|  | // This file is part of Eigen, a lightweight C++ template library | 
|  | // for linear algebra. | 
|  | // | 
|  | // Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@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" | 
|  |  | 
|  | template<typename MatrixType> 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>(); | 
|  |  | 
|  | 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()); | 
|  |  | 
|  | eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol); | 
|  | eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow,&maxcol); | 
|  | VERIFY(maxrow == eigen_maxrow); | 
|  | VERIFY(maxcol == eigen_maxcol); | 
|  | } | 
|  |  | 
|  | template<typename VectorType> 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>(); | 
|  |  | 
|  | Scalar minc = v(0), maxc = v(0); | 
|  | 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()); | 
|  |  | 
|  | Index idx0 = internal::random<Index>(0,size-1); | 
|  | Index idx1 = eigen_minidx; | 
|  | Index idx2 = eigen_maxidx; | 
|  | VectorType v1(v), v2(v); | 
|  | v1(idx0) = v1(idx1); | 
|  | v2(idx0) = v2(idx2); | 
|  | v1.minCoeff(&eigen_minidx); | 
|  | v2.maxCoeff(&eigen_maxidx); | 
|  | VERIFY(eigen_minidx == (std::min)(idx0,idx1)); | 
|  | VERIFY(eigen_maxidx == (std::min)(idx0,idx2)); | 
|  | } | 
|  |  | 
|  | void test_visitor() | 
|  | { | 
|  | for(int i = 0; i < g_repeat; i++) { | 
|  | CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) ); | 
|  | CALL_SUBTEST_2( matrixVisitor(Matrix2f()) ); | 
|  | CALL_SUBTEST_3( matrixVisitor(Matrix4d()) ); | 
|  | CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) ); | 
|  | CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) ); | 
|  | CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) ); | 
|  | } | 
|  | for(int i = 0; i < g_repeat; i++) { | 
|  | CALL_SUBTEST_7( vectorVisitor(Vector4f()) ); | 
|  | CALL_SUBTEST_7( vectorVisitor(Matrix<int,12,1>()) ); | 
|  | CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) ); | 
|  | CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) ); | 
|  | CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) ); | 
|  | } | 
|  | } |