194 lines
6.2 KiB
C++
194 lines
6.2 KiB
C++
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "main.h"
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template<typename MatrixType> void matrixVisitor(const MatrixType& p)
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{
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typedef typename MatrixType::Scalar Scalar;
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Index rows = p.rows();
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Index cols = p.cols();
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// construct a random matrix where all coefficients are different
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MatrixType m;
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m = MatrixType::Random(rows, cols);
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for(Index i = 0; i < m.size(); i++)
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for(Index i2 = 0; i2 < i; i2++)
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while(m(i) == m(i2)) // yes, ==
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m(i) = internal::random<Scalar>();
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Scalar minc = Scalar(1000), maxc = Scalar(-1000);
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Index minrow=0,mincol=0,maxrow=0,maxcol=0;
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for(Index j = 0; j < cols; j++)
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for(Index i = 0; i < rows; i++)
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{
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if(m(i,j) < minc)
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{
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minc = m(i,j);
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minrow = i;
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mincol = j;
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}
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if(m(i,j) > maxc)
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{
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maxc = m(i,j);
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maxrow = i;
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maxcol = j;
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}
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}
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Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
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Scalar eigen_minc, eigen_maxc;
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eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol);
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eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol);
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VERIFY(minrow == eigen_minrow);
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VERIFY(maxrow == eigen_maxrow);
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VERIFY(mincol == eigen_mincol);
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VERIFY(maxcol == eigen_maxcol);
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VERIFY_IS_APPROX(minc, eigen_minc);
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VERIFY_IS_APPROX(maxc, eigen_maxc);
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VERIFY_IS_APPROX(minc, m.minCoeff());
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VERIFY_IS_APPROX(maxc, m.maxCoeff());
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eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol);
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Index maxrow2=0,maxcol2=0;
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eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow2,&maxcol2);
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VERIFY(maxrow2 == eigen_maxrow);
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VERIFY(maxcol2 == eigen_maxcol);
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if (!NumTraits<Scalar>::IsInteger && m.size() > 2) {
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// Test NaN propagation by replacing an element with NaN.
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bool stop = false;
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for (Index j = 0; j < cols && !stop; ++j) {
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for (Index i = 0; i < rows && !stop; ++i) {
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if (!(j == mincol && i == minrow) &&
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!(j == maxcol && i == maxrow)) {
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m(i,j) = NumTraits<Scalar>::quiet_NaN();
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stop = true;
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break;
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}
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}
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}
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eigen_minc = m.template minCoeff<PropagateNumbers>(&eigen_minrow, &eigen_mincol);
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eigen_maxc = m.template maxCoeff<PropagateNumbers>(&eigen_maxrow, &eigen_maxcol);
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VERIFY(minrow == eigen_minrow);
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VERIFY(maxrow == eigen_maxrow);
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VERIFY(mincol == eigen_mincol);
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VERIFY(maxcol == eigen_maxcol);
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VERIFY_IS_APPROX(minc, eigen_minc);
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VERIFY_IS_APPROX(maxc, eigen_maxc);
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VERIFY_IS_APPROX(minc, m.template minCoeff<PropagateNumbers>());
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VERIFY_IS_APPROX(maxc, m.template maxCoeff<PropagateNumbers>());
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eigen_minc = m.template minCoeff<PropagateNaN>(&eigen_minrow, &eigen_mincol);
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eigen_maxc = m.template maxCoeff<PropagateNaN>(&eigen_maxrow, &eigen_maxcol);
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VERIFY(minrow != eigen_minrow || mincol != eigen_mincol);
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VERIFY(maxrow != eigen_maxrow || maxcol != eigen_maxcol);
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VERIFY((numext::isnan)(eigen_minc));
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VERIFY((numext::isnan)(eigen_maxc));
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}
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}
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template<typename VectorType> void vectorVisitor(const VectorType& w)
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{
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typedef typename VectorType::Scalar Scalar;
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Index size = w.size();
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// construct a random vector where all coefficients are different
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VectorType v;
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v = VectorType::Random(size);
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for(Index i = 0; i < size; i++)
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for(Index i2 = 0; i2 < i; i2++)
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while(v(i) == v(i2)) // yes, ==
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v(i) = internal::random<Scalar>();
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Scalar minc = v(0), maxc = v(0);
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Index minidx=0, maxidx=0;
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for(Index i = 0; i < size; i++)
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{
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if(v(i) < minc)
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{
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minc = v(i);
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minidx = i;
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}
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if(v(i) > maxc)
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{
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maxc = v(i);
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maxidx = i;
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}
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}
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Index eigen_minidx, eigen_maxidx;
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Scalar eigen_minc, eigen_maxc;
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eigen_minc = v.minCoeff(&eigen_minidx);
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eigen_maxc = v.maxCoeff(&eigen_maxidx);
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VERIFY(minidx == eigen_minidx);
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VERIFY(maxidx == eigen_maxidx);
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VERIFY_IS_APPROX(minc, eigen_minc);
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VERIFY_IS_APPROX(maxc, eigen_maxc);
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VERIFY_IS_APPROX(minc, v.minCoeff());
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VERIFY_IS_APPROX(maxc, v.maxCoeff());
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Index idx0 = internal::random<Index>(0,size-1);
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Index idx1 = eigen_minidx;
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Index idx2 = eigen_maxidx;
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VectorType v1(v), v2(v);
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v1(idx0) = v1(idx1);
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v2(idx0) = v2(idx2);
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v1.minCoeff(&eigen_minidx);
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v2.maxCoeff(&eigen_maxidx);
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VERIFY(eigen_minidx == (std::min)(idx0,idx1));
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VERIFY(eigen_maxidx == (std::min)(idx0,idx2));
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if (!NumTraits<Scalar>::IsInteger && size > 2) {
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// Test NaN propagation by replacing an element with NaN.
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for (Index i = 0; i < size; ++i) {
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if (i != minidx && i != maxidx) {
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v(i) = NumTraits<Scalar>::quiet_NaN();
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break;
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}
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}
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eigen_minc = v.template minCoeff<PropagateNumbers>(&eigen_minidx);
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eigen_maxc = v.template maxCoeff<PropagateNumbers>(&eigen_maxidx);
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VERIFY(minidx == eigen_minidx);
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VERIFY(maxidx == eigen_maxidx);
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VERIFY_IS_APPROX(minc, eigen_minc);
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VERIFY_IS_APPROX(maxc, eigen_maxc);
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VERIFY_IS_APPROX(minc, v.template minCoeff<PropagateNumbers>());
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VERIFY_IS_APPROX(maxc, v.template maxCoeff<PropagateNumbers>());
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eigen_minc = v.template minCoeff<PropagateNaN>(&eigen_minidx);
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eigen_maxc = v.template maxCoeff<PropagateNaN>(&eigen_maxidx);
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VERIFY(minidx != eigen_minidx);
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VERIFY(maxidx != eigen_maxidx);
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VERIFY((numext::isnan)(eigen_minc));
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VERIFY((numext::isnan)(eigen_maxc));
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}
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}
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EIGEN_DECLARE_TEST(visitor)
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) );
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CALL_SUBTEST_2( matrixVisitor(Matrix2f()) );
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CALL_SUBTEST_3( matrixVisitor(Matrix4d()) );
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CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) );
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CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
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CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) );
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}
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_7( vectorVisitor(Vector4f()) );
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CALL_SUBTEST_7( vectorVisitor(Matrix<int,12,1>()) );
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CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) );
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CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) );
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CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) );
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}
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}
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