[
{
"path": ".gitignore",
"content": "*.iml\n.gradle\n/local.properties\n/.idea/workspace.xml\n/.idea/libraries\n.idea/modules.xml\n.DS_Store\n/build\n/captures\n.externalNativeBuild\n.ipynb_checkpoints/\n"
},
{
"path": ".idea/compiler.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": ".idea/copyright/profiles_settings.xml",
"content": "\n \n"
},
{
"path": ".idea/gradle.xml",
"content": "\n\n \n \n \n"
},
{
"path": ".idea/misc.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": ".idea/runConfigurations.xml",
"content": "\n\n \n \n \n"
},
{
"path": ".idea/vcs.xml",
"content": "\n\n \n \n \n"
},
{
"path": "Exporting Squeezenet to mobile.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial we'll show how to export squeezenet which is implemented and trained in pytorch to run on mobile devices.\\n\",\n \"Before we start, you should have [pytorch](https://github.com/pytorch/pytorch), [caffe2](https://github.com/caffe2/caffe2), [onnx](https://github.com/onnx/onnx) and [onnx-caffe2](https://github.com/onnx/onnx-caffe2) installed in your environment and cloned [AICamera](https://github.com/bwasti/AICamera).\\n\",\n \"Please checkout their github page for installation instructions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Some standard imports\\n\",\n \"import io\\n\",\n \"import numpy as np\\n\",\n \"import torch.onnx\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following implementation of squeezenet is from [torchvision](https://github.com/pytorch/vision/blob/master/torchvision/models/squeezenet.py).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import math\\n\",\n \"import torch\\n\",\n \"import torch.nn as nn\\n\",\n \"import torch.nn.init as init\\n\",\n \"import torch.utils.model_zoo as model_zoo\\n\",\n \"\\n\",\n \"\\n\",\n \"__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']\\n\",\n \"\\n\",\n \"\\n\",\n \"model_urls = {\\n\",\n \" 'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',\\n\",\n \" 'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth',\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"class Fire(nn.Module):\\n\",\n \"\\n\",\n \" def __init__(self, inplanes, squeeze_planes,\\n\",\n \" expand1x1_planes, expand3x3_planes):\\n\",\n \" super(Fire, self).__init__()\\n\",\n \" self.inplanes = inplanes\\n\",\n \" self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)\\n\",\n \" self.squeeze_activation = nn.ReLU(inplace=True)\\n\",\n \" self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,\\n\",\n \" kernel_size=1)\\n\",\n \" self.expand1x1_activation = nn.ReLU(inplace=True)\\n\",\n \" self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,\\n\",\n \" kernel_size=3, padding=1)\\n\",\n \" self.expand3x3_activation = nn.ReLU(inplace=True)\\n\",\n \"\\n\",\n \" def forward(self, x):\\n\",\n \" x = self.squeeze_activation(self.squeeze(x))\\n\",\n \" return torch.cat([\\n\",\n \" self.expand1x1_activation(self.expand1x1(x)),\\n\",\n \" self.expand3x3_activation(self.expand3x3(x))\\n\",\n \" ], 1)\\n\",\n \"\\n\",\n \"\\n\",\n \"class SqueezeNet(nn.Module):\\n\",\n \"\\n\",\n \" def __init__(self, version=1.0, num_classes=1000):\\n\",\n \" super(SqueezeNet, self).__init__()\\n\",\n \" if version not in [1.0, 1.1]:\\n\",\n \" raise ValueError(\\\"Unsupported SqueezeNet version {version}:\\\"\\n\",\n \" \\\"1.0 or 1.1 expected\\\".format(version=version))\\n\",\n \" self.num_classes = num_classes\\n\",\n \" if version == 1.0:\\n\",\n \" self.features = nn.Sequential(\\n\",\n \" nn.Conv2d(3, 96, kernel_size=7, stride=2),\\n\",\n \" nn.ReLU(inplace=True),\\n\",\n \" nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),\\n\",\n \" Fire(96, 16, 64, 64),\\n\",\n \" Fire(128, 16, 64, 64),\\n\",\n \" Fire(128, 32, 128, 128),\\n\",\n \" nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),\\n\",\n \" Fire(256, 32, 128, 128),\\n\",\n \" Fire(256, 48, 192, 192),\\n\",\n \" Fire(384, 48, 192, 192),\\n\",\n \" Fire(384, 64, 256, 256),\\n\",\n \" nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),\\n\",\n \" Fire(512, 64, 256, 256),\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" self.features = nn.Sequential(\\n\",\n \" nn.Conv2d(3, 64, kernel_size=3, stride=2),\\n\",\n \" nn.ReLU(inplace=True),\\n\",\n \" nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),\\n\",\n \" Fire(64, 16, 64, 64),\\n\",\n \" Fire(128, 16, 64, 64),\\n\",\n \" nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),\\n\",\n \" Fire(128, 32, 128, 128),\\n\",\n \" Fire(256, 32, 128, 128),\\n\",\n \" nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),\\n\",\n \" Fire(256, 48, 192, 192),\\n\",\n \" Fire(384, 48, 192, 192),\\n\",\n \" Fire(384, 64, 256, 256),\\n\",\n \" Fire(512, 64, 256, 256),\\n\",\n \" )\\n\",\n \" # Final convolution is initialized differently form the rest\\n\",\n \" final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)\\n\",\n \" self.classifier = nn.Sequential(\\n\",\n \" nn.Dropout(p=0.5),\\n\",\n \" final_conv,\\n\",\n \" nn.ReLU(inplace=True),\\n\",\n \" nn.AvgPool2d(13)\\n\",\n \" )\\n\",\n \"\\n\",\n \" for m in self.modules():\\n\",\n \" if isinstance(m, nn.Conv2d):\\n\",\n \" if m is final_conv:\\n\",\n \" init.normal(m.weight.data, mean=0.0, std=0.01)\\n\",\n \" else:\\n\",\n \" init.kaiming_uniform(m.weight.data)\\n\",\n \" if m.bias is not None:\\n\",\n \" m.bias.data.zero_()\\n\",\n \"\\n\",\n \" def forward(self, x):\\n\",\n \" x = self.features(x)\\n\",\n \" x = self.classifier(x)\\n\",\n \" return x.view(x.size(0), self.num_classes)\\n\",\n \"\\n\",\n \"\\n\",\n \"def squeezenet1_0(pretrained=False, **kwargs):\\n\",\n \" r\\\"\\\"\\\"SqueezeNet model architecture from the `\\\"SqueezeNet: AlexNet-level\\n\",\n \" accuracy with 50x fewer parameters and <0.5MB model size\\\"\\n\",\n \" `_ paper.\\n\",\n \" Args:\\n\",\n \" pretrained (bool): If True, returns a model pre-trained on ImageNet\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" model = SqueezeNet(version=1.0, **kwargs)\\n\",\n \" if pretrained:\\n\",\n \" model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_0']))\\n\",\n \" return model\\n\",\n \"\\n\",\n \"\\n\",\n \"def squeezenet1_1(pretrained=False, **kwargs):\\n\",\n \" r\\\"\\\"\\\"SqueezeNet 1.1 model from the `official SqueezeNet repo\\n\",\n \" `_.\\n\",\n \" SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters\\n\",\n \" than SqueezeNet 1.0, without sacrificing accuracy.