diff options
Diffstat (limited to 'eigen/test/vectorwiseop.cpp')
| -rw-r--r-- | eigen/test/vectorwiseop.cpp | 75 |
1 files changed, 55 insertions, 20 deletions
diff --git a/eigen/test/vectorwiseop.cpp b/eigen/test/vectorwiseop.cpp index d32fd10..f3ab561 100644 --- a/eigen/test/vectorwiseop.cpp +++ b/eigen/test/vectorwiseop.cpp @@ -2,11 +2,13 @@ // for linear algebra. // // Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> +// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. +#define TEST_ENABLE_TEMPORARY_TRACKING #define EIGEN_NO_STATIC_ASSERT #include "main.h" @@ -101,11 +103,11 @@ template<typename ArrayType> void vectorwiseop_array(const ArrayType& m) VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose()); VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose()); - + m2 = m1; // yes, there might be an aliasing issue there but ".rowwise() /=" - // is suppposed to evaluate " m2.colwise().sum()" into to temporary to avoid - // evaluating the reducions multiple times + // is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid + // evaluating the reduction multiple times if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic) { m2.rowwise() /= m2.colwise().sum(); @@ -156,16 +158,22 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m) VERIFY_IS_APPROX(m2, m1.colwise() + colvec); VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec); - VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose()); + if(rows>1) + { + VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose()); + VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose()); + } m2 = m1; m2.rowwise() += rowvec; VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec); VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec); - VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose()); + if(cols>1) + { + VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose()); + VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose()); + } // test substraction @@ -174,29 +182,43 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m) VERIFY_IS_APPROX(m2, m1.colwise() - colvec); VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec); - VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose()); - VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose()); + if(rows>1) + { + VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose()); + VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose()); + } m2 = m1; m2.rowwise() -= rowvec; VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec); VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec); - VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose()); - VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose()); - + if(cols>1) + { + VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose()); + VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose()); + } + // test norm rrres = m1.colwise().norm(); VERIFY_IS_APPROX(rrres(c), m1.col(c).norm()); rcres = m1.rowwise().norm(); VERIFY_IS_APPROX(rcres(r), m1.row(r).norm()); - + + VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>()); + VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>()); + VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>()); + VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>()); + + // regression for bug 1158 + VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum()); + // test normalized m2 = m1.colwise().normalized(); VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized()); m2 = m1.rowwise().normalized(); VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized()); - + // test normalize m2 = m1; m2.colwise().normalize(); @@ -204,14 +226,27 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m) m2 = m1; m2.rowwise().normalize(); VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized()); + + // test with partial reduction of products + Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose(); + VERIFY_IS_APPROX( (m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum()); + Matrix<Scalar,1,MatrixType::RowsAtCompileTime> tmp(rows); + VERIFY_EVALUATION_COUNT( tmp = (m1 * m1.transpose()).colwise().sum(), 1); + + m2 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())).eval(); + m1 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())); + VERIFY_IS_APPROX( m1, m2 ); + VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/RealScalar(m1.rows())), (MatrixType::RowsAtCompileTime!=1 ? 1 : 0) ); } void test_vectorwiseop() { - CALL_SUBTEST_1(vectorwiseop_array(Array22cd())); - CALL_SUBTEST_2(vectorwiseop_array(Array<double, 3, 2>())); - CALL_SUBTEST_3(vectorwiseop_array(ArrayXXf(3, 4))); - CALL_SUBTEST_4(vectorwiseop_matrix(Matrix4cf())); - CALL_SUBTEST_5(vectorwiseop_matrix(Matrix<float,4,5>())); - CALL_SUBTEST_6(vectorwiseop_matrix(MatrixXd(7,2))); + CALL_SUBTEST_1( vectorwiseop_array(Array22cd()) ); + CALL_SUBTEST_2( vectorwiseop_array(Array<double, 3, 2>()) ); + CALL_SUBTEST_3( vectorwiseop_array(ArrayXXf(3, 4)) ); + CALL_SUBTEST_4( vectorwiseop_matrix(Matrix4cf()) ); + CALL_SUBTEST_5( vectorwiseop_matrix(Matrix<float,4,5>()) ); + CALL_SUBTEST_6( vectorwiseop_matrix(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_7( vectorwiseop_matrix(VectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_7( vectorwiseop_matrix(RowVectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); } |