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Move eigen to third party (#282)
* remove useless statement * Add eigen to third_party dir * remove reducdant lines
This commit is contained in:
Jack Zhou
823
third_party/eigen/test/sparse_basic.cpp
vendored
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823
third_party/eigen/test/sparse_basic.cpp
vendored
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
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// Copyright (C) 2013 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_SPARSE_TEST_INCLUDED_FROM_SPARSE_EXTRA
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static long g_realloc_count = 0;
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#define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++;
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static long g_dense_op_sparse_count = 0;
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#define EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN \
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g_dense_op_sparse_count++;
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#define EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN \
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g_dense_op_sparse_count += 10;
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#define EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN \
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g_dense_op_sparse_count += 20;
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#endif
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#include "sparse.h"
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template <typename SparseMatrixType>
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void sparse_basic(const SparseMatrixType& ref) {
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typedef typename SparseMatrixType::StorageIndex StorageIndex;
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typedef Matrix<StorageIndex, 2, 1> Vector2;
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const Index rows = ref.rows();
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const Index cols = ref.cols();
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// const Index inner = ref.innerSize();
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// const Index outer = ref.outerSize();
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typedef typename SparseMatrixType::Scalar Scalar;
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typedef typename SparseMatrixType::RealScalar RealScalar;
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enum { Flags = SparseMatrixType::Flags };
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double density = (std::max)(8. / (rows * cols), 0.01);
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typedef Matrix<Scalar, Dynamic, Dynamic> DenseMatrix;
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typedef Matrix<Scalar, Dynamic, 1> DenseVector;
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Scalar eps = 1e-6;
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Scalar s1 = internal::random<Scalar>();
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{
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SparseMatrixType m(rows, cols);
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DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
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DenseVector vec1 = DenseVector::Random(rows);
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std::vector<Vector2> zeroCoords;
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std::vector<Vector2> nonzeroCoords;
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initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
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// test coeff and coeffRef
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for (std::size_t i = 0; i < zeroCoords.size(); ++i) {
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VERIFY_IS_MUCH_SMALLER_THAN(m.coeff(zeroCoords[i].x(), zeroCoords[i].y()),
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eps);
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if (internal::is_same<SparseMatrixType,
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SparseMatrix<Scalar, Flags> >::value)
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VERIFY_RAISES_ASSERT(m.coeffRef(zeroCoords[i].x(), zeroCoords[i].y()) =
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5);
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}
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VERIFY_IS_APPROX(m, refMat);
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if (!nonzeroCoords.empty()) {
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m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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}
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VERIFY_IS_APPROX(m, refMat);
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// test assertion
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VERIFY_RAISES_ASSERT(m.coeffRef(-1, 1) = 0);
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VERIFY_RAISES_ASSERT(m.coeffRef(0, m.cols()) = 0);
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}
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// test insert (inner random)
