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Pulled the latest changes from the trunk

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This commit is contained in:
Benoit Steiner
2015-02-06 05:25:03 -08:00
parent 2559fa9b0f a8f2c6eec7
commit c739102ef9
345 changed files with 19503 additions and 15221 deletions
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8
unsupported/test/CMakeLists.txt
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@@ -5,6 +5,7 @@ add_custom_target(BuildUnsupported)
include_directories(../../test ../../unsupported ../../Eigen
${CMAKE_CURRENT_BINARY_DIR}/../../test)
find_package(GoogleHash)
if(GOOGLEHASH_FOUND)
add_definitions("-DEIGEN_GOOGLEHASH_SUPPORT")
@@ -40,6 +41,7 @@ ei_add_test(matrix_function)
ei_add_test(matrix_power)
ei_add_test(matrix_square_root)
ei_add_test(alignedvector3)
ei_add_test(FFT)
find_package(MPFR 2.3.0)
@@ -74,8 +76,9 @@ if(NOT EIGEN_TEST_NO_OPENGL)
find_package(GLUT)
find_package(GLEW)
if(OPENGL_FOUND AND GLUT_FOUND AND GLEW_FOUND)
include_directories(${OPENGL_INCLUDE_DIR} ${GLUT_INCLUDE_DIR} ${GLEW_INCLUDE_DIRS})
ei_add_property(EIGEN_TESTED_BACKENDS "OpenGL, ")
set(EIGEN_GL_LIB ${GLUT_LIBRARIES} ${GLEW_LIBRARIES})
set(EIGEN_GL_LIB ${GLUT_LIBRARIES} ${GLEW_LIBRARIES} ${OPENGL_LIBRARIES})
ei_add_test(openglsupport "" "${EIGEN_GL_LIB}" )
else()
ei_add_property(EIGEN_MISSING_BACKENDS "OpenGL, ")
@@ -86,12 +89,11 @@ endif()
ei_add_test(polynomialsolver)
ei_add_test(polynomialutils)
ei_add_test(kronecker_product)
ei_add_test(splines)
ei_add_test(gmres)
ei_add_test(minres)
ei_add_test(levenberg_marquardt)
ei_add_test(bdcsvd)
ei_add_test(kronecker_product)
option(EIGEN_TEST_CXX11 "Enable testing of C++11 features (e.g. Tensor module)." ON)
if(EIGEN_TEST_CXX11)

16
unsupported/test/NonLinearOptimization.cpp
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@@ -246,9 +246,9 @@ struct hybrj_functor : Functor<double>
int operator()(const VectorXd &x, VectorXd &fvec)
{
double temp, temp1, temp2;
const int n = x.size();
const VectorXd::Index n = x.size();
assert(fvec.size()==n);
for (int k = 0; k < n; k++)
for (VectorXd::Index k = 0; k < n; k++)
{
temp = (3. - 2.*x[k])*x[k];
temp1 = 0.;
@@ -261,12 +261,12 @@ struct hybrj_functor : Functor<double>
}
int df(const VectorXd &x, MatrixXd &fjac)
{
const int n = x.size();
const VectorXd::Index n = x.size();
assert(fjac.rows()==n);
assert(fjac.cols()==n);
for (int k = 0; k < n; k++)
for (VectorXd::Index k = 0; k < n; k++)
{
for (int j = 0; j < n; j++)
for (VectorXd::Index j = 0; j < n; j++)
fjac(k,j) = 0.;
fjac(k,k) = 3.- 4.*x[k];
if (k) fjac(k,k-1) = -1.;
@@ -351,10 +351,10 @@ struct hybrd_functor : Functor<double>
int operator()(const VectorXd &x, VectorXd &fvec) const
{
double temp, temp1, temp2;
const int n = x.size();
const VectorXd::Index n = x.size();
assert(fvec.size()==n);
for (int k=0; k < n; k++)
for (VectorXd::Index k=0; k < n; k++)
{
temp = (3. - 2.*x[k])*x[k];
temp1 = 0.;
@@ -455,7 +455,7 @@ struct lmstr_functor : Functor<double>
assert(jac_row.size()==x.size());
double tmp1, tmp2, tmp3, tmp4;
int i = rownb-2;
VectorXd::Index i = rownb-2;
tmp1 = i+1;
tmp2 = 16 - i - 1;
tmp3 = (i>=8)? tmp2 : tmp1;

213
unsupported/test/bdcsvd.cpp
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@@ -1,213 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/
#include "svd_common.h"
#include <iostream>
#include <Eigen/LU>
// check if "svd" is the good image of "m"
template<typename MatrixType>
void bdcsvd_check_full(const MatrixType& m, const BDCSVD<MatrixType>& svd)
{
svd_check_full< MatrixType, BDCSVD< MatrixType > >(m, svd);
}
// Compare to a reference value
template<typename MatrixType>
void bdcsvd_compare_to_full(const MatrixType& m,
unsigned int computationOptions,
const BDCSVD<MatrixType>& referenceSvd)
{
