmgr.GetParamOptional(\"efConstruction\", efConstruction_, NN_);\n pmgr.GetParamOptional(\"chunkIndexSize\", chunkIndexSize_, data_.size());\n CHECK_MSG(chunkIndexSize_ > 0, \"chunkIndexSize should be > 0\");\n\n chunkIndexSize_ = min(chunkIndexSize_, data_.size());\n efSearch_ = NN_;\n pmgr.GetParamOptional(\"initIndexAttempts\", initIndexAttempts_, 2);\n pmgr.GetParamOptional(\"indexThreadQty\", indexThreadQty_, thread::hardware_concurrency());\n if (indexThreadQty_ <=0) indexThreadQty_ = 1;\n\n LOG(LIB_INFO) << \"NN = \" << NN_;\n LOG(LIB_INFO) << \"efConstruction = \" << efConstruction_;\n LOG(LIB_INFO) << \"chunkIndexSize = \" << chunkIndexSize_;\n LOG(LIB_INFO) << \"initIndexAttempts = \" << initIndexAttempts_;\n LOG(LIB_INFO) << \"indexThreadQty = \" << indexThreadQty_;\n\n pmgr.CheckUnused();\n\n SetQueryTimeParams(getEmptyParams());\n\n if (data_.empty()) return;\n\n unique_ptr progress_bar(PrintProgress_ ?\n new ProgressDisplay(data_.size(), cerr)\n :NULL);\n\n for (size_t start = 0, chunkNum = 0; start < data_.size(); start += chunkIndexSize_, ++chunkNum) {\n size_t end = min(data_.size(), start + chunkIndexSize_);\n CHECK(end > start);\n\n vector threads(indexThreadQty_);\n vector>> threadParams;\n mutex progressBarMutex;\n\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threadParams.push_back(shared_ptr>(\n new IndexThreadParamsSplitSW(space_, *this, data_,\n i, indexThreadQty_,\n start, end,\n progress_bar.get(), progressBarMutex, 200)));\n }\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threads[i] = thread(IndexThreadSplitSW(), ref(*threadParams[i]));\n }\n for (size_t i = 0; i < indexThreadQty_; ++i) {\n threads[i].join();\n }\n }\n\n if (progress_bar) {\n (*progress_bar) += (progress_bar->expected_count() - progress_bar->count());\n }\n\n if (ElList_.size() != data_.size()) {\n stringstream err;\n err << \"Bug: Indexing seems to be incomplete ElList_.size() (\" << ElList_.size() << \") isn't equal to data_.size() (\" << data_.size() << \")\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n}\n\ntemplate \nvoid \nSmallWorldRandSplit::SetQueryTimeParams(const AnyParams& QueryTimeParams) {\n AnyParamManager pmgr(QueryTimeParams);\n pmgr.GetParamOptional(\"initSearchAttempts\", initSearchAttempts_, 3);\n pmgr.GetParamOptional(\"efSearch\", efSearch_, NN_);\n pmgr.CheckUnused();\n LOG(LIB_INFO) << \"Set SmallWorldRandSplit query-time parameters:\";\n LOG(LIB_INFO) << \"initSearchAttempts =\" << initSearchAttempts_;\n LOG(LIB_INFO) << \"efSearch =\" << efSearch_;\n}\n\ntemplate \nconst std::string SmallWorldRandSplit::StrDesc() const {\n return METH_SMALL_WORLD_RAND_SPLIT;\n}\n\ntemplate \nSmallWorldRandSplit::~SmallWorldRandSplit() {\n}\n\ntemplate \ntypename SmallWorldRandSplit::MSWNode* SmallWorldRandSplit::getRandomEntryPointLocked(size_t start, size_t end) const\n{\n unique_lock lock(ElListGuard_);\n MSWNode* res = getRandomEntryPoint(start, end);\n return res;\n}\n\ntemplate \nsize_t SmallWorldRandSplit::getEntryQtyLocked() const\n{\n unique_lock lock(ElListGuard_);\n size_t res = ElList_.size();\n return res;\n}\n\ntemplate \ntypename SmallWorldRandSplit::MSWNode* SmallWorldRandSplit::getRandomEntryPoint(size_t start, size_t end) const {\n if(end <= start) {\n return NULL;\n } else {\n size_t num = RandomInt()% (end - start);\n return ElList_[start + num];\n }\n}\n\ntemplate \nvoid \nSmallWorldRandSplit::searchForIndexing(const Object *queryObj,\n size_t chunkStart, size_t chunkEnd, size_t randomEntryPointEnd,\n vector& visitedBitset,\n priority_queue &resultSet) const\n{\n/*\n * The trick of using large dense bitsets instead of unordered_set was \n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n visitedBitset.assign(visitedBitset.size(), false); // clear the bitset\n\n vector neighborCopy;\n\n for (size_t i=0; i < initIndexAttempts_; i++){\n /**\n * Search for the k most closest elements to the query.\n */\n\n MSWNode* provider = NULL;\n\n // Some entries will hold NULLs temporarily\n for (int att = 0; ((provider = getRandomEntryPointLocked(chunkStart, randomEntryPointEnd)) == NULL) && att < 100; ++att);\n\n if (provider == NULL) {\n unique_lock lock(ElListGuard_);\n provider = ElList_[chunkStart];\n }\n\n priority_queue closestDistQueue;\n priority_queue candidateSet;\n\n#ifdef USE_ALTERNATIVE_FOR_INDEXING\n dist_t d = space_.ProxyDistance(queryObj, provider->getData());\n #pragma message \"Using an alternative/proxy function for indexing, not the original one!\" \n#else\n dist_t d = space_.IndexTimeDistance(queryObj, provider->getData());\n#endif\n EvaluatedMSWNodeReverse ev(d, provider);\n\n candidateSet.push(ev);\n closestDistQueue.push(d);\n\n if (closestDistQueue.size() > efConstruction_) {\n closestDistQueue.pop();\n }\n\n size_t nodeId = provider->getId();\n CHECK_MSG(nodeId >= chunkStart && nodeId < chunkEnd,\n \"Bug, expecting node ID in the semi-open interval [\" + ConvertToString(chunkStart) + \",\" + ConvertToString(chunkEnd) + \")\");\n\n visitedBitset[nodeId - chunkStart] = true;\n\n resultSet.emplace(d, provider);\n \n if (resultSet.size() > NN_) { // TODO check somewhere that NN > 0\n resultSet.pop();\n } \n\n while (!candidateSet.empty()) {\n const EvaluatedMSWNodeReverse& currEv = candidateSet.top();\n dist_t lowerBound = closestDistQueue.top();\n\n /*\n * Check if we reached a local minimum.\n */\n if (currEv.getDistance() > lowerBound) {\n break;\n }\n MSWNode* currNode = currEv.getMSWNode();\n\n /*\n * This lock protects currNode from being modified\n * while we are accessing elements of currNode.\n */\n size_t neighborQty = 0;\n {\n unique_lock lock(currNode->accessGuard_);\n\n //const vector& neighbor = currNode->getAllFriends();\n const vector& neighbor = currNode->getAllFriends();\n neighborQty = neighbor.size();\n if (neighborQty > neighborCopy.size()) neighborCopy.resize(neighborQty);\n for (size_t k = 0; k < neighborQty; ++k)\n neighborCopy[k]=neighbor[k];\n }\n\n // Can't access curEv anymore! The reference would become invalid\n candidateSet.pop();\n\n // calculate distance to each neighbor\n for (size_t neighborId = 0; neighborId < neighborQty; ++neighborId) {\n MSWNode* pNeighbor = neighborCopy[neighborId];\n\n size_t nodeId = pNeighbor->getId();\n CHECK_MSG(nodeId >= chunkStart && nodeId < chunkEnd,\n \"Bug, expecting node ID in the semi-open interval [\" + ConvertToString(chunkStart) + \",\" + ConvertToString(chunkEnd) + \")\");\n\n if (!visitedBitset[nodeId - chunkStart]) {\n visitedBitset[nodeId - chunkStart] = true;\n\n#ifdef USE_ALTERNATIVE_FOR_INDEXING\n d = space_.ProxyDistance(queryObj, pNeighbor->getData());\n #pragma message \"Using an alternative/proxy function for indexing, not the original one!\" \n#else\n d = space_.IndexTimeDistance(queryObj, pNeighbor->getData());\n#endif\n\n if (closestDistQueue.size() < efConstruction_ || d < closestDistQueue.top()) {\n closestDistQueue.push(d);\n if (closestDistQueue.size() > efConstruction_) {\n closestDistQueue.pop();\n }\n candidateSet.emplace(d, pNeighbor);\n }\n\n if (resultSet.size() < NN_ || resultSet.top().getDistance() > d) {\n resultSet.emplace(d, pNeighbor);\n if (resultSet.size() > NN_) { // TODO check somewhere that NN > 0\n resultSet.pop();\n }\n }\n }\n }\n }\n }\n}\n\n\ntemplate \nvoid SmallWorldRandSplit::add(MSWNode *newElement,\n const size_t chunkStart, const size_t chunkEnd,\n vector& visitedBitset){\n newElement->removeAllFriends();\n\n size_t randomEntryPointEnd = 0;\n\n size_t insertIndex = 0;\n {\n unique_lock lock(ElListGuard_);\n CHECK_MSG(chunkIndexSize_ > 0, \"chunkIndexSize should be > 0\");\n CHECK(ElList_.size() >= chunkStart && ElList_.size() < chunkEnd);\n size_t chunkIndexId = ElList_.size() % chunkIndexSize_ ;\n if (0 == chunkIndexId) {\n // If we start a new chunk, don't connect chunk elements to previously inserted entries!\n ElList_.push_back(newElement);\n return;\n }\n\n CHECK(chunkIndexSize_ <= data_.size());\n#ifdef START_WITH_E0\n randomEntryPointEnd = chunkStart + 1;\n#else\n // don't think that we can ever have a data set so large that this summation would cause an overflow,\n // also we ensure that chunkIndexSize_ <= data_.size()\n randomEntryPointEnd = min(ElList_.size(), chunkStart + chunkIndexSize_);\n#endif\n insertIndex = ElList_.size();\n /*\n * We need to claim the element space, otherwise we will get overlapping partitions in the multi-threaded mode.\n * NULL shouldn't cause problems during indexing, because NULLed entries do not appear as neighbors,\n * they can only be retrieved via a getRandomEntryPointLocked(). However, the function searchForIndexing\n * calls getRandomEntryPointLocked() until a non-NULL entry is returned.\n * After several failed attempts, we will use the first entry in the chunk as the starting point,\n * as this entry is guranteed to be non-NULL.\n */\n ElList_.push_back(NULL);\n }\n\n CHECK(randomEntryPointEnd > chunkStart);\n\n {\n priority_queue resultSet;\n\n searchForIndexing(newElement->getData(), chunkStart, chunkEnd, randomEntryPointEnd, visitedBitset, resultSet);\n\n // TODO actually we might need to add elements in the reverse order in the future.\n // For the current implementation, however, the order doesn't seem to matter\n while (!resultSet.empty()) {\n link(resultSet.top().getMSWNode(), newElement);\n resultSet.pop();\n }\n }\n\n {\n unique_lock lock(ElListGuard_);\n CHECK(NULL == ElList_[insertIndex]);\n ElList_[insertIndex] = newElement;\n }\n\n}\n\ntemplate \nvoid SmallWorldRandSplit::Search(RangeQuery* query, IdType) const {\n throw runtime_error(\"Range search is not supported!\");\n}\n\n\ntemplate \nvoid SmallWorldRandSplit::Search(KNNQuery* query, IdType) const {\n if (ElList_.empty()) return;\n/*\n * The trick of using large dense bitsets instead of unordered_set was \n * borrowed from Wei Dong's kgraph: https://github.com/aaalgo/kgraph\n *\n * This trick works really well even in a multi-threaded mode. Indeed, the amount\n * of allocated memory is small. For example, if one has 8M entries, the size of\n * the bitmap is merely 1 MB. Furthermore, setting 1MB of entries to zero via memset would take only\n * a fraction of millisecond.\n */\n vector visitedBitset(chunkIndexSize_);\n\n // don't think that we can every have a data set so this would cause an overflow, also\n // we ensure that chunkIndexSize_ <= data_.size()\n CHECK(chunkIndexSize_ <= data_.size());\n for (size_t start = 0; start < ElList_.size(); start += chunkIndexSize_) {\n size_t end = min(ElList_.size(), start + chunkIndexSize_);\n CHECK(end > start);\n if (start) visitedBitset.assign(visitedBitset.size(), false); // Clear the visited array when moving to another chunk\n for (size_t attempId =0; attempId < initSearchAttempts_; attempId++) {\n /**\n * Search of most k-closest elements to the query.\n */\n\n priority_queue closestDistQueue; //The set of all elements which distance was calculated\n priority_queue candidateQueue; //the set of elements which we can use to evaluate\n\n#ifdef START_WITH_E0_AT_QUERY_TIME\n size_t randomEntryPointEnd = start + 1;\n#else\n size_t randomEntryPointEnd = end;\n#endif\n MSWNode *provider = getRandomEntryPoint(start, randomEntryPointEnd);\n\n const Object* currObj = provider->getData();\n dist_t d = query->DistanceObjLeft(currObj);\n query->CheckAndAddToResult(d, currObj); // This should be done before the object goes to the queue: otherwise it will not be compared to the query at all!\n\n EvaluatedMSWNodeReverse ev(d, provider);\n candidateQueue.push(ev);\n closestDistQueue.emplace(d);\n\n size_t nodeId = provider->getId();\n CHECK_MSG(nodeId >= start && nodeId < end,\n \"Bug, expecting node ID in the semi-open interval [\" + ConvertToString(start) + \",\" + ConvertToString(end) + \")\");\n visitedBitset[nodeId-start] = true;\n\n while(!candidateQueue.empty()) {\n auto iter = candidateQueue.top(); // This one was already compared to the query\n const EvaluatedMSWNodeReverse& currEv = iter;\n\n dist_t lowerBound = closestDistQueue.top();\n\n // Did we reach a local minimum?\n if (currEv.getDistance() > lowerBound) {\n break;\n }\n\n const vector& neighbor = (currEv.getMSWNode())->getAllFriends();\n\n // Can't access curEv anymore! The reference would become invalid\n candidateQueue.pop();\n\n //calculate distance to each neighbor\n for (auto iter = neighbor.begin(); iter != neighbor.end(); ++iter){\n size_t nodeId = (*iter)->getId();\n CHECK_MSG(nodeId >= start && nodeId < end,\n \"Bug, expecting node ID in the semi-open interval [\" + ConvertToString(start) + \",\" + ConvertToString(end) + \")\");\n\n size_t nodeIdDiff = nodeId - start;\n if (!visitedBitset[nodeIdDiff]) {\n const Object* currObj = (*iter)->getData();\n dist_t d = query->DistanceObjLeft(currObj);\n\n visitedBitset[nodeIdDiff] = true;\n\n if (closestDistQueue.size() < efSearch_ || d < closestDistQueue.top()) {\n closestDistQueue.emplace(d);\n if (closestDistQueue.size() > efSearch_) {\n closestDistQueue.pop(); \n }\n candidateQueue.emplace(d, *iter);\n }\n\n query->CheckAndAddToResult(d, currObj);\n }\n }\n }\n }\n }\n}\n\ntemplate \nvoid SmallWorldRandSplit::SaveIndex(const string &location) {\n ofstream outFile(location);\n CHECK_MSG(outFile, \"Cannot open file '\" + location + \"' for writing\");\n outFile.exceptions(std::ios::badbit);\n size_t lineNum = 0;\n\n WriteField(outFile, METHOD_DESC, StrDesc()); lineNum++;\n WriteField(outFile, \"NN\", NN_); lineNum++;\n WriteField(outFile, \"chunkIndexSize\", chunkIndexSize_); lineNum++;\n\n for (const MSWNode* pNode: ElList_) {\n IdType nodeID = pNode->getId();\n CHECK_MSG(nodeID >= 0 && nodeID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeID) +\n \" for object ID \" + ConvertToString(pNode->getData()->id()) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n outFile << nodeID << \":\" << pNode->getData()->id() << \":\";\n for (const MSWNode* pNodeFriend: pNode->getAllFriends()) {\n IdType nodeFriendID = pNodeFriend->getId();\n CHECK_MSG(nodeFriendID >= 0 && nodeFriendID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeFriendID) +\n \" for object ID \" + ConvertToString(pNodeFriend->getData()->id()) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n outFile << ' ' << nodeFriendID;\n }\n outFile << endl; lineNum++;\n }\n outFile << endl; lineNum++; // The empty line indicates the end of data entries\n WriteField(outFile, LINE_QTY, lineNum + 1 /* including this line */);\n outFile.close();\n}\n\ntemplate \nvoid SmallWorldRandSplit::LoadIndex(const string &location) {\n vector ptrMapper(data_.size());\n\n for (unsigned pass = 0; pass < 2; ++ pass) {\n ifstream inFile(location);\n CHECK_MSG(inFile, \"Cannot open file '\" + location + \"' for reading\");\n inFile.exceptions(std::ios::badbit);\n\n size_t lineNum = 1;\n string methDesc;\n ReadField(inFile, METHOD_DESC, methDesc);\n lineNum++;\n CHECK_MSG(methDesc == StrDesc(),\n \"Looks like you try to use an index created by a different method: \" + methDesc);\n ReadField(inFile, \"NN\", NN_);\n lineNum++;\n\n ReadField(inFile, \"chunkIndexSize\", chunkIndexSize_);\n CHECK_MSG(chunkIndexSize_ <= data_.size(), \"chunkIndexSize is larger than the # of data points, did you create this index for a larger data set?\");\n lineNum++;\n\n string line;\n while (getline(inFile, line)) {\n if (line.empty()) {\n lineNum++; break;\n }\n stringstream str(line);\n str.exceptions(std::ios::badbit);\n char c1, c2;\n IdType nodeID, objID;\n CHECK_MSG((str >> nodeID) && (str >> c1) && (str >> objID) && (str >> c2),\n \"Bug or inconsitent data, wrong format, line: \" + ConvertToString(lineNum)\n );\n CHECK_MSG(c1 == ':' && c2 == ':',\n string(\"Bug or inconsitent data, wrong format, c1=\") + c1 + \",c2=\" + c2 +\n \" line: \" + ConvertToString(lineNum)\n );\n CHECK_MSG(nodeID >= 0 && nodeID < data_.size(),\n DATA_MUTATION_ERROR_MSG + \" (unexpected node ID \" + ConvertToString(nodeID) +\n \" for object ID \" + ConvertToString(objID) +\n \" data_.size() = \" + ConvertToString(data_.size()) + \")\");\n CHECK_MSG(data_[nodeID]->id() == objID,\n DATA_MUTATION_ERROR_MSG + \" (unexpected object ID \" + ConvertToString(data_[nodeID]->id()) +\n \" for data element with ID \" + ConvertToString(nodeID) +\n \" expected object ID: \" + ConvertToString(objID) + \")\"\n );\n if (pass == 0) {\n unique_ptr node(new MSWNode(data_[nodeID], nodeID));\n ptrMapper[nodeID] = node.get();\n ElList_.push_back(node.release());\n } else {\n MSWNode *pNode = ptrMapper[nodeID];\n CHECK_MSG(pNode != NULL,\n \"Bug, got NULL pointer in the second pass for nodeID \" + ConvertToString(nodeID));\n IdType nodeFriendID;\n while (str >> nodeFriendID) {\n CHECK_MSG(nodeFriendID >= 0 && nodeFriendID < data_.size(),\n \"Bug: unexpected node ID \" + ConvertToString(nodeFriendID) +\n \"data_.size() = \" + ConvertToString(data_.size()));\n MSWNode *pFriendNode = ptrMapper[nodeFriendID];\n CHECK_MSG(pFriendNode != NULL,\n \"Bug, got NULL pointer in the second pass for nodeID \" + ConvertToString(nodeFriendID));\n pNode->addFriend(pFriendNode, false /* don't check for duplicates */);\n }\n CHECK_MSG(str.eof(),\n \"It looks like there is some extract erroneous stuff in the end of the line \" +\n ConvertToString(lineNum));\n }\n ++lineNum;\n }\n\n size_t ExpLineNum;\n ReadField(inFile, LINE_QTY, ExpLineNum);\n CHECK_MSG(lineNum == ExpLineNum,\n DATA_MUTATION_ERROR_MSG + \" (expected number of lines \" + ConvertToString(ExpLineNum) +\n \" read so far doesn't match the number of read lines: \" + ConvertToString(lineNum) + \")\");\n inFile.close();\n }\n}\n\ntemplate class SmallWorldRandSplit;\ntemplate class SmallWorldRandSplit;\ntemplate class SmallWorldRandSplit;\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/method/spatial_approx_tree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"method/spatial_approx_tree.h\"\n\nusing namespace std;\n\nnamespace similarity {\n\ntemplate \nstruct SpatialApproxTree::SATKnn {\n dist_t lbound; // lower bound\n dist_t mind;\n dist_t dist_qp;\n typename SpatialApproxTree::SATNode* node;\n\n SATKnn() : lbound(0), mind(0), dist_qp(0), node(NULL) {}\n SATKnn(dist_t lbound, dist_t mind, dist_t dist_qp, SATNode* node)\n : lbound(lbound), mind(mind), dist_qp(dist_qp), node(node) {}\n\n bool operator<(const SATKnn& other) const {\n return lbound > other.lbound;\n }\n};\n\ntemplate \nclass SpatialApproxTree::SATNode {\n public:\n SATNode(const Space* space,\n const Object* pivot,\n const DistObjectPairVector& dp);\n\n ~SATNode();\n\n void Search(RangeQuery* query,\n dist_t dist_qp,\n dist_t mind) const;\n\n private:\n const Object* pivot_;\n dist_t covering_radius_;\n vector> neighbors_;\n\n friend class SpatialApproxTree;\n};\n\ntemplate \nSpatialApproxTree::SATNode::SATNode(\n const Space* space,\n const Object* pivot,\n const DistObjectPairVector& dp)\n : pivot_(pivot), covering_radius_(0) {\n if (dp.empty()) { // leaf node\n return;\n }\n\n // dp is already sorted in ascending order\n covering_radius_ = dp.back().first;\n vector> non_neighbors;\n\n for (size_t i = 0; i < dp.size(); ++i) {\n dist_t dist_p = dp[i].first;\n const Object* v = dp[i].second;\n\n dist_t min_dist = dist_p;\n size_t min_idx = 0;\n bool found = false;\n for (size_t j = 0; j < neighbors_.size(); ++j) {\n dist_t d = space->IndexTimeDistance(v, neighbors_[j].first);\n if (min_dist > d) {\n min_dist = d;\n min_idx = j;\n found = true;\n }\n }\n\n if (found) {\n non_neighbors.push_back(make_tuple(v, min_idx, min_dist));\n } else {\n neighbors_.push_back(make_pair(v, nullptr));\n }\n }\n\n vector> buckets(neighbors_.size());\n\n for (size_t i = 0; i < non_neighbors.size(); ++i) {\n const Object* v = get<0>(non_neighbors[i]);\n size_t min_idx = get<1>(non_neighbors[i]);\n dist_t min_dist = get<2>(non_neighbors[i]);\n for (size_t j = min_idx + 1; j < neighbors_.size(); ++j) {\n dist_t d = space->IndexTimeDistance(v, neighbors_[j].first);\n if (min_dist > d) {\n min_dist = d;\n min_idx = j;\n }\n }\n buckets[min_idx].push_back(make_pair(min_dist, v));\n }\n\n for (size_t i = 0; i < neighbors_.size(); ++i) {\n sort(buckets[i].begin(), buckets[i].end(),\n DistObjectPairAscComparator());\n neighbors_[i].second =\n new SATNode(space, neighbors_[i].first, buckets[i]);\n }\n}\n\ntemplate \nSpatialApproxTree::SATNode::~SATNode() {\n for (auto& n : neighbors_) {\n delete n.second;\n }\n}\n\ntemplate \nvoid SpatialApproxTree::SATNode::Search (\n RangeQuery* query,\n dist_t dist_qp,\n dist_t mind) const {\n if (dist_qp <= covering_radius_ + query->Radius()) {\n query->CheckAndAddToResult(dist_qp, pivot_);\n\n vector d(neighbors_.size());\n for (size_t i = 0; i < neighbors_.size(); ++i) {\n d[i] = query->DistanceObjLeft(neighbors_[i].first);\n if (mind > d[i]) {\n mind = d[i];\n }\n }\n\n for (size_t i = 0; i < neighbors_.size(); ++i) {\n if ((d[i] - mind) / 2 <= query->Radius()) {\n neighbors_[i].second->Search(query, d[i], mind);\n }\n }\n }\n}\n\ntemplate \nSpatialApproxTree::SpatialApproxTree(\n const Space& space,\n const ObjectVector& data) : space_(space), data_(data) {\n}\n\ntemplate \nvoid SpatialApproxTree::CreateIndex(const AnyParams& ) {\n size_t index = RandomInt() % data_.size();\n const Object* pivot = data_[index];\n\n DistObjectPairVector dp;\n for (size_t i = 0; i < data_.size(); ++i) {\n if (i != index) {\n dp.push_back(\n make_pair(space_.IndexTimeDistance(data_[i], pivot), data_[i]));\n }\n }\n\n sort(dp.begin(), dp.end(), DistObjectPairAscComparator());\n root_.reset(new SATNode(&space_, pivot, dp));\n}\n\ntemplate \nSpatialApproxTree::~SpatialApproxTree() {\n}\n\ntemplate \nconst string SpatialApproxTree::StrDesc() const {\n return \"satree\";\n}\n\ntemplate \nvoid SpatialApproxTree::Search(KNNQuery* query, IdType const) const {\n static dist_t kZERO = static_cast(0);\n priority_queue heap;\n dist_t dist_qp = query->DistanceObjLeft(root_->pivot_);\n heap.push(SATKnn((max(kZERO, dist_qp - root_->covering_radius_)),\n dist_qp, dist_qp, root_.get()));\n\n while (!heap.empty()) {\n SATKnn top = heap.top();\n dist_t lbound = (top.lbound);\n dist_t mind = top.mind;\n dist_t dist_qp = top.dist_qp;\n\n SATNode* node = top.node;\n\n heap.pop();\n//cout << \" bnd \" << lbound << \" -> \" << query->Radius() << endl;\n if (lbound > query->Radius()) break;\n\n query->CheckAndAddToResult(dist_qp, node->pivot_);\n\n\n vector d(node->neighbors_.size());\n for (size_t i = 0; i < node->neighbors_.size(); ++i) {\n d[i] = query->DistanceObjLeft(node->neighbors_[i].first);\n mind = min(mind, d[i]);\n }\n\n for (size_t i = 0; i < node->neighbors_.size(); ++i) {\n /* \n * In the original VLDB journal paper Fig. 7\n * The new lbound is computed as: max(lbound, mind/2, d(q,v)-R(v))\n *\n * This is most likely to be an error, because it contradicts to what\n * is written in section 4.3, in the discussion on lower bounds.\n *\n * The correct version, which seems to be the one in the Metric Spaces Library,\n * replaces the second MAX argument with:\n * (d(q,v)-mind)/2\n */\n dist_t new_lbound = std::max(std::max(lbound, (d[i] - mind)/2),\n (d[i] - node->neighbors_[i].second->covering_radius_));\n\n if (new_lbound < query->Radius()) {\n heap.push(SATKnn(new_lbound, mind, d[i], node->neighbors_[i].second));\n }\n }\n }\n}\n\ntemplate \nvoid SpatialApproxTree::Search(RangeQuery* query, IdType) const {\n dist_t dist_qp = query->DistanceObjLeft(root_->pivot_);\n root_->Search(query, dist_qp, dist_qp);\n}\n\ntemplate class SpatialApproxTree;\ntemplate class SpatialApproxTree;\ntemplate class SpatialApproxTree;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/method/vptree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"rangequery.h\"\n#include \"knnquery.h\"\n#include \"searchoracle.h\"\n#include \"method/vptree.h\"\n#include \"method/vptree_utils.h\"\n#include \"methodfactory.h\"\n\nnamespace similarity {\n\nusing std::string;\nusing std::stringstream;\nusing std::endl;\nusing std::cout;\nusing std::cerr;\n \ntemplate \nVPTree::VPTree(\n bool PrintProgress,\n Space& space,\n const ObjectVector& data,\n bool use_random_center) : \n space_(space),\n data_(data),\n PrintProgress_(PrintProgress),\n use_random_center_(use_random_center),\n oracle_(space, data, PrintProgress),\n QueryTimeParams_(oracle_.GetQueryTimeParamNames()) { \n QueryTimeParams_.push_back(\"maxLeavesToVisit\");\n }\n\ntemplate \nvoid VPTree::CreateIndex(const AnyParams& IndexParams) {\n AnyParamManager pmgr(IndexParams);\n\n pmgr.GetParamOptional(\"bucketSize\", BucketSize_, 50);\n pmgr.GetParamOptional(\"chunkBucket\", ChunkBucket_, true);\n\n LOG(LIB_INFO) << \"bucketSize = \" << BucketSize_;\n LOG(LIB_INFO) << \"chunkBucket = \" << ChunkBucket_;\n\n // Call this function *ONLY AFTER* the bucket size is obtained!\n oracle_.SetIndexTimeParams(pmgr);\n oracle_.LogParams();\n\n pmgr.CheckUnused();\n\n this->ResetQueryTimeParams(); // reset query-time parameters\n\n unique_ptr progress_bar(PrintProgress_ ? \n new ProgressDisplay(data_.size(), cerr):\n NULL);\n\n root_.reset(new VPNode(0,\n progress_bar.get(), \n oracle_, \n space_, data_,\n BucketSize_, ChunkBucket_,\n use_random_center_ /* use random center */));\n\n if (progress_bar) { // make it 100%\n (*progress_bar) += (progress_bar->expected_count() - progress_bar->count());\n }\n}\n\ntemplate \nVPTree::~VPTree() {\n}\n\ntemplate \nconst std::string VPTree::StrDesc() const {\n return \"vptree: \" + SearchOracle::GetName();\n}\n\ntemplate \nvoid VPTree::Search(RangeQuery* query, IdType) const {\n int mx = MaxLeavesToVisit_;\n root_->GenericSearch(query, mx);\n}\n\ntemplate \nvoid VPTree::Search(KNNQuery* query, IdType) const {\n int mx = MaxLeavesToVisit_;\n root_->GenericSearch(query, mx);\n}\n\ntemplate \nvoid VPTree::VPNode::CreateBucket(bool ChunkBucket, \n const ObjectVector& data, \n ProgressDisplay* progress_bar) {\n if (ChunkBucket) {\n CreateCacheOptimizedBucket(data, CacheOptimizedBucket_, bucket_);\n } else {\n bucket_ = new ObjectVector(data);\n }\n if (progress_bar) (*progress_bar) += data.size();\n}\n\ntemplate \nVPTree::VPNode::VPNode(\n unsigned level,\n ProgressDisplay* progress_bar,\n const SearchOracle& oracle,\n const Space& space, const ObjectVector& data,\n size_t BucketSize, bool ChunkBucket,\n bool use_random_center)\n : oracle_(oracle),\n pivot_(NULL), mediandist_(0),\n left_child_(NULL), right_child_(NULL),\n bucket_(NULL), CacheOptimizedBucket_(NULL) \n{\n CHECK(!data.empty());\n\n if (!data.empty() && data.size() <= BucketSize) {\n CreateBucket(ChunkBucket, data, progress_bar);\n return;\n }\n\n const size_t index = SelectVantagePoint(data, use_random_center);\n pivot_ = data[index];\n\n if (data.size() >= 2) {\n DistObjectPairVector dp;\n for (size_t i = 0; i < data.size(); ++i) {\n if (i == index) {\n continue;\n }\n // Distance can be asymmetric, the pivot is always on the left side!\n dp.push_back(std::make_pair(space.IndexTimeDistance(pivot_, data[i]), data[i]));\n }\n\n std::sort(dp.begin(), dp.end(), DistObjectPairAscComparator());\n DistObjectPair medianDistObj = GetMedian(dp);\n mediandist_ = medianDistObj.first; \n\n ObjectVector left;\n ObjectVector right;\n\n for (auto it = dp.begin(); it != dp.end(); ++it) {\n const Object* v = it->second;\n\n /* \n * Note that here we compare a pair (distance, pointer)\n * If distances are equal, pointers are compared.\n * Thus, we would get a balanced split, even if the median\n * occurs many times in the array dp[].\n */\n if (*it < medianDistObj) {\n left.push_back(v);\n } else {\n right.push_back(v);\n }\n }\n\n /*\n * Sometimes, e.g.., for integer-valued distances,\n * mediandist_ will be non-discriminative. In this case\n * it is more efficient to put everything into a single bucket.\n */\n size_t LeastSize = dp.size() / BalanceConst;\n\n if (left.size() < LeastSize || right.size() < LeastSize) {\n CreateBucket(ChunkBucket, data, progress_bar);\n return;\n }\n\n if (!left.empty()) {\n left_child_ = new VPNode(level + 1, progress_bar, oracle_, space, left, BucketSize, ChunkBucket, use_random_center);\n }\n\n if (!right.empty()) {\n right_child_ = new VPNode(level + 1, progress_bar, oracle_, space, right, BucketSize, ChunkBucket, use_random_center);\n }\n }\n}\n\ntemplate \nVPTree::VPNode::~VPNode() {\n delete left_child_;\n delete right_child_;\n ClearBucket(CacheOptimizedBucket_, bucket_);\n}\n\ntemplate \ntemplate \nvoid VPTree::VPNode::GenericSearch(QueryType* query,\n int& MaxLeavesToVisit) const {\n if (MaxLeavesToVisit <= 0) return; // early termination\n if (bucket_) {\n --MaxLeavesToVisit;\n\n for (unsigned i = 0; i < bucket_->size(); ++i) {\n const Object* Obj = (*bucket_)[i];\n dist_t distQC = query->DistanceObjLeft(Obj);\n query->CheckAndAddToResult(distQC, Obj);\n }\n return;\n }\n\n // Distance can be asymmetric, the pivot is always the left argument (see the function that creates the node)!\n dist_t distQC = query->DistanceObjLeft(pivot_);\n query->CheckAndAddToResult(distQC, pivot_);\n\n if (distQC < mediandist_) { // the query is inside\n // then first check inside\n if (left_child_ != NULL && oracle_.Classify(distQC, query->Radius(), mediandist_) != kVisitRight)\n left_child_->GenericSearch(query, MaxLeavesToVisit);\n\n /* \n * After potentially visiting the left child, we need to reclassify the node,\n * because the query radius might have decreased.\n */\n\n\n // after that outside\n if (right_child_ != NULL && oracle_.Classify(distQC, query->Radius(), mediandist_) != kVisitLeft)\n right_child_->GenericSearch(query, MaxLeavesToVisit);\n } else { // the query is outside\n // then first check outside\n if (right_child_ != NULL && oracle_.Classify(distQC, query->Radius(), mediandist_) != kVisitLeft)\n right_child_->GenericSearch(query, MaxLeavesToVisit);\n\n /* \n * After potentially visiting the left child, we need to reclassify the node,\n * because the query radius might have decreased.\n */\n\n // after that inside\n if (left_child_ != NULL && oracle_.Classify(distQC, query->Radius(), mediandist_) != kVisitRight)\n left_child_->GenericSearch(query, MaxLeavesToVisit);\n }\n}\n\ntemplate class VPTree >;\ntemplate class VPTree >;\ntemplate class VPTree >;\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/src/params.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n#include \n#include \n#include \n\n#include \"utils.h\"\n#include \"params.h\"\n#include \"logging.h\"\n#include \"space.h\"\n\n#include \n\nusing namespace std;\n\nnamespace similarity {\n\nusing std::multimap;\nusing std::string;\n\nvoid ParseSpaceArg(const string& descStr, string& SpaceType, vector& SpaceDesc) {\n vector tmp;\n if (!SplitStr(descStr, tmp, ':') || tmp.size() > 2 || !tmp.size()) {\n throw runtime_error(\"Wrong format of the space argument: '\" + descStr + \"'\");\n }\n\n SpaceType = tmp[0];\n SpaceDesc.clear();\n\n if (tmp.size() == 2) {\n if (!SplitStr(tmp[1], SpaceDesc, ',')) {\n throw runtime_error(\"Cannot split space arguments in: '\" + tmp[1] + \"'\");\n }\n }\n}\n\nvoid ParseArg(const string& descStr, vector& vDesc) {\n vDesc.clear();\n\n if (descStr.empty()) return;\n\n if (!SplitStr(descStr, vDesc, ',')) {\n throw runtime_error(\"Cannot split arguments in: '\" + descStr + \"'\");\n }\n}\n\n\n};\n\n\n" }, { "path": "algorithms/NMSLIB/code/src/params_cmdline.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n#include \n#include \n#include \n\n#include \"utils.h\"\n#include \"params_cmdline.h\"\n#include \"params_def.h\"\n#include \"logging.h\"\n#include \"space.h\"\n\n#include \n#include \n\nusing namespace std;\n\nnamespace similarity {\n\nusing std::multimap;\nusing std::string;\n\nnamespace po = boost::program_options;\n\nstatic void Usage(const char *prog,\n const po::options_description& desc) {\n std::cout << prog << std::endl\n << desc << std::endl;\n}\n\nvoid ParseCommandLine(int argc, char* argv[], bool& bPrintProgress,\n string& LogFile,\n string& LoadIndexLoc,\n string& SaveIndexLoc,\n string& DistType,\n string& SpaceType,\n shared_ptr& SpaceParams,\n unsigned& ThreadTestQty,\n bool& AppendToResFile,\n string& ResFilePrefix,\n unsigned& TestSetQty,\n string& DataFile,\n string& QueryFile,\n string& CacheGSFilePrefix,\n float& maxCacheGSRelativeQty,\n unsigned& MaxNumData,\n unsigned& MaxNumQuery,\n vector& knn,\n float& eps,\n string& RangeArg,\n string& MethodName,\n shared_ptr& IndexTimeParams,\n vector>& QueryTimeParams) {\n knn.clear();\n RangeArg.clear();\n QueryTimeParams.clear();\n\n string indexTimeParamStr;\n vector vQueryTimeParamStr;\n string spaceParamStr;\n string knnArg;\n // Conversion to double is due to an Intel's bug with __builtin_signbit being undefined for float\n double epsTmp;\n\n bPrintProgress = true;\n\n bool bSuppressPrintProgress;\n\n po::options_description ProgOptDesc(\"Allowed options\");\n ProgOptDesc.add_options()\n (HELP_PARAM_OPT, HELP_PARAM_MSG)\n (SPACE_TYPE_PARAM_OPT, po::value(&spaceParamStr)->required(), SPACE_TYPE_PARAM_MSG)\n (DIST_TYPE_PARAM_OPT, po::value(&DistType)->default_value(DIST_TYPE_FLOAT), DIST_TYPE_PARAM_MSG)\n (DATA_FILE_PARAM_OPT, po::value(&DataFile)->required(), DATA_FILE_PARAM_MSG)\n (MAX_NUM_DATA_PARAM_OPT, po::value(&MaxNumData)->default_value(MAX_NUM_DATA_PARAM_DEFAULT), MAX_NUM_DATA_PARAM_MSG)\n (QUERY_FILE_PARAM_OPT, po::value(&QueryFile)->default_value(QUERY_FILE_PARAM_DEFAULT), QUERY_FILE_PARAM_MSG)\n (LOAD_INDEX_PARAM_OPT, po::value(&LoadIndexLoc)->default_value(LOAD_INDEX_PARAM_DEFAULT), LOAD_INDEX_PARAM_MSG)\n (SAVE_INDEX_PARAM_OPT, po::value(&SaveIndexLoc)->default_value(SAVE_INDEX_PARAM_DEFAULT), SAVE_INDEX_PARAM_MSG)\n (CACHE_PREFIX_GS_PARAM_OPT, po::value(&CacheGSFilePrefix)->default_value(CACHE_PREFIX_GS_PARAM_DEFAULT), CACHE_PREFIX_GS_PARAM_MSG)\n (MAX_CACHE_GS_QTY_PARAM_OPT, po::value(&maxCacheGSRelativeQty)->default_value(MAX_CACHE_GS_QTY_PARAM_DEFAULT), MAX_CACHE_GS_QTY_PARAM_MSG)\n (LOG_FILE_PARAM_OPT, po::value(&LogFile)->default_value(LOG_FILE_PARAM_DEFAULT), LOG_FILE_PARAM_MSG)\n (MAX_NUM_QUERY_PARAM_OPT, po::value(&MaxNumQuery)->default_value(MAX_NUM_QUERY_PARAM_DEFAULT), MAX_NUM_QUERY_PARAM_MSG)\n (TEST_SET_QTY_PARAM_OPT, po::value(&TestSetQty)->default_value(TEST_SET_QTY_PARAM_DEFAULT), TEST_SET_QTY_PARAM_MSG)\n (KNN_PARAM_OPT, po::value< string>(&knnArg), KNN_PARAM_MSG)\n (RANGE_PARAM_OPT, po::value(&RangeArg), RANGE_PARAM_MSG)\n (EPS_PARAM_OPT, po::value(&epsTmp)->default_value(EPS_PARAM_DEFAULT), EPS_PARAM_MSG)\n (QUERY_TIME_PARAMS_PARAM_OPT, po::value< vector >(&vQueryTimeParamStr)->multitoken()->zero_tokens(), QUERY_TIME_PARAMS_PARAM_MSG)\n (INDEX_TIME_PARAMS_PARAM_OPT, po::value(&indexTimeParamStr)->default_value(\"\"), INDEX_TIME_PARAMS_PARAM_MSG)\n (METHOD_PARAM_OPT, po::value(&MethodName)->default_value(METHOD_PARAM_DEFAULT), METHOD_PARAM_MSG)\n (THREAD_TEST_QTY_PARAM_OPT,po::value(&ThreadTestQty)->default_value(THREAD_TEST_QTY_PARAM_DEFAULT), THREAD_TEST_QTY_PARAM_MSG)\n (OUT_FILE_PREFIX_PARAM_OPT, po::value(&ResFilePrefix)->default_value(OUT_FILE_PREFIX_PARAM_DEFAULT), OUT_FILE_PREFIX_PARAM_MSG)\n (APPEND_TO_RES_FILE_PARAM_OPT, po::bool_switch(&AppendToResFile), APPEND_TO_RES_FILE_PARAM_MSG)\n (NO_PROGRESS_PARAM_OPT, po::bool_switch(&bSuppressPrintProgress), NO_PROGRESS_PARAM_MSG)\n ;\n\n po::variables_map vm;\n try {\n po::store(po::parse_command_line(argc, argv, ProgOptDesc), vm);\n po::notify(vm);\n } catch (const exception& e) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << e.what();\n }\n\n bPrintProgress = !bSuppressPrintProgress;\n\n eps = epsTmp;\n\n if (vm.count(\"help\") ) {\n Usage(argv[0], ProgOptDesc);\n exit(0);\n }\n\n ToLower(DistType);\n ToLower(spaceParamStr);\n ToLower(MethodName);\n \n try {\n {\n vector desc;\n ParseSpaceArg(spaceParamStr, SpaceType, desc);\n SpaceParams = shared_ptr(new AnyParams(desc));\n }\n\n\n {\n vector desc;\n ParseArg(indexTimeParamStr, desc);\n IndexTimeParams = shared_ptr(new AnyParams(desc));\n }\n\n if (vQueryTimeParamStr.empty())\n vQueryTimeParamStr.push_back(\"\");\n\n for(const auto s: vQueryTimeParamStr) {\n vector desc;\n\n ParseArg(s, desc);\n QueryTimeParams.push_back(shared_ptr(new AnyParams(desc)));\n }\n\n if (vm.count(\"knn\")) {\n if (!SplitStr(knnArg, knn, ',')) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << \"Wrong format of the KNN argument: '\" << knnArg;\n }\n }\n\n if (DataFile.empty()) {\n LOG(LIB_FATAL) << \"data file is not specified!\";\n }\n\n if (!DoesFileExist(DataFile)) {\n LOG(LIB_FATAL) << \"data file \" << DataFile << \" doesn't exist\";\n }\n\n if (!QueryFile.empty() && !DoesFileExist(QueryFile)) {\n LOG(LIB_FATAL) << \"query file \" << QueryFile << \" doesn't exist\";\n }\n\n if (!MaxNumQuery && QueryFile.empty()) {\n LOG(LIB_FATAL) << \"Set a positive # of queries or specify a query file!\"; \n }\n\n CHECK_MSG(MaxNumData < MAX_DATASET_QTY, \"The maximum number of points should not exceed\" + ConvertToString(MAX_DATASET_QTY));\n CHECK_MSG(MaxNumQuery < MAX_DATASET_QTY, \"The maximum number of queries should not exceed\" + ConvertToString(MAX_DATASET_QTY));\n } catch (const exception& e) {\n LOG(LIB_FATAL) << \"Exception: \" << e.what();\n }\n}\n\n};\n\n\n" }, { "path": "algorithms/NMSLIB/code/src/projection.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"utils.h\"\n#include \"projection.h\"\n\n#include \"randproj_util.h\"\n#include \"permutation_utils.h\"\n\n#include \"space/space_vector.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\n/*\n * Convert to a dense vector. If the space is already a dense-vector\n * space, this should be an identity transformation.\n */\n\ntemplate \nclass ProjectionVectDense : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n if (NULL == pObj) pObj = pQuery->QueryObject();\n vector intermBuffer(dstDim_);\n Projection::fillIntermBuffer(space_,\n pObj,\n dstDim_,\n intermBuffer);\n for (size_t i = 0; i < dstDim_; ++i)\n pDstVect[i] = static_cast(intermBuffer[i]);\n }\n\n friend class Projection;\nprivate:\n ProjectionVectDense(const Space& space, size_t nDstDim) :\n space_(space), dstDim_(nDstDim) {\n }\n\n const Space& space_;\n size_t dstDim_;\n\n};\n\n\n/*\n * Classic random projections.\n */\n\ntemplate \nclass ProjectionRand : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n if (NULL == pObj) pObj = pQuery->QueryObject();\n /*\n * For dense vector spaces, we ignore the specified \"projection\"\n * dimensionality and use an actual number of vector elements. \n * For sparse vector spaces, we obtain an intermediate dense vector \n * with projDim elements.\n */\n size_t nDim = space_.GetElemQty(pObj);\n if (!nDim) nDim = projDim_;\n vector intermBuffer(nDim);\n Projection::fillIntermBuffer(space_,\n pObj,\n nDim,\n intermBuffer);\n\n vector dstBuffer(dstDim_);\n compRandProj(_projMatr, &intermBuffer[0],\n nDim,\n &dstBuffer[0],\n dstDim_);\n for (size_t i = 0; i < dstDim_; ++i)\n pDstVect[i] = static_cast(dstBuffer[i]);\n }\n\n friend class Projection;\nprivate:\n ProjectionRand(const Space& space, const ObjectVector& data,\n size_t nProjDim, size_t nDstDim, bool bDoOrth) :\n space_(space), projDim_(nProjDim), dstDim_(nDstDim) {\n if (data.empty()) {\n stringstream err;\n err << \"Cannot initialize projection type '\" <<\n PROJ_TYPE_RAND << \"'\" <<\n \" without a single data point\";\n throw runtime_error(err.str());\n }\n size_t nDim = space.GetElemQty(data[0]);\n if (nDim == 0) {\n if (!projDim_) {\n throw runtime_error(\"Specify a non-zero value for the intermediate dimensionaity.\");\n }\n nDim = projDim_;\n }\n\n initRandProj(nDim, dstDim_, bDoOrth, _projMatr);\n }\n\n vector> _projMatr;\n const Space& space_;\n size_t projDim_;\n size_t dstDim_;\n\n};\n\n/*\n * Distances to random reference points.\n */\ntemplate \nclass ProjectionRandRefPoint : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n for (size_t i = 0; i < dstDim_; ++i) {\n pDstVect[i] = static_cast(\n Projection::DistanceObjLeft(space_, pQuery,\n ref_pts_[i], pObj));\n }\n }\n\n friend class Projection;\nprivate:\n ProjectionRandRefPoint(const Space& space,\n const ObjectVector& data,\n size_t nDstDim) :\n space_(space), data_(data),\n dstDim_(nDstDim) {\n\n\n CHECK(data_.size() > dstDim_);\n\n GetPermutationPivot(data_, space_, dstDim_, &ref_pts_);\n\n }\n\n const Space& space_;\n const ObjectVector& data_;\n ObjectVector ref_pts_;\n size_t dstDim_;\n\n};\n\n/*\n * Permutations.\n * See, Edgar Chávez et al., Effective Proximity Retrieval by Ordering Permutations.\n * IEEE Trans. Pattern Anal. Mach. Intell. (2008)\n *\n */\ntemplate \nclass ProjectionPermutation : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n Permutation perm;\n\n if (NULL == pQuery) {\n GetPermutation(ref_pts_, space_, pObj, &perm);\n } else {\n GetPermutation(ref_pts_, pQuery, &perm);\n }\n\n for (size_t i = 0; i < dstDim_; ++i) {\n pDstVect[i] = static_cast(perm[i]);\n }\n\n }\n\n friend class Projection;\n\nprivate:\n ProjectionPermutation(const Space& space,\n const ObjectVector& data,\n size_t nDstDim) : space_(space), data_(data),\n dstDim_(nDstDim) {\n GetPermutationPivot(data_, space_, nDstDim, &ref_pts_);\n }\n const Space& space_;\n const ObjectVector& data_;\n ObjectVector ref_pts_;\n size_t dstDim_;\n};\n\ntemplate \nclass ProjectionPermutationTrunc : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n Permutation perm;\n\n if (NULL == pQuery) {\n GetPermutation(ref_pts_, space_, pObj, &perm);\n } else {\n GetPermutation(ref_pts_, pQuery, &perm);\n }\n\n for (size_t i = 0; i < dstDim_; ++i) {\n pDstVect[i] = perm[i] <= trunc_threshold_ ? static_cast(perm[i]) : 0;\n }\n\n }\n\n friend class Projection;\n\nprivate:\n ProjectionPermutationTrunc(const Space& space,\n const ObjectVector& data,\n size_t nDstDim,\n unsigned trunc_threshold) : space_(space), data_(data),\n dstDim_(nDstDim), \n trunc_threshold_(trunc_threshold) {\n GetPermutationPivot(data_, space_, nDstDim, &ref_pts_);\n }\n const Space& space_;\n const ObjectVector& data_;\n ObjectVector ref_pts_;\n size_t dstDim_;\n unsigned trunc_threshold_;\n};\n\n// Binarized permutations\ntemplate \nclass ProjectionPermutationBin : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n Permutation perm;\n\n if (NULL == pQuery) {\n GetPermutation(ref_pts_, space_, pObj, &perm);\n } else {\n GetPermutation(ref_pts_, pQuery, &perm);\n }\n\n for (size_t i = 0; i < dstDim_; ++i) {\n // See also the function binarize\n pDstVect[i] = static_cast(perm[i] >= binThreshold_ ? 1:0);\n }\n\n }\n\n friend class Projection;\n\nprivate:\n ProjectionPermutationBin(const Space& space,\n const ObjectVector& data,\n size_t nDstDim,\n unsigned binThreshold)\n : space_(space), data_(data),\n dstDim_(nDstDim),\n binThreshold_(binThreshold) {\n GetPermutationPivot(data_, space_, nDstDim, &ref_pts_);\n }\n const Space& space_;\n const ObjectVector& data_;\n ObjectVector ref_pts_;\n size_t dstDim_;\n unsigned binThreshold_;\n};\n\n/*\n * FastMap, see.\n *\n * Christos Faloutsos & King-Ip (David) Lin.\n * FastMap: A Fast Algorithm for Indexing, Data-Mining and\n * Visualization of Traditional and Multimedia Datasets,\n *\n */\ntemplate \nclass ProjectionFastMap : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n\n\n for (size_t i = 0; i < dstDim_; ++i) {\n dist_t distAI = Projection::DistanceObjLeft(space_, pQuery,\n ref_pts_a_[i], pObj);\n dist_t distBI = Projection::DistanceObjLeft(space_, pQuery,\n ref_pts_b_[i], pObj);\n dist_t distAB = dist_ab_[i];\n pDstVect[i] = static_cast(\n (\n (distAI*distAI) - (distBI*distBI) + (distAB*distAB)\n )/(distAB * dist_t(2))\n );\n }\n\n }\n\n friend class Projection;\n\nprivate:\n ProjectionFastMap(const Space& space,\n const ObjectVector& data,\n size_t nDstDim) : space_(space), data_(data),\n dstDim_(nDstDim) {\n ref_pts_a_.resize(nDstDim);\n ref_pts_b_.resize(nDstDim);\n dist_ab_.resize(nDstDim);\n\n dist_t eps = numeric_limits::epsilon() * 2; /* two is a bit ad-hoc */\n\n for (size_t i = 0; i < nDstDim; ++i) {\n std::unordered_set pivot_idx;\n\n for (size_t rep = 0; ; ++rep) {\n if (rep > MAX_RAND_ITER_BEFORE_GIVE_UP) {\n throw runtime_error(\"Cannot find the next pair of pivot, perhaps, the data set is too small.\");\n }\n int pa = RandomInt() % data.size(),\n pb = RandomInt() % data.size();\n dist_ab_[i] = space_.IndexTimeDistance(data[pa], data[pb]);\n if (pivot_idx.count(pa) !=0 || pivot_idx.count(pb) !=0 ||\n fabs((double)dist_ab_[i]) < eps) continue;\n pivot_idx.insert(pa);\n pivot_idx.insert(pb);\n ref_pts_a_[i] = data_[pa];\n ref_pts_b_[i] = data_[pb];\n break;\n }\n }\n }\n const Space& space_;\n const ObjectVector& data_;\n ObjectVector ref_pts_a_;\n ObjectVector ref_pts_b_;\n vector dist_ab_;\n size_t dstDim_;\n};\n\ntemplate \nclass ProjectionNone : public Projection {\npublic:\n virtual void compProj(const Query* pQuery,\n const Object* pObj,\n float* pDstVect) const {\n if (NULL == pObj) pObj = pQuery->QueryObject();\n /*\n * For dense vector spaces, we ignore the specified \"projection\"\n * dimensionality and use an actual number of vector elements. \n * For sparse vector spaces, we obtain an intermediate dense vector \n * with projDim elements.\n */\n size_t nDim = space_.GetElemQty(pObj);\n if (!nDim) nDim = projDim_;\n vector intermBuffer(nDim);\n Projection::fillIntermBuffer(space_,\n pObj,\n nDim,\n intermBuffer);\n\n CHECK(nDim == dstDim_);\n\n for (size_t i = 0; i < dstDim_; ++i)\n pDstVect[i] = static_cast(intermBuffer[i]);\n }\n\n friend class Projection;\nprivate:\n ProjectionNone(const Space& space, const ObjectVector& data,\n size_t nProjDim, size_t nDstDim) :\n space_(space), projDim_(nProjDim), dstDim_(nDstDim) {\n if (data.empty()) {\n stringstream err;\n err << \"Cannot initialize projection type '\" <<\n PROJ_TYPE_RAND << \"'\" <<\n \" without a single data point\";\n throw runtime_error(err.str());\n }\n size_t nDim = space.GetElemQty(data[0]);\n if (nDim == 0) {\n if (!projDim_) {\n throw runtime_error(\"Specify a non-zero value for the intermediate dimensionaity.\");\n }\n nDim = projDim_;\n }\n if (nDim != dstDim_) {\n throw runtime_error(\"The dimensionality of the projected space should be equal to either source or to the intermediate dimensionality!\");\n }\n }\n\n const Space& space_;\n size_t projDim_;\n size_t dstDim_;\n\n};\n\n\n/*\n * Projection factory function.\n */\n\ntemplate \nProjection*\nProjection::createProjection(const Space& space,\n const ObjectVector& data,\n string projType,\n size_t nProjDim,\n size_t nDstDim,\n unsigned binThreshold) {\n ToLower(projType);\n\n if (PROJ_TYPE_RAND == projType) {\n return new ProjectionRand(space, data, nProjDim, nDstDim, true);\n } else if (PROJ_TYPE_RAND_REF_POINT == projType) {\n return new ProjectionRandRefPoint(space, data, nDstDim);\n } else if (PROJ_TYPE_PERM == projType) {\n return new ProjectionPermutation(space, data, nDstDim);\n } else if (PROJ_TYPE_PERM_TRUNC == projType) {\n return new ProjectionPermutationTrunc(space, data, nDstDim, binThreshold);\n } else if (PROJ_TYPE_PERM_BIN == projType) {\n return new ProjectionPermutationBin(space, data, nDstDim, binThreshold);\n } else if (PROJ_TYPE_VECTOR_DENSE == projType) {\n return new ProjectionVectDense(space, nDstDim);\n } else if (PROJ_TYPE_FAST_MAP == projType) {\n return new ProjectionFastMap(space, data, nDstDim);\n } else if (PROJ_TYPE_NONE == projType) {\n return new ProjectionNone(space, data, nProjDim, nDstDim);\n }\n\n throw runtime_error(\"Unknown projection type '\" + projType + \"'\");\n\n return NULL;\n}\n\n\ntemplate class Projection;\ntemplate class Projection;\ntemplate class Projection;\n\ntemplate class ProjectionRand;\ntemplate class ProjectionRand;\ntemplate class ProjectionRand;\n\ntemplate class ProjectionRandRefPoint;\ntemplate class ProjectionRandRefPoint;\ntemplate class ProjectionRandRefPoint;\n\ntemplate class ProjectionPermutation;\ntemplate class ProjectionPermutation;\ntemplate class ProjectionPermutation;\n\ntemplate class ProjectionFastMap;\ntemplate class ProjectionFastMap;\ntemplate class ProjectionFastMap;\n\ntemplate class ProjectionNone;\ntemplate class ProjectionNone;\ntemplate class ProjectionNone;\n\n}\n\n" }, { "path": "algorithms/NMSLIB/code/src/query.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n\n#include \"space.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"query.h\"\n\nnamespace similarity {\n\ntemplate \nQuery::Query(const Space& space, const Object* query_object)\n : space_(space),\n query_object_(query_object),\n distance_computations_(0) {\n}\n\ntemplate \nQuery::~Query() {\n}\n\ntemplate \nconst Object* Query::QueryObject() const {\n return query_object_;\n}\n\ntemplate \nuint64_t Query::DistanceComputations() const {\n return distance_computations_;\n}\n\ntemplate \nvoid Query::ResetStats() {\n distance_computations_ = 0;\n}\n\ntemplate \ndist_t Query::Distance(\n const Object* object1,\n const Object* object2) const {\n ++distance_computations_;\n return space_.HiddenDistance(object1, object2);\n}\n\ntemplate \ndist_t Query::DistanceObjLeft(const Object* object) const {\n return Distance(object, query_object_);\n}\n\ntemplate \ndist_t Query::DistanceObjRight(const Object* object) const {\n return Distance(query_object_, object);\n}\n\ntemplate class Query;\ntemplate class Query;\ntemplate class Query;\ntemplate class Query;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/randproj_util.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"randproj_util.h\"\n#include \"distcomp.h\"\n#include \"logging.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate void initRandProj(size_t nSrcDim, size_t nDstDim,\n bool bDoOrth,\n vector>& projMatr) {\n // Static is thread-safe in C++-11\n static std::random_device rd;\n static std::mt19937 engine(rd());\n static std::normal_distribution<> normGen(0.0f, 1.0f);\n\n // 1. Create normally distributed vectors\n projMatr.resize(nDstDim);\n for (size_t i = 0; i < nDstDim; ++i) {\n projMatr[i].resize(nSrcDim);\n for (size_t j = 0; j < nSrcDim; ++j)\n projMatr[i][j] = normGen(engine);\n }\n /* \n * 2. If bDoOrth == true, normalize the basis using the numerically stable\n variant of the Gram–Schmidt process (see Wikipedia for details \n http://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process#Algorithm).\n * Otherwise, just divide each vector by its norm. \n */\n size_t maxNormDim = min(nDstDim, nSrcDim);\n\n for (size_t i = 0; i < nDstDim; ++i) {\n if (bDoOrth) {\n // Normalize the outcome (in particular, to ensure the above mentioned invariant is true)\n dist_t normCoeff = sqrt(ScalarProductSIMD(&projMatr[i][0], &projMatr[i][0], nSrcDim));\n for (size_t n = 0; n < nSrcDim; ++n) projMatr[i][n] /= normCoeff;\n\n for (size_t k = i + 1; k < maxNormDim; ++k) {\n dist_t coeff = ScalarProductSIMD(&projMatr[i][0], &projMatr[k][0], nSrcDim);\n /* \n * Invariant the all previously processed vectors have been normalized.\n * Therefore, we we subtract the projection to a previous vector u,\n * we don't divide elements by the norm of the vector u\n */\n for (size_t n = 0; n < nSrcDim; ++n) projMatr[k][n] -= coeff * projMatr[i][n];\n }\n }\n }\n}\n\ntemplate <> void initRandProj(size_t nSrcDim, size_t nDstDim,\n bool bDoOrth,\n vector>& projMatr) {\n throw runtime_error(\"random projections are not supported for integer-valued distances!\");\n}\n\ntemplate void initRandProj(size_t nSrcDim, size_t nDstDim,\n bool bDoOrth,\n vector>& projMatr);\ntemplate void initRandProj(size_t nSrcDim, size_t nDstDim,\n bool bDoOrth,\n vector>& projMatr);\n\ntemplate void compRandProj(const vector>& projMatr, \n const dist_t* pSrcVect, size_t nSrcDim,\n dist_t* pDstVect, size_t nDstDim) {\n if (projMatr.empty()) throw runtime_error(\"Bug: empty projection matrix\");\n if (projMatr.size() != nDstDim) { \n stringstream err;\n err << \"Bug: the # of rows in the projection matrix (\" << projMatr.size() << \")\"\n << \" isn't equal to the number of vector elements in the target space \"\n << \"(\" << nDstDim << \")\";\n throw runtime_error(err.str());\n }\n\n for (size_t i = 0; i < nDstDim; ++i) {\n if (projMatr[i].size() != nSrcDim) {\n stringstream err;\n err << \"Bug: row index \" << i << \" the number of columns \"\n << \"(\" << projMatr[i].size() << \")\"\n << \" isn't equal to the number of vector elements in the source space \"\n << \"(\" << nSrcDim << \")\";\n throw runtime_error(err.str());\n }\n pDstVect[i] = ScalarProductSIMD(&projMatr[i][0], pSrcVect, nSrcDim);\n }\n}\n\ntemplate <> void compRandProj(const vector>& projMatr,\n const int* pSrcVect, size_t nSrcDim,\n int* pDstVect, size_t nDstQty) {\n throw runtime_error(\"random projections are not supported for integer-valued distances!\");\n}\n\ntemplate void compRandProj(const vector>& projMatr,\n const float* pSrcVect, size_t nSrcDim,\n float* pDstVect, size_t nDstQty);\ntemplate void compRandProj(const vector>& projMatr,\n const double* pSrcVect, size_t nSrcDim,\n double* pDstVect, size_t nDstQty);\n}\n" }, { "path": "algorithms/NMSLIB/code/src/rangequery.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space.h\"\n#include \"object.h\"\n#include \"utils.h\"\n#include \"rangequery.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nRangeQuery::RangeQuery(const Space& space,\n const Object* query_object,\n const dist_t radius)\n : Query(space, query_object),\n radius_(radius) {\n}\n\ntemplate \nconst ObjectVector* RangeQuery::Result() const {\n return &result_;\n}\n\ntemplate \nstd::set RangeQuery::ResultSet() const {\n std::set res;\n for (auto it = result_.begin(); it != result_.end(); ++it) {\n res.insert(*it);\n }\n return res;\n}\n\ntemplate \ndist_t RangeQuery::Radius() const {\n return radius_;\n}\n\ntemplate \nunsigned RangeQuery::ResultSize() const {\n return static_cast(result_.size());\n}\n\ntemplate \nvoid RangeQuery::Reset() {\n this->ResetStats();\n result_.clear();\n}\n\ntemplate \nbool RangeQuery::CheckAndAddToResult(const dist_t distance,\n const Object* object) {\n if (distance <= radius_) {\n result_.push_back(object);\n resultDists_.push_back(distance);\n return true;\n }\n return false;\n}\n\ntemplate \nbool RangeQuery::CheckAndAddToResult(const Object* object) {\n // Distance can be asymmetric, but query is on the left side here\n return CheckAndAddToResult(this->DistanceObjLeft(object), object);\n}\n\ntemplate \nsize_t RangeQuery::CheckAndAddToResult(const ObjectVector& bucket) {\n size_t res = 0;\n for (size_t i = 0; i < bucket.size(); ++i) {\n if (CheckAndAddToResult(bucket[i])) {\n ++res;\n }\n }\n return res;\n}\n\ntemplate \nbool RangeQuery::Equals(const RangeQuery* query) const {\n std::set res1, res2;\n copy(result_.begin(), result_.end(), inserter(res1, res1.end()));\n copy(query->result_.begin(), query->result_.end(), inserter(res2, res2.end()));\n return res1 == res2;\n}\n\ntemplate \nvoid RangeQuery::Print() const {\n std::cerr << \"queryID = \" << this->QueryObject()->id()\n << \"size = \" << ResultSize() << std::endl;\n for (auto iter = result_.begin(); iter != result_.end(); ++iter) {\n const Object* object = *iter;\n std::cerr << object->id() << \"(\"\n << this->space_.HiddenDistance(this->QueryObject(), object) << \") \";\n }\n std::cerr << std::endl;\n}\n\ntemplate class RangeQuery;\ntemplate class RangeQuery;\ntemplate class RangeQuery;\ntemplate class RangeQuery;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/searchoracle.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \"searchoracle.h\"\n#include \"method/vptree_utils.h\"\n#include \"space.h\"\n#include \"method/vptree.h\"\n#include \"tune.h\"\n\n#include \n#include \n#include \n#include \n\nnamespace similarity {\n\nconst size_t TUNE_QTY_DEFAULT = 20000;\nconst size_t TUNE_QUERY_QTY = 200;\nconst size_t TUNE_SPLIT_QTY = 5;\nconst size_t TOTAL_QUERY_QTY = TUNE_QUERY_QTY * TUNE_SPLIT_QTY;\nconst size_t MIN_TUNE_QTY = TOTAL_QUERY_QTY; \n\nusing std::vector;\nusing std::round;\nusing std::endl;\nusing std::cout;\nusing std::pow;\nusing std::log;\n\ntemplate \nvoid PolynomialPruner::SetQueryTimeParams(AnyParamManager& pmgr) {\n /* \n * If no tunning was carried out previously, \n * default values are for the classic triangle inequality.\n * However, after tunning is done, we will use default values\n * obtained after tunning.\n */\n pmgr.GetParamOptional(ALPHA_LEFT_PARAM, alpha_left_, alpha_left_default_);\n pmgr.GetParamOptional(ALPHA_RIGHT_PARAM, alpha_right_, alpha_right_default_);\n pmgr.GetParamOptional(EXP_LEFT_PARAM, exp_left_, exp_left_default_);\n pmgr.GetParamOptional(EXP_RIGHT_PARAM, exp_right_, exp_right_default_);\n\n LOG(LIB_INFO) << \"Set polynomial pruner query-time parameters:\";\n LOG(LIB_INFO) << Dump();\n}\n\ntemplate \nvoid PolynomialPruner::SetIndexTimeParams(AnyParamManager& pmgr) {\n if (pmgr.hasParam(TUNE_R_PARAM) && pmgr.hasParam(TUNE_K_PARAM)) {\n stringstream err;\n\n err << \"Specify only one parameter: \" << TUNE_R_PARAM << \" or \" << TUNE_K_PARAM;\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n\n /*\n * The tuning part is hardcoded to make only with the VP-tree.\n */\n if (pmgr.hasParam(TUNE_R_PARAM) || pmgr.hasParam(TUNE_K_PARAM)) {\n // Obtain optimal parameters automatically\n size_t fullBucketSize;\n pmgr.GetParamRequired(\"bucketSize\", fullBucketSize);\n\n if (data_.size() < TOTAL_QUERY_QTY) {\n stringstream err;\n err << \"The data size is too small: it should have \" << (TOTAL_QUERY_QTY - data_.size()) << \" MORE entries!\";\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n\n size_t tuneQty;\n pmgr.GetParamOptional(TUNE_QTY_PARAM, tuneQty, TUNE_QTY_DEFAULT);\n\n if (tuneQty < MIN_TUNE_QTY) {\n stringstream err;\n err << \"The value of parameter \" << TUNE_QTY_PARAM << \" should be >= \" << MIN_TUNE_QTY;\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n\n float desiredRecallRequested = 0;\n pmgr.GetParamRequired(DESIRED_RECALL_PARAM, desiredRecallRequested);\n\n float desiredRecallAdjusted = desiredRecallRequested;\n size_t bucketSizeAdjusted = fullBucketSize;\n\n if (tuneQty >= data_.size()) {\n tuneQty = data_.size(); // Perfect, nothing to do at this point\n } else {\n float treeHeightQty = float(data_.size())/ fullBucketSize; \n\n if (treeHeightQty > tuneQty) {\n bucketSizeAdjusted = 1;\n // Because treeHeightQty > tuneQty > 0, logs are well defined.\n // Furthermore, the ratio of logs should be < 1, so we increase the desired recall\n desiredRecallAdjusted = pow(desiredRecallRequested, log(tuneQty)/log(treeHeightQty));\n LOG(LIB_INFO) << \"Adjusting recall value from \" << desiredRecallRequested << \" to \" << desiredRecallAdjusted;\n } else {\n // in this conditional branch we expect treeHeightQty to be <= tuneQty so the adjusted bucket size should be > 0\n bucketSizeAdjusted = tuneQty / treeHeightQty;\n }\n }\n\n LOG(LIB_INFO) << \"Adjusted values for tuneQty: \" << tuneQty << \n \" bucket size: \" << bucketSizeAdjusted << \n \" recall: \" << desiredRecallAdjusted;\n\n vector paramDesc;\n stringstream methParamDesc;\n string methName;\n\n methParamDesc << \"bucketSize=\" << bucketSizeAdjusted;\n\n ParseArg(methParamDesc.str(), paramDesc);\n\n size_t minExp = 0, maxExp = 0;\n\n pmgr.GetParamOptional(MIN_EXP_PARAM, minExp, MIN_EXP_DEFAULT);\n pmgr.GetParamOptional(MAX_EXP_PARAM, maxExp, MAX_EXP_DEFAULT);\n\n if (!maxExp) throw runtime_error(string(MIN_EXP_PARAM) + \" can't be zero!