1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328
|
/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#include <fstream>
#include "itkPointSetToListSampleAdaptor.h"
#include "itkSampleClassifierFilter.h"
#include "itkMaximumDecisionRule.h"
#include "itkGaussianMixtureModelComponent.h"
#include "itkExpectationMaximizationMixtureModelEstimator.h"
#include "itkTestingMacros.h"
// Sample classifier test using Gaussian Mixture model and EM estimator
int
itkSampleClassifierFilterTest7(int argc, char * argv[])
{
using PointSetType = itk::PointSet<double, 2>;
using DataSampleType = itk::Statistics::PointSetToListSampleAdaptor<PointSetType>;
using EstimatorType = itk::Statistics::ExpectationMaximizationMixtureModelEstimator<DataSampleType>;
using ComponentType = itk::Statistics::GaussianMixtureModelComponent<DataSampleType>;
if (argc < 3)
{
std::cerr << "Missing parameters." << std::endl;
std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
std::cout << "Input_data_sample Target_data_sample" << std::endl;
return EXIT_FAILURE;
}
constexpr int maximumIteration = 200;
constexpr double minStandardDeviation = 28.54746;
constexpr unsigned int numberOfClasses = 2;
using ParametersType = itk::Array<double>;
std::vector<ParametersType> trueParameters(numberOfClasses);
ParametersType params(6);
params[0] = 99.261;
params[1] = 100.078;
params[2] = 814.95741;
params[3] = 38.40308;
params[4] = 38.40308;
params[5] = 817.64446;
trueParameters[0] = params;
params[0] = 200.1;
params[1] = 201.3;
params[2] = 859.785295;
params[3] = -3.617316;
params[4] = -3.617316;
params[5] = 848.991508;
trueParameters[1] = params;
// only the means are altered
std::vector<ParametersType> initialParameters(numberOfClasses);
params[0] = 80.0;
params[1] = 80.0;
params[2] = 814.95741;
params[3] = 38.40308;
params[4] = 38.40308;
params[5] = 817.64446;
initialParameters[0] = params;
params[0] = 180.0;
params[1] = 180.0;
params[2] = 859.785295;
params[3] = -3.617316;
params[4] = -3.617316;
params[5] = 848.991508;
initialParameters[1] = params;
itk::Array<double> trueProportions(numberOfClasses);
trueProportions[0] = 0.5;
trueProportions[1] = 0.5;
itk::Array<double> initialProportions(numberOfClasses);
initialProportions[0] = 0.5;
initialProportions[1] = 0.5;
/* Loading point data */
auto pointSet = PointSetType::New();
PointSetType::PointsContainerPointer pointsContainer = PointSetType::PointsContainer::New();
constexpr int dataSizeBig = 2000;
pointsContainer->Reserve(dataSizeBig);
pointSet->SetPoints(pointsContainer);
PointSetType::PointsContainerIterator p_iter = pointsContainer->Begin();
PointSetType::PointType point;
char * const dataFileName = argv[1];
std::ifstream dataStream(dataFileName);
if (!dataStream)
{
std::cout << "ERROR: fail to open the data file." << std::endl;
return EXIT_FAILURE;
}
while (p_iter != pointsContainer->End())
{
for (unsigned int i = 0; i < PointSetType::PointDimension; ++i)
{
double temp;
dataStream >> temp;
point[i] = temp;
}
p_iter.Value() = point;
++p_iter;
}
dataStream.close();
/* Importing the point set to the sample */
auto sample = DataSampleType::New();
sample->SetPointSet(pointSet);
/* Preparing the gaussian mixture components */
using ComponentPointer = ComponentType::Pointer;
std::vector<ComponentPointer> components;
for (unsigned int i = 0; i < numberOfClasses; ++i)
{
components.push_back(ComponentType::New());
(components[i])->SetSample(sample);
(components[i])->SetParameters(initialParameters[i]);
}
/* Estimating */
auto estimator = EstimatorType::New();
estimator->SetSample(sample);
estimator->SetMaximumIteration(maximumIteration);
estimator->SetInitialProportions(initialProportions);
for (unsigned int i = 0; i < numberOfClasses; ++i)
{
estimator->AddComponent((ComponentType::Superclass *)(components[i]).GetPointer());
}
estimator->Update();
std::cout << "DEBUG: current iteration = " << estimator->GetCurrentIteration() << std::endl;
bool passed = true;
for (unsigned int i = 0; i < numberOfClasses; ++i)
{
std::cout << "Cluster[" << i << ']' << std::endl;
std::cout << " Parameters:" << std::endl;
std::cout << " " << (components[i])->GetFullParameters() << std::endl;
std::cout << " Proportion: ";
std::cout << " " << (estimator->GetProportions())[i] << std::endl;
double displacement = 0.0;
const unsigned int measurementVectorSize = sample->GetMeasurementVectorSize();
for (unsigned int j = 0; j < measurementVectorSize; ++j)
{
double temp;
temp = (components[i])->GetFullParameters()[j] - trueParameters[i][j];
displacement += (temp * temp);
}
displacement = std::sqrt(displacement);
std::cout << " Mean displacement: " << std::endl;