svd_compare_to_full< MatrixType, BDCSVD< MatrixType > >(m, computationOptions, referenceSvd);
} // end bdcsvd_compare_to_full
template<typename MatrixType>
void bdcsvd_solve(const MatrixType& m, unsigned int computationOptions)
{
svd_solve< MatrixType, BDCSVD< MatrixType > >(m, computationOptions);
} // end template bdcsvd_solve
// test the computations options
template<typename MatrixType>
void bdcsvd_test_all_computation_options(const MatrixType& m)
{
BDCSVD<MatrixType> fullSvd(m, ComputeFullU|ComputeFullV);
svd_test_computation_options_1< MatrixType, BDCSVD< MatrixType > >(m, fullSvd);
svd_test_computation_options_2< MatrixType, BDCSVD< MatrixType > >(m, fullSvd);
} // end bdcsvd_test_all_computation_options
// Call a test with all the computations options
template<typename MatrixType>
void bdcsvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
{
MatrixType m = pickrandom ? MatrixType::Random(a.rows(), a.cols()) : a;
bdcsvd_test_all_computation_options<MatrixType>(m);
} // end template bdcsvd
// verify assert
template<typename MatrixType>
void bdcsvd_verify_assert(const MatrixType& m)
{
svd_verify_assert< MatrixType, BDCSVD< MatrixType > >(m);
}// end template bdcsvd_verify_assert
// test weird values
template<typename MatrixType>
void bdcsvd_inf_nan()
{
svd_inf_nan< MatrixType, BDCSVD< MatrixType > >();
}// end template bdcsvd_inf_nan
void bdcsvd_preallocate()
{
svd_preallocate< BDCSVD< MatrixXf > >();
} // end bdcsvd_preallocate
// compare the Singular values returned with Jacobi and Bdc
template<typename MatrixType>
void compare_bdc_jacobi(const MatrixType& a = MatrixType(), unsigned int computationOptions = 0)
{
std::cout << "debut compare" << std::endl;
MatrixType m = MatrixType::Random(a.rows(), a.cols());
BDCSVD<MatrixType> bdc_svd(m);
JacobiSVD<MatrixType> jacobi_svd(m);
VERIFY_IS_APPROX(bdc_svd.singularValues(), jacobi_svd.singularValues());
if(computationOptions & ComputeFullU)
VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
if(computationOptions & ComputeThinU)
VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
if(computationOptions & ComputeFullV)
VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
if(computationOptions & ComputeThinV)
VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
std::cout << "fin compare" << std::endl;
} // end template compare_bdc_jacobi
// call the tests
void test_bdcsvd()
{
// test of Dynamic defined Matrix (42, 42) of float
CALL_SUBTEST_11(( bdcsvd_verify_assert<Matrix<float,Dynamic,Dynamic> >
(Matrix<float,Dynamic,Dynamic>(42,42)) ));
CALL_SUBTEST_11(( compare_bdc_jacobi<Matrix<float,Dynamic,Dynamic> >
(Matrix<float,Dynamic,Dynamic>(42,42), 0) ));
CALL_SUBTEST_11(( bdcsvd<Matrix<float,Dynamic,Dynamic> >
(Matrix<float,Dynamic,Dynamic>(42,42)) ));
// test of Dynamic defined Matrix (50, 50) of double
CALL_SUBTEST_13(( bdcsvd_verify_assert<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(50,50)) ));
CALL_SUBTEST_13(( compare_bdc_jacobi<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(50,50), 0) ));
CALL_SUBTEST_13(( bdcsvd<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(50, 50)) ));
// test of Dynamic defined Matrix (22, 22) of complex double
CALL_SUBTEST_14(( bdcsvd_verify_assert<Matrix<std::complex<double>,Dynamic,Dynamic> >
(Matrix<std::complex<double>,Dynamic,Dynamic>(22,22)) ));
CALL_SUBTEST_14(( compare_bdc_jacobi<Matrix<std::complex<double>,Dynamic,Dynamic> >
(Matrix<std::complex<double>, Dynamic, Dynamic> (22,22), 0) ));
CALL_SUBTEST_14(( bdcsvd<Matrix<std::complex<double>,Dynamic,Dynamic> >
(Matrix<std::complex<double>,Dynamic,Dynamic>(22, 22)) ));
// test of Dynamic defined Matrix (10, 10) of int
//CALL_SUBTEST_15(( bdcsvd_verify_assert<Matrix<int,Dynamic,Dynamic> >
// (Matrix<int,Dynamic,Dynamic>(10,10)) ));
//CALL_SUBTEST_15(( compare_bdc_jacobi<Matrix<int,Dynamic,Dynamic> >
// (Matrix<int,Dynamic,Dynamic>(10,10), 0) ));