\");\n if (maxExp < minExp) throw runtime_error(string(MAX_EXP_PARAM) + \" can't be < \" + string(MIN_EXP_PARAM));\n\n\n string metricName;\n\n pmgr.GetParamOptional(OPTIM_METRIC_PARAMETER, metricName, OPTIM_METRIC_DEFAULT);\n\n OptimMetric metric = getOptimMetric(metricName);\n\n if (IMPR_INVALID == metric) {\n stringstream err;\n \n err << \"Invalid metric name: \" << metricName;\n LOG(LIB_INFO) << err.str();\n throw runtime_error(err.str());\n }\n\n\n unique_ptr> config;\n\n dist_t eps = 0;\n\n ObjectVector emptyQueries;\n\n vector knn;\n vector range;\n\n if (pmgr.hasParam(TUNE_R_PARAM)) {\n dist_t r;\n pmgr.GetParamRequired(TUNE_R_PARAM, r);\n range.push_back(r);\n \n config.reset(new ExperimentConfig(space_, \n data_, emptyQueries, \n TUNE_SPLIT_QTY,\n tuneQty, TUNE_QUERY_QTY,\n knn, eps, range));\n \n }\n\n if (pmgr.hasParam(TUNE_K_PARAM)) {\n size_t k;\n pmgr.GetParamRequired(TUNE_K_PARAM, k);\n knn.push_back(k);\n \n config.reset(new ExperimentConfig(space_, \n data_, emptyQueries, \n TUNE_SPLIT_QTY,\n tuneQty, TUNE_QUERY_QTY,\n knn, eps, range));\n \n }\n\n CHECK(config.get());\n\n config->ReadDataset();\n\n size_t maxCacheGSQty;\n pmgr.GetParamOptional(MAX_CACHE_GS_QTY_PARAM, maxCacheGSQty, MAX_CACHE_GS_QTY_DEFAULT);\n size_t maxIter = 0;\n pmgr.GetParamOptional(MAX_ITER_PARAM, maxIter, MAX_ITER_DEFAULT);\n size_t maxRecDepth = 0;\n pmgr.GetParamOptional(MAX_REC_DEPTH_PARAM, maxRecDepth, MAX_REC_DEPTH_DEFAULT);\n size_t stepN = 0;\n pmgr.GetParamOptional(STEP_N_PARAM, stepN, STEP_N_DEFAULT);\n size_t addRestartQty = 0;\n pmgr.GetParamOptional(ADD_RESTART_QTY_PARAM, addRestartQty, ADD_RESTART_QTY_DEFAULT);\n float fullFactor = 0;\n pmgr.GetParamOptional(FULL_FACTOR_PARAM, fullFactor, (float)FULL_FACTOR_DEFAULT);\n\n float recall = 0, time_best = 0, impr_best = -1, alpha_left = 0, alpha_right = 0; \n unsigned exp_left = 0, exp_right = 0;\n\n static std::random_device rd;\n static std::mt19937 engine(rd());\n static std::normal_distribution<> normGen(0.0f, log(fullFactor));\n\n\n for (unsigned ce = minExp; ce <= maxExp; ++ce)\n for (unsigned k = 0; k < 1 + addRestartQty; ++k) {\n unsigned expLeft = ce, expRight = ce;\n float recall_loc, time_best_loc, impr_best_loc, \n alpha_left_loc = 1.0, alpha_right_loc = 1.0; // These are initial values\n\n if (k > 0) {\n // Let's do some random normal fun\n alpha_left_loc = exp(normGen(engine));\n alpha_right_loc = exp(normGen(engine));\n LOG(LIB_INFO) << \" RANDOM STARTING POINTS: \" << alpha_left_loc << \" \" << alpha_right_loc;\n } \n\n string SpaceType; // VP-tree doesn't use the space type during creation.\n\n GetOptimalAlphas(printProgress_,\n *config, \n metric, desiredRecallAdjusted,\n SpaceType, \n METH_VPTREE, \n AnyParams(paramDesc), getEmptyParams(),\n recall_loc, \n time_best_loc, impr_best_loc,\n alpha_left_loc, expLeft, alpha_right_loc, expRight,\n maxIter, maxRecDepth, stepN, fullFactor, maxCacheGSQty);\n\n if (impr_best_loc > impr_best) {\n recall = recall_loc; \n time_best = time_best_loc; \n impr_best = impr_best_loc;\n alpha_left = alpha_left_loc; \n alpha_right = alpha_right_loc;\n exp_left = expLeft;\n exp_right = expRight;\n }\n }\n\n /*\n * Note that the tunning procedure overrides the default values,\n * to prevent the procedure SetQueryTimeParams from overriding them.\n * If one calls SetQueryTimeParams with the set of empty parameters,\n * all parameters are reset to default values. So, we want to this default\n * values to be equal to the values obtained during tuning.\n */\n alpha_left_default_ = alpha_left_ = alpha_left;\n alpha_right_default_ = alpha_right_ = alpha_right;\n exp_left_default_ = exp_left_ = exp_left;\n exp_right_default_ = exp_right_ = exp_right;\n\n stringstream bestParams;\n bestParams << ALPHA_LEFT_PARAM << \"=\" << alpha_left_ << \",\" << ALPHA_RIGHT_PARAM << \"=\" << alpha_right_ << \",\"\n << EXP_LEFT_PARAM << \"=\" << exp_left_ << \",\" << EXP_RIGHT_PARAM << \"=\" << exp_right_;\n\n if (printProgress_) {\n cout << \"===============================================================\" << endl;\n cout << \"| OPTIMIZATION RESULTS |\" << endl;\n cout << \"===============================================================\" << endl;\n if (!knn.empty()) {\n cout << \"K: \" << knn[0] << endl;\n } else {\n cout << \"Range: \" << range[0] << endl;\n }\n cout << \"Recall: \" << recall << endl;\n cout << \"Best time: \" << time_best << endl;\n cout << \"Best impr. \" << impr_best << \" (\" << getOptimMetricName(metric) << \")\" << endl; \n cout << \"alpha_left: \" << alpha_left << endl;\n cout << \"exp_left: \" << exp_left << endl;\n cout << \"alpha_right: \" << alpha_right << endl;\n cout << \"exp_right: \" << exp_right << endl;\n cout << \"optimal parameters: \" << bestParams.str() << endl;\n cout << \"===============================================================\" << endl;\n }\n\n if (recall < desiredRecallAdjusted) {\n LOG(LIB_INFO) << \"Failed to get the desired recall!\";\n throw runtime_error(\"Failed to get the desired recall!\");\n }\n \n }\n}\n\ntemplate class PolynomialPruner;\ntemplate class PolynomialPruner;\ntemplate class PolynomialPruner;\n\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_bit_hamming.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"space/space_bit_hamming.h\"\n#include \"permutation_utils.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\nint SpaceBitHamming::HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const uint32_t* x = reinterpret_cast(obj1->data());\n const uint32_t* y = reinterpret_cast(obj2->data());\n const size_t length = obj1->datalength() / sizeof(uint32_t)\n - 1; // the last integer is an original number of elements \n\n return BitHamming(x, y, length);\n}\n\nvoid SpaceBitHamming::ReadBitMaskVect(std::string line, LabelType& label, std::vector& binVect) const\n{\n binVect.clear();\n\n label = Object::extractLabel(line);\n\n std::stringstream str(line);\n\n str.exceptions(std::ios::badbit);\n\n unsigned val;\n\n ReplaceSomePunct(line); \n\n vector v;\n\n try {\n while (str >> val) {\n if (val != 0 && val != 1) {\n throw runtime_error(\"Only zeros and ones are allowed\");\n }\n v.push_back(val);\n }\n } catch (const std::exception &e) {\n LOG(LIB_ERROR) << \"Exception: \" << e.what() << std::endl;\n PREPARE_RUNTIME_ERR(err) << \"Failed to parse the line: '\" << line << \"'\";\n LOG(LIB_ERROR) << err.stream().str() << std::endl;\n THROW_RUNTIME_ERR(err);\n }\n Binarize(v, 1, binVect); // Create the binary vector\n binVect.push_back(v.size()); // Put the number of elements in the end\n}\n\nObject* SpaceBitHamming::CreateObjFromBitMaskVect(IdType id, LabelType label, const std::vector& bitMaskVect) const {\n return new Object(id, label, bitMaskVect.size() * sizeof(uint32_t), &bitMaskVect[0]);\n};\n\n/** Standard functions to read/write/create objects */ \n\nunique_ptr SpaceBitHamming::OpenReadFileHeader(const string& inpFileName) const {\n return unique_ptr(new DataFileInputStateVec(inpFileName));\n}\n\nunique_ptr SpaceBitHamming::OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outFileName) const {\n return unique_ptr(new DataFileOutputState(outFileName));\n}\n\nunique_ptr \nSpaceBitHamming::CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpStateBase) const {\n DataFileInputStateVec* pInpState = NULL;\n if (pInpStateBase != NULL) {\n pInpState = dynamic_cast(pInpStateBase);\n if (NULL == pInpState) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: unexpected pointer type\";\n THROW_RUNTIME_ERR(err);\n }\n }\n vector vec;\n ReadBitMaskVect(s, label, vec);\n if (pInpState != NULL) {\n size_t elemQty = vec[vec.size() - 1];\n if (pInpState->dim_ == 0) pInpState->dim_ = elemQty;\n else if (elemQty != pInpState->dim_) {\n PREPARE_RUNTIME_ERR(err) << \"The # of bit-vector elements (\" << elemQty << \")\" <<\n \" doesn't match the # of elements in previous lines. (\" << pInpState->dim_ << \" )\";\n THROW_RUNTIME_ERR(err);\n }\n }\n return unique_ptr(CreateObjFromVectInternal(id, label, vec));\n}\n\nObject* SpaceBitHamming::CreateObjFromVectInternal(IdType id, LabelType label, const std::vector& InpVect) const {\n return new Object(id, label, InpVect.size() * sizeof(uint32_t), &InpVect[0]);\n};\n\nbool SpaceBitHamming::ApproxEqual(const Object& obj1, const Object& obj2) const {\n const uint32_t* p1 = reinterpret_cast(obj1.data());\n const uint32_t* p2 = reinterpret_cast(obj2.data());\n const size_t len1 = obj1.datalength() / sizeof(uint32_t)\n - 1; // the last integer is an original number of elements \n const size_t len2 = obj2.datalength() / sizeof(uint32_t)\n - 1; // the last integer is an original number of elements \n if (len1 != len2) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: comparing vectors of different lengths: \" << len1 << \" and \" << len2;\n THROW_RUNTIME_ERR(err);\n }\n for (size_t i = 0; i < len1; ++i) {\n uint32_t v1 = ((p1[i/32] >> (i & 31)) & 1);\n uint32_t v2 = ((p2[i/32] >> (i & 31)) & 1);\n if (v1 != v2) return false;\n }\n\n return true;\n}\n\n\nstring SpaceBitHamming::CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const {\n stringstream out;\n const uint32_t* p = reinterpret_cast(pObj->data());\n const size_t length = pObj->datalength() / sizeof(uint32_t)\n - 1; // the last integer is an original number of elements \n const size_t elemQty = p[length]; // last elem\n\n for (size_t i = 0; i < elemQty; ++i) {\n if (i) out << \" \";\n out << ((p[i/32] >> (i & 31)) & 1);\n }\n\n return out.str();\n}\n\nbool SpaceBitHamming::ReadNextObjStr(DataFileInputState &inpStateBase, string& strObj, LabelType& label, string& externId) const {\n externId.clear();\n DataFileInputStateOneFile* pInpState = dynamic_cast(&inpStateBase);\n CHECK_MSG(pInpState != NULL, \"Bug: unexpected pointer type\");\n if (!pInpState->inp_file_) return false;\n if (!getline(pInpState->inp_file_, strObj)) return false;\n pInpState->line_num_++;\n return true;\n}\n\n\n/** End of standard functions to read/write/create objects */ \n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_bregman.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space/space_bregman.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\ntemplate \nObject* BregmanDiv::Mean(const ObjectVector& data) const {\n CHECK(!data.empty());\n\n // the caller is responsible for releasing the pointer\n Object* mean = Object::CreateNewEmptyObject(data[0]->datalength());\n\n const size_t length = GetElemQty(data[0]);\n dist_t* x = reinterpret_cast(mean->data());\n\n // init\n for (size_t d = 0; d < length; ++d) {\n x[d] = 0.0;\n }\n\n // sum\n for (auto it = data.begin(); it != data.end(); ++it) {\n const dist_t* y = reinterpret_cast((*it)->data());\n for (size_t d = 0; d < length; ++d) {\n x[d] += y[d];\n }\n }\n\n // average\n for (size_t d = 0; d < length; ++d) {\n x[d] /= static_cast(data.size());\n }\n\n return mean;\n}\n\n//=============================================================\n\ntemplate \ndist_t KLDivAbstract::Function(const Object* object) const {\n DCHECK(object->datalength() > 0);\n const dist_t* x = reinterpret_cast(object->data());\n const size_t length = GetElemQty(object);\n\n dist_t result = 0.0;\n for (size_t i = 0; i < length; ++i) {\n result += x[i] * log(x[i]);\n }\n return result;\n}\n\ntemplate \nObject* KLDivAbstract::GradientFunction(const Object* object) const {\n DCHECK(object->datalength() > 0);\n const dist_t* x = reinterpret_cast(object->data());\n const size_t length = GetElemQty(object);\n\n // the caller is responsible for releasing the pointer\n Object* result = Object::CreateNewEmptyObject(object->datalength());\n dist_t* y = reinterpret_cast(result->data());\n for (size_t i = 0; i < length; ++i) {\n y[i] = log(x[i]) + 1.0;\n }\n return result;\n}\n\ntemplate \nObject* KLDivAbstract::InverseGradientFunction(const Object* object) const {\n DCHECK(object->datalength() > 0);\n const dist_t* x = reinterpret_cast(object->data());\n const size_t length = GetElemQty(object);\n\n // the caller is responsible for releasing the pointer\n Object* result = Object::CreateNewEmptyObject(object->datalength());\n dist_t* y = reinterpret_cast(result->data());\n for (size_t i = 0; i < length; ++i) {\n y[i] = exp(x[i] - 1.0);\n }\n return result;\n}\n\n//=============================================================\n\ntemplate \nObject* KLDivGenFast::Mean(const ObjectVector& data) const {\n CHECK(!data.empty());\n\n // the caller is responsible for releasing the pointer\n Object* mean = BregmanDiv::Mean(data);\n\n const size_t length = GetElemQty(data[0]);\n dist_t* x = reinterpret_cast(mean->data());\n\n PrecompLogarithms(x, length);\n\n return mean;\n}\n\ntemplate \nObject* KLDivGenFast::InverseGradientFunction(const Object* object) const {\n DCHECK(object->datalength() > 0);\n\n Object* result = KLDivAbstract::InverseGradientFunction(object);\n dist_t* y = reinterpret_cast(result->data());\n const size_t length = GetElemQty(object);\n PrecompLogarithms(y, length);\n return result;\n}\n\ntemplate \ndist_t KLDivGenFast::HiddenDistance(const Object* obj1, const Object* obj2) const {\n DCHECK(obj1->datalength() > 0);\n DCHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n \n const size_t length = GetElemQty(obj1);\n\n return KLGeneralPrecompSIMD(x, y, length);\n}\n\ntemplate \nObject* KLDivGenFast::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n std::vector temp(InpVect);\n\n // Reserve space to store logarithms\n temp.resize(2 * InpVect.size());\n // Compute logarithms\n PrecompLogarithms(&temp[0], InpVect.size());\n return new Object(id, label, temp.size() * sizeof(dist_t), &temp[0]);\n}\n\n\n//=============================================================\n\ntemplate \nObject* ItakuraSaitoFast::Mean(const ObjectVector& data) const {\n CHECK(!data.empty());\n\n // the caller is responsible for releasing the pointer\n Object* mean = BregmanDiv::Mean(data);\n\n const size_t length = GetElemQty(data[0]);\n dist_t* x = reinterpret_cast(mean->data());\n\n PrecompLogarithms(x, length);\n\n return mean;\n}\n\ntemplate \ndist_t ItakuraSaitoFast::Function(const Object* object) const {\n DCHECK(object->datalength() > 0);\n const dist_t* x = reinterpret_cast(object->data());\n const size_t length = GetElemQty(object);\n\n dist_t result = 0.0;\n for (size_t i = 0; i < length; ++i) {\n result += - log(x[i]);\n }\n return result;\n}\n\ntemplate \nObject* ItakuraSaitoFast::GradientFunction(const Object* object) const {\n DCHECK(object->datalength() > 0);\n const dist_t* x = reinterpret_cast(object->data());\n const size_t length = GetElemQty(object);\n\n // the caller is responsible for releasing the pointer\n Object* result = Object::CreateNewEmptyObject(object->datalength());\n dist_t* y = reinterpret_cast(result->data());\n for (size_t i = 0; i < length; ++i) {\n y[i] = -1/x[i];\n }\n return result;\n}\n\ntemplate \nObject* ItakuraSaitoFast::InverseGradientFunction(const Object* object) const {\n DCHECK(object->datalength() > 0);\n const dist_t* x = reinterpret_cast(object->data());\n const size_t length = GetElemQty(object);\n\n // the caller is responsible for releasing the pointer\n Object* result = Object::CreateNewEmptyObject(object->datalength());\n dist_t* y = reinterpret_cast(result->data());\n for (size_t i = 0; i < length; ++i) {\n y[i] = -1/x[i];\n }\n return result;\n}\n\ntemplate \ndist_t ItakuraSaitoFast::HiddenDistance(const Object* obj1, const Object* obj2) const {\n DCHECK(obj1->datalength() > 0);\n DCHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n \n const size_t length = GetElemQty(obj1);\n\n return ItakuraSaitoPrecompSIMD(x, y, length);\n}\n\ntemplate \nObject* ItakuraSaitoFast::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n std::vector temp(InpVect);\n\n // Reserve space to store logarithms\n temp.resize(2 * InpVect.size());\n // Compute logarithms\n PrecompLogarithms(&temp[0], InpVect.size());\n return new Object(id, label, temp.size() * sizeof(dist_t), &temp[0]);\n}\n\n//=============================================================\n\ntemplate \ndist_t KLDivGenSlow::HiddenDistance(const Object* obj1, const Object* obj2) const {\n DCHECK(obj1->datalength() > 0);\n DCHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n \n const size_t length = GetElemQty(obj1);\n\n return KLGeneralStandard(x, y, length);\n}\n\ntemplate \nObject* KLDivGenSlow::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n return new Object(id, label, InpVect.size() * sizeof(dist_t), &InpVect[0]);\n}\n\ntemplate \ndist_t KLDivGenFastRightQuery::HiddenDistance(const Object* obj1, const Object* obj2) const {\n DCHECK(obj1->datalength() > 0);\n DCHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n \n const size_t length = GetElemQty(obj1);\n\n return KLGeneralPrecompSIMD(y, x, length);\n}\n\ntemplate \nObject* KLDivGenFastRightQuery::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n std::vector temp(InpVect);\n\n // Reserve space to store logarithms\n temp.resize(2 * InpVect.size());\n // Compute logarithms\n PrecompLogarithms(&temp[0], InpVect.size());\n return new Object(id, label, temp.size() * sizeof(dist_t), &temp[0]);\n}\n\n//=============================================================\n\ntemplate \ndist_t KLDivFast::HiddenDistance(const Object* obj1, const Object* obj2) const {\n DCHECK(obj1->datalength() > 0);\n DCHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n \n const size_t length = GetElemQty(obj1);\n\n return KLPrecompSIMD(x, y, length);\n}\n\ntemplate \nObject* KLDivFast::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n std::vector temp(InpVect);\n\n // Reserve space to store logarithms\n temp.resize(2 * InpVect.size());\n // Compute logarithms\n PrecompLogarithms(&temp[0], InpVect.size());\n return new Object(id, label, temp.size() * sizeof(dist_t), &temp[0]);\n}\n\n//=============================================================\n\ntemplate \ndist_t KLDivFastRightQuery::HiddenDistance(const Object* obj1, const Object* obj2) const {\n DCHECK(obj1->datalength() > 0);\n DCHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n \n const size_t length = GetElemQty(obj1);\n\n return KLPrecompSIMD(y, x, length);\n}\n\ntemplate \nObject* KLDivFastRightQuery::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n std::vector temp(InpVect);\n\n // Reserve space to store logarithms\n temp.resize(2 * InpVect.size());\n // Compute logarithms\n PrecompLogarithms(&temp[0], InpVect.size());\n return new Object(id, label, temp.size() * sizeof(dist_t), &temp[0]);\n}\n\ntemplate class BregmanDiv;\ntemplate class BregmanDiv;\ntemplate class KLDivAbstract;\ntemplate class KLDivAbstract;\ntemplate class KLDivGenSlow;\ntemplate class KLDivGenSlow;\ntemplate class KLDivGenFast;\ntemplate class KLDivGenFast;\ntemplate class ItakuraSaitoFast;\ntemplate class ItakuraSaitoFast;\ntemplate class KLDivGenFastRightQuery;\ntemplate class KLDivGenFastRightQuery;\ntemplate class KLDivFast;\ntemplate class KLDivFast;\ntemplate class KLDivFastRightQuery;\ntemplate class KLDivFastRightQuery;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_dummy.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space/space_dummy.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\ntemplate \ndist_t SpaceDummy::HiddenDistance(const Object* obj1, const Object* obj2) const {\n LOG(LIB_INFO) << \"Calculating the distance between objects: \" << obj1->id() << \" and \" << obj2->id() << endl;\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n /* \n * Data in the object is accessed using functions:\n * obj1->datalength()\n * obj1->data();\n * obj2->datalength()\n * obj2->data();\n */\n return static_cast(0);\n\n} \n\n/** Sample standard functions to read/write/create objects */ \n\ntemplate \nunique_ptr SpaceDummy::OpenReadFileHeader(const string& inpFileName) const {\n return unique_ptr(new DataFileInputStateOneFile(inpFileName));\n}\n\ntemplate \nunique_ptr SpaceDummy::OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outFileName) const {\n return unique_ptr(new DataFileOutputState(outFileName));\n}\n\ntemplate \nunique_ptr \nSpaceDummy::CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpStateBase) const {\n // Object that stores the string\n return unique_ptr(new Object(id, label, s.size(), s.data())); \n}\n\ntemplate \nstring SpaceDummy::CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const {\n // Return a string that is a copy of the Object buffer\n return string(pObj->data(), pObj->datalength());\n}\n\ntemplate \nbool SpaceDummy::ReadNextObjStr(DataFileInputState &inpStateBase, string& strObj, LabelType& label, string& externId) const {\n externId.clear();\n DataFileInputStateOneFile* pInpState = dynamic_cast(&inpStateBase);\n CHECK_MSG(pInpState != NULL, \"Bug: unexpected pointer type\");\n if (!pInpState->inp_file_) return false;\n // Read the next line\n if (!getline(pInpState->inp_file_, strObj)) return false;\n // Increase the line number counter\n pInpState->line_num_++;\n return true;\n}\n\ntemplate \nvoid SpaceDummy::WriteNextObj(const Object& obj, const string& externId, DataFileOutputState &outState) const {\n string s = CreateStrFromObj(&obj, externId);\n outState.out_file_ << s << endl;\n}\n/** End of standard functions to read/write/create objects */ \n\n \n\ntemplate class SpaceDummy;\ntemplate class SpaceDummy;\ntemplate class SpaceDummy;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_js.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space/space_js.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\ntemplate \nObject* SpaceJSBase::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n if (type_ == kJSSlow) {\n return new Object(id, label, InpVect.size() * sizeof(dist_t), &InpVect[0]);\n }\n std::vector temp(InpVect);\n\n // Reserve space to store logarithms\n temp.resize(2 * InpVect.size());\n // Compute logarithms\n PrecompLogarithms(&temp[0], InpVect.size());\n return new Object(id, label, temp.size() * sizeof(dist_t), &temp[0]);\n}\n\n\ntemplate \ndist_t SpaceJSBase::JensenShannonFunc(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n\n size_t length = obj1->datalength() / sizeof(dist_t);\n\n if (type_ != kJSSlow) length /= 2;\n\n dist_t val = 0;\n\n switch (type_) {\n case kJSSlow: val = JSStandard(x, y, length); break;\n case kJSFastPrecomp: val = JSPrecomp(x, y, length); break;\n case kJSFastPrecompApprox: val = JSPrecompSIMDApproxLog(x, y, length); break;\n default: {\n PREPARE_RUNTIME_ERR(err) << \"Unknown JS function type code: \" << type_;\n THROW_RUNTIME_ERR(err);\n }\n }\n\n return val;\n}\n\ntemplate class SpaceJSBase;\ntemplate class SpaceJSBase;\n\ntemplate class SpaceJSDiv;\ntemplate class SpaceJSDiv;\n\ntemplate class SpaceJSMetric;\ntemplate class SpaceJSMetric;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_lp.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space/space_lp.h\"\n#include \"logging.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\ntemplate \ndist_t SpaceLp::HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n const size_t length = obj1->datalength() / sizeof(dist_t);\n\n return distObj_(x, y, length);\n}\n\ntemplate \nstd::string SpaceLp::StrDesc() const {\n std::stringstream stream;\n stream << \"SpaceLp: p = \" << distObj_.getP() << \" do we have a special implementation for this p? : \" << bool(distObj_.getCustom());\n return stream.str();\n}\n\ntemplate class SpaceLp;\ntemplate class SpaceLp;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_scalar.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space/space_scalar.h\"\n#include \"logging.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\ntemplate \ndist_t SpaceCosineSimilarity::HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n const size_t length = obj1->datalength() / sizeof(dist_t);\n\n dist_t val = CosineSimilarity(x, y, length);\n // TODO: @leo shouldn't happen any more, but let's keep this check here for a while\n if (std::isnan(val)) throw runtime_error(\"Bug: NAN dist! (SpaceCosineSimilarity)\");\n return val;\n}\n\ntemplate class SpaceCosineSimilarity;\ntemplate class SpaceCosineSimilarity;\n\ntemplate \ndist_t SpaceAngularDistance::HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n const size_t length = obj1->datalength() / sizeof(dist_t);\n\n dist_t val = AngularDistance(x, y, length);\n // TODO: @leo shouldn't happen any more, but let's keep this check here for a while\n if (std::isnan(val)) throw runtime_error(\"Bug: NAN dist! (SpaceAngularDistance)\");\n return val;\n}\n\ntemplate class SpaceAngularDistance;\ntemplate class SpaceAngularDistance;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_sparse_lp.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"space/space_sparse_lp.h\"\n#include \"space/space_sparse_vector.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\ntemplate \ndist_t SpaceSparseLp::HiddenDistance(const Object* obj1, const Object* obj2) const {\n return SpaceSparseVectorSimpleStorage::ComputeDistanceHelper(obj1, obj2, distObj_);\n}\n\ntemplate \nstd::string SpaceSparseLp::StrDesc() const {\n std::stringstream stream;\n stream << \"SpaceSparseLp: p = \" << distObj_.getP() << \" (custom implement.) = \" << distObj_.getCustom();\n return stream.str();\n}\n\ntemplate class SpaceSparseLp;\ntemplate class SpaceSparseLp;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_sparse_scalar_fast.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"utils.h\"\n#include \"space/space_sparse_scalar_fast.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\nfloat \nSpaceSparseCosineSimilarityFast::HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj2->datalength() > 0);\n\n float val = 1 - ScalarProductFast(obj1->data(), obj1->datalength(),\n obj2->data(), obj2->datalength());\n\n return max(val, float(0));\n}\n\nfloat \nSpaceSparseAngularDistanceFast::HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj2->datalength() > 0);\n\n return acos(ScalarProductFast(obj1->data(), obj1->datalength(),\n obj2->data(), obj2->datalength()));\n}\n\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_sparse_vector.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"space/space_sparse_vector.h\"\n#include \"object.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nvoid SpaceSparseVector::ReadSparseVec(std::string line, size_t line_num, LabelType& label, vector& v) const\n{\n v.clear();\n\n label = Object::extractLabel(line);\n\n ReplaceSomePunct(line); \n std::stringstream str(line);\n\n str.exceptions(std::ios::badbit);\n\n uint32_t id;\n dist_t val;\n\n try {\n while (str >> id && str >> val) {\n v.push_back(ElemType(id, val));\n }\n sort(v.begin(), v.end());\n\n for (unsigned i = 1; i < v.size(); ++i) {\n uint32_t prevId = v[i-1].id_;\n uint32_t id = v[i].id_;\n\n if (id == prevId) {\n stringstream err;\n err << \"Repeating ID: prevId = \" << prevId << \" prev val: \" << v[i-1].val_ << \" current id: \" << id << \" val = \" << v[i].val_ << \" (i=\" << i << \")\";\n throw std::runtime_error(err.str());\n }\n\n if (id < prevId) {\n stringstream err;\n err << \"But: Ids are not sorted, prevId = \" << prevId << \" prev val: \" << v[i-1].val_ << \" current id: \" << id << \" val = \" << v[i].val_ << \" (i=\" << i << \")\";\n throw std::runtime_error(err.str());\n }\n }\n } catch (const std::exception &e) {\n LOG(LIB_ERROR) << \"Exception: \" << e.what() << std::endl;\n PREPARE_RUNTIME_ERR(err) << \"Failed to parse the line # \" << line_num << \": '\" << line << \"'\" << std::endl;\n LOG(LIB_ERROR) << err.stream().str() << std::endl;\n THROW_RUNTIME_ERR(err);\n }\n\n CHECK_MSG(!v.empty(), \"Encountered an empty sparse vector: this is not allowed!\");\n}\n\n/** Standard functions to read/write/create objects */ \n\ntemplate \nunique_ptr SpaceSparseVector::OpenReadFileHeader(const string& inpFileName) const {\n return unique_ptr(new DataFileInputStateOneFile(inpFileName));\n}\n\ntemplate \nunique_ptr SpaceSparseVector::OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outputFile) const {\n return unique_ptr(new DataFileOutputState(outputFile));\n}\n\ntemplate \nunique_ptr \nSpaceSparseVector::CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpStateBase) const {\n size_t line_num = 0;\n if (NULL != pInpStateBase) {\n DataFileInputStateOneFile* pInpState = dynamic_cast(pInpStateBase);\n CHECK_MSG(pInpState != NULL, \"Bug: unexpected pointer type\");\n line_num = pInpState->line_num_; \n }\n\n vector vec;\n ReadSparseVec(s, line_num, label, vec);\n return unique_ptr(CreateObjFromVect(id, label, vec));\n}\n\ntemplate \nbool \nSpaceSparseVector::ApproxEqual(const Object& obj1, const Object& obj2) const {\n vector> target1, target2;\n CreateVectFromObj(&obj1, target1);\n CreateVectFromObj(&obj2, target2);\n return target1 == target2;\n}\n\ntemplate \nstring SpaceSparseVector::CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const {\n stringstream out;\n\n vector elems;\n\n vector> target;\n CreateVectFromObj(pObj, target);\n\n for (size_t i = 0; i < target.size(); ++i) {\n if (i) out << \" \";\n // Clear all previous flags & set to the maximum precision available\n out.unsetf(ios_base::floatfield);\n out << target[i].id_ << \" \" << setprecision(numeric_limits::max_digits10) << target[i].val_;\n }\n\n return out.str();\n}\n\n\ntemplate \nbool SpaceSparseVector::ReadNextObjStr(DataFileInputState &inpStateBase, string& strObj, LabelType& label, string& externId) const {\n externId.clear();\n DataFileInputStateOneFile* pInpState = dynamic_cast(&inpStateBase);\n CHECK_MSG(pInpState != NULL, \"Bug: unexpected reference type\");\n if (!pInpState->inp_file_) return false;\n if (!getline(pInpState->inp_file_, strObj)) return false;\n if (strObj.empty()) {\n PREPARE_RUNTIME_ERR(err) << \"Encountered an empty line (not allowed), line # \" << pInpState->line_num_; \n THROW_RUNTIME_ERR(err);\n }\n pInpState->line_num_++;\n return true;\n}\n\n/* \n * We don't instantiate sparse vector spaces for types other than float & double\n */\ntemplate class SpaceSparseVector;\ntemplate class SpaceSparseVector;\n\ntemplate class SpaceSparseVectorSimpleStorage;\ntemplate class SpaceSparseVectorSimpleStorage;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_sparse_vector_inter.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"space/space_sparse_vector_inter.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n\nnamespace similarity {\n\ntemplate \nvoid\nSpaceSparseVectorInter::CreateDenseVectFromObj(const Object* obj, \n dist_t* pVect,\n size_t nElem) const {\n static std::hash indexHash;\n fill(pVect, pVect + nElem, static_cast(0));\n\n vector> target;\n UnpackSparseElements(obj->data(), obj->datalength(), target);\n\n for (SparseVectElem e: target) {\n size_t idx = indexHash(e.id_) % nElem;\n pVect[idx] += e.val_;\n }\n}\n\ntemplate \nvoid\nSpaceSparseVectorInter::CreateVectFromObj(const Object* obj, vector& v) const {\n UnpackSparseElements(obj->data(), obj->datalength(), v);\n}\n\ntemplate \nObject* SpaceSparseVectorInter::CreateObjFromVect(IdType id, LabelType label, const std::vector& InpVect) const {\n char *pData = NULL;\n size_t dataLen = 0;\n\n try {\n PackSparseElements(InpVect, pData, dataLen);\n return new Object(id, label, dataLen, pData);\n } catch (...) {\n delete [] pData;\n throw;\n }\n}\n\ntemplate class SpaceSparseVectorInter;\ntemplate class SpaceSparseVectorInter;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_sqfd.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#if !defined(_MSC_VER)\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n#include \"space/space_sqfd.h\"\n\nnamespace similarity {\n\nusing Eigen::MatrixXd;\nusing Eigen::VectorXd;\n\nusing namespace std;\n\ntemplate \nSpaceSqfd::SpaceSqfd(SqfdFunction* func)\n : func_(func) {\n}\n\ntemplate \nSpaceSqfd::~SpaceSqfd() {\n delete func_;\n}\n\nstruct DataFileInputStateSQFD : public DataFileInputStateOneFile {\n DataFileInputStateSQFD(const string& inpFileName) : \n DataFileInputStateOneFile(inpFileName), \n num_clusters_(0), \n feature_dimension_(0),\n num_rand_pixels_(0) { \n string line;\n if (!getline(inp_file_, line))\n throw runtime_error(\"Expecting a non-empty first line in '\" + inpFileName + \"'\");\n line_num_++;\n\n stringstream ss(line);\n ss.exceptions(std::ios::badbit);\n ss >> num_clusters_ >> feature_dimension_ >> num_rand_pixels_;\n\n if (!getline(inp_file_, line) || !line.empty())\n throw runtime_error(\"Expecting an empty second line in '\" + inpFileName + \"'\");\n\n line_num_++;\n }\n \n uint32_t num_clusters_;\n uint32_t feature_dimension_;\n uint32_t num_rand_pixels_;\n};\n\n/** Standard functions to read/write/create objects */ \n\ntemplate \nunique_ptr SpaceSqfd::OpenReadFileHeader(const string& inpFileName) const {\n return unique_ptr(new DataFileInputStateSQFD(inpFileName));\n}\n\ntemplate \nunique_ptr SpaceSqfd::OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outFileName) const {\n unique_ptr outState(new DataFileOutputState(outFileName));\n uint32_t num_clusters = 0, feature_dimension = 0, num_rand_pixels = 0;\n if (!dataset.empty()) {\n const uint32_t* h = reinterpret_cast(dataset[0]->data());\n num_clusters = h[0]; \n feature_dimension = h[1];\n }\n outState->out_file_ << num_clusters << \" \" << feature_dimension << \" \" << num_rand_pixels << endl << endl; \n return outState;\n}\n\ntemplate \nbool SpaceSqfd::ApproxEqual(const Object& obj1, const Object& obj2) const {\n if (obj1.datalength() < 8) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: object size \" << obj1.datalength() << \" is smaller than 8 bytes!\";\n THROW_RUNTIME_ERR(err);\n }\n if (obj2.datalength() < 8) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: object size \" << obj2.datalength() << \" is smaller than 8 bytes!\";\n THROW_RUNTIME_ERR(err);\n }\n const uint32_t* h1 = reinterpret_cast(obj1.data());\n const uint32_t* h2 = reinterpret_cast(obj2.data());\n const uint32_t num_clusters1 = h1[0], feature_dimension1 = h1[1];\n const uint32_t num_clusters2 = h2[0], feature_dimension2 = h2[1];\n const dist_t* x = reinterpret_cast(obj1.data() + 2*sizeof(uint32_t));\n const dist_t* y = reinterpret_cast(obj2.data() + 2*sizeof(uint32_t));\n if (feature_dimension1 != feature_dimension2) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: different feature dimensions: \" \n << feature_dimension1 << \" vs \" << feature_dimension2;\n THROW_RUNTIME_ERR(err);\n }\n if (num_clusters1 != num_clusters2) return false;\n\n for (size_t i = 0; i < num_clusters1; ++i) {\n const dist_t* p1 = x + i * (feature_dimension1 + 1);\n const dist_t* p2 = y + i * (feature_dimension1 + 1);\n for (size_t k = 0; k < feature_dimension1; ++k) {\n if (!similarity::ApproxEqual(p1[k], p2[k])) return false;\n }\n }\n\n return true;\n}\n\ntemplate \nstring SpaceSqfd::CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const {\n if (pObj->datalength() < 8) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: object size \" << pObj->datalength() << \" is smaller than 8 bytes!\";\n THROW_RUNTIME_ERR(err);\n }\n const uint32_t* h = reinterpret_cast(pObj->data());\n const uint32_t num_clusters = h[0], feature_dimension = h[1];\n const dist_t* pElems = reinterpret_cast(pObj->data() + 2*sizeof(uint32_t));\n stringstream out;\n size_t pos = 0;\n out << FAKE_FILE_NAME << endl;\n for (uint32_t i = 0; i < num_clusters; ++i) {\n for (uint32_t j = 0; j < feature_dimension + 1; ++j) {\n if (j) out << \" \";\n // Clear all previous flags & set to the maximum precision available\n out.unsetf(ios_base::floatfield);\n out << setprecision(numeric_limits::max_digits10) << pElems[pos++];\n }\n out << endl;\n }\n return out.str();\n}\n\ntemplate \nunique_ptr SpaceSqfd::CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpStateBase) const {\n DataFileInputStateSQFD* pInpState = NULL;\n if (pInpStateBase != NULL) {\n pInpState = dynamic_cast(pInpStateBase);\n if (NULL == pInpState) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: unexpected pointer type\";\n THROW_RUNTIME_ERR(err);\n }\n }\n stringstream stream1(s);\n string line;\n\n int prevQty = -1;\n\n vector obj;\n if (pInpState != NULL) \n obj.reserve(pInpState->num_clusters_ * pInpState->feature_dimension_);\n\n size_t currLine = 0;\n\n if (pInpState != NULL) {\n currLine = pInpState->line_num_;\n if (!currLine) {\n throw runtime_error(\"Bug: got a zero line number while expecting a positive one!\");\n }\n currLine--; // One is for the empty line that is not present in the input string\n // line_num_ points to the last read line, we need to identify the number of the first line\n for (char c: s) \n if (c =='\\n') {\n if (!currLine) {\n throw runtime_error(\"Bug: got a zero line number while expecting a positive one!\");\n }\n --currLine;\n }\n }\n\n if (!getline(stream1, line)) {\n PREPARE_RUNTIME_ERR(err) << \"Expecting a non-empty line # \" << currLine;\n THROW_RUNTIME_ERR(err);\n }\n ++currLine;\n\n while (getline(stream1, line)) {\n ++currLine;\n\n if (line.length() == 0) break; // Empty line\n stringstream stream2(line);\n stream2.exceptions(ios::badbit);\n\n int qty = 0;\n dist_t v;\n\n while (stream2 >> v) {\n obj.push_back(v);\n qty++;\n }\n\n if (prevQty == -1) prevQty = qty;\n else if (qty != prevQty) {\n PREPARE_RUNTIME_ERR(err) << \"The number of elements \" << qty << \" in line \" << currLine << \n \" doesn't match the number of elements \" << prevQty << \" in previous lines \" << \n \" offending line: '\" << line << \"'\" << endl <<\n \" offending block:\" << endl << s;\n THROW_RUNTIME_ERR(err);\n }\n // +1 is because one element is the feature weight\n if (pInpState != NULL && qty != pInpState->feature_dimension_ + 1) {\n PREPARE_RUNTIME_ERR(err) << \"The number of elements in line \" << currLine << \n \" doesn't match the number of elements in the file header'\" <<\n \" expected: \" << (pInpState->feature_dimension_ + 1) << \" but got: \" << qty;\n THROW_RUNTIME_ERR(err);\n }\n }\n\n const uint32_t feature_weight = prevQty;\n const uint32_t num_clusters = obj.size() / prevQty;\n const int object_size =\n 2 * sizeof(uint32_t) + // num_clusters & feature_dimension\n num_clusters * feature_weight * sizeof(dist_t);\n\n vector buf(object_size);\n uint32_t* h = reinterpret_cast(&buf[0]);\n h[0] = num_clusters;\n h[1] = feature_weight - 1;\n\n dist_t* pVect = reinterpret_cast(h+2);\n copy(obj.begin(), obj.end(), pVect);\n\n return unique_ptr(new Object(id, label, object_size, &buf[0]));\n}\n\ntemplate \nbool SpaceSqfd::ReadNextObjStr(DataFileInputState &inpState, string& strObj, LabelType& label, string& externId) const {\n externId.clear();\n DataFileInputStateSQFD* pInpState = NULL;\n pInpState = dynamic_cast(&inpState);\n if (NULL == pInpState) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: unexpected reference type\";\n THROW_RUNTIME_ERR(err);\n }\n\n stringstream stream1;\n string line;\n\n size_t currLine = pInpState->line_num_;\n\n if (!getline(pInpState->inp_file_, line)) {\n return false;\n }\n ++currLine;\n\n stream1 << line << endl;\n\n while (getline(pInpState->inp_file_, line)) {\n ++currLine;\n\n if (line.length() == 0) break; // Empty line, don't append it to the object representation!\n stream1 << line << endl;\n }\n pInpState->line_num_ = currLine;\n strObj = stream1.str();\n return true;\n}\n\ntemplate \ndist_t SpaceSqfd::HiddenDistance(\n const Object* obj1, const Object* obj2) const {\n if (obj1->datalength() < 8) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: object size \" << obj1->datalength() << \" is smaller than 8 bytes!\";\n THROW_RUNTIME_ERR(err);\n }\n if (obj2->datalength() < 8) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: object size \" << obj2->datalength() << \" is smaller than 8 bytes!\";\n THROW_RUNTIME_ERR(err);\n }\n const uint32_t* h1 = reinterpret_cast(obj1->data());\n const uint32_t* h2 = reinterpret_cast(obj2->data());\n const uint32_t num_clusters1 = h1[0], feature_dimension1 = h1[1];\n const uint32_t num_clusters2 = h2[0], feature_dimension2 = h2[1];\n const dist_t* x = reinterpret_cast(\n obj1->data() + 2*sizeof(uint32_t));\n const dist_t* y = reinterpret_cast(\n obj2->data() + 2*sizeof(uint32_t));\n if (feature_dimension1 != feature_dimension2) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: different feature dimensions: \" \n << feature_dimension1 << \" vs \" << feature_dimension2;\n THROW_RUNTIME_ERR(err);\n }\n const uint32_t sz = num_clusters1 + num_clusters2;\n VectorXd W(sz);\n size_t pos = feature_dimension1;\n for (uint32_t i = 0; i < num_clusters1; ++i) {\n W(i) = x[pos];\n pos += feature_dimension1 + 1;\n }\n pos = feature_dimension2;\n for (uint32_t i = 0; i < num_clusters2; ++i) {\n W(num_clusters1 + i) = -y[pos];\n pos += feature_dimension2 + 1;\n }\n MatrixXd A(sz,sz);\n for (uint32_t i = 0; i < sz; ++i) {\n for (uint32_t j = i; j < sz; ++j) {\n const dist_t* p1 = i < num_clusters1\n ? x + i * (feature_dimension1 + 1)\n : y + (i - num_clusters1) * (feature_dimension1 + 1);\n const dist_t* p2 = j < num_clusters1\n ? x + j * (feature_dimension1 + 1)\n : y + (j - num_clusters1) * (feature_dimension1 + 1);\n A(i,j) = A(j,i) = func_->f(p1, p2, feature_dimension1);\n }\n }\n auto res = W.transpose() * A * W;\n return sqrt(res(0,0));\n}\n\ntemplate \nstring SpaceSqfd::StrDesc() const {\n stringstream stream;\n stream << \"SpaceSqfd: \" << func_->StrDesc();\n return stream.str();\n}\n\ntemplate class SpaceSqfd;\ntemplate class SpaceSqfd;\n} // namespace similarity\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_string.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n#include \"space/space_string.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nvoid StringSpace::ReadStr(string line, LabelType& label, string& str, size_t* pLineNum) const\n{\n label = Object::extractLabel(line);\n\n if (EMPTY_LABEL == label) {\n stringstream lineStr;\n if (pLineNum != NULL) lineStr << \" in line \" << *pLineNum;\n throw runtime_error(\"Missing label\" + lineStr.str());\n }\n\n str = line;\n}\n\n/** Standard functions to read/write/create objects */ \n\ntemplate \nunique_ptr StringSpace::OpenReadFileHeader(const string& inpFileName) const {\n return unique_ptr(new DataFileInputStateVec(inpFileName));\n}\n\ntemplate \nunique_ptr StringSpace::OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outFileName) const {\n return unique_ptr(new DataFileOutputState(outFileName));\n}\n\ntemplate \nbool StringSpace::ApproxEqual(const Object& obj1, const Object& obj2) const {\n return CreateStrFromObj(&obj1, \"\") == CreateStrFromObj(&obj2, \"\");\n}\n\ntemplate \nstring StringSpace::CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const {\n const char* p = reinterpret_cast(pObj->data());\n // TODO double-check that sizeof(char) should always be 1 as guaranteed by the C++ standard\n // then sizeof can be removed\n const size_t length = pObj->datalength() / sizeof(char);\n return string(p, length);\n}\n\ntemplate \nbool StringSpace::ReadNextObjStr(DataFileInputState &inpStateBase, string& strObj, LabelType& label, string& externId) const {\n externId.clear();\n DataFileInputStateOneFile* pInpState = dynamic_cast(&inpStateBase);\n CHECK_MSG(pInpState != NULL, \"Bug: unexpected pointer type\");\n if (!pInpState->inp_file_) return false;\n string line;\n if (!getline(pInpState->inp_file_, line)) return false;\n pInpState->line_num_++;\n ReadStr(line, label, strObj, &pInpState->line_num_);\n return true;\n}\n\ntemplate \nvoid StringSpace::WriteNextObj(const Object& obj, const string& externId, DataFileOutputState &outState) const {\n string s = CreateStrFromObj(&obj, externId);\n outState.out_file_ << LABEL_PREFIX << obj.label() << \" \" << s << endl;\n}\n/** End of standard functions to read/write/create objects */ \n\n\ntemplate class StringSpace;\n// TODO: do we really need floating-point distances for string spaces? \ntemplate class StringSpace;\ntemplate class StringSpace;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_vector.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"utils.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n#include \"space/space_vector.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\n/** Standard functions to read/write/create objects */ \n\ntemplate \nunique_ptr VectorSpace::OpenReadFileHeader(const string& inpFileName) const {\n return unique_ptr(new DataFileInputStateVec(inpFileName));\n}\n\ntemplate \nunique_ptr VectorSpace::OpenWriteFileHeader(const ObjectVector& dataset,\n const string& outFileName) const {\n return unique_ptr(new DataFileOutputState(outFileName));\n}\n\ntemplate \nunique_ptr \nVectorSpace::CreateObjFromStr(IdType id, LabelType label, const string& s,\n DataFileInputState* pInpStateBase) const {\n DataFileInputStateVec* pInpState = NULL;\n if (pInpStateBase != NULL) {\n pInpState = dynamic_cast(pInpStateBase);\n if (NULL == pInpState) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: unexpected pointer type\";\n THROW_RUNTIME_ERR(err);\n }\n }\n vector vec;\n ReadVec(s, label, vec);\n if (pInpState != NULL) {\n if (pInpState->dim_ == 0) pInpState->dim_ = vec.size();\n else if (vec.size() != pInpState->dim_) {\n stringstream lineStr;\n if (pInpStateBase != NULL) lineStr << \" line:\" << pInpState->line_num_ << \" \";\n PREPARE_RUNTIME_ERR(err) << \"The # of vector elements (\" << vec.size() << \")\" << lineStr.str() << \n \" doesn't match the # of elements in previous lines. (\" << pInpState->dim_ << \" )\";\n THROW_RUNTIME_ERR(err);\n }\n }\n return unique_ptr(CreateObjFromVect(id, label, vec));\n}\n\ntemplate \nbool VectorSpace::ApproxEqual(const Object& obj1, const Object& obj2) const {\n const dist_t* p1 = reinterpret_cast(obj1.data());\n const dist_t* p2 = reinterpret_cast(obj2.data());\n const size_t len1 = GetElemQty(&obj1);\n const size_t len2 = GetElemQty(&obj2);\n if (len1 != len2) {\n PREPARE_RUNTIME_ERR(err) << \"Bug: comparing vectors of different lengths: \" << len1 << \" and \" << len2;\n THROW_RUNTIME_ERR(err);\n }\n for (size_t i = 0; i < len1; ++i) \n // We have to specify the namespace here, otherwise a compiler\n // thinks that it should use the equally named member function\n if (!similarity::ApproxEqual(p1[i], p2[i])) return false;\n return true;\n}\n\ntemplate \nstring VectorSpace::CreateStrFromObj(const Object* pObj, const string& externId /* ignored */) const {\n stringstream out;\n const dist_t* p = reinterpret_cast(pObj->data());\n const size_t length = GetElemQty(pObj);\n for (size_t i = 0; i < length; ++i) {\n if (i) out << \" \";\n // Clear all previous flags & set to the maximum precision available\n out.unsetf(ios_base::floatfield);\n out << setprecision(numeric_limits::max_digits10) << noshowpoint << p[i];\n }\n\n return out.str();\n}\n\ntemplate \nbool VectorSpace::ReadNextObjStr(DataFileInputState &inpStateBase, string& strObj, LabelType& label, string& externId) const {\n externId.clear();\n DataFileInputStateOneFile* pInpState = dynamic_cast(&inpStateBase);\n CHECK_MSG(pInpState != NULL, \"Bug: unexpected pointer type\");\n if (!pInpState->inp_file_) return false;\n if (!getline(pInpState->inp_file_, strObj)) return false;\n pInpState->line_num_++;\n return true;\n}\n\n/** End of standard functions to read/write/create objects */ \n\ntemplate \nvoid VectorSpace::ReadVec(string line, LabelType& label, vector& v)\n{\n v.clear();\n\n label = Object::extractLabel(line);\n\n ReplaceSomePunct(line); \n stringstream str(line);\n\n str.exceptions(ios::badbit);\n\n dist_t val;\n\n\n try {\n while (str >> val) {\n v.push_back(val);\n }\n } catch (const exception &e) {\n LOG(LIB_ERROR) << \"Exception: \" << e.what();\n LOG(LIB_FATAL) << \"Failed to parse the line: '\" << line << \"'\";\n }\n}\n\ntemplate \nObject* VectorSpace::CreateObjFromVect(IdType id, LabelType label, const vector& InpVect) const {\n return new Object(id, label, InpVect.size() * sizeof(dist_t), &InpVect[0]);\n};\n\n/* \n * Note that we don't instantiate vector spaces for types other than float & double\n * The only exception is the VectorSpace\n */\ntemplate class VectorSpace;\ntemplate class VectorSpace;\ntemplate class VectorSpace;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space/space_word_embed.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"object.h\"\n#include \"utils.h\"\n#include \"logging.h\"\n#include \"distcomp.h\"\n#include \"experimentconf.h\"\n#include \"space/space_word_embed.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\n/** Standard functions to read/write/create objects */ \n\ntemplate \nstring WordEmbedSpace::CreateStrFromObj(const Object* pObj, const string& externId) const {\n if (HasWhiteSpace(externId)) {\n throw runtime_error(\"The id '\" + externId + \"' has the white space (but it shouldn't)\");\n }\n\n string res = VectorSpace::CreateStrFromObj(pObj, \"\" /* ignored here */);\n if (!externId.empty()) res = externId + \" \" + res;\n return res;\n}\n\ntemplate \nbool WordEmbedSpace::ReadNextObjStr(DataFileInputState &inpStateBase, string& strObj, LabelType& label, string& externId) const {\n externId.clear();\n DataFileInputStateOneFile* pInpState = dynamic_cast(&inpStateBase);\n CHECK_MSG(pInpState != NULL, \"Bug: unexpected pointer type\");\n if (!pInpState->inp_file_) return false;\n if (!getline(pInpState->inp_file_, strObj)) return false;\n pInpState->line_num_++;\n ssize_t pos = -1;\n for (size_t i = 0; i < strObj.size(); ++i) \n if (isspace(strObj[i])) { pos = i ; break; }\n\n if (-1 == pos) {\n PREPARE_RUNTIME_ERR(err) << \"No white space in line #\" << pInpState->line_num_ << \" line: '\" << strObj << \"'\";\n THROW_RUNTIME_ERR(err);\n }\n externId = strObj.substr(0, pos);\n strObj = strObj.substr(pos + 1);\n\n return true;\n}\n\n/** End of standard functions to read/write/create objects */ \n\ntemplate \nstring WordEmbedSpace::StrDesc() const {\n string distType;\n if (distType_ == kEmbedDistL2) {\n distType=SPACE_WORD_EMBED_DIST_L2;\n } else if (distType_ == kEmbedDistCosine) {\n distType = SPACE_WORD_EMBED_DIST_COSINE;\n } else {\n PREPARE_RUNTIME_ERR(err) << \"Bug: Invalid distance type code: \" << distType_;\n THROW_RUNTIME_ERR(err);\n }\n return \"word embeddings, distance type: \" + distType;\n}\n\ntemplate \ndist_t WordEmbedSpace::HiddenDistance(const Object* obj1, const Object* obj2) const {\n CHECK(obj1->datalength() > 0);\n CHECK(obj1->datalength() == obj2->datalength());\n const dist_t* x = reinterpret_cast(obj1->data());\n const dist_t* y = reinterpret_cast(obj2->data());\n const size_t length = obj1->datalength() / sizeof(dist_t);\n\n if (distType_ == kEmbedDistL2) \n return L2NormSIMD(x, y, length);\n if (distType_ == kEmbedDistCosine) \n return CosineSimilarity(x, y, length);\n PREPARE_RUNTIME_ERR(err) << \"Bug: Invalid distance type code: \" << distType_;\n THROW_RUNTIME_ERR(err);\n}\n\n/* \n * Note that we don't instantiate vector spaces for types other than float & double\n */\ntemplate class WordEmbedSpace;\ntemplate class WordEmbedSpace;\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/src/space.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"space.h\"\n\nnamespace similarity {\n\nusing std::vector;\nusing std::string;\n\ntemplate \nunique_ptr Space::ReadDataset(ObjectVector& dataset,\n vector& vExternIds,\n const string& inputFile,\n const IdTypeUnsign MaxNumObjects) const {\n CHECK_MSG(MaxNumObjects >=0, \"Bug: MaxNumObjects should be >= 0\");\n unique_ptr inpState(OpenReadFileHeader(inputFile));\n string line;\n IdType label;\n string externId;\n for (size_t id = 0; id < MaxNumObjects || !MaxNumObjects; ++id) {\n if (!ReadNextObjStr(*inpState, line, label, externId)) break;\n dataset.push_back(CreateObjFromStr(id, label, line, inpState.get()).release());\n vExternIds.push_back(externId);\n }\n inpState->Close();\n return inpState;\n}\n\ntemplate \nvoid Space::WriteDataset(const ObjectVector& dataset,\n const vector& vExternIds,\n const string& outputFile,\n const IdTypeUnsign MaxNumObjects) const {\n CHECK_MSG(MaxNumObjects >=0, \"Bug: MaxNumObjects should be >= 0\");\n if (dataset.size() != vExternIds.size()) {\n PREPARE_RUNTIME_ERR(err) << \"Bug, dataset.size() != vExternIds.size(): \" << dataset.size() << \" != \" << vExternIds.size();\n THROW_RUNTIME_ERR(err);\n }\n unique_ptr outState(OpenWriteFileHeader(dataset, outputFile));\n for (size_t i = 0; i < MaxNumObjects && i < dataset.size(); ++i) {\n WriteNextObj(*dataset[i], vExternIds[i], *outState);\n }\n outState->Close();\n}\n\ntemplate class Space;\ntemplate class Space;\ntemplate class Space;\n\n\n}\n" }, { "path": "algorithms/NMSLIB/code/src/tune_vptree.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2015\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \"init.h\"\n#include \"global.h\"\n#include \"utils.h\"\n#include \"memory.h\"\n#include \"ztimer.h\"\n#include \"experiments.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n#include \"index.h\"\n#include \"tune.h\"\n#include \"method/vptree.h\"\n#include \"method/proj_vptree.h\"\n#include \"method/perm_bin_vptree.h\"\n#include \"logging.h\"\n#include \"spacefactory.h\"\n#include \"methodfactory.h\"\n#include \"params_def.h\"\n\n#include \"meta_analysis.h\"\n#include \"params.h\"\n\n//#define DETAILED_LOG_INFO 1\n\nusing namespace similarity;\n\nusing std::vector;\nusing std::map;\nusing std::make_pair;\nusing std::string;\nusing std::stringstream;\n\nnamespace po = boost::program_options;\n\nstatic void Usage(const char *prog,\n const po::options_description& desc) {\n std::cout << prog << std::endl\n << desc << std::endl;\n}\n\ntemplate \nvoid RunExper(unsigned AddRestartQty, \n const string& MethodName,\n const AnyParams& IndexParams,\n const AnyParams& QueryTimeParams,\n const string& SpaceType,\n const AnyParams& SpaceParams,\n unsigned TestSetQty,\n const string& DataFile,\n const string& QueryFile,\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQuery,\n vector knnAll,\n float eps,\n const string& RangeArg,\n const string& ResFile,\n unsigned MinExp,\n unsigned MaxExp,\n unsigned MaxIter,\n unsigned MaxRecDepth,\n unsigned StepN,\n float FullFactor,\n float maxCacheGSRelativeQty\n)\n{\n vector rangeAll;\n\n if (!RangeArg.empty()) {\n if (!SplitStr(RangeArg, rangeAll, ',')) {\n LOG(LIB_FATAL) << \"Wrong format of the range argument: '\" << RangeArg << \"' Should be a list of coma-separated values.\";\n }\n }\n\n vector vAllowedMeth = {METH_VPTREE, METH_PROJ_VPTREE, METH_PERM_BIN_VPTREE};\n string allowedMethList;\n\n for (string s: vAllowedMeth) allowedMethList += s + \" \";\n\n bool ok = false;\n for (string s: vAllowedMeth) {\n if (MethodName == s) {\n ok = true;\n break;\n }\n }\n\n if (!ok) {\n LOG(LIB_FATAL) << \"Wrong method name, \" << \n \"you should specify only a single method from the list: \" << allowedMethList;\n }\n\n LOG(LIB_INFO) << \"We are going to tune parameters for \" << MethodName;\n\n static std::random_device rd;\n static std::mt19937 engine(rd());\n static std::normal_distribution<> normGen(0.0f, log(FullFactor));\n\n AnyParamManager pmgr(IndexParams);\n\n float desiredRecall = 0;\n\n pmgr.GetParamRequired(DESIRED_RECALL_PARAM, desiredRecall);\n\n string metricName;\n\n pmgr.GetParamOptional(OPTIM_METRIC_PARAMETER, metricName, OPTIM_METRIC_DEFAULT);\n\n OptimMetric metric = getOptimMetric(metricName);\n\n if (IMPR_INVALID == metric) {\n stringstream err;\n \n err << \"Invalid metric name: \" << metricName;\n LOG(LIB_FATAL) << err.str();\n }\n\n try {\n\n if (!MaxExp) throw runtime_error(string(MIN_EXP_PARAM) + \" can't be zero!\");\n if (MaxExp < MinExp) throw runtime_error(string(MAX_EXP_PARAM) + \" can't be < \" + string(MIN_EXP_PARAM));\n\n if (rangeAll.size() + knnAll.size() != 1) {\n LOG(LIB_FATAL) << \"You need to specify exactly one range or one knn search!\";\n }\n\n unique_ptr> config;\n unique_ptr> space(SpaceFactoryRegistry::Instance().CreateSpace(SpaceType, SpaceParams));\n\n if (NULL == space.get()) {\n LOG(LIB_FATAL) << \"Cannot create space: '\" << SpaceType;\n }\n\n for (unsigned i = 0; i < rangeAll.size(); ++i) {\n vector range;\n vector knn;\n\n range.push_back(rangeAll[i]);\n \n config.reset(new ExperimentConfig(*space,\n DataFile, QueryFile, TestSetQty,\n MaxNumData, MaxNumQuery,\n knn, eps, range));\n }\n\n for (unsigned i = 0; i < knnAll.size(); ++i) {\n vector range;\n vector knn;\n\n knn.push_back(knnAll[i]);\n\n config.reset(new ExperimentConfig(*space,\n DataFile, QueryFile, TestSetQty,\n MaxNumData, MaxNumQuery,\n knn, eps, range));\n }\n\n CHECK(config.get());\n\n config->ReadDataset();\n\n float recall = 0, time_best = 0, impr_best = -1, alpha_left = 0, alpha_right = 0; \n unsigned exp_left = 0, exp_right = 0;\n\n for (unsigned ce = MinExp; ce <= MaxExp; ++ce)\n for (unsigned k = 0; k < 1 + AddRestartQty; ++k) {\n unsigned expLeft = ce, expRight = ce;\n float recall_loc, time_best_loc, impr_best_loc, \n alpha_left_loc = 1.0, alpha_right_loc = 1.0; // These are initial values\n\n if (k > 0) {\n // Let's do some random normal fun\n alpha_left_loc = exp(normGen(engine));\n alpha_right_loc = exp(normGen(engine));\n LOG(LIB_INFO) << \" RANDOM STARTING POINTS: \" << alpha_left_loc << \" \" << alpha_right_loc;\n } \n\n GetOptimalAlphas(true,\n *config, \n metric, desiredRecall,\n SpaceType, \n MethodName, \n pmgr.ExtractParametersExcept({DESIRED_RECALL_PARAM, OPTIM_METRIC_PARAMETER}), \n QueryTimeParams,\n recall_loc, \n time_best_loc, impr_best_loc,\n alpha_left_loc, expLeft, alpha_right_loc, expRight,\n MaxIter, MaxRecDepth, StepN, FullFactor, maxCacheGSRelativeQty);\n\n if (impr_best_loc > impr_best) {\n recall = recall_loc; \n time_best = time_best_loc; \n impr_best = impr_best_loc;\n alpha_left = alpha_left_loc; \n alpha_right = alpha_right_loc;\n exp_left = expLeft;\n exp_right = expRight;\n }\n }\n\n stringstream bestParams;\n bestParams << ALPHA_LEFT_PARAM << \"=\" << alpha_left << \",\" << ALPHA_RIGHT_PARAM << \"=\" << alpha_right << \",\"\n << EXP_LEFT_PARAM << \"=\" << exp_left << \",\" << EXP_RIGHT_PARAM << \"=\" << exp_right;\n\n LOG(LIB_INFO) << \"Optimization results\";\n if (!knnAll.empty()) {\n LOG(LIB_INFO) << \"K: \" << knnAll[0];\n } else {\n LOG(LIB_INFO) << \"Range: \" << rangeAll[0];\n }\n LOG(LIB_INFO) << \"Recall: \" << recall;\n LOG(LIB_INFO) << \"Best time: \" << time_best;\n LOG(LIB_INFO) << \"Best impr. \" << impr_best << \" (\" << getOptimMetricName(metric) << \")\" << endl; \n LOG(LIB_INFO) << \"alpha_left: \" << alpha_left;\n LOG(LIB_INFO) << \"exp_left: \" << exp_left;\n LOG(LIB_INFO) << \"alpha_right: \" << alpha_right;\n LOG(LIB_INFO) << \"exp_right: \" << exp_right;\n LOG(LIB_INFO) << \"optimal parameters: \" << bestParams.str();\n\n if (recall < desiredRecall) {\n LOG(LIB_FATAL) << \"Failed to get the desired recall!