ITK  5.3.0
Insight Toolkit
SphinxExamples/src/Numerics/Statistics/DistributeSamplingUsingGMM/Code.cxx
/*=========================================================================
*
* 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
*
* http://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 "itkVector.h"
#include "itkListSample.h"
int
main(int, char *[])
{
unsigned int numberOfClasses = 2;
using MeasurementVectorType = itk::Vector<double, 1>;
SampleType::Pointer sample = SampleType::New();
using NormalGeneratorType = itk::Statistics::NormalVariateGenerator;
NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();
normalGenerator->Initialize(101);
MeasurementVectorType mv;
double mean = 100;
double standardDeviation = 30;
for (unsigned int i = 0; i < 10; ++i)
{
mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
std::cout << "m[" << i << "] = " << mv[0] << std::endl;
sample->PushBack(mv);
}
normalGenerator->Initialize(3024);
mean = 200;
standardDeviation = 30;
for (unsigned int i = 0; i < 10; ++i)
{
mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
std::cout << "m[" << i << "] = " << mv[0] << std::endl;
sample->PushBack(mv);
}
using ParametersType = itk::Array<double>;
ParametersType params1(2);
std::vector<ParametersType> initialParameters(numberOfClasses);
params1[0] = 110.0;
params1[1] = 50.0;
initialParameters[0] = params1;
ParametersType params2(2);
params2[0] = 210.0;
params2[1] = 50.0;
initialParameters[1] = params2;
std::vector<ComponentType::Pointer> components;
for (unsigned int i = 0; i < numberOfClasses; i++)
{
components.push_back(ComponentType::New());
components[i]->SetSample(sample);
components[i]->SetParameters(initialParameters[i]);
}
EstimatorType::Pointer estimator = EstimatorType::New();
estimator->SetSample(sample);
estimator->SetMaximumIteration(500);
itk::Array<double> initialProportions(numberOfClasses);
initialProportions[0] = 0.5;
initialProportions[1] = 0.5;
estimator->SetInitialProportions(initialProportions);
for (unsigned int i = 0; i < numberOfClasses; i++)
{
estimator->AddComponent((ComponentType::Superclass *)components[i].GetPointer());
}
estimator->Update();
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;
}
return EXIT_SUCCESS;
}
itk::Vector
A templated class holding a n-Dimensional vector.
Definition: itkVector.h:62
itk::Statistics::ListSample
This class is the native implementation of the a Sample with an STL container.
Definition: itkListSample.h:51
itkExpectationMaximizationMixtureModelEstimator.h
itk::Statistics::ExpectationMaximizationMixtureModelEstimator
This class generates the parameter estimates for a mixture model using expectation maximization strat...
Definition: itkExpectationMaximizationMixtureModelEstimator.h:86
itk::Statistics::NormalVariateGenerator
Normal random variate generator.
Definition: itkNormalVariateGenerator.h:98
itkListSample.h
itkGaussianMixtureModelComponent.h
itk::Statistics::GaussianMixtureModelComponent
is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in Exp...
Definition: itkGaussianMixtureModelComponent.h:51
itkNormalVariateGenerator.h
itkVector.h
itk::Array< double >