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{
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DenseMatrix m1(rows, cols);
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m1.setZero();
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SparseMatrixType m2(rows, cols);
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bool call_reserve = internal::random<int>() % 2;
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Index nnz = internal::random<int>(1, int(rows) / 2);
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if (call_reserve) {
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if (internal::random<int>() % 2)
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m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz)));
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else
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m2.reserve(m2.outerSize() * nnz);
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}
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g_realloc_count = 0;
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for (Index j = 0; j < cols; ++j) {
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for (Index k = 0; k < nnz; ++k) {
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Index i = internal::random<Index>(0, rows - 1);
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if (m1.coeff(i, j) == Scalar(0))
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m2.insert(i, j) = m1(i, j) = internal::random<Scalar>();
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}
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}
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if (call_reserve && !SparseMatrixType::IsRowMajor) {
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VERIFY(g_realloc_count == 0);
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}
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m2.finalize();
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VERIFY_IS_APPROX(m2, m1);
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}
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// test insert (fully random)
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{
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DenseMatrix m1(rows, cols);
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m1.setZero();
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SparseMatrixType m2(rows, cols);
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if (internal::random<int>() % 2)
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m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
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for (int k = 0; k < rows * cols; ++k) {
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Index i = internal::random<Index>(0, rows - 1);
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Index j = internal::random<Index>(0, cols - 1);
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if ((m1.coeff(i, j) == Scalar(0)) && (internal::random<int>() % 2))
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m2.insert(i, j) = m1(i, j) = internal::random<Scalar>();
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else {
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Scalar v = internal::random<Scalar>();
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m2.coeffRef(i, j) += v;
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m1(i, j) += v;
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}
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}
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VERIFY_IS_APPROX(m2, m1);
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}
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// test insert (un-compressed)
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for (int mode = 0; mode < 4; ++mode) {
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DenseMatrix m1(rows, cols);
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m1.setZero();
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SparseMatrixType m2(rows, cols);
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VectorXi r(VectorXi::Constant(
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m2.outerSize(), ((mode % 2) == 0)
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? int(m2.innerSize())
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: std::max<int>(1, int(m2.innerSize()) / 8)));
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m2.reserve(r);
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for (Index k = 0; k < rows * cols; ++k) {
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Index i = internal::random<Index>(0, rows - 1);
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Index j = internal::random<Index>(0, cols - 1);
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if (m1.coeff(i, j) == Scalar(0))
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m2.insert(i, j) = m1(i, j) = internal::random<Scalar>();
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if (mode == 3) m2.reserve(r);
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}
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if (internal::random<int>() % 2) m2.makeCompressed();
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VERIFY_IS_APPROX(m2, m1);
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}
