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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 Eigen;
template <typename Scalar, int Storage>
void run_matrix_tests()
{
typedef Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Storage> MatrixType;
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, Eigen::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, Eigen::RowMajor>()));
CALL_SUBTEST_1((run_matrix_tests<int, Eigen::ColMajor>()));
CALL_SUBTEST_2((run_matrix_tests<float, Eigen::RowMajor>()));
CALL_SUBTEST_2((run_matrix_tests<float, Eigen::ColMajor>()));
CALL_SUBTEST_3((run_matrix_tests<double, Eigen::RowMajor>()));
CALL_SUBTEST_3((run_matrix_tests<double, Eigen::ColMajor>()));
CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, Eigen::RowMajor>()));
CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, Eigen::ColMajor>()));
CALL_SUBTEST_5((run_matrix_tests<std::complex<double>, Eigen::RowMajor>()));
CALL_SUBTEST_6((run_matrix_tests<std::complex<double>, Eigen::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> >()));
}
}