[opengm] 247/386: add second dataset to mll test

[Ghislain Vaillant]

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

commit 9e91646ddb98654431b09630434fa319ed4fd6b1
Author: joergkappes <kappes at math.uni-heidelberg.de>
Date:   Fri Jan 16 10:14:05 2015 +0100

     add second dataset to mll test
---
 .../learning/test_maximum_likelihood_learner.cxx   | 25 ++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/src/unittest/learning/test_maximum_likelihood_learner.cxx b/src/unittest/learning/test_maximum_likelihood_learner.cxx
index a074fa9..9387dfa 100644
--- a/src/unittest/learning/test_maximum_likelihood_learner.cxx
+++ b/src/unittest/learning/test_maximum_likelihood_learner.cxx
@@ -58,6 +58,7 @@ int main() {
    }
 */
 
+
    {
       DS1 dataset;
       std::cout << "Dataset includes " << dataset.getNumberOfModels() << " instances and has " << dataset.getNumberOfWeights() << " parameters."<<std::endl;
@@ -81,6 +82,30 @@ int main() {
       learner.learn<BeliefPropagation>(parametersBP);
       
    }
+
+   {
+      DS2 dataset;
+      std::cout << "Dataset includes " << dataset.getNumberOfModels() << " instances and has " << dataset.getNumberOfWeights() << " parameters."<<std::endl;
+      opengm::learning::MaximumLikelihoodLearner<DS2>::Parameter gradientParameter;
+      gradientParameter.maximumNumberOfIterations_ = 3;
+      gradientParameter.gradientStep_ = 0.1111;
+      gradientParameter.weightAccuracy_ = 0.0000111;
+      gradientParameter.gradientStoppingCriteria_ = 0.000000011;
+      gradientParameter.infoFlag_ = true;
+      gradientParameter.infoEveryStep_ = true;
+      opengm::learning::MaximumLikelihoodLearner<DS2> learner(dataset,gradientParameter);
+
+      //INF::Parameter infParam;
+      //learner.learn<INF>(infParam);
+      //learner.learn();
+      const size_t maxNumberOfBPIterations = 40;
+      const ValueType convergenceBound = 1e-7;
+      const ValueType damping = 0.5;
+      BeliefPropagation::Parameter parametersBP(maxNumberOfBPIterations, convergenceBound, damping);
+
+      learner.learn<BeliefPropagation>(parametersBP);
+      
+   }
 /*
 
    {

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