Full Code of rymc/n2d for AI

Repository: rymc/n2d
Branch: master
Commit: 6c359365601f
Files: 45
Total size: 338.3 MB

Directory structure:
gitextract__t5w6mwm/

├── LICENSE
├── README.md
├── data/
│   ├── har/
│   │   ├── README.txt
│   │   ├── activity_labels.txt
│   │   ├── features.txt
│   │   ├── features_info.txt
│   │   ├── test/
│   │   │   ├── Inertial Signals/
│   │   │   │   ├── body_acc_x_test.txt
│   │   │   │   ├── body_acc_y_test.txt
│   │   │   │   ├── body_acc_z_test.txt
│   │   │   │   ├── body_gyro_x_test.txt
│   │   │   │   ├── body_gyro_y_test.txt
│   │   │   │   ├── body_gyro_z_test.txt
│   │   │   │   ├── total_acc_x_test.txt
│   │   │   │   ├── total_acc_y_test.txt
│   │   │   │   └── total_acc_z_test.txt
│   │   │   ├── X_test.txt
│   │   │   ├── subject_test.txt
│   │   │   └── y_test.txt
│   │   └── train/
│   │       ├── Inertial Signals/
│   │       │   ├── body_acc_x_train.txt
│   │       │   ├── body_acc_y_train.txt
│   │       │   ├── body_acc_z_train.txt
│   │       │   ├── body_gyro_x_train.txt
│   │       │   ├── body_gyro_y_train.txt
│   │       │   ├── body_gyro_z_train.txt
│   │       │   ├── total_acc_x_train.txt
│   │       │   ├── total_acc_y_train.txt
│   │       │   └── total_acc_z_train.txt
│   │       ├── X_train.txt
│   │       ├── subject_train.txt
│   │       └── y_train.txt
│   ├── pendigits/
│   │   ├── pendigits.names
│   │   ├── pendigits.tes
│   │   └── pendigits.tra
│   └── usps/
│       ├── usps_test.jf
│       └── usps_train.jf
├── datasets.py
├── n2d.py
├── requirements.txt
├── run.sh
└── weights/
    ├── fashion-1000-ae_weights.h5
    ├── har-1000-ae_weights.h5
    ├── mnist-1000-ae_weights.h5
    ├── mnist-test-1000-ae_weights.h5
    ├── pendigits-1000-ae_weights.h5
    └── usps-1000-ae_weights.h5

================================================
FILE CONTENTS
================================================

================================================
FILE: LICENSE
================================================
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  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.

  16. Limitation of Liability.

  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.

  17. Interpretation of Sections 15 and 16.

  If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.

                     END OF TERMS AND CONDITIONS

            How to Apply These Terms to Your New Programs

  If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.

  To do so, attach the following notices to the program.  It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.

    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) <year>  <name of author>

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

  If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:

    <program>  Copyright (C) <year>  <name of author>
    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License.  Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".

  You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.

  The GNU General Public License does not permit incorporating your program
into proprietary programs.  If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library.  If this is what you want to do, use the GNU Lesser General
Public License instead of this License.  But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.


================================================
FILE: README.md
================================================
## N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding.

## Abstract
Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. 
Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss.
In such cases, an autoencoder is typically connected with a clustering network, and the final clustering is jointly learned by both the autoencoder and clustering network.
Instead, we propose to learn an autoencoded embedding and then search this further for the underlying manifold.
For simplicity, we then cluster this with a shallow clustering algorithm, rather than a deeper network.
We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is able to find the best clusterable manifold of the embedding. This suggests that local manifold learning on an autoencoded embedding is effective for discovering higher quality clusters.
We quantitatively show across a range of image and time-series datasets that our method has competitive performance against the latest deep clustering algorithms, including out-performing current state-of-the-art on several.
We postulate that these results show a promising research direction for deep clustering.

## Results
![N2D results](https://seis.bristol.ac.uk/~rm17770/publications/n2d-results.png)

## Visualizations
### MNIST
<img src="https://seis.bristol.ac.uk/~rm17770/publications/mnist-n2d.png" width="600px">

### HAR (Human Activity Recognition)
<img src="https://seis.bristol.ac.uk/~rm17770/publications/har-n2d.png" width="600px">
Note: clusters 'look' better in higher dimensions (based on clustering metrics) than they do here in 2d. The intended use of n2d is for clustering. Visualized here are the first 5000 points.


## Paper

https://arxiv.org/abs/1908.05968

## Install

### Install Anaconda
```sh
wget https://repo.anaconda.com/archive/Anaconda3-2019.07-Linux-x86_64.sh
bash Anaconda3-2019.07-Linux-x86_64.sh
source anaconda3/bin/activate
```

### Create environment
```sh
conda create -n n2d python=3.7  
conda activate n2d
```
### Clone repo
```sh
git clone https://github.com/rymc/n2d.git
```
### Install packages
```sh
pip install -r requirements.txt
```

### Reproduce results
```sh
bash run.sh
```

### For training a new network
If you remove the --ae_weights argument when running n2d then it will train a new network, rather than load the pretrained weights.

