1 files changed, 0 insertions, 129 deletions
diff --git a/test/eigen2/product.h b/test/eigen2/product.h
deleted file mode 100644
index ae1b4bae4..000000000
--- a/test/eigen2/product.h
+++ /dev/null
@@ -1,129 +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) 2006-2008 Benoit Jacob <jacob.benoit.1@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 "main.h"
-#include <Eigen/Array>
-#include <Eigen/QR>
-
-template<typename Derived1, typename Derived2>
-bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = precision<typename Derived1::RealScalar>())
-{
-  return !((m1-m2).cwise().abs2().maxCoeff() < epsilon * epsilon
-                          * std::max(m1.cwise().abs2().maxCoeff(), m2.cwise().abs2().maxCoeff()));
-}
-
-template<typename MatrixType> void product(const MatrixType& m)
-{
-  /* this test covers the following files:
-     Identity.h Product.h
-  */
-
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::FloatingPoint FloatingPoint;
-  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType;
-  typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType;
-  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RowSquareMatrixType;
-  typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> ColSquareMatrixType;
-  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
-                         MatrixType::Options^RowMajor> OtherMajorMatrixType;
-
-  int rows = m.rows();
-  int cols = m.cols();
-
-  // this test relies a lot on Random.h, and there's not much more that we can do
-  // to test it, hence I consider that we will have tested Random.h
-  MatrixType m1 = MatrixType::Random(rows, cols),
-             m2 = MatrixType::Random(rows, cols),
-             m3(rows, cols);
-  RowSquareMatrixType
-             identity = RowSquareMatrixType::Identity(rows, rows),
-             square = RowSquareMatrixType::Random(rows, rows),
-             res = RowSquareMatrixType::Random(rows, rows);
-  ColSquareMatrixType
-             square2 = ColSquareMatrixType::Random(cols, cols),
-             res2 = ColSquareMatrixType::Random(cols, cols);
-  RowVectorType v1 = RowVectorType::Random(rows);
-  ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
-  OtherMajorMatrixType tm1 = m1;
-
-  Scalar s1 = ei_random<Scalar>();
-
-  int r = ei_random<int>(0, rows-1),
-      c = ei_random<int>(0, cols-1);
-
-  // begin testing Product.h: only associativity for now
-  // (we use Transpose.h but this doesn't count as a test for it)
-
-  VERIFY_IS_APPROX((m1*m1.transpose())*m2,  m1*(m1.transpose()*m2));
-  m3 = m1;
-  m3 *= m1.transpose() * m2;
-  VERIFY_IS_APPROX(m3,                      m1 * (m1.transpose()*m2));
-  VERIFY_IS_APPROX(m3,                      m1.lazy() * (m1.transpose()*m2));
-
-  // continue testing Product.h: distributivity
-  VERIFY_IS_APPROX(square*(m1 + m2),        square*m1+square*m2);
-  VERIFY_IS_APPROX(square*(m1 - m2),        square*m1-square*m2);
-
-  // continue testing Product.h: compatibility with ScalarMultiple.h
-  VERIFY_IS_APPROX(s1*(square*m1),          (s1*square)*m1);
-  VERIFY_IS_APPROX(s1*(square*m1),          square*(m1*s1));
-
-  // again, test operator() to check const-qualification
-  s1 += (square.lazy() * m1)(r,c);
-
-  // test Product.h together with Identity.h
-  VERIFY_IS_APPROX(v1,                      identity*v1);
-  VERIFY_IS_APPROX(v1.transpose(),          v1.transpose() * identity);
-  // again, test operator() to check const-qualification
-  VERIFY_IS_APPROX(MatrixType::Identity(rows, cols)(r,c), static_cast<Scalar>(r==c));
-
-  if (rows!=cols)
-     VERIFY_RAISES_ASSERT(m3 = m1*m1);
-
-  // test the previous tests were not screwed up because operator* returns 0
-  // (we use the more accurate default epsilon)
-  if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
-  {
-    VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1));
-  }
-
-  // test optimized operator+= path
-  res = square;
-  res += (m1 * m2.transpose()).lazy();
-  VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
-  if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
-  {
-    VERIFY(areNotApprox(res,square + m2 * m1.transpose()));
-  }
-  vcres = vc2;
-  vcres += (m1.transpose() * v1).lazy();
-  VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1);
-  tm1 = m1;
-  VERIFY_IS_APPROX(tm1.transpose() * v1, m1.transpose() * v1);
-  VERIFY_IS_APPROX(v1.transpose() * tm1, v1.transpose() * m1);
-
-  // test submatrix and matrix/vector product
-  for (int i=0; i<rows; ++i)
-    res.row(i) = m1.row(i) * m2.transpose();
-  VERIFY_IS_APPROX(res, m1 * m2.transpose());
-  // the other way round:
-  for (int i=0; i<rows; ++i)
-    res.col(i) = m1 * m2.transpose().col(i);
-  VERIFY_IS_APPROX(res, m1 * m2.transpose());
-
-  res2 = square2;
-  res2 += (m1.transpose() * m2).lazy();
-  VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2);
-
-  if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
-  {
-    VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1));
-  }
-}
-