\";\n }\n\n if (!ResFile.empty()) {\n ofstream out(ResFile, ios::trunc);\n\n if (!out) {\n LOG(LIB_FATAL) << \"Can't open file: '\" << ResFile << \"' for writing\";\n }\n\n out << bestParams.str() << endl;\n out.close();\n }\n\n } catch (const std::exception& e) {\n LOG(LIB_FATAL) << \"Exception: \" << e.what();\n } catch (...) {\n LOG(LIB_FATAL) << \"Unknown exception\";\n }\n}\n\nvoid ParseCommandLineForTuning(int argc, char*argv[],\n string& LogFile,\n string& DistType,\n string& SpaceType,\n shared_ptr& SpaceParams,\n string& ResFile,\n unsigned& TestSetQty,\n string& DataFile,\n string& QueryFile,\n float& MaxCacheGSRelativeQty,\n unsigned& MaxNumData,\n unsigned& MaxNumQuery,\n vector& knn,\n float& eps,\n string& RangeArg,\n unsigned& MinExp, \n unsigned& MaxExp, \n unsigned& MaxIter, \n unsigned& MaxRecDepth,\n unsigned& StepN, \n float& FullFactor,\n unsigned& addRestartQty,\n string& MethodName,\n shared_ptr& IndexParams,\n shared_ptr& QueryTimeParams) {\n knn.clear();\n RangeArg.clear();\n\n string methParams;\n string knnArg;\n // Using double due to an Intel's bug with __builtin_signbit being undefined for float\n double epsTmp;\n double fullFactorTmp;\n\n string indexTimeParamStr;\n string queryTimeParamStr;\n\n po::options_description ProgOptDesc(\"Allowed options\");\n ProgOptDesc.add_options()\n (HELP_PARAM_OPT, HELP_PARAM_MSG)\n (SPACE_TYPE_PARAM_OPT, po::value(&SpaceType)->required(), SPACE_TYPE_PARAM_MSG)\n (DIST_TYPE_PARAM_OPT, po::value(&DistType)->default_value(DIST_TYPE_FLOAT), DIST_TYPE_PARAM_MSG)\n (DATA_FILE_PARAM_OPT, po::value(&DataFile)->required(), DATA_FILE_PARAM_MSG)\n (MAX_NUM_DATA_PARAM_OPT, po::value(&MaxNumData)->default_value(MAX_NUM_DATA_PARAM_DEFAULT), MAX_NUM_DATA_PARAM_MSG)\n (QUERY_FILE_PARAM_OPT, po::value(&QueryFile)->default_value(QUERY_FILE_PARAM_DEFAULT), QUERY_FILE_PARAM_MSG)\n (MAX_CACHE_GS_QTY_PARAM_OPT, po::value(&MaxCacheGSRelativeQty)->default_value(MAX_CACHE_GS_QTY_PARAM_DEFAULT), MAX_CACHE_GS_QTY_PARAM_MSG)\n (LOG_FILE_PARAM_OPT, po::value(&LogFile)->default_value(LOG_FILE_PARAM_DEFAULT), LOG_FILE_PARAM_MSG)\n (MAX_NUM_QUERY_PARAM_OPT, po::value(&MaxNumQuery)->default_value(MAX_NUM_QUERY_PARAM_DEFAULT), MAX_NUM_QUERY_PARAM_MSG)\n (TEST_SET_QTY_PARAM_OPT, po::value(&TestSetQty)->default_value(TEST_SET_QTY_PARAM_DEFAULT), TEST_SET_QTY_PARAM_MSG)\n (KNN_PARAM_OPT, po::value< string>(&knnArg), KNN_PARAM_MSG)\n (RANGE_PARAM_OPT, po::value(&RangeArg), RANGE_PARAM_MSG)\n (EPS_PARAM_OPT, po::value(&epsTmp)->default_value(EPS_PARAM_DEFAULT), EPS_PARAM_MSG)\n (METHOD_PARAM_OPT, po::value(&MethodName)->required(), METHOD_PARAM_MSG)\n (\"outFile,o\", po::value(&ResFile)->default_value(\"\"), \"output file\")\n\n (QUERY_TIME_PARAMS_PARAM_OPT, po::value(&queryTimeParamStr)->default_value(\"\"), QUERY_TIME_PARAMS_PARAM_MSG)\n (INDEX_TIME_PARAMS_PARAM_OPT, po::value(&indexTimeParamStr)->default_value(\"\"), INDEX_TIME_PARAMS_PARAM_MSG)\n\n (MIN_EXP_PARAM, po::value(&MinExp)->default_value(MIN_EXP_DEFAULT),\n \"the minimum exponent in the pruning oracle.\")\n (MAX_EXP_PARAM, po::value(&MaxExp)->default_value(MAX_EXP_DEFAULT),\n \"the maximum exponent in the pruning oracle.\")\n (MAX_ITER_PARAM, po::value(&MaxIter)->default_value(MAX_ITER_DEFAULT),\n \"the maximum number of iteration while we are looking for a point where a desired recall can be achieved.\")\n (MAX_REC_DEPTH_PARAM, po::value(&MaxRecDepth)->default_value(MAX_REC_DEPTH_DEFAULT),\n \"the maximum recursion in the maximization algorithm (each recursion leads to decrease in the grid search step).\")\n (STEP_N_PARAM, po::value(&StepN)->default_value(STEP_N_DEFAULT),\n \"each local step of the grid search involves (2StepN+1)^2 mini-iterations.\")\n (ADD_RESTART_QTY_PARAM, po::value(&addRestartQty)->default_value(ADD_RESTART_QTY_DEFAULT),\n \"number of *ADDITIONAL* restarts, initial values are selected randomly\")\n (FULL_FACTOR_PARAM, po::value(&fullFactorTmp)->default_value(FULL_FACTOR_DEFAULT),\n \"the maximum factor used in the local grid search (i.e., if (A, B) is a starting point for the grid search, the first element will be in the range: [A/Factor,A*Factor], while the second element will be in the range [B/Factor,B*Factor]. In the beginning, Factor==FullFactor, but it gradually decreases as the algorithm converges.\")\n ;\n\n po::variables_map vm;\n try {\n po::store(po::parse_command_line(argc, argv, ProgOptDesc), vm);\n po::notify(vm);\n } catch (const exception& e) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << e.what();\n }\n\n eps = epsTmp;\n FullFactor = fullFactorTmp;\n\n if (vm.count(\"help\") ) {\n Usage(argv[0], ProgOptDesc);\n exit(0);\n }\n\n ToLower(DistType);\n ToLower(SpaceType);\n \n try {\n {\n vector SpaceDesc;\n string str = SpaceType;\n ParseSpaceArg(str, SpaceType, SpaceDesc);\n SpaceParams = shared_ptr(new AnyParams(SpaceDesc));\n }\n\n if (vm.count(\"knn\")) {\n if (!SplitStr(knnArg, knn, ',')) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << \"Wrong format of the KNN argument: '\" << knnArg;\n }\n }\n\n if (DataFile.empty()) {\n LOG(LIB_FATAL) << \"data file is not specified!\";\n }\n\n if (!DoesFileExist(DataFile)) {\n LOG(LIB_FATAL) << \"data file \" << DataFile << \" doesn't exist\";\n }\n\n if (!QueryFile.empty() && !DoesFileExist(QueryFile)) {\n LOG(LIB_FATAL) << \"query file \" << QueryFile << \" doesn't exist\";\n }\n\n if (!MaxNumQuery && QueryFile.empty()) {\n LOG(LIB_FATAL) << \"Set a positive # of queries or specify a query file!\"; \n }\n\n CHECK_MSG(MaxNumData < MAX_DATASET_QTY, \"The maximum number of points should not exceed\" + ConvertToString(MAX_DATASET_QTY));\n CHECK_MSG(MaxNumQuery < MAX_DATASET_QTY, \"The maximum number of queries should not exceed\" + ConvertToString(MAX_DATASET_QTY));\n\n {\n vector desc;\n ParseArg(indexTimeParamStr, desc);\n IndexParams = shared_ptr(new AnyParams(desc));\n }\n\n {\n vector desc;\n ParseArg(queryTimeParamStr, desc);\n QueryTimeParams = shared_ptr(new AnyParams(desc));\n }\n\n } catch (const exception& e) {\n LOG(LIB_FATAL) << \"Exception: \" << e.what();\n }\n}\n\nint main(int ac, char* av[]) {\n WallClockTimer timer;\n timer.reset();\n\n string LogFile;\n string DistType;\n string SpaceType;\n shared_ptr SpaceParams;\n string ResFile;\n unsigned TestSetQty;\n string DataFile;\n string QueryFile;\n float MaxCacheGSRelativeQty;\n IdTypeUnsign MaxNumData;\n IdTypeUnsign MaxNumQuery;\n vector knn;\n string RangeArg;\n float eps;\n string MethodName;\n shared_ptr IndexParams;\n shared_ptr QueryTimeParams;\n\n unsigned MinExp;\n unsigned MaxExp;\n unsigned MaxIter;\n unsigned MaxRecDepth;\n unsigned StepN;\n float FullFactor;\n unsigned AddRestartQty;\n\n ParseCommandLineForTuning(ac, av, \n LogFile,\n DistType,\n SpaceType,\n SpaceParams,\n ResFile,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxCacheGSRelativeQty,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg,\n MinExp,\n MaxExp,\n MaxIter,\n MaxRecDepth,\n StepN,\n FullFactor,\n AddRestartQty,\n MethodName,\n IndexParams,\n QueryTimeParams);\n\n initLibrary(LogFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, LogFile.c_str());\n\n ToLower(DistType);\n\n if (!SpaceParams) {\n LOG(LIB_FATAL) << \"Failed to initialized space parameters!\";\n }\n if (!IndexParams) {\n LOG(LIB_FATAL) << \"Failed to initialized index-time method parameters!\";\n }\n if (!QueryTimeParams) {\n LOG(LIB_FATAL) << \"Failed to initialized query-time method parameters!\";\n }\n\n if (DIST_TYPE_INT == DistType) {\n RunExper(AddRestartQty, \n MethodName, *IndexParams, *QueryTimeParams,\n SpaceType,\n *SpaceParams,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg,\n ResFile,\n MinExp,\n MaxExp,\n MaxIter,\n MaxRecDepth,\n StepN,\n FullFactor,\n MaxCacheGSRelativeQty\n );\n } else if (DIST_TYPE_FLOAT == DistType) {\n RunExper(AddRestartQty, \n MethodName, *IndexParams, *QueryTimeParams,\n SpaceType,\n *SpaceParams,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg,\n ResFile,\n MinExp,\n MaxExp,\n MaxIter,\n MaxRecDepth,\n StepN,\n FullFactor,\n MaxCacheGSRelativeQty\n );\n } else if (DIST_TYPE_DOUBLE == DistType) {\n RunExper(AddRestartQty, \n MethodName, *IndexParams, *QueryTimeParams,\n SpaceType,\n *SpaceParams,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n knn,\n eps,\n RangeArg,\n ResFile,\n MinExp,\n MaxExp,\n MaxIter,\n MaxRecDepth,\n StepN,\n FullFactor,\n MaxCacheGSRelativeQty\n );\n } else {\n LOG(LIB_FATAL) << \"Unknown distance value type: \" << DistType;\n }\n\n timer.split();\n LOG(LIB_INFO) << \"Time elapsed = \" << timer.elapsed() / 1e6;\n LOG(LIB_INFO) << \"Finished at \" << LibGetCurrentTime();\n\n return 0;\n}\n" }, { "path": "algorithms/NMSLIB/code/src/tune_vptree.vcxproj", "content": "\n\n \n \n Debug\n x64\n \n \n Release\n x64\n \n \n RelWithDebInfo\n x64\n \n \n \n {509D6427-87B7-40B5-82AC-40503FD213D8}\n Win32Proj\n Win32\n tune_vptree\n \n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n \n \n \n \n \n \n \n <_ProjectFileVersion>10.0.20506.1\n tune_vptree\n .exe\n true\n true\n tune_vptree\n .exe\n false\n true\n tune_vptree\n .exe\n true\n true\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\tune_vptree\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\tune_vptree\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\tune_vptree\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\tune_vptree\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\tune_vptree\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\tune_vptree\\\n \n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)include;%(AdditionalIncludeDirectories)\n Debug/\n EnableFastChecks\n CompileAsCpp\n ProgramDatabase\n Sync\n Disabled\n Disabled\n NotUsing\n MultiThreadedDebugDLL\n true\n Level3\n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\"Debug\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n true\n \n \n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\\\"Debug\\\";%(PreprocessorDefinitions)\n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n c:/local/boost_1_59_0/lib64-msvc-14.0;$(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)include;%(AdditionalIncludeDirectories)\n Release/\n CompileAsCpp\n Sync\n AnySuitable\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n \n \n 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from listfiles\n# because they might be regenerated.\ncmake_check_build_system:\n\tcd /home/yzhang4/HD/release/algorithms/NMSLIB/code && $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0\n.PHONY : cmake_check_build_system\n\n" }, { "path": "algorithms/NMSLIB/code/test/bench_distfunc.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan, Leonid Boytsov.\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifdef _MSC_VER\n#define _SCL_SECURE_NO_WARNINGS\n#endif\n\n#include \n#include \n#include \n\n#include \"init.h\"\n#include \"space.h\"\n#include \"space/space_leven.h\"\n#include \"space/space_sparse_lp.h\"\n#include \"space/space_sparse_scalar.h\"\n#include \"space/space_sparse_vector_inter.h\"\n#include \"space/space_sparse_scalar_fast.h\"\n#include \"space/space_sparse_vector.h\"\n#include \"space/space_scalar.h\"\n#ifndef _MSC_VER\n#include \"space/space_sqfd.h\"\n#endif\n#include \"distcomp.h\"\n#include \"permutation_utils.h\"\n#include \"ztimer.h\"\n#include \"pow.h\"\n\n#include \"../test/testdataset.h\"\n\n#define TEST_SPEED_DOUBLE 1\n\n#define TEST_SPEED_ LP 1\n#define RANGE 8.0f\n#define RANGE_SMALL 1e-6f\n\nnamespace similarity {\n\nusing std::unique_ptr;\nusing std::vector;\n\n\ntemplate \ninline void Normalize(T* pVect, size_t qty) {\n T sum = 0;\n for (size_t i = 0; i < qty; ++i) {\n sum += pVect[i];\n }\n if (sum != 0) {\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] /= sum;\n }\n }\n}\n\n\ntemplate \ninline void GenRandVect(T* pVect, size_t qty, T MinElem = T(0), T MaxElem = T(1), bool DoNormalize = false) {\n T sum = 0;\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] = MinElem + (MaxElem - MinElem) * RandomReal();\n sum += fabs(pVect[i]);\n }\n if (DoNormalize && sum != 0) {\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] /= sum;\n }\n }\n}\n\ninline void GenRandIntVect(int* pVect, size_t qty) {\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] = RandomInt();\n }\n}\n\ntemplate \ninline void SetRandZeros(T* pVect, size_t qty, double pZero) {\n for (size_t j = 0; j < qty; ++j) if (RandomReal() < pZero) pVect[j] = T(0);\n}\n\nusing namespace std;\n\n\n/*\n\n#ifdef __INTEL_COMPILER\n\n// TODO: @Leo figure out how to use this function with Intel and cmake\n\n#include \"mkl.h\"\n\nTEST(set_intel) {\n vmlSetMode(VML_HA);\n LOG(LIB_INFO) << \"Set high-accuracy mode.\";\n}\n\n#endif\n\n*/\n\n\ntemplate \nvoid GenSparseVectZipf(size_t maxSize, vector>& res) {\n maxSize = max(maxSize, (size_t)1);\n\n for (size_t i = 1; i < maxSize; ++i) {\n float f = RandomReal();\n if (f <= sqrt((float)i)/i) { // This is a bit ad hoc, but is ok for testing purposes\n res.push_back(SparseVectElem(i, RandomReal()));\n }\n }\n};\n\n// Efficiency test functions\n\ntemplate \nvoid TestLInfNormStandard(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * LInfNormStandard(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of standard LInfs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestLInfNorm(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * LInfNorm(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of optim. LInfs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestLInfNormSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n T* p = pArr;\n\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * LInfNormSIMD(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SIMD LInfs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\n\ntemplate \nvoid TestL1NormStandard(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * L1NormStandard(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of standard L1s per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestL1Norm(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * L1Norm(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of optim. L1s per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestL1NormSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n T* p = pArr;\n\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * L1NormSIMD(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SIMD L1s per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestL2NormStandard(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * L2NormStandard(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of standard L2s per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestL2Norm(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * L2Norm(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of optim. L2s per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestL2NormSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n \n T fract = T(1)/N;\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * L2NormSIMD(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n \n uint64_t tDiff = t.split();\n \n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SIMD L2s per second: \" << (1e6/tDiff) * N * Rep ;\n \n delete [] pArr;\n \n}\n\n\ntemplate \nvoid TestLPGeneric(size_t N, size_t dim, size_t Rep, T power) {\n T* pArr = new T[N * dim];\n T* p = pArr;\n\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, T(-RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * LPGenericDistance(pArr + j*dim, pArr + (j-1)*dim, dim, power) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of Generic L\" << power << \" per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestLPGenericOptim(size_t N, size_t dim, size_t Rep, T power) {\n T* pArr = new T[N * dim];\n T* p = pArr;\n\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, T(-RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * LPGenericDistanceOptim(pArr + j*dim, pArr + (j-1)*dim, dim, power) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of Optimized generic L\" << power << \" per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestItakuraSaitoPrecomp(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0));\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * ItakuraSaitoPrecomp(pArr + j*dim*2, pArr + (j-1)*dim*2, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of precomp. ItakuraSaito per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestItakuraSaitoPrecompSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0));\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * ItakuraSaitoPrecompSIMD(pArr + j*dim*2, pArr + (j-1)*dim*2, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SIMD precomp. ItakuraSaito per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestItakuraSaitoStandard(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * ItakuraSaito(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of ItakuraSaito per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\n\ntemplate \nvoid TestKLPrecomp(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0), true /* norm. for regular KL */);\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * KLPrecomp(pArr + j*dim*2, pArr + (j-1)*dim*2, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of precomp. KLs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestKLPrecompSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0), true /* norm. for regular KL */);\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * KLPrecompSIMD(pArr + j*dim*2, pArr + (j-1)*dim*2, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SIMD precomp. KLs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestKLStandard(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0), true /* norm. for regular KL */);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * KLStandard(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of KLs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\n\ntemplate \nvoid TestKLGeneralPrecomp(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0));\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * KLGeneralPrecomp(pArr + j*dim*2, pArr + (j-1)*dim*2, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of precomp. general. KLs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestKLGeneralPrecompSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0));\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * KLGeneralPrecompSIMD(pArr + j*dim*2, pArr + (j-1)*dim*2, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SIMD precomp. general. KLs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestKLGeneralStandard(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, T(RANGE_SMALL), T(1.0));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * KLGeneralStandard(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of general. KLs per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestJSStandard(size_t N, size_t dim, size_t Rep, float pZero) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, T(0), T(1), true);\n SetRandZeros(p, dim, pZero);\n Normalize(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * JSStandard(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of JSs (sparsity:\" << pZero << \") per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestJSPrecomp(size_t N, size_t dim, size_t Rep, float pZero) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(0), T(1), true);\n SetRandZeros(p, dim, pZero);\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * JSPrecomp(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of JSs (precomp) (sparsity:\" << pZero << \") per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestJSPrecompApproxLog(size_t N, size_t dim, size_t Rep, float pZero) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(0), T(1), true);\n SetRandZeros(p, dim, pZero);\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * JSPrecompApproxLog(pArr + 2*j*dim, pArr + 2*(j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of JSs (precomp, one log approx) (sparsity:\" << pZero << \") per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestJSPrecompSIMDApproxLog(size_t N, size_t dim, size_t Rep, float pZero) {\n T* pArr = new T[N * dim * 2];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= 2 * dim) {\n GenRandVect(p, dim, T(0), T(1), true);\n SetRandZeros(p, dim, pZero);\n PrecompLogarithms(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * JSPrecompSIMDApproxLog(pArr + 2*j*dim, pArr + 2*(j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of JSs (precomp, one log approx, SIMD) (sparsity:\" << pZero << \") per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\nvoid TestSpearmanRho(size_t N, size_t dim, size_t Rep) {\n int* pArr = new int[N * dim];\n\n int *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandIntVect(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n float DiffSum = 0.0f;\n\n float fract = 1.0f/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * SpearmanRho(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of standard SpearmanRho per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\nvoid TestSpearmanRhoSIMD(size_t N, size_t dim, size_t Rep) {\n int* pArr = new int[N * dim];\n\n int *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandIntVect(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n float DiffSum = 0.0f;\n\n float fract = 1.0f/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * SpearmanRhoSIMD(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SpearmanRhoSIMD per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\nvoid TestSpearmanFootrule(size_t N, size_t dim, size_t Rep) {\n int* pArr = new int[N * dim];\n\n int *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandIntVect(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n float DiffSum = 0;\n\n float fract = 1.0f/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * SpearmanFootrule(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of standard SpearmanFootrule per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\nvoid TestSpearmanFootruleSIMD(size_t N, size_t dim, size_t Rep) {\n int* pArr = new int[N * dim];\n\n int *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandIntVect(p, dim);\n }\n\n WallClockTimer t;\n\n t.reset();\n\n float DiffSum = 0;\n\n float fract = 1.0f/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * SpearmanFootruleSIMD(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of SpearmanFootruleSIMD per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\n#if !defined(_MSC_VER)\n\ntemplate \nvoid TestSQFDGeneric(size_t N, size_t Rep, SqfdFunction& func) {\n // Space will try to delete the function\n unique_ptr> space(new SpaceSqfd(func.Clone()));\n ObjectVector elems;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(elems, tmp, (sampleDataPrefix + \"sqfd20_10k_10k.txt\"), N)); \n space->UpdateParamsFromFile(*inpState);\n\n N = min(N, elems.size());\n\n WallClockTimer t;\n\n t.reset();\n\n float DiffSum = 0;\n\n float fract = 1.0f/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * space->IndexTimeDistance(elems[j-1], elems[j]) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" Elapsed: \" << tDiff / 1e3 << \" ms \" << \n \" # of \" << space->StrDesc() << \" distances per second: \" << (1e6/tDiff) * N * Rep ;\n\n}\n\n\ntemplate \nvoid TestSQFDMinus(size_t N, size_t Rep) {\n SqfdMinusFunction func;\n TestSQFDGeneric(N, Rep, func);\n}\n\ntemplate \nvoid TestSQFDHeuristic(size_t N, size_t Rep) {\n SqfdHeuristicFunction func(T(1));\n TestSQFDGeneric(N, Rep, func);\n}\n\ntemplate \nvoid TestSQFDGaussian(size_t N, size_t Rep) {\n SqfdGaussianFunction func(T(1));\n TestSQFDGeneric(N, Rep, func);\n}\n\n#endif\n\nvoid TestLevenshtein(size_t N, size_t Rep) {\n unique_ptr space(new SpaceLevenshtein);\n ObjectVector elems;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(elems, tmp, (sampleDataPrefix + \"dna32_4_5K.txt\"), N)); \n space->UpdateParamsFromFile(*inpState);\n\n N = min(N, elems.size());\n\n WallClockTimer t;\n\n t.reset();\n\n float DiffSum = 0;\n\n float fract = 1.0f/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * space->IndexTimeDistance(elems[j-1], elems[j]) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << \" Elapsed: \" << tDiff / 1e3 << \" ms \" << \n \" # of unoptimized unweighted Levenshtein distances per second: \" << (1e6/tDiff) * N * Rep ;\n\n}\n\ntemplate \nvoid TestSparseLp(size_t N, size_t Rep, T power) {\n unique_ptr> space(new SpaceSparseLp(power));\n ObjectVector elems;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(elems, tmp, (sampleDataPrefix + \"sparse_5K.txt\"), N)); \n space->UpdateParamsFromFile(*inpState);\n\n N = min(N, elems.size());\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * space->IndexTimeDistance(elems[j-1], elems[j]) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \n \" # of sparse LP (p=\" << power << \") per second: \" << (1e6/tDiff) * N * Rep ;\n\n}\n\ntemplate \nvoid TestSparseAngularDistance(const string& dataFile, size_t N, size_t Rep) {\n unique_ptr> space(new SpaceSparseAngularDistance());\n ObjectVector elems;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(elems, tmp, dataFile, N)); \n space->UpdateParamsFromFile(*inpState);\n\n N = min(N, elems.size());\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * space->IndexTimeDistance(elems[j-1], elems[j]) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" File: \" << dataFile \n << \" Elapsed: \" << tDiff / 1e3 << \" ms \" << \n \" # of sparse angular dist per second: \" << (1e6/tDiff) * N * Rep ;\n\n}\n\nvoid TestSparseCosineSimilarityFast(const string& dataFile, size_t N, size_t Rep) {\n typedef float T;\n\n unique_ptr space(new SpaceSparseCosineSimilarityFast());\n ObjectVector elems;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(elems, tmp, dataFile, N)); \n space->UpdateParamsFromFile(*inpState);\n\n N = min(N, elems.size());\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * space->IndexTimeDistance(elems[j-1], elems[j]) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" File: \" << dataFile << \n \" Elapsed: \" << tDiff / 1e3 << \" ms \" << \n \" # of (fast) fast sparse cosine similarity dist second: \" << (1e6/tDiff) * N * Rep ;\n\n}\n\nvoid TestSparseAngularDistanceFast(const string& dataFile, size_t N, size_t Rep) {\n typedef float T;\n\n unique_ptr space(new SpaceSparseAngularDistanceFast());\n ObjectVector elems;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(elems, tmp, dataFile, N));\n space->UpdateParamsFromFile(*inpState);\n\n N = min(N, elems.size());\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1) / N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * space->IndexTimeDistance(elems[j - 1], elems[j]) / N;\n }\n /*\n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" File: \" << dataFile <<\n \" Elapsed: \" << tDiff / 1e3 << \" ms \" <<\n \" # of (fast) fast sparse angular dist second: \" << (1e6 / tDiff) * N * Rep;\n\n}\n\n\ntemplate \nvoid TestSparseCosineSimilarity(const string& dataFile, size_t N, size_t Rep) {\n unique_ptr> space(new SpaceSparseCosineSimilarity());\n ObjectVector elems;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(elems, tmp, dataFile, N)); \n space->UpdateParamsFromFile(*inpState);\n\n N = min(N, elems.size());\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * space->IndexTimeDistance(elems[j-1], elems[j]) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" File: \" << dataFile \n << \" Elapsed: \" << tDiff / 1e3 << \" ms \" << \n \" # of sparse cosine similarity dist second: \" << (1e6/tDiff) * N * Rep ;\n\n}\n\ntemplate \nvoid TestScalarProduct(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * ScalarProduct(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of ScalarProduct per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestScalarProductSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * ScalarProductSIMD(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of ScalarProduct SIMD per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestNormScalarProduct(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * NormScalarProduct(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of NormScalarProduct per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestNormScalarProductSIMD(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * NormScalarProductSIMD(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of NormScalarProduct SIMD per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestCosineSimilarity(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * CosineSimilarity(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of standard CosineSimilarity per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\ntemplate \nvoid TestAngularDistance(size_t N, size_t dim, size_t Rep) {\n T* pArr = new T[N * dim];\n\n T *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= dim) {\n GenRandVect(p, dim, -T(RANGE), T(RANGE));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n T DiffSum = 0;\n T fract = T(1)/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * AngularDistance(pArr + j*dim, pArr + (j-1)*dim, dim) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << typeid(T).name() << \" \" << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of standard AngularDistance per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\nvoid TestBitHamming(size_t N, size_t dim, size_t Rep) {\n size_t WordQty = (dim + 31)/32; \n uint32_t* pArr = new uint32_t[N * WordQty];\n\n uint32_t *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= WordQty) {\n vector perm(dim);\n GenRandIntVect(&perm[0], dim);\n for (unsigned j = 0; j < dim; ++j)\n perm[j] = perm[j] % 2;\n vector h;\n Binarize(perm, 1, h); \n CHECK(h.size() == WordQty);\n memcpy(p, &h[0], WordQty * sizeof(h[0]));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n float DiffSum = 0;\n\n float fract = 1.0f/N;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n DiffSum += 0.01f * BitHamming(pArr + j*WordQty, pArr + (j-1)*WordQty, WordQty) / N;\n }\n /* \n * Multiplying by 0.01 and dividing the sum by N is to prevent Intel from \"cheating\":\n *\n * http://searchivarius.org/blog/problem-previous-version-intels-library-benchmark\n */\n DiffSum *= fract;\n }\n\n uint64_t tDiff = t.split();\n\n LOG(LIB_INFO) << \"Ignore: \" << DiffSum;\n LOG(LIB_INFO) << \"Elapsed: \" << tDiff / 1e3 << \" ms \" << \" # of BitHamming per second: \" << (1e6/tDiff) * N * Rep ;\n\n delete [] pArr;\n\n}\n\n} // namespace similarity\n\nusing namespace similarity;\n\nint main(int argc, char* argv[]) {\n string LogFile;\n if (argc == 2) LogFile = argv[1];\n initLibrary(LogFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, LogFile.c_str());\n\n int nTest = 0;\n\n int dim = 128;\n\n#if !defined(_MSC_VER)\n nTest++;\n TestSQFDMinus(2000, 50);\n nTest++;\n TestSQFDHeuristic(2000, 50);\n nTest++;\n TestSQFDGaussian(2000, 50);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestSQFDMinus(2000, 50);\n nTest++;\n TestSQFDHeuristic(2000, 50);\n nTest++;\n TestSQFDGaussian(2000, 50);\n#endif\n#endif\n\n nTest++;\n TestLevenshtein(10000, 50);\n\n nTest++;\n TestBitHamming(1000, 32, 50000);\n nTest++;\n TestBitHamming(1000, 64, 25000);\n nTest++;\n TestBitHamming(1000, 128, 12000);\n nTest++;\n TestBitHamming(1000, 256, 6000);\n nTest++;\n TestBitHamming(1000, 512, 3000);\n nTest++;\n TestBitHamming(1000, 1024, 1500);\n\n float pZero1 = 0.5f;\n float pZero2 = 0.25f;\n float pZero3 = 0.0f;\n\n nTest++;\n TestScalarProduct(1000, dim, 1000);\n nTest++;\n TestScalarProductSIMD(1000, dim, 1000);\n nTest++;\n\n\n nTest++;\n TestNormScalarProduct(1000, dim, 1000);\n nTest++;\n TestNormScalarProductSIMD(1000, dim, 1000);\n nTest++;\n\n\n TestCosineSimilarity(1000, dim, 1000);\n nTest++;\n TestAngularDistance(1000, dim, 1000);\n\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestScalarProduct(1000, dim, 1000);\n nTest++;\n TestScalarProductSIMD(1000, dim, 1000);\n\n nTest++;\n TestNormScalarProduct(1000, dim, 1000);\n nTest++;\n TestNormScalarProductSIMD(1000, dim, 1000);\n nTest++;\n\n\n nTest++;\n TestCosineSimilarity(1000, dim, 1000);\n nTest++;\n TestAngularDistance(1000, dim, 1000);\n#endif\n\n nTest++;\n TestSparseCosineSimilarityFast(sampleDataPrefix + \"sparse_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseCosineSimilarityFast(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseAngularDistanceFast(sampleDataPrefix + \"sparse_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseAngularDistanceFast(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 1000);\n\n nTest++;\n TestSparseCosineSimilarity(sampleDataPrefix + \"sparse_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseCosineSimilarity(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseAngularDistance(sampleDataPrefix + \"sparse_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseAngularDistance(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 1000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestSparseCosineSimilarity(sampleDataPrefix + \"sparse_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseCosineSimilarity(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseAngularDistance(sampleDataPrefix + \"sparse_5K.txt\", 1000, 1000);\n nTest++;\n TestSparseAngularDistance(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 1000);\n#endif\n\n\n LOG(LIB_INFO) << \"Single-precision (sparse) LP-distance tests\";\n nTest++;\n TestSparseLp(1000, 1000, -1);\n nTest++;\n nTest++;\n TestSparseLp(1000, 1000, 1/3.0f);\n\n nTest++;\n TestSpearmanRho(1024, dim, 2000);\n\n nTest++;\n TestSpearmanRhoSIMD(1024, dim, 2000);\n\n nTest++;\n TestSpearmanFootrule(1024, dim, 2000);\n\n nTest++;\n TestSpearmanFootruleSIMD(1024, dim, 2000);\n\n nTest++;\n TestJSStandard(1024, dim, 1000, pZero1);\n nTest++;\n TestJSStandard(1024, dim, 1000, pZero2);\n nTest++;\n TestJSStandard(1024, dim, 1000, pZero3);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestJSStandard(1024, dim, 500, pZero1);\n nTest++;\n TestJSStandard(1024, dim, 500, pZero2);\n nTest++;\n TestJSStandard(1024, dim, 500, pZero3);\n#endif\n\n nTest++;\n TestJSPrecomp(1024, dim, 500, pZero1);\n nTest++;\n TestJSPrecomp(1024, dim, 500, pZero2);\n nTest++;\n TestJSPrecomp(1024, dim, 500, pZero3);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestJSPrecomp(1024, dim, 500, pZero1);\n nTest++;\n TestJSPrecomp(1024, dim, 500, pZero2);\n nTest++;\n TestJSPrecomp(1024, dim, 500, pZero3);\n#endif\n\n nTest++;\n TestJSPrecompApproxLog(1024, dim, 1000, pZero1);\n nTest++;\n TestJSPrecompApproxLog(1024, dim, 1000, pZero2);\n nTest++;\n TestJSPrecompApproxLog(1024, dim, 1000, pZero3);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestJSPrecompApproxLog(1024, dim, 1000, pZero1);\n nTest++;\n TestJSPrecompApproxLog(1024, dim, 1000, pZero2);\n nTest++;\n TestJSPrecompApproxLog(1024, dim, 1000, pZero3);\n#endif\n\n nTest++;\n TestJSPrecompSIMDApproxLog(1024, dim, 2000, pZero1);\n nTest++;\n TestJSPrecompSIMDApproxLog(1024, dim, 2000, pZero2);\n nTest++;\n TestJSPrecompSIMDApproxLog(1024, dim, 2000, pZero3);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestJSPrecompSIMDApproxLog(1024, dim, 2000, pZero1);\n nTest++;\n TestJSPrecompSIMDApproxLog(1024, dim, 2000, pZero2);\n nTest++;\n TestJSPrecompSIMDApproxLog(1024, dim, 2000, pZero3);\n#endif\n\n nTest++;\n TestL1Norm(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestL1Norm(1024, dim, 10000);\n#endif\n\n nTest++;\n TestL1NormStandard(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestL1NormStandard(1024, dim, 10000);\n#endif\n\n nTest++;\n TestL1NormSIMD(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestL1NormSIMD(1024, dim, 10000);\n#endif\n\n\n nTest++;\n TestLInfNorm(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestLInfNorm(1024, dim, 10000);\n#endif\n\n nTest++;\n TestLInfNormStandard(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestLInfNormStandard(1024, dim, 10000);\n#endif\n\n nTest++;\n TestLInfNormSIMD(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestLInfNormSIMD(1024, dim, 10000);\n#endif\n\n nTest++;\n TestItakuraSaitoStandard(1024, dim, 1000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestItakuraSaitoStandard(1024, dim, 1000);\n#endif\n\n\n nTest++;\n TestItakuraSaitoPrecomp(1024, dim, 2000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestItakuraSaitoPrecomp(1024, dim, 2000);\n#endif\n\n nTest++;\n TestItakuraSaitoPrecompSIMD(1024, dim, 4000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestItakuraSaitoPrecompSIMD(1024, dim, 4000);\n#endif\n\n nTest++;\n TestL2Norm(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestL2Norm(1024, dim, 10000);\n#endif\n\n nTest++;\n TestL2NormStandard(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestL2NormStandard(1024, dim, 10000);\n#endif\n\n nTest++;\n TestL2NormSIMD(1024, dim, 10000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestL2NormSIMD(1024, dim, 10000);\n#endif\n\n\n nTest++;\n TestKLStandard(1024, dim, 1000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestKLStandard(1024, dim, 1000);\n#endif\n\n\n nTest++;\n TestKLPrecomp(1024, dim, 2000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestKLPrecomp(1024, dim, 2000);\n#endif\n\n nTest++;\n TestKLPrecompSIMD(1024, dim, 4000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestKLPrecompSIMD(1024, dim, 4000);\n#endif\n\n nTest++;\n TestKLGeneralStandard(1024, dim, 1000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestKLGeneralStandard(1024, dim, 1000);\n#endif\n\n nTest++;\n TestKLGeneralPrecomp(1024, dim, 2000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestKLGeneralPrecomp(1024, dim, 2000);\n#endif\n\n nTest++;\n TestKLGeneralPrecompSIMD(1024, dim, 2000);\n#if TEST_SPEED_DOUBLE\n nTest++;\n TestKLGeneralPrecompSIMD(1024, dim, 2000);\n#endif\n\n#if TEST_SPEED_LP\n float delta = 0.125/2.0;\n\n for (float power = delta, step = delta; power <= 24; power += step) {\n nTest++;\n // This one should use an optimized LP function\n TestSparseLp(1000, 1000, power);\n if (power == 3) step = 0.125;\n if (power == 8) step = 0.5;\n }\n LOG(LIB_INFO) << \"========================================\";\n\n#if TEST_SPEED_DOUBLE\n LOG(LIB_INFO) << \"Double-precision (sparse) LP-distance tests\";\n TestSparseLp(1000, 1000, -1);\n nTest++;\n TestSparseLp(1000, 1000, 1/3.0);\n nTest++;\n for (double power = delta, step = delta; power <= 24; power += step) {\n nTest++;\n // This one should use an optimized LP function\n TestSparseLp(1000, 1000, power);\n if (power == 3) step = 0.125;\n if (power == 8) step = 0.5;\n }\n#endif\n LOG(LIB_INFO) << \"========================================\";\n\n LOG(LIB_INFO) << \"Single-precision LP-distance tests\";\n for (float power = delta, step = delta; power <= 24; power += step) {\n nTest++;\n TestLPGeneric(128, dim, 200, power);\n nTest++;\n TestLPGenericOptim(128, dim, 200, power);\n if (power == 3) step = 0.125;\n if (power == 8) step = 0.5;\n }\n LOG(LIB_INFO) << \"========================================\";\n#if TEST_SPEED_DOUBLE\n LOG(LIB_INFO) << \"Double-precision LP-distance tests\";\n for (double power = delta, step = delta; power <= 24; power += step) {\n nTest++;\n TestLPGeneric(128, dim, 200, power);\n nTest++;\n TestLPGenericOptim(128, dim, 200, power);\n if (power == 3) step = 0.125;\n if (power == 8) step = 0.5;\n }\n LOG(LIB_INFO) << \"========================================\";\n#endif\n#endif\n\n LOG(LIB_INFO) << \"Dimensionality of dense vectors: \" << dim; \n LOG(LIB_INFO) << \" \" << nTest << \" tests performed\";\n return 0;\n}\n\n" }, { "path": "algorithms/NMSLIB/code/test/bench_distfunc.vcxproj", "content": "\n\n \n \n Debug\n x64\n \n \n Release\n x64\n \n \n RelWithDebInfo\n x64\n \n \n \n {3244B2CA-B3D3-41BC-BD55-D3C937D7D00B}\n Win32Proj\n Win32\n bench_distfunc\n \n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n \n \n \n \n \n \n \n <_ProjectFileVersion>10.0.20506.1\n bench_distfunc\n .exe\n true\n true\n bench_distfunc\n .exe\n false\n true\n bench_distfunc\n .exe\n true\n true\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\bench_distfunc\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\bench_distfunc\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\bench_distfunc\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\bench_distfunc\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\bench_distfunc\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\bench_distfunc\\\n \n \n \n $(SolutionDir)include;%(AdditionalIncludeDirectories)\n Debug/\n EnableFastChecks\n CompileAsCpp\n ProgramDatabase\n Sync\n Disabled\n Disabled\n NotUsing\n MultiThreadedDebugDLL\n true\n Level3\n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\"Debug\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n true\n \n \n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\\\"Debug\\\";%(PreprocessorDefinitions)\n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n $(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n $(OutDir)$(TargetName)$(TargetExt).pgd\n \n \n false\n \n \n \n \n $(SolutionDir)include;%(AdditionalIncludeDirectories)\n Release/\n CompileAsCpp\n Sync\n AnySuitable\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"Release\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n Speed\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"Release\\\";%(PreprocessorDefinitions)\n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n $(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n false\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n RelWithDebInfo/\n CompileAsCpp\n ProgramDatabase\n Sync\n OnlyExplicitInline\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"RelWithDebInfo\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n Speed\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"RelWithDebInfo\\\";%(PreprocessorDefinitions)\n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n $(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n \n \n \n 08B5B5CC-2938-4C1C-B835-A3868076D791\n \n \n \n \n \n" }, { "path": "algorithms/NMSLIB/code/test/bench_projection.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#include \n#include \n\n#include \n\n#include \"knnquery.h\"\n#include \"knnqueue.h\"\n#include \"space.h\"\n#include \"params.h\"\n#include \"projection.h\"\n#include \"spacefactory.h\"\n#include \"init.h\"\n\nusing namespace similarity;\nusing namespace std;\n\nnamespace po = boost::program_options;\n\nstatic void Usage(const char *prog,\n const po::options_description& desc) {\n std::cout << prog << std::endl\n << desc << std::endl;\n}\n\ntemplate \nvoid benchProjection(size_t repeatQty,\n string spaceType,\n string inFile, string outFile,\n string projType, unsigned knn, string projSpaceType,\n unsigned nIntermDim,\n unsigned nDstDim,\n unsigned binThreshold,\n unsigned maxNumData,\n unsigned sampleRandPairQty, unsigned sampleKNNQueryQty, unsigned sampleKNNTotalQty) {\n ToLower(spaceType);\n vector spaceDesc;\n\n const string descStr = spaceType;\n ParseSpaceArg(descStr, spaceType, spaceDesc);\n unique_ptr spaceParams =\n unique_ptr(new AnyParams(spaceDesc));\n\n unique_ptr> space(SpaceFactoryRegistry::\n Instance().CreateSpace(spaceType, *spaceParams));\n\n if (NULL == space.get()) {\n LOG(LIB_FATAL) << \"Cannot create the space: '\" << spaceType\n << \"' (desc: '\" << descStr << \"')\";\n }\n\n ToLower(projSpaceType);\n vector projSpaceDesc;\n\n const string projDescStr = projSpaceType;\n ParseSpaceArg(projDescStr, projSpaceType, projSpaceDesc);\n unique_ptr projSpaceParams =\n unique_ptr(new AnyParams(projSpaceDesc));\n\n unique_ptr> projSpace(SpaceFactoryRegistry::\n Instance().CreateSpace(projSpaceType, *projSpaceParams));\n\n if (NULL == projSpace.get()) {\n LOG(LIB_FATAL) << \"Cannot create the projection space: '\" << projSpaceType\n << \"' (desc: '\" << projDescStr << \"')\";\n }\n\n const VectorSpaceSimpleStorage* ps =\n dynamic_cast*>(projSpace.get());\n\n if (NULL == ps) {\n LOG(LIB_FATAL) << \"The target projection space: '\" << projDescStr << \"' \"\n << \" should be a simple-storage dense vector space, e.g., l2\";\n }\n\n ObjectVector data;\n vector tmp;\n LOG(LIB_INFO) << \"maxNumData=\" << maxNumData;\n unique_ptr inpState(space->ReadDataset(data, tmp, inFile, maxNumData));\n space->UpdateParamsFromFile(*inpState);\n\n size_t N = data.size();\n\n\n unique_ptr > projObj;\n\n ofstream out(outFile);\n\n LOG(LIB_INFO) << \"sampleRandPairQty=\" << sampleRandPairQty;\n LOG(LIB_INFO) << \"sampleKNNQueryQty=\" << sampleKNNQueryQty;\n LOG(LIB_INFO) << \"sampleKNNTotalQty=\" << sampleKNNTotalQty;\n LOG(LIB_INFO) << \"recreating projections #times=\" << repeatQty;\n\n if (N > 0) \n for (size_t rr = 0; rr < repeatQty; ++rr) {\n LOG(LIB_INFO) << \"Creating projection object set \" << (rr + 1) << \" out of \" << repeatQty;\n projObj.reset(\n Projection::createProjection(\n *space,\n data,\n projType,\n nIntermDim,\n nDstDim,\n binThreshold));\n\n vector v1(nDstDim), v2(nDstDim);\n\n vector vId1, vId2;\n vector vOrigDist;\n\n for (size_t i = 0; i < sampleRandPairQty; ++i) {\n IdType id1 = RandomInt() % N;\n IdType id2 = RandomInt() % N;\n vId1.push_back(id1);\n vId2.push_back(id2);\n vOrigDist.push_back(space->IndexTimeDistance(data[id1], data[id2]));\n }\n\n CHECK(vId1.size() == vId2.size());\n CHECK(vId1.size() == vOrigDist.size());\n \n\n size_t iter = 0;\n size_t startId = vOrigDist.size();\n\n for (size_t i = 0; i < sampleKNNQueryQty; ++i) {\n IdType id1 = RandomInt() % N;\n\n KNNQuery query(*space, data[id1], knn);\n\n // Brute force search\n for (size_t i = 0; i < N; ++i) {\n query.CheckAndAddToResult(data[i]);\n }\n\n unique_ptr> knnQ(query.Result()->Clone());\n\n // Extracting results\n while (!knnQ->Empty()) {\n // Reservoir sampling\n int selectIndex = -1;\n ++iter;\n if (iter > sampleKNNTotalQty) {\n selectIndex = RandomInt() % iter; // from 0 to iter\n } else {\n vOrigDist.push_back(-1);\n vId1.push_back(-1);\n vId2.push_back(-1);\n selectIndex = vOrigDist.size() - 1 - startId;\n }\n\n if (selectIndex >= 0 && selectIndex < vOrigDist.size() - startId) {\n size_t replIndex = selectIndex + startId;\n vOrigDist[replIndex] = knnQ->TopDistance(); \n vId1[replIndex] = id1;\n vId2[replIndex] = knnQ->TopObject()->id();\n }\n\n knnQ->Pop(); \n }\n }\n\n for (size_t i = 0; i < vOrigDist.size(); ++i) {\n projObj->compProj(NULL, data[vId1[i]], &v1[0]);\n projObj->compProj(NULL, data[vId2[i]], &v2[0]);\n\n CHECK(vId1[i] >= 0);\n CHECK(vId2[i] >= 0);\n\n unique_ptr obj1(ps->CreateObjFromVect(-1, -1, v1));\n unique_ptr obj2(ps->CreateObjFromVect(-1, -1, v2));\n\n float projDist = ps->IndexTimeDistance(obj1.get(), obj2.get());\n\n out << vOrigDist[i] << \"\\t\" << projDist << endl;\n }\n }\n}\n\nint main(int argc, char *argv[]) {\n string spaceType, distType, projSpaceType;\n string inFile, outFile;\n string projType;\n string logFile;\n unsigned maxNumData = 0;\n unsigned sampleRandPairQty = 0;\n unsigned sampleKNNQueryQty = 0;\n unsigned sampleKNNTotalQty = 0;\n unsigned nIntermDim = 0;\n unsigned binThreshold = 0;\n unsigned nDstDim;\n unsigned knn = 0;\n unsigned repeatQty = 1;\n\n\n po::options_description ProgOptDesc(\"Allowed options\");\n ProgOptDesc.add_options()\n (\"help,h\", \"produce help message\")\n (\"spaceType,s\", po::value(&spaceType)->required(),\n \"space type, e.g., l1, l2, lp:p=0.5\")\n (\"projSpaceType\", po::value(&projSpaceType)->default_value(\"l2\"),\n \"space type in the projection space, e.g., l1, l2, lp:p=0.5. \"\n \"should be a dense vector space!\")\n (\"distType\", po::value(&distType)->default_value(DIST_TYPE_FLOAT),\n \"distance value type: float, double\")\n (\"inFile,i\", po::value(&inFile)->required(),\n \"input data file\")\n (\"outFile,o\", po::value(&outFile)->required(),\n \"output data file\")\n (\"projType,p\", po::value(&projType)->required(),\n \"projection type\")\n (\"sampleRandPairQty\",po::value(&sampleRandPairQty)->default_value(0),\n \"number of randomly selected pairs\")\n (\"sampleKNNQueryQty\",po::value(&sampleKNNQueryQty)->default_value(0),\n \"number of randomly selected queries\")\n (\"sampleKNNTotalQty\",po::value(&sampleKNNTotalQty)->default_value(0),\n \"a total number of randomly selected queries' nearest neighbors (should be >= sampleKNNQueryQty)\")\n (\"knn,k\", po::value(&knn)->default_value(0),\n \"use this number of nearest neighbors (should be > 0 if sampleKNNQueryQty > 0)\")\n (\"repeat,r\", po::value(&repeatQty)->default_value(10),\n \"recreate projections this number of times\")\n (\"intermDim\", po::value(&nIntermDim)->default_value(0),\n \"intermediate dimensionality, used only for sparse vector spaces\")\n (\"projDim\", po::value(&nDstDim)->required(),\n \"dimensionality in the target space (where we project to)\")\n (\"binThreshold\", po::value(&binThreshold)->default_value(0),\n \"binarization threshold, used only for permutations\")\n (\"maxNumData\", po::value(&maxNumData)->default_value(0),\n \"if non-zero, only the first maxNumData elements are used\")\n (\"logFile,l\", po::value(&logFile)->default_value(\"\"),\n \"log file\")\n ;\n\n po::variables_map vm;\n\n try {\n po::store(po::parse_command_line(argc, argv, ProgOptDesc), vm);\n po::notify(vm);\n } catch (const exception& e) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << e.what();\n }\n\n if (vm.count(\"help\") ) {\n Usage(argv[0], ProgOptDesc);\n exit(0);\n }\n\n initLibrary(logFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, logFile.c_str());\n\n LOG(LIB_INFO) << \"Program arguments are processed\";\n \n\n ToLower(distType);\n\n\n try {\n if (sampleKNNQueryQty) {\n if (!knn) LOG(LIB_FATAL) << \"Please, specify knn > 0\";\n if (sampleKNNTotalQty < sampleKNNQueryQty) {\n LOG(LIB_FATAL) << \"sampleKNNTotalQty should be at least as large as sampleKNNQueryQty\";\n }\n }\n if (distType == DIST_TYPE_FLOAT) {\n benchProjection(repeatQty, spaceType, inFile, outFile,\n projType, knn, projSpaceType,\n nIntermDim, nDstDim, binThreshold,\n maxNumData,\n sampleRandPairQty, sampleKNNQueryQty, sampleKNNTotalQty);\n } else if (distType == DIST_TYPE_DOUBLE) {\n benchProjection(repeatQty, spaceType, inFile, outFile,\n projType, knn, projSpaceType,\n nIntermDim, nDstDim, binThreshold,\n maxNumData,\n sampleRandPairQty, sampleKNNQueryQty, sampleKNNTotalQty);\n } else {\n LOG(LIB_FATAL) << \"Unsupported distance type: '\" << distType << \"'\";\n }\n } catch (const exception& e) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << \"Exception: \" << e.what();\n }\n\n \n\n return 0;\n};\n" }, { "path": "algorithms/NMSLIB/code/test/bunit.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \n\n#include \"bunit.h\"\n#include \"init.h\"\n\nnamespace similarity {\n\nTestRunner& TestRunner::Instance() {\n static TestRunner instance;\n return instance;\n}\n\nvoid TestRunner::AddTest(const std::string& test_name, TestBase* test_instance) {\n const size_t pos = test_name.find(kDisable);\n bool disabled = false;\n if (pos != std::string::npos) {\n if (pos == 0) {\n disabled = true;\n } else {\n std::cout << \"Incorrect test name \" << test_name << std::endl;\n exit(1);\n }\n }\n tests_.push_back(std::make_tuple(test_name, test_instance, disabled));\n}\n\nint TestRunner::RunAllTests() {\n int num_failed = 0;\n int num_disabled = 0;\n for (auto it = tests_.begin(); it != tests_.end(); ++it) {\n std::cout << \"----- \" << std::get<0>(*it) << \" -----\" << std::endl;\n if (std::get<2>(*it)) {\n ++num_disabled;\n std::cout << yellow << \"disabled\" << no_color << std::endl;\n } else {\n try {\n std::get<1>(*it)->Test();\n std::cout << green << \"passed\" << no_color << std::endl;\n } catch (const TestException& ex) {\n ++num_failed;\n std::cout << red << \"failed\" << no_color << std::endl;\n std::cout << ex.what() << std::endl;\n } catch (const std::exception& ex) {\n ++num_failed;\n std::cout << red << \"failed\" << no_color << std::endl;\n std::cout << ex.what() << std::endl;\n } catch (...) {\n std::cout << red << \"failed\" << no_color << std::endl;\n std::cout << \"unknown cause\" << std::endl;\n }\n }\n }\n\n std::cout << \"======================================\" << std::endl;\n std::cout << green << \"In total \" << tests_.size() << \" testcases\";\n if (num_disabled > 0) {\n std::cout << \" (\" << num_disabled << \" tests disabled)\";\n }\n std::cout << no_color << std::endl;\n\n if (num_failed == 0) {\n std::cout << green << \"ALL TESTS PASSED\" << no_color << std::endl;\n } else {\n std::cout << red << \"FAILED \" << num_failed\n << \" TESTS !!!\" << no_color << std::endl;\n }\n std::cout << \"======================================\" << std::endl;\n\n return num_failed;\n}\n\n} // namespace similarity\n\nint main(int argc, char *argv[]) {\n std::string LogFile;\n if (argc == 2) LogFile = argv[1];\n similarity::initLibrary(LogFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, LogFile.c_str());\n\n return similarity::TestRunner::Instance().RunAllTests();\n}\n" }, { "path": "algorithms/NMSLIB/code/test/bunit.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/).\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n\n#ifndef _BUNIT_H_\n#define _BUNIT_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n// Uncomment this to disable tests taking long time\n//#define DISABLE_LONG_TESTS\n\nnamespace similarity {\n\nusing TestFunc = std::function;\n\nclass TestException : public std::exception {\n public:\n TestException(const std::string& msg) : msg_(msg) {}\n const char* what() const \n#ifndef _MSC_VER\n// TODO: @leo/bileg Do we really need noexcept here, MSVS chokes on it\n\t noexcept \n#endif\n { return msg_.c_str(); }\n private:\n const std::string msg_;\n};\n\nconst std::string kDisable = \"DISABLE_\";\n\nclass TestBase {\npublic:\n virtual void Test() = 0;\n virtual ~TestBase(){}\n};\n\nclass TestRunner {\n public:\n ~TestRunner() {}\n\n static TestRunner& Instance();\n void AddTest(const std::string& test_name, TestBase* test_instance);\n int RunAllTests();\n\n private:\n TestRunner() {}\n // \n std::vector> tests_;\n};\n\n#define TEST(test_name) \\\n class _Test_##test_name##_ : TestBase { \\\n public: \\\n _Test_##test_name##_() { \\\n similarity::TestRunner::Instance().AddTest(#test_name, this); \\\n } \\\n virtual ~_Test_##test_name##_() {} \\\n private: \\\n virtual void Test(); \\\n }; \\\n similarity::_Test_##test_name##_ _test_##test_name##_; \\\n void _Test_##test_name##_::Test()\n\n\ntemplate \ninline bool EQ(const T& x, const T& y, T eps) {\n return x == y;\n}\n\ntemplate <>\ninline bool EQ(const float& x, const float& y, float eps) {\n // In C++ 11, std::abs is also defined for real-valued arguments\n return std::abs(x - y) <= eps;\n}\n\ntemplate <>\ninline bool EQ(const double& x, const double& y, double eps) {\n // In C++ 11, std::abs is also defined for real-valued arguments\n return std::abs(x - y) <= eps;\n}\n\ntemplate \nstatic inline void Expect_EQ(const std::string& msg,\n const T& expected,\n const T& actual,\n const std::string& file_name,\n int line_num,\n T eps = static_cast(1e-10)) {\n if (!EQ(expected, actual, eps)) {\n std::stringstream ss;\n ss << file_name << \"(\" << line_num << \"): \"\n << \"EXPECT_EQ(\" << msg << \") \" << std::endl\n << \" expected: \" << expected << std::endl\n << \" actual : \" << actual\n << std::endl;\n throw TestException(ss.str());\n }\n}\n\ntemplate \nstatic inline void Expect_NE(const std::string& msg,\n const T& expected,\n const T& actual,\n const std::string& file_name,\n int line_num,\n T eps = static_cast(1e-10)) {\n if (EQ(expected, actual, eps)) {\n std::stringstream ss;\n ss << file_name << \"(\" << line_num << \"): \"\n << \"EXPECT_NE(\" << msg << \") \" << std::endl\n << \" expected: \" << expected << std::endl\n << \" actual : \" << actual\n << std::endl;\n throw TestException(ss.str());\n }\n}\n\nstatic inline void Expect_True(const std::string& msg,\n const int condition,\n const std::string& file_name,\n int line_num) {\n if (!condition) {\n std::stringstream ss;\n ss << file_name << \"(\" << line_num << \"): \"\n << \"EXPECT_TRUE(\" << msg << \") \" << std::endl;\n throw TestException(ss.str());\n }\n}\n\nstatic inline void Expect_False(const std::string& msg,\n const int condition,\n const std::string& file_name,\n int line_num) {\n if (condition) {\n std::stringstream ss;\n ss << file_name << \"(\" << line_num << \"): \"\n << \" EXPECT_FALSE(\" << msg << \") \" << std::endl;\n throw TestException(ss.str());\n }\n}\n\n#define EXPECT_EQ(expected, actual) \\\n similarity::Expect_EQ(#expected \", \" #actual, (expected), (actual), __FILE__, __LINE__)\n\n#define EXPECT_EQ_EPS(expected, actual, eps) \\\n similarity::Expect_EQ(#expected \", \" #actual, (expected), (actual), __FILE__, __LINE__, eps)\n\n#define EXPECT_NE(expected, actual) \\\n similarity::Expect_NE(#expected \", \" #actual, (expected), (actual), __FILE__, __LINE__)\n\n#define EXPECT_NE_EPS(expected, actual, eps) \\\n similarity::Expect_NE(#expected \", \" #actual, (expected), (actual), __FILE__, __LINE__, eps)\n\n#define EXPECT_TRUE(condition) \\\n similarity::Expect_True(#condition, (condition), __FILE__, __LINE__)\n\n#define EXPECT_FALSE(condition) \\\n similarity::Expect_False(#condition, (condition), __FILE__, __LINE__)\n\n#ifdef __linux\nconst std::string green = \"\\x1b[32m\";\nconst std::string red = \"\\x1b[31m\";\nconst std::string yellow = \"\\x1b[33m\";\nconst std::string no_color = \"\\x1b[0m\";\n#else\nconst std::string green;\nconst std::string red;\nconst std::string yellow;\nconst std::string no_color;\n#endif\n\n} // namespace similarity\n\n#endif // _BUNIT_H_\n" }, { "path": "algorithms/NMSLIB/code/test/bunit.vcxproj", "content": "\n\n \n \n Debug\n x64\n \n \n Release\n x64\n \n \n RelWithDebInfo\n x64\n \n \n \n {111510A1-E568-49D8-8EFE-196F5053EA0C}\n Win32Proj\n Win32\n bunit\n \n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n \n \n \n \n \n \n \n <_ProjectFileVersion>10.0.20506.1\n bunit\n .exe\n true\n true\n bunit\n .exe\n false\n true\n bunit\n .exe\n true\n true\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\bunit\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\bunit\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\bunit\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\bunit\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\bunit\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\bunit\\\n \n \n \n $(SolutionDir)include;%(AdditionalIncludeDirectories)\n Debug/\n EnableFastChecks\n CompileAsCpp\n ProgramDatabase\n Sync\n Disabled\n Disabled\n NotUsing\n MultiThreadedDebugDLL\n true\n Level3\n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\"Debug\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n true\n \n \n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\\\"Debug\\\";%(PreprocessorDefinitions)\n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n $(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n $(OutDir)$(TargetName)$(TargetExt).pgd\n \n \n false\n \n \n \n \n $(SolutionDir)include;%(AdditionalIncludeDirectories)\n Release/\n CompileAsCpp\n Sync\n AnySuitable\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"Release\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n Speed\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"Release\\\";%(PreprocessorDefinitions)\n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n $(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n false\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n RelWithDebInfo/\n CompileAsCpp\n ProgramDatabase\n Sync\n OnlyExplicitInline\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"RelWithDebInfo\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n Speed\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"RelWithDebInfo\\\";%(PreprocessorDefinitions)\n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n $(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n $(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 08B5B5CC-2938-4C1C-B835-A3868076D791\n \n \n \n \n \n \n \n \n \n\n" }, { "path": "algorithms/NMSLIB/code/test/bunit.vcxproj.filters", "content": "\n\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n Source Files\n \n \n \n \n {3EF538C7-CC7F-4CC0-AA2E-461C0B950388}\n \n \n \n \n Source Files\n \n \n Source Files\n \n \n" }, { "path": "algorithms/NMSLIB/code/test/bunit.vcxproj.user", "content": "\n\n \n" }, { "path": "algorithms/NMSLIB/code/test/cmake_install.cmake", "content": "# Install script for directory: /home/yzhang4/HD/release/algorithms/NMSLIB/code/test\n\n# Set the install prefix\nif(NOT DEFINED CMAKE_INSTALL_PREFIX)\n set(CMAKE_INSTALL_PREFIX \"/usr/local\")\nendif()\nstring(REGEX REPLACE \"/$\" \"\" CMAKE_INSTALL_PREFIX \"${CMAKE_INSTALL_PREFIX}\")\n\n# Set the install configuration name.