For adding a new dataset you should add a load function to datasets.py (you can use the existing ones to understand how) and a function to call your data loading function from n2d.py

I used the following packages for training the networks using the GPU.
```sh
conda install tensorflow-gpu=1.13.1 cudatoolkit=9.0
```

### Visualization
If you would like to produce some plots for visualization purposes add the agument '--visualize'. I also reccomend setting the argument '--umap_dim' to be 2.

## Citation
```
@inproceedings{McConville2020,
  author = {Ryan McConville and Raul Santos-Rodriguez and Robert J Piechocki and Ian Craddock},
  title = {N2D:(Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding},
  booktitle = {25th International Conference on Pattern Recognition, {ICPR} 2020},
  publisher = {{IEEE} Computer Society},
  year = {2020},
}
```


================================================
FILE: data/har/README.txt
================================================
===================================================================================================
Human Activity Recognition Using Smartphones Dataset
Version 1.0
===================================================================================================
Jorge L. Reyes-Ortiz(1,2), Davide Anguita(1), Alessandro Ghio(1), Luca Oneto(1) and Xavier Parra(2)
1 - Smartlab - Non-Linear Complex Systems Laboratory
DITEN - Universit  degli Studi di Genova, Genoa (I-16145), Italy. 
2 - CETpD - Technical Research Centre for Dependency Care and Autonomous Living
Universitat Politcnica de Catalunya (BarcelonaTech). Vilanova i la Geltr (08800), Spain
activityrecognition '@' smartlab.ws 
===================================================================================================

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. 

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. See 'features_info.txt' for more details. 

For each record it is provided:
======================================

- Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
- Triaxial Angular velocity from the gyroscope. 
- A 561-feature vector with time and frequency domain variables. 
- Its activity label. 
- An identifier of the subject who carried out the experiment.

The dataset includes the following files:
=========================================

- 'README.txt'

- 'features_info.txt': Shows information about the variables used on the feature vector.

- 'features.txt': List of all features.

- 'activity_labels.txt': Links the class labels with their activity name.

- 'train/X_train.txt': Training set.

- 'train/y_train.txt': Training labels.

- 'test/X_test.txt': Test set.

- 'test/y_test.txt': Test labels.

The following files are available for the train and test data. Their descriptions are equivalent. 

- 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30. 

- 'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and 'total_acc_z_train.txt' files for the Y and Z axis. 

- 'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration. 

- 'train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second. 

Notes: 
======
- Features are normalized and bounded within [-1,1].
- Each feature vector is a row on the text file.
- The units used for the accelerations (total and body) are 'g's (gravity of earth -> 9.80665 m/seg2).
- The gyroscope units are rad/seg.
- A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: http://www.youtube.com/watch?v=XOEN9W05_4A

For more information about this dataset please contact: activityrecognition '@' smartlab.ws

License:
========
Use of this dataset in publications must be acknowledged by referencing the following publication [1] 

[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013. 

This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.

Other Related Publications:
===========================
[2] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge L. Reyes-Ortiz.  Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. Journal of Universal Computer Science. Special Issue in Ambient Assisted Living: Home Care.   Volume 19, Issue 9. May 2013

[3] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. 4th International Workshop of Ambient Assited Living, IWAAL 2012, Vitoria-Gasteiz, Spain, December 3-5, 2012. Proceedings. Lecture Notes in Computer Science 2012, pp 216-223. 

[4] Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra-Llanas, Davide Anguita, Joan Cabestany, Andreu Catal. Human Activity and Motion Disorder Recognition: Towards Smarter Interactive Cognitive Environments. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.  

==================================================================================================
Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita and Xavier Parra. November 2013.


================================================
FILE: data/har/activity_labels.txt
================================================
1 WALKING
2 WALKING_UPSTAIRS
3 WALKING_DOWNSTAIRS
4 SITTING
5 STANDING
6 LAYING