\\n\",\n \" Args:\\n\",\n \" pretrained (bool): If True, returns a model pre-trained on ImageNet\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" model = SqueezeNet(version=1.1, **kwargs)\\n\",\n \" if pretrained:\\n\",\n \" model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_1']))\\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can get the torch model by calling the following function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Get pretrained squeezenet model\\n\",\n \"torch_model = squeezenet1_1(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and export the pytorch model as onnx model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from torch.autograd import Variable\\n\",\n \"batch_size = 1 # just a random number\\n\",\n \"\\n\",\n \"# Input to the model\\n\",\n \"x = Variable(torch.randn(batch_size, 3, 224, 224), requires_grad=True)\\n\",\n \"\\n\",\n \"# Export the model\\n\",\n \"torch_out = torch.onnx._export(torch_model, # model being run\\n\",\n \" x, # model input (or a tuple for multiple inputs)\\n\",\n \" \\\"squeezenet.onnx\\\", # where to save the model (can be a file or file-like object)\\n\",\n \" export_params=True) # store the trained parameter weights inside the model file\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After that, we can prepare and run the model and verify that the result of the model running on pytorch matches the result running on onnx-caffe2 backend.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.\\n\",\n \"WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu\\n\"\n ]\n }\n ],\n \"source\": [\n \"import onnx\\n\",\n \"import onnx_caffe2.backend\\n\",\n \"from onnx import helper\\n\",\n \"\\n\",\n \"# Load the ONNX GraphProto object. Graph is a standard Python protobuf object\\n\",\n \"model = onnx.load(\\\"squeezenet.onnx\\\")\\n\",\n \"\\n\",\n \"# prepare the caffe2 backend for executing the model this converts the ONNX graph into a\\n\",\n \"# Caffe2 NetDef that can execute it. Other ONNX backends, like one for CNTK will be\\n\",\n \"# availiable soon.\\n\",\n \"prepared_backend = onnx_caffe2.backend.prepare(model)\\n\",\n \"\\n\",\n \"# run the model in Caffe2\\n\",\n \"\\n\",\n \"# Construct a map from input names to Tensor data.\\n\",\n \"# The graph itself contains inputs for all weight parameters, followed by the input image.\\n\",\n \"# Since the weights are already embedded, we just need to pass the input image.\\n\",\n \"# last input the grap\\n\",\n \"W = {model.graph.input[0].name: x.data.numpy()}\\n\",\n \"\\n\",\n \"# Run the Caffe2 net:\\n\",\n \"c2_out = prepared_backend.run(W)[0]\\n\",\n \"\\n\",\n \"# Verify the numerical correctness upto 3 decimal places\\n\",\n \"np.testing.assert_almost_equal(torch_out.data.cpu().numpy(), c2_out, decimal=3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we can export the model to run on mobile devices, leveraging the cross-platform capability of caffe2.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Export to mobile\\n\",\n \"from onnx_caffe2.backend import Caffe2Backend as c2\\n\",\n \"\\n\",\n \"init_net, predict_net = c2.onnx_graph_to_caffe2_net(model.graph, True)\\n\",\n \"with open(\\\"squeeze_init_net.pb\\\", \\\"wb\\\") as f:\\n\",\n \" f.write(init_net.SerializeToString())\\n\",\n \"with open(\\\"squeeze_predict_net.pb\\\", \\\"wb\\\") as f:\\n\",\n \" f.write(predict_net.SerializeToString())\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You'll see squeeze_init_net.pb and squeeze_predict_net.pb in the same directory of this notebook. Let's make sure it can run with predictor since that's what we'll use in the Mobile App.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Verify it runs with predictor\\n\",\n \"with open(\\\"squeeze_init_net.pb\\\") as f:\\n\",\n \" init_net = f.read()\\n\",\n \"with open(\\\"squeeze_predict_net.pb\\\") as f:\\n\",\n \" predict_net = f.read()\\n\",\n \"from caffe2.python import workspace\\n\",\n \"p = workspace.Predictor(init_net, predict_net)\\n\",\n \"# The following code should run:\\n\",\n \"# img = np.random.rand(1, 3, 224, 224).astype(np.float32)\\n\",\n \"# p.run([img])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we are sure that it runs with predictor, we can copy squeeze_init_net.pb and squeeze_predict_net.pb to \\n\",\n \"AICamera/app/src/main/assets.\\n\",\n \"Now we can open Android Studio and import the AICamera project, run the app by clicking the green play button.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "LICENSE",
"content": "Copyright (c) 2017-present, Facebook, Inc. All rights reserved.\n\nThe examples provided by Facebook are for non-commercial testing and evaluation\npurposes only. Facebook reserves all rights not expressly granted.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\nFACEBOOK BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\nACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\nWITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n"
},
{
"path": "README.md",
"content": "## AICamera\n\nAICamera is a demo app that was displayed at Facebook's F8 event. The previous version (also on this repo) was getting quite old and attempted to demonstrate a build system that happened inside Android Studio. This led to some hacky techniques and I decided to rewrite the demo with a prebuilt Caffe2 library (which can be built using `build_android.sh` in the Caffe2 source).\n\n\n\n### Download\n\n git clone https://github.com/caffe2/AICamera.git\n\n### Build\n\nClick the green play button in Android Studio 3.0.1 and everything should build :)\n\n### Tests\n\n| Device | Network | FPS |\n| ------------------ | ------------- | ----- |\n| Samsung Galaxy S7 | SqueezeNet | 5.8 |\n| Google Pixel | SqueezeNet | 5.7 |\n\n### License\n\nPlease see the LICENSE file in the root directory the source tree.\n"
},
{
"path": "app/.gitignore",
"content": "/build\n"
},
{
"path": "app/CMakeLists.txt",
"content": "cmake_minimum_required(VERSION 3.4.1)\n\nadd_library(\n native-lib\n SHARED\n src/main/cpp/native-lib.cpp\n )\nfind_library(\n android-lib\n android\n )\n\ninclude(AndroidNdkModules)\nandroid_ndk_import_module_cpufeatures()\n\nadd_library(\n caffe2\n STATIC\n IMPORTED\n )\nset_target_properties(\n caffe2\n PROPERTIES IMPORTED_LOCATION\n ${CMAKE_CURRENT_LIST_DIR}/src/main/jniLibs/${ANDROID_ABI}/libCaffe2_CPU.a\n )\nadd_library(\n thread_pool\n STATIC\n IMPORTED\n )\nset_target_properties(\n thread_pool\n PROPERTIES IMPORTED_LOCATION\n ${CMAKE_CURRENT_LIST_DIR}/src/main/jniLibs/${ANDROID_ABI}/libCAFFE2_PTHREADPOOL.a\n )\nadd_library(\n glog\n SHARED\n IMPORTED\n )\nset_target_properties(\n glog\n PROPERTIES IMPORTED_LOCATION\n ${CMAKE_CURRENT_LIST_DIR}/src/main/jniLibs/${ANDROID_ABI}/libglog.so\n )\n\nadd_library(\n protobuf\n SHARED\n IMPORTED\n )\nset_target_properties(\n protobuf\n PROPERTIES IMPORTED_LOCATION\n ${CMAKE_CURRENT_LIST_DIR}/src/main/jniLibs/${ANDROID_ABI}/libprotobuf.a\n )\n\nadd_library(\n NNPACK\n STATIC\n IMPORTED\n )\nset_target_properties(\n NNPACK\n PROPERTIES IMPORTED_LOCATION\n ${CMAKE_CURRENT_LIST_DIR}/src/main/jniLibs/${ANDROID_ABI}/libCAFFE2_NNPACK.a\n )\n\ninclude_directories( src/main/cpp )\n\nfind_library(\n log-lib\n log\n )\n\ntarget_link_libraries(\n native-lib\n -Wl,--whole-archive\n caffe2\n -Wl,--no-whole-archive\n NNPACK\n thread_pool\n glog\n protobuf\n cpufeatures\n ${log-lib}\n ${android-lib})"
