- add diagonal * sparse product as an expression

- split sparse_basic unit test
- various fixes in sparse module

1 files changed, 132 insertions, 0 deletions

diff --git a/test/sparse_product.cpp b/test/sparse_product.cpp
new file mode 100644
index 000000000..95b3546c1
--- /dev/null
+++ b/test/sparse_product.cpp
@@ -0,0 +1,132 @@
+// 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 SparseMatrixType> void sparse_product(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;
+
+  // test matrix-matrix product
+  /*
+  {
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refMat4 = DenseMatrix::Zero(rows, rows);
+    DenseMatrix dm4 = DenseMatrix::Zero(rows, rows);
+    SparseMatrixType m2(rows, rows);
+    SparseMatrixType m3(rows, rows);
+    SparseMatrixType m4(rows, rows);
+    initSparse<Scalar>(density, refMat2, m2);
+    initSparse<Scalar>(density, refMat3, m3);
+    initSparse<Scalar>(density, refMat4, m4);
+    VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
+    VERIFY_IS_APPROX(m4=m2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
+    VERIFY_IS_APPROX(m4=m2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
+    VERIFY_IS_APPROX(m4=m2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
+
+    // sparse * dense
+    VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
+    VERIFY_IS_APPROX(dm4=m2*refMat3.transpose(), refMat4=refMat2*refMat3.transpose());
+    VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3, refMat4=refMat2.transpose()*refMat3);
+    VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
+
+    // dense * sparse
+    VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3);
+    VERIFY_IS_APPROX(dm4=refMat2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
+    VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
+    VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
+  }
+  */
+  
+  // test matrix - diagonal product
+  {
+    DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
+    DiagonalMatrix<Scalar,Dynamic> d1(DenseVector::Random(rows));
+    SparseMatrixType m2(rows, rows);
+    SparseMatrixType m3(rows, rows);
+    initSparse<Scalar>(density, refM2, m2);
+    initSparse<Scalar>(density, refM3, m3);
+//     std::cerr << "foo\n" << (m2*d1).toDense() << "\n\n" << refM2*d1 << "\n\n";
+    VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
+    VERIFY_IS_APPROX(m3=m2.transpose()*d1, refM3=refM2.transpose()*d1);
+    VERIFY_IS_APPROX(m3=d1*m2, refM3=d1*refM2);
+//     std::cerr << "foo\n" << (d1*m2.transpose()).toDense() << "\n\n" << d1 * refM2.transpose() << "\n\n";
+    VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1 * refM2.transpose());
+  }
+  
+  // test self adjoint products
+//   {
+//     DenseMatrix b = DenseMatrix::Random(rows, rows);
+//     DenseMatrix x = DenseMatrix::Random(rows, rows);
+//     DenseMatrix refX = DenseMatrix::Random(rows, rows);
+//     DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
+//     DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
+//     DenseMatrix refS = DenseMatrix::Zero(rows, rows);
+//     SparseMatrixType mUp(rows, rows);
+//     SparseMatrixType mLo(rows, rows);
+//     SparseMatrixType mS(rows, rows);
+//     do {
+//       initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
+//     } while (refUp.isZero());
+//     refLo = refUp.transpose().conjugate();
+//     mLo = mUp.transpose().conjugate();
+//     refS = refUp + refLo;
+//     refS.diagonal() *= 0.5;
+//     mS = mUp + mLo;
+//     for (int k=0; k<mS.outerSize(); ++k)
+//       for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
+//         if (it.index() == k)
+//           it.valueRef() *= 0.5;
+//     
+//     VERIFY_IS_APPROX(refS.adjoint(), refS);
+//     VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
+//     VERIFY_IS_APPROX(mS, refS);
+//     VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
+//     VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b);
+//     VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b);
+//     VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b);
+//   }
+
+}
+
+void test_sparse_product()
+{
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST( sparse_product(SparseMatrix<double>(8, 8)) );
+    CALL_SUBTEST( sparse_product(SparseMatrix<std::complex<double> >(16, 16)) );
+    CALL_SUBTEST( sparse_product(SparseMatrix<double>(33, 33)) );
+    
+    CALL_SUBTEST( sparse_product(DynamicSparseMatrix<double>(8, 8)) );
+  }
+}