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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@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"
#include <Eigen/Core>
using namespace StormEigen;
template <typename Scalar, int Storage> void run_matrix_tests() { typedef Matrix<Scalar, StormEigen::Dynamic, StormEigen::Dynamic, Storage> MatrixType; typedef typename MatrixType::Index Index;
MatrixType m, n;
// boundary cases ...
m = n = MatrixType::Random(50,50); m.conservativeResize(1,50); VERIFY_IS_APPROX(m, n.block(0,0,1,50));
m = n = MatrixType::Random(50,50); m.conservativeResize(50,1); VERIFY_IS_APPROX(m, n.block(0,0,50,1));
m = n = MatrixType::Random(50,50); m.conservativeResize(50,50); VERIFY_IS_APPROX(m, n.block(0,0,50,50));
// random shrinking ...
for (int i=0; i<25; ++i) { const Index rows = internal::random<Index>(1,50); const Index cols = internal::random<Index>(1,50); m = n = MatrixType::Random(50,50); m.conservativeResize(rows,cols); VERIFY_IS_APPROX(m, n.block(0,0,rows,cols)); }
// random growing with zeroing ...
for (int i=0; i<25; ++i) { const Index rows = internal::random<Index>(50,75); const Index cols = internal::random<Index>(50,75); m = n = MatrixType::Random(50,50); m.conservativeResizeLike(MatrixType::Zero(rows,cols)); VERIFY_IS_APPROX(m.block(0,0,n.rows(),n.cols()), n); VERIFY( rows<=50 || m.block(50,0,rows-50,cols).sum() == Scalar(0) ); VERIFY( cols<=50 || m.block(0,50,rows,cols-50).sum() == Scalar(0) ); } }
template <typename Scalar> void run_vector_tests() { typedef Matrix<Scalar, 1, StormEigen::Dynamic> VectorType;
VectorType m, n;
// boundary cases ...
m = n = VectorType::Random(50); m.conservativeResize(1); VERIFY_IS_APPROX(m, n.segment(0,1));
m = n = VectorType::Random(50); m.conservativeResize(50); VERIFY_IS_APPROX(m, n.segment(0,50)); m = n = VectorType::Random(50); m.conservativeResize(m.rows(),1); VERIFY_IS_APPROX(m, n.segment(0,1));
m = n = VectorType::Random(50); m.conservativeResize(m.rows(),50); VERIFY_IS_APPROX(m, n.segment(0,50));
// random shrinking ...
for (int i=0; i<50; ++i) { const int size = internal::random<int>(1,50); m = n = VectorType::Random(50); m.conservativeResize(size); VERIFY_IS_APPROX(m, n.segment(0,size)); m = n = VectorType::Random(50); m.conservativeResize(m.rows(), size); VERIFY_IS_APPROX(m, n.segment(0,size)); }
// random growing with zeroing ...
for (int i=0; i<50; ++i) { const int size = internal::random<int>(50,100); m = n = VectorType::Random(50); m.conservativeResizeLike(VectorType::Zero(size)); VERIFY_IS_APPROX(m.segment(0,50), n); VERIFY( size<=50 || m.segment(50,size-50).sum() == Scalar(0) ); m = n = VectorType::Random(50); m.conservativeResizeLike(Matrix<Scalar,Dynamic,Dynamic>::Zero(1,size)); VERIFY_IS_APPROX(m.segment(0,50), n); VERIFY( size<=50 || m.segment(50,size-50).sum() == Scalar(0) ); } }
void test_conservative_resize() { for(int i=0; i<g_repeat; ++i) { CALL_SUBTEST_1((run_matrix_tests<int, StormEigen::RowMajor>())); CALL_SUBTEST_1((run_matrix_tests<int, StormEigen::ColMajor>())); CALL_SUBTEST_2((run_matrix_tests<float, StormEigen::RowMajor>())); CALL_SUBTEST_2((run_matrix_tests<float, StormEigen::ColMajor>())); CALL_SUBTEST_3((run_matrix_tests<double, StormEigen::RowMajor>())); CALL_SUBTEST_3((run_matrix_tests<double, StormEigen::ColMajor>())); CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, StormEigen::RowMajor>())); CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, StormEigen::ColMajor>())); CALL_SUBTEST_5((run_matrix_tests<std::complex<double>, StormEigen::RowMajor>())); CALL_SUBTEST_6((run_matrix_tests<std::complex<double>, StormEigen::ColMajor>()));
CALL_SUBTEST_1((run_vector_tests<int>())); CALL_SUBTEST_2((run_vector_tests<float>())); CALL_SUBTEST_3((run_vector_tests<double>())); CALL_SUBTEST_4((run_vector_tests<std::complex<float> >())); CALL_SUBTEST_5((run_vector_tests<std::complex<double> >())); } }
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