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f0394edfa7
* bugfix in Dot unroller * added special random generator for the unit tests and reduced the tolerance threshold by an order of magnitude this fixes issues with sum.cpp but other tests still failed sometimes, this have to be carefully checked...
89 lines
3.5 KiB
C++
89 lines
3.5 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra. Eigen itself is part of the KDE project.
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//
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// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
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// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
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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 <functional>
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#include <Eigen/Array>
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using namespace std;
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template<typename Scalar> struct AddIfNull {
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const Scalar operator() (const Scalar a, const Scalar b) const {return a<=1e-3 ? b : a;}
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enum { Cost = NumTraits<Scalar>::AddCost };
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};
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template<typename MatrixType> void cwiseops(const MatrixType& m)
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{
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typedef typename MatrixType::Scalar Scalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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int rows = m.rows();
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int cols = m.cols();
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MatrixType m1 = test_random_matrix<MatrixType>(rows, cols),
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m2 = test_random_matrix<MatrixType>(rows, cols),
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m3(rows, cols),
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mzero = MatrixType::Zero(rows, cols),
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mones = MatrixType::Ones(rows, cols),
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identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
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::Identity(rows, rows),
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square = test_random_matrix<Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> >(rows, rows);
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VectorType v1 = test_random_matrix<VectorType>(rows),
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v2 = test_random_matrix<VectorType>(rows),
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vzero = VectorType::Zero(rows);
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m2 = m2.template binaryExpr<AddIfNull<Scalar> >(mones);
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VERIFY_IS_APPROX( mzero, m1-m1);
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VERIFY_IS_APPROX( m2, m1+m2-m1);
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#ifdef EIGEN_VECTORIZE
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if(NumTraits<Scalar>::HasFloatingPoint)
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#endif
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{
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VERIFY_IS_APPROX( mones, m2.cwise()/m2);
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}
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VERIFY_IS_APPROX( m1.cwise() * m2, m2.cwise() * m1);
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VERIFY( (m1.cwise()<m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).all() );
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VERIFY( !(m1.cwise()<m1.unaryExpr(bind2nd(minus<Scalar>(), Scalar(1)))).all() );
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VERIFY( !(m1.cwise()>m1.unaryExpr(bind2nd(plus<Scalar>(), Scalar(1)))).any() );
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//VERIFY_IS_APPROX( m1, m2.cwiseProduct(m1).cwiseQuotient(m2));
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// VERIFY_IS_APPROX( cwiseMin(m1,m2), cwiseMin(m2,m1) );
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// VERIFY_IS_APPROX( cwiseMin(m1,m1+mones), m1 );
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// VERIFY_IS_APPROX( cwiseMin(m1,m1-mones), m1-mones );
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}
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void test_cwiseop()
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{
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for(int i = 0; i < g_repeat ; i++) {
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CALL_SUBTEST( cwiseops(Matrix<float, 1, 1>()) );
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CALL_SUBTEST( cwiseops(Matrix4d()) );
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CALL_SUBTEST( cwiseops(MatrixXf(3, 3)) );
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CALL_SUBTEST( cwiseops(MatrixXi(8, 12)) );
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CALL_SUBTEST( cwiseops(MatrixXd(20, 20)) );
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}
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}
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