//CALL_SUBTEST_15(( bdcsvd<Matrix<int,Dynamic,Dynamic> >
// (Matrix<int,Dynamic,Dynamic>(10, 10)) ));
// test of Dynamic defined Matrix (8, 6) of double
CALL_SUBTEST_16(( bdcsvd_verify_assert<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(8,6)) ));
CALL_SUBTEST_16(( compare_bdc_jacobi<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(8, 6), 0) ));
CALL_SUBTEST_16(( bdcsvd<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(8, 6)) ));
// test of Dynamic defined Matrix (36, 12) of float
CALL_SUBTEST_17(( compare_bdc_jacobi<Matrix<float,Dynamic,Dynamic> >
(Matrix<float,Dynamic,Dynamic>(36, 12), 0) ));
CALL_SUBTEST_17(( bdcsvd<Matrix<float,Dynamic,Dynamic> >
(Matrix<float,Dynamic,Dynamic>(36, 12)) ));
// test of Dynamic defined Matrix (5, 8) of double
CALL_SUBTEST_18(( compare_bdc_jacobi<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(5, 8), 0) ));
CALL_SUBTEST_18(( bdcsvd<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(5, 8)) ));
// non regression tests
CALL_SUBTEST_3(( bdcsvd_verify_assert(Matrix3f()) ));
CALL_SUBTEST_4(( bdcsvd_verify_assert(Matrix4d()) ));
CALL_SUBTEST_7(( bdcsvd_verify_assert(MatrixXf(10,12)) ));
CALL_SUBTEST_8(( bdcsvd_verify_assert(MatrixXcd(7,5)) ));
// SUBTESTS 1 and 2 on specifics matrix
for(int i = 0; i < g_repeat; i++) {
Matrix2cd m;
m << 0, 1,
0, 1;
CALL_SUBTEST_1(( bdcsvd(m, false) ));
m << 1, 0,
1, 0;
CALL_SUBTEST_1(( bdcsvd(m, false) ));
Matrix2d n;
n << 0, 0,
0, 0;
CALL_SUBTEST_2(( bdcsvd(n, false) ));
n << 0, 0,
0, 1;
CALL_SUBTEST_2(( bdcsvd(n, false) ));
// Statics matrix don't work with BDSVD yet
// bdc algo on a random 3x3 float matrix
// CALL_SUBTEST_3(( bdcsvd<Matrix3f>() ));
// bdc algo on a random 4x4 double matrix
// CALL_SUBTEST_4(( bdcsvd<Matrix4d>() ));
// bdc algo on a random 3x5 float matrix
// CALL_SUBTEST_5(( bdcsvd<Matrix<float,3,5> >() ));
int r = internal::random<int>(1, 30),
c = internal::random<int>(1, 30);
CALL_SUBTEST_7(( bdcsvd<MatrixXf>(MatrixXf(r,c)) ));
CALL_SUBTEST_8(( bdcsvd<MatrixXcd>(MatrixXcd(r,c)) ));
(void) r;
(void) c;
// Test on inf/nan matrix
CALL_SUBTEST_7( bdcsvd_inf_nan<MatrixXf>() );
}
CALL_SUBTEST_7(( bdcsvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
CALL_SUBTEST_8(( bdcsvd<MatrixXcd>(MatrixXcd(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3))) ));
// Test problem size constructors
CALL_SUBTEST_7( BDCSVD<MatrixXf>(10,10) );
} // end test_bdcsvd

198
unsupported/test/jacobisvd.cpp
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@@ -1,198 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "svd_common.h"
template<typename MatrixType, int QRPreconditioner>
void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd)
{
svd_check_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner > >(m, svd);
}
template<typename MatrixType, int QRPreconditioner>
void jacobisvd_compare_to_full(const MatrixType& m,
unsigned int computationOptions,
const JacobiSVD<MatrixType, QRPreconditioner>& referenceSvd)
{
svd_compare_to_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner> >(m, computationOptions, referenceSvd);
}
template<typename MatrixType, int QRPreconditioner>
void jacobisvd_solve(const MatrixType& m, unsigned int computationOptions)
{
svd_solve< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, computationOptions);
}
template<typename MatrixType, int QRPreconditioner>
void jacobisvd_test_all_computation_options(const MatrixType& m)
{
if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
return;
JacobiSVD< MatrixType, QRPreconditioner > fullSvd(m, ComputeFullU|ComputeFullV);
svd_test_computation_options_1< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
if(QRPreconditioner == FullPivHouseholderQRPreconditioner)
return;
svd_test_computation_options_2< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
}
