add unit tests for true array objects

1 files changed, 168 insertions, 0 deletions

diff --git a/test/array_for_matrix.cpp b/test/array_for_matrix.cpp
new file mode 100644
index 000000000..deb73889c
--- /dev/null
+++ b/test/array_for_matrix.cpp
@@ -0,0 +1,168 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// 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 "main.h"
+#include <Eigen/Array>
+
+template<typename MatrixType> void array_for_matrix(const MatrixType& m)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
+  typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
+
+  int rows = m.rows();
+  int cols = m.cols();
+
+  MatrixType m1 = MatrixType::Random(rows, cols),
+             m2 = MatrixType::Random(rows, cols),
+             m3(rows, cols);
+             
+  ColVectorType cv1 = ColVectorType::Random(rows);
+  RowVectorType rv1 = RowVectorType::Random(cols);
+             
+  Scalar  s1 = ei_random<Scalar>(),
+          s2 = ei_random<Scalar>();
+
+  // scalar addition
+  VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array());
+  VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows,cols,s1) + m1);
+  VERIFY_IS_APPROX(((m1*Scalar(2)).array() - s2).matrix(), (m1+m1) - MatrixType::Constant(rows,cols,s2) );
+  m3 = m1;
+  m3.array() += s2;
+  VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix());
+  m3 = m1;
+  m3.array() -= s1;
+  VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix());
+
+  // reductions
+  VERIFY_IS_APPROX(m1.colwise().sum().sum(), m1.sum());
+  VERIFY_IS_APPROX(m1.rowwise().sum().sum(), m1.sum());
+  if (!ei_isApprox(m1.sum(), (m1+m2).sum()))
+    VERIFY_IS_NOT_APPROX(((m1+m2).rowwise().sum()).sum(), m1.sum());
+  VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(ei_scalar_sum_op<Scalar>()));
+
+  // vector-wise ops
+  m3 = m1;
+  VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1);
+  m3 = m1;
+  VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1);
+  m3 = m1;
+  VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
+  m3 = m1;
+  VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
+}
+
+template<typename MatrixType> void comparisons(const MatrixType& m)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
+
+  int rows = m.rows();
+  int cols = m.cols();
+
+  int r = ei_random<int>(0, rows-1),
+      c = ei_random<int>(0, cols-1);
+
+  MatrixType m1 = MatrixType::Random(rows, cols),
+             m2 = MatrixType::Random(rows, cols),
+             m3(rows, cols);
+
+  VERIFY(((m1.array() + Scalar(1)) > m1.array()).all());
+  VERIFY(((m1.array() - Scalar(1)) < m1.array()).all());
+  if (rows*cols>1)
+  {
+    m3 = m1;
+    m3(r,c) += 1;
+    VERIFY(! (m1.array() < m3.array()).all() );
+    VERIFY(! (m1.array() > m3.array()).all() );
+  }
+
+  // comparisons to scalar
+  VERIFY( (m1.array() != (m1(r,c)+1) ).any() );
+  VERIFY( (m1.array() > (m1(r,c)-1) ).any() );
+  VERIFY( (m1.array() < (m1(r,c)+1) ).any() );
+  VERIFY( (m1.array() == m1(r,c) ).any() );
+
+  // test Select
+  VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) );
+  VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) );
+  Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2);
+  for (int j=0; j<cols; ++j)
+  for (int i=0; i<rows; ++i)
+    m3(i,j) = ei_abs(m1(i,j))<mid ? 0 : m1(i,j);
+  VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
+                        .select(MatrixType::Zero(rows,cols),m1), m3);
+  // shorter versions:
+  VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
+                        .select(0,m1), m3);
+  VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array())
+                        .select(m1,0), m3);
+  // even shorter version:
+  VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3);
+
+  // count
+  VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols);
+  // TODO allows colwise/rowwise for array
+  VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), RowVectorXi::Constant(cols,rows));
+  VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorXi::Constant(rows, cols));
+}
+
+template<typename VectorType> void lpNorm(const VectorType& v)
+{
+  VectorType u = VectorType::Random(v.size());
+
+  VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
+  VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum());
+  VERIFY_IS_APPROX(u.template lpNorm<2>(), ei_sqrt(u.array().abs().square().sum()));
+  VERIFY_IS_APPROX(ei_pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum());
+}
+
+void test_array_for_matrix()
+{
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) );
+    CALL_SUBTEST_2( array_for_matrix(Matrix2f()) );
+    CALL_SUBTEST_3( array_for_matrix(Matrix4d()) );
+    CALL_SUBTEST_4( array_for_matrix(MatrixXcf(3, 3)) );
+    CALL_SUBTEST_5( array_for_matrix(MatrixXf(8, 12)) );
+    CALL_SUBTEST_6( array_for_matrix(MatrixXi(8, 12)) );
+  }
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
+    CALL_SUBTEST_2( comparisons(Matrix2f()) );
+    CALL_SUBTEST_3( comparisons(Matrix4d()) );
+    CALL_SUBTEST_5( comparisons(MatrixXf(8, 12)) );
+    CALL_SUBTEST_6( comparisons(MatrixXi(8, 12)) );
+  }
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
+    CALL_SUBTEST_2( lpNorm(Vector2f()) );
+    CALL_SUBTEST_7( lpNorm(Vector3d()) );
+    CALL_SUBTEST_8( lpNorm(Vector4f()) );
+    CALL_SUBTEST_5( lpNorm(VectorXf(16)) );
+    CALL_SUBTEST_4( lpNorm(VectorXcf(10)) );
+  }
+}