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// 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/
// discard stack allocation as that too bypasses malloc
#define EIGEN_STACK_ALLOCATION_LIMIT 0
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <Eigen/SVD>
#include <iostream>
#include <Eigen/LU>
#define SVD_DEFAULT(M) BDCSVD<M>
#define SVD_FOR_MIN_NORM(M) BDCSVD<M>
#include "svd_common.h"
// Check all variants of JacobiSVD
template<typename MatrixType>
void bdcsvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
{
MatrixType m = a;
if(pickrandom)
svd_fill_random(m);
CALL_SUBTEST(( svd_test_all_computation_options<BDCSVD<MatrixType> >(m, false) ));
}
template<typename MatrixType>
void bdcsvd_method()
{
enum { Size = MatrixType::RowsAtCompileTime };
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<RealScalar, Size, 1> RealVecType;
MatrixType m = MatrixType::Identity();
VERIFY_IS_APPROX(m.bdcSvd().singularValues(), RealVecType::Ones());
VERIFY_RAISES_ASSERT(m.bdcSvd().matrixU());
VERIFY_RAISES_ASSERT(m.bdcSvd().matrixV());
VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).solve(m), m);
}
// compare the Singular values returned with Jacobi and Bdc
template<typename MatrixType>
void compare_bdc_jacobi(const MatrixType& a = MatrixType(), unsigned int computationOptions = 0)
{
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());
}
void test_bdcsvd()
{
CALL_SUBTEST_3(( svd_verify_assert<BDCSVD<Matrix3f> >(Matrix3f()) ));
CALL_SUBTEST_4(( svd_verify_assert<BDCSVD<Matrix4d> >(Matrix4d()) ));
CALL_SUBTEST_7(( svd_verify_assert<BDCSVD<MatrixXf> >(MatrixXf(10,12)) ));
CALL_SUBTEST_8(( svd_verify_assert<BDCSVD<MatrixXcd> >(MatrixXcd(7,5)) ));
CALL_SUBTEST_1(( svd_all_trivial_2x2(bdcsvd<Matrix2cd>) ));
CALL_SUBTEST_1(( svd_all_trivial_2x2(bdcsvd<Matrix2d>) ));
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_3(( bdcsvd<Matrix3f>() ));
CALL_SUBTEST_4(( bdcsvd<Matrix4d>() ));
CALL_SUBTEST_5(( bdcsvd<Matrix<float,3,5> >() ));
int r = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2),
c = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2);
TEST_SET_BUT_UNUSED_VARIABLE(r)
TEST_SET_BUT_UNUSED_VARIABLE(c)
CALL_SUBTEST_6(( bdcsvd(Matrix<double,Dynamic,2>(r,2)) ));
CALL_SUBTEST_7(( bdcsvd(MatrixXf(r,c)) ));
CALL_SUBTEST_7(( compare_bdc_jacobi(MatrixXf(r,c)) ));
CALL_SUBTEST_10(( bdcsvd(MatrixXd(r,c)) ));
CALL_SUBTEST_10(( compare_bdc_jacobi(MatrixXd(r,c)) ));
CALL_SUBTEST_8(( bdcsvd(MatrixXcd(r,c)) ));
CALL_SUBTEST_8(( compare_bdc_jacobi(MatrixXcd(r,c)) ));
// Test on inf/nan matrix
CALL_SUBTEST_7( (svd_inf_nan<BDCSVD<MatrixXf>, MatrixXf>()) );
CALL_SUBTEST_10( (svd_inf_nan<BDCSVD<MatrixXd>, MatrixXd>()) );
}
// test matrixbase method
CALL_SUBTEST_1(( bdcsvd_method<Matrix2cd>() ));
CALL_SUBTEST_3(( bdcsvd_method<Matrix3f>() ));
// Test problem size constructors
CALL_SUBTEST_7( BDCSVD<MatrixXf>(10,10) );
// Check that preallocation avoids subsequent mallocs
CALL_SUBTEST_9( svd_preallocate() );
CALL_SUBTEST_2( svd_underoverflow() );
}
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