template<typename MatrixType>
void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
{
MatrixType m = pickrandom ? MatrixType::Random(a.rows(), a.cols()) : a;
jacobisvd_test_all_computation_options<MatrixType, FullPivHouseholderQRPreconditioner>(m);
jacobisvd_test_all_computation_options<MatrixType, ColPivHouseholderQRPreconditioner>(m);
jacobisvd_test_all_computation_options<MatrixType, HouseholderQRPreconditioner>(m);
jacobisvd_test_all_computation_options<MatrixType, NoQRPreconditioner>(m);
}
template<typename MatrixType>
void jacobisvd_verify_assert(const MatrixType& m)
{
svd_verify_assert<MatrixType, JacobiSVD< MatrixType > >(m);
typedef typename MatrixType::Index Index;
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
MatrixType a = MatrixType::Zero(rows, cols);
a.setZero();
if (ColsAtCompileTime == Dynamic)
{
JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr;
VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV))
VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV))
VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV))
}
}
template<typename MatrixType>
void jacobisvd_method()
{
enum { Size = MatrixType::RowsAtCompileTime };
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<RealScalar, Size, 1> RealVecType;
MatrixType m = MatrixType::Identity();
VERIFY_IS_APPROX(m.jacobiSvd().singularValues(), RealVecType::Ones());
VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU());
VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV());
VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
}
template<typename MatrixType>
void jacobisvd_inf_nan()
{
svd_inf_nan<MatrixType, JacobiSVD< MatrixType > >();
}
// Regression test for bug 286: JacobiSVD loops indefinitely with some
// matrices containing denormal numbers.
void jacobisvd_bug286()
{
#if defined __INTEL_COMPILER
// shut up warning #239: floating point underflow
#pragma warning push
#pragma warning disable 239
#endif
Matrix2d M;
M << -7.90884e-313, -4.94e-324,
0, 5.60844e-313;
#if defined __INTEL_COMPILER
#pragma warning pop
#endif
JacobiSVD<Matrix2d> svd;
svd.compute(M); // just check we don't loop indefinitely
}
void jacobisvd_preallocate()
{
svd_preallocate< JacobiSVD <MatrixXf> >();
}
void test_jacobisvd()
{
CALL_SUBTEST_11(( jacobisvd<Matrix<double,Dynamic,Dynamic> >
(Matrix<double,Dynamic,Dynamic>(16, 6)) ));
CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) ));
CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) ));
CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) ));
CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) ));
for(int i = 0; i < g_repeat; i++) {
Matrix2cd m;
m << 0, 1,
0, 1;
CALL_SUBTEST_1(( jacobisvd(m, false) ));
m << 1, 0,
1, 0;
CALL_SUBTEST_1(( jacobisvd(m, false) ));
Matrix2d n;
n << 0, 0,
0, 0;
CALL_SUBTEST_2(( jacobisvd(n, false) ));
n << 0, 0,
0, 1;
CALL_SUBTEST_2(( jacobisvd(n, false) ));
CALL_SUBTEST_3(( jacobisvd<Matrix3f>() ));
CALL_SUBTEST_4(( jacobisvd<Matrix4d>() ));
CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() ));
CALL_SUBTEST_6(( jacobisvd<Matrix<double,Dynamic,2> >(Matrix<double,Dynamic,2>(10,2)) ));
int r = internal::random<int>(1, 30),
c = internal::random<int>(1, 30);
CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(r,c)) ));
CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(r,c)) ));
(void) r;
(void) c;
// Test on inf/nan matrix
CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() );
}
CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3))) ));
// test matrixbase method
CALL_SUBTEST_1(( jacobisvd_method<Matrix2cd>() ));
CALL_SUBTEST_3(( jacobisvd_method<Matrix3f>() ));
// Test problem size constructors
CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) );
// Check that preallocation avoids subsequent mallocs
CALL_SUBTEST_9( jacobisvd_preallocate() );
// Regression check for bug 286
