Commit 4261895b authored by Jakub Klinkovský's avatar Jakub Klinkovský
Browse files

Update tests

parent f36eebf2

Catch2 @ ff151d28

Compare 2f631bb8 to ff151d28
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Subproject commit 2f631bb8087a0355d2b23a75a28d936ce237659d
 
Subproject commit ff151d2833d48aa630564859cd1401a93a2bb333

test/CMakeLists.txt

+0 −6
Original line number Diff line number Diff line 
# CMakeLists.txt

#

#     Author: Fabian Meyer

# Created On: 12 Jul 2019



include_directories(

    ${CMAKE_CURRENT_LIST_DIR}

    ${CATCH2_INCLUDE_DIR}
@@ -10,7 +5,6 @@ include_directories( 


set(TEST_SRC

    "main.cpp"



    "gdcpp.cpp"

)

test/assert/eigen_require.h

deleted100644 → 0
+0 −29
Original line number Diff line number Diff line 
/* eigen_require.h

 *

 *  Created on: 27 Jun 2019

 *      Author: Fabian Meyer

 */



#ifndef GDC_EIGEN_REQUIRE_H_

#define GDC_EIGEN_REQUIRE_H_



#include <Eigen/Geometry>

#include <catch2/catch.hpp>



#define REQUIRE_MATRIX_APPROX(a, b, eps) do {                                 \

        REQUIRE(a.cols() == b.cols());                                        \

        REQUIRE(a.rows() == b.rows());                                        \

        for(Index _c = 0; _c < a.cols(); ++_c)                                \

            for(Index _r = 0; _r < a.rows(); ++_r)                            \

                REQUIRE(Approx(a(_r, _c)).margin(eps) == b(_r, _c));          \

    } while(0)



#define REQUIRE_MATRIX(a, b) do {                                             \

        REQUIRE(a.cols() == b.cols());                                        \

        REQUIRE(a.rows() == b.rows());                                        \

        for(Index _c = 0; _c < a.cols(); ++_c)                                \

            for(Index _r = 0; _r < a.rows(); ++_r)                            \

                REQUIRE(a(_r, _c) == b(_r, _c));                              \

    } while(0)



#endif

test/assert/tnl_require.h

0 → 100644
+18 −0
Original line number Diff line number Diff line 
#ifndef GDC_TNL_REQUIRE_H_

#define GDC_TNL_REQUIRE_H_



#include <catch2/catch.hpp>



#define REQUIRE_VECTOR_APPROX(a, b, eps) do {                                 \

        REQUIRE(a.getSize() == b.getSize());                                  \

        for(Index _i = 0; _i < a.getSize(); ++_i)                             \

            REQUIRE(Approx(a[_i]).margin(eps) == b[_i]);                      \

    } while(0)



#define REQUIRE_VECTOR(a, b) do {                                             \

        REQUIRE(a.getSize() == b.getSize());                                  \

        for(Index _i = 0; _i < a.getSize(); ++_i)                             \

            REQUIRE(a[_i] == b[_i]);                                          \

    } while(0)



#endif

test/gdcpp.cpp

+31 −58
Original line number Diff line number Diff line 
/* gdcpp.cpp

 *

 *     Author: Fabian Meyer

 * Created On: 12 Jul 2019

 */



#include "assert/eigen_require.h"

#include "assert/tnl_require.h"

#include <gdcpp.h>



using namespace gdc;
@@ -12,7 +6,7 @@ using namespace gdc; 
template<typename Scalar>

struct Paraboloid

{

    typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1> Vector;

    using Vector = TNL::Containers::Vector<float, TNL::Devices::Host, gdc::Index>;

    Scalar operator()(const Vector &state, Vector &)

    {

        return state(0) * state(0) + state(1) * state(1);
@@ -22,7 +16,7 @@ struct Paraboloid 
template<typename Scalar>

struct Rosenbrock

{

    typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1> Vector;

    using Vector = TNL::Containers::Vector<float, TNL::Devices::Host, gdc::Index>;

    Scalar operator()(const Vector &state, Vector &)

    {

        Scalar delta1 = 1 - state(0);
@@ -32,7 +26,7 @@ struct Rosenbrock 
    }

};



typedef Eigen::Matrix<float, Eigen::Dynamic, 1> Vector;

using Vector = TNL::Containers::Vector<float, TNL::Devices::Host, gdc::Index>;



TEST_CASE("gradient_descent")

{
@@ -45,17 +39,14 @@ TEST_CASE("gradient_descent") 
            GradientDescent<float,

                Paraboloid<float>,

                ConstantStepSize<float>,

                NoCallback<float>,

                ForwardDifferences<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }



        SECTION("backward differences")
@@ -63,17 +54,14 @@ TEST_CASE("gradient_descent") 
            GradientDescent<float,

                Paraboloid<float>,

                ConstantStepSize<float>,

                NoCallback<float>,

                BackwardDifferences<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }



        SECTION("central differences")
@@ -81,17 +69,14 @@ TEST_CASE("gradient_descent") 
            GradientDescent<float,

                Paraboloid<float>,

                ConstantStepSize<float>,

                NoCallback<float>,

                CentralDifferences<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }



        SECTION("constant step size")
@@ -101,13 +86,11 @@ TEST_CASE("gradient_descent") 
                ConstantStepSize<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }



        SECTION("Barzilai-Borwein step")
@@ -117,13 +100,11 @@ TEST_CASE("gradient_descent") 
                BarzilaiBorwein<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }



        SECTION("Wolfe linesearch")
@@ -133,13 +114,11 @@ TEST_CASE("gradient_descent") 
                WolfeBacktracking<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }



        SECTION("Armijo linesearch")
@@ -149,13 +128,11 @@ TEST_CASE("gradient_descent") 
                ArmijoBacktracking<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }



        SECTION("Decrease linesearch")
@@ -165,13 +142,11 @@ TEST_CASE("gradient_descent") 
                DecreaseBacktracking<float>> optimizer;

            optimizer.setMaxIterations(100);



            Vector xval(2);

            xval << 2, 2;

            Vector xvalExp(2);

            xvalExp << 0, 0;

            Vector xval = {2, 2};

            Vector xvalExp = {0, 0};



            auto result = optimizer.minimize(xval);

            REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

            REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

        }

    }
@@ -181,12 +156,10 @@ TEST_CASE("gradient_descent") 
            WolfeBacktracking<float>> optimizer;

        optimizer.setMaxIterations(3000);

        optimizer.setMomentum(0.9);

        Vector xval(2);

        xval << -0.5, 0.5;

        Vector xvalExp(2);

        xvalExp << 1, 1;

        Vector xval = {-0.5, 0.5};

        Vector xvalExp = {1, 1};



        auto result = optimizer.minimize(xval);

        REQUIRE_MATRIX_APPROX(xvalExp, result.xval, eps);

        REQUIRE_VECTOR_APPROX(xvalExp, result.xval, eps);

    }

}