[opengm] 113/386: Remove Loss from StructMaxMargin template parameters. Update parameter object, and actually pass it to the optimizer. Use infParameter in Oracle. Use Gurobi OR Cplex as INF in test.

[Ghislain Vaillant]

This is an automated email from the git hooks/post-receive script.

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

commit 45d07c34a7f56efb7dfb622b7b95b868670cd24d
Author: Carsten Haubold <carstenhaubold at googlemail.com>
Date:   Thu Dec 18 11:50:37 2014 +0100

    Remove Loss from StructMaxMargin template parameters. Update parameter object, and actually pass it to the optimizer. Use infParameter in Oracle. Use Gurobi OR Cplex as INF in test.
---
 include/opengm/learning/struct-max-margin.hxx | 35 ++++++++++++---------------
 src/unittest/learning/test_learning.cxx       | 25 +++++++++++++------
 2 files changed, 33 insertions(+), 27 deletions(-)

diff --git a/include/opengm/learning/struct-max-margin.hxx b/include/opengm/learning/struct-max-margin.hxx
index 405489e..775732d 100644
--- a/include/opengm/learning/struct-max-margin.hxx
+++ b/include/opengm/learning/struct-max-margin.hxx
@@ -11,34 +11,27 @@ namespace learning {
 
 template <
 		typename DS,
-		typename LG,
 		typename O = BundleOptimizer<typename DS::ValueType> >
 class StructMaxMargin {
 
 public:
 
 	typedef DS DatasetType;
-	typedef LG LossGeneratorType;
 	typedef O  OptimizerType;
 
 	typedef typename DatasetType::ValueType       ValueType;
     typedef typename DatasetType::Weights         Weights;
 
 	struct Parameter {
-
-		Parameter() :
-			regularizerWeight(1.0) {}
-
-		typedef typename OptimizerType::Parameter OptimizerParameter;
-
-		ValueType regularizerWeight;
-
-		OptimizerParameter optimizerParameter;
+        typedef typename OptimizerType::Parameter OptimizerParameter;
+        OptimizerParameter optimizerParameter_;
 	};
 
 	StructMaxMargin(DatasetType& dataset, const Parameter& parameter = Parameter()) :
 		_dataset(dataset),
-		_parameter(parameter) {}
+        _parameter(parameter),
+        _optimizer(parameter.optimizerParameter_)
+    {}
 
 	Parameter& parameter() { return _parameter; }
 
@@ -54,8 +47,10 @@ private:
 
 		public:
 
-			Oracle(DatasetType& dataset) :
-				_dataset(dataset) {}
+            Oracle(DatasetType& dataset, typename InferenceType::Parameter& infParam) :
+                _dataset(dataset),
+                _infParam(infParam)
+            {}
 
 			/**
 			 * Evaluate the loss-augmented energy value of the dataset and its 
@@ -118,8 +113,7 @@ private:
 
 					// find the minimizer y* of F(y,w)
 					ConfigurationType mostViolated;
-					typename InferenceType::Parameter p;
-					InferenceType inference(gml, p);
+                    InferenceType inference(gml, _infParam);
 					inference.infer();
 					inference.arg(mostViolated);
 
@@ -143,6 +137,7 @@ private:
 		private:
 
 			DatasetType& _dataset;
+            typename InferenceType::Parameter& _infParam;
 	};
 
 	DatasetType& _dataset;
@@ -154,12 +149,12 @@ private:
     Weights _weights;
 };
 
-template <typename DS, typename LG, typename O>
+template <typename DS, typename O>
 template <typename InfereneType>
 void
-StructMaxMargin<DS, LG, O>::learn(typename InfereneType::Parameter& infParams) {
+StructMaxMargin<DS, O>::learn(typename InfereneType::Parameter& infParams) {
 
-	Oracle<InfereneType> oracle(_dataset);
+    Oracle<InfereneType> oracle(_dataset, infParams);
 
