add a stable_norm unit test

1 files changed, 80 insertions, 0 deletions

diff --git a/test/stable_norm.cpp b/test/stable_norm.cpp
new file mode 100644
index 000000000..0326f7bc9
--- /dev/null
+++ b/test/stable_norm.cpp
@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 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"
+
+template<typename MatrixType> void stable_norm(const MatrixType& m)
+{
+  /* this test covers the following files:
+     StableNorm.h
+  */
+
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  int rows = m.rows();
+  int cols = m.cols();
+
+  Scalar big = ei_random<Scalar>() * std::numeric_limits<RealScalar>::max() * 1e-4;
+  Scalar small = 1/big;
+
+  MatrixType  vzero = MatrixType::Zero(rows, cols),
+              vrand = MatrixType::Random(rows, cols),
+              vbig(rows, cols),
+              vsmall(rows,cols);
+
+  vbig.fill(big);
+  vsmall.fill(small);
+
+  VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1));
+  VERIFY_IS_APPROX(vrand.stableNorm(),      vrand.norm());
+  VERIFY_IS_APPROX(vrand.blueNorm(),        vrand.norm());
+  VERIFY_IS_APPROX(vrand.hypotNorm(),       vrand.norm());
+
+  RealScalar size = m.size();
+
+  // test overflow
+  VERIFY_IS_NOT_APPROX(vbig.norm(),   ei_sqrt(size)*big); // here the default norm must fail
+  VERIFY_IS_APPROX(vbig.stableNorm(), ei_sqrt(size)*big);
+  VERIFY_IS_APPROX(vbig.blueNorm(),   ei_sqrt(size)*big);
+  VERIFY_IS_APPROX(vbig.hypotNorm(),  ei_sqrt(size)*big);
+
+  // test underflow
+  VERIFY_IS_NOT_APPROX(vsmall.norm(),   ei_sqrt(size)*small); // here the default norm must fail
+  VERIFY_IS_APPROX(vsmall.stableNorm(), ei_sqrt(size)*small);
+  VERIFY_IS_APPROX(vsmall.blueNorm(),   ei_sqrt(size)*small);
+  VERIFY_IS_APPROX(vsmall.hypotNorm(),  ei_sqrt(size)*small);
+}
+
+void test_stable_norm()
+{
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST( stable_norm(Matrix<float, 1, 1>()) );
+    CALL_SUBTEST( stable_norm(Vector4d()) );
+    CALL_SUBTEST( stable_norm(VectorXd(ei_random<int>(10,2000))) );
+    CALL_SUBTEST( stable_norm(VectorXf(ei_random<int>(10,2000))) );
+    CALL_SUBTEST( stable_norm(VectorXcd(ei_random<int>(10,2000))) );
+  }
+}
+