112 lines
4.1 KiB
C++
112 lines
4.1 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
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// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
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// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
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// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/
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// discard stack allocation as that too bypasses malloc
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#define EIGEN_STACK_ALLOCATION_LIMIT 0
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#define EIGEN_RUNTIME_NO_MALLOC
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#include "main.h"
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#include <Eigen/SVD>
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#include <iostream>
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#include <Eigen/LU>
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#define SVD_DEFAULT(M) BDCSVD<M>
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#define SVD_FOR_MIN_NORM(M) BDCSVD<M>
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#include "svd_common.h"
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// Check all variants of JacobiSVD
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template<typename MatrixType>
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void bdcsvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
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{
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MatrixType m = a;
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if(pickrandom)
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svd_fill_random(m);
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CALL_SUBTEST(( svd_test_all_computation_options<BDCSVD<MatrixType> >(m, false) ));
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}
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template<typename MatrixType>
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void bdcsvd_method()
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{
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enum { Size = MatrixType::RowsAtCompileTime };
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typedef typename MatrixType::RealScalar RealScalar;
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typedef Matrix<RealScalar, Size, 1> RealVecType;
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MatrixType m = MatrixType::Identity();
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VERIFY_IS_APPROX(m.bdcSvd().singularValues(), RealVecType::Ones());
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VERIFY_RAISES_ASSERT(m.bdcSvd().matrixU());
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VERIFY_RAISES_ASSERT(m.bdcSvd().matrixV());
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VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).solve(m), m);
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}
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// compare the Singular values returned with Jacobi and Bdc
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template<typename MatrixType>
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void compare_bdc_jacobi(const MatrixType& a = MatrixType(), unsigned int computationOptions = 0)
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{
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MatrixType m = MatrixType::Random(a.rows(), a.cols());
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BDCSVD<MatrixType> bdc_svd(m);
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JacobiSVD<MatrixType> jacobi_svd(m);
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VERIFY_IS_APPROX(bdc_svd.singularValues(), jacobi_svd.singularValues());
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if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
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if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
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if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
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if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
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}
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void test_bdcsvd()
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{
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CALL_SUBTEST_3(( svd_verify_assert<BDCSVD<Matrix3f> >(Matrix3f()) ));
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CALL_SUBTEST_4(( svd_verify_assert<BDCSVD<Matrix4d> >(Matrix4d()) ));
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CALL_SUBTEST_7(( svd_verify_assert<BDCSVD<MatrixXf> >(MatrixXf(10,12)) ));
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CALL_SUBTEST_8(( svd_verify_assert<BDCSVD<MatrixXcd> >(MatrixXcd(7,5)) ));
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CALL_SUBTEST_101(( svd_all_trivial_2x2(bdcsvd<Matrix2cd>) ));
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CALL_SUBTEST_102(( svd_all_trivial_2x2(bdcsvd<Matrix2d>) ));
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_3(( bdcsvd<Matrix3f>() ));
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CALL_SUBTEST_4(( bdcsvd<Matrix4d>() ));
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CALL_SUBTEST_5(( bdcsvd<Matrix<float,3,5> >() ));
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int r = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2),
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c = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2);
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TEST_SET_BUT_UNUSED_VARIABLE(r)
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TEST_SET_BUT_UNUSED_VARIABLE(c)
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CALL_SUBTEST_6(( bdcsvd(Matrix<double,Dynamic,2>(r,2)) ));
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CALL_SUBTEST_7(( bdcsvd(MatrixXf(r,c)) ));
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CALL_SUBTEST_7(( compare_bdc_jacobi(MatrixXf(r,c)) ));
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CALL_SUBTEST_10(( bdcsvd(MatrixXd(r,c)) ));
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CALL_SUBTEST_10(( compare_bdc_jacobi(MatrixXd(r,c)) ));
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CALL_SUBTEST_8(( bdcsvd(MatrixXcd(r,c)) ));
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CALL_SUBTEST_8(( compare_bdc_jacobi(MatrixXcd(r,c)) ));
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// Test on inf/nan matrix
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CALL_SUBTEST_7( (svd_inf_nan<BDCSVD<MatrixXf>, MatrixXf>()) );
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CALL_SUBTEST_10( (svd_inf_nan<BDCSVD<MatrixXd>, MatrixXd>()) );
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}
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// test matrixbase method
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CALL_SUBTEST_1(( bdcsvd_method<Matrix2cd>() ));
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CALL_SUBTEST_3(( bdcsvd_method<Matrix3f>() ));
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// Test problem size constructors
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CALL_SUBTEST_7( BDCSVD<MatrixXf>(10,10) );
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// Check that preallocation avoids subsequent mallocs
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CALL_SUBTEST_9( svd_preallocate<void>() );
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CALL_SUBTEST_2( svd_underoverflow<void>() );
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}
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