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-rw-r--r--test/eigen2/eigen2_sparse_basic.cpp317
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diff --git a/test/eigen2/eigen2_sparse_basic.cpp b/test/eigen2/eigen2_sparse_basic.cpp
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--- a/test/eigen2/eigen2_sparse_basic.cpp
+++ /dev/null
@@ -1,317 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra. Eigen itself is part of the KDE project.
-//
-// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@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/.
-
-#include "sparse.h"
-
-template<typename SetterType,typename DenseType, typename Scalar, int Options>
-bool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
-{
- typedef SparseMatrix<Scalar,Options> SparseType;
- {
- sm.setZero();
- SetterType w(sm);
- std::vector<Vector2i> remaining = nonzeroCoords;
- while(!remaining.empty())
- {
- int i = ei_random<int>(0,remaining.size()-1);
- w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
- remaining[i] = remaining.back();
- remaining.pop_back();
- }
- }
- return sm.isApprox(ref);
-}
-
-template<typename SetterType,typename DenseType, typename T>
-bool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
-{
- sm.setZero();
- std::vector<Vector2i> remaining = nonzeroCoords;
- while(!remaining.empty())
- {
- int i = ei_random<int>(0,remaining.size()-1);
- sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
- remaining[i] = remaining.back();
- remaining.pop_back();
- }
- return sm.isApprox(ref);
-}
-
-template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
-{
- const int rows = ref.rows();
- const int cols = ref.cols();
- typedef typename SparseMatrixType::Scalar Scalar;
- enum { Flags = SparseMatrixType::Flags };
-
- double density = std::max(8./(rows*cols), 0.01);
- typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
- typedef Matrix<Scalar,Dynamic,1> DenseVector;
- Scalar eps = 1e-6;
-
- SparseMatrixType m(rows, cols);
- DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
- DenseVector vec1 = DenseVector::Random(rows);
- Scalar s1 = ei_random<Scalar>();
-
- std::vector<Vector2i> zeroCoords;
- std::vector<Vector2i> nonzeroCoords;
- initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
-
- if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
- return;
-
- // test coeff and coeffRef
- for (int i=0; i<(int)zeroCoords.size(); ++i)
- {
- VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
- if(ei_is_same_type<SparseMatrixType,SparseMatrix<Scalar,Flags> >::ret)
- VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
- }
- VERIFY_IS_APPROX(m, refMat);
-
- m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
- refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
-
- VERIFY_IS_APPROX(m, refMat);
- /*
- // test InnerIterators and Block expressions
- for (int t=0; t<10; ++t)
- {
- int j = ei_random<int>(0,cols-1);
- int i = ei_random<int>(0,rows-1);
- int w = ei_random<int>(1,cols-j-1);
- int h = ei_random<int>(1,rows-i-1);
-
-// VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
- for(int c=0; c<w; c++)
- {
- VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
- for(int r=0; r<h; r++)
- {
-// VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
- }
- }
-// for(int r=0; r<h; r++)
-// {
-// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
-// for(int c=0; c<w; c++)
-// {
-// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
-// }
-// }
- }
-
- for(int c=0; c<cols; c++)
- {
- VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
- VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
- }
-
- for(int r=0; r<rows; r++)
- {
- VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
- VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
- }
- */
-
- // test SparseSetters
- // coherent setter
- // TODO extend the MatrixSetter
-// {
-// m.setZero();
-// VERIFY_IS_NOT_APPROX(m, refMat);
-// SparseSetter<SparseMatrixType, FullyCoherentAccessPattern> w(m);
-// for (int i=0; i<nonzeroCoords.size(); ++i)
-// {
-// w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y());
-// }
-// }
-// VERIFY_IS_APPROX(m, refMat);
-
- // random setter
-// {
-// m.setZero();
-// VERIFY_IS_NOT_APPROX(m, refMat);
-// SparseSetter<SparseMatrixType, RandomAccessPattern> w(m);
-// std::vector<Vector2i> remaining = nonzeroCoords;
-// while(!remaining.empty())
-// {
-// int i = ei_random<int>(0,remaining.size()-1);
-// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
-// remaining[i] = remaining.back();
-// remaining.pop_back();
-// }
-// }
-// VERIFY_IS_APPROX(m, refMat);
-
- VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) ));
- #ifdef EIGEN_UNORDERED_MAP_SUPPORT
- VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) ));
