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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>
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void matrixVisitor_impl(MatrixType& m) {
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typedef typename MatrixType::Scalar Scalar;
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Index rows = m.rows();
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Index cols = m.cols();
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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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if (m(i, j) < minc) {
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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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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) && !(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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// Test matrix of all NaNs.
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m.fill(NumTraits<Scalar>::quiet_NaN());
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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(eigen_minrow == 0);
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VERIFY(eigen_maxrow == 0);
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VERIFY(eigen_mincol == 0);
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VERIFY(eigen_maxcol == 0);
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VERIFY((numext::isnan)(eigen_minc));
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VERIFY((numext::isnan)(eigen_maxc));
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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(eigen_minrow == 0);
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VERIFY(eigen_maxrow == 0);
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VERIFY(eigen_mincol == 0);
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VERIFY(eigen_maxcol == 0);
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VERIFY((numext::isnan)(eigen_minc));
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VERIFY((numext::isnan)(eigen_maxc));
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eigen_minc = m.template minCoeff<PropagateFast>(&eigen_minrow, &eigen_mincol);
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eigen_maxc = m.template maxCoeff<PropagateFast>(&eigen_maxrow, &eigen_maxcol);
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VERIFY(eigen_minrow == 0);
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VERIFY(eigen_maxrow == 0);
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VERIFY(eigen_mincol == 0);
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VERIFY(eigen_maxcol == 0);
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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 MatrixType>
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void matrixVisitor(const MatrixType& p) {
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MatrixType m(p.rows(), p.cols());
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// construct a random matrix where all coefficients are different
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m.setRandom();
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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 (numext::equal_strict(m(i), m(i2))) // yes, strict equality
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m(i) = internal::random<typename DenseBase<MatrixType>::Scalar>();
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MatrixType n = m;
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matrixVisitor_impl(m);
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// force outer-inner access pattern
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using BlockType = Block<MatrixType, Dynamic, Dynamic>;
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BlockType m_block = n.block(0, 0, n.rows(), n.cols());
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matrixVisitor_impl(m_block);
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}
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template <typename VectorType>
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void vectorVisitor(const VectorType& w) {
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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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if (v(i) < minc) {
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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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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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template <typename Derived, bool Vectorizable>
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struct TrackedVisitor {
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using Scalar = typename DenseBase<Derived>::Scalar;
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static constexpr int PacketSize = Eigen::internal::packet_traits<Scalar>::size;
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static constexpr bool RowMajor = Derived::IsRowMajor;
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void init(Scalar v, Index i, Index j) { return this->operator()(v, i, j); }
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template <typename Packet>
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void initpacket(Packet p, Index i, Index j) {
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return this->packet(p, i, j);
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}
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void operator()(Scalar v, Index i, Index j) {
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EIGEN_UNUSED_VARIABLE(v)
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visited.emplace_back(i, j);
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scalarOps++;
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}
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template <typename Packet>
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void packet(Packet p, Index i, Index j) {
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EIGEN_UNUSED_VARIABLE(p)
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for (int k = 0; k < PacketSize; k++)
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if (RowMajor)
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visited.emplace_back(i, j + k);
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else
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visited.emplace_back(i + k, j);
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vectorOps++;
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}
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std::vector<std::pair<Index, Index>> visited;
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Index scalarOps = 0;
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Index vectorOps = 0;
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};
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namespace Eigen {
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namespace internal {
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template <typename T, bool Vectorizable>
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struct functor_traits<TrackedVisitor<T, Vectorizable>> {
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enum { PacketAccess = Vectorizable, LinearAccess = false, Cost = 1 };
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};
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} // namespace internal
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} // namespace Eigen
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template <typename Derived, bool Vectorized>
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void checkOptimalTraversal_impl(const DenseBase<Derived>& mat) {
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using Scalar = typename DenseBase<Derived>::Scalar;
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static constexpr int PacketSize = Eigen::internal::packet_traits<Scalar>::size;
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static constexpr bool RowMajor = Derived::IsRowMajor;
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Derived X(mat.rows(), mat.cols());
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X.setRandom();
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TrackedVisitor<Derived, Vectorized> visitor;
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visitor.visited.reserve(X.size());
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X.visit(visitor);
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Index count = 0;
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for (Index j = 0; j < X.outerSize(); ++j) {
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for (Index i = 0; i < X.innerSize(); ++i) {
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Index r = RowMajor ? j : i;
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Index c = RowMajor ? i : j;
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VERIFY_IS_EQUAL(visitor.visited[count].first, r);
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VERIFY_IS_EQUAL(visitor.visited[count].second, c);
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++count;
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}
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}
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Index vectorOps = Vectorized ? ((X.innerSize() / PacketSize) * X.outerSize()) : 0;
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Index scalarOps = X.size() - (vectorOps * PacketSize);
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VERIFY_IS_EQUAL(vectorOps, visitor.vectorOps);
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VERIFY_IS_EQUAL(scalarOps, visitor.scalarOps);
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}
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void checkOptimalTraversal() {
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using Scalar = float;
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constexpr int PacketSize = Eigen::internal::packet_traits<Scalar>::size;
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// use sizes that mix vector and scalar ops
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constexpr int Rows = 3 * PacketSize + 1;
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constexpr int Cols = 4 * PacketSize + 1;
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int rows = internal::random(PacketSize + 1, EIGEN_TEST_MAX_SIZE);
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int cols = internal::random(PacketSize + 1, EIGEN_TEST_MAX_SIZE);
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using UnrollColMajor = Matrix<Scalar, Rows, Cols, ColMajor>;
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using UnrollRowMajor = Matrix<Scalar, Rows, Cols, RowMajor>;
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using DynamicColMajor = Matrix<Scalar, Dynamic, Dynamic, ColMajor>;
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using DynamicRowMajor = Matrix<Scalar, Dynamic, Dynamic, RowMajor>;
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// Scalar-only visitors
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checkOptimalTraversal_impl<UnrollColMajor, false>(UnrollColMajor(Rows, Cols));
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checkOptimalTraversal_impl<UnrollRowMajor, false>(UnrollRowMajor(Rows, Cols));
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checkOptimalTraversal_impl<DynamicColMajor, false>(DynamicColMajor(rows, cols));
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checkOptimalTraversal_impl<DynamicRowMajor, false>(DynamicRowMajor(rows, cols));
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// Vectorized visitors
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checkOptimalTraversal_impl<UnrollColMajor, true>(UnrollColMajor(Rows, Cols));
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checkOptimalTraversal_impl<UnrollRowMajor, true>(UnrollRowMajor(Rows, Cols));
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checkOptimalTraversal_impl<DynamicColMajor, true>(DynamicColMajor(rows, cols));
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checkOptimalTraversal_impl<DynamicRowMajor, true>(DynamicRowMajor(rows, cols));
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const Eigen::Array<bool, Eigen::Dynamic, 1> a = Eigen::Array<bool, 2, 1>{false, true};
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Eigen::Index i = -1;
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VERIFY(!a.minCoeff(&i));
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VERIFY(i == 0);
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VERIFY(!(!a).minCoeff(&i));
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VERIFY(i == 1);
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Eigen::Index j = -1;
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VERIFY(a.maxCoeff(&j));
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VERIFY(j == 1);
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VERIFY((!a).maxCoeff(&j));
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VERIFY(j == 0);
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}
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EIGEN_DECLARE_TEST(visitor) {
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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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CALL_SUBTEST_11(checkOptimalTraversal());
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}
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