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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 <set>
#include "itkSPSAOptimizer.h"
#include "itkTestingMacros.h"
/**
* \class
* The objective function is the quadratic form:
*
* 1/2 x^T A x - b^T x
*
* Where A is a matrix and b is a vector
* The system in this example is:
*
* | 3 2 ||x| | 2| |0|
* | 2 6 ||y| + |-8| = |0|
*
*
* the solution is the vector | 2 -2 |
*
*/
class SPSACostFunction : public itk::SingleValuedCostFunction
{
public:
using Self = SPSACostFunction;
using Superclass = itk::SingleValuedCostFunction;
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
itkNewMacro(Self);
enum
{
SpaceDimension = 2
};
using ParametersType = Superclass::ParametersType;
using DerivativeType = Superclass::DerivativeType;
using MeasureType = Superclass::MeasureType;
SPSACostFunction() = default;
MeasureType
GetValue(const ParametersType & parameters) const override
{
double x = parameters[0];
double y = parameters[1];
std::cout << "GetValue( ";
std::cout << x << ' ';
std::cout << y << ") = ";
MeasureType measure = 0.5 * (3 * x * x + 4 * x * y + 6 * y * y) - 2 * x + 8 * y;
std::cout << measure << std::endl;
return measure;
}
void
GetDerivative(const ParametersType & parameters, DerivativeType & derivative) const override
{
double x = parameters[0];
double y = parameters[1];
std::cout << "GetDerivative( ";
std::cout << x << ' ';
std::cout << y << ") = ";
derivative = DerivativeType(SpaceDimension);
derivative[0] = 3 * x + 2 * y - 2;
derivative[1] = 2 * x + 6 * y + 8;
}
unsigned int
GetNumberOfParameters() const override
{
return SpaceDimension;
}
private:
};
int
itkSPSAOptimizerTest(int, char *[])
{
std::cout << "SPSAOptimizer Test ";
std::cout << std::endl << std::endl;
using OptimizerType = itk::SPSAOptimizer;
using ScalesType = OptimizerType::ScalesType;
// Declaration of an itkOptimizer
auto itkOptimizer = OptimizerType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(itkOptimizer, SPSAOptimizer, SingleValuedNonLinearOptimizer);
// Declaration of the CostFunction
auto costFunction = SPSACostFunction::New();
itkOptimizer->SetCostFunction(costFunction);
using ParametersType = SPSACostFunction::ParametersType;
const unsigned int spaceDimension = costFunction->GetNumberOfParameters();
ScalesType parametersScale(spaceDimension);
parametersScale[0] = 1.0;
parametersScale[1] = 2.0;
itkOptimizer->SetScales(parametersScale);
bool maximize = false;
ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, Maximize, maximize);
bool minimize = !maximize;
ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, Minimize, minimize);
double a = 10.0;
itkOptimizer->SetA(a);
ITK_TEST_SET_GET_VALUE(a, itkOptimizer->GetA());
double alpha = 0.602;
itkOptimizer->SetAlpha(alpha);
ITK_TEST_SET_GET_VALUE(alpha, itkOptimizer->GetAlpha());
double c = 0.0001;
itkOptimizer->Setc(c);
ITK_TEST_SET_GET_VALUE(c, itkOptimizer->Getc());
itkOptimizer->SetSc(c);
ITK_TEST_SET_GET_VALUE(c, itkOptimizer->GetSc());
double gamma = 0.101;
itkOptimizer->SetGamma(gamma);
ITK_TEST_SET_GET_VALUE(gamma, itkOptimizer->GetGamma());
double tolerance = 1e-5;
itkOptimizer->SetTolerance(tolerance);
ITK_TEST_SET_GET_VALUE(tolerance, itkOptimizer->GetTolerance());
double stateOfConvergenceDecayRate = 0.5;
itkOptimizer->SetStateOfConvergenceDecayRate(stateOfConvergenceDecayRate);
ITK_TEST_SET_GET_VALUE(stateOfConvergenceDecayRate, itkOptimizer->GetStateOfConvergenceDecayRate());
itk::SizeValueType minimumNumberOfIterations = 10;
itkOptimizer->SetMinimumNumberOfIterations(10);
