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/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2013 Soumyajit De
*/
#include <shogun/lib/common.h>
#ifdef HAVE_EIGEN3
#include <shogun/lib/SGMatrix.h>
#include <shogun/lib/SGVector.h>
#include <shogun/lib/SGSparseMatrix.h>
#include <shogun/features/SparseFeatures.h>
#include <shogun/mathematics/eigen3.h>
#include <shogun/mathematics/linalg/linop/SparseMatrixOperator.h>
#include <gtest/gtest.h>
using namespace shogun;
using namespace Eigen;
TEST(SparseMatrixOperator, symmetric_apply)
{
const index_t size=2;
SGMatrix<float64_t> m(size, size);
m.set_const(0.0);
m(0,0)=1.0;
m(0,1)=0.01;
m(1,1)=2.0;
CSparseFeatures<float64_t> feat(m);
SGSparseMatrix<float64_t> mat=feat.get_sparse_feature_matrix();
CSparseMatrixOperator<float64_t> op(mat);
SGVector<float64_t> b(size);
b.set_const(0.25);
SGVector<float64_t> result=op.apply(b);
Map<VectorXd> map_result(result.vector, result.vlen);
#ifdef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
EXPECT_NEAR(map_result.norm(), 0.56125417593101246, 1E-16);
#else
const SparseMatrix<float64_t> &eig_m
=EigenSparseUtil<float64_t>::toEigenSparse(mat);
Map<VectorXd> map_b(b.vector, b.vlen);
EXPECT_NEAR(map_result.norm(), (eig_m*map_b).norm(), 1E-16);
#endif
}
TEST(SparseMatrixOperator, symmetric_apply_complex)
{
const index_t size=2;
SGMatrix<complex128_t> m(size, size);
m.set_const(complex128_t(0.0));
m(0,0)=complex128_t(1.0);
m(0,1)=complex128_t(0.01);
m(1,1)=complex128_t(2.0);
CSparseFeatures<complex128_t> feat(m);
SGSparseMatrix<complex128_t> mat=feat.get_sparse_feature_matrix();
CSparseMatrixOperator<complex128_t> op(mat);
SGVector<complex128_t> b(size);
b.set_const(0.25);
SGVector<complex128_t> result=op.apply(b);
Map<VectorXcd> map_result(result.vector, result.vlen);
#ifdef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
EXPECT_NEAR(map_result.norm(), 0.56125417593101246, 1E-16);
#else
const SparseMatrix<complex128_t> &eig_m
=EigenSparseUtil<complex128_t>::toEigenSparse(mat);
Map<VectorXcd> map_b(b.vector, b.vlen);
EXPECT_NEAR(map_result.norm(), (eig_m*map_b).norm(), 1E-16);
#endif
}
TEST(SparseMatrixOperator, asymmetric_apply)
{
const index_t size=2;
SGMatrix<float64_t> m(size*10, size);
m.set_const(0.0);
m(0,0)=-0.3435774457;
m(9,0)=0.1253463474;
m(19,0)=-2.34654537245;
m(2,1)=1.23534643643;
m(15,1)=-0.23462346332;
m(17,1)=-1.12351352;
CSparseFeatures<float64_t> feat(m);
SGSparseMatrix<float64_t> mat=feat.get_sparse_feature_matrix();
CSparseMatrixOperator<float64_t> op(mat);
SGVector<float64_t> b(size*10);
b.set_const(0.25);
SGVector<float64_t> result=op.apply(b);
Map<VectorXd> map_result(result.vector, result.vlen);
#ifdef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
EXPECT_NEAR(map_result.norm(), 0.64192853298275987, 1E-16);
#else
const SparseMatrix<float64_t> &eig_m
=EigenSparseUtil<float64_t>::toEigenSparse(mat);
Map<VectorXd> map_b(b.vector, b.vlen);
EXPECT_NEAR(map_result.norm(), (eig_m*map_b).norm(), 1E-16);
#endif
}
TEST(SparseMatrixOperator, get_set_diagonal_no_alloc)
{
const index_t size=2;
SGMatrix<float64_t> m(size, size);
m.set_const(0.0);
m(0,0)=1.0;
m(0,1)=0.01;
m(1,1)=2.0;
CSparseFeatures<float64_t> feat(m);
SGSparseMatrix<float64_t> mat=feat.get_sparse_feature_matrix();
CSparseMatrixOperator<float64_t> op(mat);
// get the old diagonal and check if it works fine
SGVector<float64_t> old_diag=op.get_diagonal();
Map<VectorXd> map_old_diag(old_diag.vector, old_diag.vlen);
VectorXd eig_old_diag(size);
eig_old_diag << 1.0, 2.0;
EXPECT_NEAR(map_old_diag.norm(), eig_old_diag.norm(), 1E-16);
// set the new diagonal and check if it works fine
SGVector<float64_t> diag(size);
diag.set_const(3.0);
op.set_diagonal(diag);
SGVector<float64_t> new_diag=op.get_diagonal();