- #endif
- #ifdef _DENSE_HASH_MAP_H_
- VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
- #endif
- #ifdef _SPARSE_HASH_MAP_H_
- VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
- #endif
-
- // test fillrand
- {
- DenseMatrix m1(rows,cols);
- m1.setZero();
- SparseMatrixType m2(rows,cols);
- m2.startFill();
- for (int j=0; j<cols; ++j)
- {
- for (int k=0; k<rows/2; ++k)
- {
- int i = ei_random<int>(0,rows-1);
- if (m1.coeff(i,j)==Scalar(0))
- m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>();
- }
- }
- m2.endFill();
- VERIFY_IS_APPROX(m2,m1);
- }
-
- // test RandomSetter
- /*{
- SparseMatrixType m1(rows,cols), m2(rows,cols);
- DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
- initSparse<Scalar>(density, refM1, m1);
- {
- Eigen::RandomSetter<SparseMatrixType > setter(m2);
- for (int j=0; j<m1.outerSize(); ++j)
- for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i)
- setter(i.index(), j) = i.value();
- }
- VERIFY_IS_APPROX(m1, m2);
- }*/
-// std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n";
-// VERIFY_IS_APPROX(m, refMat);
-
- // test basic computations
- {
- DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
- DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
- DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
- DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m1(rows, rows);
- SparseMatrixType m2(rows, rows);
- SparseMatrixType m3(rows, rows);
- SparseMatrixType m4(rows, rows);
- initSparse<Scalar>(density, refM1, m1);
- initSparse<Scalar>(density, refM2, m2);
- initSparse<Scalar>(density, refM3, m3);
- initSparse<Scalar>(density, refM4, m4);
-
- VERIFY_IS_APPROX(m1+m2, refM1+refM2);
- VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
- VERIFY_IS_APPROX(m3.cwise()*(m1+m2), refM3.cwise()*(refM1+refM2));
- VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
-
- VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
- VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
-
- VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
- VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
-
- VERIFY_IS_APPROX(m1.col(0).eigen2_dot(refM2.row(0)), refM1.col(0).eigen2_dot(refM2.row(0)));
-
- refM4.setRandom();
- // sparse cwise* dense
- VERIFY_IS_APPROX(m3.cwise()*refM4, refM3.cwise()*refM4);
-// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
- }
-
- // test innerVector()
- {
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
- initSparse<Scalar>(density, refMat2, m2);
- int j0 = ei_random(0,rows-1);
- int j1 = ei_random(0,rows-1);
- VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
- VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
- //m2.innerVector(j0) = 2*m2.innerVector(j1);
- //refMat2.col(j0) = 2*refMat2.col(j1);
- //VERIFY_IS_APPROX(m2, refMat2);
- }
-
- // test innerVectors()
- {
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
- initSparse<Scalar>(density, refMat2, m2);
- int j0 = ei_random(0,rows-2);
- int j1 = ei_random(0,rows-2);
- int n0 = ei_random<int>(1,rows-std::max(j0,j1));
- VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
- VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
- refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
- //m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
- //refMat2.block(0,j0,rows,n0) = refMat2.block(0,j0,rows,n0) + refMat2.block(0,j1,rows,n0);
- }
-
- // test transpose
- {
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
- initSparse<Scalar>(density, refMat2, m2);
- VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
- VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
- }
-
- // test prune
- {
- SparseMatrixType m2(rows, rows);
- DenseMatrix refM2(rows, rows);
- refM2.setZero();
- int countFalseNonZero = 0;
- int countTrueNonZero = 0;
- m2.startFill();
- for (int j=0; j<m2.outerSize(); ++j)
- for (int i=0; i<m2.innerSize(); ++i)
- {
- float x = ei_random<float>(0,1);
- if (x<0.1)
- {
- // do nothing
- }
- else if (x<0.5)
- {
- countFalseNonZero++;
- m2.fill(i,j) = Scalar(0);
- }
- else
- {
- countTrueNonZero++;
- m2.fill(i,j) = refM2(i,j) = Scalar(1);
- }
- }
- m2.endFill();
- VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
- VERIFY_IS_APPROX(m2, refM2);
- m2.prune(1);
- VERIFY(countTrueNonZero==m2.nonZeros());
- VERIFY_IS_APPROX(m2, refM2);
- }
-}
-
-void test_eigen2_sparse_basic()
-{
- for(int i = 0; i < g_repeat; i++) {
- CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(8, 8)) );
- CALL_SUBTEST_2( sparse_basic(SparseMatrix<std::complex<double> >(16, 16)) );
- CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(33, 33)) );
-
- CALL_SUBTEST_3( sparse_basic(DynamicSparseMatrix<double>(8, 8)) );
- }
-}
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