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90 lines
3.2 KiB
C++
90 lines
3.2 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) 2009 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/Householder>
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template<typename MatrixType> void householder(const MatrixType& m)
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{
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/* this test covers the following files:
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Householder.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::RowsAtCompileTime, 1> VectorType;
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typedef Matrix<Scalar, ei_decrement_size<MatrixType::RowsAtCompileTime>::ret, 1> EssentialVectorType;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
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RealScalar beta;
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EssentialVectorType essential;
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VectorType v1 = VectorType::Random(rows), v2;
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v2 = v1;
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v1.makeHouseholder(&essential, &beta);
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v1.applyHouseholderOnTheLeft(essential,beta);
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VERIFY_IS_APPROX(v1.norm(), v2.norm());
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VERIFY_IS_MUCH_SMALLER_THAN(v1.end(rows-1).norm(), v1.norm());
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v1 = VectorType::Random(rows);
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v2 = v1;
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v1.applyHouseholderOnTheLeft(essential,beta);
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VERIFY_IS_APPROX(v1.norm(), v2.norm());
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MatrixType m1(rows, cols),
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m2(rows, cols);
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v1 = VectorType::Random(rows);
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m1.colwise() = v1;
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m2 = m1;
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m1.col(0).makeHouseholder(&essential, &beta);
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m1.applyHouseholderOnTheLeft(essential,beta);
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VERIFY_IS_APPROX(m1.norm(), m2.norm());
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VERIFY_IS_MUCH_SMALLER_THAN(m1.block(1,0,rows-1,cols).norm(), m1.norm());
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v1 = VectorType::Random(rows);
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SquareMatrixType m3(rows,rows), m4(rows,rows);
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m3.rowwise() = v1.transpose();
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m4 = m3;
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m3.row(0).makeHouseholder(&essential, &beta);
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m3.applyHouseholderOnTheRight(essential,beta);
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VERIFY_IS_APPROX(m3.norm(), m4.norm());
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VERIFY_IS_MUCH_SMALLER_THAN(m3.block(0,1,rows,rows-1).norm(), m3.norm());
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}
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void test_householder()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST( householder(Matrix<double,2,2>()) );
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CALL_SUBTEST( householder(Matrix<float,2,3>()) );
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CALL_SUBTEST( householder(Matrix<double,3,5>()) );
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CALL_SUBTEST( householder(Matrix<float,4,4>()) );
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CALL_SUBTEST( householder(MatrixXd(10,12)) );
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CALL_SUBTEST( householder(MatrixXcf(16,17)) );
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}
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}
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