\nif(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)\n if(BUILD_TYPE)\n string(REGEX REPLACE \"^[^A-Za-z0-9_]+\" \"\"\n CMAKE_INSTALL_CONFIG_NAME \"${BUILD_TYPE}\")\n else()\n set(CMAKE_INSTALL_CONFIG_NAME \"Release\")\n endif()\n message(STATUS \"Install configuration: \\\"${CMAKE_INSTALL_CONFIG_NAME}\\\"\")\nendif()\n\n# Set the component getting installed.\nif(NOT CMAKE_INSTALL_COMPONENT)\n if(COMPONENT)\n message(STATUS \"Install component: \\\"${COMPONENT}\\\"\")\n set(CMAKE_INSTALL_COMPONENT \"${COMPONENT}\")\n else()\n set(CMAKE_INSTALL_COMPONENT)\n endif()\nendif()\n\n# Install shared libraries without execute permission?\nif(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)\n set(CMAKE_INSTALL_SO_NO_EXE \"0\")\nendif()\n\n" }, { "path": "algorithms/NMSLIB/code/test/genrand_vect.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef GENRAND_VECT_HPP\n#define GENRAND_VECT_HPP\n\n#include \"space/space_sparse_vector.h\"\n\nnamespace similarity {\n\ntemplate \ninline void Normalize(T* pVect, size_t qty) {\n T sum = 0;\n for (size_t i = 0; i < qty; ++i) {\n sum += pVect[i];\n }\n if (sum != 0) {\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] /= sum;\n }\n }\n}\n\n\ntemplate \ninline void GenRandVect(T* pVect, size_t qty, T MinElem = T(0), T MaxElem = T(1), bool DoNormalize = false) {\n T sum = 0;\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] = MinElem + (MaxElem - MinElem) * RandomReal();\n sum += fabs(pVect[i]);\n }\n if (DoNormalize && sum != 0) {\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] /= sum;\n }\n }\n}\n\ninline void GenRandIntVect(int* pVect, size_t qty) {\n for (size_t i = 0; i < qty; ++i) {\n pVect[i] = RandomInt();\n }\n}\n\ntemplate \ninline void SetRandZeros(T* pVect, size_t qty, double pZero) {\n for (size_t j = 0; j < qty; ++j) if (RandomReal() < pZero) pVect[j] = T(0);\n}\n\ntemplate \nvoid GenSparseVectZipf(size_t maxSize, vector>& res) {\n maxSize = max(maxSize, (size_t)1);\n\n for (size_t i = 1; i < maxSize; ++i) {\n float f = RandomReal();\n if (f <= sqrt((float)i)/i) { // This is a bit ad hoc, but is ok for testing purposes\n res.push_back(SparseVectElem(i, RandomReal()));\n }\n }\n};\n\n}\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/test/report_intr_dim.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n\n#include \n\n#include \"space.h\"\n#include \"params.h\"\n#include \"init.h\"\n#include \"report_intr_dim.h\"\n#include \"spacefactory.h\"\n\nusing namespace similarity;\nusing namespace std;\n\nnamespace po = boost::program_options;\n\nconst unsigned defaultSampleQty = 1000000;\n\nstatic void Usage(const char *prog,\n const po::options_description& desc) {\n std::cout << prog << std::endl\n << desc << std::endl;\n}\n\ntemplate \nvoid ComputeMuDeffect(const Space& space, \n const ObjectVector& dataset,\n double & dleft, double & dright,\n size_t SampleQty = 1000000) {\n dleft = dright = -1;\n for (size_t n = 0; n < SampleQty; ++n) {\n size_t r1 = RandomInt() % dataset.size();\n size_t r2 = RandomInt() % dataset.size();\n size_t r3 = RandomInt() % dataset.size();\n CHECK(r1 < dataset.size());\n CHECK(r2 < dataset.size());\n CHECK(r3 < dataset.size());\n const Object* q = dataset[r1];\n const Object* a = dataset[r2];\n const Object* b = dataset[r3];\n {\n dist_t d1 = space.IndexTimeDistance(q, a);\n dist_t d2 = space.IndexTimeDistance(q, b);\n dist_t d3 = space.IndexTimeDistance(a, b);\n if (ISNAN(d1) || ISNAN(d2) || ISNAN(d3)) {\n throw runtime_error(\"!!! Bug: a distance returned NAN!\");\n }\n if (d3 != 0) {\n dright = max(dright, (double)fabs(double(d1)-double(d2))/double(d3));\n }\n }\n {\n dist_t d1 = space.IndexTimeDistance(a, q);\n dist_t d2 = space.IndexTimeDistance(b, q);\n dist_t d3 = space.IndexTimeDistance(b, a);\n if (ISNAN(d1) || ISNAN(d2) || ISNAN(d3)) {\n throw runtime_error(\"!!! Bug: a distance returned NAN!\");\n }\n if (d3 != 0) {\n dleft = max(dleft, (double)fabs(double(d1)-double(d2))/double(d3));\n }\n }\n }\n}\n\ntemplate \nvoid TestSpace(\n string spaceDesc,\n string dataFile,\n bool compMuDeffect,\n unsigned maxNumData,\n unsigned sampleQty\n ) {\n string spaceType;\n vector vSpaceArgs;\n\n ParseSpaceArg(spaceDesc, spaceType, vSpaceArgs);\n AnyParams spaceParams({AnyParams(vSpaceArgs)});\n\n unique_ptr> space(SpaceFactoryRegistry::\n Instance().CreateSpace(spaceType, spaceParams));\n\n ObjectVector data;\n vector tmp;\n unique_ptr inpState(space->ReadDataset(data, tmp, dataFile, maxNumData));\n space->UpdateParamsFromFile(*inpState);\n\n // Prints the report\n ReportIntrinsicDimensionality(\"********\", *space, data, sampleQty);\n if (compMuDeffect) {\n double dleft, dright;\n ComputeMuDeffect(\n *space,\n data,\n dleft, dright,\n sampleQty\n );\n LOG(LIB_INFO) << \"### left mu-defect. : \" << dleft << \" right mu-defect. :\" << dright;\n }\n}\n\nint main(int argc, char* argv[]) {\n string spaceDesc, distType;\n string dataFile;\n unsigned maxNumData;\n unsigned sampleQty;\n bool compMuDeffect;\n\n po::options_description ProgOptDesc(\"Allowed options\");\n ProgOptDesc.add_options()\n (\"help,h\", \"produce help message\")\n (\"spaceType,s\", po::value(&spaceDesc)->required(),\n \"space type, e.g., l1, l2, lp:p=0.5\")\n (\"distType\", po::value(&distType)->default_value(DIST_TYPE_FLOAT),\n \"distance value type: int, float, double\")\n (\"dataFile,i\", po::value(&dataFile)->required(),\n \"input data file\")\n (\"maxNumData\", po::value(&maxNumData)->default_value(0),\n \"if non-zero, only the first maxNumData elements are used\")\n (\"sampleQty\", po::value(&sampleQty)->default_value(defaultSampleQty),\n \"a number of samples (a sample is a pair of data points)\")\n (\"muDeffect,m\", po::value(&compMuDeffect)->default_value(false),\n \"estimate the left and the right mu deffectiveness\")\n \n ;\n\n po::variables_map vm;\n try {\n po::store(po::parse_command_line(argc, argv, ProgOptDesc), vm);\n po::notify(vm);\n\n if (!DoesFileExist(dataFile)) {\n PREPARE_RUNTIME_ERR(err) << \"data file \" << dataFile << \" doesn't exist\";\n THROW_RUNTIME_ERR(err);\n }\n\n initLibrary(LIB_LOGSTDERR);\n\n if (DIST_TYPE_INT == distType) {\n TestSpace(\n spaceDesc,\n dataFile,\n compMuDeffect,\n maxNumData,\n sampleQty\n );\n } else if (DIST_TYPE_FLOAT == distType) {\n TestSpace(\n spaceDesc,\n dataFile,\n compMuDeffect,\n maxNumData,\n sampleQty\n );\n } else if (DIST_TYPE_DOUBLE == distType) {\n TestSpace(\n spaceDesc,\n dataFile,\n compMuDeffect,\n maxNumData,\n sampleQty\n );\n }\n\n } catch (const exception& e) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << e.what();\n }\n\n return 0;\n}\n" }, { "path": "algorithms/NMSLIB/code/test/test_clust.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2015\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"init.h\"\n#include \"global.h\"\n#include \"utils.h\"\n#include \"ztimer.h\"\n#include \"experiments.h\"\n#include \"tune.h\"\n#include \"method/vptree.h\"\n#include \"method/proj_vptree.h\"\n#include \"method/perm_bin_vptree.h\"\n#include \"logging.h\"\n#include \"spacefactory.h\"\n#include \"cluster_util.h\"\n#include \"params_def.h\"\n\n#include \"meta_analysis.h\"\n#include \"params.h\"\n\nusing namespace similarity;\n\n#define CLUST_TYPE_PARAM_OPT \"clustType,t\"\nconst string CLUST_TYPE_PARAM_MSG=\"A type of cluster: \" + CLUST_TYPE_CLARAN + \",\" + CLUST_TYPE_FIRMAL;\n#define CLUST_QTY_PARAM_OPT \"clustQty,c\"\n#define CLUST_QTY_PARAM_MSG \"A # of clusters\"\nconst IdTypeUnsign CLUST_QTY_PARAM_DEFAULT = 100;\n#define IN_CLUST_SWAP_ATT_PARAM_OPT \"swapAtt,W\"\n#define IN_CLUST_SWAP_ATT_PARAM_MSG \"The number of in claster swap attempts (in order to find a better center)\"\nconst IdTypeUnsign IN_CLUST_SWAP_ATT_PARAM_DEFAULT = CLARANS_SWAP_ATTEMPTS;\n#define IN_CLUST_SAMPLE_QTY_PARAM_OPT \"clustSampleQty,Q\"\n#define IN_CLUST_SAMPLE_QTY_PARAM_MSG \"The number of sampled points inside the cluster to compute a cluster configuration cost\"\nconst IdTypeUnsign IN_CLUST_SAMPLE_QTY_PARAM_DEFAULT = CLARANS_SAMPLE_QTY;\n#define RAND_REST_QTY_PARAM_OPT \"randRestartQty,R\"\n#define RAND_REST_QTY_PARAM_MSG \"The number of random restarts\"\nconst size_t RAND_REST_QTY_PARAM_DEFAULT = 5;\n#define SEARCH_CLOSE_ITER_QTY_PARAM_OPT \"searchCloseIterQty,I\"\n#define SEARCH_CLOSE_ITER_QTY_PARAM_MSG \"A number of search iterations to find a point that is close to already selected centers\"\nconst IdTypeUnsign SEARCH_CLOSE_ITER_QTY_PARAM_DEFAULT = 200;\n#define DIST_SAMPLE_QTY_PARAM_OPT \"distSampleQty,S\"\n#define DIST_SAMPLE_QTY_PARAM_MSG \"A number of samples to determine the distribution of distances\"\nconst IdTypeUnsign DIST_SAMPLE_QTY_PARAM_DEFAULT = SAMPLE_LIST_CLUST_DEFAULT_SAMPLE_QTY;\n#define MAX_META_ITER_QTY_PARAM_OPT \"maxMetaIterQty,M\"\n#define MAX_META_ITER_QTY_PARAM_MSG \"A maximum number of meta iterations\"\nconst IdTypeUnsign MAX_META_ITER_QTY_PARAM_DEFAULT = 10;\n#define KEEP_FRAC_QTY_PARAM_OPT \"keepFrac,F\"\n#define KEEP_FRAC_QTY_PARAM_MSG \"Percentage of assigned points kept after a meta-iteration is finished\"\nconst float KEEP_FRAC_QTY_PARAM_DEFAULT = 0.2;\n\nusing std::vector;\nusing std::map;\nusing std::make_pair;\nusing std::string;\nusing std::stringstream;\n\nnamespace po = boost::program_options;\n\nstatic void Usage(const char *prog,\n const po::options_description& desc) {\n std::cout << prog << std::endl\n << desc << std::endl;\n}\n\ntemplate \nvoid RunExper(\n bool PrintProgress,\n const string& DistType,\n const string& SpaceType,\n shared_ptr SpaceParams,\n const string& DataFile,\n IdTypeUnsign MaxNumData,\n const string& ClustType,\n IdTypeUnsign ClustQty,\n // reductive CLARANS parameters\n IdTypeUnsign maxMetaIterQty,\n float keepFrac,\n // CLARANS & reductive CLARANS parameters\n IdTypeUnsign inClusterSwapAttempts,\n IdTypeUnsign inClusterSampleQty, // Number of random points to estimate if the swap was beneficial\n size_t randRestQty,\n // FIRMAL parameters\n size_t searchCloseIterQty, // A # of search iterations to find a point that is close to already selected centers\n // For good performance it should be in the order of sqrt(data.size())\n size_t sampleDistQty // A # of sample to determine the distribution of distances)\n) {\n\n unique_ptr> space (SpaceFactoryRegistry::\n Instance().CreateSpace(SpaceType, *SpaceParams));\n\n ObjectVector data;\n vector tmp;\n\n unique_ptr inpState(space->ReadDataset(data, tmp, DataFile, MaxNumData));\n space->UpdateParamsFromFile(*inpState);\n\n LOG(LIB_INFO) << \"Read: \" << data.size() << \" entries.\";\n\n ObjectVector vCenters;\n ObjectVector vUnassigned;\n vector>> vClusterAssign;\n\n if (ClustType == CLUST_TYPE_CLARAN) {\n ClusterUtils::doCLARANS(PrintProgress, *space, data, ClustQty, vCenters, vClusterAssign,\n inClusterSwapAttempts, inClusterSampleQty, randRestQty);\n } else if (ClustType == CLUST_TYPE_REDUCT_CLARAN) {\n ClusterUtils::doReductiveCLARANS(PrintProgress, *space, data,\n maxMetaIterQty, keepFrac,\n ClustQty, vCenters, vClusterAssign, vUnassigned,\n inClusterSwapAttempts, inClusterSampleQty);\n } else if (ClustType == CLUST_TYPE_FIRMAL) {\n ClusterUtils::doFIRMAL(PrintProgress, *space, data, ClustQty, vCenters, vClusterAssign, vUnassigned,\n searchCloseIterQty, sampleDistQty);\n } else {\n throw runtime_error(\"Unsupported clustering type: \" + ClustType);\n }\n\n LOG(LIB_INFO) << \"The number of unassigned points: \" << vUnassigned.size();\n\n ClusterUtils::printAndVerifyClusterStat(*space, vCenters, vClusterAssign, 1000);\n}\n\nvoid ParseCommandLineForClustering(int argc, char*argv[],\n bool& PrintProgress,\n string& LogFile,\n string& DistType,\n string& SpaceType,\n shared_ptr& SpaceParams,\n string& DataFile,\n unsigned& MaxNumData,\n string& ClustType,\n IdTypeUnsign& ClustQty,\n// reductive CLARANS parameters\n IdTypeUnsign& maxMetaIterQty,\n float& keepFrac,\n// CLARANS parameters\n IdTypeUnsign& inClusterSwapAttempts,\n IdTypeUnsign& inClusterSampleQty, // Number of random points to estimate if the swap was beneficial\n IdTypeUnsign& RandRestQty,\n// FIRMAL parameters\n IdTypeUnsign& SearchCloseIterQty,// A # of search iterations to find a point that is close to already selected centers\n// For good performance it should be in the order of sqrt(data.size())\n IdTypeUnsign& SampleDistQty // A # of sample to determine the distribution of distances)\n\n) {\n bool NoPrintProgress;\n\n\n po::options_description ProgOptDesc(\"Allowed options\");\n ProgOptDesc.add_options()\n (HELP_PARAM_OPT, HELP_PARAM_MSG)\n (NO_PROGRESS_PARAM_OPT, po::bool_switch(&NoPrintProgress), NO_PROGRESS_PARAM_MSG)\n (SPACE_TYPE_PARAM_OPT, po::value(&SpaceType)->required(), SPACE_TYPE_PARAM_MSG)\n (DIST_TYPE_PARAM_OPT, po::value(&DistType)->default_value(DIST_TYPE_FLOAT), DIST_TYPE_PARAM_MSG)\n (DATA_FILE_PARAM_OPT, po::value(&DataFile)->required(), DATA_FILE_PARAM_MSG)\n (MAX_NUM_DATA_PARAM_OPT, po::value(&MaxNumData)->default_value(MAX_NUM_DATA_PARAM_DEFAULT), MAX_NUM_DATA_PARAM_MSG)\n (LOG_FILE_PARAM_OPT, po::value(&LogFile)->default_value(LOG_FILE_PARAM_DEFAULT), LOG_FILE_PARAM_MSG)\n (CLUST_TYPE_PARAM_OPT, po::value(&ClustType)->required(), CLUST_TYPE_PARAM_MSG.c_str())\n (CLUST_QTY_PARAM_OPT, po::value(&ClustQty)->default_value(CLUST_QTY_PARAM_DEFAULT), CLUST_QTY_PARAM_MSG)\n (IN_CLUST_SWAP_ATT_PARAM_OPT, po::value(&inClusterSwapAttempts)->default_value(IN_CLUST_SWAP_ATT_PARAM_DEFAULT), IN_CLUST_SWAP_ATT_PARAM_MSG)\n (IN_CLUST_SAMPLE_QTY_PARAM_OPT, po::value(&inClusterSampleQty)->default_value(IN_CLUST_SAMPLE_QTY_PARAM_DEFAULT), IN_CLUST_SAMPLE_QTY_PARAM_MSG)\n (RAND_REST_QTY_PARAM_OPT, po::value(&RandRestQty)->default_value(RAND_REST_QTY_PARAM_DEFAULT), RAND_REST_QTY_PARAM_MSG)\n (SEARCH_CLOSE_ITER_QTY_PARAM_OPT,po::value(&SearchCloseIterQty)->default_value(SEARCH_CLOSE_ITER_QTY_PARAM_DEFAULT), SEARCH_CLOSE_ITER_QTY_PARAM_MSG)\n (DIST_SAMPLE_QTY_PARAM_OPT, po::value(&SampleDistQty)->default_value(DIST_SAMPLE_QTY_PARAM_DEFAULT), DIST_SAMPLE_QTY_PARAM_MSG)\n (MAX_META_ITER_QTY_PARAM_OPT, po::value(&maxMetaIterQty)->default_value(MAX_META_ITER_QTY_PARAM_DEFAULT), MAX_META_ITER_QTY_PARAM_MSG)\n (KEEP_FRAC_QTY_PARAM_OPT, po::value(&keepFrac)->default_value(KEEP_FRAC_QTY_PARAM_DEFAULT), KEEP_FRAC_QTY_PARAM_MSG)\n ;\n\n PrintProgress = !NoPrintProgress;\n\n po::variables_map vm;\n try {\n po::store(po::parse_command_line(argc, argv, ProgOptDesc), vm);\n po::notify(vm);\n } catch (const exception& e) {\n Usage(argv[0], ProgOptDesc);\n LOG(LIB_FATAL) << e.what();\n }\n\n if (vm.count(\"help\") ) {\n Usage(argv[0], ProgOptDesc);\n exit(0);\n }\n\n ToLower(ClustType);\n ToLower(SpaceType);\n \n try {\n {\n vector SpaceDesc;\n string str = SpaceType;\n ParseSpaceArg(str, SpaceType, SpaceDesc);\n SpaceParams = shared_ptr(new AnyParams(SpaceDesc));\n }\n\n if (DataFile.empty()) {\n LOG(LIB_FATAL) << \"data file is not specified!\";\n }\n\n if (!DoesFileExist(DataFile)) {\n LOG(LIB_FATAL) << \"data file \" << DataFile << \" doesn't exist\";\n }\n CHECK_MSG(MaxNumData < MAX_DATASET_QTY, \"The maximum number of points should not exceed\" + ConvertToString(MAX_DATASET_QTY));\n } catch (const exception& e) {\n LOG(LIB_FATAL) << \"Exception: \" << e.what();\n }\n}\n\nint main(int argc, char* argv[]) {\n WallClockTimer timer;\n timer.reset();\n\n bool PrintProgress;\n string LogFile;\n string DistType;\n string SpaceType;\n shared_ptr SpaceParams;\n string ClustType;\n IdTypeUnsign ClustQty;\n string DataFile;\n IdTypeUnsign MaxNumData;\n\n// reductive CLARANS parameters\n IdTypeUnsign maxMetaIterQty;\n float keepFrac;\n // CLARANS parameters\n IdTypeUnsign inClusterSwapAttempts;\n IdTypeUnsign inClusterSampleQty; // Number of random points to estimate if the swap was beneficial\n IdTypeUnsign RandRestQty;\n // FIRMAL parameters\n IdTypeUnsign SearchCloseIterQty;// A # of search iterations to find a point that is close to already selected centers\n // For good performance it should be in the order of sqrt(data.size())\n IdTypeUnsign SampleDistQty; // A # of sample to determine the distribution of distances)\n\n ParseCommandLineForClustering(argc, argv,\n PrintProgress,\n LogFile,\n DistType,\n SpaceType,\n SpaceParams,\n DataFile,\n MaxNumData,\n ClustType,\n ClustQty,\n // Reductive CLARANS parameters\n maxMetaIterQty,\n keepFrac,\n // CLARANS parameters\n inClusterSwapAttempts,\n inClusterSampleQty,\n RandRestQty,\n // FIRMAL paramaters\n SearchCloseIterQty,\n SampleDistQty\n );\n\n initLibrary(LogFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, LogFile.c_str());\n\n ToLower(DistType);\n ToLower(ClustType);\n\n if (!SpaceParams) {\n LOG(LIB_FATAL) << \"Failed to initialized space parameters!\";\n }\n\n\n if (DIST_TYPE_INT == DistType) {\n RunExper(PrintProgress,\n DistType,\n SpaceType,\n SpaceParams,\n DataFile,\n MaxNumData,\n ClustType,\n ClustQty,\n maxMetaIterQty,\n keepFrac,\n inClusterSwapAttempts,\n inClusterSampleQty,\n RandRestQty,\n SearchCloseIterQty,\n SampleDistQty\n );\n } else if (DIST_TYPE_FLOAT == DistType) {\n RunExper(PrintProgress,\n DistType,\n SpaceType,\n SpaceParams,\n DataFile,\n MaxNumData,\n ClustType,\n ClustQty,\n maxMetaIterQty,\n keepFrac,\n inClusterSwapAttempts,\n inClusterSampleQty,\n RandRestQty,\n SearchCloseIterQty,\n SampleDistQty);\n } else if (DIST_TYPE_DOUBLE == DistType) {\n RunExper(PrintProgress,\n DistType,\n SpaceType,\n SpaceParams,\n DataFile,\n MaxNumData,\n ClustType,\n ClustQty,\n maxMetaIterQty,\n keepFrac,\n inClusterSwapAttempts,\n inClusterSampleQty,\n RandRestQty,\n SearchCloseIterQty,\n SampleDistQty);\n } else {\n LOG(LIB_FATAL) << \"Unknown distance value type: \" << DistType;\n }\n\n timer.split();\n LOG(LIB_INFO) << \"Time elapsed = \" << timer.elapsed() / 1e6;\n LOG(LIB_INFO) << \"Finished at \" << LibGetCurrentTime();\n\n return 0;\n}\n" }, { "path": "algorithms/NMSLIB/code/test/test_distfunc.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifdef _MSC_VER\n#define _SCL_SECURE_NO_WARNINGS\n#endif\n\n#include \n#include \n#include \n\n#include \"bunit.h\"\n#include \"space.h\"\n#include \"space/space_sparse_lp.h\"\n#include \"space/space_sparse_scalar.h\"\n#include \"space/space_sparse_vector_inter.h\"\n#include \"space/space_sparse_scalar_fast.h\"\n#include \"space/space_sparse_vector.h\"\n#include \"space/space_scalar.h\"\n#include \"testdataset.h\"\n#include \"distcomp.h\"\n#include \"genrand_vect.h\"\n#include \"permutation_utils.h\"\n#include \"ztimer.h\"\n#include \"pow.h\"\n\n#define RANGE 8.0f\n#define RANGE_SMALL 1e-6f\n\nnamespace similarity {\n\nusing namespace std;\n\n\n/*\n\n#ifdef __INTEL_COMPILER\n\n// TODO: @Leo figure out how to use this function with Intel and cmake\n\n#include \"mkl.h\"\n\nTEST(set_intel) {\n vmlSetMode(VML_HA);\n LOG(LIB_INFO) << \"Set high-accuracy mode.\";\n}\n\n#endif\n\n*/\n\n\nTEST(Platform64) {\n EXPECT_EQ(8 == sizeof(size_t), true);\n}\n\ntemplate \nbool checkElemVectEq(const vector>& source,\n const vector>& target) {\n if (source.size() != target.size()) return false;\n\n for (size_t i = 0; i < source.size(); ++i)\n if (source[i] != target[i]) return false;\n\n return true;\n}\n\ntemplate \nvoid TestSparsePackUnpack() {\n for (size_t maxSize = 1024 ; maxSize < 1024*1024; maxSize += 8192) {\n vector> source;\n GenSparseVectZipf(maxSize, source);\n\n LOG(LIB_INFO) << \"testing maxSize: \" << maxSize << \"\\nqty: \" << source.size()\n << \" maxId: \" << source.back().id_;\n\n char* pBuff = NULL; \n size_t dataLen = 0;\n\n PackSparseElements(source, pBuff, dataLen);\n \n vector> target;\n UnpackSparseElements(pBuff, dataLen, target);\n\n bool eqFlag = checkElemVectEq(source, target);\n\n if (!eqFlag) {\n LOG(LIB_INFO) << \"Different source and target, source.size(): \" << source.size()\n << \" target.size(): \" << target.size();\n // Let's print the first different in the case of equal # of elements\n size_t i = 0;\n for (; i < min(source.size(), target.size()); ++i) {\n if (!(source[i] == target[i])) {\n LOG(LIB_INFO) << \"First diff, i = \" << i << \" \" << source[i] << \" vs \" << target[i];\n break;\n }\n }\n }\n\n EXPECT_EQ(eqFlag, true);\n }\n}\n\nTEST(BlockZeros) {\n for (size_t id = 0 ; id <= 3*65536; id++) {\n size_t id1 = removeBlockZeros(id);\n \n size_t id2 = addBlockZeros(id1); \n EXPECT_EQ(id, id2);\n }\n}\n\n#ifdef DISABLE_LONG_TESTS\nTEST(DISABLE_SparsePackUnpack) {\n#else\nTEST(SparsePackUnpack) {\n#endif\n TestSparsePackUnpack();\n TestSparsePackUnpack();\n}\n\nTEST(TestEfficientPower) {\n double f = 2.0;\n\n for (unsigned i = 1; i <= 64; i++) {\n double p1 = std::pow(f, i);\n double p2 = EfficientPow(f, i);\n\n EXPECT_EQ(p1, p2);\n }\n}\n\nTEST(TestEfficientFract) {\n unsigned MaxNumDig = 16;\n\n for (float a = 1.1f ; a <= 2.0f; a+= 0.1f) {\n for (unsigned NumDig = 1; NumDig < MaxNumDig; ++NumDig) {\n uint64_t MaxFract = uint64_t(1) << NumDig;\n\n for (uint64_t intFract = 0; intFract < MaxFract; ++intFract) {\n float fract = float(intFract) / float(MaxFract);\n float v1 = pow(a, fract);\n float v2 = EfficientFractPow(a, fract, NumDig);\n\n EXPECT_EQ_EPS(v1, v2, 1e-5f);\n }\n }\n } \n}\n\ntemplate \nbool TestScalarProductAgree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n\n float maxRelDiff = 1e-6f;\n float maxAbsDiff = 1e-6f;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, T(1), T(2), true /* do normalize */);\n GenRandVect(pVect2, dim, T(1), T(2), true /* do normalize */);\n\n T val1 = ScalarProduct(pVect1, pVect2, dim);\n T val2 = ScalarProductSIMD(pVect1, pVect2, dim);\n\n bool bug = false;\n T diff = fabs(val1 - val2);\n T diffRel = diff/max(max(fabs(val1),fabs(val2)),T(1e-18));\n if (diffRel > maxRelDiff && diff > maxAbsDiff) {\n bug = true;\n cerr << \"Bug ScalarProduct !!! Dim = \" << dim << \" val1 = \" << val1 << \" val2 = \" << val2 << \" diff=\" << diff << \" diffRel=\" << diffRel << endl;\n }\n\n if (bug) return false;\n }\n }\n\n return true;\n}\n\ntemplate \nbool TestNormScalarProductAgree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n\n float maxRelDiff = 1e-6f;\n float maxAbsDiff = 1e-6f;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, T(1), T(2), true /* do normalize */);\n GenRandVect(pVect2, dim, T(1), T(2), true /* do normalize */);\n\n T val1 = NormScalarProduct(pVect1, pVect2, dim);\n T val2 = NormScalarProductSIMD(pVect1, pVect2, dim);\n\n bool bug = false;\n T diff = fabs(val1 - val2);\n T diffRel = diff/max(max(fabs(val1),fabs(val2)),T(1e-18));\n if (diffRel > maxRelDiff && diff > maxAbsDiff) {\n bug = true;\n cerr << \"Bug NormScalarProduct !!! Dim = \" << dim << \" val1 = \" << val1 << \" val2 = \" << val2 << \" diff=\" << diff << \" diffRel=\" << diffRel << endl;\n }\n\n if (bug) return false;\n }\n }\n\n return true;\n}\n\n// Agreement test functions\ntemplate \nbool TestLInfAgree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, -T(RANGE), T(RANGE));\n GenRandVect(pVect2, dim, -T(RANGE), T(RANGE));\n\n T val1 = LInfNormStandard(pVect1, pVect2, dim);\n T val2 = LInfNorm(pVect1, pVect2, dim);\n T val3 = LInfNormSIMD(pVect1, pVect2, dim);\n\n bool bug = false;\n\n if (fabs(val1 - val2)/max(max(val1,val2),T(1e-18)) > 1e-6) {\n cerr << \"Bug LInf !!! Dim = \" << dim << \" val1 = \" << val1 << \" val2 = \" << val2 << endl;\n bug = true;\n }\n if (fabs(val1 - val3)/max(max(val1,val2),T(1e-18)) > 1e-6) {\n cerr << \"Bug LInf !!! Dim = \" << dim << \" val1 = \" << val1 << \" val3 = \" << val3 << endl;\n bug = true;\n }\n if (bug) return false;\n }\n }\n\n\n return true;\n}\n\ntemplate \nbool TestL1Agree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, -T(RANGE), T(RANGE));\n GenRandVect(pVect2, dim, -T(RANGE), T(RANGE));\n\n T val1 = L1NormStandard(pVect1, pVect2, dim);\n T val2 = L1Norm(pVect1, pVect2, dim);\n T val3 = L1NormSIMD(pVect1, pVect2, dim);\n\n bool bug = false;\n\n if (fabs(val1 - val2)/max(max(val1,val2),T(1e-18)) > 1e-6) {\n cerr << \"Bug L1 !!! Dim = \" << dim << \" val1 = \" << val1 << \" val2 = \" << val2 << endl;\n bug = true;\n }\n if (fabs(val1 - val3)/max(max(val1,val2),T(1e-18)) > 1e-6) {\n cerr << \"Bug L1 !!! Dim = \" << dim << \" val1 = \" << val1 << \" val3 = \" << val3 << endl;\n bug = true;\n }\n if (bug) return false;\n }\n }\n\n return true;\n}\n\ntemplate \nbool TestL2Agree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, -T(RANGE), T(RANGE));\n GenRandVect(pVect2, dim, -T(RANGE), T(RANGE));\n\n T val1 = L2NormStandard(pVect1, pVect2, dim);\n T val2 = L2Norm(pVect1, pVect2, dim);\n T val3 = L2NormSIMD(pVect1, pVect2, dim);\n\n bool bug = false;\n\n if (fabs(val1 - val2)/max(max(val1,val2),T(1e-18)) > 1e-6) {\n cerr << \"Bug L2 !!! Dim = \" << dim << \" val1 = \" << val1 << \" val2 = \" << val2 << endl;\n bug = true;\n }\n if (fabs(val1 - val3)/max(max(val1,val2),T(1e-18)) > 1e-6) {\n cerr << \"Bug L2 !!! Dim = \" << dim << \" val1 = \" << val1 << \" val3 = \" << val3 << endl;\n bug = true;\n }\n if (bug) return false;\n }\n }\n\n\n return true;\n}\n\ntemplate \nbool TestItakuraSaitoAgree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n vector precompVect1(dim *2), precompVect2(dim * 2);\n T* pPrecompVect1 = &precompVect1[0];\n T* pPrecompVect2 = &precompVect2[0];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, T(RANGE_SMALL), T(1.0), true);\n GenRandVect(pVect2, dim, T(RANGE_SMALL), T(1.0), true);\n\n copy(pVect1, pVect1 + dim, pPrecompVect1); \n copy(pVect2, pVect2 + dim, pPrecompVect2); \n \n PrecompLogarithms(pPrecompVect1, dim);\n PrecompLogarithms(pPrecompVect2, dim);\n\n T val0 = ItakuraSaito(pVect1, pVect2, dim);\n T val1 = ItakuraSaitoPrecomp(pPrecompVect1, pPrecompVect2, dim);\n T val2 = ItakuraSaitoPrecompSIMD(pPrecompVect1, pPrecompVect2, dim);\n\n bool bug = false;\n\n T AbsDiff1 = fabs(val1 - val0);\n T RelDiff1 = AbsDiff1/max(max(fabs(val1),fabs(val0)),T(1e-18));\n\n if (RelDiff1 > 1e-5 && AbsDiff1 > 1e-5) {\n cerr << \"Bug ItakuraSaito !!! Dim = \" << dim << \" val1 = \" << val1 << \" val0 = \" << val0 << \" Diff: \" << (val1 - val0) << \" RelDiff1: \" << RelDiff1 << \" << AbsDiff1: \" << AbsDiff1 << endl;\n bug = true;\n }\n\n T AbsDiff2 = fabs(val1 - val2);\n T RelDiff2 = AbsDiff2/max(max(fabs(val1),fabs(val2)),T(1e-18));\n if (RelDiff2 > 1e-5 && AbsDiff2 > 1e-5) {\n cerr << \"Bug ItakuraSaito !!! Dim = \" << dim << \" val1 = \" << val1 << \" val2 = \" << val2 << \" Diff: \" << (val1 - val2) << \" RelDiff2: \" << RelDiff2 << \" AbsDiff2: \" << AbsDiff2 << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n }\n\n\n return true;\n}\n\ntemplate \nbool TestKLAgree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n vector precompVect1(dim *2), precompVect2(dim * 2);\n T* pPrecompVect1 = &precompVect1[0];\n T* pPrecompVect2 = &precompVect2[0];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, T(RANGE_SMALL), T(1.0), true);\n GenRandVect(pVect2, dim, T(RANGE_SMALL), T(1.0), true);\n\n copy(pVect1, pVect1 + dim, pPrecompVect1); \n copy(pVect2, pVect2 + dim, pPrecompVect2); \n \n PrecompLogarithms(pPrecompVect1, dim);\n PrecompLogarithms(pPrecompVect2, dim);\n\n T val0 = KLStandard(pVect1, pVect2, dim);\n T val1 = KLStandardLogDiff(pVect1, pVect2, dim);\n T val2 = KLPrecomp(pPrecompVect1, pPrecompVect2, dim);\n T val3 = KLPrecompSIMD(pPrecompVect1, pPrecompVect2, dim);\n\n bool bug = false;\n\n /* \n * KLStandardLog has a worse accuracy due to computing the log of ratios\n * as opposed to difference of logs, but it is more efficient (log can be\n * expensive to compute)\n */\n\n T AbsDiff1 = fabs(val1 - val0);\n T RelDiff1 = AbsDiff1/max(max(fabs(val1),fabs(val0)),T(1e-18));\n if (RelDiff1 > 1e-5 && AbsDiff1 > 1e-5) {\n cerr << \"Bug KL !!! Dim = \" << dim << \" val0 = \" << val0 << \" val1 = \" << val1 << \" Diff: \" << (val0 - val1) << \" RelDiff1: \" << RelDiff1 << \" AbsDiff1: \" << AbsDiff1 << endl;\n bug = true;\n }\n\n T AbsDiff2 = fabs(val1 - val2);\n T RelDiff2 = AbsDiff2/max(max(fabs(val1),fabs(val2)),T(1e-18));\n if (RelDiff2 > 1e-5 && AbsDiff2 > 1e-5) {\n cerr << \"Bug KL !!! Dim = \" << dim << \" val2 = \" << val2 << \" val1 = \" << val1 << \" Diff: \" << (val2 - val1) << \" RelDiff2: \" << RelDiff2 << \" AbsDiff2: \" << AbsDiff2 << endl;\n bug = true;\n }\n\n T AbsDiff3 = fabs(val1 - val3);\n T RelDiff3 = AbsDiff3/max(max(fabs(val1),fabs(val3)),T(1e-18));\n if (RelDiff3 > 1e-5 && AbsDiff3 > 1e-5) {\n cerr << \"Bug KL !!! Dim = \" << dim << \" val3 = \" << val3 << \" val1 = \" << val1 << \" Diff: \" << (val3 - val1) << \" RelDiff3: \" << RelDiff3 << \" AbsDiff3: \" << AbsDiff3 << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n }\n\n\n return true;\n}\n\ntemplate \nbool TestKLGeneralAgree(size_t N, size_t dim, size_t Rep) {\n T* pVect1 = new T[dim];\n T* pVect2 = new T[dim];\n T* pPrecompVect1 = new T[dim * 2];\n T* pPrecompVect2 = new T[dim * 2];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, T(RANGE_SMALL), T(1.0), false);\n GenRandVect(pVect2, dim, T(RANGE_SMALL), T(1.0), false);\n\n copy(pVect1, pVect1 + dim, pPrecompVect1); \n copy(pVect2, pVect2 + dim, pPrecompVect2); \n \n PrecompLogarithms(pPrecompVect1, dim);\n PrecompLogarithms(pPrecompVect2, dim);\n\n T val0 = KLGeneralStandard(pVect1, pVect2, dim);\n T val2 = KLGeneralPrecomp(pPrecompVect1, pPrecompVect2, dim);\n T val3 = KLGeneralPrecompSIMD(pPrecompVect1, pPrecompVect2, dim);\n\n bool bug = false;\n\n T AbsDiff1 = fabs(val2 - val0);\n T RelDiff1 = AbsDiff1/max(max(fabs(val2),fabs(val0)),T(1e-18));\n if (RelDiff1 > 1e-5 && AbsDiff1 > 1e-5) {\n cerr << \"Bug KL !!! Dim = \" << dim << \" val0 = \" << val0 << \" val2 = \" << val2 << \" Diff: \" << (val0 - val2) << \" RelDiff1: \" << RelDiff1 << \" AbsDiff1: \" << AbsDiff1 << endl;\n bug = true;\n }\n\n T AbsDiff2 = fabs(val3 - val2);\n T RelDiff2 = AbsDiff2/max(max(fabs(val3),fabs(val2)),T(1e-18));\n if (RelDiff2 > 1e-5 && AbsDiff2 > 1e-5) {\n cerr << \"Bug KL !!! Dim = \" << dim << \" val2 = \" << val2 << \" val3 = \" << val3 << \" Diff: \" << (val2 - val3) << \" RelDiff2: \" << RelDiff2 << \" AbsDiff2: \" << AbsDiff2 << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n }\n\n\n return true;\n}\n\ntemplate \nbool TestJSAgree(size_t N, size_t dim, size_t Rep, double pZero) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n vector precompVect1(dim *2), precompVect2(dim * 2);\n T* pPrecompVect1 = &precompVect1[0];\n T* pPrecompVect2 = &precompVect2[0];\n\n T Dist = 0;\n T Error = 0;\n T TotalQty = 0;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, T(RANGE_SMALL), T(1.0), true);\n SetRandZeros(pVect1, dim, pZero);\n Normalize(pVect1, dim);\n GenRandVect(pVect2, dim, T(RANGE_SMALL), T(1.0), true);\n SetRandZeros(pVect2, dim, pZero);\n Normalize(pVect2, dim);\n\n copy(pVect1, pVect1 + dim, pPrecompVect1); \n copy(pVect2, pVect2 + dim, pPrecompVect2); \n \n PrecompLogarithms(pPrecompVect1, dim);\n PrecompLogarithms(pPrecompVect2, dim);\n\n T val0 = JSStandard(pVect1, pVect2, dim);\n T val1 = JSPrecomp(pPrecompVect1, pPrecompVect2, dim);\n\n bool bug = false;\n\n T AbsDiff1 = fabs(val1 - val0);\n T RelDiff1 = AbsDiff1/max(max(fabs(val1),fabs(val0)),T(1e-18));\n\n if (RelDiff1 > 1e-5 && AbsDiff1 > 1e-5) {\n cerr << \"Bug JS (1) \" << typeid(T).name() << \" !!! Dim = \" << dim << \" val0 = \" << val0 << \" val1 = \" << val1 << \" Diff: \" << (val0 - val1) << \" RelDiff1: \" << RelDiff1 << \" AbsDiff1: \" << AbsDiff1 << endl;\n bug = true;\n }\n\n T val2 = JSPrecompApproxLog(pPrecompVect1, pPrecompVect2, dim);\n T val3 = JSPrecompSIMDApproxLog(pPrecompVect1, pPrecompVect2, dim);\n\n T AbsDiff2 = fabs(val2 - val3);\n T RelDiff2 = AbsDiff2/max(max(fabs(val2),fabs(val3)),T(1e-18));\n\n if (RelDiff2 > 1e-5 && AbsDiff2 > 1e-5) {\n cerr << \"Bug JS (2) \" << typeid(T).name() << \" !!! Dim = \" << dim << \" val2 = \" << val2 << \" val3 = \" << val3 << \" Diff: \" << (val2 - val3) << \" RelDiff2: \" << RelDiff2 << \" AbsDiff2: \" << AbsDiff2 << endl;\n bug = true;\n }\n\n T AbsDiff3 = fabs(val1 - val2);\n T RelDiff3 = AbsDiff3/max(max(fabs(val1),fabs(val2)),T(1e-18));\n\n Dist += val1;\n Error += AbsDiff3;\n ++TotalQty;\n\n if (RelDiff3 > 1e-4 && AbsDiff3 > 1e-4) {\n cerr << \"Bug JS (3) \" << typeid(T).name() << \" !!! Dim = \" << dim << \" val1 = \" << val1 << \" val2 = \" << val2 << \" Diff: \" << (val1 - val2) << \" RelDiff3: \" << RelDiff3 << \" AbsDiff2: \" << AbsDiff3 << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n }\n\n LOG(LIB_INFO) << typeid(T).name() << \" JS approximation error: average absolute: \" << Error / TotalQty << \n \" avg. dist: \" << Dist / TotalQty << \" average relative: \" << Error/Dist;\n\n\n return true;\n}\n\nbool TestSpearmanFootruleAgree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n PivotIdType* pVect1 = &vect1[0]; \n PivotIdType* pVect2 = &vect2[0];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandIntVect(pVect1, dim);\n GenRandIntVect(pVect2, dim);\n\n int val0 = SpearmanFootrule(pVect1, pVect2, dim);\n int val1 = SpearmanFootruleSIMD(pVect1, pVect2, dim);\n\n bool bug = false;\n\n\n if (val0 != val1) {\n cerr << \"Bug SpearmanFootrule !!! Dim = \" << dim << \" val0 = \" << val0 << \" val1 = \" << val1 << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n }\n\n\n return true;\n}\n\nbool TestSpearmanRhoAgree(size_t N, size_t dim, size_t Rep) {\n vector vect1(dim), vect2(dim);\n PivotIdType* pVect1 = &vect1[0]; \n PivotIdType* pVect2 = &vect2[0];\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandIntVect(pVect1, dim);\n GenRandIntVect(pVect2, dim);\n\n int val0 = SpearmanRho(pVect1, pVect2, dim);\n int val1 = SpearmanRhoSIMD(pVect1, pVect2, dim);\n\n bool bug = false;\n\n\n if (val0 != val1) {\n cerr << \"Bug SpearmanRho !!! Dim = \" << dim << \" val0 = \" << val0 << \" val1 = \" << val1 << \" Diff: \" << (val0 - val1) << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n }\n\n\n return true;\n}\n\ntemplate \nbool TestLPGenericAgree(size_t N, size_t dim, size_t Rep, T power) {\n vector vect1(dim), vect2(dim);\n T* pVect1 = &vect1[0]; \n T* pVect2 = &vect2[0];\n\n T TotalQty = 0, Error = 0, Dist = 0;\n\n for (size_t i = 0; i < Rep; ++i) {\n for (size_t j = 1; j < N; ++j) {\n GenRandVect(pVect1, dim, -T(RANGE), T(RANGE));\n GenRandVect(pVect2, dim, -T(RANGE), T(RANGE));\n\n T val0 = LPGenericDistance(pVect1, pVect2, dim, power);\n T val1 = LPGenericDistanceOptim(pVect1, pVect2, dim, power);\n\n bool bug = false;\n\n T AbsDiff1 = fabs(val1 - val0);\n T RelDiff1 = AbsDiff1/max(max(fabs(val1),fabs(val0)),T(1e-18));\n\n T maxRelDiff = 1e-5f;\n T maxAbsDiff = 1e-5f;\n /* \n * For large powers, the difference can be larger,\n * because our approximations are efficient, but not very\n * precise\n */\n if (power > 8) { maxAbsDiff = maxRelDiff = 1e-3f;}\n if (power > 12) { maxAbsDiff = maxRelDiff = 0.01f;}\n if (power > 22) { maxAbsDiff = maxRelDiff = 0.1f;}\n\n ++TotalQty;\n Error += RelDiff1;\n Dist += val0;\n\n if (RelDiff1 > maxRelDiff && AbsDiff1 > maxAbsDiff) {\n cerr << \"Bug LP\" << power << \" !!! Dim = \" << dim << \n \" val1 = \" << val1 << \" val0 = \" << val0 << \n \" Diff: \" << (val1 - val0) << \n \" RelDiff1: \" << RelDiff1 << \n \" (max for this power: \" << maxRelDiff << \") \" <<\n \" AbsDiff1: \" << AbsDiff1 << \" (max for this power: \" << maxAbsDiff << \")\" << endl;\n }\n\n if (bug) return false;\n }\n }\n\n if (power < 4) {\n LOG(LIB_INFO) << typeid(T).name() << \" LP approximation error: average absolute \" << Error / TotalQty << \" avg. dist: \" << Dist / TotalQty << \" average relative: \" << Error/Dist;\n\n }\n\n return true;\n}\n\nbool TestBitHammingAgree(size_t N, size_t dim, size_t Rep) {\n size_t WordQty = (dim + 31)/32; \n vector arr(N * WordQty);\n uint32_t* pArr = &arr[0];\n\n uint32_t *p = pArr;\n for (size_t i = 0; i < N; ++i, p+= WordQty) {\n vector perm(dim);\n GenRandIntVect(&perm[0], dim);\n for (unsigned j = 0; j < dim; ++j)\n perm[j] = perm[j] % 2;\n vector h;\n Binarize(perm, 1, h); \n CHECK(h.size() == WordQty);\n memcpy(p, &h[0], WordQty * sizeof(h[0]));\n }\n\n WallClockTimer t;\n\n t.reset();\n\n bool res = true;\n\n for (size_t j = 1; j < N; ++j) {\n uint32_t* pVect1 = pArr + j*WordQty;\n uint32_t* pVect2 = pArr + (j-1)*WordQty;\n int d1 = BitHamming(pVect1, pVect2, WordQty);\n int d2 = 0;\n\n for (unsigned t = 0; t < WordQty; ++t) {\n for (unsigned k = 0; k < 32; ++k) {\n d2 += ((pVect1[t]>>k)&1) != ((pVect2[t]>>k)&1);\n }\n }\n if (d1 != d2) {\n cerr << \"Bug bit hamming, WordQty = \" << WordQty << \" d1 = \" << d1 << \" d2 = \" << d2 << endl;\n res = false;\n break;\n }\n }\n\n return res;\n}\n\n\nbool TestSparseAngularDistanceAgree(const string& dataFile, size_t N, size_t Rep) {\n typedef float T;\n\n unique_ptr spaceFast(new SpaceSparseAngularDistanceFast());\n unique_ptr> spaceReg(new SpaceSparseAngularDistance());\n\n ObjectVector elemsFast;\n ObjectVector elemsReg;\n vector tmp;\n\n unique_ptr inpStateFast(spaceFast->ReadDataset(elemsFast, tmp, dataFile, N));\n spaceFast->UpdateParamsFromFile(*inpStateFast);\n unique_ptr inpStateReg(spaceReg->ReadDataset(elemsReg, tmp, dataFile, N));\n spaceReg->UpdateParamsFromFile(*inpStateReg);\n\n CHECK(elemsFast.size() == elemsReg.size());\n\n N = min(N, elemsReg.size());\n\n bool bug = false;\n\n float maxRelDiff = 2e-5f;\n float maxAbsDiff = 1e-6f;\n\n for (size_t j = Rep; j < N; ++j)\n for (size_t k = j - Rep; k < j; ++k) {\n float val1 = spaceFast->IndexTimeDistance(elemsFast[k], elemsFast[j]);\n float val2 = spaceReg->IndexTimeDistance(elemsReg[k], elemsReg[j]);\n\n float AbsDiff1 = fabs(val1 - val2);\n float RelDiff1 = AbsDiff1 / max(max(fabs(val1), fabs(val2)), T(1e-18));\n\n if (RelDiff1 > maxRelDiff && AbsDiff1 > maxAbsDiff) {\n cerr << \"Bug fast vs non-fast angular dist \" <<\n \" val1 = \" << val1 << \" val2 = \" << val2 <<\n \" Diff: \" << (val1 - val2) <<\n \" RelDiff1: \" << RelDiff1 <<\n \" AbsDiff1: \" << AbsDiff1 << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n\n return true;\n}\n\n\n\nbool TestSparseCosineSimilarityAgree(const string& dataFile, size_t N, size_t Rep) {\n typedef float T;\n\n unique_ptr spaceFast(new SpaceSparseCosineSimilarityFast());\n unique_ptr> spaceReg (new SpaceSparseCosineSimilarity());\n\n ObjectVector elemsFast;\n ObjectVector elemsReg;\n vector tmp;\n\n unique_ptr inpStateFast(spaceFast->ReadDataset(elemsFast, tmp, dataFile, N)); \n spaceFast->UpdateParamsFromFile(*inpStateFast);\n unique_ptr inpStateReg(spaceReg->ReadDataset(elemsReg, tmp, dataFile, N)); \n spaceReg->UpdateParamsFromFile(*inpStateReg);\n\n CHECK(elemsFast.size() == elemsReg.size());\n\n N = min(N, elemsReg.size());\n\n bool bug = false;\n\n float maxRelDiff = 1e-6f;\n float maxAbsDiff = 1e-6f;\n\n for (size_t j = Rep; j < N; ++j) \n for (size_t k = j - Rep; k < j; ++k) {\n float val1 = spaceFast->IndexTimeDistance(elemsFast[k], elemsFast[j]);\n float val2 = spaceReg->IndexTimeDistance(elemsReg[k], elemsReg[j]);\n\n float AbsDiff1 = fabs(val1 - val2);\n float RelDiff1 = AbsDiff1/max(max(fabs(val1),fabs(val2)),T(1e-18));\n\n if (RelDiff1 > maxRelDiff && AbsDiff1 > maxAbsDiff) {\n cerr << \"Bug fast vs non-fast cosine \" << \n \" val1 = \" << val1 << \" val2 = \" << val2 << \n \" Diff: \" << (val1 - val2) << \n \" RelDiff1: \" << RelDiff1 << \n \" AbsDiff1: \" << AbsDiff1 << endl;\n bug = true;\n }\n\n if (bug) return false;\n }\n\n return true;\n}\n\n#ifdef DISABLE_LONG_TESTS\nTEST(DISABLE_TestAgree) {\n#else\nTEST(TestAgree) {\n#endif\n int nTest = 0;\n int nFail = 0;\n\n nTest++;\n nFail += !TestSparseAngularDistanceAgree(sampleDataPrefix + \"sparse_5K.txt\", 1000, 200);\n\n nTest++;\n nFail += !TestSparseAngularDistanceAgree(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 200);\n\n nTest++;\n nFail += !TestSparseCosineSimilarityAgree(sampleDataPrefix + \"sparse_5K.txt\", 1000, 200);\n\n nTest++;\n nFail += !TestSparseCosineSimilarityAgree(sampleDataPrefix + \"sparse_wiki_5K.txt\", 1000, 200);\n \n /* \n * 32 should be more than enough for almost all methods,\n * where loop-unrolling includes at most 16 distance computations.\n *\n * Bit-Hamming is an exception.\n * \n */\n for (unsigned dim = 1; dim <= 1024; dim+=2) {\n LOG(LIB_INFO) << \"Dim = \" << dim;\n\n nFail += !TestBitHammingAgree(1000, dim, 1000);\n }\n\n for (unsigned dim = 1; dim <= 32; ++dim) {\n LOG(LIB_INFO) << \"Dim = \" << dim;\n\n /* \n * This is a costly check, we don't need to do it for large # dimensions.\n * Anyways, the function is not using any loop unrolling, so 8 should be sufficient.\n */\n if (dim <= 8) {\n\n for (float power = 0.125; power <= 32; power += 0.125) {\n TestLPGenericAgree(1024, dim, 10, power);\n }\n\n for (double power = 0.125; power <= 32; power += 0.125) {\n TestLPGenericAgree(1024, dim, 10, power);\n }\n }\n\n nTest++;\n nFail += !TestNormScalarProductAgree(1024, dim, 10);\n nTest++;\n nFail += !TestNormScalarProductAgree(1024, dim, 10);\n\n nTest++;\n nFail += !TestScalarProductAgree(1024, dim, 10);\n nTest++;\n nFail += !TestScalarProductAgree(1024, dim, 10);\n\n nTest++;\n nFail += !TestSpearmanFootruleAgree(1024, dim, 10);\n\n nTest++;\n nFail += !TestSpearmanRhoAgree(1024, dim, 10);\n\n nTest++;\n nFail += !TestJSAgree(1024, dim, 10, 0.5);\n nTest++;\n nFail += !TestJSAgree(1024, dim, 10, 0.5);\n\n nTest++;\n nFail += !TestKLGeneralAgree(1024, dim, 10);\n nTest++;\n nFail += !TestKLGeneralAgree(1024, dim, 10);\n\n nTest++;\n nFail += !TestLInfAgree(1024, dim, 10);\n nTest++;\n nFail += !TestLInfAgree(1024, dim, 10);\n\n nTest++;\n nFail += !TestL1Agree(1024, dim, 10);\n nTest++;\n nFail += !TestL1Agree(1024, dim, 10);\n\n nTest++;\n nFail += !TestL2Agree(1024, dim, 10);\n nTest++;\n nFail += !TestL2Agree(1024, dim, 10);\n\n nTest++;\n nFail += !TestKLAgree(1024, dim, 10);\n nTest++;\n nFail += !TestKLAgree(1024, dim, 10);\n\n nTest++;\n nFail += !TestItakuraSaitoAgree(1024, dim, 10);\n nTest++;\n nFail += !TestItakuraSaitoAgree(1024, dim, 10);\n }\n\n LOG(LIB_INFO) << nTest << \" (sub) tests performed \" << nFail << \" failed\";\n\n EXPECT_EQ(0, nFail);\n}\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/test/test_editdist.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n\n#include \"space/space_leven.h\"\n#include \"distcomp_edist.h\"\n#include \"bunit.h\"\n#include \"testdataset.h\"\n\nnamespace similarity {\n\nconst size_t NUM_TEST_SHORT_STR = 16;\n\nusing namespace std;\n\nconst char* pTestArr[NUM_TEST_SHORT_STR] = {\n \"xyz\", \"beagcfa\", \"cea\", \"cb\",\n \"d\", \"c\", \"bdaf\", \"ddcd\",\n \"egbfa\", \"a\", \"fba\", \"bcccfe\",\n \"ab\", \"bfgbfdc\", \"bcbbgf\", \"bfbb\"\n};\n\nclass ShortStringDataSet : public TestDataset {\n public:\n ShortStringDataSet(SpaceLevenshtein& space) {\n\n for (unsigned i = 0; i < NUM_TEST_SHORT_STR; ++i) {\n dataobjects_.push_back(space.CreateObjFromStr(i, -1, pTestArr[i], NULL).release());\n }\n }\n};\n\n\nTEST(EditDistanceShort) {\n unique_ptr space(new SpaceLevenshtein());\n\n ShortStringDataSet dataset(*space);\n const ObjectVector& dataobjects = dataset.GetDataObjects();\n\n const int expected[16][16] = {\n {0, 7, 3, 3, 3, 3, 4, 4, 5, 3, 3, 6, 3, 7, 6, 4},\n {7, 0, 5, 6, 7, 6, 4, 6, 3, 6, 6, 4, 6, 5, 5, 6},\n {3, 5, 0, 2, 3, 2, 3, 4, 4, 2, 2, 5, 3, 7, 5, 4},\n {3, 6, 2, 0, 2, 1, 4, 3, 4, 2, 2, 5, 1, 6, 4, 3},\n {3, 7, 3, 2, 0, 1, 3, 3, 5, 1, 3, 6, 2, 6, 6, 4},\n {3, 6, 2, 1, 1, 0, 4, 3, 5, 1, 3, 5, 2, 6, 5, 4},\n {4, 4, 3, 4, 3, 4, 0, 3, 4, 3, 3, 4, 3, 5, 4, 3},\n {4, 6, 4, 3, 3, 3, 3, 0, 5, 4, 4, 5, 4, 6, 6, 4},\n {5, 3, 4, 4, 5, 5, 4, 5, 0, 4, 3, 5, 4, 4, 5, 4},\n {3, 6, 2, 2, 1, 1, 3, 4, 4, 0, 2, 6, 1, 7, 6, 4},\n {3, 6, 2, 2, 3, 3, 3, 4, 3, 2, 0, 6, 2, 5, 5, 2},\n {6, 4, 5, 5, 6, 5, 4, 5, 5, 6, 6, 0, 6, 5, 4, 5},\n {3, 6, 3, 1, 2, 2, 3, 4, 4, 1, 2, 6, 0, 6, 5, 3},\n {7, 5, 7, 6, 6, 6, 5, 6, 4, 7, 5, 5, 6, 0, 5, 4},\n {6, 5, 5, 4, 6, 5, 4, 6, 5, 6, 5, 4, 5, 5, 0, 3},\n {4, 6, 4, 3, 4, 4, 3, 4, 4, 4, 2, 5, 3, 4, 3, 0} };\n\n for (size_t i = 0; i < NUM_TEST_SHORT_STR; ++i) {\n for (size_t j = 0; j < NUM_TEST_SHORT_STR; ++j) {\n const int d = space->IndexTimeDistance(dataobjects[i], dataobjects[j]);\n if (expected[i][j] != d) {\n LOG(LIB_ERROR) << \"Bug, expected: \" << expected[i][j] << \" got \" << d\n << \"Strings: '\" << pTestArr[i] << \"' vs '\" << pTestArr[j] << \"'\";\n }\n EXPECT_EQ(expected[i][j], d);\n }\n }\n}\n\n/*\n * This test is needed to merely verify the correctness\n * of the hybrid memory allocation strategy:\n * relatively short strings will use stack memory,\n * for longer ones we use malloc-based methods.\n */\n\nstring str1, str2;\n\nclass LongStringDataSet : public TestDataset {\n public:\n LongStringDataSet(SpaceLevenshtein& space) {\n\n str1 = string(MAX_LEVEN_BUFFER_QTY + 1, 'a');\n str2 = string(MAX_LEVEN_BUFFER_QTY, 'c') + str1 + string(MAX_LEVEN_BUFFER_QTY, 'b');\n\n dataobjects_.push_back(space.CreateObjFromStr(0, -1, str1, NULL).release());\n dataobjects_.push_back(space.CreateObjFromStr(0, -1, str2, NULL).release());\n }\n};\n\n\nTEST(EditDistanceLong) {\n unique_ptr space(new SpaceLevenshtein());\n\n LongStringDataSet dataset(*space);\n const ObjectVector& dataobjects = dataset.GetDataObjects();\n\n int d = space->IndexTimeDistance(dataobjects[0], dataobjects[1]);\n EXPECT_EQ(2 * MAX_LEVEN_BUFFER_QTY, d);\n d = space->IndexTimeDistance(dataobjects[1], dataobjects[0]);\n EXPECT_EQ(2 * MAX_LEVEN_BUFFER_QTY, d);\n\n}\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/test/test_eval.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"logging.h\"\n#include \"utils.h\"\n#include \"bunit.h\"\n\n#include \n#include \n\n#include \n\nusing namespace std;\n\nnamespace similarity {\n\ntemplate \nvoid testMetric(\n size_t ExactResultSize,\n vector> exactEntries,\n vector> approxEntries,\n const double expVal) {\n unordered_set exactIds;\n unordered_set approxIds;\n\n // Let's sort the entries\n std::sort(exactEntries.begin(), exactEntries.end());\n std::sort(approxEntries.begin(), approxEntries.end());\n\n {\n size_t i = 0;\n for (const auto& e:exactEntries) {\n ++i;\n if (i > ExactResultSize) break;\n exactIds.insert(e.mId);\n }\n }\n for (const auto& e:approxEntries) approxIds.insert(e.mId);\n\n double val = EvalObj()(ExactResultSize, exactEntries, exactIds, approxEntries, approxIds);\n\n EXPECT_EQ_EPS(expVal, val, 1e-4);\n};\n\ntypedef ResultEntry RESF;\ntypedef ResultEntry RESD;\ntypedef ResultEntry RESI;\n\nconst float EPSF = numeric_limits::min();\nconst double EPSD = numeric_limits::min();\n\nconst size_t KNN = 10;\n\nTEST(TestRecallDouble) {\n vector> exactEntries = {\n {},\n {},\n {RESD(0, 0, 100)},\n { RESD(0, 0, 1), RESD(1, 0, 2), RESD(3, 0, 3), RESD(4, 0, 4), RESD(5, 0, 5), RESD(6, 0, 6), RESD(7, 0, 7), RESD(8, 0, 8), RESD(9, 0, 9), RESD(10, 0, 10), },\n { RESD(0, 0, 1), RESD(1, 0, 2), RESD(3, 0, 3), RESD(4, 0, 4), RESD(5, 0, 5), RESD(6, 0, 6), RESD(7, 0, 7), RESD(8, 0, 8), RESD(9, 0, 9), RESD(10, 0, 10), },\n };\n vector> approxEntries = {\n {},\n {RESD(0, 0, 100)},\n {},\n { RESD(0, 0, 1), RESD(3, 0, 3), RESD(5, 0, 5), RESD(7, 0, 7), RESD(9, 0, 9), },\n { RESD(0, 0, 1), RESD(1, 0, 2), RESD(3, 0, 3), RESD(4, 0, 4), RESD(5, 0, 5), RESD(6, 0, 6), RESD(7, 0, 7), RESD(8, 0, 8), RESD(9, 0, 9), RESD(10, 0, 10), },\n };\n vector expRecall {\n 1,\n 1,\n 0.0,\n 0.5,\n 1.0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expRecall.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expRecall[i]); \n // In a special case when there are no results recall should be equal to 1\n testMetric>(0, exactEntries[i], approxEntries[i], 1.0);\n }\n}\n\n\nTEST(TestRecallFloat) {\n vector> exactEntries = {\n {},\n {},\n {RESF(0, 0, 100)},\n { RESF(0, 0, 1), RESF(1, 0, 2), RESF(3, 0, 3), RESF(4, 0, 4), RESF(5, 0, 5), RESF(6, 0, 6), RESF(7, 0, 7), RESF(8, 0, 8), RESF(9, 0, 9), RESF(10, 0, 10), },\n { RESF(0, 0, 1), RESF(1, 0, 2), RESF(3, 0, 3), RESF(4, 0, 4), RESF(5, 0, 5), RESF(6, 0, 6), RESF(7, 0, 7), RESF(8, 0, 8), RESF(9, 0, 9), RESF(10, 0, 10), },\n };\n vector> approxEntries = {\n {},\n {RESF(0, 0, 100)},\n {},\n { RESF(0, 0, 1), RESF(3, 0, 3), RESF(5, 0, 5), RESF(7, 0, 7), RESF(9, 0, 9), },\n { RESF(0, 0, 1), RESF(1, 0, 2), RESF(3, 0, 3), RESF(4, 0, 4), RESF(5, 0, 5), RESF(6, 0, 6), RESF(7, 0, 7), RESF(8, 0, 8), RESF(9, 0, 9), RESF(10, 0, 10), },\n };\n vector expRecall {\n 1,\n 1,\n 0.0,\n 0.5,\n 1.0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expRecall.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expRecall[i]); \n // In a special case when there are no results recall should be equal to 1\n testMetric>(0, exactEntries[i], approxEntries[i], 1.0);\n }\n}\n\nTEST(TestRecallInt) {\n vector> exactEntries = {\n {},\n {},\n {RESI(0, 0, 100)},\n { RESI(0, 0, 1), RESI(1, 0, 2), RESI(3, 0, 3), RESI(4, 0, 4), RESI(5, 0, 5), RESI(6, 0, 6), RESI(7, 0, 7), RESI(8, 0, 8), RESI(9, 0, 9), RESI(10, 0, 10), },\n { RESI(0, 0, 1), RESI(1, 0, 2), RESI(3, 0, 3), RESI(4, 0, 4), RESI(5, 0, 5), RESI(6, 0, 6), RESI(7, 0, 7), RESI(8, 0, 8), RESI(9, 0, 9), RESI(10, 0, 10), },\n };\n vector> approxEntries = {\n {},\n {RESI(0, 0, 100)},\n {},\n { RESI(0, 0, 1), RESI(3, 0, 3), RESI(5, 0, 5), RESI(7, 0, 7), RESI(9, 0, 9), },\n { RESI(0, 0, 1), RESI(1, 0, 2), RESI(3, 0, 3), RESI(4, 0, 4), RESI(5, 0, 5), RESI(6, 0, 6), RESI(7, 0, 7), RESI(8, 0, 8), RESI(9, 0, 9), RESI(10, 0, 10), },\n };\n vector expRecall {\n 1,\n 1,\n 0.0,\n 0.5,\n 1.0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expRecall.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expRecall[i]); \n // In a special case when there are no results recall should be equal to 1\n testMetric>(0, exactEntries[i], approxEntries[i], 1.0);\n }\n}\n\nTEST(TestNumCloserDouble) {\n vector> exactEntries = {\n {},\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2) },\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2) },\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2) },\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESD(33, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2) },\n { RESD(33, 0, EPSD), RESD(1, 0, 1 + EPSD), RESD(2, 0, 2 + EPSD) },\n { RESD(11, 0, 2), RESD(12, 0, 2.0001), RESD(13, 0, 2.0001) },\n };\n vector expNumCloser {\n 0,\n static_cast(exactEntries[1].size()),\n 0,\n 0,\n 2\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n // In a special case when there are no results the number of points that are closer is 0 \n testMetric>(0, exactEntries[i], approxEntries[i], 0.0); \n }\n}\n\nTEST(TestNumCloserFloat) {\n vector> exactEntries = {\n {},\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2) },\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2) },\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2) },\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESF(33, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2) },\n { RESF(33, 0, EPSF), RESF(1, 0, 1 + EPSF), RESF(2, 0, 2 + EPSF) },\n { RESF(11, 0, 2), RESF(12, 0, 2.0001), RESF(13, 0, 2.0001) },\n };\n vector expNumCloser {\n 0,\n static_cast(exactEntries[1].size()),\n 0,\n 0,\n 2\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n // In a special case when there are no results the number of points that are closer is 0 \n testMetric>(0, exactEntries[i], approxEntries[i], 0.0); \n }\n}\n\nTEST(TestNumCloserInt) {\n vector> exactEntries = {\n {},\n { RESI(0, 0, 1), RESI(1, 0, 1), RESI(2, 0, 1) },\n { RESI(0, 0, 1), RESI(1, 0, 1), RESI(2, 0, 1) },\n { RESI(0, 0, 1), RESI(1, 0, 3), RESI(2, 0, 3) },\n { RESI(0, 0, 1), RESI(1, 0, 1), RESI(2, 0, 1) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESI(0, 0, 1), RESI(1, 0, 1), RESI(2, 0, 1) },\n { RESI(33, 0, 2), RESI(1, 0, 3), RESI(2, 0, 3) },\n { RESI(10, 0, 1), RESI(11, 0, 1), RESI(12, 0, 1) },\n };\n vector expNumCloser {\n 0,\n static_cast(exactEntries[1].size()),\n 0,\n 1,\n 0\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n // In a special case when there are no results the number of points that are closer is 0 \n testMetric>(0, exactEntries[i], approxEntries[i], 0.0); \n }\n}\n\nTEST(TestRelPosErrorDouble) {\n vector> exactEntries = {\n {},\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2) },\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2), RESD(3, 0, 3) },\n { RESD(0, 0, 33), RESD(1, 0, 33), RESD(2, 0, 33) },\n { RESD(0, 0, 33), RESD(1, 0, 33), RESD(2, 0, 33) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESD(10, 0, 1), RESD(11, 0, 3) },\n { RESD(10, 0, 33), RESD(11, 0, 33), RESD(12, 0, 33) },\n { RESD(10, 0, 33 + + numeric_limits::epsilon()), RESD(11, 0, 33 + + numeric_limits::epsilon()), RESD(12, 0, 33 + numeric_limits::epsilon()) },\n };\n vector expNumCloser {\n 0,\n log(static_cast(exactEntries[1].size())),\n log(2),\n 0,\n 0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n /* \n * In a special case when there are no results the relative position error should be 1.0 and\n * its logarithm should be zero\n */\n testMetric>(0, exactEntries[i], approxEntries[i], 0.0); \n }\n}\n\nTEST(TestRelPosErrorFloat) {\n vector> exactEntries = {\n {},\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2) },\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2), RESF(3, 0, 3) },\n { RESF(0, 0, 33), RESF(1, 0, 33), RESF(2, 0, 33) },\n { RESF(0, 0, 33), RESF(1, 0, 33), RESF(2, 0, 33) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESF(10, 0, 1), RESF(11, 0, 3) },\n { RESF(10, 0, 33), RESF(11, 0, 33), RESF(12, 0, 33) },\n { RESF(10, 0, 33 + + numeric_limits::epsilon()), RESF(11, 0, 33 + + numeric_limits::epsilon()), RESF(12, 0, 33 + numeric_limits::epsilon()) },\n };\n vector expNumCloser {\n 0,\n log(static_cast(exactEntries[1].size())),\n log(2),\n 0,\n 0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n /* \n * In a special case when there are no results the relative position error should be 1.0 and\n * its logarithm should be zero\n */\n testMetric>(0, exactEntries[i], approxEntries[i], 0.0); \n }\n}\n\nTEST(TestRelPosErrorInt) {\n vector> exactEntries = {\n {},\n { RESI(0, 0, 0), RESI(1, 0, 1), RESI(2, 0, 2) },\n { RESI(0, 0, 0), RESI(1, 0, 1), RESI(2, 0, 2), RESI(3, 0, 3) },\n { RESI(0, 0, 33), RESI(1, 0, 33), RESI(2, 0, 33) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESI(10, 0, 1), RESI(11, 0, 3) },\n { RESI(10, 0, 33), RESI(11, 0, 33), RESI(12, 0, 33) },\n };\n vector expNumCloser {\n 0,\n log(static_cast(exactEntries[1].size())),\n log(2),\n 0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n /* \n * In a special case when there are no results the relative position error should be 1.0 and\n * its logarithm should be zero\n */\n testMetric>(0, exactEntries[i], approxEntries[i], 0.0); \n }\n}\n\nTEST(TestPrecisionOfApproxDouble) {\n vector> exactEntries = {\n {},\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2) },\n { RESD(0, 0, 0), RESD(1, 0, 1), RESD(2, 0, 2), RESD(3, 0, 3) },\n { RESD(0, 0, 33), RESD(1, 0, 33), RESD(2, 0, 33) },\n { RESD(0, 0, 33), RESD(1, 0, 33), RESD(2, 0, 33) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESD(10, 0, 1), RESD(11, 0, 3) },\n { RESD(10, 0, 33), RESD(11, 0, 33), RESD(12, 0, 33) },\n { RESD(10, 0, 33 + + numeric_limits::epsilon()), RESD(11, 0, 33 + + numeric_limits::epsilon()), RESD(12, 0, 33 + numeric_limits::epsilon()) },\n };\n vector expNumCloser {\n 1.0,\n 0.0,\n 0.5,\n 1.0,\n 1.0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n // In a special case when there are no results, the precision of approximation should be equal to 1.0\n testMetric>(0, exactEntries[i], approxEntries[i], 1.0); \n }\n}\n\nTEST(TestPrecisionOfApproxFloat) {\n vector> exactEntries = {\n {},\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2) },\n { RESF(0, 0, 0), RESF(1, 0, 1), RESF(2, 0, 2), RESF(3, 0, 3) },\n { RESF(0, 0, 33), RESF(1, 0, 33), RESF(2, 0, 33) },\n { RESF(0, 0, 33), RESF(1, 0, 33), RESF(2, 0, 33) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESF(10, 0, 1), RESF(11, 0, 3) },\n { RESF(10, 0, 33), RESF(11, 0, 33), RESF(12, 0, 33) },\n { RESF(10, 0, 33 + + numeric_limits::epsilon()), RESF(11, 0, 33 + + numeric_limits::epsilon()), RESF(12, 0, 33 + numeric_limits::epsilon()) },\n };\n vector expNumCloser {\n 1.0,\n 0.0,\n 0.5,\n 1.0,\n 1.0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n // In a special case when there are no results, the precision of approximation should be equal to 1.0\n testMetric>(0, exactEntries[i], approxEntries[i], 1.0); \n }\n}\n\nTEST(TestPrecisionOfApproxInt) {\n vector> exactEntries = {\n {},\n { RESI(0, 0, 0), RESI(1, 0, 1), RESI(2, 0, 2) },\n { RESI(0, 0, 0), RESI(1, 0, 1), RESI(2, 0, 2), RESI(3, 0, 3) },\n { RESI(0, 0, 33), RESI(1, 0, 33), RESI(2, 0, 33) },\n };\n vector> approxEntries = {\n {},\n {},\n { RESI(10, 0, 1), RESI(11, 0, 3) },\n { RESI(10, 0, 33), RESI(11, 0, 33), RESI(12, 0, 33) },\n };\n vector expNumCloser {\n 1.0,\n 0.0,\n 0.5,\n 1.0,\n };\n \n EXPECT_EQ(exactEntries.size(), approxEntries.size());\n EXPECT_EQ(exactEntries.size(), expNumCloser.size());\n\n for (size_t i = 0; i < exactEntries.size(); ++i) {\n testMetric>(KNN, exactEntries[i], approxEntries[i], expNumCloser[i]); \n // In a special case when there are no results, the precision of approximation should be equal to 1.0\n testMetric>(0, exactEntries[i], approxEntries[i], 1.0); \n }\n}\n\ntemplate \nvoid testResultEntryIO(string fileName, const vector>& testData) {\n unique_ptr cacheGSBinary;\n\n cacheGSBinary.reset(new fstream(fileName.c_str(),\n std::ios::trunc | std::ios::out | ios::binary));\n for (const ResultEntry& e: testData) {\n e.writeBinary(*cacheGSBinary);\n }\n\n cacheGSBinary->close();\n \n cacheGSBinary.reset(new fstream(fileName.c_str(),\n std::ios::in | ios::binary));\n\n ResultEntry tmp;\n for (size_t i = 0; i < testData.size(); ++i) {\n EXPECT_FALSE(cacheGSBinary->eof());\n tmp.readBinary(*cacheGSBinary);\n EXPECT_EQ(testData[i], tmp);\n }\n\n cacheGSBinary->close();\n}\n\nTEST(TestResultEntryIntIO) {\n vector testData;\n\n for (int i = 0; i < 300000; ++i) {\n testData.push_back(RESI(i, i % 10, i * 2));\n }\n\n testResultEntryIO(\"tmpfile.bin\", testData);\n}\n\n\nTEST(TestResultEntryFloatIO) {\n vector testData;\n\n for (int i = 0; i < 300000; ++i) {\n testData.push_back(RESF(i, i % 10, i * 0.01f));\n }\n\n testResultEntryIO(\"tmpfile.bin\", testData);\n}\n\nTEST(TestResultEntryDoubleIO) {\n vector testData;\n\n for (int i = 0; i < 300000; ++i) {\n testData.push_back(RESD(i, i % 10, i * 0.01));\n }\n\n testResultEntryIO(\"tmpfile.bin\", testData);\n}\n\n}\n" }, { "path": "algorithms/NMSLIB/code/test/test_fp.