Map<VectorXd> map_diag(diag.vector, diag.vlen);
Map<VectorXd> map_new_diag(new_diag.vector, new_diag.vlen);
EXPECT_NEAR(map_diag.norm(), map_new_diag.norm(), 1E-16);
}
TEST(SparseMatrixOperator, get_set_diagonal_realloc)
{
const index_t size=2;
SGMatrix<float64_t> m(size, size);
m.set_const(0.0);
m(0,1)=0.01;
m(1,1)=2.0;
CSparseFeatures<float64_t> feat(m);
SGSparseMatrix<float64_t> mat=feat.get_sparse_feature_matrix();
CSparseMatrixOperator<float64_t> op(mat);
// get the old diagonal and check if it works fine
SGVector<float64_t> old_diag=op.get_diagonal();
Map<VectorXd> map_old_diag(old_diag.vector, old_diag.vlen);
VectorXd eig_old_diag(size);
eig_old_diag << 0.0, 2.0;
EXPECT_NEAR(map_old_diag.norm(), eig_old_diag.norm(), 1E-16);
// set the new diagonal and check if it works fine
SGVector<float64_t> diag(size);
diag.set_const(3.0);
op.set_diagonal(diag);
SGVector<float64_t> new_diag=op.get_diagonal();
Map<VectorXd> map_diag(diag.vector, diag.vlen);
Map<VectorXd> map_new_diag(new_diag.vector, new_diag.vlen);
EXPECT_NEAR(map_diag.norm(), map_new_diag.norm(), 1E-16);
}
TEST(SparseMatrixOperator, get_set_diagonal_realloc_complex128)
{
const index_t size=2;
SGMatrix<complex128_t> m(size, size);
m.set_const(complex128_t(0.0));
m(0,1)=complex128_t(0.01);
m(1,1)=complex128_t(2.0);
CSparseFeatures<complex128_t> feat(m);
SGSparseMatrix<complex128_t> mat=feat.get_sparse_feature_matrix();
CSparseMatrixOperator<complex128_t> op(mat);
// get the old diagonal and check if it works fine
SGVector<complex128_t> old_diag=op.get_diagonal();
Map<VectorXcd> map_old_diag(old_diag.vector, old_diag.vlen);
VectorXcd eig_old_diag(size);
eig_old_diag(0)=complex128_t(0.0);
eig_old_diag(1)=complex128_t(2.0);
EXPECT_NEAR(map_old_diag.norm(), eig_old_diag.norm(), 1E-16);
// set the new diagonal and check if it works fine
SGVector<complex128_t> diag(size);
diag.set_const(complex128_t(3.0));
op.set_diagonal(diag);
SGVector<complex128_t> new_diag=op.get_diagonal();
Map<VectorXcd> map_diag(diag.vector, diag.vlen);
Map<VectorXcd> map_new_diag(new_diag.vector, new_diag.vlen);
EXPECT_NEAR(map_diag.norm(), map_new_diag.norm(), 1E-16);
}
TEST(SparseMatrixOperator, get_sparsity_structure)
{
const int32_t size=9;
const int32_t max_pow=10;
SGMatrix<float64_t> m(size, size);
m.set_const(0.0);
for (int32_t i=0; i<size; ++i)
m(i,i)=2.0;
for (int32_t i=0; i<size; i+=4)
m(i,size-1)=2.0;
for (int32_t i=0; i<size; i+=4)
m(size-1,i)=2.0;
CSparseFeatures<float64_t> feat(m);
SGSparseMatrix<float64_t> sm=feat.get_sparse_feature_matrix();
CSparseMatrixOperator<float64_t> op(sm);
CSparseMatrixOperator<bool>* b_op
=static_cast<CSparseMatrixOperator<bool>*>(op);
SparseMatrix<bool, RowMajor, int32_t> sp
=EigenSparseUtil<bool>::toEigenSparse(b_op->get_matrix_operator());
SparseMatrix<float64_t, RowMajor, int32_t> sm2
=EigenSparseUtil<float64_t>::toEigenSparse(sm);
// compute direct matrix power and then the sparsity structure
for (int32_t i=2; i<=max_pow; ++i)
#if EIGEN_VERSION_AT_LEAST(3,2,91)
sp=(sp.cast<float64_t>()*sm2).cast<bool>();
#else
sp=sp*sm2;
#endif
int32_t* outerIndexPtr=const_cast<int32_t*>(sp.outerIndexPtr());
int32_t* innerIndexPtr=const_cast<int32_t*>(sp.innerIndexPtr());
SparsityStructure* sp_struct1
=new SparsityStructure(outerIndexPtr, innerIndexPtr, sp.cols());
// compute the sparsity structure using the method added in
// sparse matrix operator
SparsityStructure* sp_struct2=op.get_sparsity_structure(max_pow);
for (index_t i=0; i<sp_struct2->m_num_rows; ++i)
{
index_t nnzs=sp_struct2->m_ptr[i][0];
EXPECT_EQ(nnzs, sp_struct1->m_ptr[i][0]);
for(index_t j=1; j<=nnzs; ++j)
EXPECT_EQ(sp_struct1->m_ptr[i][j], sp_struct2->m_ptr[i][j]);
}
SG_UNREF(b_op);
delete sp_struct1;
delete sp_struct2;
}
#endif // HAVE_EIGEN3
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