================================================
FILE: data/har/features.txt
================================================
1 tBodyAcc-mean()-X
2 tBodyAcc-mean()-Y
3 tBodyAcc-mean()-Z
4 tBodyAcc-std()-X
5 tBodyAcc-std()-Y
6 tBodyAcc-std()-Z
7 tBodyAcc-mad()-X
8 tBodyAcc-mad()-Y
9 tBodyAcc-mad()-Z
10 tBodyAcc-max()-X
11 tBodyAcc-max()-Y
12 tBodyAcc-max()-Z
13 tBodyAcc-min()-X
14 tBodyAcc-min()-Y
15 tBodyAcc-min()-Z
16 tBodyAcc-sma()
17 tBodyAcc-energy()-X
18 tBodyAcc-energy()-Y
19 tBodyAcc-energy()-Z
20 tBodyAcc-iqr()-X
21 tBodyAcc-iqr()-Y
22 tBodyAcc-iqr()-Z
23 tBodyAcc-entropy()-X
24 tBodyAcc-entropy()-Y
25 tBodyAcc-entropy()-Z
26 tBodyAcc-arCoeff()-X,1
27 tBodyAcc-arCoeff()-X,2
28 tBodyAcc-arCoeff()-X,3
29 tBodyAcc-arCoeff()-X,4
30 tBodyAcc-arCoeff()-Y,1
31 tBodyAcc-arCoeff()-Y,2
32 tBodyAcc-arCoeff()-Y,3
33 tBodyAcc-arCoeff()-Y,4
34 tBodyAcc-arCoeff()-Z,1
35 tBodyAcc-arCoeff()-Z,2
36 tBodyAcc-arCoeff()-Z,3
37 tBodyAcc-arCoeff()-Z,4
38 tBodyAcc-correlation()-X,Y
39 tBodyAcc-correlation()-X,Z
40 tBodyAcc-correlation()-Y,Z
41 tGravityAcc-mean()-X
42 tGravityAcc-mean()-Y
43 tGravityAcc-mean()-Z
44 tGravityAcc-std()-X
45 tGravityAcc-std()-Y
46 tGravityAcc-std()-Z
47 tGravityAcc-mad()-X
48 tGravityAcc-mad()-Y
49 tGravityAcc-mad()-Z
50 tGravityAcc-max()-X
51 tGravityAcc-max()-Y
52 tGravityAcc-max()-Z
53 tGravityAcc-min()-X
54 tGravityAcc-min()-Y
55 tGravityAcc-min()-Z
56 tGravityAcc-sma()
57 tGravityAcc-energy()-X
58 tGravityAcc-energy()-Y
59 tGravityAcc-energy()-Z
60 tGravityAcc-iqr()-X
61 tGravityAcc-iqr()-Y
62 tGravityAcc-iqr()-Z
63 tGravityAcc-entropy()-X
64 tGravityAcc-entropy()-Y
65 tGravityAcc-entropy()-Z
66 tGravityAcc-arCoeff()-X,1
67 tGravityAcc-arCoeff()-X,2
68 tGravityAcc-arCoeff()-X,3
69 tGravityAcc-arCoeff()-X,4
70 tGravityAcc-arCoeff()-Y,1
71 tGravityAcc-arCoeff()-Y,2
72 tGravityAcc-arCoeff()-Y,3
73 tGravityAcc-arCoeff()-Y,4
74 tGravityAcc-arCoeff()-Z,1
75 tGravityAcc-arCoeff()-Z,2
76 tGravityAcc-arCoeff()-Z,3
77 tGravityAcc-arCoeff()-Z,4
78 tGravityAcc-correlation()-X,Y
79 tGravityAcc-correlation()-X,Z
80 tGravityAcc-correlation()-Y,Z
81 tBodyAccJerk-mean()-X
82 tBodyAccJerk-mean()-Y
83 tBodyAccJerk-mean()-Z
84 tBodyAccJerk-std()-X
85 tBodyAccJerk-std()-Y
86 tBodyAccJerk-std()-Z
87 tBodyAccJerk-mad()-X
88 tBodyAccJerk-mad()-Y
89 tBodyAccJerk-mad()-Z
90 tBodyAccJerk-max()-X
91 tBodyAccJerk-max()-Y
92 tBodyAccJerk-max()-Z
93 tBodyAccJerk-min()-X
94 tBodyAccJerk-min()-Y
95 tBodyAccJerk-min()-Z
96 tBodyAccJerk-sma()
97 tBodyAccJerk-energy()-X
98 tBodyAccJerk-energy()-Y
99 tBodyAccJerk-energy()-Z
100 tBodyAccJerk-iqr()-X
101 tBodyAccJerk-iqr()-Y
102 tBodyAccJerk-iqr()-Z
103 tBodyAccJerk-entropy()-X
104 tBodyAccJerk-entropy()-Y
105 tBodyAccJerk-entropy()-Z
106 tBodyAccJerk-arCoeff()-X,1
107 tBodyAccJerk-arCoeff()-X,2
108 tBodyAccJerk-arCoeff()-X,3
109 tBodyAccJerk-arCoeff()-X,4
110 tBodyAccJerk-arCoeff()-Y,1
111 tBodyAccJerk-arCoeff()-Y,2
112 tBodyAccJerk-arCoeff()-Y,3
113 tBodyAccJerk-arCoeff()-Y,4
114 tBodyAccJerk-arCoeff()-Z,1
115 tBodyAccJerk-arCoeff()-Z,2
116 tBodyAccJerk-arCoeff()-Z,3
117 tBodyAccJerk-arCoeff()-Z,4
118 tBodyAccJerk-correlation()-X,Y
119 tBodyAccJerk-correlation()-X,Z
120 tBodyAccJerk-correlation()-Y,Z