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"logging.h\"\n#include \"utils.h\"\n#include \"bunit.h\"\n\n#include \n\nusing namespace std;\n\nnamespace similarity {\n\ntemplate \nvoid testEqualInt(dist_t num1, dist_t num2) {\n EXPECT_TRUE(ApproxEqual(num1, num2, 4));\n}\n\ntemplate \nvoid testNotEqualInt(dist_t num1, dist_t num2) {\n EXPECT_FALSE(ApproxEqual(num1, num2, 4));\n}\n\ntemplate \nvoid testEqualFP(dist_t baseNum, float epsFact) {\n EXPECT_TRUE(ApproxEqual(baseNum, baseNum * (1 + epsFact * numeric_limits::epsilon()), 4));\n EXPECT_TRUE(ApproxEqual(-baseNum, -baseNum * (1 + epsFact * numeric_limits::epsilon()), 4));\n}\n\ntemplate \nvoid testNotEqualFP(dist_t baseNum, float epsFact) {\n EXPECT_FALSE(ApproxEqual(baseNum, baseNum * (1 + epsFact * numeric_limits::epsilon()), 4));\n EXPECT_FALSE(ApproxEqual(-baseNum, -baseNum * (1 + epsFact * numeric_limits::epsilon()), 4));\n}\n\nTEST(FP_Char1) {\n testEqualInt(1, 1);\n}\n\nTEST(FP_Char2) {\n testEqualInt(125, 125);\n}\n\nTEST(FP_Char3) {\n testNotEqualInt(1, 2);\n}\n\nTEST(FP_Char4) {\n testNotEqualInt(0, -1);\n}\n\nTEST(FP_Short1) {\n testEqualInt(1, 1);\n}\n\nTEST(FP_Short2) {\n testEqualInt(32767, 32767);\n}\n\nTEST(FP_Short3) {\n testNotEqualInt(1, 2);\n}\n\nTEST(FP_Short4) {\n testNotEqualInt(0, -1);\n}\n\nTEST(FP_Int1) {\n testEqualInt(1, 1);\n}\n\nTEST(FP_Int2) {\n testEqualInt(65535, 65535);\n}\n\nTEST(FP_Int3) {\n testNotEqualInt(1, 2);\n}\n\nTEST(FP_Int4) {\n testNotEqualInt(0, -1);\n}\n\nTEST(FP_Unsigned1) {\n testEqualInt(1, 1);\n}\n\nTEST(FP_Unsigned2) {\n testEqualInt(65535, 65535);\n}\n\nTEST(FP_Unsigned3) {\n testNotEqualInt(1, 2);\n}\n\nTEST(FP_Float1) {\n testEqualFP(1.0, 1);\n testEqualFP(1.0, 2);\n testEqualFP(1.0, 3);\n testEqualFP(1.0, 4);\n}\n\nTEST(FP_Float2) {\n testEqualFP(1e4f, 1);\n testEqualFP(1e4f, 2);\n testEqualFP(1e4f, 3);\n}\n\nTEST(FP_Float3) {\n testEqualFP(1e-5f, 1);\n testEqualFP(1e-5f, 2);\n testEqualFP(1e-5f, 3);\n}\n\nTEST(FP_Float4) {\n testNotEqualFP(1.0, 5);\n}\n\nTEST(FP_Float5) {\n testNotEqualFP(0.5, 5);\n}\n\nTEST(FP_Double1) {\n testEqualFP(1.0, 1);\n testEqualFP(1.0, 2);\n testEqualFP(1.0, 3);\n testEqualFP(1.0, 4);\n}\n\nTEST(FP_Double2) {\n testEqualFP(1e7, 1);\n testEqualFP(1e7, 2);\n testEqualFP(1e7, 3);\n}\n\nTEST(FP_Double3) {\n testEqualFP(1e-6, 1);\n testEqualFP(1e-6, 2);\n testEqualFP(1e-6, 3);\n}\n\nTEST(FP_Double4) {\n testNotEqualFP(1.0, 5);\n}\n\nTEST(FP_Double5) {\n testNotEqualFP(0.5, 5);\n}\n\nTEST(FP_LongDouble1) {\n testEqualFP(1.0, 1);\n testEqualFP(1.0, 2);\n testEqualFP(1.0, 3);\n}\n\nTEST(FP_LongDouble2) {\n testEqualFP(2, 1);\n testEqualFP(2, 2);\n testEqualFP(2, 3);\n}\n\nTEST(FP_LongDouble3) {\n testNotEqualFP(1.0, 5);\n testNotEqualFP(10.0, 5);\n testNotEqualFP(10e10, 5);\n}\n\nTEST(FP_LongDouble4) {\n testNotEqualFP(0.5, 5);\n testNotEqualFP(0.05, 5);\n testNotEqualFP(1e-10, 5);\n}\n\nTEST(FP_NANFloat) {\n static float __nan = numeric_limits::quiet_NaN();\n testNotEqualFP(__nan, __nan);\n testNotEqualFP(__nan, -__nan);\n};\n\nTEST(FP_NANDouble) {\n static double __nan = numeric_limits::quiet_NaN();\n testNotEqualFP(__nan, __nan);\n testNotEqualFP(__nan, -__nan);\n};\n\nTEST(FP_NANLongDouble) {\n static long double __nan = numeric_limits::quiet_NaN();\n testNotEqualFP(__nan, __nan);\n testNotEqualFP(__nan, -__nan);\n};\n\nTEST(FP_ZEROFloat) {\n EXPECT_TRUE(ApproxEqual(0.0f, -0.0f));\n EXPECT_TRUE(ApproxEqual(0.0f, numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0f, 1.9f*numeric_limits::min()));\n EXPECT_FALSE(ApproxEqual(0.0f, 5000000.0f*numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0f, -numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0f, -1.9f*numeric_limits::min()));\n EXPECT_FALSE(ApproxEqual(0.0f, -5000000.0f*numeric_limits::min()));\n}\n\nTEST(FP_ZERODouble) {\n EXPECT_TRUE(ApproxEqual(0.0, -0.0));\n EXPECT_TRUE(ApproxEqual(0.0, numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0, 1.9*numeric_limits::min()));\n EXPECT_FALSE(ApproxEqual(0.0, 2.1*numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0, -numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0, -1.9*numeric_limits::min()));\n EXPECT_FALSE(ApproxEqual(0.0, -2.1*numeric_limits::min()));\n}\n\nTEST(FP_ZEROLongDouble) {\n EXPECT_TRUE(ApproxEqual(0.0l, -0.0l));\n EXPECT_TRUE(ApproxEqual(0.0l, numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0l, 1.9*numeric_limits::min()));\n EXPECT_FALSE(ApproxEqual(0.0l, 100.1*numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0l, -numeric_limits::min()));\n EXPECT_TRUE(ApproxEqual(0.0l, -1.9*numeric_limits::min()));\n EXPECT_FALSE(ApproxEqual(0.0l, -10.1*numeric_limits::min()));\n}\n\n}\n" }, { "path": "algorithms/NMSLIB/code/test/test_integr.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"init.h\"\n#include \"global.h\"\n#include \"utils.h\"\n#include \"memory.h\"\n#include \"ztimer.h\"\n#include \"experiments.h\"\n#include \"experimentconf.h\"\n#include \"space.h\"\n#include \"spacefactory.h\"\n#include \"logging.h\"\n#include \"methodfactory.h\"\n\n#include \"meta_analysis.h\"\n#include \"params.h\"\n\n#include \"test_integr_util.h\"\n#include \"testdataset.h\"\n\nusing namespace similarity;\n\nusing std::set;\nusing std::multimap;\nusing std::vector;\nusing std::string;\nusing std::stringstream;\n\n//#define MAX_THREAD_QTY 4\n#define MAX_THREAD_QTY 1\n#define TEST_SET_QTY 20\n#define MAX_NUM_QUERY 100\n\nvector vTestCaseDesc = {\n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"nonmetr_list_clust\", \"clusterType=clarans,centerQty=10\", \"dbScanFrac=0.1\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.85, 0.95, 0.01, 5, 2, 7), \n // ************** Tests for non-metric clustering *********** //\n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"nonmetr_list_clust\", \"clusterType=firmal,centerQty=10\", \"dbScanFrac=0.1\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.8, 0.92, 0.1, 20, 2.5, 6), \n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"nonmetr_list_clust\", \"clusterType=reduct_clarans,centerQty=10\", \"dbScanFrac=0.1\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.85, 0.95, 0.01, 5, 2, 7), \n#if 1\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"napp\", \"numPivot=8,numPivotIndex=8,chunkIndexSize=102\", \"numPivotSearch=8\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n\n MethodTestCase(DIST_TYPE_FLOAT, \"cosinesimil\", \"final8_10K.txt\", \"hnsw\", \"efConstruction=50,M=10,skip_optimized_index=1\", \"ef=50\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.97, 1, 0, 0.1, 40, 60), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"hnsw\", \"efConstruction=50,M=10,skip_optimized_index=1\", \"ef=50\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.97, 1, 0, 0.1, 40, 60), \n // *************** NEW versions of permutation & projection-based filtering method tests ******************** //\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"seq_search\", \"\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0, 0, 1, 1), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"seq_search\", \"\", \"\",\n 0 /* no-knn search */, 0.2 /* range 0.2 */ , 1.0, 1.0, 0, 0, 1, 1), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"seq_search\", \"multiThread=1,threadQty=4\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0, 0, 1, 1), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"seq_search\", \"multiThread=1,threadQty=4\", \"\",\n 0 /* no-knn search */, 0.2 /* range 0.2 */ , 1.0, 1.0, 0, 0, 1, 1), \n#if !defined(_MSC_VER)\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"nndes\", \"NN=10,rho=0.5,delta=0.001\", \"initSearchAttempts=10\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.9, 1.0, 0, 1.0, 5, 12), \n#endif\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"small_world_rand\", \"NN=10,initIndexAttempts=1\", \"initSearchAttempts=1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.9, 1.0, 0, 1.0, 40, 52), \n // 4 different types of projections\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=perm,projDim=4\", \"dbScanFrac=1.0\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=rand,projDim=4\", \"dbScanFrac=1.0\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=fastmap,projDim=4\", \"dbScanFrac=1.0\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=randrefpt,projDim=4\", \"dbScanFrac=1.0\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n\n // Proj. VP-tree\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_vptree\", \"projType=perm,projDim=4\", \"alphaLeft=2,alphaRight=2,dbScanFrac=1.0\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\",\"pp-index\", \"numPivot=4,prefixLength=4\", \"minCandidate=10000\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mi-file\", \"numPivot=16,numPivotIndex=16\", \"numPivotSearch=16,dbScanFrac=1.0\",\n\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"napp\", \"numPivot=8,numPivotIndex=8,chunkIndexSize=102\", \"numPivotSearch=8\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n\n // Binarized permutations\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"perm_incsort_bin\", \"numPivot=32\", \"dbScanFrac=1.0\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"perm_bin_vptree\", \"numPivot=32\", \"alphaLeft=2,alphaRight=2,dbScanFrac=1.0\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0, 0.01, 0.99, 1.01), \n\n\n // 4 different types of projections\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=perm,projDim=4\", \"dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.4, 0.7, 0.5, 4, 8, 12), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=rand,projDim=4\", \"dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.9, 1.01, 0.0, 0.2, 8, 12), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=fastmap,projDim=4\", \"dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.9, 1.01, 0.0, 0.2, 8, 12), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_incsort\", \"projType=randrefpt,projDim=4\", \"dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.9, 1.01, 0.0, 0.2, 8, 12), \n\n // Proj. VP-tree\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"proj_vptree\", \"projType=perm,projDim=4\", \"alphaLeft=2,alphaRight=2,dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.4, 0.7, 0.5, 4, 8, 12), \n\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\",\"pp-index\", \"numPivot=4,prefixLength=4\", \"minCandidate=100\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.8, 1.0, 0.1, 2, 3, 8), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mi-file\", \"numPivot=16,numPivotIndex=16\", \"numPivotSearch=16,dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.95, 1.0, 0, 0.5, 8, 12), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"napp\", \"numPivot=32,numPivotIndex=8,chunkIndexSize=102\", \"numPivotSearch=8\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.6, 0.8, 1, 4, 22, 37),\n\n // Binarized\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"perm_incsort_bin\", \"numPivot=32\", \"dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.9, 1.0, 0.01, 0.3, 8, 12), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"perm_bin_vptree\", \"numPivot=32\", \"alphaLeft=2,alphaRight=2,dbScanFrac=0.1\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.9, 1.0, 0.01, 0.5, 8, 12), \n\n\n // *************** omedrank tests ******************** //\n\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"omedrank\", \"numPivot=4,chunkIndexSize=16536\", \"dbScanFrac=0.01,minFreq=0.5\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.7, 0.97, 0.1, 3, 70, 120), \n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"omedrank\", \"numPivot=4,chunkIndexSize=16536\", \"dbScanFrac=0.01,minFreq=0.5\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.6, 0.9, 0.1, 3, 70, 120), \n\n // *************** VP-tree tests ******************** //\n // knn\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 40, 70), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"alphaLeft=2,alphaRight=2\", \n 1 /* KNN-1 */, 0 /* no range search */ , 0.93, 0.97, 0.03, 0.09, 120, 160), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final128_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 1.5, 1.8), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final128_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"alphaLeft=2,alphaRight=2\", \n 1 /* KNN-1 */, 0 /* no range search */ , 0.98, 1.0, 0.0, 0.02, 2.8, 3.4), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 20, 24), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"alphaLeft=2,alphaRight=2\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.93, 0.96, 0.0, 0.02, 56, 63), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final128_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 1.1, 1.3), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final128_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"alphaLeft=2,alphaRight=2\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.99, 0.999, 0.0, 0.01, 1.5, 2.5), \n // range\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"\",\n 0 /* no KNN */, 0.1 /* range search radius 0.1 */ , 1.0, 1.0, 0.0, 0.0, 23, 26), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"vptree\", \"chunkBucket=1,bucketSize=10\", \"\",\n 0 /* no KNN */, 0.5 /* range search radius 0.5 */ , 1.0, 1.0, 0.0, 0.0, 2.4, 3), \n\n // *************** MVP-tree tests ******************** //\n // knn\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mvptree\", \"maxPathLen=4,bucketSize=10\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 120, 140), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mvptree\", \"maxPathLen=4,bucketSize=10\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 40, 50), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mvptree\", \"maxPathLen=4,bucketSize=10\", \"maxLeavesToVisit=10\", \n 1 /* KNN-1 */, 0 /* no range search */ , 0.82, 0.9, 0.2, 3.5, 230, 250), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mvptree\", \"maxPathLen=4,bucketSize=10\", \"maxLeavesToVisit=20\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.75, 0.82, 0.2, 2.0, 85, 100), \n\n // range\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mvptree\", \"maxPathLen=4,bucketSize=10\", \"\",\n 0 /* no KNN */, 0.1 /* range search radius 0.1 */ , 1.0, 1.0, 0.0, 0.0, 40, 55), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"mvptree\", \"maxPathLen=4,bucketSize=10\", \"\",\n 0 /* no KNN */, 0.5 /* range search radius 0.5*/ , 1.0, 1.0, 0.0, 0.0, 3, 4), \n\n // *************** GH-tree tests ******************** //\n // knn\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"ghtree\", \"bucketSize=10\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 28, 35), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"ghtree\", \"bucketSize=10\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 8, 10.2), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"ghtree\", \"bucketSize=10\", \"maxLeavesToVisit=10\", \n 1 /* KNN-1 */, 0 /* no range search */ , 0.8, 0.87, 0.2, 1.5, 95, 115), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"ghtree\", \"bucketSize=10\", \"maxLeavesToVisit=20\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.75, 0.82, 0.1, 1.0, 52, 62), \n // range\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"ghtree\", \"bucketSize=10\", \"\", \n 0 /* no KNN */, 0.1 /* range search radius 0.1 */ , 1.0, 1.0, 0.0, 0.0, 10, 16), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"ghtree\", \"bucketSize=10\", \"\",\n 0 /* no KNN */, 0.5 /* range search radius 0.5*/ , 1.0, 1.0, 0.0, 0.0, 1, 1.2), \n\n // *************** SA-tree tests ******************** //\n // knn\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"satree\", \"bucketSize=10\", \"\", \n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 25, 33), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"satree\", \"bucketSize=10\", \"\", \n 10 /* KNN-10 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 10, 25), \n // range\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"satree\", \"bucketSize=10\", \"\", \n 0 /* no KNN */, 0.1 /* range search radius 0.1 */ , 1.0, 1.0, 0.0, 0.0, 13, 18), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"satree\", \"bucketSize=10\", \"\", \n 0 /* no KNN */, 0.5 /* range search radius 0.5*/ , 1.0, 1.0, 0.0, 0.0, 2.8, 3.4), \n\n // *************** List of clusters tests ******************** //\n // knn\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"list_clusters\", \"strategy=random,useBucketSize=1,bucketSize=10\", \"\", \n 1 /* KNN-1 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 9.5, 11.5), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"list_clusters\", \"strategy=random,useBucketSize=1,bucketSize=10\", \"\", \n 10 /* KNN-10 */, 0 /* no range search */ , 1.0, 1.0, 0.0, 0.0, 7.5, 8.5), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"list_clusters\", \"strategy=random,useBucketSize=1,bucketSize=10\", \"maxLeavesToVisit=10\", \n 1 /* KNN-1 */, 0 /* no range search */ , 0.78, 0.9, 0.2, 1.5, 9.5, 11.5), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"list_clusters\", \"strategy=random,useBucketSize=1,bucketSize=10\", \"maxLeavesToVisit=20\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.85, 0.97, 0.05, 0.7, 8.5, 10.5), \n // range\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"list_clusters\", \"strategy=random,useBucketSize=1,bucketSize=10\", \"\",\n 0 /* no KNN */, 0.1 /* range search radius 0.1 */ , 1.0, 1.0, 0.0, 0.0, 8, 10), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"list_clusters\", \"strategy=random,useBucketSize=1,bucketSize=10\", \"\",\n 0 /* no KNN */, 0.5 /* range search radius 0.5*/ , 1.0, 1.0, 0.0, 0.0, 2.4, 3.4), \n\n // *************** bbtree tests ******************** //\n // knn\n /* \n * TODO @leo \n * bbtree seems to be a bit wacky (missing a tiny fraction of answers), \n * need to debug it in the future.\n * Therefore, we expect a slightly imperfect recall sometimes.\n */\n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"bbtree\", \"bucketSize=10\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.999, 1.0, 0.0, 0.0, 9.5, 11.5), \n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"bbtree\", \"bucketSize=10\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 0.999, 1.0, 0.0, 0.0, 5.5, 8), \n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"bbtree\", \"bucketSize=10\", \"maxLeavesToVisit=10\", \n 1 /* KNN-1 */, 0 /* no range search */ , 0.75, 0.85, 0.3, 1.5, 48, 52), \n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"bbtree\", \"bucketSize=10\", \"maxLeavesToVisit=20\", \n 10 /* KNN-10 */, 0 /* no range search */ , 0.7, 0.78, 0.3, 1.6, 28, 37), \n // range\n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"bbtree\", \"bucketSize=10\", \"\",\n 0 /* no KNN */, 0.1 /* range search radius 0.1 */ , 0.999, 1.0, 0.0, 0.0, 4.5, 6.5), \n MethodTestCase(DIST_TYPE_FLOAT, \"kldivgenfast\", \"final8_10K.txt\", \"bbtree\", \"bucketSize=10\", \"\",\n 0 /* no KNN */, 0.5 /* range search radius 0.5*/ , 0.999, 1.0, 0.0, 0.0, 1.2, 2.4), \n\n#ifndef _MSC_VER\n // no LSH for Windows\n // *************** multi-probe LSH tests ******************** //\n // knn\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"lsh_multiprobe\", \"desiredRecall=0.5,tuneK=1,T=5,L=25,H=16535\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.45, 0.6, 45, 80, 90, 130), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"lsh_multiprobe\", \"desiredRecall=0.5,tuneK=10,T=5,L=25,H=16535\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 0.45, 0.6, 10, 40, 80, 120), \n // *************** Guassian LSH tests ******************** //\n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"lsh_gaussian\", \"W=2,L=5,M=40,H=16535\", \"\",\n\n 1 /* KNN-1 */, 0 /* no range search */ , 0.85, 0.95, 0.1, 40, 90, 130), \n MethodTestCase(DIST_TYPE_FLOAT, \"l2\", \"final8_10K.txt\", \"lsh_gaussian\", \"W=2,L=5,M=40,H=16535\", \"\",\n\n 10 /* KNN-10 */, 0 /* no range search */ , 0.68, 0.82, 0.1, 50, 80, 120), \n // *************** Cauchy LSH tests ******************** //\n MethodTestCase(DIST_TYPE_FLOAT, \"l1\", \"final8_10K.txt\", \"lsh_cauchy\", \"W=2,L=5,M=10,H=16535\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.7, 0.9, 0.1, 50, 90, 130), \n MethodTestCase(DIST_TYPE_FLOAT, \"l1\", \"final8_10K.txt\", \"lsh_cauchy\", \"W=2,L=5,M=10,H=16535\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 0.5, 0.8, 0.1, 50, 80, 120), \n // *************** Thresholding LSH tests ******************** //\n MethodTestCase(DIST_TYPE_FLOAT, \"l1\", \"final8_10K.txt\", \"lsh_threshold\", \"L=5,M=60,H=16535\", \"\",\n 1 /* KNN-1 */, 0 /* no range search */ , 0.8, 0.99, 0.1, 50, 40, 70), \n MethodTestCase(DIST_TYPE_FLOAT, \"l1\", \"final8_10K.txt\", \"lsh_threshold\", \"L=5,M=60,H=16535\", \"\",\n 10 /* KNN-10 */, 0 /* no range search */ , 0.65, 0.85, 0.1, 50, 40, 70), \n#endif\n#endif\n};\n\n\nint main(int ac, char* av[]) {\n // This should be the first function called before\n string LogFile;\n if (ac == 2) LogFile = av[1];\n\n initLibrary(LogFile.empty() ? LIB_LOGSTDERR:LIB_LOGFILE, LogFile.c_str());\n\n WallClockTimer timer;\n timer.reset();\n\n set setDistType;\n set setSpaceType;\n set setDataSet;\n set setKNN;\n set setRange;\n\n for (const auto& testCase: vTestCaseDesc) {\n setDistType.insert(testCase.mDistType);\n setSpaceType.insert(testCase.mSpaceType);\n setDataSet.insert(testCase.mDataSet);\n if (testCase.mKNN > 0)\n setKNN.insert(testCase.mKNN);\n if (testCase.mRange > 0)\n setRange.insert(testCase.mRange);\n }; \n\n size_t nTest = 0;\n size_t nFail = 0;\n\n try {\n /* \n * 1. Let's iterate over all combinations of data sets,\n * distance, and space types. \n * 2. For each combination, we select test cases \n * with exactly same data set, distance and space type. \n * 3. Create an array of arguments in the same format\n * as used by the main benchmarking utility.\n * 4. Use a standard function to parse these arguments.\n */\n for (string dataSet : setDataSet)\n for (string distType : setDistType)\n for (string spaceType: setSpaceType) {\n string dataFile = sampleDataPrefix + dataSet;\n\n for (unsigned K: setKNN) {\n vector vTestCases; \n\n // Select appropriate test cases to share the same gold-standard data\n for (const auto& testCase: vTestCaseDesc) {\n if (testCase.mDataSet == dataSet &&\n testCase.mDistType == distType && \n testCase.mSpaceType == spaceType &&\n testCase.mKNN == K) {\n vTestCases.push_back(MethodTestCase(testCase));\n }\n }\n\n if (!vTestCases.empty()) { // Not all combinations of spaces, data sets, and search types are non-empty\n for (size_t threadQty = 1; threadQty <= MAX_THREAD_QTY; ++threadQty) {\n nTest += vTestCases.size();\n nFail += RunOneTest(vTestCases, \n distType,\n spaceType,\n threadQty,\n TEST_SET_QTY,\n dataFile,\n \"\",\n 0,\n MAX_NUM_QUERY,\n ConvertToString(K),\n 0,\n \"\"\n );\n }\n }\n }\n\n for (float R: setRange) {\n vector vTestCases; \n\n\n // Select appropriate test cases to share the same gold-standard data\n for (const auto& testCase: vTestCaseDesc) {\n if (testCase.mDataSet == dataSet &&\n testCase.mDistType == distType && \n testCase.mSpaceType == spaceType &&\n testCase.mRange == R) {\n vTestCases.push_back(MethodTestCase(testCase));\n }\n }\n\n if (!vTestCases.empty()) { // Not all combinations of spaces, data sets, and search types are non-empty\n for (size_t threadQty = 1; threadQty <= MAX_THREAD_QTY; ++threadQty) {\n nTest += vTestCases.size();\n nFail += RunOneTest(vTestCases, \n distType,\n spaceType,\n threadQty,\n TEST_SET_QTY,\n dataFile,\n \"\",\n 0,\n MAX_NUM_QUERY,\n \"\",\n 0,\n ConvertToString(R)\n );\n }\n }\n\n }\n\n }\n\n } catch (const std::exception& e) {\n cout << red << \"Failure to test due to exception: \" << e.what() << no_color << endl;\n ++nFail;\n } catch (...) {\n ++nFail;\n cout << red << \"Failure to test due to unknown exception: \" << no_color << endl;\n }\n\n timer.split();\n\n LOG(LIB_INFO) << \"Time elapsed = \" << timer.elapsed() / 1e6;\n LOG(LIB_INFO) << \"Finished at \" << LibGetCurrentTime();\n\n cerr << endl << \"==================================================\" << endl;\n cerr << (nFail ? \"FAILURE\" : \"SUCCESS\") << endl;\n cerr << \"Carried out: \" << nTest << \" tests. Failed: \" << nFail << \" tests\" << endl;\n\n return nFail ? 1:0;\n}\n" }, { "path": "algorithms/NMSLIB/code/test/test_integr.vcxproj", "content": "\n\n \n \n Debug\n x64\n \n \n Release\n x64\n \n \n RelWithDebInfo\n x64\n \n \n \n {B078DA10-B28A-4CB8-B07E-61E93B100B1F}\n Win32Proj\n Win32\n test_integr\n \n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n Application\n false\n MultiByte\n v140\n \n \n \n \n \n \n \n \n \n <_ProjectFileVersion>10.0.20506.1\n test_integr\n .exe\n true\n true\n test_integr\n .exe\n false\n true\n test_integr\n .exe\n true\n true\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\test_integr\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\test_integr\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\test_integr\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\test_integr\\\n \n \n $(SolutionDir)$(Platform)\\$(Configuration)\\test_integr\\\n $(SolutionDir)$(Platform)\\$(Configuration)\\test_integr\\\n \n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)include;%(AdditionalIncludeDirectories)\n Debug/\n EnableFastChecks\n CompileAsCpp\n ProgramDatabase\n Sync\n Disabled\n Disabled\n NotUsing\n MultiThreadedDebugDLL\n true\n Level3\n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\"Debug\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n true\n \n \n WIN32;_WINDOWS;_DEBUG;CMAKE_INTDIR=\\\"Debug\\\";%(PreprocessorDefinitions)\n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n c:/local/boost_1_59_0/lib64-msvc-14.0;$(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n $(OutDir)$(TargetName)$(TargetExt).pgd\n \n \n false\n \n \n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)include;%(AdditionalIncludeDirectories)\n Release/\n CompileAsCpp\n Sync\n AnySuitable\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"Release\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n Speed\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"Release\\\";%(PreprocessorDefinitions)\n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n c:/local/boost_1_59_0/lib64-msvc-14.0;$(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n false\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n RelWithDebInfo/\n CompileAsCpp\n ProgramDatabase\n Sync\n OnlyExplicitInline\n MaxSpeed\n NotUsing\n MultiThreadedDLL\n true\n Level3\n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\"RelWithDebInfo\";%(PreprocessorDefinitions)\n $(IntDir)\n $(OutDir)$(TargetName)$(TargetExt)_compile.pdb\n NotSet\n Fast\n Speed\n true\n true\n \n \n WIN32;_WINDOWS;NDEBUG;CMAKE_INTDIR=\\\"RelWithDebInfo\\\";%(PreprocessorDefinitions)\n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n \n \n c:\\local\\boost_1_59_0;$(SolutionDir)\\include;%(AdditionalIncludeDirectories)\n $(IntDir)\n %(Filename).h\n %(Filename).tlb\n %(Filename)_i.c\n %(Filename)_p.c\n \n \n /machine:X64 /debug %(AdditionalOptions)\n kernel32.lib;user32.lib;gdi32.lib;winspool.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;comdlg32.lib;advapi32.lib;NonMetricSpaceLib.lib\n c:/local/boost_1_59_0/lib64-msvc-14.0;$(SolutionDir)$(Platform)\\$(Configuration)\\MainLib\\;%(AdditionalLibraryDirectories)\n true\n \n \n $(OutDir)$(TargetName)$(TargetExt)_link.pdb\n Console\n \n \n \n \n false\n \n \n \n \n \n \n \n \n 08B5B5CC-2938-4C1C-B835-A3868076D791\n \n \n \n \n \n \n \n \n" }, { "path": "algorithms/NMSLIB/code/test/test_integr_util.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef TEST_INTEGR_UTILS_H\n#define TEST_INTEGR_UTILS_H\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"utils.h\"\n#include \"bunit.h\"\n#include \"report.h\"\n#include \"params_def.h\"\n#include \"params_cmdline.h\"\n#include \"report_intr_dim.h\"\n\nusing std::set;\nusing std::multimap;\nusing std::vector;\nusing std::copy;\nusing std::string;\nusing std::stringstream;\n\nusing namespace similarity;\n\n/*\n * A data structure that defines \n * 1) Search parameters\n * 2) Method parameters\n * 3) Search outcome (recall range, range for the improvement in distance computation)\n */\nstruct MethodTestCase {\n string mDistType;\n string mSpaceType;\n string mDataSet;\n string mMethodName;\n string mIndexParams;\n string mQueryTimeParams;\n float mRecallMin;\n float mRecallMax;\n float mNumCloserMin;\n float mNumCloserMax;\n float mImprDistCompMin;\n float mImprDistCompMax;\n\n unsigned mKNN;\n float mRange;\n\n MethodTestCase(\n string distType,\n string spaceType,\n string dataSet,\n string methodName,\n string indexParams,\n string queryTimeParams,\n unsigned knn,\n float range, \n float recallMin = 0, \n float recallMax = 0,\n float numCloserMin = 0, \n float numCloserMax = 0,\n float imprDistCompMin = 0, \n float imprDistCompMax = 0) :\n mDistType(distType),\n mSpaceType(spaceType),\n mDataSet(dataSet),\n mMethodName(methodName),\n mIndexParams(indexParams),\n mQueryTimeParams(queryTimeParams),\n mRecallMin(recallMin),\n mRecallMax(recallMax),\n mNumCloserMin(numCloserMin),\n mNumCloserMax(numCloserMax),\n mImprDistCompMin(imprDistCompMin),\n mImprDistCompMax(imprDistCompMax),\n mKNN(knn), mRange(range)\n {\n ToLower(mDistType);\n ToLower(spaceType);\n }\n};\n\ntemplate \nbool ProcessAndCheckResults(\n const string& cmdStr,\n const string& distType,\n const string& spaceType,\n const MethodTestCase& testCase,\n const ExperimentConfig& config,\n MetaAnalysis& ExpRes,\n string& PrintStr // For display\n ) {\n std::stringstream Print, Data, Header;\n\n ExpRes.ComputeAll();\n\n PrintStr = produceHumanReadableReport(config, ExpRes, testCase.mMethodName, testCase.mIndexParams, testCase.mQueryTimeParams);\n\n bool bFail = false;\n\n if (ExpRes.GetRecallAvg() < testCase.mRecallMin) {\n cerr << \"Failed to meet min recall requirement, expect >= \" << testCase.mRecallMin \n << \" got \" << ExpRes.GetRecallAvg() << endl \n << \" method: \" << testCase.mMethodName << \" ; \"\n << \" index-time params: \" << testCase.mIndexParams << \" ; \"\n << \" query-time params: \" << testCase.mQueryTimeParams << \" ; \"\n << \" data set: \" << testCase.mDataSet << \" ; \"\n << \" dist value type: \" << distType << \" ; \"\n << \" space type: \" << spaceType << endl << cmdStr << endl;\n bFail = true;\n }\n\n if (ExpRes.GetRecallAvg() > testCase.mRecallMax) {\n cerr << \"Failed to meet max recall requirement, expect <= \" << testCase.mRecallMax \n << \" got \" << ExpRes.GetRecallAvg() << endl \n << \" method: \" << testCase.mMethodName << \" ; \"\n << \" index-time params: \" << testCase.mIndexParams << \" ; \"\n << \" query-time params: \" << testCase.mQueryTimeParams << \" ; \"\n << \" data set: \" << testCase.mDataSet << \" ; \"\n << \" dist value type: \" << distType << \" ; \"\n << \" space type: \" << spaceType << endl << cmdStr << endl;\n bFail = true;\n }\n\n if (ExpRes.GetNumCloserAvg() < testCase.mNumCloserMin) {\n cerr << \"Failed to meet min # of points closer requirement, expect >= \" << testCase.mNumCloserMin \n << \" got \" << ExpRes.GetNumCloserAvg() << endl \n << \" method: \" << testCase.mMethodName << \" ; \"\n << \" index-time params: \" << testCase.mIndexParams << \" ; \"\n << \" query-time params: \" << testCase.mQueryTimeParams << \" ; \"\n << \" data set: \" << testCase.mDataSet << \" ; \"\n << \" dist value type: \" << distType << \" ; \"\n << \" space type: \" << spaceType << endl << cmdStr << endl;\n bFail = true;\n }\n\n if (ExpRes.GetNumCloserAvg() > testCase.mNumCloserMax) {\n cerr << \"Failed to meet max # of points closer requirement, expect <= \" << testCase.mNumCloserMax \n << \" got \" << ExpRes.GetNumCloserAvg() << endl \n << \" method: \" << testCase.mMethodName << \" ; \"\n << \" index-time params: \" << testCase.mIndexParams << \" ; \"\n << \" query-time params: \" << testCase.mQueryTimeParams << \" ; \"\n << \" data set: \" << testCase.mDataSet << \" ; \"\n << \" dist value type: \" << distType << \" ; \"\n << \" space type: \" << spaceType << endl << cmdStr << endl;\n bFail = true;\n }\n\n if (ExpRes.GetImprDistCompAvg() < testCase.mImprDistCompMin) {\n cerr << \"Failed to meet min improvement requirement in the # of distance computations, expect >= \"\n << testCase.mImprDistCompMin << \" got \" << ExpRes.GetImprDistCompAvg() << endl \n << \" method: \" << testCase.mMethodName << \" ; \"\n << \" index-time params: \" << testCase.mIndexParams << \" ; \"\n << \" query-time params: \" << testCase.mQueryTimeParams << \" ; \"\n << \" data set: \" << testCase.mDataSet << \" ; \"\n << \" dist value type: \" << distType << \" ; \"\n << \" space type: \" << spaceType << endl << cmdStr << endl;\n bFail = true;\n }\n\n if (ExpRes.GetImprDistCompAvg() > testCase.mImprDistCompMax) {\n cerr << \"Failed to meet max improvement requirement in the # of distance computations, expect <= \"\n << testCase.mImprDistCompMax << \" got \" << ExpRes.GetImprDistCompAvg() << endl \n << \" method: \" << testCase.mMethodName << \" ; \"\n << \" index-time params: \" << testCase.mIndexParams << \" ; \"\n << \" query-time params: \" << testCase.mQueryTimeParams << \" ; \"\n << \" data set: \" << testCase.mDataSet << \" ; \"\n << \" dist value type: \" << distType << \" ; \"\n << \" space type: \" << spaceType << endl << cmdStr << endl;\n bFail = true;\n }\n\n return !bFail;\n};\n\ninline string getFirstParam(const string& paramDef) {\n vector vParDefs;\n \n if (!SplitStr(paramDef, vParDefs, ',')) {\n PREPARE_RUNTIME_ERR(err) << \"Cannot comma-split the parameter definition: '\" << paramDef << \"'\";\n THROW_RUNTIME_ERR(err);\n }\n if (vParDefs.empty()) {\n PREPARE_RUNTIME_ERR(err) << \"Empty definition list in the parameter definition: '\" << paramDef << \"'\";\n THROW_RUNTIME_ERR(err);\n }\n return \"--\" + vParDefs[0]; \n}\n\ninline string quoteEmpty(const string& str) {\n return str.empty() ? string(\"\\\"\\\"\") : str;\n};\n\nstring CreateCmdStr(\n const MethodTestCase& testCase,\n bool isRange,\n const string& rangeOrKnnArg,\n const string& DistType, \n string SpaceTypeStr,\n unsigned ThreadTestQty,\n unsigned TestSetQty,\n const string& DataFile,\n const string& QueryFile,\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQuery,\n const float eps) {\n stringstream res;\n // our test data files shouldn't have spaces, so we won't escape them\n // anyways, this is only a debug output \n res << getFirstParam(DATA_FILE_PARAM_OPT) << \" \" << DataFile << \" \" \n << getFirstParam(MAX_NUM_DATA_PARAM_OPT) << \" \" << MaxNumData << \" \" \n << getFirstParam(DIST_TYPE_PARAM_OPT) << \" \" << DistType << \" \" \n << getFirstParam(SPACE_TYPE_PARAM_OPT) << \" \" << SpaceTypeStr << \" \" \n << getFirstParam(THREAD_TEST_QTY_PARAM_OPT)<< \" \" << ThreadTestQty << \" \" \n << getFirstParam(EPS_PARAM_OPT) << \" \" << eps << \" \" ;\n if (QueryFile.empty())\n res << getFirstParam(TEST_SET_QTY_PARAM_OPT) << \" \" << TestSetQty << \" \" ;\n else\n res << getFirstParam(QUERY_FILE_PARAM_OPT) << \" \" << QueryFile << \" \" ;\n\n res << getFirstParam(MAX_NUM_QUERY_PARAM_OPT) << \" \" << MaxNumQuery << \" \" \n << getFirstParam(isRange ? RANGE_PARAM_OPT : KNN_PARAM_OPT) << \" \" << rangeOrKnnArg << \" \" \n << getFirstParam(METHOD_PARAM_OPT) << \" \" << testCase.mMethodName << \" \" \n << getFirstParam(INDEX_TIME_PARAMS_PARAM_OPT) << \" \" << quoteEmpty(testCase.mIndexParams) << \" \" \n << getFirstParam(QUERY_TIME_PARAMS_PARAM_OPT) << \" \" << quoteEmpty(testCase.mQueryTimeParams);\n\n return res.str();\n};\n \n\n/*\n * returns the # of failed tests\n */\ntemplate \nsize_t RunTestExper(const vector& vTestCases,\n const string& DistType, \n string SpaceTypeStr,\n unsigned ThreadTestQty,\n unsigned TestSetQty,\n const string& DataFile,\n const string& QueryFile,\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQuery,\n const string& KnnArg,\n const float eps,\n const string& RangeArg\n)\n{\n vector knn;\n vector range;\n\n size_t nFail = 0;\n\n if (!KnnArg.empty()) {\n if (!SplitStr(KnnArg, knn, ',')) {\n PREPARE_RUNTIME_ERR(err) << \"Wrong format of the knn argument: '\" << KnnArg << \"' Should be a list of coma-separated int > 0 values.\";\n THROW_RUNTIME_ERR(err);\n }\n }\n\n if (!RangeArg.empty()) {\n if (!SplitStr(RangeArg, range, ',')) {\n PREPARE_RUNTIME_ERR(err) << \"Wrong format of the range argument: '\" << RangeArg << \"' Should be a list of coma-separated distance-type values.\";\n THROW_RUNTIME_ERR(err);\n }\n }\n\n ToLower(SpaceTypeStr);\n string SpaceType;\n\n shared_ptr SpaceParams;\n\n {\n vector desc;\n ParseSpaceArg(SpaceTypeStr, SpaceType, desc);\n SpaceParams = shared_ptr(new AnyParams(desc));\n }\n\n\n unique_ptr> space(SpaceFactoryRegistry::\n Instance().CreateSpace(SpaceType, *SpaceParams));\n\n if (NULL == space.get()) {\n PREPARE_RUNTIME_ERR(err) << \"Cannot create space: '\" << SpaceType;\n THROW_RUNTIME_ERR(err);\n }\n \n ExperimentConfig config(*space,\n DataFile, QueryFile, TestSetQty,\n MaxNumData, MaxNumQuery,\n knn, eps, range);\n\n config.ReadDataset();\n MemUsage mem_usage_measure;\n\n\n std::vector MethDescStr;\n std::vector MethParams;\n vector MemUsage;\n\n vector> ExpResRange(config.GetRange().size(),\n vector(vTestCases.size()));\n vector> ExpResKNN(config.GetKNN().size(),\n vector(vTestCases.size()));\n\n for (size_t MethNum = 0; MethNum < vTestCases.size(); ++MethNum) {\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n ExpResRange[i][MethNum] = new MetaAnalysis(config.GetTestSetToRunQty());\n }\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n ExpResKNN[i][MethNum] = new MetaAnalysis(config.GetTestSetToRunQty());\n }\n }\n\n\n for (int TestSetId = 0; TestSetId < config.GetTestSetToRunQty(); ++TestSetId) {\n config.SelectTestSet(TestSetId);\n\n LOG(LIB_INFO) << \">>>> Test set id: \" << TestSetId << \" (set qty: \" << config.GetTestSetToRunQty() << \")\";\n\n GoldStandardManager managerGS(config);\n managerGS.Compute(ThreadTestQty, 0); // Keeping all GS entries, should be Ok here because our data sets are smallish\n \n for (size_t MethNum = 0; MethNum < vTestCases.size(); ++MethNum) {\n const string& MethodName = vTestCases[MethNum].mMethodName;\n\n shared_ptr IndexParams; \n vector> vQueryTimeParams;\n\n {\n vector desc;\n ParseArg(vTestCases[MethNum].mIndexParams, desc);\n IndexParams = shared_ptr(new AnyParams(desc));\n }\n\n {\n vector desc;\n ParseArg(vTestCases[MethNum].mQueryTimeParams, desc);\n vQueryTimeParams.push_back(shared_ptr(new AnyParams(desc)));\n }\n\n LOG(LIB_INFO) << \">>>> Index type : \" << MethodName;\n LOG(LIB_INFO) << \">>>> Index-time parameters: \" << IndexParams->ToString();\n\n const double vmsize_before = mem_usage_measure.get_vmsize();\n\n WallClockTimer wtm;\n\n wtm.reset();\n \n\n LOG(LIB_INFO) << \"Creating a new index\" ;\n\n shared_ptr> IndexPtr(\n MethodFactoryRegistry::Instance().\n CreateMethod(false /* don't print progress */,\n MethodName, \n SpaceType, config.GetSpace(), \n config.GetDataObjects())\n );\n\n IndexPtr->CreateIndex(*IndexParams);\n\n LOG(LIB_INFO) << \"==============================================\";\n\n const double vmsize_after = mem_usage_measure.get_vmsize();\n\n const double data_size = DataSpaceUsed(config.GetDataObjects()) / 1024.0 / 1024.0;\n\n const double TotalMemByMethod = vmsize_after - vmsize_before + data_size;\n\n wtm.split();\n\n LOG(LIB_INFO) << \">>>> Process memory usage: \" << vmsize_after << \" MBs\";\n LOG(LIB_INFO) << \">>>> Virtual memory usage: \" << TotalMemByMethod << \" MBs\";\n LOG(LIB_INFO) << \">>>> Data size: \" << data_size << \" MBs\";\n LOG(LIB_INFO) << \">>>> Time elapsed: \" << (wtm.elapsed()/double(1e6)) << \" sec\";\n\n /* \n * We need to repackage MetaAnalysis arrays:\n * RunAll will deal with only a single method and \n * a single set of query-time parameters.\n */ \n\n vector> ExpResRangeTmp(config.GetRange().size(), vector(1));\n vector> ExpResKNNTmp(config.GetKNN().size(), vector(1));\n\n\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n MetaAnalysis* res = ExpResRange[i][MethNum];\n res->SetMem(TestSetId, TotalMemByMethod);\n ExpResRangeTmp[i][0] = res;\n }\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n MetaAnalysis* res = ExpResKNN[i][MethNum];\n res->SetMem(TestSetId, TotalMemByMethod);\n ExpResKNNTmp[i][0] = res;\n }\n\n CHECK_MSG(vQueryTimeParams.size() == 1, \n \"Test integration code is currently can execute only one set of query-time parameters!\");\n\n Experiments::RunAll(true /* print progress */, \n ThreadTestQty,\n TestSetId,\n managerGS,\n ExpResRangeTmp, ExpResKNNTmp,\n config, \n *IndexPtr, \n vQueryTimeParams);\n\n }\n\n }\n\n for (size_t MethNum = 0; MethNum < vTestCases.size(); ++MethNum) {\n string Print, Data, Header;\n\n for (size_t i = 0; i < config.GetRange().size(); ++i) {\n MetaAnalysis* res = ExpResRange[i][MethNum];\n\n string cmdStr = CreateCmdStr(vTestCases[MethNum],\n true,\n ConvertToString(config.GetRange()[i]),\n DistType, \n SpaceTypeStr,\n ThreadTestQty,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n eps);\n cout << cmdStr << endl;\n LOG(LIB_INFO) << \"Command line params: \" << cmdStr;\n if (!ProcessAndCheckResults(cmdStr, \n DistType, SpaceType, \n vTestCases[MethNum], config, *res, Print)) {\n nFail++;\n cout << red << \"failed\" << no_color << \" (see logs for more details) \" << endl;\n } else {\n cout << green << \"passed\" << no_color << endl;\n }\n LOG(LIB_INFO) << \"Range: \" << config.GetRange()[i];\n LOG(LIB_INFO) << Print;\n LOG(LIB_INFO) << \"Data: \" << Header << Data;\n\n delete res;\n }\n\n for (size_t i = 0; i < config.GetKNN().size(); ++i) {\n MetaAnalysis* res = ExpResKNN[i][MethNum];\n\n string cmdStr = CreateCmdStr(vTestCases[MethNum],\n false,\n ConvertToString(config.GetKNN()[i]),\n DistType, \n SpaceTypeStr,\n ThreadTestQty,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n eps);\n\n cout << cmdStr << endl;\n LOG(LIB_INFO) << \"Command line params: \" << cmdStr;\n if (!ProcessAndCheckResults(cmdStr,\n DistType, SpaceType, \n vTestCases[MethNum], config, *res, Print)) {\n cout << red << \"failed\" << no_color << \" (see logs for more details) \" << endl;\n nFail++;\n } else {\n cout << green << \"passed\" << no_color << endl;\n }\n LOG(LIB_INFO) << \"KNN: \" << config.GetKNN()[i];\n LOG(LIB_INFO) << Print;\n LOG(LIB_INFO) << \"Data: \" << Header << Data;\n\n delete res;\n }\n }\n\n return nFail;\n}\n\ninline bool RunOneTest(const vector& vTestCases,\n string DistType, \n string SpaceTypeStr,\n unsigned ThreadTestQty,\n unsigned TestSetQty,\n const string& DataFile,\n const string& QueryFile,\n IdTypeUnsign MaxNumData,\n IdTypeUnsign MaxNumQuery,\n const string& KnnArg,\n const float eps,\n const string& RangeArg) {\n bool bTestRes = false;\n ToLower(DistType);\n if (DIST_TYPE_INT == DistType) {\n bTestRes = RunTestExper(vTestCases,\n DistType,\n SpaceTypeStr,\n ThreadTestQty,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n KnnArg,\n eps,\n RangeArg\n );\n } else if (DIST_TYPE_FLOAT == DistType) {\n bTestRes = RunTestExper(vTestCases,\n DistType,\n SpaceTypeStr,\n ThreadTestQty,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n KnnArg,\n eps,\n RangeArg\n );\n } else if (DIST_TYPE_DOUBLE == DistType) {\n bTestRes = RunTestExper(vTestCases,\n DistType,\n SpaceTypeStr,\n ThreadTestQty,\n TestSetQty,\n DataFile,\n QueryFile,\n MaxNumData,\n MaxNumQuery,\n KnnArg,\n eps,\n RangeArg\n );\n } else {\n PREPARE_RUNTIME_ERR(err) << \"Unknown distance value type: \" << DistType;\n THROW_RUNTIME_ERR(err);\n }\n\n return bTestRes;\n};\n\n#endif\n" }, { "path": "algorithms/NMSLIB/code/test/test_lpnorm.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n\n#include \"space/space_lp.h\"\n#include \"bunit.h\"\n#include \"testdataset.h\"\n\nnamespace similarity {\n\n#define FLOAT_TYPE double\n\nclass VectorDataset1 : public TestDataset {\n public:\n VectorDataset1() {\n FLOAT_TYPE arr[8][5] = {\n {0.459, 0.04, 0.086, 0.599, 0.555},\n {0.842, 0.572, 0.801, 0.136, 0.87},\n {0.42, 0.773, 0.554, 0.198, 0.461},\n {0.958, 0.057, 0.376, 0.663, 0.419},\n {0.261, 0.312, 0.7, 0.108, 0.588},\n {0.079, 0.663, 0.921, 0.901, 0.564},\n {0.463, 0.806, 0.672, 0.388, 0.225},\n {0.174, 0.884, 0.801, 0.563, 0.092}\n };\n\n for (int i = 0; i < 8; ++i) {\n dataobjects_.push_back(new Object(i + 1, -1, 5 * sizeof(FLOAT_TYPE), &arr[i]));\n }\n }\n};\n\n\nTEST(EuclideanDistance) {\n VectorDataset1 dataset;\n const ObjectVector& dataobjects = dataset.GetDataObjects();\n\n unique_ptr> space(new SpaceLp(2));\n const FLOAT_TYPE expected[8][8] = {\n {0.0, 1.120059, 0.96305295, 0.59664227, 0.85577684, 1.1493646, 1.04095581, 1.23306569},\n {1.120059, 0.0, 0.67128161, 0.96976079, 0.70403835, 1.1330097, 0.83340686, 1.15376817},\n {0.96305295, 0.67128161, 0.0, 1.02555984, 0.53230348, 0.87629218, 0.32963312, 0.63500551},\n {0.59664227, 0.96976079, 1.02555984, 0.0, 0.99619074, 1.23067908, 1.00344556, 1.26338394},\n {0.85577684, 0.70403835, 0.53230348, 0.99619074, 0.0, 0.91355952, 0.70412569, 0.89330565},\n {1.1493646, 1.1330097, 0.87629218, 1.23067908, 0.91355952, 0.0, 0.77974098, 0.63976089},\n {1.04095581, 0.83340686, 0.32963312, 1.00344556, 0.70412569, 0.77974098, 0.0, 0.39314119},\n {1.23306569, 1.15376817, 0.63500551, 1.26338394, 0.89330565, 0.63976089, 0.39314119, 0.0}\n };\n\n for (size_t i = 0; i < 8; ++i) {\n for (size_t j = 0; j < 8; ++j) {\n const FLOAT_TYPE d = space->IndexTimeDistance(dataobjects[i], dataobjects[j]);\n EXPECT_EQ_EPS(expected[i][j], d, 1e-5);\n }\n }\n}\n\nTEST(ManhattanDistance) {\n VectorDataset1 dataset;\n const ObjectVector& dataobjects = dataset.GetDataObjects();\n\n unique_ptr> space(new SpaceLp(1));\n const FLOAT_TYPE expected[8][8] = {\n {0.0, 2.408, 1.735, 1.006, 1.608, 2.149, 1.897, 2.343},\n {2.408, 0.0, 1.341, 2.034, 1.252, 2.045, 1.639, 2.185},\n {1.735, 1.341, 0.0, 1.939, 0.98299999, 1.624, 0.62, 1.338},\n {1.006, 2.034, 1.939, 0.0, 2.0, 2.41299999, 2.009, 2.463},\n {1.608, 1.252, 0.98299999, 2.0, 0.0, 1.571, 1.367, 1.711},\n {2.149, 2.045, 1.624, 2.41299999, 1.571, 0.0, 1.628, 1.246},\n {1.897, 1.639, 0.62, 2.009, 1.367, 1.628, 0.0, 0.804},\n {2.343, 2.185, 1.338, 2.463, 1.711, 1.246, 0.804, 0.0}\n };\n\n for (size_t i = 0; i < 8; ++i) {\n for (size_t j = 0; j < 8; ++j) {\n const FLOAT_TYPE d = space->IndexTimeDistance(dataobjects[i], dataobjects[j]);\n EXPECT_EQ_EPS(expected[i][j], d, 1e-5);\n }\n }\n}\n\nTEST(ChebyshevDistance) {\n VectorDataset1 dataset;\n const ObjectVector& dataobjects = dataset.GetDataObjects();\n\n unique_ptr> space(new SpaceLp(-1));\n const FLOAT_TYPE expected[8][8] = {\n {0.0, 0.715, 0.733, 0.499, 0.614, 0.835, 0.766, 0.844},\n {0.715, 0.0, 0.422, 0.527, 0.581, 0.765, 0.645, 0.778},\n {0.733, 0.422, 0.0, 0.716, 0.461, 0.703, 0.236, 0.369},\n {0.499, 0.527, 0.716, 0.0, 0.697, 0.879, 0.749, 0.827},\n {0.614, 0.581, 0.461, 0.697, 0.0, 0.793, 0.494, 0.572},\n {0.835, 0.765, 0.703, 0.879, 0.793, 0.0, 0.513, 0.472},\n {0.766, 0.645, 0.236, 0.749, 0.494, 0.513, 0.0, 0.289},\n {0.844, 0.778, 0.369, 0.827, 0.572, 0.472, 0.289, 0.0}\n };\n\n for (size_t i = 0; i < 8; ++i) {\n for (size_t j = 0; j < 8; ++j) {\n const FLOAT_TYPE d = space->IndexTimeDistance(dataobjects[i], dataobjects[j]);\n EXPECT_EQ_EPS(expected[i][j], d, 1e-5);\n }\n }\n}\n\n\n} // namespace similarity\n\n" }, { "path": "algorithms/NMSLIB/code/test/test_object.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \n#include \"object.h\"\n#include \"bunit.h\"\n\n#include \n#include \n\nusing namespace std;\n\nnamespace similarity {\n\nTEST(Object) {\n vector strs = {\n \"xyz\", \"beagcfa\", \"cea\", \"cb\",\n \"d\", \"c\", \"bdaf\", \"ddcd\",\n \"egbfa\", \"a\", \"fba\", \"bcccfe\",\n \"ab\", \"bfgbfdc\", \"bcbbgf\", \"bfbb\"\n };\n\n for (int i = 0; i < 16; ++i) {\n LabelType label = i * 1000 + i;\n IdType id = i + 1;\n Object* obj = new Object(id, label, strs[i].size(), strs[i].c_str());\n EXPECT_EQ(id, obj->id()); \n EXPECT_EQ(label, obj->label()); \n EXPECT_EQ(strs[i].size(), obj->datalength());\n\n string tmp(obj->data(), obj->datalength());\n EXPECT_TRUE(tmp == strs[i]);\n delete obj;\n }\n}\n\nTEST(ExtractLabel) {\n string str;\n int lab;\n\n str = \"label:3456 12 34 56\";\n lab = Object::extractLabel(str); \n\n EXPECT_EQ(lab, 3456);\n EXPECT_TRUE(str == \"12 34 56\");\n\n str = \"label:9 1\";\n\n lab = Object::extractLabel(str); \n\n EXPECT_EQ(lab, 9);\n EXPECT_TRUE(str == \"1\");\n\n str = \"33\";\n\n lab = Object::extractLabel(str); \n\n EXPECT_EQ(lab, EMPTY_LABEL);\n EXPECT_TRUE(str == \"33\");\n}\n\nTEST(AddLabel) {\n string str = \"1 23 4 5 6\";\n Object::addLabel(str, 2345);\n \n EXPECT_TRUE(str == \"label:2345 1 23 4 5 6\");\n}\n\n\n} // namespace similarity\n" }, { "path": "algorithms/NMSLIB/code/test/test_proj.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#include \"bunit.h\"\n#include \"distcomp.h\"\n#include \"randproj_util.h\"\n#include \"genrand_vect.h\"\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nbool TestRandProj(size_t srcDim, size_t dstDim, size_t rep, dist_t eps) {\n vector> projMatr;\n\n for (size_t r = 0; r < rep; ++r) {\n // 1. Generate the random matrix\n initRandProj(srcDim, dstDim, \n true, // yes, do create an orthonormal basis\n projMatr); \n // 2. Check if we got an orthonormal basis\n for (int i = 0; i < dstDim; ++i) {\n dist_t v = ScalarProductSIMD(&projMatr[i][0], &projMatr[i][0], srcDim);\n if (fabs(v - static_cast(1)) > eps) {\n LOG(LIB_ERROR) << \"Expected norm 1, got: \" << v \n << \" srcDim = \" << srcDim << \" dstDim = \" << dstDim\n << \" type: \" << typeid(dist_t).name();\n return false;\n }\n for (int k = i + 1; k < dstDim; ++k) {\n v = ScalarProductSIMD(&projMatr[i][0], &projMatr[k][0], srcDim);\n if (fabs(v - static_cast(0)) > eps) {\n LOG(LIB_ERROR) << \"Expected product 0, got: \" << v \n << \" srcDim = \" << srcDim << \" dstDim = \" << dstDim\n << \" type: \" << typeid(dist_t).name();\n return false;\n }\n }\n }\n } \n return true;\n}\n\nTEST(TestRandProj) {\n int nTest = 0;\n int nFail = 0;\n\n for (int srcDim = 1; srcDim <= 128; ) {\n for (int dstDim = 1; dstDim < srcDim; ) {\n ++nTest;\n nFail += !TestRandProj(srcDim, dstDim, 2, 1e-4f);\n ++nTest;\n nFail += !TestRandProj(srcDim, dstDim, 2, 1e-8);\n dstDim += (srcDim < 32 ? 1:8);\n }\n srcDim += (srcDim < 32 ? 1:8);\n }\n\n LOG(LIB_INFO) << nTest << \" (sub) tests performed \" << nFail << \" failed\";\n\n EXPECT_EQ(0, nFail);\n}\n\n}\n" }, { "path": "algorithms/NMSLIB/code/test/test_space_serial.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifdef _MSC_VER\n#define _SCL_SECURE_NO_WARNINGS\n#endif\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"bunit.h\"\n#include \"space.h\"\n#include \"spacefactory.h\"\n#include \"testdataset.h\"\n#include \"params.h\"\n#include \"space/space_sparse_lp.h\"\n#include \"space/space_sparse_scalar.h\"\n#include \"space/space_sparse_vector_inter.h\"\n#include \"space/space_sparse_scalar_fast.h\"\n#include \"space/space_sparse_vector.h\"\n#include \"space/space_scalar.h\"\n#include \"space/space_word_embed.h\"\n\n#define MAX_NUM_REC 10\n\nnamespace similarity {\n\nusing namespace std;\n\ntemplate \nbool fullTestCommon(Space* pSpace, const ObjectVector& dataSet1, const vector& vExternIds1, size_t maxNumRec, const string& tmpFileName,bool bTestExternId) {\n pSpace->WriteDataset(dataSet1, vExternIds1, tmpFileName);\n\n ObjectVector dataSet2;\n vector vExternIds2;\n\n unique_ptr inpState(pSpace->ReadDataset(dataSet2, vExternIds2, tmpFileName));\n pSpace->UpdateParamsFromFile(*inpState);\n\n if (maxNumRec != dataSet2.size()) {\n LOG(LIB_ERROR) << \"Expected to read \" << maxNumRec << \" records from \" \n \"dataSet, but read only: \" << dataSet2.size();\n return false;\n }\n\n if (vExternIds2.size() != dataSet2.size()) {\n LOG(LIB_ERROR) << \"The number of external IDs (\" << vExternIds1.size() << \") is different from the number of records: \" << dataSet2.size();\n return false;\n }\n\n for (size_t i = 0; i < maxNumRec; ++i) {\n if (bTestExternId) {\n if (vExternIds1[i] != vExternIds2[i]) {\n LOG(LIB_ERROR) << \"External IDs are different, i = \" << i << \" id1 = '\" << vExternIds1[i] << \"' id2 = '\" << vExternIds2[i] << \"'\" ;\n return false;\n }\n }\n if (!pSpace->ApproxEqual(*dataSet1[i], *dataSet2[i])) {\n LOG(LIB_ERROR) << \"Objects are different, i = \" << i;\n LOG(LIB_ERROR) << \"Object 1 string representation produced by the space:\" <<\n pSpace->CreateStrFromObj(dataSet1[i], vExternIds1[i]);\n LOG(LIB_ERROR) << \"Object 2 string representation produced by the space:\" <<\n pSpace->CreateStrFromObj(dataSet2[i], vExternIds2[i]);\n return false;\n }\n }\n\n return true;\n}\n\ntemplate \nbool fullTest(const string& dataSetFileName, size_t maxNumRec, const string& tmpFileName, const string& spaceName, const char* pSpaceParams[], bool bTestExternId) {\n vector spaceParams;\n\n LOG(LIB_INFO) << \"Space name: \" << spaceName << \" distance type: \" << DistTypeName() << \" data file: \" << dataSetFileName << \" maxNumRec=\" << maxNumRec;\n\n while (*pSpaceParams) {\n spaceParams.push_back(*pSpaceParams++);\n }\n\n unique_ptr> space(SpaceFactoryRegistry::\n Instance().CreateSpace(spaceName, spaceParams));\n\n ObjectVector dataSet1;\n vector vExternIds1;\n\n unique_ptr inpState(space->ReadDataset(dataSet1, vExternIds1, sampleDataPrefix + dataSetFileName, maxNumRec));\n space->UpdateParamsFromFile(*inpState);\n\n if (dataSet1.size() != maxNumRec) {\n LOG(LIB_ERROR) << \"Bug or poorly designed test, expected to read \" << maxNumRec << \" records from \" \n << dataSetFileName << \", but read only: \" << dataSet1.size();\n return false;\n }\n\n return fullTestCommon(space.get(), dataSet1, vExternIds1, maxNumRec, tmpFileName, bTestExternId); \n}\n\ntemplate \nbool fullTest(const vector& dataSetStr, size_t maxNumRec, const string& tmpFileName, const string& spaceName, const char* pSpaceParams[], bool bTestExternId) {\n vector spaceParams;\n\n LOG(LIB_INFO) << \"Space name: \" << spaceName << \" maxNumRec=\" << maxNumRec;\n\n while (*pSpaceParams) {\n spaceParams.push_back(*pSpaceParams++);\n }\n\n unique_ptr> space(SpaceFactoryRegistry::\n Instance().CreateSpace(spaceName, spaceParams));\n\n ObjectVector dataSet1;\n vector vExternIds1;\n\n IdType id = 0;\n for (string s: dataSetStr) {\n stringstream ss;\n ss << id;\n \n dataSet1.push_back(space->CreateObjFromStr(id++, -1, s, NULL).release());\n vExternIds1.push_back(ss.str());\n \n if (id >= maxNumRec) break;\n }\n\n if (dataSet1.size() < maxNumRec) {\n LOG(LIB_ERROR) << \"Bug or poorly designed test, expected to create \" << maxNumRec << \" records from array but read only: \" << dataSet1.size();\n return false;\n }\n\n return fullTestCommon(space.get(), dataSet1, vExternIds1, maxNumRec, tmpFileName, bTestExternId); \n}\n\nconst char *emptyParams[] = {NULL};\nconst char *paramsDistL2[] = {\"dist=\" SPACE_WORD_EMBED_DIST_L2, NULL};\nconst char *paramsDistCosine[] = {\"dist=\" SPACE_WORD_EMBED_DIST_COSINE, NULL};\n\nTEST(Test_WordEmbedSpace) {\n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(\"glove.6B.100d_100.txt\", maxNumRec, \"tmp_out_file.txt\", SPACE_WORD_EMBED, paramsDistL2, true));\n EXPECT_EQ(true, fullTest(\"glove.6B.100d_100.txt\", maxNumRec, \"tmp_out_file.txt\",SPACE_WORD_EMBED, paramsDistCosine, true));\n EXPECT_EQ(true, fullTest(\"glove.6B.100d_100.txt\", maxNumRec, \"tmp_out_file.txt\", SPACE_WORD_EMBED, paramsDistL2, true));\n EXPECT_EQ(true, fullTest(\"glove.6B.100d_100.txt\", maxNumRec, \"tmp_out_file.txt\",SPACE_WORD_EMBED, paramsDistCosine, true));\n }\n}\n\nTEST(Test_DenseVectorSpace) {\n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(\"final128_10K.txt\", maxNumRec, \"tmp_out_file.txt\", \"l2\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"final128_10K.txt\", maxNumRec, \"tmp_out_file.txt\", \"l2\", emptyParams, false));\n }\n}\n\nTEST(Test_DenseVectorKLDiv) {\n // Test KL-diverg. with and without precomputation of logarithms\n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(\"final128_10K.txt\", maxNumRec, \"tmp_out_file.txt\", \"kldivgenfast\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"final128_10K.txt\", maxNumRec, \"tmp_out_file.txt\", \"kldivgenfast\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"final128_10K.txt\", maxNumRec, \"tmp_out_file.txt\", \"kldivgenslow\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"final128_10K.txt\", maxNumRec, \"tmp_out_file.txt\", \"kldivgenslow\", emptyParams, false));\n }\n}\n\nTEST(Test_SparseVectorSpace) {\n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(\"sparse_5K.txt\", maxNumRec, \"tmp_out_file.txt\", \"cosinesimil_sparse\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"sparse_5K.txt\", maxNumRec, \"tmp_out_file.txt\", \"angulardist_sparse\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"sparse_5K.txt\", maxNumRec, \"tmp_out_file.txt\", \"cosinesimil_sparse\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"sparse_5K.txt\", maxNumRec, \"tmp_out_file.txt\", \"angulardist_sparse\", emptyParams, false));\n }\n}\n\nTEST(Test_SparseVectorSpaceFast) {\n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(\"sparse_5K.txt\", maxNumRec, \"tmp_out_file.txt\", \"cosinesimil_sparse_fast\", emptyParams, false));\n EXPECT_EQ(true, fullTest(\"sparse_5K.txt\", maxNumRec, \"tmp_out_file.txt\", \"angulardist_sparse_fast\", emptyParams, false));\n }\n}\n\nTEST(Test_StringSpace) {\n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(\"dna32_4_5K.txt\", maxNumRec, \"tmp_out_file.txt\", \"leven\", emptyParams, false));\n }\n}\n\nTEST(Test_BitHamming) {\n vector testVect;\n\n for (size_t i = 0; i < MAX_NUM_REC; ++i) {\n stringstream ss;\n\n for (size_t k = 0; k < 128; ++k) {\n if (k) ss << \" \";\n ss << (RandomInt() % 2);\n }\n testVect.push_back(ss.str());\n }\n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(testVect, maxNumRec, \"tmp_out_file.txt\", \"bit_hamming\", emptyParams, false));\n }\n}\n\n#if !defined(_MSC_VER)\nTEST(Test_SFQD) {\n const char* sqfdParams[] = {\"alpha=1\", NULL} ; \n for (size_t maxNumRec = 1; maxNumRec < MAX_NUM_REC; ++maxNumRec) {\n EXPECT_EQ(true, fullTest(\"sqfd20_10k_10k.txt\", maxNumRec, \"tmp_out_file.txt\", \"sqfd_heuristic_func\", sqfdParams, false));\n EXPECT_EQ(true, fullTest(\"sqfd20_10k_10k.txt\", maxNumRec, \"tmp_out_file.txt\", \"sqfd_heuristic_func\", sqfdParams, false));\n }\n}\n#endif\n\n\n}\n" }, { "path": "algorithms/NMSLIB/code/test/test_sqfd.