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// test basic computations
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{
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DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
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DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
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DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
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DenseMatrix refM4 = DenseMatrix::Zero(rows, cols);
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SparseMatrixType m1(rows, cols);
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SparseMatrixType m2(rows, cols);
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SparseMatrixType m3(rows, cols);
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SparseMatrixType m4(rows, cols);
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initSparse<Scalar>(density, refM1, m1);
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initSparse<Scalar>(density, refM2, m2);
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initSparse<Scalar>(density, refM3, m3);
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initSparse<Scalar>(density, refM4, m4);
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if (internal::random<bool>()) m1.makeCompressed();
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Index m1_nnz = m1.nonZeros();
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VERIFY_IS_APPROX(m1 * s1, refM1 * s1);
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VERIFY_IS_APPROX(m1 + m2, refM1 + refM2);
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VERIFY_IS_APPROX(m1 + m2 + m3, refM1 + refM2 + refM3);
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VERIFY_IS_APPROX(m3.cwiseProduct(m1 + m2),
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refM3.cwiseProduct(refM1 + refM2));
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VERIFY_IS_APPROX(m1 * s1 - m2, refM1 * s1 - refM2);
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VERIFY_IS_APPROX(m4 = m1 / s1, refM1 / s1);
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VERIFY_IS_EQUAL(m4.nonZeros(), m1_nnz);
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if (SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)),
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refM1.row(0).dot(refM2.row(0)));
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else
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VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.col(0)),
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refM1.col(0).dot(refM2.col(0)));
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DenseVector rv = DenseVector::Random(m1.cols());
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DenseVector cv = DenseVector::Random(m1.rows());
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Index r = internal::random<Index>(0, m1.rows() - 2);
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Index c = internal::random<Index>(0, m1.cols() - 1);
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VERIFY_IS_APPROX(
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(m1.template block<1, Dynamic>(r, 0, 1, m1.cols()).dot(rv)),
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refM1.row(r).dot(rv));
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VERIFY_IS_APPROX(m1.row(r).dot(rv), refM1.row(r).dot(rv));
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VERIFY_IS_APPROX(m1.col(c).dot(cv), refM1.col(c).dot(cv));
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VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate());
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VERIFY_IS_APPROX(m1.real(), refM1.real());
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refM4.setRandom();
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// sparse cwise* dense
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VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4));
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// dense cwise* sparse
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VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3));
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// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
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// mixed sparse-dense
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VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3);
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VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4);
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VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3);
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VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4);
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + RealScalar(0.5) * m3).eval(),
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RealScalar(0.5) * refM4 + RealScalar(0.5) * refM3);
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + m3 * RealScalar(0.5)).eval(),
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RealScalar(0.5) * refM4 + RealScalar(0.5) * refM3);
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + m3.cwiseProduct(m3)).eval(),
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RealScalar(0.5) * refM4 + refM3.cwiseProduct(refM3));