121 tBodyGyro-mean()-X
122 tBodyGyro-mean()-Y
123 tBodyGyro-mean()-Z
124 tBodyGyro-std()-X
125 tBodyGyro-std()-Y
126 tBodyGyro-std()-Z
127 tBodyGyro-mad()-X
128 tBodyGyro-mad()-Y
129 tBodyGyro-mad()-Z
130 tBodyGyro-max()-X
131 tBodyGyro-max()-Y
132 tBodyGyro-max()-Z
133 tBodyGyro-min()-X
134 tBodyGyro-min()-Y
135 tBodyGyro-min()-Z
136 tBodyGyro-sma()
137 tBodyGyro-energy()-X
138 tBodyGyro-energy()-Y
139 tBodyGyro-energy()-Z
140 tBodyGyro-iqr()-X
141 tBodyGyro-iqr()-Y
142 tBodyGyro-iqr()-Z
143 tBodyGyro-entropy()-X
144 tBodyGyro-entropy()-Y
145 tBodyGyro-entropy()-Z
146 tBodyGyro-arCoeff()-X,1
147 tBodyGyro-arCoeff()-X,2
148 tBodyGyro-arCoeff()-X,3
149 tBodyGyro-arCoeff()-X,4
150 tBodyGyro-arCoeff()-Y,1
151 tBodyGyro-arCoeff()-Y,2
152 tBodyGyro-arCoeff()-Y,3
153 tBodyGyro-arCoeff()-Y,4
154 tBodyGyro-arCoeff()-Z,1
155 tBodyGyro-arCoeff()-Z,2
156 tBodyGyro-arCoeff()-Z,3
157 tBodyGyro-arCoeff()-Z,4
158 tBodyGyro-correlation()-X,Y
159 tBodyGyro-correlation()-X,Z
160 tBodyGyro-correlation()-Y,Z
161 tBodyGyroJerk-mean()-X
162 tBodyGyroJerk-mean()-Y
163 tBodyGyroJerk-mean()-Z
164 tBodyGyroJerk-std()-X
165 tBodyGyroJerk-std()-Y
166 tBodyGyroJerk-std()-Z
167 tBodyGyroJerk-mad()-X
168 tBodyGyroJerk-mad()-Y
169 tBodyGyroJerk-mad()-Z
170 tBodyGyroJerk-max()-X
171 tBodyGyroJerk-max()-Y
172 tBodyGyroJerk-max()-Z
173 tBodyGyroJerk-min()-X
174 tBodyGyroJerk-min()-Y
175 tBodyGyroJerk-min()-Z
176 tBodyGyroJerk-sma()
177 tBodyGyroJerk-energy()-X
178 tBodyGyroJerk-energy()-Y
179 tBodyGyroJerk-energy()-Z
180 tBodyGyroJerk-iqr()-X
181 tBodyGyroJerk-iqr()-Y
182 tBodyGyroJerk-iqr()-Z
183 tBodyGyroJerk-entropy()-X
184 tBodyGyroJerk-entropy()-Y
185 tBodyGyroJerk-entropy()-Z
186 tBodyGyroJerk-arCoeff()-X,1
187 tBodyGyroJerk-arCoeff()-X,2
188 tBodyGyroJerk-arCoeff()-X,3
189 tBodyGyroJerk-arCoeff()-X,4
190 tBodyGyroJerk-arCoeff()-Y,1
191 tBodyGyroJerk-arCoeff()-Y,2
192 tBodyGyroJerk-arCoeff()-Y,3
193 tBodyGyroJerk-arCoeff()-Y,4
194 tBodyGyroJerk-arCoeff()-Z,1
195 tBodyGyroJerk-arCoeff()-Z,2
196 tBodyGyroJerk-arCoeff()-Z,3
197 tBodyGyroJerk-arCoeff()-Z,4
198 tBodyGyroJerk-correlation()-X,Y
199 tBodyGyroJerk-correlation()-X,Z
200 tBodyGyroJerk-correlation()-Y,Z
201 tBodyAccMag-mean()
202 tBodyAccMag-std()
203 tBodyAccMag-mad()
204 tBodyAccMag-max()
205 tBodyAccMag-min()
206 tBodyAccMag-sma()
207 tBodyAccMag-energy()
208 tBodyAccMag-iqr()
209 tBodyAccMag-entropy()
210 tBodyAccMag-arCoeff()1
211 tBodyAccMag-arCoeff()2
212 tBodyAccMag-arCoeff()3
213 tBodyAccMag-arCoeff()4
214 tGravityAccMag-mean()
215 tGravityAccMag-std()
216 tGravityAccMag-mad()
217 tGravityAccMag-max()
218 tGravityAccMag-min()
219 tGravityAccMag-sma()
220 tGravityAccMag-energy()
221 tGravityAccMag-iqr()
222 tGravityAccMag-entropy()
223 tGravityAccMag-arCoeff()1
224 tGravityAccMag-arCoeff()2
225 tGravityAccMag-arCoeff()3
226 tGravityAccMag-arCoeff()4
227 tBodyAccJerkMag-mean()
228 tBodyAccJerkMag-std()
229 tBodyAccJerkMag-mad()
230 tBodyAccJerkMag-max()
231 tBodyAccJerkMag-min()
232 tBodyAccJerkMag-sma()
233 tBodyAccJerkMag-energy()
234 tBodyAccJerkMag-iqr()
235 tBodyAccJerkMag-entropy()
236 tBodyAccJerkMag-arCoeff()1
237 tBodyAccJerkMag-arCoeff()2
238 tBodyAccJerkMag-arCoeff()3