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib\n *\n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#if !defined(_MSC_VER)\n\n#include \n#include \"space.h\"\n#include \"bunit.h\"\n#include \"testdataset.h\"\n#include \"space_sqfd.h\"\n\nnamespace similarity {\n\nObject* CreateSqfdObject(\n const std::vector>& cluster,\n const std::vector& weight) {\n CHECK(cluster.size() > 0);\n CHECK(weight.size() == cluster.size());\n std::vector buf(\n 2*sizeof(int) + (cluster[0].size() + 1) * cluster.size() * sizeof(float));\n int* h = reinterpret_cast(&buf[0]);\n h[0] = cluster.size();\n h[1] = cluster[0].size();\n float* obj = reinterpret_cast(&buf[2*sizeof(int)]);\n int pos = 0;\n for (size_t i = 0; i < cluster.size(); ++i) {\n for (size_t j = 0; j < cluster[i].size(); ++j) {\n obj[pos++] = cluster[i][j];\n }\n obj[pos++] = weight[i];\n }\n return new Object(-1, -1, buf.size(), &buf[0]);\n}\n\nTEST(Sqfd_From_Article) {\n std::vector> cq = {{3,3}, {8,7}};\n std::vector wq = {0.5, 0.5};\n Object* q = CreateSqfdObject(cq, wq);\n\n std::vector> co = {{4,7}, {9,5}, {8,1}};\n std::vector wo = {0.5, 0.25, 0.25};\n Object* o = CreateSqfdObject(co, wo);\n\n SqfdFunction* f = new SqfdHeuristicFunction(1.0);\n Space* space = new SpaceSqfd(f);\n\n /*\n >>> import numpy as np\n >>> import math\n >>> w = np.array([0.5,0.5,-0.5,-0.25,-0.25])\n >>> a = np.array([[1.0, 0.135, 0.195, 0.137, 0.157],\n [0.135, 1.0, 0.2, 0.309, 0.143],\n [0.195, 0.2, 1.0, 0.157, 0.122],\n [0.137, 0.309, 0.157, 1.0, 0.195],\n [0.157, 0.143, 0.122, 0.195, 1.0]])\n >>> math.sqrt(w.dot(a).dot(w.transpose()))\n 0.807\n */\n\n float d = space->IndexTimeDistance(q, o);\n EXPECT_EQ_EPS(d, 0.808f, 0.01f);\n\n delete space;\n delete q;\n delete o;\n}\n\nTEST(Sqfd) {\n std::vector> cq = {\n {0.382806,0.397073,0.661498,0.683582,0.203314,0.0871583,1},\n {0.482246,0.368699,0.701657,0.731006,0.175442,0.132232,0.20056},\n {0.740454,0.434634,0.661071,0.850084,0.681469,0.0610024,0.218037},\n {0.178604,0.416208,0.62079,0.437091,0.757451,0.0982573,0.256335},\n {0.518211,0.432369,0.639439,0.23629,0.690716,0.191468,0.193767},\n {0.250961,0.416317,0.621276,0.344846,0.763613,0.0738424,1},\n {0.609122,0.331734,0.760648,0.710042,0.769226,0.0996582,1},\n {0.744822,0.425876,0.551634,0.223641,0.23818,0.0885243,1},\n {0.843675,0.543647,0.541379,0.798141,0.496724,0.0357157,1},\n {0.612551,0.408074,0.600394,0.266899,0.234377,0.143155,0.252654}};\n std::vector wq = {\n 0.0822,0.1005,0.1314,0.0878,0.1087,0.1413,0.0397,0.0886,0.0832,0.1366};\n Object* q = CreateSqfdObject(cq, wq);\n\n std::vector> co = {\n {0.720299,0.460648,0.609983,0.733792,0.279245,0.0940223,0.940909},\n {0.732504,0.470709,0.584041,0.849335,0.51135,0.105338,0.100655},\n {0.790358,0.446342,0.585623,0.633481,0.834874,0.0901916,0.855607},\n {0.265014,0.441256,0.551832,0.435405,0.199537,0.111734,0.240841},\n {0.714692,0.469428,0.333677,0.1261,0.297041,0.0146298,0.987917},\n {0.194637,0.449039,0.533339,0.482084,0.214012,0.0459264,1},\n {0.288555,0.430071,0.558277,0.17054,0.765986,0.0694933,1},\n {0.268943,0.460447,0.544101,0.583028,0.829013,0.0607609,1},\n {0.23752,0.443694,0.554333,0.345023,0.773767,0.089284,0.310363},\n {0.55076,0.411417,0.602403,0.311277,0.628119,0.171292,0.163618}};\n std::vector wo = {\n 0.066,0.2385,0.0651,0.1085,0.12,0.0968,0.0684,0.0541,0.0965,0.0861};\n Object* o = CreateSqfdObject(co, wo);\n\n SqfdFunction* f = new SqfdHeuristicFunction(1.0);\n Space* space = new SpaceSqfd(f);\n\n float d = space->IndexTimeDistance(q, o);\n EXPECT_EQ_EPS(d, 0.214f, 0.01f);\n\n delete space;\n delete q;\n delete o;\n}\n\n} // namespace similarity\n\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/test/test_timer.cc", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/) and others.\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n#ifndef _MSC_VER\n\n#include \n\n#include \"logging.h\"\n#include \"ztimer.h\"\n#include \"bunit.h\"\n\nnamespace similarity {\n\n/*\n * These test is intended to run only on Unix-like systems.\n */\n\n/**\n * author: Preston Bannister\n */\nclass WallClockTimerBannister {\npublic:\n struct timeval t1, t2;\n WallClockTimerBannister() :\n t1(), t2() {\n gettimeofday(&t1, 0);\n t2 = t1;\n }\n void reset() {\n gettimeofday(&t1, 0);\n t2 = t1;\n }\n uint64_t elapsed() {\n return ((t2.tv_sec - t1.tv_sec) * 1000ULL * 1000ULL) + ((t2.tv_usec - t1. tv_usec));\n }\n uint64_t split() {\n gettimeofday(&t2, 0);\n return elapsed();\n }\n};\n\nvoid BurnCPU(size_t qty = 10000000000) {\n size_t sum = 0;\n for (size_t i = 0; i < qty; ++i) {\n sum += i;\n sum *= qty;\n }\n LOG(LIB_INFO) << \"Ignore: \" << sum;\n}\n\n#ifdef DISABLE_LONG_TESTS\nTEST(DISABLE_TestTimer) {\n#else\nTEST(TestTimer) {\n#endif\n WallClockTimerBannister oldz;\n WallClockTimer z;\n\n BurnCPU();\n oldz.split();\n z.split();\n\n LOG(LIB_INFO) << \"Timer: \" << z.elapsed() << \" : \" << oldz.elapsed();\n // We expect both timers to differ in at most 0.1 ms\n EXPECT_EQ(std::abs(static_cast(z.elapsed()) - static_cast(oldz.elapsed())) < 100, true);\n\n BurnCPU();\n oldz.split();\n z.split();\n\n LOG(LIB_INFO) << \"Timer: \" << z.elapsed() << \" : \" << oldz.elapsed();\n // We expect both timers to differ in at most 0.1 ms\n EXPECT_EQ(std::abs(static_cast(z.elapsed()) - static_cast(oldz.elapsed())) < 100, true);\n\n z.reset();\n oldz.reset();\n\n BurnCPU();\n oldz.split();\n z.split();\n\n LOG(LIB_INFO) << \"Timer: \" << z.elapsed() << \" : \" << oldz.elapsed();\n // We expect both timers to differ in at most 0.1 ms\n EXPECT_EQ(std::abs(static_cast(z.elapsed()) - static_cast(oldz.elapsed())) < 100, true);\n}\n\n} // namespace similarity\n#endif\n\n" }, { "path": "algorithms/NMSLIB/code/test/testdataset.h", "content": "/**\n * Non-metric Space Library\n *\n * Authors: Bilegsaikhan Naidan (https://github.com/bileg), Leonid Boytsov (http://boytsov.info).\n * With contributions from Lawrence Cayton (http://lcayton.com/).\n *\n * For the complete list of contributors and further details see:\n * https://github.com/searchivarius/NonMetricSpaceLib \n * \n * Copyright (c) 2014\n *\n * This code is released under the\n * Apache License Version 2.0 http://www.apache.org/licenses/.\n *\n */\n\n#ifndef _TEST_DATASET_H_\n#define _TEST_DATASET_H_\n\n#include \"object.h\"\n#include \"space.h\"\n\n#include \n\nnamespace similarity {\n\nusing std::string;\n\n#ifdef _MSC_VER\nconst string sampleDataPrefix = string(\"..\") + PATH_SEPARATOR +\n string(\"..\") + PATH_SEPARATOR +\n string(\"..\") + PATH_SEPARATOR +\n string(\"..\") + PATH_SEPARATOR + string(\"sample_data\") + PATH_SEPARATOR;\n#else\nconst string sampleDataPrefix = string(\"..\") + PATH_SEPARATOR + string(\"sample_data\") + PATH_SEPARATOR;\n#endif\n\nclass TestDataset {\n public:\n virtual ~TestDataset() {\n for (auto it = dataobjects_.begin(); it != dataobjects_.end(); ++it) {\n delete *it;\n }\n }\n\n const ObjectVector& GetDataObjects() const { return dataobjects_; }\n\n protected:\n ObjectVector dataobjects_;\n};\n\n} // namespace similarity\n\n#endif // _TEST_DATASET_H_\n" }, { "path": "algorithms/NMSLIB/script/run_HNSW.sh", "content": "data_path=../../../data\nquery_path=../../../data\nresult_path=../results\nmkdir ${result_path}\nmkdir ../indices\n\n\nfor data in \"audio\"\ndo\nn=53387\nm=200\nK=20\nM=10\nNN=200\n\ndataset=${data_path}/${data}/${data}_base.txt\nquery=${data_path}/${data}/${data}_query.txt\nresult_path=${result_path}/${data}_HNSW_${M}M_${NN}NN.txt\nindices_path=\"../indices/${data}_${M}M_${NN}NN.hnsw\"\n\nStr=\"--queryTimeParams efSearch=1 \"\nfor i in 2 3 4 5 6 8 10 15 20 30 50 70 80 100 150 200 250 300 400 500 600 700 800 1000 1300 1500 1700 2000\ndo\nStr=\"${Str} -t efSearch=$i \"\ndone\n\n../code/release/experiment \\\n--distType float --spaceType l2 \\\n--knn $K \\\n--dataFile ${dataset} --maxNumData $n --queryFile ${query} --maxNumQuery 200 -o ${result_path} -a \\\n--method hnsw \\\n--createIndex M=${M},efConstruction=${NN},indexThreadQty=100,skip_optimized_index=1 \\\n${Str} \n\ndone\n" }, { "path": "algorithms/NMSLIB/script/run_NAPP.sh", "content": "data_path=../../../data\nquery_path=../../../data\nresult_path=../results\nmkdir ${result_path}\n\nfor data in \"audio\"\ndo\nn=53387\nm=200\nK=20\nPP=512\nPI=64\n\ndataset=${data_path}/${data}/${data}_base.txt\nquery=${data_path}/${data}/${data}_query.txt\nresult_path=${result_path}/${data}_NAPP_${PP}PP_${PI}PI.txt\n\nStr=\"--queryTimeParams numPivotSearch=10 \"\nfor i in 15 20 25 30 35 40 43 45 47 48 50 52 53 55 60\ndo\nStr=\"${Str} -t numPivotSearch=$i \"\ndone\n\n../code/release/experiment \\\n--distType float --spaceType l2 \\\n--knn $K \\\n--dataFile ${dataset} --maxNumData $n --queryFile ${query} --maxNumQuery $m -o ${result_path} -a \\\n--method napp \\\n--createIndex numPivot=${PP},numPivotIndex=${PI},chunkIndexSize=1024,indexThreadQty=1 \\\n--saveIndex ../indices/${data}_${PP}PP_${PI}PI.napp \\\n${Str}\n\n\ndone\n" }, { "path": "algorithms/NMSLIB/script/run_SW.sh", "content": "data_path=../../../data\nquery_path=../../../data\nresult_path=../results\nmkdir ${result_path}\nmkdir \"../indices\"\n\nfor data in \"audio\"\ndo\nn=53387\nm=200\nK=20\nNN=10\nI=5\n\ndataset=${data_path}/${data}/${data}_base.txt\nquery=${data_path}/${data}/${data}_query.txt\nresult_path=${result_path}/${data}_SW_${NN}NN_${I}I.txt\n\nStr=\"--queryTimeParams initSearchAttempts=1,efSearch=1 \"\nfor i in 2 3 4 5 6 8 10 15 20 30 50 70 80 100 150 200 250 300 500 700 1000 1500 1700 2000 2500\ndo\nStr=\"${Str} -t initSearchAttempts=1,efSearch=$i \"\ndone\n\n../code/release/experiment \\\n--distType float --spaceType l2 \\\n--knn 20 \\\n--dataFile ${dataset} --maxNumData $n --queryFile ${query} --maxNumQuery 200 -o ${result_path} -a \\\n--method sw-graph \\\n--createIndex NN=${NN},indexThreadQty=41 \\\n--saveIndex ../indices/${data}_${NN}NN_${I}I.sw \\\n${Str}\n\ndone\n \n" }, { "path": "algorithms/NMSLIB/script/run_vptree.sh", "content": "data_path=../../../data\nquery_path=../../../data\nresult_path=../results\nmkdir ${result_path}\n\nfor data in \"audio\"\ndo\nn=53387\nm=200\nK=20\nbucket_size=100\n\ndataset=${data_path}/${data}/${data}_base.txt\nquery=${data_path}/${data}/${data}_query.txt\nresult_path=${result_path}/${data}_vptree_${bucket_size}B.txt\n\nfor i in 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.95\ndo\n../code/release/experiment \\\n--distType float --spaceType l2 \\\n--knn $K \\\n--dataFile ${dataset} --maxNumData $n --queryFile ${query} --maxNumQuery 200 -o ${result_path} -a \\\n--method vptree \\\n--createIndex bucketSize=${bucket_size},desiredRecall=$i,tuneK=$K,chunkBucket=1\ndone\ndone\n" }, { "path": "algorithms/NSH/README.md", "content": "# NSH - Neighbor-Sensitive Hashing\n\nThis is a modified version of NSH. The original version can be downloaded from [here](https://github.com/pyongjoo/nsh), which is implemented with MATLAB. \n\nWe use the original code to generate the hashcodes of the data points and transformation parameters. We rewrite the searching part in C++ to transform the query points to binrary vectors and use the hierarchical clustering trees in FLANN (C++) to index the data binary vectors to support more efficient search.\n\n## Compile (NSH/src)\n\nTo compile NSH, go to NSH/src and run \n```\nmake\n```\nto get the executable file `NSH`.\n\n## Index (NSH/preprocess)\n\nTo build the index of NSH, enter into preprocess folder and run \n```\nmain.m\n```\nin MATLAB to get the transformed data and transformation parameters.\nAlternatively, one may run\n```\nmatlab -nodisplay -nojvm -nosplash -nodesktop -r \"try, run('main.m'), end, exit(0)\"\n```\n\n## Search (NSH/script)\n\ngo to NSH/src and run \n```\nsearcher.sh \n```\nto obtain the searching performance of NSH. We use c value (e.g. the number of the visited points) to achieve the trade-off between search speed and search quality (recall).\n\n\n" }, { "path": "algorithms/NSH/preprocess/compactbit.m", "content": "function cb = compactbit(b)\n%\n% b = bits array\n% cb = compacted string of bits (using words of 'word' bits)\n\n[nSamples nbits] = size(b);\nnwords = ceil(nbits/8);\ncb = zeros([nSamples nwords], 'uint8');\n\nfor j = 1:nbits\n w = ceil(j/8);\n cb(:,w) = bitset(cb(:,w), mod(j-1,8)+1, b(:,j));\nend\n\nend" }, { "path": "algorithms/NSH/preprocess/computeRecall.m", "content": "function recall = computeRecall(DB, G, r)\n% return the recall of k-NN retrieval\n% \n% Input\n% DB: distance matrix calculated from the hashed matrix\n% r: number of the documents retrieved \n%\n% The dimensions of DX and DB are [#query, #data].\n\n[qn xn] = size(DB);\nassert(r <= xn);\n\nk = size(G,2);\nrecall = zeros(qn, 1);\n\nparfor i = 1:qn\n %qid = i;\n\n %groundtruth_dist = distMat(q, X(G(qid,:),:));\n %min_index = mink(gold_standard_dist, k);\n groundtruth = G(i, :);\n [B,I] = sort(double(DB(i,:)));\n hash_neighbor = I(1:r);\n %hash_neighbor = mink(double(DB(i,:)), r);\n\n recall(i) = length(intersect(groundtruth, hash_neighbor)) / k;\nend\n\nrecall = mean(recall);\n\n" }, { "path": "algorithms/NSH/preprocess/distMat.m", "content": "function D=distMat(P1, P2)\r\n%\r\n% Euclidian distances between vectors\r\n% each vector is one row\r\n \r\nif nargin == 2\r\n P1 = double(P1);\r\n P2 = double(P2);\r\n \r\n X1=repmat(sum(P1.^2,2),[1 size(P2,1)]);\r\n X2=repmat(sum(P2.^2,2),[1 size(P1,1)]);\r\n R=P1*P2';\r\n D=real(sqrt(X1+X2'-2*R));\r\nelse\r\n P1 = double(P1);\r\n\r\n % each vector is one row\r\n X1=repmat(sum(P1.^2,2),[1 size(P1,1)]);\r\n R=P1*P1';\r\n D=X1+X1'-2*R;\r\n D = real(sqrt(D));\r\nend\r\n\r\n\r\n" }, { "path": "algorithms/NSH/preprocess/fvecs_read.m", "content": "% Read a set of vectors stored in the fvec format (int + n * float)\r\n% The function returns a set of output vector (one vector per column)\r\n%\r\n% Syntax: \r\n% v = fvecs_read (filename) -> read all vectors\r\n% v = fvecs_read (filename, n) -> read n vectors \r\n% v = fvecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\r\nfunction v = fvecs_read (filename, bounds)\r\n\r\n% open the file and count the number of descriptors\r\nfid = fopen (filename, 'rb');\r\n \r\nif fid == -1\r\n error ('I/O error : Unable to open the file %s\\n', filename)\r\nend\r\n\r\n% Read the vector size\r\nd = fread (fid, 1, 'int');\r\nvecsizeof = 1 * 4 + d * 4;\r\n\r\n% Get the number of vectrors\r\nfseek (fid, 0, 1);\r\na = 1;\r\nbmax = ftell (fid) / vecsizeof;\r\nb = bmax;\r\n\r\nif nargin >= 2\r\n if length (bounds) == 1\r\n b = bounds;\r\n \r\n elseif length (bounds) == 2\r\n a = bounds(1);\r\n b = bounds(2); \r\n end\r\nend\r\n\r\nassert (a >= 1);\r\nif b > bmax\r\n b = bmax;\r\nend\r\n\r\nif b == 0 | b < a\r\n v = [];\r\n fclose (fid);\r\n return;\r\nend\r\n\r\n% compute the number of vectors that are really read and go in starting positions\r\nn = b - a + 1;\r\nfseek (fid, (a - 1) * vecsizeof, -1);\r\n\r\n% read n vectors\r\nv = fread (fid, (d + 1) * n, 'float=>single');\r\nv = reshape (v, d + 1, n);\r\n\r\n% Check if the first column (dimension of the vectors) is correct\r\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\r\nv = v (2:end, :);\r\n \r\nfclose (fid);\r\n\r\n" }, { "path": "algorithms/NSH/preprocess/hammingDist.m", "content": "function Dh=hammingDist(B1, B2)\r\n%\r\n% Compute hamming distance between two sets of samples (B1, B2)\r\n%\r\n% Dh=hammingDist(B1, B2);\r\n%\r\n% Input\r\n% B1, B2: compact bit vectors. Each datapoint is one row.\r\n% size(B1) = [ndatapoints1, nwords]\r\n% size(B2) = [ndatapoints2, nwords]\r\n% It is faster if ndatapoints1 < ndatapoints2\r\n% \r\n% Output\r\n% Dh = hamming distance. \r\n% size(Dh) = [ndatapoints1, ndatapoints2]\r\n\r\n% example query\r\n% Dhamm = hammingDist(B2, B1);\r\n% this will give the same result than:\r\n% Dhamm = distMat(U2>0, U1>0).^2;\r\n% the size of the distance matrix is:\r\n% size(Dhamm) = [Ntest x Ntraining]\r\n\r\n% loop-up table:\r\nbit_in_char = uint16([...\r\n 0 1 1 2 1 2 2 3 1 2 2 3 2 3 3 4 1 2 2 3 2 3 ...\r\n 3 4 2 3 3 4 3 4 4 5 1 2 2 3 2 3 3 4 2 3 3 4 ...\r\n 3 4 4 5 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 1 2 ...\r\n 2 3 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 ...\r\n 3 4 4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 ...\r\n 5 6 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 1 2 2 3 ...\r\n 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 3 4 ...\r\n 4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 ...\r\n 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 2 3 3 4 3 4 ...\r\n 4 5 3 4 4 5 4 5 5 6 3 4 4 5 4 5 5 6 4 5 5 6 ...\r\n 5 6 6 7 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 4 5 ...\r\n 5 6 5 6 6 7 5 6 6 7 6 7 7 8]);\r\n\r\nn1 = size(B1,1);\r\n[n2, nwords] = size(B2);\r\n\r\nDh = zeros([n1 n2], 'uint16');\r\nparfor j = 1:n1\r\n for n=1:nwords\r\n y = bitxor(B1(j,n),B2(:,n));\r\n Dh(j,:) = Dh(j,:) + bit_in_char(y+1);\r\n end\r\nend\r\n" }, { "path": "algorithms/NSH/preprocess/ivecs_read.m", "content": "% Read a set of vectors stored in the ivec format (int + n * int)\r\n% The function returns a set of output vector (one vector per column)\r\n%\r\n% Syntax: \r\n% v = ivecs_read (filename) -> read all vectors\r\n% v = ivecs_read (filename, n) -> read n vectors \r\n% v = ivecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\r\nfunction v = ivecs_read (filename, bounds)\r\n\r\n% open the file and count the number of descriptors\r\nfid = fopen (filename, 'rb');\r\n \r\nif fid == -1\r\n error ('I/O error : Unable to open the file %s\\n', filename)\r\nend\r\n\r\n% Read the vector size\r\nd = fread (fid, 1, 'int');\r\nvecsizeof = 1 * 4 + d * 4;\r\n\r\n% Get the number of vectrors\r\nfseek (fid, 0, 1);\r\na = 1;\r\nbmax = ftell (fid) / vecsizeof;\r\nb = bmax;\r\n\r\nif nargin >= 2\r\n if length (bounds) == 1\r\n b = bounds;\r\n \r\n elseif length (bounds) == 2\r\n a = bounds(1);\r\n b = bounds(2); \r\n end\r\nend\r\n\r\nassert (a >= 1);\r\nif b > bmax\r\n b = bmax;\r\nend\r\n\r\nif b == 0 | b < a\r\n v = [];\r\n fclose (fid);\r\n return;\r\nend\r\n\r\n% compute the number of vectors that are really read and go in starting positions\r\nn = b - a + 1;\r\nfseek (fid, (a - 1) * vecsizeof, -1);\r\n\r\n% read n vectors\r\nv = fread (fid, (d + 1) * n, 'int=>double');\r\nv = reshape (v, d + 1, n);\r\n\r\n% Check if the first column (dimension of the vectors) is correct\r\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\r\nv = v (2:end, :);\r\nfclose (fid);\r\n\r\n" }, { "path": "algorithms/NSH/preprocess/ivecs_write.m", "content": "% This function writes a vector from a file in the ivecs format\n\nfunction ivecs_write (filename, v)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'wb');\nd = size (v, 1);\nn = size (v, 2);\n\nfor i = 1:n\n \n % first write the vector size\n count = fwrite (fid, d, 'int');\n\n if count ~= 1 \n error ('Unable to write vector dimension: count !=1 \\n');\n end\n\n % write the vector components\n count = fwrite (fid, v(:,i), 'int');\n\n if count ~= d \n error ('Unable to write vector elements: count !=1 \\n');\n end\nend\n\nfclose (fid);\n" }, { "path": "algorithms/NSH/preprocess/main.m", "content": "data_name='audio';\ndata_path=['../../../data/' data_name '_base.fvecs'];\n\nindex_path=['../index/' data_name];\nmkdir(index_path);\n\nparams_path=[index_path '/' data_name '_params.NSH'];\ndataset_binary_path=[index_path '/' data_name '_B1.NSH'];\nbit=8;\n\nrun_NSH(data_path, params_path, dataset_binary_path, bit);\n" }, { "path": "algorithms/NSH/preprocess/readme.markdown", "content": "# NEIGHBOR-SENSITIVE HASHING\n\nNeighbor-Sensitive Hashing is a state-of-the-art approximate k-Nearest Neighbor\nalgorithm for high-dimensional objects which are represetned as vectors of\nhundreds of dimensions. Our work is based on a seemingly counter-intuitive (but\neffective) idea that increasing the distances between nearby objects in\nhashed-space (or equivanlently, Hamming space) results in hash functions that\nare only neighbor-sensitive. Our technique may not be an optimal choice if users\nare interested in sorting all data objects in a database in terms of their\ndistances from a query. However, our techinique is greatly successful at\ndistinguishing k-Nearest Neighbor objects when the value of k is small enough\ncompared to the number of the total objects in a database.\n\nOur work has shown superior performances over other state-of-the-art techniques\nsuch as Spectral Hashing, Spherical Hashing, Complementary Projection Hashing,\nand so on, according to our comprehensive experiments not only including\n\"codesize vs. recall\" but also \"latency vs. recall\"; for search latency measure,\nwe used a state-of-the-art Hamming search algorithm Multi-Index Hashing with\nthose hashing algorithms.\n\nhttp://www-personal.umich.edu/~pyongjoo/#nsh\n" }, { "path": "algorithms/NSH/preprocess/run_NSH.m", "content": "function run_NSH(data_path, params_path, binary_path, codesize)\n\ndataset = fvecs_read(data_path);\nX=dataset';\nclear dataset;\n\ntic;\n[model,pivots,W,eps] = trainNSH(X, codesize);\nXB = model.hash(X);\nindex_time=toc;\n\nm=size(pivots,1);\nfile = fopen(params_path, 'w');\nfprintf(file,'%d\\n',codesize);\nfprintf(file,'%f\\n',eps);\nfor t=1:m %anchor\n p_=pivots(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfor t=1:m+1 %W\n p_=W(t,:);\n fprintf(file,'%f ',p_);\n fprintf(file,'\\n');\nend\nfclose(file);\n \nivecs_write(binary_path ,XB');\n \ndisp(['indextime: ' num2str(index_time) ' (s)']);\n \nend\n" }, { "path": "algorithms/NSH/preprocess/trainNSH.m", "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%\n% Neighbor-Sensitive Hashing (NSH)\n% Yongjoo Park, Michael Cafarella, and Barzan Mozafari. In PVLDB Vol 9(3) 2015\n% pyongjoo@umich.edu\n% http://www-personal.umich.edu/~pyongjoo/\n%\n% Main training module of Neighbor-Sensitive Hashing. For simplicity of running\n% demo, we are using a few heuristics. For better performance, one may want to\n% tune those parameters.\n%\n% param 1: number of pivots (m), currently this is set to 4 * codesize\n% param 2: neighbor parameter (epsilon), currently this is set to 1.9 times the\n% average distance between a pivot and its another closest pivot.\n%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\nfunction [model,pivots,W,epsilon] = trainNSH(X, codesize)\n% @param X a data matrix (row is instance and column is feature)\n% @param codesize is the hascode length of generated binary hashcodes.\n% @ret model is a model that contains a hash function that transforms arbitrary data\n% matrix into a hashcode matrix.\n\n b = codesize;\n m = 4 * codesize; % number of pivots (chosen heuristically)\n [N d] = size(X);\n\n % Obtain pivots by k-means\n warning off;\n kmeans_sample_size = min(N, 10000); %randomly selct 50000 samples to do kmeans \n [idx pivots] = kmeans(X(randperm(N, kmeans_sample_size),:), m, 'MaxIter', 10, 'Display', 'off');\n warning on;\n\n\n % Compute neighbor parameter\n anchor_dist = distMat(pivots);\n first_dist = zeros(size(anchor_dist,1),1);\n\n for i = 1:size(anchor_dist,1)\n [B,Idx] = sort(anchor_dist(i,:));\n min_idx = Idx(1:2);\n %min_idx = mink(anchor_dist(i,:), 2);\n first_dist(i) = anchor_dist(i,min_idx(2)); % the distance from the closest pivot\n end\n epsilon = mean(first_dist) * 1.9; % 1.9 is chosen heuristically (other numbers also works too though)\n\n\n % Transform all data using multiple pivots\n KK = exp(-distMat(X, pivots).^2 / epsilon^2);\n KK = [KK ones(size(KK,1), 1)]; % the projection respect to each pivot , last column is 1\n\n\n % This peforms orthogonal projections to make different bits learn different\n % hash functions\n kd = size(KK, 2); % dimension in transformed space m+1\n\n W = randn(kd, b); % hyperplane in transformed space %b random hyperplane\n for i = 1:b\n W(:,i) = W(:,i) / norm(W(:,i));\n end\n\n V = zeros(kd, b+1);\n v = KK' * ones(N,1); %(m+1)*1\n v = v / norm(v);\n V(:,1) = v;\n for i = 1:b\n w = W(:,i); % [kd 1]\n w = w - V(:,1:i) * V(:,1:i)' * w; % [kd b+1] * [b+1 kd] * [kd 1]\n W(:,i) = w / norm(w);\n\n a = KK * w;\n a(a>0) = 1;\n a(a<=0) = -1;\n\n if i ~= b\n v = KK' * a; % [kd N] * [N 1] = [kd 1] -- O(kd * N)\n v = v - V * V' * v; % [kd b+1] * [b+1 kd] * [kd 1]\n V(:,i+1) = v / norm(v);\n end\n end\n\n model.hash = generateHashFunc(pivots, W, epsilon);\n \nend\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\nfunction hash = generateHashFunc(pivots, W, epsilon)\n\n function QB = hash_func(Q)\n QK = exp(-distMat(Q, pivots).^2 / epsilon^2);\n QK = [QK ones(size(QK,1), 1)];\n HH = QK * W;\n HH(HH>0) = 1;\n HH(HH<=0) = 0;\n QB = compactbit(HH);\n end\n\n hash = @hash_func;\nend\n" }, { "path": "algorithms/NSH/script/searcher.sh", "content": "data_path=\"../../../data\"\nresult_path=\"../results\"\nmkdir ${result_path}\n\n\nfor data in \"audio\"\ndo\n\ndataset_path=${data_path}/${data}_base.fvecs\nquery_path=${data_path}/${data}_query.fvecs\ngnd_path=${data_path}/${data}_groundtruth.ivecs\n\nparams_path=../index/${data}/${data}_params.NSH\ndataset_binary_path=../index/${data}/${data}_B1.NSH\nk=20\nradis=-2\n\nfor c in 100 500 1000\ndo\n\n../src/NSH -s ${dataset_path} -q ${query_path} -g ${gnd_path} -k $k -r ${radis} -c $c -b \"${dataset_binary_path}\" -p \"${params_path}\" >> \"${result_path}/${data}.txt\"\n\ndone\ndone\n" }, { "path": "algorithms/NSH/src/Makefile", "content": "##############\n# Make the SRS project\n##############\n\nPROD\t:= DEBUG\nOPT := -O3 -march=native #-ffast-math \nVERSION := \\\"0.1_${PROD}\\\"\nTARGETS := NSH\nDEFINES := #-DREAL_PROF\nSRCS := NSH.cpp\n\nCCFLAGS = ${OPT} -Wno-deprecated -ggdb -D${PROD} ${DEFINES} -I./ -DVERSION=${VERSION}\nLDFLAGS = ${OPT} -ggdb \nLIBS = \nCC\t= g++ \nOBJS := ${SRCS:.cpp=.o}\n\n\n.PHONY: all clean distclean \nall:: ${TARGETS} \n\nsrs: NSH.o\n\t${CC} ${LDFLAGS} -o $@ $^ ${LIBS}\n\n${OBJS}: %.o: %.cpp\n\t${CC} ${CCFLAGS} -o $@ -c $< \n\nclean:: \n\t-rm -f *~ *.o ${TARGETS}\n\ndistclean:: clean\n" }, { "path": "algorithms/NSH/src/NSH.cpp", "content": "#include \"data_util.h\"\n#include \"NSH.h\"\n#include \"hammingDist.h\"\n#include \"eval.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nvector > points;\nvector > query;\nvector > gnds;\n\nint n;\nint nq;\nint dim;\nint k;\nint radis;\n\nfloat diff_timeval(timeval t1, timeval t2) {\n return (float) (t1.tv_sec - t2.tv_sec) + (t1.tv_usec - t2.tv_usec) * 1e-6;\n}\n\nint main(int argc, char * argv[]) {\n\tconst struct option longopts[] ={\n\t {\"help\", no_argument, 0, 'h'},\n\t {\"dataset_filepath\", required_argument, 0, 's'},\n\t {\"query_filepath\" , required_argument, 0, 'q'},\n\t {\"groundtruth_filepath\", required_argument, 0, 'g'},\n\t\t{\"k\", required_argument, 0, 'k'},\n\t\t{\"r\", required_argument, 0, 'r'},\n\t\t{\"c\", required_argument, 0, 'c'},\n\t {\"dataset_binary_path\", required_argument, 0, 'b'},\n\t {\"params_path\", required_argument, 0, 'p'},\n\t };\n\t int ind;\n\t int iarg = 0;\n\t opterr = 1;\n\t int checks;\n\t char groundtruth_filepath[100] = \"\";\n\t char query_filepath[100] = \"\";\n\t char dataset_filepath[100] = \"\";\n\t char dataset_binary_path[100] = \"\";\n\t char params_path[100] = \"\";\n\t //char checks_path[20] =\"\";\n\n\t while (iarg != -1) {\n\t iarg = getopt_long(argc, argv, \"s:q:g:k:r:c:b:p:h\",\n\t longopts, &ind);\n\n\t switch (iarg) {\n\t case 's':\n\t if (optarg) {\n\t \tstrcpy(dataset_filepath, optarg);\n\t }\n\t break;\n\t case 'q':\n\t if (optarg) {\n\t \tstrcpy(query_filepath, optarg);\n\t }\n\t break;\n\t case 'g':\n\t if (optarg) {\n\t \tstrcpy(groundtruth_filepath, optarg);\n\t }\n\t break;\n\t\t case 'k':\n\t \t if (optarg) {\n\t \t k = atoi(optarg);\n\t \t }\n\t \t break;\n\t\t case 'r':\n\t \t if (optarg) {\n\t \t radis = atoi(optarg);\n\t \t }\n\t \t break;\n\t\t case 'c':\n\t \t if (optarg) {\n\t\t\t checks = atoi(optarg);\n\t\t\t //strcpy(checks_path, optarg);\n\t \t }\n\t \t break;\n\t case 'b':\n\t if (optarg) {\n\t \tstrcpy(dataset_binary_path, optarg);\n\t }\n\t break;\n\t case 'p':\n\t if (optarg) {\n\t \tstrcpy(params_path, optarg);\n\t }\n\t break;\n\t }\n\t }\n\n\t ///*\n\t ReadPoints(dataset_filepath, &points, &n, &dim);\n\t ReadPoints(query_filepath, &query,&nq, &dim);\n\n\t int nn;\n\t ReadGroundtruth(groundtruth_filepath, &gnds, &nq, &nn);\n\n\t //ReadChecks(checks_path,&maxChecks);\n\t //read SH params\n\t NSH NSH_;\n\t NSH_.ReadNSHParams(params_path,dim);\n\t int nbits = NSH_.nbits;\n\n\n\t vector > B1;\n\t ReadB1(dataset_binary_path,&B1,n);\n\n\n\t HierarchicalClusteringIndex HCI_ ;\n\t HCI_.set_params(B1,32,4,100);\n\t if(radis!=-1)\n\t \tHCI_.buildIndex();\n\n timeval start;\n\t gettimeofday(&start, NULL);\n\t vector > B2;\n\t NSH_.compressNSH(&query,&B2);\n\t timeval end;\n\t gettimeofday(&end, NULL);\n\t double transform_time = diff_timeval(end, start);\n\n\t gettimeofday(&start, NULL);\n\t vector > indices;\n\t vector > dists;\n\n\t\t //compute the distance between B1 and B2\n\t if(radis==-1)\n\t\t hammingDist(&B2,&B1,&indices,checks);\n\t else if(radis==-2)\n\t\t HCI_.knnSearch(B2,indices,dists,checks,checks);\n\t else\n\t\t hammingDist_ByRange(&B2,&B1,&indices,radis);\n\n\t // get results after re-ranking\n\t vector > results;\n\t results = Search(&points, &query, &indices, k);\n\n\t gettimeofday(&end, NULL);\n\t double search_time = diff_timeval(end, start);\n\t float recall = compute_recall(&gnds, &results);\n\t cout<\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\n#ifndef NSH_H_\n#define NSH_H_\n\nclass NSH\n{\n\tpublic:\n\tint nbits;\n\tint m;\n\tfloat eps;\n\tvector > pivots;\n\tvector > W;\n\n\tvoid ReadNSHParams(string filename, int dim)\n\t{\n\t\tFILE* fp=fopen(filename.c_str(),\"r\");\n\t\t//read nbits\n\t\tfscanf(fp,\"%d\",&nbits);\n\t\tfscanf(fp,\"%f\",&eps);\n\n\t\t//cout< tmp;\n\t\t\ttmp.resize(dim);\n\t\t\tfor(int d = 0; d < dim; ++d) \n\t\t\t{\n\t\t \t\tfscanf(fp,\"%f\",&tmp[d]);\n\t\t\t}\n\t\t\tfscanf(fp,\"\\n\");\n\t\t\tpivots.push_back(tmp);\n\t\t}\n\t\t//read W\n\t\tfor (int i=0; i tmp;\n\t\t\ttmp.resize(nbits);\n\t\t\tfor(int d = 0; d < nbits; ++d)\n\t\t\t{\n\t\t\t\tfscanf(fp,\"%f\",&tmp[d]);\n\t\t\t}\n\t\t\tfscanf(fp,\"\\n\");\n\t\t\tW.push_back(tmp);\n\t\t}\n\t}\n\n\tvoid compressNSH(vector >* X, vector >* U)\n\t{\n\t}\n \n\t\n\t/*\n\tvoid compactBits(vector > U,vector >* B)\n\t{\n\t\t//U nbits*n\n\t\tint nbits = U.size();\n\t\tint n = U.at(0).size();\n\t\tfor(int i=0;i B_;\n\t\t\tunsigned tmp=0;\n\t\t\tfor(int j=0;j<=nbits;j++)\n\t\t\t{\n\t\t\t\tif(j==nbits)\n\t\t\t\t{\n\t\t\t\t\tB_.push_back(tmp);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif(j%8==0 && j!=0)\n\t\t\t\t{\n\t\t\t\t\tB_.push_back(tmp);\n\t\t\t\t\ttmp=0;\n\t\t\t\t}\n\t\t\t\tif(U.at(j)[i]>0)\n\t\t\t\t{\n\t\t\t\t\tunsigned pos=1<<(j%8);\n\t\t\t\t\ttmp |= pos;\n\t\t\t\t}\n\t\t\t}\n\t\t\tB->push_back(B_);\n\t\t}\n\t}*/\n\n\tvoid compactBits(vector > U,vector >* B)\n\t{\n\t\t\t//U n*nbits\n\t\tint n = U.size();\n\t\tint nbits = U.at(0).size();\n\t\tfor(int i=0;i B_;\n\t\t\tunsigned tmp=0;\n\t\t\tfor(int j=0;j<=nbits;j++)\n\t\t\t{\n\t\t\t\tif(j==nbits)\n\t\t\t\t{\n\t\t\t\t\tB_.push_back(tmp);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif(j%8==0 && j!=0)\n\t\t\t\t{\n\t\t\t\t\tB_.push_back(tmp);\n\t\t\t\t\ttmp=0;\n\t\t\t\t}\n\t\t\t\tif(U.at(i)[j]>0)\n\t\t\t\t{\n\t\t\t\t\tunsigned pos=1<<(j%8);\n\t\t\t\t\ttmp |= pos;\n\t\t\t\t}\n\t\t\t}\n\t\t\tB->push_back(B_);\n\t\t}\n\t}\n\tfloat get_distance(vector point, vector query)\n\t{\n\t\tint dim = query.size();\n\t\tfloat dist=0.0;\n\t\tfor(int i=0; i >* X, vector >* B)\n\t{\n\t\t//X nq*dim\n\t\tint n = X->size();\n\t\tint dim = X->at(0).size();\n\n\t\tvector > QK;\n\t\tQK.resize(n);\n\n\t\tfor(int i=0; iat(i),pivots.at(j));\n\t\t\t\t//cout<<\"dist:\"< > HH;\n\t\tHH.resize(n);\n\t\tfor(int i=0; i >* X, vector >* U)\n\t{\n\t}\n};\n\n\n\n#endif\n" }, { "path": "algorithms/NSH/src/center_chooser.h", "content": "#include \"random.h\"\n//#include \"dist.h\"\n#include \"char_bit_cnt.h\"\n\nclass CenterChooser\n{\n\tvector* > dataset_;\npublic:\n\n\tvoid setDataset(vector >& dataset)\n {\n\t\tsize_t size_ = dataset.size();\n\n\t\tdataset_.resize(size_);\n\t\tfor (size_t i=0;isize();\n\t\tfor(int t=0; tat(t) ^ dataset_.at(p2)->at(t);\n\t\t\tdist += char_bit_cnt[y];\n\t\t}\n\t\treturn dist;\n\t} //*/\n\n\tvoid chooseCenters(int k, int* indices, int indices_length, int* centers, int& centers_length)\n\t{\n\t\tUniqueRandom r(indices_length);\n\n int index;\n for (index=0; index\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nvoid transpose(vector > X,vector >* Y)\n{\n\n\tint dim=X.size();\n\tint n=X[0].size();\n\tfor(int i=0;i y_;\n\t\tfor(int j=0;jpush_back(y_);\n\t}\n}\n\nvoid ReadPoints(char* filepath,vector >* points, int* len, int* d)\n{\n\tFILE * dfp = fopen(filepath, \"rb\");\n\tint dim;\n\tfread(&dim,sizeof(int),1,dfp);\n\tfseek(dfp, 0, SEEK_END);\n\toff_t size = ftell(dfp);\n fseek(dfp, 0, SEEK_SET);\n int n = (int)(size / (sizeof(int)+ sizeof(float)*dim));\n\t*d = dim;\n\t*len = n;\n\tpoints->resize(n);\n\tfor (int i=0;iat(i).resize(dim);\n \tfor(int j = 0; j < dim; ++j) {\n \t\tfloat buffer;\n\t\t\tfread(&(buffer), sizeof(float),1,dfp);\n \t\tpoints->at(i)[j] = buffer;\n \t}\n\t}\n}\n\nvoid ReadGroundtruth(char* filepath, vector >* gnds, int* len, int* d)\n{\n\tFILE * dfp = fopen(filepath, \"rb\");\n\tint k;\n\tfread(&k,sizeof(int),1,dfp);\n\tfseek(dfp, 0, SEEK_END);\n\toff_t size = ftell(dfp);\n fseek(dfp, 0, SEEK_SET);\n int n = (int)(size / (sizeof(int)+ sizeof(int)*k));\n\t*d = k;\n\t*len = n;\n\tgnds->resize(n);\n\tfor (int i=0;iat(i).resize(k);\n \tfor(int j = 0; j < k; ++j) {\n \t\tint buffer;\n\t\t\tfread(&(buffer), sizeof(int),1,dfp);\n \t\tgnds->at(i)[j] = buffer;\n \t}\n\t}\n}\n\nvoid ReadB1(const string& filename, vector >* B1,int n) {//\n ifstream input;\n\tinput.open(filename.c_str(), ios::binary);\n if(!input.good()) {\n throw std::logic_error(\"Invalid filename\");\n }\n B1->resize(n);\n int cbits;\n for(int pid = 0; pid < n; ++pid) {\n input.read((char*)&cbits, sizeof(cbits));\n if(cbits <= 0) {\n throw std::logic_error(\"Bad file content: non-positive dimension\");\n }\n B1->at(pid).resize(cbits);\n for(int d = 0; d < cbits; ++d) {\n int buffer;\n input.read((char*)&(buffer), sizeof(int));\n B1->at(pid)[d] = (unsigned)buffer;//(unsigned)\n }\n }\n}\n\n\nvoid ReadChecks(const string& filename, vector* checks) {\n FILE *fp = fopen(filename.c_str(), \"r\");\n int n;\n fscanf(fp, \"%d \", &n);\n checks->resize(n);\n int tmp;\n for (int i = 0; i < n; ++i) {\n fscanf(fp, \"%d \", &tmp);\n checks->at(i)=tmp;\n }\n}\n\n\n\n\n\n" }, { "path": "algorithms/NSH/src/dynamic_bitset.h", "content": "#include \n\nclass DynamicBitset\n{\npublic:\n /** @param only constructor we use in our code\n * @param the size of the bitset (in bits)\n */\n DynamicBitset(size_t size)\n {\n resize(size);\n reset();\n }\n\n /** Sets all the bits to 0\n */\n void clear()\n {\n std::fill(bitset_.begin(), bitset_.end(), 0);\n }\n\n void reset()\n {\n std::fill(bitset_.begin(), bitset_.end(), 0);\n }\n\n \n void resize(size_t size)\n {\n size_ = size;\n bitset_.resize(size / cell_bit_size_ + 1);\n }\n size_t size() const\n {\n return size_;\n }\n\n\tvoid set(size_t index)\n {\n bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_);\n }\n\n /** @param check if a bit is set\n * @param index the index of the bit to check\n * @return true if the bit is set\n */\n bool test(size_t index) const\n {\n return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0;\n }\n\nprivate:\n std::vector bitset_;\n size_t size_;\n static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t);\n};\n" }, { "path": "algorithms/NSH/src/eval.h", "content": "/*\n * eval.h\n *\n * Created on: 14/10/2015\n * Author: liwen\n */\n\n#ifndef EVAL_H_\n#define EVAL_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nfloat compute_recall(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tint avg = 0;\n\tfor (size_t i=0; iat(i)[j] == gnds->at(i)[k]) {\n\t\t\t\t\tavg ++;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn float(avg)/(nn*nq);\n}\n\nfloat compute_mean_reciprocal_rank(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[0]==results->at(i)[k])\n\t\t\t{\n\t\t\t\trate +=1.0/(k+1);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tavg+=rate;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_number_closer(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[j]==results->at(i)[k])\n\t\t\t\t{\n\t\t\t\t\trate +=(float)(j+1)/(k+1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tavg+=rate/nn;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_relative_distance_error(vector >* points, vector >* query, vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat count = 0.0;\n\tfor (size_t i=0; iat(gnds->at(i)[j]),query->at(i));\n\t\t\tif(min_distance==0)\n\t\t\t\tcontinue;\n\t\t\tfloat test_distance = get_distance(points->at(results->at(i)[j]),query->at(i));\n\t\t\tfloat d= (test_distance - min_distance )/min_distance;\n\t\t\tif(d<0)\n\t\t\t\tcout<at(i)[j]<<\" \"<at(i)[j]<<\" \"<4)\n\t\t\t\tsum +=4;\n\t\t\telse\n\t\t\t\tsum +=d;\n\t\t}\n\t\tcount += sum /nn;\n\t}\n\treturn count/ nq;\n}\n\nfloat compute_mean_average_precision (vector >* gnds, vector >* results)\n{\n\tint nq = results->size();\n\tint nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (int i = 0; i < nq; ++i)\n\t{\n\t\tfloat rate = 0.0;\n\t\tint found = 0;\n\n\t\tstd::set gnd_row;\n \t\tfor(int j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(int j=0;j at(i)[j]) != gnd_row.end()) \n\t\t\t{\n\t\t\t\tfound ++;\n\t\t\t\trate += 1.0 * found/(j+1);\n \t\t}\n \t}\n\t\tsum += rate/nn;\n\t}\n\treturn sum / nq;\n}\n\nfloat compute_discounted_culmulative_gain (vector >* gnds, vector >* results) \n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (size_t i = 0; i < nq; ++i) \n\t{\n\t\tfloat rate = 0.0;\n\n\t\tstd::set gnd_row;\n \t\tfor(size_t j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(size_t j=0;j at(i)[j]) != gnd_row.end()) \n\t\t\t{\n\t\t\t\trate += 1.0 /log2(j+2);\n \t\t}\n \t}\n\t\tsum += rate;\n\t}\n\treturn sum / nq;\n}\n\nfloat compute_recall(vector >* gnds, vector >* results, float* recall)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tcout<<\"nq:\"<at(i)[j].id == gnds->at(i)[k]) {\n\t\t\t\t\tavg ++;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn float(avg)/(nn*nq);\n}\n\nfloat compute_mean_reciprocal_rank(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[0]==results->at(i)[k].id)\n\t\t\t{\n\t\t\t\trate +=1.0/(k+1);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tavg+=rate;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_number_closer(vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat avg = 0.0;\n\tfor (size_t i=0; iat(i)[j]==results->at(i)[k].id)\n\t\t\t\t{\n\t\t\t\t\trate +=(float)(j+1)/(k+1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tavg+=rate/nn;\n\t}\n\treturn avg/nq;\n}\n\nfloat compute_relative_distance_error(vector >* points, vector >* query, vector >* gnds, vector >* results)\n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat count = 0.0;\n\tfor (size_t i=0; iat(gnds->at(i)[j]),query->at(i));\n\t\t\tfloat test_distance=results->at(i)[j].dist;\n\t\t\tfloat d= (test_distance - min_distance )/min_distance;\n \n\t\t\tif(d >4)\n\t\t\t\tsum +=4;\n\t\t\telse\n\t\t\t\tsum +=d;\n\t\t}\n\t\tcount += sum /nn;\n\t}\n\treturn count/ nq;\n}\n\n\nfloat compute_mean_average_precision (vector >* gnds, vector >* results)\n{\n\tint nq = results->size();\n\tint nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (int i = 0; i < nq; ++i)\n\t{\n\t\tfloat rate = 0.0;\n\t\tint found = 0;\n\n\t\tstd::set gnd_row;\n \t\tfor(int j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(int j=0;j at(i)[j].id) != gnd_row.end()) \n\t\t\t{\n\t\t\t\tfound ++;\n\t\t\t\trate += 1.0 * found/(j+1);\n \t\t}\n \t}\n\t\tsum += rate/nn;\n\t}\n\treturn sum / nq;\n}\n\nfloat compute_discounted_culmulative_gain (vector >* gnds, vector >* results) \n{\n\tsize_t nq = results->size();\n\tsize_t nn = results->at(0).size();\n\tfloat sum = 0;\n\tfor (size_t i = 0; i < nq; ++i) \n\t{\n\t\tfloat rate = 0.0;\n\n\t\tstd::set gnd_row;\n \t\tfor(size_t j = 0; j at(i)[j]);\n \t\t}\n\n\t\tfor(size_t j=0;j at(i)[j].id) != gnd_row.end()) \n\t\t\t{\n\t\t\t\trate += 1.0 /log2(j+2);\n \t\t}\n \t}\n\t\tsum += rate;\n\t}\n\treturn sum / nq;\n}\n\n#endif /* EVAL_H_ */\n" }, { "path": "algorithms/NSH/src/hammingDist.h", "content": "/*\n * hammingDist.h\n *\n * Created on: 14/10/2015\n * Author: liwen\n */\n\n#ifndef HAMMINGDIST_H_\n#define HAMMINGDIST_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"hierarchical_clustering_index.h\"\n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\nstruct B_Neighbor{\n\tint id;\n\tint dist;\n\tB_Neighbor()\n\t{\n\t\tid=0;\n\t\tdist=-1;\n\t}\n\tint get_id(){return id;}\n};\n\nstruct E_Neighbor{\n\tint id;\n\tfloat dist;\n\tE_Neighbor()\n\t{\n\t\tid=0;\n\t\tdist=-1;\n\t}\n};\n\nfloat get_distance(vector point, vector query)\n{\n\tint dim = query.size();\n\tfloat dist=0.0;\n\tfor(int i=0; i* p1, const vector* p2)\n{\n\tint dist=0;\n\tint cbits = p1->size();\n\tfor(int t=0; tat(t) ^ p2->at(t);\n\t\tdist += char_bit_cnt[y];\n\t}\n\treturn dist;\n}\n\nvoid hammingDist(vector >* B2,vector >* B1, vector >* Dist)\n{\n\tint n = B1->size();\n\tint nq = B2->size();\n\t//int cbits = B2->at(0).size();\n\tfor(int i=0; i d_;\n\t\tfor(int j=0; jat(j),&B2->at(i));\n\t\t\t//for(int t=0; tat(j)[t] ^ B2->at(i)[t];\n\t\t\t\t//tmp += char_bit_cnt[y];\n\t\t\t//}\n\t\t\td_.push_back(tmp);\n\t\t}\n\t\tDist->push_back(d_);\n\t}\n}\n\nvoid UpdatePriorityList_Binary (B_Neighbor *BList, B_Neighbor tmp, int len) {\n\t// find the location to insert\n\tint j;\n int i = len;\n while (i > 0) {\n j = i - 1;\n if (BList[j].dist <= tmp.dist && BList[j].dist!=-1) break;\n i = j;\n }\n\tif(i < len)\n\t{\n \tj = len-1;\n \twhile (j > i) {\n \tBList[j] = BList[j-1];\n \t--j;\n \t}\n \tBList[i] = tmp;\n\t}\n}\n\nvoid hammingDist(vector >* B2,vector >* B1, vector >* Dist,int nn)\n{\n\tint len = nn;\n\tint n = B1->size();\n\tint nq = B2->size();\n\t//int cbits = B2->at(0).size();\n\n\tfor(int i=0; i BNN(len);\n\t\tfor(int j=0; jat(j),&B2->at(i));\n\t\t\t//for(int t=0; tat(j)[t] ^ B2->at(i)[t];\n\t\t\t\t//tmp += char_bit_cnt[y];\n\t\t\t//}\n\n\t\t\tB_Neighbor b_;\n\t\t\tb_.id=j;\n\t\t\tb_.dist=tmp;\n\n\t\t\tUpdatePriorityList_Binary ( &BNN[0], b_, len);\n\t\t}\n\t\tvector d_;\n\t\tfor(int j=0;jpush_back(d_);\n\t}\n}\n\nvoid hammingDist_ByRange(vector >* B2,vector >* B1, vector >* IDs,int radis)\n{\n\tint n = B1->size();\n\tint nq = B2->size();\n\t//int cbits = B2->at(0).size();\n\n\tfor(int i=0; i BNN;\n\t\tfor(int j=0; jat(j),&B2->at(i));\n\t\t\t//for(int t=0; tat(j)[t] ^ B2->at(i)[t];\n\t\t\t\t//tmp += char_bit_cnt[y];\n\t\t\t//}\n\t\t\tif(tmp<=radis)\n\t\t\t\tBNN.push_back(j);\n\t\t}\n\t\tIDs->push_back(BNN);\n\t}\n}\n\nvoid UpdatePriorityList_Euclidean (E_Neighbor *BList, E_Neighbor tmp, int len) {\n\t// find the location to insert\n\tint j;\n int i = len;\n while (i > 0) {\n j = i - 1;\n if (BList[j].dist <= tmp.dist && BList[j].dist!=-1) break;\n i = j;\n }\n\tif(i < len)\n\t{\n \tj = len-1;\n \twhile (j > i) {\n \tBList[j] = BList[j-1];\n \t--j;\n \t}\n \tBList[i] = tmp;\n\t}\n}\n\nvector > Search(vector >* points, vector >* query, vector >* IDs, int K)\n{\n\tint nq=query->size();\n\tvector > results;\n\tfor(int i=0; i result;\n\t\tvector ENN(K);\n\t\tint len = IDs->at(i).size();\n\t\tvector ids_ = IDs->at(i);\n\t\tfor(int j=0; jat(i),points->at(ids_.at(j)));\n\t\t\tE_Neighbor e_;\n\t\t\te_.id=ids_.at(j);\n\t\t\te_.dist=dist;\n\t\t\tUpdatePriorityList_Euclidean (&ENN[0], e_, K);\n\t\t}\n\t\tfor(int j=0;j > Search(vector >* points, vector >* query, vector > IDs, int K)\n{\n\tint nq=query->size();\n\tvector > results;\n\tfor(int i=0; i ENN(K);\n\t\tint len = IDs.at(i).size();\n\t\tvector ids_ = IDs.at(i);\n\t\tfor(int j=0; jat(i),points->at(ids_.at(j)));\n\t\t\tE_Neighbor e_;\n\t\t\te_.id=ids_.at(j);\n\t\t\te_.dist=dist;\n\t\t\tUpdatePriorityList_Euclidean (&ENN[0], e_, K);\n\t\t}\n\t\t//for(int j=0;j\n#include \n\n\ntemplate \nclass Heap\n{\n\n /**\n * Storage array for the heap.\n * Type T must be comparable.\n */\n std::vector heap;\n int length;\n\n /**\n * Number of element in the heap\n */\n int count;\n\n\n\npublic:\n /**\n * Constructor.\n *\n * Params:\n * size = heap size\n */\n\n Heap(int size)\n {\n length = size;\n heap.reserve(length);\n count = 0;\n }\n\n /**\n *\n * Returns: heap size\n */\n int size()\n {\n return count;\n }\n\n /**\n * Tests if the heap is empty\n *\n * Returns: true is heap empty, false otherwise\n */\n bool empty()\n {\n return size()==0;\n }\n\n /**\n * Clears the heap.\n */\n void clear()\n {\n heap.clear();\n count = 0;\n }\n\n struct CompareT : public std::binary_function\n {\n bool operator()(const T& t_1, const T& t_2) const\n {\n return t_2 < t_1;\n }\n };\n\n /**\n * Insert a new element in the heap.\n *\n * We select the next empty leaf node, and then keep moving any larger\n * parents down until the right location is found to store this element.\n *\n * Params:\n * value = the new element to be inserted in the heap\n */\n void insert(const T& value)\n {\n /* If heap is full, then return without adding this element. */\n if (count == length) {\n return;\n }\n\n heap.push_back(value);\n static CompareT compareT;\n std::push_heap(heap.begin(), heap.end(), compareT);\n ++count;\n }\n\n\n\n /**\n * Returns the node of minimum value from the heap (top of the heap).\n *\n * Params:\n * value = out parameter used to return the min element\n * Returns: false if heap empty\n */\n bool popMin(T& value)\n {\n if (count == 0) {\n return false;\n }\n\n value = heap[0];\n static CompareT compareT;\n std::pop_heap(heap.begin(), heap.end(), compareT);\n heap.pop_back();\n --count;\n\n return true; /* Return old last node. */\n }\n};\n\n\ntemplate \nclass IntervalHeap\n{\n\tstruct Interval\n\t{\n\t\tT left;\n\t\tT right;\n\t};\n\n /**\n * Storage array for the heap.\n * Type T must be comparable.\n */\n std::vector heap;\n size_t capacity_;\n size_t size_;\n\npublic:\n /**\n * Constructor.\n *\n * Params:\n * size = heap size\n */\n\n IntervalHeap(int capacity) : capacity_(capacity), size_(0)\n {\n heap.resize(capacity/2 + capacity%2 + 1); // 1-based indexing\n }\n\n /**\n * @return Heap size\n */\n size_t size()\n {\n return size_;\n }\n\n /**\n * Tests if the heap is empty\n * @return true is heap empty, false otherwise\n */\n bool empty()\n {\n return size_==0;\n }\n\n /**\n * Clears the heap.\n */\n void clear()\n {\n size_ = 0;\n }\n\n void insert(const T& value)\n {\n /* If heap is full, then return without adding this element. */\n if (size_ == capacity_) {\n return;\n }\n\n // insert into the root\n if (size_<2) {\n \tif (size_==0) {\n \t\theap[1].left = value;\n \t\theap[1].right = value;\n \t}\n \telse {\n \t\tif (value1 && value < heap[par].left) {\n \t\theap[pos].left = heap[par].left;\n \t\tpos = par;\n \t\tpar = pos/2;\n \t}\n \theap[pos].left = value;\n \t++size_;\n\n \tif (size_%2) { // duplicate element in last position if size is odd\n \t\theap[last_pos].right = heap[last_pos].left;\n \t}\n }\n else {\n \tsize_t pos = last_pos;\n \tsize_t par = pos/2;\n \twhile (pos>1 && heap[par].right < value) {\n \t\theap[pos].right = heap[par].right;\n \t\tpos = par;\n \t\tpar = pos/2;\n \t}\n \theap[pos].right = value;\n \t++size_;\n\n \tif (size_%2) { // duplicate element in last position if size is odd\n \t\theap[last_pos].left = heap[last_pos].right;\n \t}\n }\n }\n\n\n /**\n * Returns the node of minimum value from the heap\n * @param value out parameter used to return the min element\n * @return false if heap empty\n */\n bool popMin(T& value)\n {\n if (size_ == 0) {\n return false;\n }\n\n value = heap[1].left;\n size_t last_pos = size_/2 + size_%2;\n T elem = heap[last_pos].left;\n\n if (size_ % 2) { // odd number of elements\n \t--last_pos;\n }\n else {\n \theap[last_pos].left = heap[last_pos].right;\n }\n --size_;\n if (size_<2) return true;\n\n size_t crt=1; // root node\n size_t child = crt*2;\n\n while (child <= last_pos) {\n \tif (child < last_pos && heap[child+1].left < heap[child].left) ++child; // pick the child with min\n\n \tif (!(heap[child].left\nclass BoundedHeap\n{\n\tIntervalHeap interval_heap_;\n\tsize_t capacity_;\npublic:\n\tBoundedHeap(size_t capacity) : interval_heap_(capacity), capacity_(capacity)\n\t{\n\n\t}\n\n /**\n * Returns: heap size\n */\n int size()\n {\n return interval_heap_.size();\n }\n\n /**\n * Tests if the heap is empty\n * Returns: true is heap empty, false otherwise\n */\n bool empty()\n {\n return interval_heap_.empty();\n }\n\n /**\n * Clears the heap.\n */\n void clear()\n {\n \tinterval_heap_.clear();\n }\n\n void insert(const T& value)\n {\n \tif (interval_heap_.size()==capacity_) {\n \t\tT max;\n \t\tinterval_heap_.getMax(max);\n \t\tif (max\n#include \n#include \n#include \n#include \n#include \"center_chooser.h\"\n//#include \"char_bit_cnt.h\"\n#include \"dynamic_bitset.h\"\n#include \"heap.h\"\n#include \"result_set.h\"\n\nusing namespace std;\nusing std::string;\nusing std::vector;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\n\n//typedef unsigned ElementType;\n//typedef int DistanceType;\n\nclass HierarchicalClusteringIndex\n{\n\tprivate:\n\n\tint branching_;\n int trees_;\n int leaf_max_size_;\n\n\tsize_t size_;\n \tsize_t veclen_;\n\tsize_t size_at_build_;\n\tsize_t last_id_;\n\n\tvector ids_;\n\tvector* > points_;\n\tCenterChooser Centers_;\n\t\n\t//DynamicBitset removed_points_;\n\n\tvoid setDataset(vector >& dataset)\n {\n \tsize_ = dataset.size();\n\n\t\tpoints_.resize(size_);\n\t\tfor (size_t i=0;iat(0)< >& inputData, int b=32,int t=4,int leaf=100)\n {\n branching_ = b;\n trees_ = t;\n leaf_max_size_ = leaf;\n\n\t\tsetDataset(inputData);\n\n Centers_.setDataset(inputData);\n\t\tDynamicBitset checked(size_);\n }\n\n\n /**\n * Builds the index\n */\n void buildIndex()\n {\n if (branching_<2) {\n cerr<<\"Branching factor must be at least 2\"< indices(size_);\n for (int i=0; iat(0)<<\" \"<at(0)<<\" \"<< get_distance(0,1)<* point; /** Point data */\n };\n\n\n\n\tstruct Node\n {\n \n \tvector* pivot; /** The cluster center */\n\n \tsize_t pivot_index;\n \n vector childs; /** Child nodes (only for non-terminal nodes) */\n \n vector points; /** Node points (only for terminal nodes) */\n\n ~Node()\n {\n \tfor(size_t i=0; i~Node();\n \t}\n };\n };\n\n\ttypedef Node* NodePtr;\n\tstruct BranchStruct\n\t{\n \tNode* node; /* Tree node at which search resumes */\n \tint mindist; /* Minimum distance to query for all nodes below. */\n\n \tBranchStruct() {}\n \tBranchStruct(Node* aNode, int dist) : node(aNode), mindist(dist)\n \t{//cout<<\"branch node:\"<pivot_index <<\" \"<pivot_index<size();\n\t\tfor(int t=0; tat(t) ^ points_.at(p2)->at(t);\n\t\t\tdist += char_bit_cnt[y];\n\t\t}\n\t\treturn dist;\n\t}\n\n\t\n\tint get_distance(const vector* p1, const vector* p2)\n\t{\n\t\tint dist=0;\n\t\tint cbits = p1->size();\n\t\tfor(int t=0; tat(t) ^ p2->at(t);\n\t\t\tdist += char_bit_cnt[y];\n\t\t}\n\t\treturn dist;\n\t}\n//*/\n\n\tvoid computeLabels(int* indices, int indices_length, int* centers, int centers_length, int* labels)\n {\n for (int i=0; inew_dist) {\n labels[i] = j;\n dist = new_dist;\n }\n }\n }\n }\n\n\tvoid computeClustering(Node* node, int* indices, int indices_length)\n {\n\t\t//cout<<\"begin:\"<points.resize(indices_length);\n for (int i=0;ipoints[i].index = indices[i];\n \tnode->points[i].point = points_.at(indices[i]);\n }\n node->childs.clear();\n\t\t\t//cout<<\"indices:\"<points.size()< centers(branching_);\n std::vector labels(indices_length);\n\n int centers_length;\n\t\tCenters_.chooseCenters(branching_, indices, indices_length, ¢ers[0], centers_length);\n\t\t//cout<<\"choose centers_length : \"<points.resize(indices_length);\n for (int i=0;ipoints[i].index = indices[i];\n \tnode->points[i].point = points_.at(indices[i]);\n }\n node->childs.clear();\n\t\t\t//cout<<\" centers: \"<points.size()<childs.resize(branching_);\n int start = 0;\n int end = start;\n for (int i=0; ichilds[i] = new Node();\n node->childs[i]->pivot_index = centers[i];\n node->childs[i]->pivot = points_.at(centers[i]);\n node->childs[i]->points.clear();\n computeClustering(node->childs[i],indices+start, end-start);\n start=end;\n }\n }\n\n\ttypedef BranchStruct BranchSt;\n\n\tvoid findNN(NodePtr node, ResultSet& result, const vector* vec, int& checks,const int maxChecks,\n\t Heap* heap, DynamicBitset& checked)\n\t{\n\t if (node->childs.empty())\n\t {\n\t if (checks>=maxChecks) {\n\t if (result.full()) return;\n\t }\n\n\t for (size_t i=0; ipoints.size(); ++i)\n\t {\n\t PointInfo& pointInfo = node->points[i];\n\t if (checked.test(pointInfo.index)) continue;\n\t int dist = get_distance(pointInfo.point, vec);\n\t result.addPoint(dist, pointInfo.index);\n\t checked.set(pointInfo.index);\n\t ++checks;\n\t }\n\t }\n\t else {\n\t int* domain_distances = new int[branching_];\n\t int best_index = 0;\n\t domain_distances[best_index] = get_distance(vec, node->childs[best_index]->pivot);\n\t for (int i=1; ichilds[i]->pivot);\n\t if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i]));\n\t }\n\t }\n\t delete[] domain_distances;\n\t findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked);\n\t }\n\t }\n\n\n\n\tvoid findNeighborsWithRemoved(ResultSet& result, const vector* vec, const int maxChecks)\n {\n //int maxChecks = searchParams.checks;\n\n // Priority queue storing intermediate branches in the best-bin-first search\n Heap* heap = new Heap(size_);\n\n DynamicBitset checked(size_);\n int checks = 0;\n for (int i=0; ipopMin(branch) && (checks >& queries, vector< vector >& indices,\tvector< vector >& dists,size_t knn,const int maxChecks)\n {\n if (indices.size() < queries.size() ) indices.resize(queries.size());\n\t\tif (dists.size() < queries.size() ) dists.resize(queries.size());\n\n\t\tint count = 0;\n\n\t\tfor (int i = 0; i < (int)queries.size(); i++)\n\t\t{\n\t\t\t\t//resultSet.clear();\n\t\t\t\tResultSet resultSet(knn);\n\t\t\t\tfindNeighborsWithRemoved(resultSet, &queries.at(i), maxChecks);\n\t\t\t\tsize_t n = std::min(resultSet.size(), knn);\n\t\t\t\tindices[i].resize(n);\n\t\t\t\tdists[i].resize(n);\n\t\t\t\tif (n>0)\n\t\t\t\t{\n\t\t\t\t\tresultSet.copy(&indices.at(i), &dists.at(i), n);\n\t\t\t\t}\n\t\t\t\tcount += n;\n\t\t}\n\t\treturn count;\n }\n\n\tvector tree_roots_;\n};\n\n" }, { "path": "algorithms/NSH/src/random.h", "content": "/***********************************************************************\n * Software License Agreement (BSD License)\n *\n * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.\n * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.\n *\n * THE BSD LICENSE\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions\n * are met:\n *\n * 1. Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * 2. Redistributions in binary form must reproduce the above copyright\n * notice, this list of conditions and the following disclaimer in the\n * documentation and/or other materials provided with the distribution.\n *\n * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR\n * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES\n * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.\n * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,\n * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT\n * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF\n * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n *************************************************************************/\n\n#include \n#include \n#include \n#include \n/**\n * Seeds the random number generator\n * @param seed Random seed\n */\ninline void seed_random(unsigned int seed)\n{\n srand(seed);\n}\n\n/*\n * Generates a random double value.\n */\n/**\n * Generates a random double value.\n * @param high Upper limit\n * @param low Lower limit\n * @return Random double value\n */\ninline double rand_double(double high = 1.0, double low = 0)\n{\n return low + ((high-low) * (std::rand() / (RAND_MAX + 1.0)));\n}\n\n/**\n * Generates a random integer value.\n * @param high Upper limit\n * @param low Lower limit\n * @return Random integer value\n */\ninline int rand_int(int high = RAND_MAX, int low = 0)\n{\n return low + (int) ( double(high-low) * (std::rand() / (RAND_MAX + 1.0)));\n}\n\n\nclass RandomGenerator\n{\npublic:\n ptrdiff_t operator() (ptrdiff_t i) { return rand_int(i); }\n};\n\n\n/**\n * Random number generator that returns a distinct number from\n * the [0,n) interval each time.\n */\nclass UniqueRandom\n{\n std::vector vals_;\n int size_;\n int counter_;\n\npublic:\n /**\n * Constructor.\n * @param n Size of the interval from which to generate\n * @return\n */\n UniqueRandom(int n)\n {\n init(n);\n }\n\n /**\n * Initializes the number generator.\n * @param n the size of the interval from which to generate random numbers.\n */\n void init(int n)\n {\n static RandomGenerator generator;\n // create and initialize an array of size n\n vals_.resize(n);\n size_ = n;\n for (int i = 0; i < size_; ++i) vals_[i] = i;\n\n // shuffle the elements in the array\n std::random_shuffle(vals_.begin(), vals_.end(), generator);\n\n counter_ = 0;\n }\n\n /**\n * Return a distinct random integer in greater or equal to 0 and less\n * than 'n' on each call. It should be called maximum 'n' times.\n * Returns: a random integer\n */\n int next()\n {\n if (counter_ == size_) {\n return -1;\n }\n else {\n return vals_[counter_++];\n }\n }\n};\n\n\n\n" }, { "path": "algorithms/NSH/src/result_set.h", "content": "#include \n#include \n#include \n#include \n#include \n#include \n\n\nstruct DistanceIndex\n{\n DistanceIndex(int dist, int index) :\n dist_(dist), index_(index)\n {\n }\n bool operator<(const DistanceIndex& dist_index) const\n {\n return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);\n }\n int dist_;\n int index_;\n};\n\n\nclass ResultSet\n{\npublic:\n\t//typedef DistanceIndex DistIndex;\n\n\tResultSet(size_t capacity_) :\n capacity_(capacity_)\n {\n\t\t// reserving capacity to prevent memory re-allocations\n\t\tdist_index_.reserve(capacity_);\n \tclear();\n }\n\n ~ResultSet()\n {\n }\n\n /**\n * Clears the result set\n */\n void clear()\n {\n dist_index_.clear();\n worst_dist_ = std::numeric_limits::max();\n is_full_ = false;\n }\n\n /**\n *\n * @return Number of elements in the result set\n */\n size_t size() const\n {\n return dist_index_.size();\n }\n\n /**\n * Radius search result set always reports full\n * @return\n */\n bool full()\n {\n return is_full_;\n }\n\n /**\n * Add another point to result set\n * @param dist distance to point\n * @param index index of point\n * Pre-conditions: capacity_>0\n */\n void addPoint(int dist, int index)\n {\n \tif (dist>=worst_dist_) return;\n\n \tif (dist_index_.size()==capacity_) {\n \t\t// if result set if filled to capacity, remove farthest element\n \t\tstd::pop_heap(dist_index_.begin(), dist_index_.end());\n \tdist_index_.pop_back();\n \t}\n\n \t// add new element\n \tdist_index_.push_back(DistanceIndex(dist,index));\n \tif (is_full_) { // when is_full_==true, we have a heap\n \t\tstd::push_heap(dist_index_.begin(), dist_index_.end());\n \t}\n\n \tif (dist_index_.size()==capacity_) {\n \t\tif (!is_full_) {\n \t\t\tstd::make_heap(dist_index_.begin(), dist_index_.end());\n \tis_full_ = true;\n \t\t}\n \t\t// we replaced the farthest element, update worst distance\n \tworst_dist_ = dist_index_[0].dist_;\n }\n }\n\n /**\n * Copy indices and distances to output buffers\n * @param indices\n * @param dists\n * @param num_elements Number of elements to copy\n * @param sorted Indicates if results should be sorted\n */\n void copy(vector* indices, vector* dists, size_t num_elements)\n {\n \t\t// std::sort_heap(dist_index_.begin(), dist_index_.end());\n \t\t// sort seems faster here, even though dist_index_ is a heap\n \t//\tstd::sort(dist_index_.begin(), dist_index_.end());\n\n \tsize_t n = std::min(dist_index_.size(), num_elements);\n \tfor (size_t i=0; iat(i) = dist_index_[i].index_;\n \t\tdists->at(i) = dist_index_[i].dist_;\n \t}\n }\n\n int worstDist() const\n {\n \treturn worst_dist_;\n }\n\nprivate:\n size_t capacity_;\n int worst_dist_;\n std::vector dist_index_;\n bool is_full_;\n};\n" }, { "path": "algorithms/OPQ/README.html", "content": "\n\n\n \n \n \n \n \n\n\n