CALL_SUBTEST_2( jacobisvd_bug286() );
}

12
unsupported/test/kronecker_product.cpp
View File

@@ -216,5 +216,17 @@ void test_kronecker_product()
sC2 = kroneckerProduct(sA,sB);
dC = kroneckerProduct(dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
sC2 = kroneckerProduct(dA,sB);
dC = kroneckerProduct(dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
sC2 = kroneckerProduct(sA,dB);
dC = kroneckerProduct(dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
sC2 = kroneckerProduct(2*sA,sB);
dC = kroneckerProduct(2*dA,dB);
VERIFY_IS_APPROX(MatrixXf(sC2),dC);
}
}

261
unsupported/test/svd_common.h
View File

@@ -1,261 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// discard stack allocation as that too bypasses malloc
#define EIGEN_STACK_ALLOCATION_LIMIT 0
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <unsupported/Eigen/BDCSVD>
#include <Eigen/LU>
// check if "svd" is the good image of "m"
template<typename MatrixType, typename SVD>
void svd_check_full(const MatrixType& m, const SVD& svd)
{
typedef typename MatrixType::Index Index;
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
MatrixType sigma = MatrixType::Zero(rows, cols);
sigma.diagonal() = svd.singularValues().template cast<Scalar>();
MatrixUType u = svd.matrixU();
MatrixVType v = svd.matrixV();
VERIFY_IS_APPROX(m, u * sigma * v.adjoint());
VERIFY_IS_UNITARY(u);
VERIFY_IS_UNITARY(v);
} // end svd_check_full
// Compare to a reference value
template<typename MatrixType, typename SVD>
void svd_compare_to_full(const MatrixType& m,
unsigned int computationOptions,
const SVD& referenceSvd)
{
typedef typename MatrixType::Index Index;
Index rows = m.rows();
Index cols = m.cols();
Index diagSize = (std::min)(rows, cols);
SVD svd(m, computationOptions);
VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues());
if(computationOptions & ComputeFullU)
VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU());
if(computationOptions & ComputeThinU)
VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU().leftCols(diagSize));
if(computationOptions & ComputeFullV)
VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV());
if(computationOptions & ComputeThinV)
VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV().leftCols(diagSize));
} // end svd_compare_to_full
template<typename MatrixType, typename SVD>
void svd_solve(const MatrixType& m, unsigned int computationOptions)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::Index Index;
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef Matrix<Scalar, RowsAtCompileTime, Dynamic> RhsType;
typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
RhsType rhs = RhsType::Random(rows, internal::random<Index>(1, cols));
SVD svd(m, computationOptions);
SolutionType x = svd.solve(rhs);
// evaluate normal equation which works also for least-squares solutions
VERIFY_IS_APPROX(m.adjoint()*m*x,m.adjoint()*rhs);
} // end svd_solve
// test computations options
// 2 functions because Jacobisvd can return before the second function
template<typename MatrixType, typename SVD>
void svd_test_computation_options_1(const MatrixType& m, const SVD& fullSvd)
{
svd_check_full< MatrixType, SVD >(m, fullSvd);
svd_solve< MatrixType, SVD >(m, ComputeFullU | ComputeFullV);
}
template<typename MatrixType, typename SVD>
void svd_test_computation_options_2(const MatrixType& m, const SVD& fullSvd)
{
svd_compare_to_full< MatrixType, SVD >(m, ComputeFullU, fullSvd);
svd_compare_to_full< MatrixType, SVD >(m, ComputeFullV, fullSvd);
svd_compare_to_full< MatrixType, SVD >(m, 0, fullSvd);
if (MatrixType::ColsAtCompileTime == Dynamic) {
// thin U/V are only available with dynamic number of columns
svd_compare_to_full< MatrixType, SVD >(m, ComputeFullU|ComputeThinV, fullSvd);
svd_compare_to_full< MatrixType, SVD >(m, ComputeThinV, fullSvd);
