Sparse module:
* extend unit tests
* add support for generic sum reduction and dot product
* optimize the cwise()* : this is a special case of CwiseBinaryOp where
we only have to process the coeffs which are not null for *both* matrices.
Perhaps there exist some other binary operations like that ?
diff --git a/test/sparse_vector.cpp b/test/sparse_vector.cpp
new file mode 100644
index 0000000..0a25884
--- /dev/null
+++ b/test/sparse_vector.cpp
@@ -0,0 +1,92 @@
+// 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>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, 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 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#include "sparse.h"
+
+template<typename Scalar> void sparse_vector(int rows, int cols)
+{
+ double densityMat = std::max(8./(rows*cols), 0.01);
+ double densityVec = std::max(8./float(rows), 0.1);
+ typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
+ typedef Matrix<Scalar,Dynamic,1> DenseVector;
+ typedef SparseVector<Scalar> SparseVectorType;
+ typedef SparseMatrix<Scalar> SparseMatrixType;
+ Scalar eps = 1e-6;
+
+ SparseMatrixType m1(rows,cols);
+ SparseVectorType v1(rows), v2(rows), v3(rows);
+ DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
+ 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 = ei_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[it.index()]);
+ }
+ }
+ VERIFY_IS_APPROX(v1, refV1);
+
+ 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);
+
+ std::cerr << v1.dot(v2) << " == " << refV1.dot(refV2) << "\n";
+ VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
+
+}
+
+void test_sparse_vector()
+{
+ for(int i = 0; i < g_repeat; i++) {
+ CALL_SUBTEST( sparse_vector<double>(8, 8) );
+// CALL_SUBTEST( sparse_vector<std::complex<double> >(16, 16) );
+ CALL_SUBTEST( sparse_vector<double>(299, 535) );
+ }
+}
+