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + RealScalar(0.5) * m3).eval(),
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RealScalar(0.5) * refM4 + RealScalar(0.5) * refM3);
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + m3 * RealScalar(0.5)).eval(),
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RealScalar(0.5) * refM4 + RealScalar(0.5) * refM3);
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + (m3 + m3)).eval(),
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RealScalar(0.5) * refM4 + (refM3 + refM3));
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VERIFY_IS_APPROX(((refM3 + m3) + RealScalar(0.5) * m3).eval(),
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RealScalar(0.5) * refM3 + (refM3 + refM3));
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + (refM3 + m3)).eval(),
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RealScalar(0.5) * refM4 + (refM3 + refM3));
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VERIFY_IS_APPROX((RealScalar(0.5) * refM4 + (m3 + refM3)).eval(),
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RealScalar(0.5) * refM4 + (refM3 + refM3));
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VERIFY_IS_APPROX(m1.sum(), refM1.sum());
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m4 = m1;
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refM4 = m4;
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VERIFY_IS_APPROX(m1 *= s1, refM1 *= s1);
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VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
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VERIFY_IS_APPROX(m1 /= s1, refM1 /= s1);
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VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
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VERIFY_IS_APPROX(m1 += m2, refM1 += refM2);
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VERIFY_IS_APPROX(m1 -= m2, refM1 -= refM2);
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refM3 = refM1;
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VERIFY_IS_APPROX(refM1 += m2, refM3 += refM2);
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VERIFY_IS_APPROX(refM1 -= m2, refM3 -= refM2);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 = m2 + refM4, refM3 = refM2 + refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 10);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 += m2 + refM4, refM3 += refM2 + refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 -= m2 + refM4, refM3 -= refM2 + refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 = refM4 + m2, refM3 = refM2 + refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 += refM4 + m2, refM3 += refM2 + refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 -= refM4 + m2, refM3 -= refM2 + refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 = m2 - refM4, refM3 = refM2 - refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 20);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 += m2 - refM4, refM3 += refM2 - refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 -= m2 - refM4, refM3 -= refM2 - refM4);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 = refM4 - m2, refM3 = refM4 - refM2);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 += refM4 - m2, refM3 += refM4 - refM2);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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g_dense_op_sparse_count = 0;
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VERIFY_IS_APPROX(refM1 -= refM4 - m2, refM3 -= refM4 - refM2);
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VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1);
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refM3 = m3;
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if (rows >= 2 && cols >= 2) {
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VERIFY_RAISES_ASSERT(m1 += m1.innerVector(0));
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VERIFY_RAISES_ASSERT(m1 -= m1.innerVector(0));
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VERIFY_RAISES_ASSERT(refM1 -= m1.innerVector(0));
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VERIFY_RAISES_ASSERT(refM1 += m1.innerVector(0));
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}
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m1 = m4;
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refM1 = refM4;
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// test aliasing
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VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1));
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VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
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m1 = m4;
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refM1 = refM4;
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VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval()));
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VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
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m1 = m4;
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refM1 = refM4;
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VERIFY_IS_APPROX((m1 = -m1.transpose()),
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(refM1 = -refM1.transpose().eval()));