ITK_TEST_SET_GET_VALUE(minimumNumberOfIterations, itkOptimizer->GetMinimumNumberOfIterations());
itk::SizeValueType maximumNumberOfIterations = 100;
itkOptimizer->SetMaximumNumberOfIterations(maximumNumberOfIterations);
ITK_TEST_SET_GET_VALUE(maximumNumberOfIterations, itkOptimizer->GetMaximumNumberOfIterations());
itk::SizeValueType numberOfPerturbations = 1;
itkOptimizer->SetNumberOfPerturbations(numberOfPerturbations);
ITK_TEST_SET_GET_VALUE(numberOfPerturbations, itkOptimizer->GetNumberOfPerturbations());
// We start not so far from | 2 -2 |
ParametersType initialPosition(spaceDimension);
initialPosition[0] = 100;
initialPosition[1] = -100;
itkOptimizer->SetInitialPosition(initialPosition);
try
{
itkOptimizer->GuessParameters(50, 70.0);
}
catch (const itk::ExceptionObject & e)
{
std::cout << "Exception thrown ! " << std::endl;
std::cout << "An error occurred during Guessing Parameters" << std::endl;
std::cout << "Location = " << e.GetLocation() << std::endl;
std::cout << "Description = " << e.GetDescription() << std::endl;
return EXIT_FAILURE;
}
std::cout << "\nEstimated parameter: a = " << itkOptimizer->Geta();
std::cout << "\nEstimated parameter: A = " << itkOptimizer->GetA() << '\n' << std::endl;
try
{
itkOptimizer->StartOptimization();
}
catch (const itk::ExceptionObject & e)
{
std::cout << "Exception thrown ! " << std::endl;
std::cout << "An error occurred during Optimization" << std::endl;
std::cout << "Location = " << e.GetLocation() << std::endl;
std::cout << "Description = " << e.GetDescription() << std::endl;
return EXIT_FAILURE;
}
ParametersType finalPosition = itkOptimizer->GetCurrentPosition();
std::cout << "Solution = (";
std::cout << finalPosition[0] << ',';
std::cout << finalPosition[1] << ')' << std::endl;
std::cout << "StateOfConvergence in last iteration: " << itkOptimizer->GetStateOfConvergence() << std::endl;
std::cout << "NumberOfIterations: " << itkOptimizer->GetCurrentIteration() << std::endl;
std::cout << "Stop condition: " << itkOptimizer->GetStopConditionDescription() << std::endl;
std::cout << "LearningRate: " << itkOptimizer->GetLearningRate() << std::endl;
std::cout << "GradientMagnitude: " << itkOptimizer->GetGradientMagnitude() << std::endl;
std::cout << "Gradient: " << itkOptimizer->GetGradient() << std::endl;
//
// check results to see if it is within range
//
bool pass = true;
double trueParameters[2] = { 2, -2 };
for (unsigned int j = 0; j < 2; ++j)
{
if (itk::Math::abs(finalPosition[j] - trueParameters[j]) > 0.01)
{
pass = false;
}
}
if (itkOptimizer->GetStopCondition() == itk::SPSAOptimizer::StopConditionSPSAOptimizerEnum::Unknown)
{
pass = false;
}
if (itkOptimizer->GetStopCondition() == itk::SPSAOptimizer::StopConditionSPSAOptimizerEnum::MetricError)
{
pass = false;
}
if (!pass)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
// Test streaming enumeration for SPSAOptimizerEnums::StopConditionSPSAOptimizer elements
const std::set<itk::SPSAOptimizerEnums::StopConditionSPSAOptimizer> allStopConditionSPSAOptimizer{
itk::SPSAOptimizerEnums::StopConditionSPSAOptimizer::Unknown,
itk::SPSAOptimizerEnums::StopConditionSPSAOptimizer::MaximumNumberOfIterations,
itk::SPSAOptimizerEnums::StopConditionSPSAOptimizer::BelowTolerance,
itk::SPSAOptimizerEnums::StopConditionSPSAOptimizer::MetricError
};
for (const auto & ee : allStopConditionSPSAOptimizer)
{
std::cout << "STREAMED ENUM VALUE SPSAOptimizerEnums::StopConditionSPSAOptimizer: " << ee << std::endl;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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