[mlpack-git] master: Refactor GeneralizedRosenbrockTest to deal with intermittent failures better. Also use 4 trials for RastigrinFunctionTest. (0ccb8e3)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40

>---------------------------------------------------------------

commit 0ccb8e3d226498dbc0008fc4098865ba8f249568
Author: Ryan Curtin <ryan at ratml.org>
Date:   Tue Nov 18 23:20:31 2014 +0000

    Refactor GeneralizedRosenbrockTest to deal with intermittent failures better.
    Also use 4 trials for RastigrinFunctionTest.


>---------------------------------------------------------------

0ccb8e3d226498dbc0008fc4098865ba8f249568
 src/mlpack/tests/sa_test.cpp | 27 +++++++++++++++++++--------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/src/mlpack/tests/sa_test.cpp b/src/mlpack/tests/sa_test.cpp
index 3cbcfed..b4564bc 100644
--- a/src/mlpack/tests/sa_test.cpp
+++ b/src/mlpack/tests/sa_test.cpp
@@ -27,18 +27,29 @@ BOOST_AUTO_TEST_SUITE(SATest);
 
 BOOST_AUTO_TEST_CASE(GeneralizedRosenbrockTest)
 {
+  math::RandomSeed(std::time(NULL));
   size_t dim = 50;
   GeneralizedRosenbrockFunction f(dim);
 
-  ExponentialSchedule schedule(1e-5);
-  SA<GeneralizedRosenbrockFunction, ExponentialSchedule>
-      sa(f, schedule, 10000000, 1000., 1000, 100, 1e-9, 3, 20, 0.3, 0.3);
-  arma::mat coordinates = f.GetInitialPoint();
-  const double result = sa.Optimize(coordinates);
+  double iteration = 0;
+  double result = DBL_MAX;
+  arma::mat coordinates;
+  while (result > 1e-6)
+  {
+    ExponentialSchedule schedule(1e-5);
+    SA<GeneralizedRosenbrockFunction, ExponentialSchedule>
+        sa(f, schedule, 10000000, 1000., 1000, 100, 1e-10, 3, 20, 0.3, 0.3);
+    coordinates = f.GetInitialPoint();
+    result = sa.Optimize(coordinates);
+    ++iteration;
+
+    BOOST_REQUIRE_LT(iteration, 3); // No more than three tries.
+  }
 
+  // 0.1% tolerance for each coordinate.
   BOOST_REQUIRE_SMALL(result, 1e-6);
   for (size_t j = 0; j < dim; ++j)
-      BOOST_REQUIRE_CLOSE(coordinates[j], (double) 1.0, 1e-2);
+      BOOST_REQUIRE_CLOSE(coordinates[j], (double) 1.0, 0.1);
 }
 
 // The Rosenbrock function is a simple function to optimize.
@@ -91,11 +102,11 @@ class RastrigrinFunction
 BOOST_AUTO_TEST_CASE(RastrigrinFunctionTest)
 {
   // Simulated annealing isn't guaranteed to converge (except in very specific
-  // situations).  If this works 1 of 3 times, I'm fine with that.  All I want
+  // situations).  If this works 1 of 4 times, I'm fine with that.  All I want
   // to know is that this implementation will escape from local minima.
   size_t successes = 0;
 
-  for (size_t trial = 0; trial < 3; ++trial)
+  for (size_t trial = 0; trial < 4; ++trial)
   {
     RastrigrinFunction f;
     ExponentialSchedule schedule(3e-6);