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VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
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m1 = m4;
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refM1 = refM4;
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VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1));
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VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
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m1 = m4;
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refM1 = refM4;
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if (m1.isCompressed()) {
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VERIFY_IS_APPROX(m1.coeffs().sum(), m1.sum());
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m1.coeffs() += s1;
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for (Index j = 0; j < m1.outerSize(); ++j)
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for (typename SparseMatrixType::InnerIterator it(m1, j); it; ++it)
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refM1(it.row(), it.col()) += s1;
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VERIFY_IS_APPROX(m1, refM1);
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}
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// and/or
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{
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typedef SparseMatrix<bool, SparseMatrixType::Options,
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typename SparseMatrixType::StorageIndex>
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SpBool;
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SpBool mb1 = m1.real().template cast<bool>();
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SpBool mb2 = m2.real().template cast<bool>();
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VERIFY_IS_EQUAL(mb1.template cast<int>().sum(),
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refM1.real().template cast<bool>().count());
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VERIFY_IS_EQUAL((mb1 && mb2).template cast<int>().sum(),
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(refM1.real().template cast<bool>() &&
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refM2.real().template cast<bool>())
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.count());
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VERIFY_IS_EQUAL((mb1 || mb2).template cast<int>().sum(),
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(refM1.real().template cast<bool>() ||
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refM2.real().template cast<bool>())
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.count());
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SpBool mb3 = mb1 && mb2;
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if (mb1.coeffs().all() && mb2.coeffs().all()) {
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VERIFY_IS_EQUAL(mb3.nonZeros(), (refM1.real().template cast<bool>() &&
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refM2.real().template cast<bool>())
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.count());
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}
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}
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}
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// test reverse iterators
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
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SparseMatrixType m2(rows, cols);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
std::vector<Scalar> ref_value(m2.innerSize());
|
||||
std::vector<Index> ref_index(m2.innerSize());
|
||||
if (internal::random<bool>()) m2.makeCompressed();
|
||||
for (Index j = 0; j < m2.outerSize(); ++j) {
|
||||
Index count_forward = 0;
|
||||
|
||||
for (typename SparseMatrixType::InnerIterator it(m2, j); it; ++it) {
|
||||
ref_value[ref_value.size() - 1 - count_forward] = it.value();
|
||||
ref_index[ref_index.size() - 1 - count_forward] = it.index();
|
||||
count_forward++;
|
||||
}
|
||||
Index count_reverse = 0;
|
||||
for (typename SparseMatrixType::ReverseInnerIterator it(m2, j); it;
|
||||
--it) {
|
||||
VERIFY_IS_APPROX(
|
||||
std::abs(
|
||||
ref_value[ref_value.size() - count_forward + count_reverse]) +
|
||||
1,
|
||||
std::abs(it.value()) + 1);
|
||||
VERIFY_IS_EQUAL(
|
||||
ref_index[ref_index.size() - count_forward + count_reverse],
|
||||
it.index());
|
||||
count_reverse++;
|
||||
}
|
||||
VERIFY_IS_EQUAL(count_forward, count_reverse);
|
||||
}
|
||||
}
|
||||
|
||||
// test transpose
|
||||
{
|
||||
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
||||
SparseMatrixType m2(rows, cols);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
|
||||
VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
|
||||
|
||||
VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
|
||||
|
||||
// check isApprox handles opposite storage order
|
||||
typename Transpose<SparseMatrixType>::PlainObject m3(m2);
|
||||
VERIFY(m2.isApprox(m3));
|
||||
}
|
||||
|
||||
// test prune
|
||||
{
|
||||
SparseMatrixType m2(rows, cols);
|
||||
DenseMatrix refM2(rows, cols);
|
||||
refM2.setZero();
|
||||
int countFalseNonZero = 0;
|
||||
int countTrueNonZero = 0;
|
||||
m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize())));
|
||||