239 tBodyAccJerkMag-arCoeff()4
240 tBodyGyroMag-mean()
241 tBodyGyroMag-std()
242 tBodyGyroMag-mad()
243 tBodyGyroMag-max()
244 tBodyGyroMag-min()
245 tBodyGyroMag-sma()
246 tBodyGyroMag-energy()
247 tBodyGyroMag-iqr()
248 tBodyGyroMag-entropy()
249 tBodyGyroMag-arCoeff()1
250 tBodyGyroMag-arCoeff()2
251 tBodyGyroMag-arCoeff()3
252 tBodyGyroMag-arCoeff()4
253 tBodyGyroJerkMag-mean()
254 tBodyGyroJerkMag-std()
255 tBodyGyroJerkMag-mad()
256 tBodyGyroJerkMag-max()
257 tBodyGyroJerkMag-min()
258 tBodyGyroJerkMag-sma()
259 tBodyGyroJerkMag-energy()
260 tBodyGyroJerkMag-iqr()
261 tBodyGyroJerkMag-entropy()
262 tBodyGyroJerkMag-arCoeff()1
263 tBodyGyroJerkMag-arCoeff()2
264 tBodyGyroJerkMag-arCoeff()3
265 tBodyGyroJerkMag-arCoeff()4
266 fBodyAcc-mean()-X
267 fBodyAcc-mean()-Y
268 fBodyAcc-mean()-Z
269 fBodyAcc-std()-X
270 fBodyAcc-std()-Y
271 fBodyAcc-std()-Z
272 fBodyAcc-mad()-X
273 fBodyAcc-mad()-Y
274 fBodyAcc-mad()-Z
275 fBodyAcc-max()-X
276 fBodyAcc-max()-Y
277 fBodyAcc-max()-Z
278 fBodyAcc-min()-X
279 fBodyAcc-min()-Y
280 fBodyAcc-min()-Z
281 fBodyAcc-sma()
282 fBodyAcc-energy()-X
283 fBodyAcc-energy()-Y
284 fBodyAcc-energy()-Z
285 fBodyAcc-iqr()-X
286 fBodyAcc-iqr()-Y
287 fBodyAcc-iqr()-Z
288 fBodyAcc-entropy()-X
289 fBodyAcc-entropy()-Y
290 fBodyAcc-entropy()-Z
291 fBodyAcc-maxInds-X
292 fBodyAcc-maxInds-Y
293 fBodyAcc-maxInds-Z
294 fBodyAcc-meanFreq()-X
295 fBodyAcc-meanFreq()-Y
296 fBodyAcc-meanFreq()-Z
297 fBodyAcc-skewness()-X
298 fBodyAcc-kurtosis()-X
299 fBodyAcc-skewness()-Y
300 fBodyAcc-kurtosis()-Y
301 fBodyAcc-skewness()-Z
302 fBodyAcc-kurtosis()-Z
303 fBodyAcc-bandsEnergy()-1,8
304 fBodyAcc-bandsEnergy()-9,16
305 fBodyAcc-bandsEnergy()-17,24
306 fBodyAcc-bandsEnergy()-25,32
307 fBodyAcc-bandsEnergy()-33,40
308 fBodyAcc-bandsEnergy()-41,48
309 fBodyAcc-bandsEnergy()-49,56
310 fBodyAcc-bandsEnergy()-57,64
311 fBodyAcc-bandsEnergy()-1,16
312 fBodyAcc-bandsEnergy()-17,32
313 fBodyAcc-bandsEnergy()-33,48
314 fBodyAcc-bandsEnergy()-49,64
315 fBodyAcc-bandsEnergy()-1,24
316 fBodyAcc-bandsEnergy()-25,48
317 fBodyAcc-bandsEnergy()-1,8
318 fBodyAcc-bandsEnergy()-9,16
319 fBodyAcc-bandsEnergy()-17,24
320 fBodyAcc-bandsEnergy()-25,32
321 fBodyAcc-bandsEnergy()-33,40
322 fBodyAcc-bandsEnergy()-41,48
323 fBodyAcc-bandsEnergy()-49,56
324 fBodyAcc-bandsEnergy()-57,64
325 fBodyAcc-bandsEnergy()-1,16
326 fBodyAcc-bandsEnergy()-17,32
327 fBodyAcc-bandsEnergy()-33,48
328 fBodyAcc-bandsEnergy()-49,64
329 fBodyAcc-bandsEnergy()-1,24
330 fBodyAcc-bandsEnergy()-25,48
331 fBodyAcc-bandsEnergy()-1,8
332 fBodyAcc-bandsEnergy()-9,16
333 fBodyAcc-bandsEnergy()-17,24
334 fBodyAcc-bandsEnergy()-25,32
335 fBodyAcc-bandsEnergy()-33,40
336 fBodyAcc-bandsEnergy()-41,48
337 fBodyAcc-bandsEnergy()-49,56
338 fBodyAcc-bandsEnergy()-57,64
339 fBodyAcc-bandsEnergy()-1,16
340 fBodyAcc-bandsEnergy()-17,32
341 fBodyAcc-bandsEnergy()-33,48
342 fBodyAcc-bandsEnergy()-49,64
343 fBodyAcc-bandsEnergy()-1,24
344 fBodyAcc-bandsEnergy()-25,48
345 fBodyAccJerk-mean()-X
346 fBodyAccJerk-mean()-Y
347 fBodyAccJerk-mean()-Z
348 fBodyAccJerk-std()-X
349 fBodyAccJerk-std()-Y
350 fBodyAccJerk-std()-Z
351 fBodyAccJerk-mad()-X
352 fBodyAccJerk-mad()-Y
353 fBodyAccJerk-mad()-Z
