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Diffstat (limited to 'eigen/test/array_for_matrix.cpp')
| -rw-r--r-- | eigen/test/array_for_matrix.cpp | 254 |
1 files changed, 254 insertions, 0 deletions
diff --git a/eigen/test/array_for_matrix.cpp b/eigen/test/array_for_matrix.cpp new file mode 100644 index 0000000..9667e1f --- /dev/null +++ b/eigen/test/array_for_matrix.cpp @@ -0,0 +1,254 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2009 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/. + +#include "main.h" + +template<typename MatrixType> void array_for_matrix(const MatrixType& m) +{ + typedef typename MatrixType::Index Index; + typedef typename MatrixType::Scalar Scalar; + typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType; + typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; + + Index rows = m.rows(); + Index cols = m.cols(); + + MatrixType m1 = MatrixType::Random(rows, cols), + m2 = MatrixType::Random(rows, cols), + m3(rows, cols); + + ColVectorType cv1 = ColVectorType::Random(rows); + RowVectorType rv1 = RowVectorType::Random(cols); + + Scalar s1 = internal::random<Scalar>(), + s2 = internal::random<Scalar>(); + + // scalar addition + VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array()); + VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows,cols,s1) + m1); + VERIFY_IS_APPROX(((m1*Scalar(2)).array() - s2).matrix(), (m1+m1) - MatrixType::Constant(rows,cols,s2) ); + m3 = m1; + m3.array() += s2; + VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix()); + m3 = m1; + m3.array() -= s1; + VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix()); + + // reductions + VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm()); + VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm()); + VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm()); + VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm()); + VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar>())); + + // vector-wise ops + m3 = m1; + VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1); + m3 = m1; + VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1); + m3 = m1; + VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1); + m3 = m1; + VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1); + + // empty objects + VERIFY_IS_APPROX(m1.block(0,0,0,cols).colwise().sum(), RowVectorType::Zero(cols)); + VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().prod(), ColVectorType::Ones(rows)); + + // verify the const accessors exist + const Scalar& ref_m1 = m.matrix().array().coeffRef(0); + const Scalar& ref_m2 = m.matrix().array().coeffRef(0,0); + const Scalar& ref_a1 = m.array().matrix().coeffRef(0); + const Scalar& ref_a2 = m.array().matrix().coeffRef(0,0); + VERIFY(&ref_a1 == &ref_m1); + VERIFY(&ref_a2 == &ref_m2); +} + +template<typename MatrixType> void comparisons(const MatrixType& m) +{ + using std::abs; + typedef typename MatrixType::Index Index; + typedef typename MatrixType::Scalar Scalar; + typedef typename NumTraits<Scalar>::Real RealScalar; + + Index rows = m.rows(); + Index cols = m.cols(); + + Index r = internal::random<Index>(0, rows-1), + c = internal::random<Index>(0, cols-1); + + MatrixType m1 = MatrixType::Random(rows, cols), + m2 = MatrixType::Random(rows, cols), + m3(rows, cols); + + VERIFY(((m1.array() + Scalar(1)) > m1.array()).all()); + VERIFY(((m1.array() - Scalar(1)) < m1.array()).all()); + if (rows*cols>1) + { + m3 = m1; + m3(r,c) += 1; + VERIFY(! (m1.array() < m3.array()).all() ); + VERIFY(! (m1.array() > m3.array()).all() ); + } + + // comparisons to scalar + VERIFY( (m1.array() != (m1(r,c)+1) ).any() ); + VERIFY( (m1.array() > (m1(r,c)-1) ).any() ); + VERIFY( (m1.array() < (m1(r,c)+1) ).any() ); + VERIFY( (m1.array() == m1(r,c) ).any() ); + VERIFY( m1.cwiseEqual(m1(r,c)).any() ); + + // test Select + VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) ); + VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) ); + Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2); + for (int j=0; j<cols; ++j) + for (int i=0; i<rows; ++i) + m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j); + VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array()) + .select(MatrixType::Zero(rows,cols),m1), m3); + // shorter versions: + VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array()) + .select(0,m1), m3); + VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array()) + .select(m1,0), m3); + // even shorter version: + VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3); + + // count + VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols); + + typedef Matrix<typename MatrixType::Index, Dynamic, 1> VectorOfIndices; + + // TODO allows colwise/rowwise for array + VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose()); + VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols)); +} + +template<typename VectorType> void lpNorm(const VectorType& v) +{ + using std::sqrt; + VectorType u = VectorType::Random(v.size()); + + VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff()); + VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum()); + VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum())); + VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum()); +} + +template<typename MatrixType> void cwise_min_max(const MatrixType& m) +{ + typedef typename MatrixType::Index Index; + typedef typename MatrixType::Scalar Scalar; + + Index rows = m.rows(); + Index cols = m.cols(); + + MatrixType m1 = MatrixType::Random(rows, cols); + + // min/max with array + Scalar maxM1 = m1.maxCoeff(); + Scalar minM1 = m1.minCoeff(); + + VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin(MatrixType::Constant(rows,cols, minM1))); + VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows,cols, maxM1))); + + VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows,cols, maxM1))); + VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows,cols, minM1))); + + // min/max with scalar input + VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin( minM1)); + VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1)); + VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1)); + VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)( -minM1)); + + VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax( maxM1)); + VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1)); + VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1)); + VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1)); + + VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1).array(), (m1.array().min)( minM1)); + VERIFY_IS_APPROX(m1.array(), (m1.array().min)( maxM1)); + + VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1).array(), (m1.array().max)( maxM1)); + VERIFY_IS_APPROX(m1.array(), (m1.array().max)( minM1)); + +} + +template<typename MatrixTraits> void resize(const MatrixTraits& t) +{ + typedef typename MatrixTraits::Index Index; + typedef typename MatrixTraits::Scalar Scalar; + typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType; + typedef Array<Scalar,Dynamic,Dynamic> Array2DType; + typedef Matrix<Scalar,Dynamic,1> VectorType; + typedef Array<Scalar,Dynamic,1> Array1DType; + + Index rows = t.rows(), cols = t.cols(); + + MatrixType m(rows,cols); + VectorType v(rows); + Array2DType a2(rows,cols); + Array1DType a1(rows); + + m.array().resize(rows+1,cols+1); + VERIFY(m.rows()==rows+1 && m.cols()==cols+1); + a2.matrix().resize(rows+1,cols+1); + VERIFY(a2.rows()==rows+1 && a2.cols()==cols+1); + v.array().resize(cols); + VERIFY(v.size()==cols); + a1.matrix().resize(cols); + VERIFY(a1.size()==cols); +} + +void regression_bug_654() +{ + ArrayXf a = RowVectorXf(3); + VectorXf v = Array<float,1,Dynamic>(3); +} + +void test_array_for_matrix() +{ + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) ); + CALL_SUBTEST_2( array_for_matrix(Matrix2f()) ); + CALL_SUBTEST_3( array_for_matrix(Matrix4d()) ); + CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + } + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) ); + CALL_SUBTEST_2( comparisons(Matrix2f()) ); + CALL_SUBTEST_3( comparisons(Matrix4d()) ); + CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + } + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1( cwise_min_max(Matrix<float, 1, 1>()) ); + CALL_SUBTEST_2( cwise_min_max(Matrix2f()) ); + CALL_SUBTEST_3( cwise_min_max(Matrix4d()) ); + CALL_SUBTEST_5( cwise_min_max(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_6( cwise_min_max(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + } + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) ); + CALL_SUBTEST_2( lpNorm(Vector2f()) ); + CALL_SUBTEST_7( lpNorm(Vector3d()) ); + CALL_SUBTEST_8( lpNorm(Vector4f()) ); + CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + } + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + CALL_SUBTEST_6( resize(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + } + CALL_SUBTEST_6( regression_bug_654() ); +} |