blob: 3b118f9c7deb08808f4ed69736685b088b36e525 [file] [log] [blame]
 // This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Benoit Jacob // // 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 void matrixVisitor(const MatrixType& p) { typedef typename MatrixType::Scalar Scalar; 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(numext::equal_strict(m(i), m(i2))) // yes, strict equality 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()); eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol); Index maxrow2=0,maxcol2=0; eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow2,&maxcol2); VERIFY(maxrow2 == eigen_maxrow); VERIFY(maxcol2 == eigen_maxcol); if (!NumTraits::IsInteger && m.size() > 2) { // Test NaN propagation by replacing an element with NaN. bool stop = false; for (Index j = 0; j < cols && !stop; ++j) { for (Index i = 0; i < rows && !stop; ++i) { if (!(j == mincol && i == minrow) && !(j == maxcol && i == maxrow)) { m(i,j) = NumTraits::quiet_NaN(); stop = true; break; } } } eigen_minc = m.template minCoeff(&eigen_minrow, &eigen_mincol); eigen_maxc = m.template 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.template minCoeff()); VERIFY_IS_APPROX(maxc, m.template maxCoeff()); eigen_minc = m.template minCoeff(&eigen_minrow, &eigen_mincol); eigen_maxc = m.template maxCoeff(&eigen_maxrow, &eigen_maxcol); VERIFY(minrow != eigen_minrow || mincol != eigen_mincol); VERIFY(maxrow != eigen_maxrow || maxcol != eigen_maxcol); VERIFY((numext::isnan)(eigen_minc)); VERIFY((numext::isnan)(eigen_maxc)); } } template void vectorVisitor(const VectorType& w) { typedef typename VectorType::Scalar Scalar; 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 = 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(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)); if (!NumTraits::IsInteger && size > 2) { // Test NaN propagation by replacing an element with NaN. for (Index i = 0; i < size; ++i) { if (i != minidx && i != maxidx) { v(i) = NumTraits::quiet_NaN(); break; } } eigen_minc = v.template minCoeff(&eigen_minidx); eigen_maxc = v.template 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.template minCoeff()); VERIFY_IS_APPROX(maxc, v.template maxCoeff()); eigen_minc = v.template minCoeff(&eigen_minidx); eigen_maxc = v.template maxCoeff(&eigen_maxidx); VERIFY(minidx != eigen_minidx); VERIFY(maxidx != eigen_maxidx); VERIFY((numext::isnan)(eigen_minc)); VERIFY((numext::isnan)(eigen_maxc)); } } template struct TrackedVisitor { void init(T v, Index i, Index j) { return this->operator()(v,i,j); } void operator()(T v, Index i, Index j) { EIGEN_UNUSED_VARIABLE(v) visited.push_back({i, j}); vectorized = false; } template void packet(Packet p, Index i, Index j) { EIGEN_UNUSED_VARIABLE(p) visited.push_back({i, j}); vectorized = true; } std::vector> visited; bool vectorized; }; namespace Eigen { namespace internal { template struct functor_traits > { enum { PacketAccess = Vectorizable, Cost = 1 }; }; } // namespace internal } // namespace Eigen void checkOptimalTraversal() { // Unrolled - ColMajor. { Eigen::Matrix4f X = Eigen::Matrix4f::Random(); TrackedVisitor visitor; X.visit(visitor); Index count = 0; for (Index j=0; j; { Matrix4fRowMajor X = Matrix4fRowMajor::Random(); TrackedVisitor visitor; X.visit(visitor); Index count = 0; for (Index i=0; i visitor; X.visit(visitor); Index count = 0; for (Index j=0; j; { MatrixXfRowMajor X = MatrixXfRowMajor::Random(4, 4); TrackedVisitor visitor; X.visit(visitor); Index count = 0; for (Index i=0; i::size; Eigen::MatrixXf X = Eigen::MatrixXf::Random(4 * PacketSize, 4 * PacketSize); TrackedVisitor visitor; X.visit(visitor); Index previ = -1; Index prevj = 0; for (const auto& p : visitor.visited) { Index i = p.first; Index j = p.second; VERIFY( (j == prevj && i == previ + 1) // Advance single element || (j == prevj && i == previ + PacketSize) // Advance packet || (j == prevj + 1 && i == 0) // Advance column ); previ = i; prevj = j; } if (Eigen::internal::packet_traits::Vectorizable) { VERIFY(visitor.vectorized); } } // Vectorized - RowMajor. { // Ensure rows/cols is larger than packet size. constexpr int PacketSize = Eigen::internal::packet_traits::size; MatrixXfRowMajor X = MatrixXfRowMajor::Random(4 * PacketSize, 4 * PacketSize); TrackedVisitor visitor; X.visit(visitor); Index previ = 0; Index prevj = -1; for (const auto& p : visitor.visited) { Index i = p.first; Index j = p.second; VERIFY( (i == previ && j == prevj + 1) // Advance single element || (i == previ && j == prevj + PacketSize) // Advance packet || (i == previ + 1 && j == 0) // Advance row ); previ = i; prevj = j; } if (Eigen::internal::packet_traits::Vectorizable) { VERIFY(visitor.vectorized); } } } EIGEN_DECLARE_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_7( vectorVisitor(Matrix()) ); CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) ); CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) ); CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) ); } CALL_SUBTEST_11(checkOptimalTraversal()); }