354 fBodyAccJerk-max()-X
355 fBodyAccJerk-max()-Y
356 fBodyAccJerk-max()-Z
357 fBodyAccJerk-min()-X
358 fBodyAccJerk-min()-Y
359 fBodyAccJerk-min()-Z
360 fBodyAccJerk-sma()
361 fBodyAccJerk-energy()-X
362 fBodyAccJerk-energy()-Y
363 fBodyAccJerk-energy()-Z
364 fBodyAccJerk-iqr()-X
365 fBodyAccJerk-iqr()-Y
366 fBodyAccJerk-iqr()-Z
367 fBodyAccJerk-entropy()-X
368 fBodyAccJerk-entropy()-Y
369 fBodyAccJerk-entropy()-Z
370 fBodyAccJerk-maxInds-X
371 fBodyAccJerk-maxInds-Y
372 fBodyAccJerk-maxInds-Z
373 fBodyAccJerk-meanFreq()-X
374 fBodyAccJerk-meanFreq()-Y
375 fBodyAccJerk-meanFreq()-Z
376 fBodyAccJerk-skewness()-X
377 fBodyAccJerk-kurtosis()-X
378 fBodyAccJerk-skewness()-Y
379 fBodyAccJerk-kurtosis()-Y
380 fBodyAccJerk-skewness()-Z
381 fBodyAccJerk-kurtosis()-Z
382 fBodyAccJerk-bandsEnergy()-1,8
383 fBodyAccJerk-bandsEnergy()-9,16
384 fBodyAccJerk-bandsEnergy()-17,24
385 fBodyAccJerk-bandsEnergy()-25,32
386 fBodyAccJerk-bandsEnergy()-33,40
387 fBodyAccJerk-bandsEnergy()-41,48
388 fBodyAccJerk-bandsEnergy()-49,56
389 fBodyAccJerk-bandsEnergy()-57,64
390 fBodyAccJerk-bandsEnergy()-1,16
391 fBodyAccJerk-bandsEnergy()-17,32
392 fBodyAccJerk-bandsEnergy()-33,48
393 fBodyAccJerk-bandsEnergy()-49,64
394 fBodyAccJerk-bandsEnergy()-1,24
395 fBodyAccJerk-bandsEnergy()-25,48
396 fBodyAccJerk-bandsEnergy()-1,8
397 fBodyAccJerk-bandsEnergy()-9,16
398 fBodyAccJerk-bandsEnergy()-17,24
399 fBodyAccJerk-bandsEnergy()-25,32
400 fBodyAccJerk-bandsEnergy()-33,40
401 fBodyAccJerk-bandsEnergy()-41,48
402 fBodyAccJerk-bandsEnergy()-49,56
403 fBodyAccJerk-bandsEnergy()-57,64
404 fBodyAccJerk-bandsEnergy()-1,16
405 fBodyAccJerk-bandsEnergy()-17,32
406 fBodyAccJerk-bandsEnergy()-33,48
407 fBodyAccJerk-bandsEnergy()-49,64
408 fBodyAccJerk-bandsEnergy()-1,24
409 fBodyAccJerk-bandsEnergy()-25,48
410 fBodyAccJerk-bandsEnergy()-1,8
411 fBodyAccJerk-bandsEnergy()-9,16
412 fBodyAccJerk-bandsEnergy()-17,24
413 fBodyAccJerk-bandsEnergy()-25,32
414 fBodyAccJerk-bandsEnergy()-33,40
415 fBodyAccJerk-bandsEnergy()-41,48
416 fBodyAccJerk-bandsEnergy()-49,56
417 fBodyAccJerk-bandsEnergy()-57,64
418 fBodyAccJerk-bandsEnergy()-1,16
419 fBodyAccJerk-bandsEnergy()-17,32
420 fBodyAccJerk-bandsEnergy()-33,48
421 fBodyAccJerk-bandsEnergy()-49,64
422 fBodyAccJerk-bandsEnergy()-1,24
423 fBodyAccJerk-bandsEnergy()-25,48
424 fBodyGyro-mean()-X
425 fBodyGyro-mean()-Y
426 fBodyGyro-mean()-Z
427 fBodyGyro-std()-X
428 fBodyGyro-std()-Y
429 fBodyGyro-std()-Z
430 fBodyGyro-mad()-X
431 fBodyGyro-mad()-Y
432 fBodyGyro-mad()-Z
433 fBodyGyro-max()-X
434 fBodyGyro-max()-Y
435 fBodyGyro-max()-Z
436 fBodyGyro-min()-X
437 fBodyGyro-min()-Y
438 fBodyGyro-min()-Z
439 fBodyGyro-sma()
440 fBodyGyro-energy()-X
441 fBodyGyro-energy()-Y
442 fBodyGyro-energy()-Z
443 fBodyGyro-iqr()-X
444 fBodyGyro-iqr()-Y
445 fBodyGyro-iqr()-Z
446 fBodyGyro-entropy()-X
447 fBodyGyro-entropy()-Y
448 fBodyGyro-entropy()-Z
449 fBodyGyro-maxInds-X
450 fBodyGyro-maxInds-Y
451 fBodyGyro-maxInds-Z
452 fBodyGyro-meanFreq()-X
453 fBodyGyro-meanFreq()-Y
454 fBodyGyro-meanFreq()-Z
455 fBodyGyro-skewness()-X
456 fBodyGyro-kurtosis()-X
457 fBodyGyro-skewness()-Y
458 fBodyGyro-kurtosis()-Y
459 fBodyGyro-skewness()-Z
460 fBodyGyro-kurtosis()-Z