Optimized Product Quantization

\n

This is a combination of source codes of OPQ and Inverted MultiIndex. In our impementation, we use the inverted multi-indexing technique to perform non-exhaustive search for OPQ. The original code of OPQ is implemented by MATLAB, which is used to transform the data vectors and query vectors for finding the minimal quantization distortion.

\n

Prerequisites

\n
    \n
  • Yael and VLFeat libraries (for the Matlab code)
    • \n
  • A modern compiler that supports C++11 (e.g., gcc 4.7 and above)
    • \n
  • Matlab
    • \n
  • CMake (version 2.6 or over)
    • \n
  • The boost library (dev version) installed.
    • \n
  • IntelMKL
    • \n
\n

Compile

\n

To compile, go to src directory and type

\n
sh ./build_debug.sh or ./build_release.sh
\n

Note that the local directory of Boost and IntelMKL should be provided (CMakeLists.txt).

\n

Index

\n

To get OPQ indexing, one should first enter the Transform_Data folder and run

\n
main.m
\n

file to get the transformed data vectors and query vectors, coarse vocabularies and fine vocabularies. Note that the directory of Yael and vlfeat library should be given. Then go to OPQ/script directory and run

\n
./indexer.sh
\n

to obtain the OPQ index.

\n

Search

\n
./search.sh
\n

Note that, in our experiment paper, we tune the c value of OPQ search (i.e., size of neighbors to be visited) to achieve the trade-off between search speed and search quality (recall). The search performance (time and recall) results are kept in the OPQ/results directory.

