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Diffstat (limited to 'test/redux.cpp')
| -rw-r--r-- | test/redux.cpp | 158 |
1 files changed, 158 insertions, 0 deletions
diff --git a/test/redux.cpp b/test/redux.cpp new file mode 100644 index 000000000..e07d4b1e4 --- /dev/null +++ b/test/redux.cpp @@ -0,0 +1,158 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> +// +// 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 matrixRedux(const MatrixType& m) +{ + typedef typename MatrixType::Index Index; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; + + Index rows = m.rows(); + Index cols = m.cols(); + + MatrixType m1 = MatrixType::Random(rows, cols); + + // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test + // failures if we underflow into denormals. Thus, we scale so that entires are close to 1. + MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + Scalar(0.2) * m1; + + VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1)); + VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy + Scalar s(0), p(1), minc(internal::real(m1.coeff(0))), maxc(internal::real(m1.coeff(0))); + for(int j = 0; j < cols; j++) + for(int i = 0; i < rows; i++) + { + s += m1(i,j); + p *= m1_for_prod(i,j); + minc = (std::min)(internal::real(minc), internal::real(m1(i,j))); + maxc = (std::max)(internal::real(maxc), internal::real(m1(i,j))); + } + const Scalar mean = s/Scalar(RealScalar(rows*cols)); + + VERIFY_IS_APPROX(m1.sum(), s); + VERIFY_IS_APPROX(m1.mean(), mean); + VERIFY_IS_APPROX(m1_for_prod.prod(), p); + VERIFY_IS_APPROX(m1.real().minCoeff(), internal::real(minc)); + VERIFY_IS_APPROX(m1.real().maxCoeff(), internal::real(maxc)); + + // test slice vectorization assuming assign is ok + Index r0 = internal::random<Index>(0,rows-1); + Index c0 = internal::random<Index>(0,cols-1); + Index r1 = internal::random<Index>(r0+1,rows)-r0; + Index c1 = internal::random<Index>(c0+1,cols)-c0; + VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum()); + VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean()); + VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod()); + VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff()); + VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff()); + + // test empty objects + VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0)); + VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1)); +} + +template<typename VectorType> void vectorRedux(const VectorType& w) +{ + typedef typename VectorType::Index Index; + typedef typename VectorType::Scalar Scalar; + typedef typename NumTraits<Scalar>::Real RealScalar; + Index size = w.size(); + + VectorType v = VectorType::Random(size); + VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod + + for(int i = 1; i < size; i++) + { + Scalar s(0), p(1); + RealScalar minc(internal::real(v.coeff(0))), maxc(internal::real(v.coeff(0))); + for(int j = 0; j < i; j++) + { + s += v[j]; + p *= v_for_prod[j]; + minc = (std::min)(minc, internal::real(v[j])); + maxc = (std::max)(maxc, internal::real(v[j])); + } + VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.head(i).sum()), Scalar(1)); + VERIFY_IS_APPROX(p, v_for_prod.head(i).prod()); + VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff()); + VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff()); + } + + for(int i = 0; i < size-1; i++) + { + Scalar s(0), p(1); + RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i))); + for(int j = i; j < size; j++) + { + s += v[j]; + p *= v_for_prod[j]; + minc = (std::min)(minc, internal::real(v[j])); + maxc = (std::max)(maxc, internal::real(v[j])); + } + VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.tail(size-i).sum()), Scalar(1)); + VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod()); + VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff()); + VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff()); + } + + for(int i = 0; i < size/2; i++) + { + Scalar s(0), p(1); + RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i))); + for(int j = i; j < size-i; j++) + { + s += v[j]; + p *= v_for_prod[j]; + minc = (std::min)(minc, internal::real(v[j])); + maxc = (std::max)(maxc, internal::real(v[j])); + } + VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.segment(i, size-2*i).sum()), Scalar(1)); + VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod()); + VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff()); + VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff()); + } + + // test empty objects + VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0)); + VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1)); + VERIFY_RAISES_ASSERT(v.head(0).mean()); + VERIFY_RAISES_ASSERT(v.head(0).minCoeff()); + VERIFY_RAISES_ASSERT(v.head(0).maxCoeff()); +} + +void test_redux() +{ + // the max size cannot be too large, otherwise reduxion operations obviously generate large errors. + int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE); + EIGEN_UNUSED_VARIABLE(maxsize); + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) ); + CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) ); + CALL_SUBTEST_2( matrixRedux(Matrix2f()) ); + CALL_SUBTEST_2( matrixRedux(Array2f()) ); + CALL_SUBTEST_3( matrixRedux(Matrix4d()) ); + CALL_SUBTEST_3( matrixRedux(Array4d()) ); + CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); + } + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_7( vectorRedux(Vector4f()) ); + CALL_SUBTEST_7( vectorRedux(Array4f()) ); + CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) ); + CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) ); + } +} |