for (Index j = 0; j < m2.cols(); ++j) {
|
||||
for (Index i = 0; i < m2.rows(); ++i) {
|
||||
float x = internal::random<float>(0, 1);
|
||||
if (x < 0.1f) {
|
||||
// do nothing
|
||||
} else if (x < 0.5f) {
|
||||
countFalseNonZero++;
|
||||
m2.insert(i, j) = Scalar(0);
|
||||
} else {
|
||||
countTrueNonZero++;
|
||||
m2.insert(i, j) = Scalar(1);
|
||||
refM2(i, j) = Scalar(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
if (internal::random<bool>()) m2.makeCompressed();
|
||||
VERIFY(countFalseNonZero + countTrueNonZero == m2.nonZeros());
|
||||
if (countTrueNonZero > 0) VERIFY_IS_APPROX(m2, refM2);
|
||||
m2.prune(Scalar(1));
|
||||
VERIFY(countTrueNonZero == m2.nonZeros());
|
||||
VERIFY_IS_APPROX(m2, refM2);
|
||||
}
|
||||
|
||||
// test setFromTriplets
|
||||
{
|
||||
typedef Triplet<Scalar, StorageIndex> TripletType;
|
||||
std::vector<TripletType> triplets;
|
||||
Index ntriplets = rows * cols;
|
||||
triplets.reserve(ntriplets);
|
||||
DenseMatrix refMat_sum = DenseMatrix::Zero(rows, cols);
|
||||
DenseMatrix refMat_prod = DenseMatrix::Zero(rows, cols);
|
||||
DenseMatrix refMat_last = DenseMatrix::Zero(rows, cols);
|
||||
|
||||
for (Index i = 0; i < ntriplets; ++i) {
|
||||
StorageIndex r =
|
||||
internal::random<StorageIndex>(0, StorageIndex(rows - 1));
|
||||
StorageIndex c =
|
||||
internal::random<StorageIndex>(0, StorageIndex(cols - 1));
|
||||
Scalar v = internal::random<Scalar>();
|
||||
triplets.push_back(TripletType(r, c, v));
|
||||
refMat_sum(r, c) += v;
|
||||
if (std::abs(refMat_prod(r, c)) == 0)
|
||||
refMat_prod(r, c) = v;
|
||||
else
|
||||
refMat_prod(r, c) *= v;
|
||||
refMat_last(r, c) = v;
|
||||
}
|
||||
SparseMatrixType m(rows, cols);
|
||||
m.setFromTriplets(triplets.begin(), triplets.end());
|
||||
VERIFY_IS_APPROX(m, refMat_sum);
|
||||
|
||||
m.setFromTriplets(triplets.begin(), triplets.end(),
|
||||
std::multiplies<Scalar>());
|
||||
VERIFY_IS_APPROX(m, refMat_prod);
|
||||
#if (EIGEN_COMP_CXXVER >= 11)
|
||||
m.setFromTriplets(triplets.begin(), triplets.end(),
|
||||
[](Scalar, Scalar b) { return b; });
|
||||
VERIFY_IS_APPROX(m, refMat_last);
|
||||
#endif
|
||||
}
|
||||
|
||||
// test Map
|
||||
{
|
||||
DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
|
||||
SparseMatrixType m2(rows, cols), m3(rows, cols);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
initSparse<Scalar>(density, refMat3, m3);
|
||||
{
|
||||
Map<SparseMatrixType> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(),
|
||||
m2.outerIndexPtr(), m2.innerIndexPtr(),
|
||||
m2.valuePtr(), m2.innerNonZeroPtr());
|
||||
Map<SparseMatrixType> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(),
|
||||
m3.outerIndexPtr(), m3.innerIndexPtr(),
|
||||
m3.valuePtr(), m3.innerNonZeroPtr());
|
||||
VERIFY_IS_APPROX(mapMat2 + mapMat3, refMat2 + refMat3);
|
||||
VERIFY_IS_APPROX(mapMat2 + mapMat3, refMat2 + refMat3);
|
||||
}
|
||||
{
|
||||
MappedSparseMatrix<Scalar, SparseMatrixType::Options, StorageIndex>
|
||||
mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(),
|
||||
m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
|
||||
MappedSparseMatrix<Scalar, SparseMatrixType::Options, StorageIndex>
|
||||
mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(),
|
||||
m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
|
||||
VERIFY_IS_APPROX(mapMat2 + mapMat3, refMat2 + refMat3);
|
||||
VERIFY_IS_APPROX(mapMat2 + mapMat3, refMat2 + refMat3);
|
||||
}
|
||||
|
||||
Index i = internal::random<Index>(0, rows - 1);
|
||||
Index j = internal::random<Index>(0, cols - 1);
|
||||
m2.coeffRef(i, j) = 123;
|
||||
if (internal::random<bool>()) m2.makeCompressed();
|
||||
Map<SparseMatrixType> mapMat2(rows, cols, m2.nonZeros(), m2.outerIndexPtr(),
|
||||
m2.innerIndexPtr(), m2.valuePtr(),
|
||||
m2.innerNonZeroPtr());
|
||||
VERIFY_IS_EQUAL(m2.coeff(i, j), Scalar(123));
|
||||
VERIFY_IS_EQUAL(mapMat2.coeff(i, j), Scalar(123));
|
||||
mapMat2.coeffRef(i, j) = -123;
|
||||
VERIFY_IS_EQUAL(m2.coeff(i, j), Scalar(-123));
|
||||
}
|
||||
|
||||
// test triangularView
|
||||
{
|
||||
DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
|
||||
SparseMatrixType m2(rows, cols), m3(rows, cols);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
refMat3 = refMat2.template triangularView<Lower>();
|
||||
m3 = m2.template triangularView<Lower>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
refMat3 = refMat2.template triangularView<Upper>();
|
||||
m3 = m2.template triangularView<Upper>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
{
|
||||
refMat3 = refMat2.template triangularView<UnitUpper>();
|
||||
m3 = m2.template triangularView<UnitUpper>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
refMat3 = refMat2.template triangularView<UnitLower>();
|
||||
m3 = m2.template triangularView<UnitLower>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
}
|
||||
|
||||
refMat3 = refMat2.template triangularView<StrictlyUpper>();
|
||||
m3 = m2.template triangularView<StrictlyUpper>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
refMat3 = refMat2.template triangularView<StrictlyLower>();
|
||||
m3 = m2.template triangularView<StrictlyLower>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
// check sparse-triangular to dense
|
||||
refMat3 = m2.template triangularView<StrictlyUpper>();
|
||||
VERIFY_IS_APPROX(
|
||||
refMat3, DenseMatrix(refMat2.template triangularView<StrictlyUpper>()));
