1 files changed, 119 insertions, 0 deletions
diff --git a/test/svd_fill.h b/test/svd_fill.h
new file mode 100644
index 000000000..3877c0c7e
--- /dev/null
+++ b/test/svd_fill.h
@@ -0,0 +1,119 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// 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/.
+
+template<typename T>
+Array<T,4,1> four_denorms();
+
+template<>
+Array4f four_denorms() { return Array4f(5.60844e-39f, -5.60844e-39f, 4.94e-44f, -4.94e-44f); }
+template<>
+Array4d four_denorms() { return Array4d(5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324); }
+template<typename T>
+Array<T,4,1> four_denorms() { return four_denorms<double>().cast<T>(); }
+
+template<typename MatrixType>
+void svd_fill_random(MatrixType &m, int Option = 0)
+{
+  using std::pow;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::Index Index;
+  Index diagSize = (std::min)(m.rows(), m.cols());
+  RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4;
+  s = internal::random<RealScalar>(1,s);
+  Matrix<RealScalar,Dynamic,1> d =  Matrix<RealScalar,Dynamic,1>::Random(diagSize);
+  for(Index k=0; k<diagSize; ++k)
+    d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s));
+
+  bool dup     = internal::random<int>(0,10) < 3;
+  bool unit_uv = internal::random<int>(0,10) < (dup?7:3); // if we duplicate some diagonal entries, then increase the chance to preserve them using unitary U and V factors
+  
+  // duplicate some singular values
+  if(dup)
+  {
+    Index n = internal::random<Index>(0,d.size()-1);
+    for(Index i=0; i<n; ++i)
+      d(internal::random<Index>(0,d.size()-1)) = d(internal::random<Index>(0,d.size()-1));
+  }
+  
+  Matrix<Scalar,Dynamic,Dynamic> U(m.rows(),diagSize);
+  Matrix<Scalar,Dynamic,Dynamic> VT(diagSize,m.cols());
+  if(unit_uv)
+  {
+    // in very rare cases let's try with a pure diagonal matrix
+    if(internal::random<int>(0,10) < 1)
+    {
+      U.setIdentity();
+      VT.setIdentity();
+    }
+    else
+    {
+      createRandomPIMatrixOfRank(diagSize,U.rows(), U.cols(), U);
+      createRandomPIMatrixOfRank(diagSize,VT.rows(), VT.cols(), VT);
+    }
+  }
+  else
+  {
+    U.setRandom();
+    VT.setRandom();
+  }
+  
+  Matrix<Scalar,Dynamic,1> samples(9);
+  samples << 0, four_denorms<RealScalar>(),
+            -RealScalar(1)/NumTraits<RealScalar>::highest(), RealScalar(1)/NumTraits<RealScalar>::highest(), (std::numeric_limits<RealScalar>::min)(), pow((std::numeric_limits<RealScalar>::min)(),0.8);
+  
+  if(Option==Symmetric)
+  {
+    m = U * d.asDiagonal() * U.transpose();
+    
+    // randomly nullify some rows/columns
+    {
+      Index count = internal::random<Index>(-diagSize,diagSize);
+      for(Index k=0; k<count; ++k)
+      {
+        Index i = internal::random<Index>(0,diagSize-1);
+        m.row(i).setZero();
+        m.col(i).setZero();
+      }
+      if(count<0)
+      // (partly) cancel some coeffs
+      if(!(dup && unit_uv))
+      {
+        
+        Index n = internal::random<Index>(0,m.size()-1);
+        for(Index k=0; k<n; ++k)
+        {
+          Index i = internal::random<Index>(0,m.rows()-1);
+          Index j = internal::random<Index>(0,m.cols()-1);
+          m(j,i) = m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
+          if(NumTraits<Scalar>::IsComplex)
+            *(&numext::real_ref(m(j,i))+1) = *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
+        }
+      }
+    }
+  }
+  else
+  {
+    m = U * d.asDiagonal() * VT;
+    // (partly) cancel some coeffs
+    if(!(dup && unit_uv))
+    {
+      Index n = internal::random<Index>(0,m.size()-1);
+      for(Index k=0; k<n; ++k)
+      {
+        Index i = internal::random<Index>(0,m.rows()-1);
+        Index j = internal::random<Index>(0,m.cols()-1);
+        m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
+        if(NumTraits<Scalar>::IsComplex)
+          *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
+      }
+    }
+  }
+}
+