},
{
"path": "app/build.gradle",
"content": "apply plugin: 'com.android.application'\n\nandroid {\n compileSdkVersion 25\n defaultConfig {\n applicationId \"facebook.f8demo\"\n minSdkVersion 22\n targetSdkVersion 25\n versionCode 1\n versionName \"1.0\"\n testInstrumentationRunner \"android.support.test.runner.AndroidJUnitRunner\"\n externalNativeBuild {\n cmake {\n cppFlags \"-frtti -fexceptions -std=c++11\"\n arguments \"-DANDROID_STL=gnustl_shared\"\n }\n }\n ndk {\n // Specifies the ABI configurations of your native\n // libraries Gradle should build and package with your APK.\n abiFilters 'armeabi-v7a'\n }\n }\n buildTypes {\n release {\n minifyEnabled false\n proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'\n }\n }\n externalNativeBuild {\n cmake {\n path \"CMakeLists.txt\"\n }\n }\n packagingOptions {\n pickFirst 'lib/armeabi-v7a/libgnustl_shared.so'\n }\n\n}\n\ndependencies {\n compile fileTree(dir: 'libs', include: ['*.jar', '*.so'])\n androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', {\n exclude group: 'com.android.support', module: 'support-annotations'\n })\n compile 'com.android.support:appcompat-v7:25.1.1'\n compile 'com.android.support.constraint:constraint-layout:1.0.2'\n testCompile 'junit:junit:4.12'\n}\n"
},
{
"path": "app/proguard-rules.pro",
"content": "# Add project specific ProGuard rules here.\n# By default, the flags in this file are appended to flags specified\n# in /Users/bwasti/Library/Android/sdk/tools/proguard/proguard-android.txt\n# You can edit the include path and order by changing the proguardFiles\n# directive in build.gradle.\n#\n# For more details, see\n# http://developer.android.com/guide/developing/tools/proguard.html\n\n# Add any project specific keep options here:\n\n# If your project uses WebView with JS, uncomment the following\n# and specify the fully qualified class name to the JavaScript interface\n# class:\n#-keepclassmembers class fqcn.of.javascript.interface.for.webview {\n# public *;\n#}\n\n# Uncomment this to preserve the line number information for\n# debugging stack traces.\n#-keepattributes SourceFile,LineNumberTable\n\n# If you keep the line number information, uncomment this to\n# hide the original source file name.\n#-renamesourcefileattribute SourceFile\n"
},
{
"path": "app/src/androidTest/java/facebook/f8demo/ExampleInstrumentedTest.java",
"content": "package facebook.f8demo;\n\nimport android.content.Context;\nimport android.support.test.InstrumentationRegistry;\nimport android.support.test.runner.AndroidJUnit4;\n\nimport org.junit.Test;\nimport org.junit.runner.RunWith;\n\nimport static org.junit.Assert.*;\n\n/**\n * Instrumentation test, which will execute on an Android device.\n *\n * @see Testing documentation\n */\n@RunWith(AndroidJUnit4.class)\npublic class ExampleInstrumentedTest {\n @Test\n public void useAppContext() throws Exception {\n // Context of the app under test.\n Context appContext = InstrumentationRegistry.getTargetContext();\n\n assertEquals(\"facebook.f8demo\", appContext.getPackageName());\n }\n}\n"
},
{
"path": "app/src/main/AndroidManifest.xml",
"content": "\n\n \n \n \n \n \n \n\n \n \n \n \n\n"
},
{
"path": "app/src/main/cpp/Eigen/CMakeLists.txt",
"content": "include(RegexUtils)\ntest_escape_string_as_regex()\n\nfile(GLOB Eigen_directory_files \"*\")\n\nescape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR \"${CMAKE_CURRENT_SOURCE_DIR}\")\n\nforeach(f ${Eigen_directory_files})\n if(NOT f MATCHES \"\\\\.txt\" AND NOT f MATCHES \"${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+\" AND NOT f MATCHES \"${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src\")\n list(APPEND Eigen_directory_files_to_install ${f})\n endif()\nendforeach(f ${Eigen_directory_files})\n\ninstall(FILES\n ${Eigen_directory_files_to_install}\n DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel\n )\n\ninstall(DIRECTORY src DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel FILES_MATCHING PATTERN \"*.h\")\n"
},
{
"path": "app/src/main/cpp/Eigen/Cholesky",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_CHOLESKY_MODULE_H\n#define EIGEN_CHOLESKY_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \\defgroup Cholesky_Module Cholesky module\n *\n *\n *\n * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.\n * Those decompositions are also accessible via the following methods:\n * - MatrixBase::llt()\n * - MatrixBase::ldlt()\n * - SelfAdjointView::llt()\n * - SelfAdjointView::ldlt()\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/Cholesky/LLT.h\"\n#include \"src/Cholesky/LDLT.h\"\n#ifdef EIGEN_USE_LAPACKE\n#include \"src/misc/lapacke.h\"\n#include \"src/Cholesky/LLT_LAPACKE.h\"\n#endif\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_CHOLESKY_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/CholmodSupport",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H\n#define EIGEN_CHOLMODSUPPORT_MODULE_H\n\n#include \"SparseCore\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\nextern \"C\" {\n #include \n}\n\n/** \\ingroup Support_modules\n * \\defgroup CholmodSupport_Module CholmodSupport module\n *\n * This module provides an interface to the Cholmod library which is part of the suitesparse package.\n * It provides the two following main factorization classes:\n * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.\n * - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).\n *\n * For the sake of completeness, this module also propose the two following classes:\n * - class CholmodSimplicialLLT\n * - class CholmodSimplicialLDLT\n * Note that these classes does not bring any particular advantage compared to the built-in\n * SimplicialLLT and SimplicialLDLT factorization classes.\n *\n * \\code\n * #include \n * \\endcode\n *\n * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be linked to the cholmod library and its dependencies.\n * The dependencies depend on how cholmod has been compiled.\n * For a cmake based project, you can use our FindCholmod.cmake module to help you in this task.\n *\n */\n\n#include \"src/CholmodSupport/CholmodSupport.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_CHOLMODSUPPORT_MODULE_H\n\n"
},
{
