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174 lines
7.5 KiB
C++
174 lines
7.5 KiB
C++
// 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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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "main.h"
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template<typename MatrixType> void matrixRedux(const MatrixType& m)
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{
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typedef typename MatrixType::Index Index;
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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Index rows = m.rows();
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Index cols = m.cols();
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MatrixType m1 = MatrixType::Random(rows, cols);
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// The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
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// failures if we underflow into denormals. Thus, we scale so that entires are close to 1.
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MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + Scalar(0.2) * m1;
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VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
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VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
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Scalar s(0), p(1), minc(internal::real(m1.coeff(0))), maxc(internal::real(m1.coeff(0)));
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for(int j = 0; j < cols; j++)
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for(int i = 0; i < rows; i++)
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{
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s += m1(i,j);
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p *= m1_for_prod(i,j);
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minc = (std::min)(internal::real(minc), internal::real(m1(i,j)));
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maxc = (std::max)(internal::real(maxc), internal::real(m1(i,j)));
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}
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const Scalar mean = s/Scalar(RealScalar(rows*cols));
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VERIFY_IS_APPROX(m1.sum(), s);
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VERIFY_IS_APPROX(m1.mean(), mean);
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VERIFY_IS_APPROX(m1_for_prod.prod(), p);
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VERIFY_IS_APPROX(m1.real().minCoeff(), internal::real(minc));
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VERIFY_IS_APPROX(m1.real().maxCoeff(), internal::real(maxc));
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// test slice vectorization assuming assign is ok
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Index r0 = internal::random<Index>(0,rows-1);
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Index c0 = internal::random<Index>(0,cols-1);
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Index r1 = internal::random<Index>(r0+1,rows)-r0;
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Index c1 = internal::random<Index>(c0+1,cols)-c0;
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VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
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VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
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VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
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VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
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VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
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// test empty objects
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VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
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VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
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}
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template<typename VectorType> void vectorRedux(const VectorType& w)
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{
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typedef typename VectorType::Index Index;
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typedef typename VectorType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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Index size = w.size();
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VectorType v = VectorType::Random(size);
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VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
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for(int i = 1; i < size; i++)
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{
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Scalar s(0), p(1);
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RealScalar minc(internal::real(v.coeff(0))), maxc(internal::real(v.coeff(0)));
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for(int j = 0; j < i; j++)
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{
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s += v[j];
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p *= v_for_prod[j];
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minc = (std::min)(minc, internal::real(v[j]));
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maxc = (std::max)(maxc, internal::real(v[j]));
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}
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VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.head(i).sum()), Scalar(1));
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VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
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VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
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VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
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}
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for(int i = 0; i < size-1; i++)
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{
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Scalar s(0), p(1);
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RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i)));
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for(int j = i; j < size; j++)
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{
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s += v[j];
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p *= v_for_prod[j];
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minc = (std::min)(minc, internal::real(v[j]));
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maxc = (std::max)(maxc, internal::real(v[j]));
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}
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VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.tail(size-i).sum()), Scalar(1));
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VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
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VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
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VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
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}
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for(int i = 0; i < size/2; i++)
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{
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Scalar s(0), p(1);
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RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i)));
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for(int j = i; j < size-i; j++)
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{
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s += v[j];
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p *= v_for_prod[j];
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minc = (std::min)(minc, internal::real(v[j]));
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maxc = (std::max)(maxc, internal::real(v[j]));
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}
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VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
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VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
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VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
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VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
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}
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// test empty objects
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VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
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VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
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VERIFY_RAISES_ASSERT(v.head(0).mean());
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VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
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VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
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}
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void test_redux()
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{
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// the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
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int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
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EIGEN_UNUSED_VARIABLE(maxsize);
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
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CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
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CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
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CALL_SUBTEST_2( matrixRedux(Array2f()) );
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CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
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CALL_SUBTEST_3( matrixRedux(Array4d()) );
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CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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}
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_7( vectorRedux(Vector4f()) );
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CALL_SUBTEST_7( vectorRedux(Array4f()) );
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CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
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CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
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}
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}
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