| // 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::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>(); |
| |
| // Prevent overflows for integer types. |
| if (Eigen::NumTraits<Scalar>::IsInteger) { |
| Scalar kMaxVal = Scalar(10000); |
| m1.array() = m1.array() - kMaxVal * (m1.array() / kMaxVal); |
| m2.array() = m2.array() - kMaxVal * (m2.array() / kMaxVal); |
| } |
| |
| // 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, 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_EQUAL((m1.template block<0, Dynamic>(0, 0, 0, cols).colwise().sum()), RowVectorType::Zero(cols)); |
| VERIFY_IS_EQUAL((m1.template block<Dynamic, 0>(0, 0, rows, 0).rowwise().sum()), ColVectorType::Zero(rows)); |
| VERIFY_IS_EQUAL((m1.template block<0, Dynamic>(0, 0, 0, cols).colwise().prod()), RowVectorType::Ones(cols)); |
| VERIFY_IS_EQUAL((m1.template block<Dynamic, 0>(0, 0, rows, 0).rowwise().prod()), ColVectorType::Ones(rows)); |
| |
| VERIFY_IS_EQUAL(m1.block(0, 0, 0, cols).colwise().sum(), RowVectorType::Zero(cols)); |
| VERIFY_IS_EQUAL(m1.block(0, 0, rows, 0).rowwise().sum(), ColVectorType::Zero(rows)); |
| VERIFY_IS_EQUAL(m1.block(0, 0, 0, cols).colwise().prod(), RowVectorType::Ones(cols)); |
| VERIFY_IS_EQUAL(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); |
| |
| // Check write accessors: |
| m1.array().coeffRef(0, 0) = 1; |
| VERIFY_IS_APPROX(m1(0, 0), Scalar(1)); |
| m1.array()(0, 0) = 2; |
| VERIFY_IS_APPROX(m1(0, 0), Scalar(2)); |
| m1.array().matrix().coeffRef(0, 0) = 3; |
| VERIFY_IS_APPROX(m1(0, 0), Scalar(3)); |
| m1.array().matrix()(0, 0) = 4; |
| VERIFY_IS_APPROX(m1(0, 0), Scalar(4)); |
| } |
| |
| template <typename MatrixType> |
| void comparisons(const MatrixType& m) { |
| using std::abs; |
| 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); |
| |
| // and/or |
| VERIFY(((m1.array() < RealScalar(0)).matrix() && (m1.array() > RealScalar(0)).matrix()).count() == 0); |
| VERIFY(((m1.array() < RealScalar(0)).matrix() || (m1.array() >= RealScalar(0)).matrix()).count() == rows * cols); |
| RealScalar a = m1.cwiseAbs().mean(); |
| VERIFY(((m1.array() < -a).matrix() || (m1.array() > a).matrix()).count() == (m1.cwiseAbs().array() > a).count()); |
| |
| typedef Matrix<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; |
| typedef typename VectorType::RealScalar RealScalar; |
| VectorType u = VectorType::Random(v.size()); |
| |
| if (v.size() == 0) { |
| VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), RealScalar(0)); |
| VERIFY_IS_APPROX(u.template lpNorm<1>(), RealScalar(0)); |
| VERIFY_IS_APPROX(u.template lpNorm<2>(), RealScalar(0)); |
| VERIFY_IS_APPROX(u.template lpNorm<5>(), RealScalar(0)); |
| } else { |
| 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::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)); |
| |
| // Test NaN propagation for min/max. |
| if (!NumTraits<Scalar>::IsInteger) { |
| m1(0, 0) = NumTraits<Scalar>::quiet_NaN(); |
| // Elementwise. |
| VERIFY((numext::isnan)(m1.template cwiseMax<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0))); |
| VERIFY((numext::isnan)(m1.template cwiseMin<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0))); |
| VERIFY(!(numext::isnan)(m1.template cwiseMax<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0))); |
| VERIFY(!(numext::isnan)(m1.template cwiseMin<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)))(0, 0))); |
| VERIFY((numext::isnan)(m1.template cwiseMax<PropagateNaN>(Scalar(1))(0, 0))); |
| VERIFY((numext::isnan)(m1.template cwiseMin<PropagateNaN>(Scalar(1))(0, 0))); |
| VERIFY(!(numext::isnan)(m1.template cwiseMax<PropagateNumbers>(Scalar(1))(0, 0))); |
| VERIFY(!(numext::isnan)(m1.template cwiseMin<PropagateNumbers>(Scalar(1))(0, 0))); |
| |
| VERIFY((numext::isnan)( |
| m1.array().template max<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0))); |
| VERIFY((numext::isnan)( |
| m1.array().template min<PropagateNaN>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0))); |
| VERIFY(!(numext::isnan)( |
| m1.array().template max<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0))); |
| VERIFY(!(numext::isnan)( |
| m1.array().template min<PropagateNumbers>(MatrixType::Constant(rows, cols, Scalar(1)).array())(0, 0))); |
| VERIFY((numext::isnan)(m1.array().template max<PropagateNaN>(Scalar(1))(0, 0))); |
| VERIFY((numext::isnan)(m1.array().template min<PropagateNaN>(Scalar(1))(0, 0))); |
| VERIFY(!(numext::isnan)(m1.array().template max<PropagateNumbers>(Scalar(1))(0, 0))); |
| VERIFY(!(numext::isnan)(m1.array().template min<PropagateNumbers>(Scalar(1))(0, 0))); |
| |
| // Reductions. |
| VERIFY((numext::isnan)(m1.template maxCoeff<PropagateNaN>())); |
| VERIFY((numext::isnan)(m1.template minCoeff<PropagateNaN>())); |
| if (m1.size() > 1) { |
| VERIFY(!(numext::isnan)(m1.template maxCoeff<PropagateNumbers>())); |
| VERIFY(!(numext::isnan)(m1.template minCoeff<PropagateNumbers>())); |
| } else { |
| VERIFY((numext::isnan)(m1.template maxCoeff<PropagateNumbers>())); |
| VERIFY((numext::isnan)(m1.template minCoeff<PropagateNumbers>())); |
| } |
| } |
| } |
| |
| template <typename MatrixTraits> |
| void resize(const MatrixTraits& t) { |
| 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); |
| } |
| |
| template <int> |
| void regression_bug_654() { |
| ArrayXf a = RowVectorXf(3); |
| VectorXf v = Array<float, 1, Dynamic>(3); |
| } |
| |
| // Check propagation of LvalueBit through Array/Matrix-Wrapper |
| template <int> |
| void regrrssion_bug_1410() { |
| const Matrix4i M; |
| const Array4i A; |
| ArrayWrapper<const Matrix4i> MA = M.array(); |
| MA.row(0); |
| MatrixWrapper<const Array4i> AM = A.matrix(); |
| AM.row(0); |
| |
| VERIFY((internal::traits<ArrayWrapper<const Matrix4i> >::Flags & LvalueBit) == 0); |
| VERIFY((internal::traits<MatrixWrapper<const Array4i> >::Flags & LvalueBit) == 0); |
| |
| VERIFY((internal::traits<ArrayWrapper<Matrix4i> >::Flags & LvalueBit) == LvalueBit); |
| VERIFY((internal::traits<MatrixWrapper<Array4i> >::Flags & LvalueBit) == LvalueBit); |
| } |
| |
| EIGEN_DECLARE_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)))); |
| } |
| CALL_SUBTEST_5(lpNorm(VectorXf(0))); |
| CALL_SUBTEST_4(lpNorm(VectorXcf(0))); |
| 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<0>()); |
| CALL_SUBTEST_6(regrrssion_bug_1410<0>()); |
| } |