[opengm] 246/386: reduce test to shorter time

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to branch debian/master
in repository opengm.

commit 3384042f164c64c38d799c7b024ae86631b2b9c4
Author: joergkappes <kappes at math.uni-heidelberg.de>
Date:   Fri Jan 16 10:01:29 2015 +0100

     reduce test to shorter time
---
 .../opengm/learning/maximum_likelihood_learning.hxx    | 18 ++++++++----------
 .../learning/test_maximum_likelihood_learner.cxx       | 10 +++++-----
 2 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/include/opengm/learning/maximum_likelihood_learning.hxx b/include/opengm/learning/maximum_likelihood_learning.hxx
index 9539b32..9d05bec 100644
--- a/include/opengm/learning/maximum_likelihood_learning.hxx
+++ b/include/opengm/learning/maximum_likelihood_learning.hxx
@@ -25,24 +25,22 @@ namespace opengm {
 
          class Parameter{
          public:
-	     IndexType maximumNumberOfIterations_;
-	     ValueType gradientStep_;
-	     ValueType weightAccuracy_;
-             ValueType gradientStoppingCriteria_;
+	     size_t maximumNumberOfIterations_;
+	     double gradientStep_;
+	     double weightAccuracy_;
+             double gradientStoppingCriteria_;
              bool infoFlag_;
              bool infoEveryStep_;
 
-  	     size_t maxNumSteps_;
 	     double reg_;
 	     double temperature_;
 	     Parameter():
-	         maximumNumberOfIterations_(123),
-	         gradientStep_(0.123),
-		 weightAccuracy_(0.0000123),
-		 gradientStoppingCriteria_(0.0000000123),
+	         maximumNumberOfIterations_(10),
+	         gradientStep_(0.1),
+		 weightAccuracy_(0.0001),
+		 gradientStoppingCriteria_(0.00000001),
 		 infoFlag_(true),
 		 infoEveryStep_(false),
-		 maxNumSteps_(10), 
 		 reg_(1.0), 
 		 temperature_(0.3)
 	   {;}
diff --git a/src/unittest/learning/test_maximum_likelihood_learner.cxx b/src/unittest/learning/test_maximum_likelihood_learner.cxx
index b9493ae..a074fa9 100644
--- a/src/unittest/learning/test_maximum_likelihood_learner.cxx
+++ b/src/unittest/learning/test_maximum_likelihood_learner.cxx
@@ -62,10 +62,10 @@ int main() {
       DS1 dataset;
       std::cout << "Dataset includes " << dataset.getNumberOfModels() << " instances and has " << dataset.getNumberOfWeights() << " parameters."<<std::endl;
       opengm::learning::MaximumLikelihoodLearner<DS1>::Parameter gradientParameter;
-      gradientParameter.maximumNumberOfIterations_ = (IndexType)111111;
-      gradientParameter.gradientStep_ = (ValueType)0.1111;
-      gradientParameter.weightAccuracy_ = (ValueType)0.0000111;
-      gradientParameter.gradientStoppingCriteria_ = (ValueType)0.000000011;
+      gradientParameter.maximumNumberOfIterations_ = 3;
+      gradientParameter.gradientStep_ = 0.1111;
+      gradientParameter.weightAccuracy_ = 0.0000111;
+      gradientParameter.gradientStoppingCriteria_ = 0.000000011;
       gradientParameter.infoFlag_ = true;
       gradientParameter.infoEveryStep_ = true;
       opengm::learning::MaximumLikelihoodLearner<DS1> learner(dataset,gradientParameter);
@@ -73,7 +73,7 @@ int main() {
       //INF::Parameter infParam;
       //learner.learn<INF>(infParam);
       //learner.learn();
-      const IndexType maxNumberOfBPIterations = 40;
+      const size_t maxNumberOfBPIterations = 40;
       const ValueType convergenceBound = 1e-7;
       const ValueType damping = 0.5;
       BeliefPropagation::Parameter parametersBP(maxNumberOfBPIterations, convergenceBound, damping);

-- 
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