std::cout << " " << displacement << std::endl << std::endl;
if (displacement > (minStandardDeviation / 100.0) * 3)
{
passed = false;
}
}
// Set up a classifier
using FilterType = itk::Statistics::SampleClassifierFilter<DataSampleType>;
auto filter = FilterType::New();
using ClassLabelVectorObjectType = FilterType::ClassLabelVectorObjectType;
using ClassLabelVectorType = FilterType::ClassLabelVectorType;
auto classLabelsObject = ClassLabelVectorObjectType::New();
// Add class labels
ClassLabelVectorType & classLabelVector = classLabelsObject->Get();
using ClassLabelType = FilterType::ClassLabelType;
ClassLabelType class1 = 0;
classLabelVector.push_back(class1);
ClassLabelType class2 = 1;
classLabelVector.push_back(class2);
// Set a decision rule type
using DecisionRuleType = itk::Statistics::MaximumDecisionRule;
auto decisionRule = DecisionRuleType::New();
const FilterType::MembershipFunctionVectorObjectType * membershipFunctionsObject = estimator->GetOutput();
/* Print out estimated parameters of the membership function */
const FilterType::MembershipFunctionVectorType membershipFunctions = membershipFunctionsObject->Get();
auto begin = membershipFunctions.begin();
auto end = membershipFunctions.end();
FilterType::MembershipFunctionVectorType::const_iterator functionIter;
functionIter = begin;
unsigned int counter = 1;
std::cout << "Estimator membership function output " << std::endl;
while (functionIter != end)
{
FilterType::MembershipFunctionPointer membershipFunction = *functionIter;
const auto * gaussianMemberShpFunction =
dynamic_cast<const EstimatorType::GaussianMembershipFunctionType *>(membershipFunction.GetPointer());
std::cout << "\tMembership function:\t " << counter << std::endl;
std::cout << "\t\tMean=" << gaussianMemberShpFunction->GetMean() << std::endl;
std::cout << "\t\tCovariance matrix=" << gaussianMemberShpFunction->GetCovariance() << std::endl;
functionIter++;
counter++;
}
// Set membership functions weight array
const FilterType::MembershipFunctionsWeightsArrayObjectType * weightArrayObjects =
estimator->GetMembershipFunctionsWeightsArray();
const FilterType::MembershipFunctionsWeightsArrayType weightsArray = weightArrayObjects->Get();
std::cout << "Estimator membership function Weight/proporation output: " << std::endl;
for (unsigned int i = 0; i < weightsArray.Size(); ++i)
{
std::cout << "Membership function: \t" << i << '\t' << weightsArray[i] << std::endl;
}
char * targetFileName = argv[2];
std::ifstream dataTargetStream(targetFileName);
if (!dataTargetStream)
{
std::cout << "ERROR: fail to open the target data file." << std::endl;
return EXIT_FAILURE;
}
auto pointSet2 = PointSetType::New();
PointSetType::PointsContainerPointer pointsContainer2 = PointSetType::PointsContainer::New();
constexpr int dataSizeSmall = 200;
pointsContainer2->Reserve(dataSizeSmall);
pointSet2->SetPoints(pointsContainer2);
p_iter = pointsContainer2->Begin();
while (p_iter != pointsContainer2->End())
{
for (unsigned int i = 0; i < PointSetType::PointDimension; ++i)
{
double temp;
dataTargetStream >> temp;
point[i] = temp;
}
p_iter.Value() = point;
++p_iter;
}
dataTargetStream.close();
/* Importing the point set to the sample */
auto sampleTarget = DataSampleType::New();
sampleTarget->SetPointSet(pointSet2);
filter->SetInput(sample);
filter->SetNumberOfClasses(numberOfClasses);
filter->SetClassLabels(classLabelsObject);
filter->SetDecisionRule(decisionRule);
filter->SetMembershipFunctions(membershipFunctionsObject);
filter->SetMembershipFunctionsWeightsArray(weightArrayObjects);
ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());
// Check if the measurement vectors are correctly labelled.
const FilterType::MembershipSampleType * membershipSample = filter->GetOutput();
FilterType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();
unsigned int sampleCounter = 0;
unsigned int numberOfSamplesPerClass = 100;
if (sampleCounter > numberOfSamplesPerClass)
{
if (iter.GetClassLabel() != class1)
{
std::cerr << "Classification error: " << sampleCounter << '\t' << iter.GetMeasurementVector() << '\t'
<< "Class label= " << iter.GetClassLabel() << "\tTrue label=" << class1 << std::endl;
return EXIT_FAILURE;
}
++iter;
++sampleCounter;
}
if (sampleCounter > numberOfSamplesPerClass)
{
if (iter.GetClassLabel() != class1)
{
std::cerr << "Classification error: " << sampleCounter << '\t' << iter.GetMeasurementVector() << '\t'
<< "Class label= " << iter.GetClassLabel() << "\tTrue label=" << class1 << std::endl;
return EXIT_FAILURE;
}
++iter;
++sampleCounter;
}
if (!passed)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
|