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102 lines
3.5 KiB
C++
102 lines
3.5 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) 2008 Gael Guennebaud <g.gael@free.fr>
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// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "main.h"
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#include <Eigen/LU>
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template<typename MatrixType> void inverse(const MatrixType& m)
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{
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/* this test covers the following files:
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Inverse.h
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*/
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int rows = m.rows();
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int cols = m.cols();
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
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MatrixType m1(rows, cols),
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m2(rows, cols),
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mzero = MatrixType::Zero(rows, cols),
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identity = MatrixType::Identity(rows, rows);
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createRandomMatrixOfRank(rows,rows,rows,m1);
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m2 = m1.inverse();
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VERIFY_IS_APPROX(m1, m2.inverse() );
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VERIFY_IS_APPROX((Scalar(2)*m2).inverse(), m2.inverse()*Scalar(0.5));
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VERIFY_IS_APPROX(identity, m1.inverse() * m1 );
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VERIFY_IS_APPROX(identity, m1 * m1.inverse() );
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VERIFY_IS_APPROX(m1, m1.inverse().inverse() );
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// since for the general case we implement separately row-major and col-major, test that
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VERIFY_IS_APPROX(m1.transpose().inverse(), m1.inverse().transpose());
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#if !defined(EIGEN_TEST_PART_5) && !defined(EIGEN_TEST_PART_6)
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//computeInverseAndDetWithCheck tests
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//First: an invertible matrix
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bool invertible;
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RealScalar det;
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m2.setZero();
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m1.computeInverseAndDetWithCheck(m2, det, invertible);
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VERIFY(invertible);
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VERIFY_IS_APPROX(identity, m1*m2);
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VERIFY_IS_APPROX(det, m1.determinant());
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m2.setZero();
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m1.computeInverseWithCheck(m2, invertible);
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VERIFY(invertible);
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VERIFY_IS_APPROX(identity, m1*m2);
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//Second: a rank one matrix (not invertible, except for 1x1 matrices)
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VectorType v3 = VectorType::Random(rows);
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MatrixType m3 = v3*v3.transpose(), m4(rows,cols);
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m3.computeInverseAndDetWithCheck(m4, det, invertible);
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VERIFY( rows==1 ? invertible : !invertible );
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VERIFY_IS_APPROX(det, m3.determinant());
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m3.computeInverseWithCheck(m4, invertible);
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VERIFY( rows==1 ? invertible : !invertible );
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#endif
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}
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void test_inverse()
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{
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int s;
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( inverse(Matrix<double,1,1>()) );
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CALL_SUBTEST_2( inverse(Matrix2d()) );
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CALL_SUBTEST_3( inverse(Matrix3f()) );
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CALL_SUBTEST_4( inverse(Matrix4f()) );
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s = ei_random<int>(50,320);
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CALL_SUBTEST_5( inverse(MatrixXf(s,s)) );
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s = ei_random<int>(25,100);
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CALL_SUBTEST_6( inverse(MatrixXcd(s,s)) );
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CALL_SUBTEST_7( inverse(Matrix4d()) );
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}
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}
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