\n\n\n" }, { "path": "algorithms/OPQ/README.md", "content": "# Optimized Product Quantization\n\nThis is a combination of source codes of OPQ and Inverted MultiIndex. In our impementation, we use the inverted multi-indexing technique to perform non-exhaustive search for OPQ. [The original code of OPQ](http://kaiminghe.com/cvpr13/index.html) is implemented by MATLAB, which is used to transform the data vectors and query vectors for finding the minimal quantization distortion. \nFor the search efficiency, we apply the [inverted multi-indexing technique](http://arbabenko.github.io/MultiIndex/index.html) implemented by C++ to perform non-exhaustive search. \n\n##Prerequisites\n\n- `Yael` and `VLFeat` libraries (for the Matlab code)\n- A modern compiler that supports `C++11` (e.g., `gcc 4.7` and above)\n- Matlab\n- CMake (version 2.6 or over)\n- The `boost` library (dev version) installed.\n- IntelMKL\n\n##Compile (OPQ/src)\n\nTo compile, go to src directory and type \n\n```\n./build_release.sh\n```\n\nNote that the local directory of `Boost` and `IntelMKL` should be provided (`CMakeLists.txt`).\n\n##Index (OPQ/Transform_Data and OPQ/script)\n\nTo get OPQ indexing, one should first enter the `Transform_Data` folder and run \n\n```\nmain.m \n```\nin the Matlab to get the transformed data vectors and query vectors, coarse vocabularies and fine vocabularies.\nNote that the directories of `Yael` and `vlfeat` libraries should be given in the main.m file. \n\nThen go to OPQ/script directory and run \n\n```\n./indexer.sh \n```\n\nto build the OPQ index.\n\n##Search (OPQ/script) \n\n```\n./search.sh\n```\n\nNote that, in our experiment paper, we tune the `c` value of OPQ search (i.e.,\nsize of neighbors to be visited) to achieve the trade-off between search speed\nand search quality (recall). The search performance (time and recall) results\nare kept in the `OPQ/results` directory. \n\n\n" }, { "path": "algorithms/OPQ/TransForm_Data/coarse_vocabularies.m", "content": "function coarse_vocabularies(yael_path,file_path,Rinit_path, coarse_name,K)\n\n%addpath ('~/Documents/yael/yael_v401/matlab');\nall_data = fvecs_read(file_path);\nall_data = all_data';\n\ndim=size(all_data,2);\nniter=30;\nM=2;\n\ntic;\n%%%OPQ_NP\nR_init = eye(dim);\nall_data = single(all_data);\nvocabSize = 2^K;\n% % add implementation of K-means\n\ncenter_table_init = cell (M,1);\n\n[centers_table_opq_np R_opq_np] = train_opq_np(all_data,M,center_table_init,R_init,niter/2,1,vocabSize);\nvocab1 = centers_table_opq_np{1}';\nvocab2 = centers_table_opq_np{2}';\n\nfvecs_write(Rinit_path,R_opq_np);\nfile = fopen([coarse_name '.dat'], 'w');\ndim = size(vocab1, 1);\nsz = size(vocab1, 2);\nfwrite(file, dim, 'int32');\nfwrite(file, sz, 'int32');\nfwrite(file, vocab1, 'float');\nfwrite(file, vocab2, 'float');\nfclose(file);\nsave([coarse_name '.mat'], 'vocab1', 'vocab2');\ntime=toc;\nend\n" }, { "path": "algorithms/OPQ/TransForm_Data/coarse_vocabularies.m~", "content": "function coarse_vocabularies(yael_path,file_path,Rinit_path, coarse_name,K,s)\n\n%addpath ('~/Documents/yael/yael_v401/matlab');\nall_data = fvecs_read(file_path);\nall_data = all_data';\n\ndim=size(all_data,2);\nniter=30;\nM=2;\n\ntic;\n%%%OPQ_NP\nR_init = eye(dim);\nall_data = single(all_data);\nvocabSize = 2^K;\n% % add implementation of K-means\n\ncenter_table_init = cell (M,1);\n\n[centers_table_opq_np R_opq_np] = train_opq_np(all_data,M,center_table_init,R_init,niter/2,1,vocabSize);\nvocab1 = centers_table_opq_np{1}';\nvocab2 = centers_table_opq_np{2}';\n\nfvecs_write(Rinit_path,R_opq_np);\nfile = fopen([coarse_name '.dat'], 'w');\ndim = size(vocab1, 1);\nsz = size(vocab1, 2);\nfwrite(file, dim, 'int32');\nfwrite(file, sz, 'int32');\nfwrite(file, vocab1, 'float');\nfwrite(file, vocab2, 'float');\nfclose(file);\nsave([coarse_name '.mat'], 'vocab1', 'vocab2');\ntime=toc;\nend\n" }, { "path": "algorithms/OPQ/TransForm_Data/fine_vocabularies.m", "content": "function fine_vocabularies(yael_path,vlfeat_path,file_path,Rinit_path,coarse_name, fine_name,K,M)\n%addpath ('~/Documents/yael/yael_v401/matlab');\n%run('~/Downloads/software/vlfeat-0.9.20/toolbox/vl_setup');\n\naddpath([yael_path '/matlab']);\nrun([vlfeat_path '/toolbox/vl_setup']);\nall_data = fvecs_read(file_path);\n\ntic;\nvocabSize = 2^K;\nR_opq_p = fvecs_read(Rinit_path);\nall_data = uint8(R_opq_p' * all_data);\n\n\nload([coarse_name '.mat'], 'vocab1', 'vocab2');\n\nvocab1 = int32(vocab1);\nvocab2 = int32(vocab2);\ni1 = vl_ikmeanspush((all_data(1:end/2,:)), vocab1);\ni2 = vl_ikmeanspush((all_data(end/2+1:end,:)), vocab2); \nresidual = single(all_data)- single([vocab1(:,i1); vocab2(:,i2)]);\nbytes_per_point = M;\n\nD = size(residual,1) / bytes_per_point;\nresidual_vocab = cell(bytes_per_point,1);\ndist = cell(bytes_per_point,1);\nniter = 30;\nfor m = 1:bytes_per_point\n chunk = residual(D*m-D+1:D*m,:);\n % add implementation of K-means\n residual_vocab{m} = yael_kmeans(chunk,2^K,'niter', niter, 'verbose', 0);\n dist{m} = vl_alldist2(residual_vocab{m}); \nend\n\nsave([fine_name '.mat'],'residual_vocab','dist');\n\nfile = fopen([fine_name '.dat'], 'w');\nvocabs_count = size(residual_vocab, 1);\neach_vocab_count = size(residual_vocab{1}, 2);\neach_vocab_dim = size(residual_vocab{1}, 1);\nfwrite(file, vocabs_count, 'int32');\nfwrite(file, each_vocab_count, 'int32');\nfwrite(file, each_vocab_dim, 'int32');\nfor i = 1:vocabs_count\n for j = 1:each_vocab_count\n a = residual_vocab{i}(:,j);\n fwrite(file, a, 'float');\n end\nend\nfclose(file);\n\ntime=toc;\nend\n" }, { "path": "algorithms/OPQ/TransForm_Data/fvecs_read.m", "content": "% Read a set of vectors stored in the fvec format (int + n * float)\n% The function returns a set of output vector (one vector per column)\n%\n% Syntax: \n% v = fvecs_read (filename) -> read all vectors\n% v = fvecs_read (filename, n) -> read n vectors \n% v = fvecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)\nfunction v = fvecs_read (filename, bounds)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'rb');\n \nif fid == -1\n error ('I/O error : Unable to open the file %s\\n', filename)\nend\n\n% Read the vector size\nd = fread (fid, 1, 'int');\nvecsizeof = 1 * 4 + d * 4;\n\n% Get the number of vectrors\nfseek (fid, 0, 1);\na = 1;\nbmax = ftell (fid) / vecsizeof;\nb = bmax;\n\nif nargin >= 2\n if length (bounds) == 1\n b = bounds;\n \n elseif length (bounds) == 2\n a = bounds(1);\n b = bounds(2); \n end\nend\n\nassert (a >= 1);\nif b > bmax\n b = bmax;\nend\n\nif b == 0 | b < a\n v = [];\n fclose (fid);\n return;\nend\n\n% compute the number of vectors that are really read and go in starting positions\nn = b - a + 1;\nfseek (fid, (a - 1) * vecsizeof, -1);\n\n% read n vectors\nv = fread (fid, (d + 1) * n, 'float=>single');\nv = reshape (v, d + 1, n);\n\n% Check if the first column (dimension of the vectors) is correct\nassert (sum (v (1, 2:end) == v(1, 1)) == n - 1);\nv = v (2:end, :);\n\nfclose (fid);\n\n" }, { "path": "algorithms/OPQ/TransForm_Data/fvecs_write.m", "content": "% This function reads a vector of float vectors\n% \n% Usage: fvecs_write (filename, v)\n% where v is a set of vector (stored columnwise)\nfunction fvecs_write (filename, v)\n\n% open the file and count the number of descriptors\nfid = fopen (filename, 'wb');\nd = size (v, 1);\nn = size (v, 2);\n\nfor i = 1:n\n \n % first write the vector size\n count = fwrite (fid, d, 'int');\n\n if count ~= 1 \n error ('Unable to write vector dimension: count !=1 \\n');\n end\n\n % write the vector components\n count = fwrite (fid, v(:, i), 'float');\n\n if count ~= d \n error ('Unable to write vector elements: count !=1 \\n');\n end\nend\n\nfclose (fid);" }, { "path": "algorithms/OPQ/TransForm_Data/generate_learn.m", "content": "function generate_learn(data_path,learn_path,m)\n\ndataset=fvecs_read(data_path);\nn=size(dataset,2);\nidx=randsample(n,m);\nlearn=dataset(:,idx);\n\nfvecs_write(learn_path,learn);\nend\n" }, { "path": "algorithms/OPQ/TransForm_Data/generate_learn.m~", "content": "function generate_learn(data_path,learn_path,m)\n\nmkdir(learn_path);\n\ndataset=fvecs_read(data_path);\nn=size(dataset,2);\nidx=randsample(n,m);\nlearn=dataset(:,idx);\n\nfvecs_write(learn_path,learn);\nend\n" }, { "path": "algorithms/OPQ/TransForm_Data/main.m", "content": "n=1000;\ndata_name='audio';\nK=8;\nM=8;\n\ndata_path='../../../../data';\nindex_path=['../index/' data_name];\nmkdir(index_path);\n\nyael_path = '/home/yzhang4/HD/home/liwen/Documents/yael/yael_v401';\nvlfeat_path='/home/yzhang4/HD/home/liwen/Downloads/software/vlfeat-0.9.20';\n\ngenerate_learn([data_path '/' data_name '_base.fvecs'],[index_path '/' data_name '_learn.fvecs'],n);\n\ncoarse_vocabularies(yael_path, [index_path '/' data_name '_learn.fvecs'],[index_path '/' data_name '_rinit.fvecs'],[index_path '/' data_name '_coarse'],K);\n\nfine_vocabularies(yael_path,vlfeat_path, [index_path '/' data_name '_learn.fvecs'], [index_path '/' data_name '_rinit.fvecs'], [index_path '/' data_name '_coarse'], [index_path '/' data_name '_fine'], K, M);\n\n\ntransform_base_query([data_path '/' data_name '_base.fvecs'], [data_path '/' data_name '_query.fvecs'], [index_path '/' data_name '_rinit.fvecs'], [index_path '/' data_name '_base_NP.fvecs'], [index_path '/' data_name '_query_NP.fvecs']);\n\n\n\n" }, { "path": "algorithms/OPQ/TransForm_Data/sqdist.m", "content": "function d=sqdist(a,b)\n% SQDIST - computes squared Euclidean distance matrix\n% computes a rectangular matrix of pairwise distances ABеijɶԾ\n% between points in A (given in columns) and points in B\n\n% NB: very fast implementation taken from Roland Bunschoten\n\n%aa = sum(a.*a,1); aиݵijȣ bb = sum(b.*b,1); ab = a'*b; abڻ\n%d = abs(repmat(aa',[1 size(bb,2)]) + repmat(bb,[size(aa,2) 1]) - 2*ab);\n\n%aa = sum(a.*a,1)'; bb = sum(b.*b,1);\n%d = abs(repmat(sum(a.*a,1)',[1 size(b,2)]) + repmat(sum(b.*b,1),[size(a,2) 1]) - 2*a'*b);\n\nd = bsxfun(@plus, -2*a'*b, sum(b.*b,1));\nd = abs(bsxfun(@plus, d', sum(a.*a,1)))';" }, { "path": "algorithms/OPQ/TransForm_Data/train_opq_np.m", "content": "function [centers_table, R_opq_np] = train_opq_np(X, M, centers_table_init, R_init, num_iter_outer, num_iter_inner,k)\n\n% X: [nSamples, dim] training samples\n% M: number of subspacs\n % fixed number of centers per subspaces (8 bits per subspaces)\ndim = size(X, 2);\nd = dim / M;\n\nidx_table = zeros(size(X, 1), M);\n\nR = R_init;\ncenters_table = centers_table_init;\n\nfor iter_outer = 1:num_iter_outer\n \n Y = zeros(size(X));\n \n % line 3 in Algorithm 1\n Xproj = X*R; % pre-projecting X\n \n distortion = 0; \n for m = 1:M\n Xsub = Xproj(:, (1:d) + (m-1)*d);\n \n % line 5 in Algorithm 1\n \n if iter_outer == 1\n opts = statset('Display','off','MaxIter',num_iter_outer);\n [idx, centers] = kmeans(Xsub, k, 'Options', opts, 'EmptyAction', 'singleton');\n else\n opts = statset('Display','off','MaxIter',num_iter_inner);\n [idx, centers] = kmeans(Xsub, k, 'Options', opts, 'Start', centers_table{m}, 'EmptyAction', 'singleton');\n end\n centers_table{m} = centers;\n \n % line 6 in Algorithm 1\n dist = sqdist(centers', Xsub');\n [dist, idx] = min(dist);\n idx_table(:,m) = idx(:);\n \n % compute distortion\n dist = mean(dist);\n distortion = distortion + dist;\n \n % compute Y \n Ysub = centers(idx(:),:);\n Y(:, (1:d) + (m-1)*d) = Ysub;\n end\n %fprintf('opq-np: iter: %d, distortion: %e\\n', iter_outer, distortion);\n R_opq_np = R; % save the output R\n \n % line 8 in Algorithm 1 (update R)\n [U, S, V] = svd(X'*Y);\n R = U * V';\nend\n\nend\n" }, { "path": "algorithms/OPQ/TransForm_Data/transform_base_query.m", "content": "%%%% Transform database and query\n\nfunction transform_base_query(data_path,query_path,Rinit_path,trans_data_path, trans_query_path)\n\nR_opq_p = fvecs_read(Rinit_path);\ndataset = fvecs_read(data_path);\n\ndataset = R_opq_p' * dataset;\nfvecs_write(trans_data_path,dataset);\n\nquery = fvecs_read(query_path);\nquery = R_opq_p' * query;\n\nfvecs_write (trans_query_path,query);\n\nend\n" }, { "path": "algorithms/OPQ/TransForm_Data/transform_base_query.m~", "content": "%%%% Transform database and query\n\nfunction transform_base_query(data_path,query_path,Rinit_path,trans_data_path, trans_query_path)\n\nvocabSize = 2^K;\nR_opq_p = fvecs_read(Rinit_path);\ndataset = fvecs_read(data_path);\n\ndataset = R_opq_p' * dataset;\nfvecs_write(trans_data_path,dataset);\n\nquery = fvecs_read(query_path);\nquery = R_opq_p' * query;\n\nfvecs_write (trans_query_path,query);\n\nend\n" }, { "path": "algorithms/OPQ/script/indexer.sh", "content": "for data in \"audio\"\ndo\n\ndata_path=../index/${data}/${data}_base_NP.fvecs\ncoarse_path=../index/${data}/${data}_coarse.dat\nfine_path=../index/${data}/${data}_fine.dat\nindice_path=../index/${data}\ncoarse_bin_path=../index/${data}/${data}_coarse.bin\nn=53387\ndim=192\nuse_residual=0\n\n../src/build_master/indexer_launcher \\\n--threads_count=1 \\\n--multiplicity=2 \\\n--points_file=${data_path} \\\n--coarse_vocabs_file=${coarse_path} \\\n--fine_vocabs_file=${fine_path} \\\n--input_point_type=\"FVEC\" \\\n--points_count=$n \\\n--space_dim=$dim \\\n--files_prefix=${indice_path}/ \\\n--coarse_quantization_file=${coarse_bin_path} \\\n--metainfo_file=\"fake1.txt\" \\\n--build_coarse 0 \\\n--use_residuals ${use_residual} \\\n\ndone\n" }, { "path": "algorithms/OPQ/script/searcher.sh", "content": "result_path=../results\nmkdir ${result_path}\n\nfor data in \"audio\"\ndo\n\ndata_path=../index/${data}/${data}_base_NP.fvecs\nquery_path=../index/${data}/${data}_query_NP.fvecs\ngnd_path=../../../data/${data}_groundtruth.ivecs\ncoarse_path=../index/${data}/${data}_coarse.dat\nfine_path=../index/${data}/${data}_fine.dat\nindex_path=../index/${data}/\nn=53387\ndim=192\nnq=200\nk=20\nuse_residual=1\nrerank=0\nuse_original=1\nK=8\nlet centroids=2**$K\n\nfor c in 100 500 1000\ndo\n\n../src/build_master/searcher_tester \\\n--index_files_prefix=${index_path} \\\n--queries_file=${query_path} \\\n--queries_count=${nq} \\\n--neighbours_count=$c \\\n--groundtruth_file=${gnd_path} \\\n--data_file ${data_path} \\\n--data_count ${n} \\\n--coarse_vocabs_file=${coarse_path} \\\n--fine_vocabs_file=${fine_path} \\\n--query_point_type=\"FVEC\" \\\n--use_residuals ${use_residual} \\\n--do_rerank ${rerank} \\\n--report_file=${result_path}/${data}.txt \\\n--dim ${dim} \\\n--use_originaldata ${use_original} \\\n--subspaces_centroids_count=${centroids} \\\n--k_neighbors $k \\\n\ndone\ndone\n\n\n\n" }, { "path": "algorithms/OPQ/src/CMakeLists.txt", "content": "##########################################################################################\n# CMake build script for NearestSearch.\n#\n##########################################################################################\n\n#let all libraries be static, not shared\nOPTION(BUILD_SHARED_LIBS \"Build shared libraries (DLLs).\" OFF)\n\n##########################################################################################\n# lets start describing our project.\nproject (NearestSearch CXX C)\ncmake_minimum_required(VERSION 3.2)\n\nfind_package(Boost 1.58.0 COMPONENTS program_options serialization system filesystem thread REQUIRED)\n\nIF (UNIX)\n\tSET(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -O3 -fopenmp -DMKL_ILP64 -m64\")\n\tSET(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} -O3 -fopenmp -DMKL_ILP64 -m64\")\nENDIF (UNIX)\n\nIF (NOT DEFINED CMAKE_CURRENT_LIST_DIR)\n SET(CMAKE_CURRENT_LIST_DIR ${CMAKE_CURRENT_SOURCE_DIR})\nENDIF (NOT DEFINED CMAKE_CURRENT_LIST_DIR)\n\nMESSAGE(\"current dir: ${CMAKE_CURRENT_LIST_DIR}\")\nSET (Source_Path ${CMAKE_CURRENT_LIST_DIR})\n\nMESSAGE (\"PROJECT_BINARY_DIR \" ${PROJECT_BINARY_DIR})\n\n# ADD BOOSTDIR\nIF (UNIX)\nSET(BOOST_DIR /home/yzhang4/HD/home/liwen/Downloads/software/boost_1_58_0)\nSET(BOOST_LIB /home/yzhang4/HD/home/liwen/Downloads/software/boost1.58/lib)\nENDIF (UNIX)\nIF(WIN32)\nSET(BOOST_DIR C:/Users/arbabenko/Soft/boost_1_47_0)\nSET(BOOST_LIB C:/Users/arbabenko/Soft/boost_1_47_0/lib/x64/lib)\nENDIF(WIN32)\n\n# ADD BLAS\nIF (UNIX)\nSET(BLAS_DIR /opt/intel/composer_xe_2013/mkl/include)\nSET(BLAS_LIB /opt/intel/composer_xe_2013/mkl/lib/intel64 \n /opt/intel/composer_xe_2013/compiler/lib/intel64)\nENDIF (UNIX)\nIF(WIN32)\nSET(BLAS_DIR \"C:/Program Files (x86)/Intel/Composer XE 2011 SP1/mkl/include\")\nSET(BLAS_LIB \"C:/Program Files (x86)/Intel/Composer XE 2011 SP1/mkl/lib/intel64\"\n \"C:/Program Files (x86)/Intel/Composer XE 2011 SP1/compiler/lib/intel64\")\nENDIF(WIN32)\n\n\n\n##########################################################################################\nINCLUDE (${Source_Path}/NearestSearch.cmake NO_POLICY_SCOPE)\nINCLUDE_DIRECTORIES(${BOOST_DIR})\nINCLUDE_DIRECTORIES(${BLAS_DIR})\nLINK_DIRECTORIES(${BOOST_LIB})\nLINK_DIRECTORIES(${BLAS_LIB})\nINCLUDE_DIRECTORIES(${Source_Path})\nADD_LIBRARY (nearest_search_lib STATIC ${NEAREST_ALL_CC})\n\n##########################################################################################\nINCLUDE (${Source_Path}/IndexerLauncher.cmake NO_POLICY_SCOPE)\nINCLUDE_DIRECTORIES(${BOOST_DIR})\nINCLUDE_DIRECTORIES(${BLAS_DIR})\nLINK_DIRECTORIES(${BOOST_LIB})\nLINK_DIRECTORIES(${BLAS_LIB})\nINCLUDE_DIRECTORIES(${Source_Path})\nADD_EXECUTABLE (indexer_launcher ${IndexerLauncher})\nTARGET_LINK_LIBRARIES (indexer_launcher nearest_search_lib)\ntarget_link_libraries( indexer_launcher ${Boost_LIBRARIES} )\nIF (UNIX)\nTARGET_LINK_LIBRARIES (indexer_launcher libmkl_intel_ilp64.a libmkl_gnu_thread.a libmkl_core.a dl pthread m)\nENDIF (UNIX)\nIF(WIN32)\nTARGET_LINK_LIBRARIES (indexer_launcher mkl_intel_lp64.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib)\nENDIF(WIN32)\n \n##########################################################################################\nINCLUDE (${Source_Path}/SearcherTester.cmake NO_POLICY_SCOPE)\nINCLUDE_DIRECTORIES(${BOOST_DIR})\nINCLUDE_DIRECTORIES(${BLAS_DIR})\nLINK_DIRECTORIES(${BOOST_LIB})\nLINK_DIRECTORIES(${BLAS_LIB})\nINCLUDE_DIRECTORIES(${Source_Path})\nADD_EXECUTABLE (searcher_tester ${SearchTester})\nTARGET_LINK_LIBRARIES (searcher_tester nearest_search_lib)\ntarget_link_libraries( searcher_tester ${Boost_LIBRARIES} )\nIF (UNIX)\nTARGET_LINK_LIBRARIES (searcher_tester libmkl_intel_ilp64.a libmkl_gnu_thread.a libmkl_core.a dl pthread m)\nENDIF (UNIX)\nIF(WIN32)\nTARGET_LINK_LIBRARIES (searcher_tester mkl_intel_lp64.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib)\nENDIF(WIN32)\n\n\n" }, { "path": "algorithms/OPQ/src/IndexerLauncher.cmake", "content": "# lets list sample's binaries\nset (IndexerLauncher ${Source_Path}/indexer_launcher.cpp)\n\n\n" }, { "path": "algorithms/OPQ/src/NearestSearch.cmake", "content": "# lets divide binaries in groups, for comfort navigation\nSOURCE_GROUP(util FILES ${Source_Path}/data_util.h\n ${Source_Path}/data_util.cpp\n ${Source_Path}/multitable.hpp\n ${Source_Path}/perfomance_util.h\n ${Source_Path}/perfomance_util.cpp)\n\t\t\t\t\t\t\nSET(UTIL ${Source_Path}/data_util.h\n ${Source_Path}/data_util.cpp\n ${Source_Path}/multitable.hpp\n ${Source_Path}/perfomance_util.h\n ${Source_Path}/perfomance_util.cpp)\n\t\t\t\t\t\t\nSOURCE_GROUP(indexer FILES ${Source_Path}/indexer.h)\n\t\t\t\t\t\t \nSET(INDEXER ${Source_Path}/indexer.h)\n\t\t\t\t\t\t \nSOURCE_GROUP(searcher FILES ${Source_Path}/searcher.h\n ${Source_Path}/ordered_lists_merger.h)\n\t\t\t\t\t\t\t\nSET(SEARCHER ${Source_Path}/searcher.h\n ${Source_Path}/ordered_lists_merger.h)\n\t\t\t\t\t\t\t\n\n# lets list all Nearest's source binaries\nSET(NEAREST_ALL_CC ${UTIL} ${INDEXER} ${SEARCHER})\n\n" }, { "path": "algorithms/OPQ/src/SearcherTester.cmake", "content": "# lets list sample's binaries\nset (SearchTester ${Source_Path}/searcher_tester.cpp)\n\n\n" }, { "path": "algorithms/OPQ/src/build_release.sh", "content": "mkdir -p build_master\ncd build_master\nrm ./CMakeCache.txt\ncmake -DCMAKE_BUILD_TYPE=Release ..\nmake\n" }, { "path": "algorithms/OPQ/src/data_util.cpp", "content": "// Copyright 2012 Yandex Artem Babenko\n\n#include \"data_util.h\"\n\nDistance Eucldistance(const Point& x, const Point& y) {\n Distance result = 0;\n Distance current_coord_diff;\n\tfor(Dimensions d = 0; d < x.size(); ++d){\n current_coord_diff = x[d] - y[d];\n result += current_coord_diff * current_coord_diff; \n\t}\n return result;\n}\n\nDistance Eucldistance(const Point& x, const Point& y,\n const Dimensions start, const Dimensions finish) {\n Distance result = 0;\n Distance current_coord_diff;\n\tfor(Dimensions d = start; d < finish; ++d){\n current_coord_diff = x[d] - y[d - start];\n result += current_coord_diff * current_coord_diff;\n\t}\n return result;\n}\n\nvoid GetSubpoints(const Points& points,\n const Dimensions start_dim,\n const Dimensions final_dim,\n Points* subpoints) {\n if(final_dim < start_dim) {\n throw std::logic_error(\"Final dim < Start dim\");\n }\n subpoints->resize(points.size());\n for(PointId pid = 0; pid < points.size(); ++pid) {\n subpoints->at(pid).resize(final_dim - start_dim);\n for(Dimensions dim = start_dim; dim < final_dim; ++dim) {\n subpoints->at(pid)[dim] = points[pid][start_dim + dim];\n }\n }\n}\n\nClusterId GetNearestClusterId(const Point& point,\n const Centroids& centroids,\n const Dimensions start_dim,\n const Dimensions final_dim) {\n if(final_dim < start_dim) {\n throw std::logic_error(\"Final dim < Start dim\");\n }\n ClusterId nearest = 0;\n Distance min_distance = Eucldistance(point, centroids[0], start_dim, final_dim);\n for(PointId pid = 1; pid < centroids.size(); ++pid) {\n Distance current_distance = 0;\n current_distance = Eucldistance(point, centroids[pid], start_dim, final_dim);\n if(current_distance < min_distance) {\n min_distance = current_distance;\n nearest = pid;\n }\n }\n return nearest;\n}\n\nvoid GetResidual(const Point& point, const CoarseQuantization& coarse_quantizations,\n const vector& centroids, Point* residual) {\n residual->resize(point.size());\n Dimensions subvector_dimension = point.size() / centroids.size();\n cblas_saxpy(point.size(), 1, &(point[0]), 1, &(residual->at(0)), 1);\n for(int subvector_index = 0; subvector_index < centroids.size(); ++subvector_index) {\n Dimensions start_dim = subvector_index * subvector_dimension;\n const Point& current_coarse_centroid = centroids[subvector_index][coarse_quantizations[subvector_index]];\n cblas_saxpy(subvector_dimension, -1, &(current_coarse_centroid[0]), 1, &(residual->at(start_dim)), 1);\n }\n}\n\nvoid GetResidual(const Point& point, const CoarseQuantization& coarse_quantizations,\n const vector& centroids, Coord* residual) {\n Dimensions subvector_dimension = point.size() / centroids.size();\n cblas_scopy(point.size(), &(point[0]), 1, residual, 1);\n for(int subvector_index = 0; subvector_index < centroids.size(); ++subvector_index) {\n Dimensions start_dim = subvector_index * subvector_dimension;\n const Point& current_coarse_centroid = centroids[subvector_index][coarse_quantizations[subvector_index]];\n cblas_saxpy(subvector_dimension, -1, &(current_coarse_centroid[0]), 1, &(residual[start_dim]), 1);\n }\n}\n\nvoid GetNearestClusterIdsForPointSubset(const Points& points, const Centroids& centroids,\n const PointId start_pid, const PointId final_pid,\n vector* nearest) {\n if(final_pid < start_pid) {\n throw std::logic_error(\"Final pid < Start pid\");\n }\n cout << start_pid << \" point processing started\\n\";\n for(PointId pid = start_pid; pid < final_pid; ++pid) {\n if(pid % 10000 == 0) {\n cout << pid << endl;\n }\n nearest->at(pid) = GetNearestClusterId(points[pid], centroids, 0, points[0].size());\n } \n cout << final_pid << \" point processing finished\\n\";\n}\n\nvoid GetNearestClusterIdsForSubpoints(const Points& points, const Centroids& centroids,\n const Dimensions start_dim, const Dimensions final_dim,\n int threads_count, vector* nearest) {\n if(final_dim < start_dim) {\n throw std::logic_error(\"Final dim < Start dim\");\n }\n cout << \"Start getting nearest Cluster Ids...\" << endl;\n Points subpoints;\n GetSubpoints(points, start_dim, final_dim, &subpoints);\n boost::thread_group threads;\n int subpoints_count = points.size() / threads_count;\n for(int thread_id = 0; thread_id < threads_count; ++thread_id) {\n PointId start_pid = subpoints_count * thread_id;\n PointId final_pid = start_pid + subpoints_count;\n threads.create_thread(boost::bind(&GetNearestClusterIdsForPointSubset, subpoints, centroids,\n start_pid, final_pid, nearest));\n }\n threads.join_all();\n cout << \"Finish getting nearest Cluster Ids...\" << endl;\n}\n\nvoid GetPointsCoarseQuaintizations(const Points& points,\n const vector& centroids,\n const int threads_count,\n vector* coarse_quantizations) {\n int number_of_subvectors = centroids.size();\n coarse_quantizations->resize(points.size(), CoarseQuantization(number_of_subvectors));\n Dimensions subvector_dimension = points[0].size() / number_of_subvectors;\n for(int centroids_index = 0; centroids_index < number_of_subvectors; ++centroids_index) {\n vector cluster_labels;\n cluster_labels.resize(points.size());\n Dimensions start_dim = centroids_index * subvector_dimension;\n Dimensions final_dim = std::min((Dimensions)points[0].size(), start_dim + subvector_dimension);\n GetNearestClusterIdsForSubpoints(points, centroids[centroids_index],\n start_dim, final_dim, threads_count, &cluster_labels);\n for(PointId pid = 0; pid < points.size(); ++pid) {\n coarse_quantizations->at(pid)[centroids_index] = cluster_labels[pid];\n }\n }\n}" }, { "path": "algorithms/OPQ/src/data_util.h", "content": "/** @file */\n\n// Copyright 2012 Yandex Artem Babenko\n#pragma once\n\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"mkl_cblas.h\"\n\n#include \"multitable.hpp\"\n\nusing std::bitset;\nusing std::cout;\nusing std::ifstream;\nusing std::ios;\nusing std::endl;\nusing std::multimap;\nusing std::pair;\nusing std::set;\nusing std::string;\nusing std::vector;\n\n/**\n * \\typedef\n * Data type for coordinate (bool, char, int, float, etc.)\n */\ntypedef float Coord;\n/**\n * \\typedef\n * Data type for distance in multidimensional space\n */\ntypedef float Distance;\n/**\n * \\typedef\n * Dimensionality of space = number of point coordinates\n */\ntypedef int Dimensions;\n/**\n * \\typedef\n * Data type for point identifier\n */\ntypedef int PointId;\n/**\n * \\typedef\n * Data type for cluster identifier\n */\ntypedef int ClusterId;\n/**\n * \\typedef\n * Just vector of coordinates\n */\ntypedef vector Point;\n/**\n * \\typedef\n * Class for a number of points\n */\ntypedef vector Points;\n/**\n * \\typedef\n * Class for a number of point identifiers\n */\ntypedef vector PointIds;\n/**\n * \\typedef\n * Class for a set of points\n */\ntypedef set SetPoints;\n/**\n * \\typedef\n * Class for a number of cluster identifiers\n */\ntypedef vector ClusterIds;\n/**\n * \\typedef\n * Class for representation of point coarse quantization,\n * ids of nearest centroids for each group of dimensions\n */\ntypedef vector CoarseQuantization;\n/**\n * \\typedef\n * Data type for fine cluster identifier\n */\ntypedef unsigned char FineClusterId;\n/**\n * \\typedef\n * Class for representation of point fine quantization\n */\ntypedef vector FineQuantization;\n/**\n * \\typedef\n * Class for clusters representation\n * ClusterId -> (PointId, PointId, PointId, .... )\n */\ntypedef vector ClustersToPoints;\n/**\n * \\typedef\n * Class for belonging to clusters representation\n * PointId -> ClusterId\n */\ntypedef std::vector PointsToClusters; \n/**\n * \\typedef\n * Centroids of the clustered points set\n */\ntypedef std::vector Centroids;\n\n/**\n * \\enum This enumeration presents different types of input point\n * coordinate can be float or uint8\n */\nenum PointType {\n FVEC,\n BVEC\n};\n\n/**\n * \\enum This enumeration presents different ways to get rerank info.\n * Algorithm can quantize residuals after coarse quantization or init points without\n * centroids subtraction\n */\nenum RerankMode {\n USE_RESIDUALS, \n USE_INIT_POINTS\n};\n\n/**\n * \\struct MultiIndex incorporates all data structures we need to make search\n */\ntemplate\nstruct MultiIndex {\n vector multiindex;\n Multitable cell_edges; ///< Table with index cell edges in array\n};\n\n/**\n * Function calculates squared euclidian distance between two points (points must have the same dimensionality)\n * @param x first point\n * @param y second point\n */\nDistance Eucldistance(const Point& x, const Point& y);\n/**\n * Function calculates squared euclidian distance point with small dimensionality and \n * subpoint of point with bigger dimensionality. \n * @param x first point\n * @param y second point\n * @param start first dimension of subpoint\n * @param finish dimension after the last dimension of subpoint\n */\nDistance Eucldistance(const Point& x, const Point& y, Dimensions start, Dimensions finish);\n\n/**\n * This simple function casts number of type T to the nearest number of type U \n */\ntemplate\ninline U Round(T number) {\n return (U)(number);\n}\n\n/**\n * Function reads point written in .fvecs or .bvecs format.\n * Input points have coordinates of type T.\n * Result points have coordinates of type U\n * @param filename .fvecs or .bvecs file name\n * @param points_count how many points to read\n * @param points result list of read points\n */\ntemplate\nvoid ReadPoints(const string& filename,\n vector >* points,\n int count) {\n ifstream input;\n\tinput.open(filename.c_str(), ios::binary);\n if(!input.good()) {\n throw std::logic_error(\"Invalid filename\");\n }\n points->resize(count);\n int dimension;\n for(PointId pid = 0; pid < count; ++pid) {\n input.read((char*)&dimension, sizeof(dimension));\n if(dimension <= 0) {\n throw std::logic_error(\"Bad file content: non-positive dimension\");\n }\n points->at(pid).resize(dimension);\n for(Dimensions d = 0; d < dimension; ++d) {\n T buffer;\n input.read((char*)&(buffer), sizeof(T));\n points->at(pid)[d] = Round(buffer);\n }\n }\n}\n\n/**\n * Function reads one vector of coordinates of type T.\n * Function assumes that the first int32-number in input stream is\n * vector dimensionality. Result vector will have coordinates of type U.\n * @param input input stream\n * @param v result vector\n */\ntemplate\nvoid ReadVector(ifstream& input, vector* v) {\n if(!input.good()) {\n throw std::logic_error(\"Bad input stream\");\n }\n int dimension;\n input.read((char*)&dimension, sizeof(dimension));\n if(dimension <= 0) {\n throw std::logic_error(\"Bad file content: non-positive dimension\");\n }\n v->resize(dimension);\n for(Dimensions d = 0; d < dimension; ++d) {\n T buffer;\n input.read((char*)&buffer, sizeof(buffer));\n v->at(d) = Round(buffer);\n } \n}\n\n/**\n * Function reads vocabulary of centroids produced by matlab script.\n * @param input input stream\n * @param dimension one centroid dimensionality\n * @param vocabulary_size centroids count\n * @param Centroids* result centroids\n */\ntemplate\nvoid ReadVocabulary(ifstream& input,\n Dimensions dimension,\n int vocabulary_size,\n Centroids* centroids) {\n \n if(!input.good()) {\n throw std::logic_error(\"Bad input stream\");\n }\n centroids->resize(vocabulary_size);\n for(ClusterId centroid_index = 0; centroid_index < centroids->size(); ++centroid_index) {\n centroids->at(centroid_index).resize(dimension);\n for(Dimensions dimension_index = 0; dimension_index < dimension; ++dimension_index) {\n T buffer;\n input.read((char*)&buffer, sizeof(buffer));\n centroids->at(centroid_index)[dimension_index] = Round(buffer);\n }\n }\n}\n\n/**\n * Function reads vocabularies of centroids produced by matlab script.\n * Function assumes that the first int32 in input is dimensionality of centroids and\n * the second is the number of centroids in each vocabulary\n * @param input input stream\n * @param dimension one centroid dimensionality\n * @param vocabulary_size centroids count\n * @param Centroids* result centroids\n */\ntemplate\nvoid ReadVocabularies(const string& filename,\n Dimensions space_dimension,\n vector* centroids) {\n ifstream vocabulary;\n vocabulary.open(filename.c_str(), ios::binary);\n\t\n if(!vocabulary.good()) {\n throw std::logic_error(\"Bad vocabulary file\");\n }\n int dimension;//dimension in each subspace\n vocabulary.read((char*)&dimension, sizeof(dimension));\n if(dimension <= 0) {\n throw std::logic_error(\"Bad file content: non-positive dimension\");\n }\n int vocabs_count = space_dimension / dimension;\n if(space_dimension < dimension) {\n throw std::logic_error(\"Space dimension is less than vocabulary dimension\");\n }\n centroids->resize(vocabs_count);\n int vocabulary_size;\n vocabulary.read((char*)&vocabulary_size, sizeof(vocabulary_size));\n for(int vocab_item = 0; vocab_item < vocabs_count; ++vocab_item) {\n ReadVocabulary(vocabulary, dimension, vocabulary_size, &(centroids->at(vocab_item)));\n }\n}\n\n/**\n * This function reads fine vocabs of centroids\n * @param fine_vocabs_filename file with vocabularies\n * @param fine_vocabs fine centroids lists\n */\ntemplate\nvoid ReadFineVocabs(const string& fine_vocabs_filename, vector* fine_vocabs) {\n ifstream fine_vocabs_stream;\n fine_vocabs_stream.open(fine_vocabs_filename.c_str(), ios::binary);\n if(!fine_vocabs_stream.good()) {\n throw std::logic_error(\"Bad fine vocabulary file\");\n }\n int vocabs_count, centroids_count, vocabs_dim;\n fine_vocabs_stream.read((char*)&vocabs_count, sizeof(vocabs_count));\n if(vocabs_count < 1) {\n throw std::logic_error(\"Bad fine vocabulary file content: number of vocabularies < 1\");\n }\n fine_vocabs_stream.read((char*)¢roids_count, sizeof(centroids_count));\n if(centroids_count < 1) {\n throw std::logic_error(\"Bad fine vocabulary file content: vocabulary capacity < 1\");\n }\n fine_vocabs_stream.read((char*)&vocabs_dim, sizeof(vocabs_dim));\n if(vocabs_dim < 1) {\n throw std::logic_error(\"Bad fine vocabulary file content: vocabulary dimension < 1\");\n }\n fine_vocabs->resize(vocabs_count);\n for(int voc_index = 0; voc_index < vocabs_count; ++voc_index) {\n ReadVocabulary(fine_vocabs_stream, vocabs_dim, centroids_count, &(fine_vocabs->at(voc_index)));\n }\n}\n\n/**\n * This function returns subpoints limited by start_dim and final_dim\n * for every point in points\n * @param points all points\n * @param start_dim first dimension of subpoint\n * @param final_dim dimension after the last dimension of subpoint\n * @param subpoints result subpoints\n */\nvoid GetSubpoints(const Points& points,\n const Dimensions start_dim,\n const Dimensions final_dim,\n Points* subpoints);\n\n/**\n * This function returns identifier of clusters which centroid is the nearest to \n * subpoint limited by start_dim and final_dim\n * @param point full point\n * @param Centroids all centroids (function finds the nearest one)\n * @param start_dim first dimension of subpoint\n * @param final_dim dimension after the last dimension of subpoint\n */\nClusterId GetNearestClusterId(const Point& point, const Centroids& centroids,\n const Dimensions start_dim, const Dimensions final_dim);\n\n/**\n * This function calculates quantization residual. \n * @param point initial point\n * @param coarse_quantizations point coarse quantization\n * @param centroids lists of centroids\n * @param residual result residual\n */\nvoid GetResidual(const Point& point, const CoarseQuantization& coarse_quantizations,\n const vector& centroids, Point* residual);\n/**\n * This function calculates quantization residual. \n * @param point initial point\n * @param coarse_quantizations point coarse quantization\n * @param centroids lists of centroids\n * @param residual pointer to start of residual\n */\nvoid GetResidual(const Point& point, const CoarseQuantization& coarse_quantizations,\n const vector& centroids, Coord* residual);\n\n/**\n * This function finds nearest cluster identifiers for points from start_pid to final_pid.\n * We need this function for multi-threading\n * @param points all points\n * @param centroids centroids of clusters\n * @param start_pid first point function finds nearest cluster\n * @param final_pid point after the last point function finds nearest cluster\n */\nvoid GetNearestClusterIdsForPointSubset(const Points& points, const Centroids& centroids,\n const PointId start_pid, const PointId final_pid,\n vector* nearest);\n\n/**\n * This function finds cluster identifiers nearest to subpoints for a number of points.\n * Subpoints are limited by start_dim and finish_dim\n * @param points all points\n * @param centroids centroids of clusters\n * @param start_dim first dimesion of subpoint\n * @param final_dim dimesion after the last dimension of subpoint\n * @param threads_count number of threads\n * @param nearest result\n */\nvoid GetNearestClusterIdsForSubpoints(const Points& points, const Centroids& centroids,\n const Dimensions start_dim, const Dimensions final_dim,\n int threads_count, vector* nearest);\n\n/**\n * This function calculates points coarse product quantizations\n * @param points all points\n * @param centroids centroids of clusters\n * @param threads_count number of threads\n * @param coarse_quantizations result quantizations\n */\nvoid GetPointsCoarseQuaintizations(const Points& points, const vector& centroids,\n const int threads_count,\n vector* coarse_quantizations);\n\n\n/**\n * \\struct All indexation parameters\n */\nstruct IndexConfig {\n RerankMode rerank_mode;\n vector fine_vocabs;\n};\n\n/**\n * \\struct Type of record in multiindex, contains\n * id of point and 8 bytes for ADC reranking\n */\nstruct RerankADC8 {\n PointId pid;\n FineClusterId quantizations[8];\n template\n void serialize(Archive& arc, unsigned int version) {\n arc & pid;\n arc & quantizations;\n }\n};\n\n/**\n * \\struct Type of record in multiindex, contains\n * id of point and 16 bytes for ADC reranking\n */\nstruct RerankADC16 {\n PointId pid;\n FineClusterId quantizations[16];\n template\n void serialize(Archive& arc, unsigned int version) {\n arc & pid;\n arc & quantizations;\n }\n};\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "algorithms/OPQ/src/indexer.h", "content": "/** @file */\n\n// Copyright 2012 Yandex Artem Babenko\n#ifndef INDEXER_H_\n#define INDEXER_H_\n\n#include \n#include \n\n#include \n#include \n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\n#include \"data_util.h\"\n#include \"multitable.hpp\"\n#include \n#include \n#include \n\nusing std::ifstream;\nusing std::map;\nusing std::multimap;\nusing std::ofstream;\nusing std::string;\nusing std::pair;\nusing std::make_pair;\n\nusing boost::lexical_cast;\nusing boost::split;\n\nextern int THREADS_COUNT;\n\nextern Dimensions SPACE_DIMENSION;\n\nextern enum PointType point_type;\n\nIndexConfig gConfig;\n\n/**\n * This is the main class for creating multiindex for a set of points\n * in a multidimensional space. Clusterization and vocabs learning happen\n * outside of this class, multiindexer receives prepared vocabs in input\n */\ntemplate\nclass MultiIndexer { \n public:\n /**\n * This is the simple MultiIndexer constructor\n * @param multiplicity how many parts input points will be divide on\n */\n MultiIndexer(const int multiplicity = 2);\n /**\n * This is the main function of MultiIndexer\n * @param points_filename file with points in .fvecs or .bvecs format\n * @param points_count how many points should we index\n * @param coarse_vocabs vocabularies for coarse quantization\n * @param fine_vocabs vocabularies for fine quantization for reranking\n * @param mode determines the way of rerank info calculating\n * @param build_coarse_quantization should we get coarse quantization or not\n * @param files_prefix all index filenames will have this prefix\n * @param coarse_quantization_filename file with coarse quantization (if exists)\n */\n void BuildMultiIndex(const string& points_filename,\n const string& metainfo_filename,\n const int points_count,\n const vector& coarse_vocabs,\n const vector& fine_vocabs,\n const RerankMode& mode,\n const bool build_coarse_quantization,\n const string& files_prefix,\n const string& coarse_quantization_filename = \"\");\n private:\n\t\n\n int IsExist(const vector* inverts,const string hex);\n void report(const vector >* transposed_coarse_quantizations, int coarse_k,const vector& coarse_vocabs);\n \n\n\n /**\n * This function prepares for each point its coarse quantization\n * @param points_filename file with points in .fvecs or .bvecs format\n * @param points_count how many points should we handle\n * @param coarse_vocabs vocabularies for coarse quantization\n */\n void PrepareCoarseQuantization(const string& points_filename,\n const int points_count,\n const vector& coarse_vocabs);\n /**\n * This function prepares for each point in subset its coarse quantization\n * @param points_filename file with points in .fvecs or .bvecs format\n * @param start_pid identifier of the first point in subset\n * @param subset_size points count in subset\n * @param coarse_vocabs vocabularies for coarse quantization\n * @param transposed_coarse_quantizations result\n */\n void GetCoarseQuantizationsForSubset(const string& points_filename,\n const int start_pid,\n const int subset_size,\n const vector& coarse_vocabs,\n vector >*\n transposed_coarse_quantizations);\n /**\n * This function serializes prepared coarse quantizations to file\n * @param transposed_coarse_quantizations quantizations to serialize.\n * They are transposed because of effective memory usage\n * @param filename file we should serialize to\n */\n void SerializeCoarseQuantizations(const vector >&\n transposed_coarse_quantizations,\n const string& filename);\n /**\n * This function saves index to files.\n * All filenames start form the common files prefix\n */\n void SerializeMultiIndexFiles();\n /**\n * This function converts counts of points in cells to cell edges\n */\n void ConvertPointsInCellsCountToCellEdges();\n\n /**\n * This function fills multiindex data structures.\n * @param points_filename file with points in .fvecs or .bvecs format\n * @param points_count how many points should we index\n * @param coarse_vocabs vocabularies for coarse quantization\n * @param fine_vocabs vocabularies for fine quantization for reranking\n * @param mode determines the way of rerank info calculating\n */\n void FillMultiIndex(const string& points_filename,\n const int points_count,\n const vector& coarse_vocabs,\n const vector& fine_vocabs,\n const RerankMode& mode);\n /**\n * This function fills multiindex data structures.\n * @param points_filename file with points in .fvecs or .bvecs format\n * @param start_pid identifier of the first point in subset\n * @param subset_size points count in subset\n * @param coarse_vocabs vocabularies for coarse quantization\n * @param fine_vocabs vocabularies for fine quantization for reranking\n * @param mode determines the way of rerank info calculating\n * @param points_written_in_index auxillary structure for correct index filling\n */\n void FillMultiIndexForSubset(const string& points_filename,\n const PointId start_pid,\n const int points_count,\n const vector& coarse_vocabs,\n const vector& fine_vocabs,\n const RerankMode& mode,\n Multitable* points_written_in_index);\n\n /**\n * This function reads point coarse quantization from file\n * @param pid identifier of target point\n * @param filename file with coarse quantizations\n * @param coarse_quantization result\n */\n void GetPointCoarseQuantization(const PointId pid,\n const string& filename,\n vector* coarse_quantization);\n /**\n * This function calculates rerank info for point\n * @param point target point\n * @param pid identifier of target point\n * @param fine_vocabs vocabularies for rerank info calculation\n */\n void FillPointRerankInfo(const Point& point,\n const PointId pid,\n const vector& fine_vocabs);\n /**\n * This function restores counts of points from coarse quantizations\n * @param points_filename file with points in .fvecs or .bvecs format\n * @param points_count how many points should we index\n * @param coarse_vocabs vocabularies for coarse quantization\n * We need them to init counts table correctly\n */\n void RestorePointsInCellsCountFromCourseQuantization(const string& points_filename,\n const int points_count,\n const vector& coarse_vocabs);\n /**\n * This simple function returns size of one coordinate of input point\n */\n int GetInputCoordSizeof();\n /**\n * This simple function reads one point from input stream\n * @param input input stream\n * @param point result point\n */\n void ReadPoint(ifstream& input, Point* point);\n /**\n * Initialize all structures for BLAS operations\n * @param coarse_vocabs coarse vocabularies\n */\n void InitBlasStructures(const vector& coarse_vocabs);\t\n /**\n * All index filenames will start from this prefix\n */\n string files_prefix_;\n /**\n * Filename of file with coarse quantizations\n */\n string coarse_quantization_filename_;\n /**\n * Multiplicity (how many parts point space is divided on)\n */\n int multiplicity_;\n /**\n * Table with number of points in each cell\n */\n Multitable point_in_cells_count_;\n /**\n * Multiindex\n */\n MultiIndex multiindex_;\n /**\n * Mutex for critical section in filling index stage\n */\n boost::mutex cell_counts_mutex_;\n /**\n * Struct for BLAS\n */\n vector coarse_vocabs_matrices_;\n /**\n * Struct for BLAS\n */\n vector > coarse_centroids_norms_;\n\n};\n\ntemplate\ninline void GetRecord(const Point& point, const PointId pid,\n const vector coarse_quantization,\n const vector& coarse_vocabs,\n Record* result) {\n}\n\ntemplate\nvoid InitParameters(const vector& fine_vocabs,\n const RerankMode& mode,\n const string& metainfo_filename) {\n gConfig.fine_vocabs = fine_vocabs;\n gConfig.rerank_mode = mode;\n}\n\n\n//////////////////// IMPLEMENTATION //////////////////////\ntemplate\nMultiIndexer::MultiIndexer(const int multiplicity) {\n if(multiplicity < 0) {\n throw std::logic_error(\"Multiplicity < 0\");\n }\n multiplicity_ = multiplicity;\n}\n\ntemplate\nint MultiIndexer::GetInputCoordSizeof() {\n if(point_type == FVEC) {\n return (int)sizeof(float);\n } else if(point_type == BVEC) {\n return (int)sizeof(unsigned char);\n }\n}\n\ntemplate\nvoid MultiIndexer::ReadPoint(ifstream& input, Point* point) {\n if(!input.good()) {\n throw std::logic_error(\"Bad input stream\");\n }\n if(point_type == FVEC) {\n ReadVector(input, point);\n } else if(point_type == BVEC) {\n ReadVector(input, point);\n } \n}\n\ntemplate\nvoid MultiIndexer::SerializeCoarseQuantizations(const vector >&\n\t\t transposed_coarse_quantizations,\n const string& filename) {\n ofstream quantizations_stream;\n quantizations_stream.open(filename.c_str(), ios::binary);\n if(!quantizations_stream.good()) {\n throw std::logic_error(\"Bad input stream\");\n }\n cout << \"Writing coarse quantizations started\" << endl;\n for(PointId pid = 0; pid < transposed_coarse_quantizations[0].size(); ++pid) {\n for(int subspace_index = 0; subspace_index < multiplicity_; ++subspace_index) {\n ClusterId quantization = transposed_coarse_quantizations[subspace_index][pid];\n quantizations_stream.write((char*)&quantization, sizeof(quantization));\n }\n }\n quantizations_stream.close();\n cout << \"Writing coarse quantizations started\" << endl;\n}\n\ntemplate\nvoid MultiIndexer::SerializeMultiIndexFiles() {\n cout << \"Start multiindex serializing....\\n\";\n ofstream cell_edges(string(files_prefix_ + \"_cell_edges.bin\").c_str(), ios::binary);\n boost::archive::binary_oarchive arc_cell_edges(cell_edges);\n arc_cell_edges << multiindex_.cell_edges;\n ofstream multi_array(string(files_prefix_ + \"_multi_array.bin\").c_str(), ios::binary);\n boost::archive::binary_oarchive arc_multi_array(multi_array);\n arc_multi_array << multiindex_.multiindex;\n cout << \"Finish multiindex serializing....\\n\";\n}\n\ntemplate\nint MultiIndexer::IsExist(const vector* inverts, const string hex)\n{\n\tfor(int i=0;isize();i++)\n\t{\n\t\tif(inverts->at(i).compare(hex)==0)\n\t\t\treturn i;\n\t}\n\treturn -1;\n}\n/*\ntemplate\nvoid MultiIndexer::report(const vector >* transposed_coarse_quantizations, int coarse_k)\n{\n\tint data_size = transposed_coarse_quantizations->at(0).size();\n\tcout< bit_set;\n\tvector counts(data_size,0);\n\tfor(int i=0; iat(j)[i]<<\";\";\n\t\tstd::string hex=ss.str();\n\t\tint index = IsExist(&bit_set,hex);\n\t\tif(index==-1)\n\t\t{\n\t\t\tbit_set.push_back(hex);\n\t\t\tcounts[bit_set.size()-1]++;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tcounts[index]++;\n\t\t}\n\t}\n\tif(bit_set.size()==data_size)\n\t{\n\t\tcout<<\"Failed!!!\"<=0;i--)\n\t{\n\t\tfloat tmp = counts[i]-avg ; \n\t\tvar += tmp * tmp ;\n\t}\n\tvar /= bit_set.size();\n\tcout<<\"\\n\"<=0;i--)\n\t{\n\t\tcout<\nvoid MultiIndexer::report(const vector >* transposed_coarse_quantizations, int coarse_k,const vector& coarse_vocabs)\n{\n\tint data_size = transposed_coarse_quantizations->at(0).size();\n\tint m=2;\n vector bit_set;\n\tvector > center_ids;\n\tvector counts(data_size,0);\n\tvector > ds;\n\tfor(int i=0; iat(j)[i]<<\";\";\n\t\tstd::string hex=ss.str();\n\t\tint index = IsExist(&bit_set,hex);\n\t\tif(index==-1)\n\t\t{\n\t\t\tbit_set.push_back(hex);\n\t\t\tcounts[bit_set.size()-1]++;\n\t\t\tvector dd;\n\t\t\tdd.push_back(i);\n\t\t\tds.push_back(dd);\n\t\t\tcenter_ids.push_back(make_pair(transposed_coarse_quantizations->at(0)[i],transposed_coarse_quantizations->at(1)[i]));\n\t\t}\n\t\telse\n\t\t{\n\t\t\tcounts[index]++;\n\t\t\tvector dd=ds[index];\n\t\t\tdd.push_back(i);\n\t\t\tds[index]=dd;\n\t\t}\n\t}\n\n\tFILE * fp =fopen(\"index.opq\",\"wb\");\n\tint cluster_size = bit_set.size();\n\tfwrite(&cluster_size,sizeof(int),1,fp);\n\tcout<\nvoid MultiIndexer::GetCoarseQuantizationsForSubset(const string& points_filename,\n const int start_pid,\n const int subset_size,\n const vector& coarse_vocabs,\n vector >*\n transposed_coarse_quantizations) {\n\n\n ifstream point_stream;\n point_stream.open(points_filename.c_str(), ios::binary);\n if(!point_stream.good()) {\n throw std::logic_error(\"Bad input points stream\");\n }\n // we assume points are stored in .fvecs or .bvecs format\n point_stream.seekg(start_pid * (GetInputCoordSizeof() * SPACE_DIMENSION + sizeof(Dimensions)), ios::beg);\n vector coarse_quantization(multiplicity_);\n //cout<<\"subset_size:\" << subset_size << endl;\n for(int point_number = 0; point_number < subset_size; ++point_number) {\n /*if(point_number % 10000 == 0) {\n cout << \"Getting coarse quantization, point # \" << start_pid + point_number << endl;\n }*/\n //cout<<\"point number:\"<at(coarse_index)[start_pid + point_number] = nearest;\n coarse_quantization[coarse_index] = nearest;\n cblas_saxpy(subpoints_dimension, -1, &(coarse_vocabs.at(coarse_index)[nearest][0]), 1, &(current_point[start_dim]), 1);\n }\n \n int global_index = point_in_cells_count_.GetCellGlobalIndex(coarse_quantization);\n cell_counts_mutex_.lock();\n ++(point_in_cells_count_.table[global_index]);\n cell_counts_mutex_.unlock();\n }\n\t\n //cout<<\"transposed:\"<size()<<\" \"<at(0).size()<\nvoid MultiIndexer::PrepareCoarseQuantization(const string& points_filename,\n const int points_count,\n const vector& coarse_vocabs) {\n // we use transposed quantizations for efficient memory usage\n vector > transposed_coarse_quantizations; \n transposed_coarse_quantizations.resize(multiplicity_);\n vector multiindex_table_dimensions;\n for(int i = 0; i < multiplicity_; ++i) {\n transposed_coarse_quantizations[i].resize(points_count);\n multiindex_table_dimensions.push_back(coarse_vocabs[i].size());\n }\n point_in_cells_count_.Resize(multiindex_table_dimensions);\n cout << \"Memory for coarse quantizations allocated\" << endl;\n boost::thread_group index_threads;\n int thread_points_count = points_count / THREADS_COUNT;\n for(int thread_id = 0; thread_id < THREADS_COUNT; ++thread_id) {\n PointId start_pid = thread_points_count * thread_id;\n index_threads.create_thread(boost::bind(&MultiIndexer::GetCoarseQuantizationsForSubset,\n this, points_filename, start_pid, thread_points_count,\n boost::cref(coarse_vocabs), &transposed_coarse_quantizations));\n }\n index_threads.join_all();\n if(coarse_quantization_filename_.empty()) {\n coarse_quantization_filename_ = files_prefix_ + \"_coarse_quantizations.bin\";\n }\n cout << \"Coarse quantizations are calculated\" << endl;\n SerializeCoarseQuantizations(transposed_coarse_quantizations, coarse_quantization_filename_);\n cout << \"Coarse quantizations are serialized\" << endl;\n}\n\ntemplate\nvoid MultiIndexer::ConvertPointsInCellsCountToCellEdges() {\n cout << \"Converting points in cells to cell edges...\\n\";\n multiindex_.cell_edges = point_in_cells_count_;\n multiindex_.cell_edges.table[0] = 0;\n for(int global_index = 1;\n global_index < point_in_cells_count_.table.size();\n ++global_index) {\n multiindex_.cell_edges.table[global_index] = multiindex_.cell_edges.table[global_index - 1] +\n point_in_cells_count_.table[global_index - 1];\n }\n // we do not need this table more\n point_in_cells_count_.table.clear();\n cout << \"Finish converting points in cells to cell edges...\\n\";\n}\n\ntemplate\nvoid MultiIndexer::GetPointCoarseQuantization(const PointId pid,\n const string& filename,\n vector* coarse_quantization) {\n ifstream coarse_quantization_stream;\n coarse_quantization_stream.open(filename.c_str(), ios::binary);\n if(!coarse_quantization_stream.good()) {\n throw std::logic_error(\"Bad input coarse quantizations stream\");\n }\n coarse_quantization_stream.seekg((long long)pid * sizeof(ClusterId) * multiplicity_, ios::beg);\n for(int coarse_index = 0; coarse_index < multiplicity_; ++coarse_index) {\n coarse_quantization_stream.read((char*)&(coarse_quantization->at(coarse_index)),\n sizeof(coarse_quantization->at(coarse_index)));\n }\n}\n\ntemplate\nvoid MultiIndexer::FillMultiIndexForSubset(const string& points_filename,\n const PointId start_pid,\n const int points_count,\n const vector& coarse_vocabs,\n const vector& fine_vocabs,\n const RerankMode& mode,\n Multitable* points_written_in_index) {\n ifstream point_stream;\n point_stream.open(points_filename.c_str(), ios::binary);\n if(!point_stream.good()) {\n throw std::logic_error(\"Bad input points stream\");\n }\n point_stream.seekg((long long)start_pid * (GetInputCoordSizeof() * SPACE_DIMENSION + sizeof(Dimensions)), ios::beg);\n for(int point_number = 0; point_number < points_count; ++point_number) {\n if(point_number % 10000 == 0) {\n cout << \"Filling multiindex, point # \" << start_pid + point_number << endl;\n }\n Point current_point;\n ReadPoint(point_stream, ¤t_point);\n vector coarse_quantization(multiplicity_);\n GetPointCoarseQuantization(start_pid + point_number,\n coarse_quantization_filename_,\n &coarse_quantization);\n int current_written_count = points_written_in_index->GetValue(coarse_quantization);\n int pid_multiindex = multiindex_.cell_edges.GetValue(coarse_quantization) + current_written_count;\n GetRecord(current_point, start_pid + point_number,\n coarse_quantization, coarse_vocabs, &(multiindex_.multiindex[pid_multiindex]));\n cell_counts_mutex_.lock();\n points_written_in_index->SetValue(current_written_count + 1, coarse_quantization);\n cell_counts_mutex_.unlock();\n }\n}\n\ntemplate\nvoid MultiIndexer::FillMultiIndex(const string& points_filename,\n const int points_count,\n const vector& coarse_vocabs,\n const vector& fine_vocabs,\n const RerankMode& mode) {\n ConvertPointsInCellsCountToCellEdges();\n multiindex_.multiindex.resize(points_count);\n cout << \"Indexing started...\" << endl;\n\n Multitable points_written_in_index(multiindex_.cell_edges.dimensions);\n int thread_points_count = points_count / THREADS_COUNT;\n boost::thread_group threads;\n for(int thread_id = 0; thread_id < THREADS_COUNT; ++thread_id) {\n PointId start_pid = thread_points_count * thread_id;\n threads.create_thread(boost::bind(&MultiIndexer::FillMultiIndexForSubset, this, points_filename, start_pid,\n thread_points_count, boost::cref(coarse_vocabs),\n boost::cref(fine_vocabs), mode, &points_written_in_index));\n }\n threads.join_all();\n cout << \"Indexing finished...\" << endl;\n}\n\ntemplate\nvoid MultiIndexer::RestorePointsInCellsCountFromCourseQuantization(const string& points_filename,\n const int points_count,\n const vector& coarse_vocabs) {\n vector dimensions;\n for(int i = 0; i < multiplicity_; ++i) {\n dimensions.push_back(coarse_vocabs[i].size());\n }\n point_in_cells_count_.Resize(dimensions);\n ifstream coarse_quantization_stream;\n coarse_quantization_stream.open(coarse_quantization_filename_.c_str(), ios::binary);\n if(!coarse_quantization_stream.good()) {\n throw std::logic_error(\"Bad input coarse quantizations stream\");\n }\n CoarseQuantization quantization(multiplicity_);\n for(PointId pid = 0; pid < points_count; ++pid) {\n if(pid % 100000 == 0) {\n cout << pid << endl;\n }\n for(int subspace_index = 0; subspace_index < multiplicity_; ++subspace_index) {\n coarse_quantization_stream.read((char*)&(quantization[subspace_index]), \n sizeof(ClusterId));\n }\n int cell_global_index = point_in_cells_count_.GetCellGlobalIndex(quantization);\n point_in_cells_count_.table[cell_global_index] += 1;\n }\n}\n\ntemplate\nvoid MultiIndexer::BuildMultiIndex(const string& points_filename,\n const string& metainfo_filename,\n const int points_count,\n const vector& coarse_vocabs,\n const vector& fine_vocabs,\n const RerankMode& mode,\n const bool build_coarse_quantization,\n const string& files_prefix,\n const string& coarse_quantization_filename) {\n InitParameters(fine_vocabs, mode, metainfo_filename);\n InitBlasStructures(coarse_vocabs);\n files_prefix_ = files_prefix;\n coarse_quantization_filename_ = coarse_quantization_filename;\n if(build_coarse_quantization) {\n PrepareCoarseQuantization(points_filename, points_count, coarse_vocabs);\n } else {\n RestorePointsInCellsCountFromCourseQuantization(points_filename,\n points_count,\n coarse_vocabs);\n }\n FillMultiIndex(points_filename, points_count, coarse_vocabs, fine_vocabs, mode);\n cout << \"Multiindex created\" << endl;\n SerializeMultiIndexFiles();\n cout << \"Multiindex serialized\" << endl;\n}\n\ntemplate\nvoid MultiIndexer::InitBlasStructures(const vector& coarse_vocabs) {\n coarse_vocabs_matrices_.resize(coarse_vocabs.size());\n coarse_centroids_norms_.resize(coarse_vocabs.size(), vector(coarse_vocabs[0].size()));\n for(int coarse_id = 0; coarse_id < coarse_vocabs_matrices_.size(); ++coarse_id) {\n coarse_vocabs_matrices_[coarse_id] = new float[coarse_vocabs[0].size() * coarse_vocabs[0][0].size()];\n for(int i = 0; i < coarse_vocabs[0].size(); ++i) {\n Coord norm = 0;\n for(int j = 0; j < coarse_vocabs[0][0].size(); ++j) {\n coarse_vocabs_matrices_[coarse_id][coarse_vocabs[0][0].size() * i + j] = coarse_vocabs[coarse_id][i][j];\n norm += coarse_vocabs[coarse_id][i][j] * coarse_vocabs[coarse_id][i][j];\n }\n coarse_centroids_norms_[coarse_id][i] = norm;\n }\n }\n}\n\ntemplate<>\ninline void GetRecord (const Point& point, const PointId pid,\n const vector coarse_quantization,\n const vector& coarse_vocabs,\n PointId* result) {\n *result = pid;\n}\n\ninline void FillAdcInfo(const Point& point, const PointId pid,\n const vector& fine_vocabs,\n char* result) {\n int subvectors_count = fine_vocabs.size();\n int subvector_dim = point.size() / subvectors_count;\n for(int subvector_index = 0; subvector_index < subvectors_count; ++subvector_index) {\n Dimensions start_dim = subvector_index * subvector_dim;\n Dimensions final_dim = start_dim + subvector_dim;\n *((FineClusterId*)result) = (FineClusterId)GetNearestClusterId(point, fine_vocabs[subvector_index],\n\t\t\t start_dim, final_dim);\n result += sizeof(FineClusterId);\n }\n}\n\ntemplate<>\ninline void GetRecord (const Point& point, const PointId pid,\n const vector coarse_quantization,\n const vector& coarse_vocabs,\n RerankADC8* result) {\n result->pid = pid;\n char* rerank_info_ptr = (char*)result + sizeof(pid);\n if(gConfig.rerank_mode == USE_RESIDUALS) {\n Point residual;\n GetResidual(point, coarse_quantization, coarse_vocabs, &residual);\n FillAdcInfo(residual, pid, gConfig.fine_vocabs, rerank_info_ptr);\n } else if (gConfig.rerank_mode == USE_INIT_POINTS) {\n FillAdcInfo(point, pid, gConfig.fine_vocabs, rerank_info_ptr);\n }\n}\n\ntemplate<>\ninline void GetRecord (const Point& point, const PointId pid,\n const vector coarse_quantization,\n const vector& coarse_vocabs,\n RerankADC16* result) {\n result->pid = pid;\n char* rerank_info_ptr = (char*)result + sizeof(pid);\n if(gConfig.rerank_mode == USE_RESIDUALS) {\n Point residual;\n GetResidual(point, coarse_quantization, coarse_vocabs, &residual);\n FillAdcInfo(residual, pid, gConfig.fine_vocabs, rerank_info_ptr);\n } else if (gConfig.rerank_mode == USE_INIT_POINTS) {\n FillAdcInfo(point, pid, gConfig.fine_vocabs, rerank_info_ptr);\n }\n}\n\n#endif\n\n\n\n\n" }, { "path": "algorithms/OPQ/src/indexer_launcher.cpp", "content": "// Copyright 2012 Yandex Artem Babenko\n#include \n#include \n\n#include \n#include \n\n#include \"indexer.h\"\n\nusing namespace boost::program_options;\nusing std::ofstream;\n/**\n * Number of threads for indexing\n */\nint THREADS_COUNT;\n/**\n * Type, should be BVEC or FVEC\n */\nPointType point_type;\n/**\n * Number of coordinates in a point\n */\nDimensions SPACE_DIMENSION;\n/**\n * File with vocabularies for multiindex structure\n */\nstring coarse_vocabs_file;\n/**\n * File with vocabularies for reranking\n */\nstring fine_vocabs_file;\n/**\n * File with points to index\n */\nstring points_file;\n/**\n * File with points metainfo (imageId, etc.)\n */\nstring metainfo_file;\n/**\n * Reranking approach, should be USE_RESIDUALS or USE_INIT_POINTS\n */\nRerankMode mode;\n/**\n * Common prefix of all multiindex files\n */\nstring files_prefix;\n/**\n * Should we calculate coarse quantizations (they can be precomputed)\n */\nbool build_coarse_quantizations;\n/**\n * File with points coarse quantizations\n */\nstring coarse_quantizations_file;\n/**\n * How many points should we index\n */\nint points_count;\n/**\n * Multiplicity of multiindex\n */\nint multiplicity;\n\nstring result_path;\n\nstring index_param ;\n\nint SetOptions(int argc, char** argv) {\n options_description description(\"Options\");\n description.add_options()\n (\"threads_count,t\", value())\n (\"multiplicity,m\", value())\n (\"points_file,p\", value())\n (\"metainfo_file,z\", value())\n (\"coarse_vocabs_file,c\", value())\n (\"fine_vocabs_file,f\", value())\n (\"input_point_type,i\", value())\n (\"build_coarse,b\", value()) //bool_switch(), \"Flag B\")\n (\"use_residuals,r\", value())\n\t//(\"Use_res,a\", value())bool_switch(), \"Flag R\")\n (\"points_count,p\", value())\n (\"coarse_quantization_file,q\", value())\n (\"space_dim,d\", value())\n (\"files_prefix,_\", value());\n\n variables_map name_to_value;\n try {\n store(command_line_parser(argc, argv).options(description).run(), name_to_value);\n } catch (const invalid_command_line_syntax& inv_syntax) {\n switch (inv_syntax.kind()) {\n case invalid_syntax::missing_parameter :\n cout << \"Missing argument for option '\" << inv_syntax.tokens() << \"'.\\n\";\n break;\n default:\n cout << \"Syntax error, kind \" << int(inv_syntax.kind()) << \"\\n\";\n break;\n };\n return 1;\n } catch (const unknown_option& unkn_option) {\n cout << \"Unknown option '\" << unkn_option.get_option_name() << \"'\\n\";\n return 1;\n }\n if (name_to_value.count(\"help\")) {\n cout << description << \"\\n\";\n return 1;\n }\n\n THREADS_COUNT = name_to_value[\"threads_count\"].as();\n multiplicity = name_to_value[\"multiplicity\"].as();\n points_file = name_to_value[\"points_file\"].as();\n metainfo_file = name_to_value[\"metainfo_file\"].as();\n coarse_vocabs_file = name_to_value[\"coarse_vocabs_file\"].as();\n fine_vocabs_file = name_to_value[\"fine_vocabs_file\"].as();\n SPACE_DIMENSION = name_to_value[\"space_dim\"].as();\n files_prefix = name_to_value[\"files_prefix\"].as();\n points_count = name_to_value[\"points_count\"].as();\n\n build_coarse_quantizations = (name_to_value[\"build_coarse\"].as() == 0) ? true : false;\n //cout<() == true ? USE_RESIDUALS : USE_INIT_POINTS;\n mode = name_to_value[\"use_residuals\"].as() == 0 ? USE_RESIDUALS : USE_INIT_POINTS;\n //cout<();\n }\n if (name_to_value[\"input_point_type\"].as() == \"FVEC\") {\n point_type = FVEC;\n } else if(name_to_value[\"input_point_type\"].as() == \"BVEC\") {\n point_type = BVEC;\n }\n return 0;\n}\n\nfloat diff_timeval(timeval t1, timeval t2) {\n return (float) (t1.tv_sec - t2.tv_sec) + (t1.tv_usec - t2.tv_usec) * 1e-6;\n}\n\nint main(int argc, char** argv) {\n \n SetOptions(argc, argv);\n \n cout << \"Options are set ...\\n\";\n vector coarse_vocabs;\n vector fine_vocabs;\n ReadVocabularies(coarse_vocabs_file, SPACE_DIMENSION, &coarse_vocabs);\n cout<<\"read coarse file\\n\";\n ReadFineVocabs(fine_vocabs_file, &fine_vocabs);\n cout << \"Vocs are read ...\\n\";\n timeval start;\n timeval end;\n gettimeofday(&start, NULL);\n if(fine_vocabs.size() == 8) {\n MultiIndexer indexer(multiplicity);\n indexer.BuildMultiIndex(points_file, metainfo_file, points_count, coarse_vocabs, \n fine_vocabs, mode, build_coarse_quantizations,\n files_prefix, coarse_quantizations_file);\n } else if(fine_vocabs.size() == 16) {\n MultiIndexer indexer(multiplicity);\n indexer.BuildMultiIndex(points_file, metainfo_file, points_count, coarse_vocabs, \n fine_vocabs, mode, build_coarse_quantizations,\n files_prefix, coarse_quantizations_file); \n }\n\n gettimeofday(&end, NULL);\n float time=0;\n time += diff_timeval(end, start);\n \n\n ofstream out;\n out.open(result_path.c_str(),ios::app);\n out<