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/*=========================================================================
*
* 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 "itkCumulativeGaussianOptimizer.h"
#include "itkMath.h"
#include "itkTestingMacros.h"
#include <iostream>
/**
* Generate test data with the Cumulative Gaussian Cost Function
* given parameter values for mean, standard deviation,
* lower and upper asymptotes of a Cumulative Gaussian.
* Estimate the parameters of the test data with the
* Cumulative Gaussian optimizer. The solution should
* be within differenceTolerance of the fitError.
*/
int
itkCumulativeGaussianOptimizerTest(int, char *[])
{
double mean = 3; // Mean of the Cumulative Gaussian.
// Ranges from 0 to N-1, where N is numberOfSamples.
double standardDeviation = 2; // Standard deviation of the Cumulative Gaussian.
double lowerAsymptote = -10; // Lower asymptotic value of the Cumulative Gaussian.
int numberOfSamples = 9; // Number of data samples.
double upperAsymptote = 10; // Upper asymptotic value of the Cumulative Gaussian.
double differenceTolerance = 1e-20; // Tolerance allowed for the difference between Gaussian iterations.
// Typedef and initialization for the Cumulative Gaussian Optimizer.
using CumulativeGaussianOptimizerType = itk::CumulativeGaussianOptimizer;
auto optimizer = CumulativeGaussianOptimizerType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(optimizer, CumulativeGaussianOptimizer, MultipleValuedNonLinearOptimizer);
// Typedef and initialization for the Cumulative Gaussian Cost Function.
using CostFunctionType = itk::CumulativeGaussianCostFunction;
auto costFunction = CostFunctionType::New();
// Declare and initialize the data array.
// CostFunctionType::MeasureType * cumGaussianArray = new CostFunctionType::MeasureType();
// cumGaussianArray->SetSize(numberOfSamples);
// Set the parameters.
CostFunctionType::ParametersType parameters;
parameters.SetSize(4);
parameters[0] = mean;
parameters[1] = standardDeviation;
parameters[2] = lowerAsymptote;
parameters[3] = upperAsymptote;
// Set the range of data sampled from a Cumulative Gaussian.
costFunction->Initialize(numberOfSamples);
// Generate data given a set of parameters.
CostFunctionType::MeasureType * cumGaussianArray = costFunction->GetValuePointer(parameters);
// Set the data array.
costFunction->SetOriginalDataArray(cumGaussianArray);
// Not used; empty method body; called for coverage purposes
CostFunctionType::DerivativeType derivative;
costFunction->GetDerivative(parameters, derivative);
// Set the cost function.
optimizer->SetCostFunction(costFunction);
// Set the tolerance for the Gaussian iteration error.
optimizer->SetDifferenceTolerance(differenceTolerance);
ITK_TEST_SET_GET_VALUE(differenceTolerance, optimizer->GetDifferenceTolerance());
// Print results after each iteration.
bool verbose = true;
ITK_TEST_SET_GET_BOOLEAN(optimizer, Verbose, verbose);
// Set the data array.
optimizer->SetDataArray(cumGaussianArray);
// Start optimization;
optimizer->StartOptimization();
std::cout << "StopConditionDescription: " << optimizer->GetStopConditionDescription() << std::endl;
// The test passes if the difference between the given parameters and estimated parameters
// is less than or equal to 0.1.
if (itk::Math::abs(optimizer->GetComputedMean() - mean) <= 0.1 &&
itk::Math::abs(optimizer->GetComputedStandardDeviation() - standardDeviation) <= 0.1 &&
itk::Math::abs(optimizer->GetUpperAsymptote() - upperAsymptote) <= 0.1 &&
itk::Math::abs(optimizer->GetLowerAsymptote() - lowerAsymptote) <= 0.1)
{
std::cerr << std::endl << "Test Passed with a Fit Error of " << optimizer->GetFitError() << std::endl << std::endl;
// Print out the resulting parameters.
std::cerr << "Fitted mean = " << optimizer->GetComputedMean() << std::endl;
std::cerr << "Fitted standard deviation = " << optimizer->GetComputedStandardDeviation() << std::endl;
std::cerr << "Fitted upper intensity = " << optimizer->GetUpperAsymptote() << std::endl;
std::cerr << "Fitted lower intensity = " << optimizer->GetLowerAsymptote() << std::endl;
std::cerr << "FinalSampledArray: " << optimizer->GetFinalSampledArray() << std::endl;
std::cout << "[TEST DONE]" << std::endl;
return EXIT_SUCCESS;
}
else
{
std::cerr << std::endl << "Test Failed with a Fit Error of " << optimizer->GetFitError() << std::endl << std::endl;
// Print out the resulting parameters.
std::cerr << "Fitted mean = " << optimizer->GetComputedMean() << std::endl;
std::cerr << "Fitted standard deviation = " << optimizer->GetComputedStandardDeviation() << std::endl;
std::cerr << "Fitted upper asymptote = " << optimizer->GetUpperAsymptote() << std::endl;
std::cerr << "Fitted lower asymptote = " << optimizer->GetLowerAsymptote() << std::endl;
return EXIT_FAILURE;
}
}
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