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 ?

1 files changed, 92 insertions, 0 deletions

diff --git a/test/sparse_vector.cpp b/test/sparse_vector.cpp
new file mode 100644
index 000000000..0a25884ca
--- /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) );
+  }
+}
+