// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Benoit Jacob // // 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 or 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" template void matrixVisitor(const MatrixType& p) { typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::Index Index; Index rows = p.rows(); Index cols = p.cols(); // construct a random matrix where all coefficients are different MatrixType m; m = MatrixType::Random(rows, cols); for(Index i = 0; i < m.size(); i++) for(Index i2 = 0; i2 < i; i2++) while(m(i) == m(i2)) // yes, == m(i) = internal::random(); Scalar minc = Scalar(1000), maxc = Scalar(-1000); Index minrow=0,mincol=0,maxrow=0,maxcol=0; for(Index j = 0; j < cols; j++) for(Index i = 0; i < rows; i++) { if(m(i,j) < minc) { minc = m(i,j); minrow = i; mincol = j; } if(m(i,j) > maxc) { maxc = m(i,j); maxrow = i; maxcol = j; } } Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol; Scalar eigen_minc, eigen_maxc; eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol); eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol); VERIFY(minrow == eigen_minrow); VERIFY(maxrow == eigen_maxrow); VERIFY(mincol == eigen_mincol); VERIFY(maxcol == eigen_maxcol); VERIFY_IS_APPROX(minc, eigen_minc); VERIFY_IS_APPROX(maxc, eigen_maxc); VERIFY_IS_APPROX(minc, m.minCoeff()); VERIFY_IS_APPROX(maxc, m.maxCoeff()); } template void vectorVisitor(const VectorType& w) { typedef typename VectorType::Scalar Scalar; typedef typename VectorType::Index Index; Index size = w.size(); // construct a random vector where all coefficients are different VectorType v; v = VectorType::Random(size); for(Index i = 0; i < size; i++) for(Index i2 = 0; i2 < i; i2++) while(v(i) == v(i2)) // yes, == v(i) = internal::random(); Scalar minc = Scalar(1000), maxc = Scalar(-1000); Index minidx=0,maxidx=0; for(Index i = 0; i < size; i++) { if(v(i) < minc) { minc = v(i); minidx = i; } if(v(i) > maxc) { maxc = v(i); maxidx = i; } } Index eigen_minidx, eigen_maxidx; Scalar eigen_minc, eigen_maxc; eigen_minc = v.minCoeff(&eigen_minidx); eigen_maxc = v.maxCoeff(&eigen_maxidx); VERIFY(minidx == eigen_minidx); VERIFY(maxidx == eigen_maxidx); VERIFY_IS_APPROX(minc, eigen_minc); VERIFY_IS_APPROX(maxc, eigen_maxc); VERIFY_IS_APPROX(minc, v.minCoeff()); VERIFY_IS_APPROX(maxc, v.maxCoeff()); } void test_visitor() { for(int i = 0; i < g_repeat; i++) { CALL_SUBTEST_1( matrixVisitor(Matrix()) ); CALL_SUBTEST_2( matrixVisitor(Matrix2f()) ); CALL_SUBTEST_3( matrixVisitor(Matrix4d()) ); CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) ); CALL_SUBTEST_5( matrixVisitor(Matrix(20, 20)) ); CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) ); } for(int i = 0; i < g_repeat; i++) { CALL_SUBTEST_7( vectorVisitor(Vector4f()) ); CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) ); CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) ); CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) ); } }