|
||||
}
|
||||
|
||||
// test selfadjointView
|
||||
if (!SparseMatrixType::IsRowMajor) {
|
||||
DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
|
||||
SparseMatrixType m2(rows, rows), m3(rows, rows);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
refMat3 = refMat2.template selfadjointView<Lower>();
|
||||
m3 = m2.template selfadjointView<Lower>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
refMat3 += refMat2.template selfadjointView<Lower>();
|
||||
m3 += m2.template selfadjointView<Lower>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
refMat3 -= refMat2.template selfadjointView<Lower>();
|
||||
m3 -= m2.template selfadjointView<Lower>();
|
||||
VERIFY_IS_APPROX(m3, refMat3);
|
||||
|
||||
// selfadjointView only works for square matrices:
|
||||
SparseMatrixType m4(rows, rows + 1);
|
||||
VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>());
|
||||
VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>());
|
||||
}
|
||||
|
||||
// test sparseView
|
||||
{
|
||||
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
|
||||
SparseMatrixType m2(rows, rows);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval());
|
||||
|
||||
// sparse view on expressions:
|
||||
VERIFY_IS_APPROX((s1 * m2).eval(), (s1 * refMat2).sparseView().eval());
|
||||
VERIFY_IS_APPROX((m2 + m2).eval(), (refMat2 + refMat2).sparseView().eval());
|
||||
VERIFY_IS_APPROX((m2 * m2).eval(),
|
||||
(refMat2.lazyProduct(refMat2)).sparseView().eval());
|
||||
VERIFY_IS_APPROX((m2 * m2).eval(), (refMat2 * refMat2).sparseView().eval());
|
||||
}
|
||||
|
||||
// test diagonal
|
||||
{
|
||||
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
||||
SparseMatrixType m2(rows, cols);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
|
||||
DenseVector d = m2.diagonal();
|
||||
VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
|
||||
d = m2.diagonal().array();
|
||||
VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
|
||||
VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(),
|
||||
refMat2.diagonal().eval());
|
||||
|
||||
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag);
|
||||
m2.diagonal() += refMat2.diagonal();
|
||||
refMat2.diagonal() += refMat2.diagonal();
|
||||
VERIFY_IS_APPROX(m2, refMat2);
|
||||
}
|
||||
|
||||
// test diagonal to sparse
|
||||
{
|
||||
DenseVector d = DenseVector::Random(rows);
|
||||
DenseMatrix refMat2 = d.asDiagonal();
|
||||
SparseMatrixType m2;
|
||||
m2 = d.asDiagonal();
|
||||
VERIFY_IS_APPROX(m2, refMat2);
|
||||
SparseMatrixType m3(d.asDiagonal());
|
||||
VERIFY_IS_APPROX(m3, refMat2);
|
||||
refMat2 += d.asDiagonal();
|
||||
m2 += d.asDiagonal();
|
||||
VERIFY_IS_APPROX(m2, refMat2);
|
||||
m2.setZero();
|
||||
m2 += d.asDiagonal();
|
||||
refMat2.setZero();
|
||||
refMat2 += d.asDiagonal();
|
||||
VERIFY_IS_APPROX(m2, refMat2);
|
||||
m2.setZero();
|
||||
m2 -= d.asDiagonal();
|
||||
refMat2.setZero();
|
||||
refMat2 -= d.asDiagonal();
|
||||
VERIFY_IS_APPROX(m2, refMat2);
|
||||
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
m2.makeCompressed();
|
||||
m2 += d.asDiagonal();
|
||||
refMat2 += d.asDiagonal();
|
||||
VERIFY_IS_APPROX(m2, refMat2);
|
||||
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
m2.makeCompressed();
|
||||
VectorXi res(rows);
|
||||
for (Index i = 0; i < rows; ++i) res(i) = internal::random<int>(0, 3);
|
||||
m2.reserve(res);
|
||||
m2 -= d.asDiagonal();
|
||||
refMat2 -= d.asDiagonal();
|
||||
VERIFY_IS_APPROX(m2, refMat2);
|
||||
}
|
||||
|
||||
// test conservative resize
|
||||
{
|
||||
std::vector<std::pair<StorageIndex, StorageIndex> > inc;
|
||||
if (rows > 3 && cols > 2)
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(-3, -2));
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(0, 0));
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(3, 2));
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(3, 0));
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(0, 3));
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(0, -1));
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(-1, 0));
|
||||
inc.push_back(std::pair<StorageIndex, StorageIndex>(-1, -1));
|
||||
|
||||
for (size_t i = 0; i < inc.size(); i++) {
|
||||
StorageIndex incRows = inc[i].first;
|
||||
StorageIndex incCols = inc[i].second;
|
||||
SparseMatrixType m1(rows, cols);
|
||||
DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols);
|
||||
initSparse<Scalar>(density, refMat1, m1);
|
||||
|
||||
SparseMatrixType m2 = m1;
|
||||
m2.makeCompressed();
|
||||
|
||||
m1.conservativeResize(rows + incRows, cols + incCols);
|
||||
m2.conservativeResize(rows + incRows, cols + incCols);
|
||||
refMat1.conservativeResize(rows + incRows, cols + incCols);
|
||||
if (incRows > 0) refMat1.bottomRows(incRows).setZero();
|
||||
if (incCols > 0) refMat1.rightCols(incCols).setZero();
|
||||
|
||||
VERIFY_IS_APPROX(m1, refMat1);
|
||||
VERIFY_IS_APPROX(m2, refMat1);
|
||||
|
||||
// Insert new values
|
||||
if (incRows > 0)
|
||||
m1.insert(m1.rows() - 1, 0) = refMat1(refMat1.rows() - 1, 0) = 1;
|
||||
if (incCols > 0)
|
||||
m1.insert(0, m1.cols() - 1) = refMat1(0, refMat1.cols() - 1) = 1;
|
||||
|
||||
VERIFY_IS_APPROX(m1, refMat1);
|
||||
}
|
||||
}
|
||||
|
||||
// test Identity matrix
|
||||
{
|
||||
DenseMatrix refMat1 = DenseMatrix::Identity(rows, rows);
|
||||
SparseMatrixType m1(rows, rows);
|
||||
m1.setIdentity();
|
||||
VERIFY_IS_APPROX(m1, refMat1);
