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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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 "sparse.h"
template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols)
{
double densityMat = (std::max)(8./(rows*cols), 0.01);
double densityVec = (std::max)(8./(rows), 0.1);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType;
typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType;
Scalar eps = 1e-6;
SparseMatrixType m1(rows,rows);
SparseVectorType v1(rows), v2(rows), v3(rows);
DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
DenseVector refV1 = DenseVector::Random(rows),
refV2 = DenseVector::Random(rows),
refV3 = DenseVector::Random(rows);
std::vector<int> zerocoords, nonzerocoords;
initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
initSparse<Scalar>(densityMat, refM1, m1);
initSparse<Scalar>(densityVec, refV2, v2);
initSparse<Scalar>(densityVec, refV3, v3);
Scalar s1 = internal::random<Scalar>();
// test coeff and coeffRef
for (unsigned int i=0; i<zerocoords.size(); ++i)
{
VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
//VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
}
{
VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
int j=0;
for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
{
VERIFY(nonzerocoords[j]==it.index());
VERIFY(it.value()==v1.coeff(it.index()));
VERIFY(it.value()==refV1.coeff(it.index()));
}
}
VERIFY_IS_APPROX(v1, refV1);
// test coeffRef with reallocation
{
SparseVectorType v4(rows);
DenseVector v5 = DenseVector::Zero(rows);
for(int k=0; k<rows; ++k)
{
int i = internal::random<int>(0,rows-1);
Scalar v = internal::random<Scalar>();
v4.coeffRef(i) += v;
v5.coeffRef(i) += v;
}
VERIFY_IS_APPROX(v4,v5);
}
v1.coeffRef(nonzerocoords[0]) = Scalar(5);
refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
VERIFY_IS_APPROX(v1, refV1);
VERIFY_IS_APPROX(v1+v2, refV1+refV2);
VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);
VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);
VERIFY_IS_APPROX(v1*=s1, refV1*=s1);
VERIFY_IS_APPROX(v1/=s1, refV1/=s1);
VERIFY_IS_APPROX(v1+=v2, refV1+=refV2);
VERIFY_IS_APPROX(v1-=v2, refV1-=refV2);
VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));
VERIFY_IS_APPROX(m1*v2, refM1*refV2);
VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
{
int i = internal::random<int>(0,rows-1);
VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
}
VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm());
// test aliasing
VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));
// sparse matrix to sparse vector
SparseMatrixType mv1;
VERIFY_IS_APPROX((mv1=v1),v1);
VERIFY_IS_APPROX(mv1,(v1=mv1));
VERIFY_IS_APPROX(mv1,(v1=mv1.transpose()));
// check copy to dense vector with transpose
refV3.resize(0);
VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense());
VERIFY_IS_APPROX(DenseVector(v1),v1.toDense());
// test conservative resize
{
std::vector<StorageIndex> inc;
if(rows > 3)
inc.push_back(-3);
inc.push_back(0);
inc.push_back(3);
inc.push_back(1);
inc.push_back(10);
for(std::size_t i = 0; i< inc.size(); i++) {
StorageIndex incRows = inc[i];
SparseVectorType vec1(rows);
DenseVector refVec1 = DenseVector::Zero(rows);
initSparse<Scalar>(densityVec, refVec1, vec1);
vec1.conservativeResize(rows+incRows);
refVec1.conservativeResize(rows+incRows);
if (incRows > 0) refVec1.tail(incRows).setZero();
VERIFY_IS_APPROX(vec1, refVec1);
// Insert new values
if (incRows > 0)
vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1;
VERIFY_IS_APPROX(vec1, refVec1);
}
}
}
EIGEN_DECLARE_TEST(sparse_vector)
{
for(int i = 0; i < g_repeat; i++) {
int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500);
if(Eigen::internal::random<int>(0,4) == 0) {
r = c; // check square matrices in 25% of tries
}
EIGEN_UNUSED_VARIABLE(r+c);
CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) ));
CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
}
}
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