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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#define TEST_ENABLE_TEMPORARY_TRACKING
#include "main.h"
using namespace std;
template<typename MatrixType> void permutationmatrices(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options };
typedef PermutationMatrix<Rows> LeftPermutationType;
typedef Matrix<int, Rows, 1> LeftPermutationVectorType;
typedef Map<LeftPermutationType> MapLeftPerm;
typedef PermutationMatrix<Cols> RightPermutationType;
typedef Matrix<int, Cols, 1> RightPermutationVectorType;
typedef Map<RightPermutationType> MapRightPerm;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m_original = MatrixType::Random(rows,cols);
LeftPermutationVectorType lv;
randomPermutationVector(lv, rows);
LeftPermutationType lp(lv);
RightPermutationVectorType rv;
randomPermutationVector(rv, cols);
RightPermutationType rp(rv);
MatrixType m_permuted = MatrixType::Random(rows,cols);
VERIFY_EVALUATION_COUNT(m_permuted = lp * m_original * rp, 1); // 1 temp for sub expression "lp * m_original"
for (int i=0; i<rows; i++)
for (int j=0; j<cols; j++)
VERIFY_IS_APPROX(m_permuted(lv(i),j), m_original(i,rv(j)));
Matrix<Scalar,Rows,Rows> lm(lp);
Matrix<Scalar,Cols,Cols> rm(rp);
VERIFY_IS_APPROX(m_permuted, lm*m_original*rm);
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted = lp * m_permuted * rp, 1);
VERIFY_IS_APPROX(m_permuted, lm*m_original*rm);
VERIFY_IS_APPROX(lp.inverse()*m_permuted*rp.inverse(), m_original);
VERIFY_IS_APPROX(lv.asPermutation().inverse()*m_permuted*rv.asPermutation().inverse(), m_original);
VERIFY_IS_APPROX(MapLeftPerm(lv.data(),lv.size()).inverse()*m_permuted*MapRightPerm(rv.data(),rv.size()).inverse(), m_original);
VERIFY((lp*lp.inverse()).toDenseMatrix().isIdentity());
VERIFY((lv.asPermutation()*lv.asPermutation().inverse()).toDenseMatrix().isIdentity());
VERIFY((MapLeftPerm(lv.data(),lv.size())*MapLeftPerm(lv.data(),lv.size()).inverse()).toDenseMatrix().isIdentity());
LeftPermutationVectorType lv2;
randomPermutationVector(lv2, rows);
LeftPermutationType lp2(lv2);
Matrix<Scalar,Rows,Rows> lm2(lp2);
VERIFY_IS_APPROX((lp*lp2).toDenseMatrix().template cast<Scalar>(), lm*lm2);
VERIFY_IS_APPROX((lv.asPermutation()*lv2.asPermutation()).toDenseMatrix().template cast<Scalar>(), lm*lm2);
VERIFY_IS_APPROX((MapLeftPerm(lv.data(),lv.size())*MapLeftPerm(lv2.data(),lv2.size())).toDenseMatrix().template cast<Scalar>(), lm*lm2);
LeftPermutationType identityp;
identityp.setIdentity(rows);
VERIFY_IS_APPROX(m_original, identityp*m_original);
// check inplace permutations
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias()= lp.inverse() * m_permuted, 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, lp.inverse()*m_original);
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp.inverse(), 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, m_original*rp.inverse());
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias() = lp * m_permuted, 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, lp*m_original);
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp, 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, m_original*rp);
if(rows>1 && cols>1)
{
lp2 = lp;
Index i = internal::random<Index>(0, rows-1);
Index j;
do j = internal::random<Index>(0, rows-1); while(j==i);
lp2.applyTranspositionOnTheLeft(i, j);
lm = lp;
lm.row(i).swap(lm.row(j));
VERIFY_IS_APPROX(lm, lp2.toDenseMatrix().template cast<Scalar>());
RightPermutationType rp2 = rp;
i = internal::random<Index>(0, cols-1);
do j = internal::random<Index>(0, cols-1); while(j==i);
rp2.applyTranspositionOnTheRight(i, j);
rm = rp;
rm.col(i).swap(rm.col(j));
VERIFY_IS_APPROX(rm, rp2.toDenseMatrix().template cast<Scalar>());
}
{
// simple compilation check
Matrix<Scalar, Cols, Cols> A = rp;
Matrix<Scalar, Cols, Cols> B = rp.transpose();
VERIFY_IS_APPROX(A, B.transpose());
}
}
template<typename T>
void bug890()
{
typedef Matrix<T, Dynamic, Dynamic> MatrixType;
typedef Matrix<T, Dynamic, 1> VectorType;
typedef Stride<Dynamic,Dynamic> S;
typedef Map<MatrixType, Aligned, S> MapType;
typedef PermutationMatrix<Dynamic> Perm;
VectorType v1(2), v2(2), op(4), rhs(2);
v1 << 666,667;
op << 1,0,0,1;
rhs << 42,42;
Perm P(2);
P.indices() << 1, 0;
MapType(v1.data(),2,1,S(1,1)) = P * MapType(rhs.data(),2,1,S(1,1));
VERIFY_IS_APPROX(v1, (P * rhs).eval());
MapType(v1.data(),2,1,S(1,1)) = P.inverse() * MapType(rhs.data(),2,1,S(1,1));
VERIFY_IS_APPROX(v1, (P.inverse() * rhs).eval());
}
void test_permutationmatrices()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( permutationmatrices(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( permutationmatrices(Matrix3f()) );
CALL_SUBTEST_3( permutationmatrices(Matrix<double,3,3,RowMajor>()) );
CALL_SUBTEST_4( permutationmatrices(Matrix4d()) );
CALL_SUBTEST_5( permutationmatrices(Matrix<double,40,60>()) );
CALL_SUBTEST_6( permutationmatrices(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 30)) );
CALL_SUBTEST_7( permutationmatrices(MatrixXcf(15, 10)) );
}
CALL_SUBTEST_5( bug890<double>() );
}
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