81 lines
2.8 KiB
C++
81 lines
2.8 KiB
C++
/**
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* @file sa_test.cpp
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* @author Zhihao Lou
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* @author Marcus Edel
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* @author Conrad Sanderson
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*
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* ensmallen is free software; you may redistribute it and/or modify it under
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* the terms of the 3-clause BSD license. You should have received a copy of
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* the 3-clause BSD license along with ensmallen. If not, see
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* http://www.opensource.org/licenses/BSD-3-Clause for more information.
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*/
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#include <ensmallen.hpp>
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#include "catch.hpp"
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#include "test_function_tools.hpp"
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using namespace ens;
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using namespace ens::test;
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// The Generalized-Rosenbrock function is a simple function to optimize.
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TEST_CASE("SAGeneralizedRosenbrockTest","[SATest]")
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{
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size_t dim = 10;
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GeneralizedRosenbrockFunction f(dim);
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double iteration = 0;
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double result = DBL_MAX;
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arma::mat coordinates;
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while (result > 1e-6)
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{
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ExponentialSchedule schedule;
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// The convergence is very sensitive to the choices of maxMove and initMove.
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SA<ExponentialSchedule> sa(schedule, 1000000, 1000., 1000, 100, 1e-10, 3,
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1.5, 0.5, 0.3);
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coordinates = f.GetInitialPoint();
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result = sa.Optimize(f, coordinates);
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++iteration;
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REQUIRE(iteration < 4); // No more than three tries.
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}
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// 0.1% tolerance for each coordinate.
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REQUIRE(result == Approx(0.0).margin(1e-6));
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for (size_t j = 0; j < dim; ++j)
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REQUIRE(coordinates(j) == Approx(1.0).epsilon(0.001));
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}
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// The Rosenbrock function is a simple function to optimize.
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TEST_CASE("SARosenbrockTest", "[SATest]")
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{
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ExponentialSchedule schedule;
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// The convergence is very sensitive to the choices of maxMove and initMove.
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SA<> sa(schedule, 1000000, 1000., 1000, 100, 1e-11, 3, 1.5, 0.3, 0.3);
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FunctionTest<RosenbrockFunction>(sa, 0.01, 0.001);
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}
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// The Rosenbrock function is a simple function to optimize. Use arma::fmat.
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TEST_CASE("SARosenbrockFMatTest", "[SATest]")
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{
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ExponentialSchedule schedule;
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// The convergence is very sensitive to the choices of maxMove and initMove.
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SA<> sa(schedule, 1000000, 1000., 1000, 100, 1e-11, 3, 1.5, 0.3, 0.3);
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FunctionTest<RosenbrockFunction, arma::fmat>(sa, 0.1, 0.01);
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}
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/**
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* The Rastrigrin function, a (not very) simple nonconvex function. It has very
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* many local minima, so finding the true global minimum is difficult.
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*/
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TEST_CASE("RastrigrinFunctionTest", "[SATest]")
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{
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// Simulated annealing isn't guaranteed to converge (except in very specific
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// situations). If this works 1 of 4 times, I'm fine with that. All I want
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// to know is that this implementation will escape from local minima.
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ExponentialSchedule schedule;
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// The convergence is very sensitive to the choices of maxMove and initMove.
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// SA<> sa(schedule, 2000000, 100, 50, 1000, 1e-12, 2, 2.0, 0.5, 0.1);
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SA<> sa(schedule, 2000000, 100, 50, 1000, 1e-12, 2, 2.0, 0.5, 0.1);
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FunctionTest<RastriginFunction>(sa, 0.01, 0.001, 4);
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}
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