461 fBodyGyro-bandsEnergy()-1,8
462 fBodyGyro-bandsEnergy()-9,16
463 fBodyGyro-bandsEnergy()-17,24
464 fBodyGyro-bandsEnergy()-25,32
465 fBodyGyro-bandsEnergy()-33,40
466 fBodyGyro-bandsEnergy()-41,48
467 fBodyGyro-bandsEnergy()-49,56
468 fBodyGyro-bandsEnergy()-57,64
469 fBodyGyro-bandsEnergy()-1,16
470 fBodyGyro-bandsEnergy()-17,32
471 fBodyGyro-bandsEnergy()-33,48
472 fBodyGyro-bandsEnergy()-49,64
473 fBodyGyro-bandsEnergy()-1,24
474 fBodyGyro-bandsEnergy()-25,48
475 fBodyGyro-bandsEnergy()-1,8
476 fBodyGyro-bandsEnergy()-9,16
477 fBodyGyro-bandsEnergy()-17,24
478 fBodyGyro-bandsEnergy()-25,32
479 fBodyGyro-bandsEnergy()-33,40
480 fBodyGyro-bandsEnergy()-41,48
481 fBodyGyro-bandsEnergy()-49,56
482 fBodyGyro-bandsEnergy()-57,64
483 fBodyGyro-bandsEnergy()-1,16
484 fBodyGyro-bandsEnergy()-17,32
485 fBodyGyro-bandsEnergy()-33,48
486 fBodyGyro-bandsEnergy()-49,64
487 fBodyGyro-bandsEnergy()-1,24
488 fBodyGyro-bandsEnergy()-25,48
489 fBodyGyro-bandsEnergy()-1,8
490 fBodyGyro-bandsEnergy()-9,16
491 fBodyGyro-bandsEnergy()-17,24
492 fBodyGyro-bandsEnergy()-25,32
493 fBodyGyro-bandsEnergy()-33,40
494 fBodyGyro-bandsEnergy()-41,48
495 fBodyGyro-bandsEnergy()-49,56
496 fBodyGyro-bandsEnergy()-57,64
497 fBodyGyro-bandsEnergy()-1,16
498 fBodyGyro-bandsEnergy()-17,32
499 fBodyGyro-bandsEnergy()-33,48
500 fBodyGyro-bandsEnergy()-49,64
501 fBodyGyro-bandsEnergy()-1,24
502 fBodyGyro-bandsEnergy()-25,48
503 fBodyAccMag-mean()
504 fBodyAccMag-std()
505 fBodyAccMag-mad()
506 fBodyAccMag-max()
507 fBodyAccMag-min()
508 fBodyAccMag-sma()
509 fBodyAccMag-energy()
510 fBodyAccMag-iqr()
511 fBodyAccMag-entropy()
512 fBodyAccMag-maxInds
513 fBodyAccMag-meanFreq()
514 fBodyAccMag-skewness()
515 fBodyAccMag-kurtosis()
516 fBodyBodyAccJerkMag-mean()
517 fBodyBodyAccJerkMag-std()
518 fBodyBodyAccJerkMag-mad()
519 fBodyBodyAccJerkMag-max()
520 fBodyBodyAccJerkMag-min()
521 fBodyBodyAccJerkMag-sma()
522 fBodyBodyAccJerkMag-energy()
523 fBodyBodyAccJerkMag-iqr()
524 fBodyBodyAccJerkMag-entropy()
525 fBodyBodyAccJerkMag-maxInds
526 fBodyBodyAccJerkMag-meanFreq()
527 fBodyBodyAccJerkMag-skewness()
528 fBodyBodyAccJerkMag-kurtosis()
529 fBodyBodyGyroMag-mean()
530 fBodyBodyGyroMag-std()
531 fBodyBodyGyroMag-mad()
532 fBodyBodyGyroMag-max()
533 fBodyBodyGyroMag-min()
534 fBodyBodyGyroMag-sma()
535 fBodyBodyGyroMag-energy()
536 fBodyBodyGyroMag-iqr()
537 fBodyBodyGyroMag-entropy()
538 fBodyBodyGyroMag-maxInds
539 fBodyBodyGyroMag-meanFreq()
540 fBodyBodyGyroMag-skewness()
541 fBodyBodyGyroMag-kurtosis()
542 fBodyBodyGyroJerkMag-mean()
543 fBodyBodyGyroJerkMag-std()
544 fBodyBodyGyroJerkMag-mad()
545 fBodyBodyGyroJerkMag-max()
546 fBodyBodyGyroJerkMag-min()
547 fBodyBodyGyroJerkMag-sma()
548 fBodyBodyGyroJerkMag-energy()
549 fBodyBodyGyroJerkMag-iqr()
550 fBodyBodyGyroJerkMag-entropy()
551 fBodyBodyGyroJerkMag-maxInds
552 fBodyBodyGyroJerkMag-meanFreq()
553 fBodyBodyGyroJerkMag-skewness()
554 fBodyBodyGyroJerkMag-kurtosis()
555 angle(tBodyAccMean,gravity)
556 angle(tBodyAccJerkMean),gravityMean)
557 angle(tBodyGyroMean,gravityMean)
558 angle(tBodyGyroJerkMean,gravityMean)
559 angle(X,gravityMean)
560 angle(Y,gravityMean)
561 angle(Z,gravityMean)