svd_compare_to_full< MatrixType, SVD >(m, ComputeThinU|ComputeFullV, fullSvd);
svd_compare_to_full< MatrixType, SVD >(m, ComputeThinU , fullSvd);
svd_compare_to_full< MatrixType, SVD >(m, ComputeThinU|ComputeThinV, fullSvd);
svd_solve<MatrixType, SVD>(m, ComputeFullU | ComputeThinV);
svd_solve<MatrixType, SVD>(m, ComputeThinU | ComputeFullV);
svd_solve<MatrixType, SVD>(m, ComputeThinU | ComputeThinV);
typedef typename MatrixType::Index Index;
Index diagSize = (std::min)(m.rows(), m.cols());
SVD svd(m, ComputeThinU | ComputeThinV);
VERIFY_IS_APPROX(m, svd.matrixU().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint());
}
}
template<typename MatrixType, typename SVD>
void svd_verify_assert(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::Index Index;
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsType;
RhsType rhs(rows);
SVD svd;
VERIFY_RAISES_ASSERT(svd.matrixU())
VERIFY_RAISES_ASSERT(svd.singularValues())
VERIFY_RAISES_ASSERT(svd.matrixV())
VERIFY_RAISES_ASSERT(svd.solve(rhs))
MatrixType a = MatrixType::Zero(rows, cols);
a.setZero();
svd.compute(a, 0);
VERIFY_RAISES_ASSERT(svd.matrixU())
VERIFY_RAISES_ASSERT(svd.matrixV())
svd.singularValues();
VERIFY_RAISES_ASSERT(svd.solve(rhs))
if (ColsAtCompileTime == Dynamic)
{
svd.compute(a, ComputeThinU);
svd.matrixU();
VERIFY_RAISES_ASSERT(svd.matrixV())
VERIFY_RAISES_ASSERT(svd.solve(rhs))
svd.compute(a, ComputeThinV);
svd.matrixV();
VERIFY_RAISES_ASSERT(svd.matrixU())
VERIFY_RAISES_ASSERT(svd.solve(rhs))
}
else
{
VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinU))
VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinV))
}
}
// work around stupid msvc error when constructing at compile time an expression that involves
// a division by zero, even if the numeric type has floating point
template<typename Scalar>
EIGEN_DONT_INLINE Scalar zero() { return Scalar(0); }
// workaround aggressive optimization in ICC
template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; }
template<typename MatrixType, typename SVD>
void svd_inf_nan()
{
// all this function does is verify we don't iterate infinitely on nan/inf values
SVD svd;
typedef typename MatrixType::Scalar Scalar;
Scalar some_inf = Scalar(1) / zero<Scalar>();
VERIFY(sub(some_inf, some_inf) != sub(some_inf, some_inf));
svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV);
Scalar some_nan = zero<Scalar> () / zero<Scalar> ();
VERIFY(some_nan != some_nan);
svd.compute(MatrixType::Constant(10,10,some_nan), ComputeFullU | ComputeFullV);
MatrixType m = MatrixType::Zero(10,10);
m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_inf;
svd.compute(m, ComputeFullU | ComputeFullV);
m = MatrixType::Zero(10,10);
m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_nan;
svd.compute(m, ComputeFullU | ComputeFullV);
}
template<typename SVD>
void svd_preallocate()
{
Vector3f v(3.f, 2.f, 1.f);
MatrixXf m = v.asDiagonal();
internal::set_is_malloc_allowed(false);
VERIFY_RAISES_ASSERT(VectorXf v(10);)
SVD svd;
internal::set_is_malloc_allowed(true);
svd.compute(m);
VERIFY_IS_APPROX(svd.singularValues(), v);
SVD svd2(3,3);
internal::set_is_malloc_allowed(false);
svd2.compute(m);
internal::set_is_malloc_allowed(true);
VERIFY_IS_APPROX(svd2.singularValues(), v);
VERIFY_RAISES_ASSERT(svd2.matrixU());
VERIFY_RAISES_ASSERT(svd2.matrixV());
svd2.compute(m, ComputeFullU | ComputeFullV);
VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
internal::set_is_malloc_allowed(false);
svd2.compute(m);
internal::set_is_malloc_allowed(true);
SVD svd3(3,3,ComputeFullU|ComputeFullV);
internal::set_is_malloc_allowed(false);
svd2.compute(m);
internal::set_is_malloc_allowed(true);
VERIFY_IS_APPROX(svd2.singularValues(), v);
VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
internal::set_is_malloc_allowed(false);
svd2.compute(m, ComputeFullU|ComputeFullV);
internal::set_is_malloc_allowed(true);
}