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array_for_matrix.cpp
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// 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>();
// 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_APPROX(
(m1.template block<0, Dynamic>(0, 0, 0, cols).colwise().sum()),
RowVectorType::Zero(cols));
VERIFY_IS_APPROX(
(m1.template block<Dynamic, 0>(0, 0, rows, 0).rowwise().sum()),
ColVectorType::Zero(rows));
VERIFY_IS_APPROX(
(m1.template block<0, Dynamic>(0, 0, 0, cols).colwise().prod()),
RowVectorType::Ones(cols));
VERIFY_IS_APPROX(
(m1.template block<Dynamic, 0>(0, 0, rows, 0).rowwise().prod()),
ColVectorType::Ones(rows));
VERIFY_IS_APPROX(m1.block(0, 0, 0, cols).colwise().sum(),
RowVectorType::Zero(cols));
VERIFY_IS_APPROX(m1.block(0, 0, rows, 0).rowwise().sum(),
ColVectorType::Zero(rows));
VERIFY_IS_APPROX(m1.block(0, 0, 0, cols).colwise().prod(),
RowVectorType::Ones(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);
// 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));
}
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>());
}