|
||||
for (int k = 0; k < rows * rows / 4; ++k) {
|
||||
Index i = internal::random<Index>(0, rows - 1);
|
||||
Index j = internal::random<Index>(0, rows - 1);
|
||||
Scalar v = internal::random<Scalar>();
|
||||
m1.coeffRef(i, j) = v;
|
||||
refMat1.coeffRef(i, j) = v;
|
||||
VERIFY_IS_APPROX(m1, refMat1);
|
||||
if (internal::random<Index>(0, 10) < 2) m1.makeCompressed();
|
||||
}
|
||||
m1.setIdentity();
|
||||
refMat1.setIdentity();
|
||||
VERIFY_IS_APPROX(m1, refMat1);
|
||||
}
|
||||
|
||||
// test array/vector of InnerIterator
|
||||
{
|
||||
typedef typename SparseMatrixType::InnerIterator IteratorType;
|
||||
|
||||
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
||||
SparseMatrixType m2(rows, cols);
|
||||
initSparse<Scalar>(density, refMat2, m2);
|
||||
IteratorType static_array[2];
|
||||
static_array[0] = IteratorType(m2, 0);
|
||||
static_array[1] = IteratorType(m2, m2.outerSize() - 1);
|
||||
VERIFY(static_array[0] ||
|
||||
m2.innerVector(static_array[0].outer()).nonZeros() == 0);
|
||||
VERIFY(static_array[1] ||
|
||||
m2.innerVector(static_array[1].outer()).nonZeros() == 0);
|
||||
if (static_array[0] && static_array[1]) {
|
||||
++(static_array[1]);
|
||||
static_array[1] = IteratorType(m2, 0);
|
||||
VERIFY(static_array[1]);
|
||||
VERIFY(static_array[1].index() == static_array[0].index());
|
||||
VERIFY(static_array[1].outer() == static_array[0].outer());
|
||||
VERIFY(static_array[1].value() == static_array[0].value());
|
||||
}
|
||||
|
||||
std::vector<IteratorType> iters(2);
|
||||
iters[0] = IteratorType(m2, 0);
|
||||
iters[1] = IteratorType(m2, m2.outerSize() - 1);
|
||||
}
|
||||
|
||||
// test reserve with empty rows/columns
|
||||
{
|
||||
SparseMatrixType m1(0, cols);
|
||||
m1.reserve(ArrayXi::Constant(m1.outerSize(), 1));
|
||||
SparseMatrixType m2(rows, 0);
|
||||
m2.reserve(ArrayXi::Constant(m2.outerSize(), 1));
|
||||
}
|
||||
}
|
||||
|
||||
template <typename SparseMatrixType>
|
||||
void big_sparse_triplet(Index rows, Index cols, double density) {
|
||||
typedef typename SparseMatrixType::StorageIndex StorageIndex;
|
||||
typedef typename SparseMatrixType::Scalar Scalar;
|
||||
typedef Triplet<Scalar, Index> TripletType;
|
||||
std::vector<TripletType> triplets;
|
||||
double nelements = density * rows * cols;
|
||||
VERIFY(nelements >= 0 &&
|
||||
nelements < static_cast<double>(NumTraits<StorageIndex>::highest()));
|
||||
Index ntriplets = Index(nelements);
|
||||
triplets.reserve(ntriplets);
|
||||
Scalar sum = Scalar(0);
|
||||
for (Index i = 0; i < ntriplets; ++i) {
|
||||
Index r = internal::random<Index>(0, rows - 1);
|
||||
Index c = internal::random<Index>(0, cols - 1);
|
||||
// use positive values to prevent numerical cancellation errors in sum
|
||||
Scalar v = numext::abs(internal::random<Scalar>());
|
||||
triplets.push_back(TripletType(r, c, v));
|
||||
sum += v;
|
||||
}
|
||||
SparseMatrixType m(rows, cols);
|
||||
m.setFromTriplets(triplets.begin(), triplets.end());
|
||||
VERIFY(m.nonZeros() <= ntriplets);
|
||||
VERIFY_IS_APPROX(sum, m.sum());
|
||||
}
|
||||
|
||||
template <int>
|
||||
void bug1105() {
|
||||
// Regression test for bug 1105
|
||||
int n = Eigen::internal::random<int>(200, 600);
|
||||
SparseMatrix<std::complex<double>, 0, long> mat(n, n);
|
||||
std::complex<double> val;
|
||||
|
||||
for (int i = 0; i < n; ++i) {
|
||||
mat.coeffRef(i, i % (n / 10)) = val;
|
||||
VERIFY(mat.data().allocatedSize() < 20 * n);
|
||||
}
|
||||
}
|
||||
|
||||
#ifndef EIGEN_SPARSE_TEST_INCLUDED_FROM_SPARSE_EXTRA
|
||||
|
||||
EIGEN_DECLARE_TEST(sparse_basic) {
|
||||
g_dense_op_sparse_count = 0; // Suppresses compiler warning.
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
int r = Eigen::internal::random<int>(1, 200),
|
||||
c = Eigen::internal::random<int>(1, 200);
|
||||
if (Eigen::internal::random<int>(0, 4) == 0) {
|
||||
r = c; // check square matrices in 25% of tries
|
||||
}
|
||||
EIGEN_UNUSED_VARIABLE(r + c);
|
||||
CALL_SUBTEST_1((sparse_basic(SparseMatrix<double>(1, 1))));
|
||||
CALL_SUBTEST_1((sparse_basic(SparseMatrix<double>(8, 8))));
|
||||
CALL_SUBTEST_2(
|
||||
(sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c))));
|
||||
CALL_SUBTEST_2(
|
||||
(sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c))));
|
||||
CALL_SUBTEST_1((sparse_basic(SparseMatrix<double>(r, c))));
|
||||
CALL_SUBTEST_5(
|
||||
(sparse_basic(SparseMatrix<double, ColMajor, long int>(r, c))));
|
||||
CALL_SUBTEST_5(
|
||||
(sparse_basic(SparseMatrix<double, RowMajor, long int>(r, c))));
|
||||
|
||||
r = Eigen::internal::random<int>(1, 100);
|
||||
c = Eigen::internal::random<int>(1, 100);
|
||||
if (Eigen::internal::random<int>(0, 4) == 0) {
|
||||
r = c; // check square matrices in 25% of tries
|
||||
}
|
||||
|
||||
CALL_SUBTEST_6((sparse_basic(
|
||||
SparseMatrix<double, ColMajor, short int>(short(r), short(c)))));
|
||||
CALL_SUBTEST_6((sparse_basic(
|
||||
SparseMatrix<double, RowMajor, short int>(short(r), short(c)))));
|
||||
}
|
||||
|
||||
// Regression test for bug 900: (manually insert higher values here, if you
|
||||
// have enough RAM):
|
||||
CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(
|
||||
10000, 10000, 0.125)));
|
||||
CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(
|
||||
10000, 10000, 0.125)));
|
||||
|
||||
CALL_SUBTEST_7(bug1105<0>());
|
||||
}
|
||||
#endif
|
||||
Reference in New Issue
Block a user