mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-27 07:29:52 +08:00
2840ac7e94
* renaming, e.g. LU ---> FullPivLU * split tests framework: more robust, e.g. dont generate empty tests if a number is skipped * make all remaining tests use that splitting, as needed. * Fix 4x4 inversion (see stable branch) * Transform::inverse() and geo_transform test : adapt to new inverse() API, it was also trying to instantiate inverse() for 3x4 matrices. * CMakeLists: more robust regexp to parse the version number * misc fixes in unit tests
109 lines
4.6 KiB
C++
109 lines
4.6 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
|
|
//
|
|
// Eigen is free software; you can redistribute it and/or
|
|
// modify it under the terms of the GNU Lesser General Public
|
|
// License as published by the Free Software Foundation; either
|
|
// version 3 of the License, or (at your option) any later version.
|
|
//
|
|
// Alternatively, you can redistribute it and/or
|
|
// modify it under the terms of the GNU General Public License as
|
|
// published by the Free Software Foundation; either version 2 of
|
|
// the License, or (at your option) any later version.
|
|
//
|
|
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
|
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
|
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
|
// GNU General Public License for more details.
|
|
//
|
|
// You should have received a copy of the GNU Lesser General Public
|
|
// License and a copy of the GNU General Public License along with
|
|
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
|
|
|
#include "main.h"
|
|
using namespace std;
|
|
template<typename MatrixType> void diagonalmatrices(const MatrixType& m)
|
|
{
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename MatrixType::RealScalar RealScalar;
|
|
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
|
|
typedef Matrix<Scalar, Rows, 1> VectorType;
|
|
typedef Matrix<Scalar, 1, Cols> RowVectorType;
|
|
typedef Matrix<Scalar, Rows, Rows> SquareMatrixType;
|
|
typedef DiagonalMatrix<Scalar, Rows> LeftDiagonalMatrix;
|
|
typedef DiagonalMatrix<Scalar, Cols> RightDiagonalMatrix;
|
|
typedef Matrix<Scalar, Rows==Dynamic?Dynamic:2*Rows, Cols==Dynamic?Dynamic:2*Cols> BigMatrix;
|
|
int rows = m.rows();
|
|
int cols = m.cols();
|
|
|
|
MatrixType m1 = MatrixType::Random(rows, cols),
|
|
m2 = MatrixType::Random(rows, cols);
|
|
VectorType v1 = VectorType::Random(rows),
|
|
v2 = VectorType::Random(rows);
|
|
RowVectorType rv1 = RowVectorType::Random(cols),
|
|
rv2 = RowVectorType::Random(cols);
|
|
LeftDiagonalMatrix ldm1(v1), ldm2(v2);
|
|
RightDiagonalMatrix rdm1(rv1), rdm2(rv2);
|
|
|
|
SquareMatrixType sq_m1 (v1.asDiagonal());
|
|
VERIFY_IS_APPROX(sq_m1, v1.asDiagonal().toDenseMatrix());
|
|
sq_m1 = v1.asDiagonal();
|
|
VERIFY_IS_APPROX(sq_m1, v1.asDiagonal().toDenseMatrix());
|
|
SquareMatrixType sq_m2 = v1.asDiagonal();
|
|
VERIFY_IS_APPROX(sq_m1, sq_m2);
|
|
|
|
ldm1 = v1.asDiagonal();
|
|
LeftDiagonalMatrix ldm3(v1);
|
|
VERIFY_IS_APPROX(ldm1.diagonal(), ldm3.diagonal());
|
|
LeftDiagonalMatrix ldm4 = v1.asDiagonal();
|
|
VERIFY_IS_APPROX(ldm1.diagonal(), ldm4.diagonal());
|
|
|
|
sq_m1.block(0,0,rows,rows) = ldm1;
|
|
VERIFY_IS_APPROX(sq_m1, ldm1.toDenseMatrix());
|
|
sq_m1.transpose() = ldm1;
|
|
VERIFY_IS_APPROX(sq_m1, ldm1.toDenseMatrix());
|
|
|
|
int i = ei_random<int>(0, rows-1);
|
|
int j = ei_random<int>(0, cols-1);
|
|
|
|
VERIFY_IS_APPROX( ((ldm1 * m1)(i,j)) , ldm1.diagonal()(i) * m1(i,j) );
|
|
VERIFY_IS_APPROX( ((ldm1 * (m1+m2))(i,j)) , ldm1.diagonal()(i) * (m1+m2)(i,j) );
|
|
VERIFY_IS_APPROX( ((m1 * rdm1)(i,j)) , rdm1.diagonal()(j) * m1(i,j) );
|
|
VERIFY_IS_APPROX( ((v1.asDiagonal() * m1)(i,j)) , v1(i) * m1(i,j) );
|
|
VERIFY_IS_APPROX( ((m1 * rv1.asDiagonal())(i,j)) , rv1(j) * m1(i,j) );
|
|
VERIFY_IS_APPROX( (((v1+v2).asDiagonal() * m1)(i,j)) , (v1+v2)(i) * m1(i,j) );
|
|
VERIFY_IS_APPROX( (((v1+v2).asDiagonal() * (m1+m2))(i,j)) , (v1+v2)(i) * (m1+m2)(i,j) );
|
|
VERIFY_IS_APPROX( ((m1 * (rv1+rv2).asDiagonal())(i,j)) , (rv1+rv2)(j) * m1(i,j) );
|
|
VERIFY_IS_APPROX( (((m1+m2) * (rv1+rv2).asDiagonal())(i,j)) , (rv1+rv2)(j) * (m1+m2)(i,j) );
|
|
|
|
BigMatrix big;
|
|
big.setZero(2*rows, 2*cols);
|
|
|
|
big.block(i,j,rows,cols) = m1;
|
|
big.block(i,j,rows,cols) = v1.asDiagonal() * big.block(i,j,rows,cols);
|
|
|
|
VERIFY_IS_APPROX((big.block(i,j,rows,cols)) , v1.asDiagonal() * m1 );
|
|
|
|
big.block(i,j,rows,cols) = m1;
|
|
big.block(i,j,rows,cols) = big.block(i,j,rows,cols) * rv1.asDiagonal();
|
|
VERIFY_IS_APPROX((big.block(i,j,rows,cols)) , m1 * rv1.asDiagonal() );
|
|
|
|
}
|
|
|
|
void test_diagonalmatrices()
|
|
{
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST_1( diagonalmatrices(Matrix<float, 1, 1>()) );
|
|
CALL_SUBTEST_2( diagonalmatrices(Matrix3f()) );
|
|
CALL_SUBTEST_3( diagonalmatrices(Matrix<double,3,3,RowMajor>()) );
|
|
CALL_SUBTEST_4( diagonalmatrices(Matrix4d()) );
|
|
CALL_SUBTEST_5( diagonalmatrices(Matrix<float,4,4,RowMajor>()) );
|
|
CALL_SUBTEST_6( diagonalmatrices(MatrixXcf(3, 5)) );
|
|
CALL_SUBTEST_7( diagonalmatrices(MatrixXi(10, 8)) );
|
|
CALL_SUBTEST_8( diagonalmatrices(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
|
|
CALL_SUBTEST_9( diagonalmatrices(MatrixXf(21, 24)) );
|
|
}
|
|
}
|