================================================
FILE: data/har/features_info.txt
================================================
Feature Selection 
=================

The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz. 

Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag). 

Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals). 

These signals were used to estimate variables of the feature vector for each pattern:  
'-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.

tBodyAcc-XYZ
tGravityAcc-XYZ
tBodyAccJerk-XYZ
tBodyGyro-XYZ
tBodyGyroJerk-XYZ
tBodyAccMag
tGravityAccMag
tBodyAccJerkMag
tBodyGyroMag
tBodyGyroJerkMag
fBodyAcc-XYZ
fBodyAccJerk-XYZ
fBodyGyro-XYZ
fBodyAccMag
fBodyAccJerkMag
fBodyGyroMag
fBodyGyroJerkMag

The set of variables that were estimated from these signals are: 

mean(): Mean value
std(): Standard deviation
mad(): Median absolute deviation 
max(): Largest value in array
min(): Smallest value in array
sma(): Signal magnitude area
energy(): Energy measure. Sum of the squares divided by the number of values. 
iqr(): Interquartile range 
entropy(): Signal entropy
arCoeff(): Autorregresion coefficients with Burg order equal to 4
correlation(): correlation coefficient between two signals
maxInds(): index of the frequency component with largest magnitude
meanFreq(): Weighted average of the frequency components to obtain a mean frequency
skewness(): skewness of the frequency domain signal 
kurtosis(): kurtosis of the frequency domain signal 
bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window.
angle(): Angle between to vectors.

Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

gravityMean
tBodyAccMean
tBodyAccJerkMean
tBodyGyroMean
tBodyGyroJerkMean

The complete list of variables of each feature vector is available in 'features.txt'


================================================
FILE: data/har/test/Inertial Signals/body_acc_x_test.txt
================================================
  1.1653150e-002  1.3109090e-002  1.1268850e-002  2.7830730e-002  2.3183500e-003 -1.8965500e-002 -6.1920230e-002 -9.4248130e-002 -7.9534200e-002 -7.0083760e-002 -1.9152480e-002 -2.2668950e-003 -3.0111470e-002 -1.1910260e-002 -1.5542110e-002 -1.6666170e-002 -6.6302790e-003 -2.3480930e-002 -1.7627590e-002 -2.1150170e-002 -3.2659070e-002 -2.2478410e-002 -1.0196890e-002 -3.1196910e-003 -1.6495820e-002 -2.0441880e-002 -2.7567620e-002 -5.5032500e-002 -5.7479710e-002 -4.7095990e-002 -2.8361690e-002 -1.7184890e-002 -7.3758860e-003  2.4778770e-002  2.3929340e-002  2.1248370e-002  9.6999630e-003 -3.5118900e-002 -1.0733260e-002  2.3672330e-002  1.4929500e-002  2.1906200e-002  5.0844080e-003 -1.3070070e-002 -7.9809120e-003 -1.2763990e-002 -9.8686730e-003 -3.6970220e-003  4.7806470e-003  7.9291720e-003 -5.4944340e-004 -9.1546860e-004 -4.9868180e-003  9.0750290e-003  3.3040610e-002  1.9763320e-002  1.0038920e-005 -1.7139270e-002 -2.9957720e-002 -9.4479730e-003  5.1335230e-003 -2.5121430e-003  3.2194320e-003  1.2330130e-002  9.2796290e-003  4.9297110e-003  3.9535960e-003  9.2144290e-003  1.6156130e-002  1.4079400e-002  1.3105210e-002  2.1246960e-002  2.6823440e-002  3.1054370e-002  3.7886060e-002  2.6462870e-002 -2.1441150e-002 -6.0567950e-002 -2.4725380e-002  2.0272130e-002 -1.9340940e-003 -8.9067130e-003  9.5376650e-003 -4.3316630e-003 -1.8638360e-003  4.9550750e-003 -5.3361360e-003  2.0007890e-003 -1.7075180e-003 -6.4634980e-003  2.0412210e-004 -3.6400110e-003  8.2683690e-005 -1.4416150e-003 -5.9593910e-003  3.4398910e-004  5.5799520e-004  2.3814270e-003  1.8277850e-003 -1.4560660e-003 -2.0005970e-004 -5.7041380e-004  2.6049420e-003  2.6436190e-003  1.5970290e-003  3.8177820e-003  1.6479750e-003  2.9432210e-003  5.5816380e-003  6.2567030e-003  5.1689760e-003 -2.5212320e-003 -6.3626070e-003 -2.4966130e-003  2.4552680e-003  4.4235590e-003  2.1102270e-003 -5.5878910e-004 -2.9442470e-003 -1.2468990e-003  2.8482550e-003  2.3545580e-003  2.7209850e-003  3.2552900e-003  1.3010800e-003  1.2915110e-003  1.4699970e-003  2.5738410e-003
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