"path": "app/src/main/cpp/Eigen/Core",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// Copyright (C) 2008 Gael Guennebaud \n// Copyright (C) 2007-2011 Benoit Jacob \n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_CORE_H\n#define EIGEN_CORE_H\n\n// first thing Eigen does: stop the compiler from committing suicide\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n// Handle NVCC/CUDA/SYCL\n#if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)\n // Do not try asserts on CUDA and SYCL!\n #ifndef EIGEN_NO_DEBUG\n #define EIGEN_NO_DEBUG\n #endif\n\n #ifdef EIGEN_INTERNAL_DEBUGGING\n #undef EIGEN_INTERNAL_DEBUGGING\n #endif\n\n #ifdef EIGEN_EXCEPTIONS\n #undef EIGEN_EXCEPTIONS\n #endif\n\n // All functions callable from CUDA code must be qualified with __device__\n #ifdef __CUDACC__\n // Do not try to vectorize on CUDA and SYCL!\n #ifndef EIGEN_DONT_VECTORIZE\n #define EIGEN_DONT_VECTORIZE\n #endif\n\n #define EIGEN_DEVICE_FUNC __host__ __device__\n // We need math_functions.hpp to ensure that that EIGEN_USING_STD_MATH macro\n // works properly on the device side\n #include \n #else\n #define EIGEN_DEVICE_FUNC\n #endif\n#else\n #define EIGEN_DEVICE_FUNC\n#endif\n\n// When compiling CUDA device code with NVCC, pull in math functions from the\n// global namespace. In host mode, and when device doee with clang, use the\n// std versions.\n#if defined(__CUDA_ARCH__) && defined(__NVCC__)\n #define EIGEN_USING_STD_MATH(FUNC) using ::FUNC;\n#else\n #define EIGEN_USING_STD_MATH(FUNC) using std::FUNC;\n#endif\n\n#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(__CUDA_ARCH__) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL)\n #define EIGEN_EXCEPTIONS\n#endif\n\n#ifdef EIGEN_EXCEPTIONS\n #include \n#endif\n\n// then include this file where all our macros are defined. It's really important to do it first because\n// it's where we do all the alignment settings (platform detection and honoring the user's will if he\n// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.\n#include \"src/Core/util/Macros.h\"\n\n// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)\n// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.\n#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6)\n #pragma GCC optimize (\"-fno-ipa-cp-clone\")\n#endif\n\n#include \n\n// this include file manages BLAS and MKL related macros\n// and inclusion of their respective header files\n#include \"src/Core/util/MKL_support.h\"\n\n// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into\n// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks\n#if EIGEN_MAX_ALIGN_BYTES==0\n #ifndef EIGEN_DONT_VECTORIZE\n #define EIGEN_DONT_VECTORIZE\n #endif\n#endif\n\n#if EIGEN_COMP_MSVC\n #include // for _aligned_malloc -- need it regardless of whether vectorization is enabled\n #if (EIGEN_COMP_MSVC >= 1500) // 2008 or later\n // Remember that usage of defined() in a #define is undefined by the standard.\n // a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.\n #if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64\n #define EIGEN_SSE2_ON_MSVC_2008_OR_LATER\n #endif\n #endif\n#else\n // Remember that usage of defined() in a #define is undefined by the standard\n #if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )\n #define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC\n #endif\n#endif\n\n#ifndef EIGEN_DONT_VECTORIZE\n\n #if defined (EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)\n\n // Defines symbols for compile-time detection of which instructions are\n // used.\n // EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used\n #define EIGEN_VECTORIZE\n #define EIGEN_VECTORIZE_SSE\n #define EIGEN_VECTORIZE_SSE2\n\n // Detect sse3/ssse3/sse4:\n // gcc and icc defines __SSE3__, ...\n // there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you\n // want to force the use of those instructions with msvc.\n #ifdef __SSE3__\n #define EIGEN_VECTORIZE_SSE3\n #endif\n #ifdef __SSSE3__\n #define EIGEN_VECTORIZE_SSSE3\n #endif\n #ifdef __SSE4_1__\n #define EIGEN_VECTORIZE_SSE4_1\n #endif\n #ifdef __SSE4_2__\n #define EIGEN_VECTORIZE_SSE4_2\n #endif\n #ifdef __AVX__\n #define EIGEN_VECTORIZE_AVX\n #define EIGEN_VECTORIZE_SSE3\n #define EIGEN_VECTORIZE_SSSE3\n #define EIGEN_VECTORIZE_SSE4_1\n #define EIGEN_VECTORIZE_SSE4_2\n #endif\n #ifdef __AVX2__\n #define EIGEN_VECTORIZE_AVX2\n #define EIGEN_VECTORIZE_AVX\n #define EIGEN_VECTORIZE_SSE3\n #define EIGEN_VECTORIZE_SSSE3\n #define EIGEN_VECTORIZE_SSE4_1\n #define EIGEN_VECTORIZE_SSE4_2\n #endif\n #ifdef __FMA__\n #define EIGEN_VECTORIZE_FMA\n #endif\n #if defined(__AVX512F__)\n #define EIGEN_VECTORIZE_AVX512\n #define EIGEN_VECTORIZE_AVX2\n #define EIGEN_VECTORIZE_AVX\n #define EIGEN_VECTORIZE_FMA\n #define EIGEN_VECTORIZE_SSE3\n #define EIGEN_VECTORIZE_SSSE3\n #define EIGEN_VECTORIZE_SSE4_1\n #define EIGEN_VECTORIZE_SSE4_2\n #ifdef __AVX512DQ__\n #define EIGEN_VECTORIZE_AVX512DQ\n #endif\n #endif\n\n // include files\n\n // This extern \"C\" works around a MINGW-w64 compilation issue\n // https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354\n // In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).\n // However, intrin.h uses an extern \"C\" declaration, and g++ thus complains of duplicate declarations\n // with conflicting linkage. The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;\n // so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern \"C\" here too.\n // notice that since these are C headers, the extern \"C\" is theoretically needed anyways.\n extern \"C\" {\n // In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.\n // Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:\n #if EIGEN_COMP_ICC >= 1110\n #include \n #else\n #include \n #include \n #include \n #ifdef EIGEN_VECTORIZE_SSE3\n #include \n #endif\n #ifdef EIGEN_VECTORIZE_SSSE3\n #include \n #endif\n #ifdef EIGEN_VECTORIZE_SSE4_1\n #include \n #endif\n #ifdef EIGEN_VECTORIZE_SSE4_2\n #include \n #endif\n #if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)\n #include \n #endif\n #endif\n } // end extern \"C\"\n #elif defined __VSX__\n #define EIGEN_VECTORIZE\n #define EIGEN_VECTORIZE_VSX\n #include \n // We need to #undef all these ugly tokens defined in \n // => use __vector instead of vector\n #undef bool\n #undef vector\n #undef pixel\n #elif defined __ALTIVEC__\n #define EIGEN_VECTORIZE\n #define EIGEN_VECTORIZE_ALTIVEC\n #include \n // We need to #undef all these ugly tokens defined in \n // => use __vector instead of vector\n #undef bool\n #undef vector\n #undef pixel\n #elif (defined __ARM_NEON) || (defined __ARM_NEON__)\n #define EIGEN_VECTORIZE\n #define EIGEN_VECTORIZE_NEON\n #include \n #elif (defined __s390x__ && defined __VEC__)\n #define EIGEN_VECTORIZE\n #define EIGEN_VECTORIZE_ZVECTOR\n #include \n #endif\n#endif\n\n#if defined(__F16C__) && !defined(EIGEN_COMP_CLANG)\n // We can use the optimized fp16 to float and float to fp16 conversion routines\n #define EIGEN_HAS_FP16_C\n#endif\n\n#if defined __CUDACC__\n #define EIGEN_VECTORIZE_CUDA\n #include \n #if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500\n #define EIGEN_HAS_CUDA_FP16\n #endif\n#endif\n\n#if defined EIGEN_HAS_CUDA_FP16\n #include \n #include \n#endif\n\n#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)\n #define EIGEN_HAS_OPENMP\n#endif\n\n#ifdef EIGEN_HAS_OPENMP\n#include \n#endif\n\n// MSVC for windows mobile does not have the errno.h file\n#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM\n#define EIGEN_HAS_ERRNO\n#endif\n\n#ifdef EIGEN_HAS_ERRNO\n#include \n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include // for CHAR_BIT\n// for min/max:\n#include \n\n// for std::is_nothrow_move_assignable\n#ifdef EIGEN_INCLUDE_TYPE_TRAITS\n#include \n#endif\n\n// for outputting debug info\n#ifdef EIGEN_DEBUG_ASSIGN\n#include \n#endif\n\n// required for __cpuid, needs to be included after cmath\n#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE\n #include \n#endif\n\n#if defined(__SYCL_DEVICE_ONLY__)\n #undef min\n #undef max\n #undef isnan\n #undef isinf\n #undef isfinite\n #include \n#endif\n\n/** \\brief Namespace containing all symbols from the %Eigen library. */\nnamespace Eigen {\n\ninline static const char *SimdInstructionSetsInUse(void) {\n#if defined(EIGEN_VECTORIZE_AVX512)\n return \"AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\";\n#elif defined(EIGEN_VECTORIZE_AVX)\n return \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\";\n#elif defined(EIGEN_VECTORIZE_SSE4_2)\n return \"SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\";\n#elif defined(EIGEN_VECTORIZE_SSE4_1)\n return \"SSE, SSE2, SSE3, SSSE3, SSE4.1\";\n#elif defined(EIGEN_VECTORIZE_SSSE3)\n return \"SSE, SSE2, SSE3, SSSE3\";\n#elif defined(EIGEN_VECTORIZE_SSE3)\n return \"SSE, SSE2, SSE3\";\n#elif defined(EIGEN_VECTORIZE_SSE2)\n return \"SSE, SSE2\";\n#elif defined(EIGEN_VECTORIZE_ALTIVEC)\n return \"AltiVec\";\n#elif defined(EIGEN_VECTORIZE_VSX)\n return \"VSX\";\n#elif defined(EIGEN_VECTORIZE_NEON)\n return \"ARM NEON\";\n#elif defined(EIGEN_VECTORIZE_ZVECTOR)\n return \"S390X ZVECTOR\";\n#else\n return \"None\";\n#endif\n}\n\n} // end namespace Eigen\n\n#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT\n// This will generate an error message:\n#error Eigen2-support is only available up to version 3.2. Please go to \"http://eigen.tuxfamily.org/index.php?title=Eigen2\" for further information\n#endif\n\nnamespace Eigen {\n\n// we use size_t frequently and we'll never remember to prepend it with std:: everytime just to\n// ensure QNX/QCC support\nusing std::size_t;\n// gcc 4.6.0 wants std:: for ptrdiff_t\nusing std::ptrdiff_t;\n\n}\n\n/** \\defgroup Core_Module Core module\n * This is the main module of Eigen providing dense matrix and vector support\n * (both fixed and dynamic size) with all the features corresponding to a BLAS library\n * and much more...\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/Core/util/Constants.h\"\n#include \"src/Core/util/Meta.h\"\n#include \"src/Core/util/ForwardDeclarations.h\"\n#include \"src/Core/util/StaticAssert.h\"\n#include \"src/Core/util/XprHelper.h\"\n#include \"src/Core/util/Memory.h\"\n#include \"src/Core/util/IntegralConstant.h\"\n#include \"src/Core/util/SymbolicIndex.h\"\n\n\n#include \"src/Core/NumTraits.h\"\n#include \"src/Core/MathFunctions.h\"\n#include \"src/Core/GenericPacketMath.h\"\n#include \"src/Core/MathFunctionsImpl.h\"\n\n#if defined EIGEN_VECTORIZE_AVX512\n #include \"src/Core/arch/SSE/PacketMath.h\"\n #include \"src/Core/arch/AVX/PacketMath.h\"\n #include \"src/Core/arch/AVX512/PacketMath.h\"\n #include \"src/Core/arch/SSE/MathFunctions.h\"\n #include \"src/Core/arch/AVX/MathFunctions.h\"\n #include \"src/Core/arch/AVX512/MathFunctions.h\"\n#elif defined EIGEN_VECTORIZE_AVX\n // Use AVX for floats and doubles, SSE for integers\n #include \"src/Core/arch/SSE/PacketMath.h\"\n #include \"src/Core/arch/SSE/Complex.h\"\n #include \"src/Core/arch/SSE/MathFunctions.h\"\n #include \"src/Core/arch/AVX/PacketMath.h\"\n #include \"src/Core/arch/AVX/MathFunctions.h\"\n #include \"src/Core/arch/AVX/Complex.h\"\n #include \"src/Core/arch/AVX/TypeCasting.h\"\n#elif defined EIGEN_VECTORIZE_SSE\n #include \"src/Core/arch/SSE/PacketMath.h\"\n #include \"src/Core/arch/SSE/MathFunctions.h\"\n #include \"src/Core/arch/SSE/Complex.h\"\n #include \"src/Core/arch/SSE/TypeCasting.h\"\n#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)\n #include \"src/Core/arch/AltiVec/PacketMath.h\"\n #include \"src/Core/arch/AltiVec/MathFunctions.h\"\n #include \"src/Core/arch/AltiVec/Complex.h\"\n#elif defined EIGEN_VECTORIZE_NEON\n #include \"src/Core/arch/NEON/PacketMath.h\"\n #include \"src/Core/arch/NEON/MathFunctions.h\"\n #include \"src/Core/arch/NEON/Complex.h\"\n#elif defined EIGEN_VECTORIZE_ZVECTOR\n #include \"src/Core/arch/ZVector/PacketMath.h\"\n #include \"src/Core/arch/ZVector/MathFunctions.h\"\n #include \"src/Core/arch/ZVector/Complex.h\"\n#endif\n\n// Half float support\n#include \"src/Core/arch/CUDA/Half.h\"\n#include \"src/Core/arch/CUDA/PacketMathHalf.h\"\n#include \"src/Core/arch/CUDA/TypeCasting.h\"\n\n#if defined EIGEN_VECTORIZE_CUDA\n #include \"src/Core/arch/CUDA/PacketMath.h\"\n #include \"src/Core/arch/CUDA/MathFunctions.h\"\n#endif\n\n#include \"src/Core/arch/Default/Settings.h\"\n\n#include \"src/Core/functors/TernaryFunctors.h\"\n#include \"src/Core/functors/BinaryFunctors.h\"\n#include \"src/Core/functors/UnaryFunctors.h\"\n#include \"src/Core/functors/NullaryFunctors.h\"\n#include \"src/Core/functors/StlFunctors.h\"\n#include \"src/Core/functors/AssignmentFunctors.h\"\n\n// Specialized functors to enable the processing of complex numbers\n// on CUDA devices\n#include \"src/Core/arch/CUDA/Complex.h\"\n\n#include \"src/Core/util/IndexedViewHelper.h\"\n#include \"src/Core/ArithmeticSequence.h\"\n#include \"src/Core/IO.h\"\n#include \"src/Core/DenseCoeffsBase.h\"\n#include \"src/Core/DenseBase.h\"\n#include \"src/Core/MatrixBase.h\"\n#include \"src/Core/EigenBase.h\"\n\n#include \"src/Core/Product.h\"\n#include \"src/Core/CoreEvaluators.h\"\n#include \"src/Core/AssignEvaluator.h\"\n\n#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874\n // at least confirmed with Doxygen 1.5.5 and 1.5.6\n #include \"src/Core/Assign.h\"\n#endif\n\n#include \"src/Core/ArrayBase.h\"\n#include \"src/Core/util/BlasUtil.h\"\n#include \"src/Core/DenseStorage.h\"\n#include \"src/Core/NestByValue.h\"\n\n// #include \"src/Core/ForceAlignedAccess.h\"\n\n#include \"src/Core/ReturnByValue.h\"\n#include \"src/Core/NoAlias.h\"\n#include \"src/Core/PlainObjectBase.h\"\n#include \"src/Core/Matrix.h\"\n#include \"src/Core/Array.h\"\n#include \"src/Core/CwiseTernaryOp.h\"\n#include \"src/Core/CwiseBinaryOp.h\"\n#include \"src/Core/CwiseUnaryOp.h\"\n#include \"src/Core/CwiseNullaryOp.h\"\n#include \"src/Core/CwiseUnaryView.h\"\n#include \"src/Core/SelfCwiseBinaryOp.h\"\n#include \"src/Core/Dot.h\"\n#include \"src/Core/StableNorm.h\"\n#include \"src/Core/Stride.h\"\n#include \"src/Core/MapBase.h\"\n#include \"src/Core/Map.h\"\n#include \"src/Core/Ref.h\"\n#include \"src/Core/Block.h\"\n#include \"src/Core/VectorBlock.h\"\n#include \"src/Core/IndexedView.h\"\n#include \"src/Core/Transpose.h\"\n#include \"src/Core/DiagonalMatrix.h\"\n#include \"src/Core/Diagonal.h\"\n#include \"src/Core/DiagonalProduct.h\"\n#include \"src/Core/Redux.h\"\n#include \"src/Core/Visitor.h\"\n#include \"src/Core/Fuzzy.h\"\n#include \"src/Core/Swap.h\"\n#include \"src/Core/CommaInitializer.h\"\n#include \"src/Core/GeneralProduct.h\"\n#include \"src/Core/Solve.h\"\n#include \"src/Core/Inverse.h\"\n#include \"src/Core/SolverBase.h\"\n#include \"src/Core/PermutationMatrix.h\"\n#include \"src/Core/Transpositions.h\"\n#include \"src/Core/TriangularMatrix.h\"\n#include \"src/Core/SelfAdjointView.h\"\n#include \"src/Core/products/GeneralBlockPanelKernel.h\"\n#include \"src/Core/products/Parallelizer.h\"\n#include \"src/Core/ProductEvaluators.h\"\n#include \"src/Core/products/GeneralMatrixVector.h\"\n#include \"src/Core/products/GeneralMatrixMatrix.h\"\n#include \"src/Core/SolveTriangular.h\"\n#include \"src/Core/products/GeneralMatrixMatrixTriangular.h\"\n#include \"src/Core/products/SelfadjointMatrixVector.h\"\n#include \"src/Core/products/SelfadjointMatrixMatrix.h\"\n#include \"src/Core/products/SelfadjointProduct.h\"\n#include \"src/Core/products/SelfadjointRank2Update.h\"\n#include \"src/Core/products/TriangularMatrixVector.h\"\n#include \"src/Core/products/TriangularMatrixMatrix.h\"\n#include \"src/Core/products/TriangularSolverMatrix.h\"\n#include \"src/Core/products/TriangularSolverVector.h\"\n#include \"src/Core/BandMatrix.h\"\n#include \"src/Core/CoreIterators.h\"\n#include \"src/Core/ConditionEstimator.h\"\n\n#include \"src/Core/BooleanRedux.h\"\n#include \"src/Core/Select.h\"\n#include \"src/Core/VectorwiseOp.h\"\n#include \"src/Core/Random.h\"\n#include \"src/Core/Replicate.h\"\n#include \"src/Core/Reverse.h\"\n#include \"src/Core/ArrayWrapper.h\"\n\n#ifdef EIGEN_USE_BLAS\n#include \"src/Core/products/GeneralMatrixMatrix_BLAS.h\"\n#include \"src/Core/products/GeneralMatrixVector_BLAS.h\"\n#include \"src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h\"\n#include \"src/Core/products/SelfadjointMatrixMatrix_BLAS.h\"\n#include \"src/Core/products/SelfadjointMatrixVector_BLAS.h\"\n#include \"src/Core/products/TriangularMatrixMatrix_BLAS.h\"\n#include \"src/Core/products/TriangularMatrixVector_BLAS.h\"\n#include \"src/Core/products/TriangularSolverMatrix_BLAS.h\"\n#endif // EIGEN_USE_BLAS\n\n#ifdef EIGEN_USE_MKL_VML\n#include \"src/Core/Assign_MKL.h\"\n#endif\n\n#include \"src/Core/GlobalFunctions.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_CORE_H\n"
},
{
"path": "app/src/main/cpp/Eigen/Dense",
"content": "#include \"Core\"\n#include \"LU\"\n#include \"Cholesky\"\n#include \"QR\"\n#include \"SVD\"\n#include \"Geometry\"\n#include \"Eigenvalues\"\n"
},
{
"path": "app/src/main/cpp/Eigen/Eigen",
"content": "#include \"Dense\"\n#include \"Sparse\"\n"
},
{
"path": "app/src/main/cpp/Eigen/Eigenvalues",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_EIGENVALUES_MODULE_H\n#define EIGEN_EIGENVALUES_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n#include \"Cholesky\"\n#include \"Jacobi\"\n#include \"Householder\"\n#include \"LU\"\n#include \"Geometry\"\n\n/** \\defgroup Eigenvalues_Module Eigenvalues module\n *\n *\n *\n * This module mainly provides various eigenvalue solvers.\n * This module also provides some MatrixBase methods, including:\n * - MatrixBase::eigenvalues(),\n * - MatrixBase::operatorNorm()\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/misc/RealSvd2x2.h\"\n#include \"src/Eigenvalues/Tridiagonalization.h\"\n#include \"src/Eigenvalues/RealSchur.h\"\n#include \"src/Eigenvalues/EigenSolver.h\"\n#include \"src/Eigenvalues/SelfAdjointEigenSolver.h\"\n#include \"src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h\"\n#include \"src/Eigenvalues/HessenbergDecomposition.h\"\n#include \"src/Eigenvalues/ComplexSchur.h\"\n#include \"src/Eigenvalues/ComplexEigenSolver.h\"\n#include \"src/Eigenvalues/RealQZ.h\"\n#include \"src/Eigenvalues/GeneralizedEigenSolver.h\"\n#include \"src/Eigenvalues/MatrixBaseEigenvalues.h\"\n#ifdef EIGEN_USE_LAPACKE\n#include \"src/misc/lapacke.h\"\n#include \"src/Eigenvalues/RealSchur_LAPACKE.h\"\n#include \"src/Eigenvalues/ComplexSchur_LAPACKE.h\"\n#include \"src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h\"\n#endif\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_EIGENVALUES_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/Geometry",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_GEOMETRY_MODULE_H\n#define EIGEN_GEOMETRY_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n#include \"SVD\"\n#include \"LU\"\n#include \n\n/** \\defgroup Geometry_Module Geometry module\n *\n * This module provides support for:\n * - fixed-size homogeneous transformations\n * - translation, scaling, 2D and 3D rotations\n * - \\link Quaternion quaternions \\endlink\n * - cross products (\\ref MatrixBase::cross, \\ref MatrixBase::cross3)\n * - orthognal vector generation (\\ref MatrixBase::unitOrthogonal)\n * - some linear components: \\link ParametrizedLine parametrized-lines \\endlink and \\link Hyperplane hyperplanes \\endlink\n * - \\link AlignedBox axis aligned bounding boxes \\endlink\n * - \\link umeyama least-square transformation fitting \\endlink\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/Geometry/OrthoMethods.h\"\n#include \"src/Geometry/EulerAngles.h\"\n\n#include \"src/Geometry/Homogeneous.h\"\n#include \"src/Geometry/RotationBase.h\"\n#include \"src/Geometry/Rotation2D.h\"\n#include \"src/Geometry/Quaternion.h\"\n#include \"src/Geometry/AngleAxis.h\"\n#include \"src/Geometry/Transform.h\"\n#include \"src/Geometry/Translation.h\"\n#include \"src/Geometry/Scaling.h\"\n#include \"src/Geometry/Hyperplane.h\"\n#include \"src/Geometry/ParametrizedLine.h\"\n#include \"src/Geometry/AlignedBox.h\"\n#include \"src/Geometry/Umeyama.h\"\n\n// Use the SSE optimized version whenever possible. At the moment the\n// SSE version doesn't compile when AVX is enabled\n#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX\n#include \"src/Geometry/arch/Geometry_SSE.h\"\n#endif\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_GEOMETRY_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/Householder",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_HOUSEHOLDER_MODULE_H\n#define EIGEN_HOUSEHOLDER_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \\defgroup Householder_Module Householder module\n * This module provides Householder transformations.\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/Householder/Householder.h\"\n#include \"src/Householder/HouseholderSequence.h\"\n#include \"src/Householder/BlockHouseholder.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_HOUSEHOLDER_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/IterativeLinearSolvers",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H\n#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H\n\n#include \"SparseCore\"\n#include \"OrderingMethods\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \n * \\defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module\n *\n * This module currently provides iterative methods to solve problems of the form \\c A \\c x = \\c b, where \\c A is a squared matrix, usually very large and sparse.