// This file is part of Eigen, a lightweight C++ template library // for linear algebra. Eigen itself is part of the KDE project. // // Copyright (C) 2009 Hauke Heibel // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or1 FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #include "main.h" #include using namespace Eigen; template void run_matrix_tests() { typedef Matrix 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(1,50); const Index cols = internal::random(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(50,75); const Index cols = internal::random(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 void run_vector_tests() { typedef Matrix MatrixType; MatrixType m, n; // boundary cases ... m = n = MatrixType::Random(50); m.conservativeResize(1); VERIFY_IS_APPROX(m, n.segment(0,1)); m = n = MatrixType::Random(50); m.conservativeResize(50); VERIFY_IS_APPROX(m, n.segment(0,50)); // random shrinking ... for (int i=0; i<50; ++i) { const int size = internal::random(1,50); m = n = MatrixType::Random(50); m.conservativeResize(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(50,100); m = n = MatrixType::Random(50); m.conservativeResizeLike(MatrixType::Zero(size)); VERIFY_IS_APPROX(m.segment(0,50), n); VERIFY( size<=50 || m.segment(50,size-50).sum() == Scalar(0) ); } } void test_conservative_resize() { CALL_SUBTEST_1((run_matrix_tests())); CALL_SUBTEST_1((run_matrix_tests())); CALL_SUBTEST_2((run_matrix_tests())); CALL_SUBTEST_2((run_matrix_tests())); CALL_SUBTEST_3((run_matrix_tests())); CALL_SUBTEST_3((run_matrix_tests())); CALL_SUBTEST_4((run_matrix_tests, Eigen::RowMajor>())); CALL_SUBTEST_4((run_matrix_tests, Eigen::ColMajor>())); CALL_SUBTEST_5((run_matrix_tests, Eigen::RowMajor>())); CALL_SUBTEST_6((run_matrix_tests, Eigen::ColMajor>())); CALL_SUBTEST_1((run_vector_tests())); CALL_SUBTEST_2((run_vector_tests())); CALL_SUBTEST_3((run_vector_tests())); CALL_SUBTEST_4((run_vector_tests >())); CALL_SUBTEST_5((run_vector_tests >())); }