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160 lines
7.8 KiB
C++
160 lines
7.8 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) 2006-2008 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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#include <Eigen/Array>
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template<typename MatrixType> void product_extra(const MatrixType& m)
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{
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::FloatingPoint FloatingPoint;
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typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
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typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
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typedef Matrix<Scalar, Dynamic, Dynamic,
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MatrixType::Flags&RowMajorBit> OtherMajorMatrixType;
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int rows = m.rows();
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int cols = m.cols();
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MatrixType m1 = MatrixType::Random(rows, cols),
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m2 = MatrixType::Random(rows, cols),
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m3(rows, cols),
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mzero = MatrixType::Zero(rows, cols),
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identity = MatrixType::Identity(rows, rows),
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square = MatrixType::Random(rows, rows),
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res = MatrixType::Random(rows, rows),
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square2 = MatrixType::Random(cols, cols),
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res2 = MatrixType::Random(cols, cols);
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RowVectorType v1 = RowVectorType::Random(rows), vrres(rows);
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// v2 = RowVectorType::Random(rows),
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// vzero = RowVectorType::Zero(rows);
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ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
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OtherMajorMatrixType tm1 = m1;
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Scalar s1 = ei_random<Scalar>(),
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s2 = ei_random<Scalar>(),
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s3 = ei_random<Scalar>();
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int c0 = ei_random<int>(0,cols/2-1),
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c1 = ei_random<int>(cols/2,cols),
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r0 = ei_random<int>(0,rows/2-1),
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r1 = ei_random<int>(rows/2,rows);
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// all the expressions in this test should be compiled as a single matrix product
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// TODO: add internal checks to verify that
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/*
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VERIFY_IS_APPROX(m1 * m2.adjoint(), m1 * m2.adjoint().eval());
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VERIFY_IS_APPROX(m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval());
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VERIFY_IS_APPROX(m1.adjoint() * m2, m1.adjoint().eval() * m2);
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VERIFY_IS_APPROX( (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2);
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VERIFY_IS_APPROX( (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval());
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VERIFY_IS_APPROX( (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2);
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VERIFY_IS_APPROX( (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval());
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// a very tricky case where a scale factor has to be automatically conjugated:
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VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval());
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// test all possible conjugate combinations for the four matrix-vector product cases:
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// std::cerr << "a\n";
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VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2),
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(-m1.conjugate()*s2).eval() * (s1 * vc2).eval());
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VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()),
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(-m1*s2).eval() * (s1 * vc2.conjugate()).eval());
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VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()),
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(-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval());
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// std::cerr << "b\n";
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VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2),
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(s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval());
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VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2),
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(s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval());
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VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2),
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(s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval());
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// std::cerr << "c\n";
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VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()),
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(-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval());
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VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()),
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(-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval());
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VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
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(-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
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// std::cerr << "d\n";
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VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2),
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(s1 * v1).eval() * (-m1.conjugate()*s2).eval());
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VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2),
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(s1 * v1.conjugate()).eval() * (-m1*s2).eval());
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VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2),
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(s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval());
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VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
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(-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
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*/
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// test with sub matrices
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m2 = m1;
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m3 = m1;
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// std::cerr << (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).rows() << " " << (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).cols() << " == " << vrres.segment(r0,r1-r0).rows() << " " << vrres.segment(r0,r1-r0).cols() << "\n";
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// m2.col(c0).segment(0,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).lazy();
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// m3.col(c0).segment(0,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).eval();
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Matrix<Scalar,Dynamic,1> a = m2.col(c0), b = a;
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a.segment(5,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).lazy();
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b.segment(5,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).eval();
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// m2.col(c0).segment(0,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).lazy();
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// m3.col(c0).segment(0,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).eval();
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// if (!m2.isApprox(m3))
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std::cerr << (a-b).cwise().abs().maxCoeff() << "\n";
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VERIFY_IS_APPROX(a,b);
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// VERIFY_IS_APPROX( vrres.segment(0,r1-r0).transpose().eval(),
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// v1.segment(0,r1-r0).transpose() + m1.block(r0,c0, r1-r0, c1-c0).eval() * (vc2.segment(c0,c1-c0)).eval());
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}
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void test_product_extra()
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{
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for(int i = 0; i < g_repeat; i++) {
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int rows = ei_random<int>(2,10);
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int cols = ei_random<int>(2,10);
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int c0 = ei_random<int>(0,cols/2-1),
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c1 = ei_random<int>(cols/2,cols),
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r0 = ei_random<int>(0,rows/2-1),
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r1 = ei_random<int>(rows/2,rows);
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MatrixXf m1 = MatrixXf::Random(rows,cols), m2 = m1;
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Matrix<float,Dynamic,1> a = m2.col(c0), b = a;
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Matrix<float,Dynamic,1> vc2 = Matrix<float,Dynamic,1>::Random(cols);
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if (1+r1-r0<rows) {
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a.segment(1,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).lazy();
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b.segment(1,r1-r0) += (m1.block(r0,c0, r1-r0, c1-c0) * vc2.segment(c0,c1-c0)).eval();
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VERIFY_IS_APPROX(a,b);
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}
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// CALL_SUBTEST( product_extra(MatrixXf(ei_random<int>(1,320), ei_random<int>(1,320))) );
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// CALL_SUBTEST( product_extra(MatrixXd(ei_random<int>(1,320), ei_random<int>(1,320))) );
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// CALL_SUBTEST( product(MatrixXi(ei_random<int>(1,320), ei_random<int>(1,320))) );
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// CALL_SUBTEST( product_extra(MatrixXcf(ei_random<int>(50,50), ei_random<int>(50,50))) );
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// CALL_SUBTEST( product(Matrix<float,Dynamic,Dynamic,RowMajor>(ei_random<int>(1,320), ei_random<int>(1,320))) );
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}
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}
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