\n * Those solvers are accessible via the following classes:\n * - ConjugateGradient for selfadjoint (hermitian) matrices,\n * - LeastSquaresConjugateGradient for rectangular least-square problems,\n * - BiCGSTAB for general square matrices.\n *\n * These iterative solvers are associated with some preconditioners:\n * - IdentityPreconditioner - not really useful\n * - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.\n * - IncompleteLUT - incomplete LU factorization with dual thresholding\n *\n * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.\n *\n \\code\n #include \n \\endcode\n */\n\n#include \"src/IterativeLinearSolvers/SolveWithGuess.h\"\n#include \"src/IterativeLinearSolvers/IterativeSolverBase.h\"\n#include \"src/IterativeLinearSolvers/BasicPreconditioners.h\"\n#include \"src/IterativeLinearSolvers/ConjugateGradient.h\"\n#include \"src/IterativeLinearSolvers/LeastSquareConjugateGradient.h\"\n#include \"src/IterativeLinearSolvers/BiCGSTAB.h\"\n#include \"src/IterativeLinearSolvers/IncompleteLUT.h\"\n#include \"src/IterativeLinearSolvers/IncompleteCholesky.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H\n"
},
{
"path": "app/src/main/cpp/Eigen/Jacobi",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_JACOBI_MODULE_H\n#define EIGEN_JACOBI_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \\defgroup Jacobi_Module Jacobi module\n * This module provides Jacobi and Givens rotations.\n *\n * \\code\n * #include \n * \\endcode\n *\n * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:\n * - MatrixBase::applyOnTheLeft()\n * - MatrixBase::applyOnTheRight().\n */\n\n#include \"src/Jacobi/Jacobi.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_JACOBI_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n\n"
},
{
"path": "app/src/main/cpp/Eigen/LU",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_LU_MODULE_H\n#define EIGEN_LU_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \\defgroup LU_Module LU module\n * This module includes %LU decomposition and related notions such as matrix inversion and determinant.\n * This module defines the following MatrixBase methods:\n * - MatrixBase::inverse()\n * - MatrixBase::determinant()\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/misc/Kernel.h\"\n#include \"src/misc/Image.h\"\n#include \"src/LU/FullPivLU.h\"\n#include \"src/LU/PartialPivLU.h\"\n#ifdef EIGEN_USE_LAPACKE\n#include \"src/misc/lapacke.h\"\n#include \"src/LU/PartialPivLU_LAPACKE.h\"\n#endif\n#include \"src/LU/Determinant.h\"\n#include \"src/LU/InverseImpl.h\"\n\n// Use the SSE optimized version whenever possible. At the moment the\n// SSE version doesn't compile when AVX is enabled\n#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX\n #include \"src/LU/arch/Inverse_SSE.h\"\n#endif\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_LU_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/MetisSupport",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_METISSUPPORT_MODULE_H\n#define EIGEN_METISSUPPORT_MODULE_H\n\n#include \"SparseCore\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\nextern \"C\" {\n#include \n}\n\n\n/** \\ingroup Support_modules\n * \\defgroup MetisSupport_Module MetisSupport module\n *\n * \\code\n * #include \n * \\endcode\n * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis). \n * It can be used just as any other built-in method as explained in \\link OrderingMethods_Module here. \\endlink\n */\n\n\n#include \"src/MetisSupport/MetisSupport.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_METISSUPPORT_MODULE_H\n"
},
{
"path": "app/src/main/cpp/Eigen/OrderingMethods",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_ORDERINGMETHODS_MODULE_H\n#define EIGEN_ORDERINGMETHODS_MODULE_H\n\n#include \"SparseCore\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \n * \\defgroup OrderingMethods_Module OrderingMethods module\n *\n * This module is currently for internal use only\n * \n * It defines various built-in and external ordering methods for sparse matrices. \n * They are typically used to reduce the number of elements during \n * the sparse matrix decomposition (LLT, LU, QR).\n * Precisely, in a preprocessing step, a permutation matrix P is computed using \n * those ordering methods and applied to the columns of the matrix. \n * Using for instance the sparse Cholesky decomposition, it is expected that \n * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).\n * \n * \n * Usage : \n * \\code\n * #include \n * \\endcode\n * \n * A simple usage is as a template parameter in the sparse decomposition classes : \n * \n * \\code \n * SparseLU > solver;\n * \\endcode \n * \n * \\code \n * SparseQR > solver;\n * \\endcode\n * \n * It is possible as well to call directly a particular ordering method for your own purpose, \n * \\code \n * AMDOrdering ordering;\n * PermutationMatrix perm;\n * SparseMatrix A; \n * //Fill the matrix ...\n * \n * ordering(A, perm); // Call AMD\n * \\endcode\n * \n * \\note Some of these methods (like AMD or METIS), need the sparsity pattern \n * of the input matrix to be symmetric. When the matrix is structurally unsymmetric, \n * Eigen computes internally the pattern of \\f$A^T*A\\f$ before calling the method.\n * If your matrix is already symmetric (at leat in structure), you can avoid that\n * by calling the method with a SelfAdjointView type.\n * \n * \\code\n * // Call the ordering on the pattern of the lower triangular matrix A\n * ordering(A.selfadjointView(), perm);\n * \\endcode\n */\n\n#ifndef EIGEN_MPL2_ONLY\n#include \"src/OrderingMethods/Amd.h\"\n#endif\n\n#include \"src/OrderingMethods/Ordering.h\"\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_ORDERINGMETHODS_MODULE_H\n"
},
{
"path": "app/src/main/cpp/Eigen/PaStiXSupport",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_PASTIXSUPPORT_MODULE_H\n#define EIGEN_PASTIXSUPPORT_MODULE_H\n\n#include \"SparseCore\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\nextern \"C\" {\n#include \n#include \n}\n\n#ifdef complex\n#undef complex\n#endif\n\n/** \\ingroup Support_modules\n * \\defgroup PaStiXSupport_Module PaStiXSupport module\n * \n * This module provides an interface to the PaSTiX library.\n * PaSTiX is a general \\b supernodal, \\b parallel and \\b opensource sparse solver.\n * It provides the two following main factorization classes:\n * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.\n * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.\n * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).\n * \n * \\code\n * #include \n * \\endcode\n *\n * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be linked to the PaSTiX library and its dependencies.\n * The dependencies depend on how PaSTiX has been compiled.\n * For a cmake based project, you can use our FindPaSTiX.cmake module to help you in this task.\n *\n */\n\n#include \"src/PaStiXSupport/PaStiXSupport.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_PASTIXSUPPORT_MODULE_H\n"
