/*========================================================================= * * 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 #include "itkGradientDescentOptimizer.h" #include "itkMath.h" #include "itkTestingMacros.h" /** * The objectif 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 gradientCostFunction */ class gradientCostFunction : public itk::SingleValuedCostFunction { public: using Self = gradientCostFunction; using Superclass = itk::SingleValuedCostFunction; using Pointer = itk::SmartPointer; using ConstPointer = itk::SmartPointer; itkNewMacro(Self); itkOverrideGetNameOfClassMacro(gradientCostFunction); enum { SpaceDimension = 2 }; using ParametersType = Superclass::ParametersType; using DerivativeType = Superclass::DerivativeType; using MeasureType = Superclass::MeasureType; gradientCostFunction() = 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 << ") = "; DerivativeType temp(SpaceDimension); temp.Fill(0); derivative = temp; derivative[0] = 3 * x + 2 * y - 2; derivative[1] = 2 * x + 6 * y + 8; std::cout << derivative << std::endl; } unsigned int GetNumberOfParameters() const override { return SpaceDimension; } private: }; int itkGradientDescentOptimizerTest(int, char *[]) { std::cout << "Gradient Descent Optimizer Test "; std::cout << std::endl << std::endl; using OptimizerType = itk::GradientDescentOptimizer; // Declaration of an itkOptimizer auto itkOptimizer = OptimizerType::New(); ITK_EXERCISE_BASIC_OBJECT_METHODS(itkOptimizer, GradientDescentOptimizer, SingleValuedNonLinearOptimizer); // Declaration of the CostFunction auto costFunction = gradientCostFunction::New(); itkOptimizer->SetCostFunction(costFunction); using ParametersType = gradientCostFunction::ParametersType; const unsigned int spaceDimension = costFunction->GetNumberOfParameters(); // We start not so far from | 2 -2 | ParametersType initialPosition(spaceDimension); initialPosition[0] = 100; initialPosition[1] = -100; bool maximize = false; ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, Maximize, maximize); bool minimize = !maximize; ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, Minimize, minimize); double learningRate = 0.1; itkOptimizer->SetLearningRate(learningRate); ITK_TEST_SET_GET_VALUE(learningRate, itkOptimizer->GetLearningRate()); itk::SizeValueType numberOfIterations = 50; itkOptimizer->SetNumberOfIterations(numberOfIterations); ITK_TEST_SET_GET_VALUE(numberOfIterations, itkOptimizer->GetNumberOfIterations()); itkOptimizer->SetInitialPosition(initialPosition); ITK_TEST_SET_GET_VALUE(initialPosition, itkOptimizer->GetInitialPosition()); 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; // // 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; } } std::cout << "Stop description = " << itkOptimizer->GetStopConditionDescription() << std::endl; std::cout << "StopCondition: " << itkOptimizer->GetStopCondition() << std::endl; std::cout << "Value: " << itkOptimizer->GetValue() << std::endl; std::cout << "Gradient: " << itkOptimizer->GetGradient() << std::endl; // Test streaming enumeration for GradientDescentOptimizerEnums::StopConditionGradientDescentOptimizer elements const std::set allStopConditionGradientDescentOptimizer{ itk::GradientDescentOptimizerEnums::StopConditionGradientDescentOptimizer::MaximumNumberOfIterations, itk::GradientDescentOptimizerEnums::StopConditionGradientDescentOptimizer::MetricError }; for (const auto & ee : allStopConditionGradientDescentOptimizer) { std::cout << "STREAMED ENUM VALUE GradientDescentOptimizerEnums::StopConditionGradientDescentOptimizer: " << ee << std::endl; } if (!pass) { std::cout << "Test failed." << std::endl; return EXIT_FAILURE; } std::cout << "Test passed." << std::endl; return EXIT_SUCCESS; }