},
{
"path": "app/src/main/cpp/Eigen/PardisoSupport",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_PARDISOSUPPORT_MODULE_H\n#define EIGEN_PARDISOSUPPORT_MODULE_H\n\n#include \"SparseCore\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n#include \n\n/** \\ingroup Support_modules\n * \\defgroup PardisoSupport_Module PardisoSupport module\n *\n * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.\n *\n * \\code\n * #include \n * \\endcode\n *\n * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies.\n * See this \\ref TopicUsingIntelMKL \"page\" for more information on MKL-Eigen integration.\n * \n */\n\n#include \"src/PardisoSupport/PardisoSupport.h\"\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_PARDISOSUPPORT_MODULE_H\n"
},
{
"path": "app/src/main/cpp/Eigen/QR",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_QR_MODULE_H\n#define EIGEN_QR_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n#include \"Cholesky\"\n#include \"Jacobi\"\n#include \"Householder\"\n\n/** \\defgroup QR_Module QR module\n *\n *\n *\n * This module provides various QR decompositions\n * This module also provides some MatrixBase methods, including:\n * - MatrixBase::householderQr()\n * - MatrixBase::colPivHouseholderQr()\n * - MatrixBase::fullPivHouseholderQr()\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/QR/HouseholderQR.h\"\n#include \"src/QR/FullPivHouseholderQR.h\"\n#include \"src/QR/ColPivHouseholderQR.h\"\n#include \"src/QR/CompleteOrthogonalDecomposition.h\"\n#ifdef EIGEN_USE_LAPACKE\n#include \"src/misc/lapacke.h\"\n#include \"src/QR/HouseholderQR_LAPACKE.h\"\n#include \"src/QR/ColPivHouseholderQR_LAPACKE.h\"\n#endif\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_QR_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/QtAlignedMalloc",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_QTMALLOC_MODULE_H\n#define EIGEN_QTMALLOC_MODULE_H\n\n#include \"Core\"\n\n#if (!EIGEN_MALLOC_ALREADY_ALIGNED)\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\nvoid *qMalloc(std::size_t size)\n{\n return Eigen::internal::aligned_malloc(size);\n}\n\nvoid qFree(void *ptr)\n{\n Eigen::internal::aligned_free(ptr);\n}\n\nvoid *qRealloc(void *ptr, std::size_t size)\n{\n void* newPtr = Eigen::internal::aligned_malloc(size);\n memcpy(newPtr, ptr, size);\n Eigen::internal::aligned_free(ptr);\n return newPtr;\n}\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif\n\n#endif // EIGEN_QTMALLOC_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/SPQRSupport",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_SPQRSUPPORT_MODULE_H\n#define EIGEN_SPQRSUPPORT_MODULE_H\n\n#include \"SparseCore\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n#include \"SuiteSparseQR.hpp\"\n\n/** \\ingroup Support_modules\n * \\defgroup SPQRSupport_Module SuiteSparseQR module\n * \n * This module provides an interface to the SPQR library, which is part of the suitesparse package.\n *\n * \\code\n * #include \n * \\endcode\n *\n * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).\n * For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules\n *\n */\n\n#include \"src/CholmodSupport/CholmodSupport.h\"\n#include \"src/SPQRSupport/SuiteSparseQRSupport.h\"\n\n#endif\n"
},
{
"path": "app/src/main/cpp/Eigen/SVD",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_SVD_MODULE_H\n#define EIGEN_SVD_MODULE_H\n\n#include \"QR\"\n#include \"Householder\"\n#include \"Jacobi\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \\defgroup SVD_Module SVD module\n *\n *\n *\n * This module provides SVD decomposition for matrices (both real and complex).\n * Two decomposition algorithms are provided:\n * - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.\n * - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems.\n * These decompositions are accessible via the respective classes and following MatrixBase methods:\n * - MatrixBase::jacobiSvd()\n * - MatrixBase::bdcSvd()\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#include \"src/misc/RealSvd2x2.h\"\n#include \"src/SVD/UpperBidiagonalization.h\"\n#include \"src/SVD/SVDBase.h\"\n#include \"src/SVD/JacobiSVD.h\"\n#include \"src/SVD/BDCSVD.h\"\n#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)\n#include \"src/misc/lapacke.h\"\n#include \"src/SVD/JacobiSVD_LAPACKE.h\"\n#endif\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_SVD_MODULE_H\n/* vim: set filetype=cpp et sw=2 ts=2 ai: */\n"
},
{
"path": "app/src/main/cpp/Eigen/Sparse",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_SPARSE_MODULE_H\n#define EIGEN_SPARSE_MODULE_H\n\n/** \\defgroup Sparse_Module Sparse meta-module\n *\n * Meta-module including all related modules:\n * - \\ref SparseCore_Module\n * - \\ref OrderingMethods_Module\n * - \\ref SparseCholesky_Module\n * - \\ref SparseLU_Module\n * - \\ref SparseQR_Module\n * - \\ref IterativeLinearSolvers_Module\n *\n \\code\n #include \n \\endcode\n */\n\n#include \"SparseCore\"\n#include \"OrderingMethods\"\n#ifndef EIGEN_MPL2_ONLY\n#include \"SparseCholesky\"\n#endif\n#include \"SparseLU\"\n#include \"SparseQR\"\n#include \"IterativeLinearSolvers\"\n\n#endif // EIGEN_SPARSE_MODULE_H\n\n"
},
{
"path": "app/src/main/cpp/Eigen/SparseCholesky",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// Copyright (C) 2008-2013 Gael Guennebaud \n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_SPARSECHOLESKY_MODULE_H\n#define EIGEN_SPARSECHOLESKY_MODULE_H\n\n#include \"SparseCore\"\n#include \"OrderingMethods\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n/** \n * \\defgroup SparseCholesky_Module SparseCholesky module\n *\n * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.\n * Those decompositions are accessible via the following classes:\n * - SimplicialLLt,\n * - SimplicialLDLt\n *\n * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.\n *\n * \\code\n * #include \n * \\endcode\n */\n\n#ifdef EIGEN_MPL2_ONLY\n#error The SparseCholesky module has nothing to offer in MPL2 only mode\n#endif\n\n#include \"src/SparseCholesky/SimplicialCholesky.h\"\n\n#ifndef EIGEN_MPL2_ONLY\n#include \"src/SparseCholesky/SimplicialCholesky_impl.h\"\n#endif\n\n#include \"src/Core/util/ReenableStupidWarnings.h\"\n\n#endif // EIGEN_SPARSECHOLESKY_MODULE_H\n"
},
{
"path": "app/src/main/cpp/Eigen/SparseCore",
"content": "// This file is part of Eigen, a lightweight C++ template library\n// for linear algebra.\n//\n// This Source Code Form is subject to the terms of the Mozilla\n// Public License v. 2.0. If a copy of the MPL was not distributed\n// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef EIGEN_SPARSECORE_MODULE_H\n#define EIGEN_SPARSECORE_MODULE_H\n\n#include \"Core\"\n\n#include \"src/Core/util/DisableStupidWarnings.h\"\n\n#include \n#include