 	_weights = _dataset.getWeights();
 
@@ -173,7 +168,7 @@ StructMaxMargin<DS, LG, O>::learn(typename InfereneType::Parameter& infParams) {
 		std::cout << "optimization converged to requested precision" << std::endl;
 
 	if (result == ReachedSteps)
-		std::cout << "optimization stopped after " << parameter().optimizerParameter.steps << " iterations" << std::endl;
+        std::cout << "optimization stopped after " << parameter().optimizerParameter_.steps << " iterations" << std::endl;
 }
 
 } // namespace learning
diff --git a/src/unittest/learning/test_learning.cxx b/src/unittest/learning/test_learning.cxx
index 4a5153b..9a52ac2 100644
--- a/src/unittest/learning/test_learning.cxx
+++ b/src/unittest/learning/test_learning.cxx
@@ -6,9 +6,15 @@
 #include <opengm/operations/adder.hxx>
 #include <opengm/operations/minimizer.hxx>
 #include <opengm/inference/external/trws.hxx>
-#include <opengm/inference/lpgurobi.hxx>
 #include <opengm/utilities/metaprogramming.hxx>
 
+#if WITH_GUROBI
+#include <opengm/inference/lpgurobi.hxx>
+#else
+#include <opengm/inference/lpcplex.hxx>
+#endif
+
+
 #include <opengm/functions/learnable/lpotts.hxx>
 #include <opengm/functions/learnable/sum_of_experts.hxx>
 #include <opengm/learning/struct-max-margin.hxx>
@@ -25,7 +31,12 @@ typedef size_t LabelType;
 typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::SumOfExperts<ValueType,IndexType,LabelType> >::type FunctionListType;
 typedef opengm::GraphicalModel<ValueType,opengm::Adder, FunctionListType, opengm::DiscreteSpace<IndexType,LabelType> > GM; 
 typedef opengm::learning::HammingLoss     LOSS;
+
+#if WITH_GUROBI
 typedef opengm::LPGurobi<GM,opengm::Minimizer> INF;
+#else
+typedef opengm::LPCplex<GM,opengm::Minimizer> INF;
+#endif
 typedef opengm::datasets::TestDataset1<GM,LOSS> DS1;
 typedef opengm::datasets::TestDataset2<GM,LOSS> DS2;
 typedef opengm::datasets::TestDatasetSimple<GM,LOSS> DSS;
@@ -41,8 +52,8 @@ int main() {
 	  std::cout << "Dataset includes " << dataset.getNumberOfModels() << " instances and has " << dataset.getNumberOfWeights() << " parameters."<<std::endl;
 	  
 	  
-	  opengm::learning::StructMaxMargin<DSS,LOSS>::Parameter para;
-	  opengm::learning::StructMaxMargin<DSS,LOSS> learner(dataset,para);
+      opengm::learning::StructMaxMargin<DSS>::Parameter para;
+      opengm::learning::StructMaxMargin<DSS> learner(dataset,para);
 	  
 	  
 	  INF::Parameter infPara;
@@ -60,8 +71,8 @@ int main() {
 	  std::cout << "Dataset includes " << dataset.getNumberOfModels() << " instances and has " << dataset.getNumberOfWeights() << " parameters."<<std::endl;
 	  
 	  
-	  opengm::learning::StructMaxMargin<DS1,LOSS>::Parameter para;
-	  opengm::learning::StructMaxMargin<DS1,LOSS> learner(dataset,para);
+      opengm::learning::StructMaxMargin<DS1>::Parameter para;
+      opengm::learning::StructMaxMargin<DS1> learner(dataset,para);
 	  
 	  
 	  INF::Parameter infPara;
@@ -80,8 +91,8 @@ int main() {
 	  std::cout << "Dataset includes " << dataset.getNumberOfModels() << " instances and has " << dataset.getNumberOfWeights() << " parameters."<<std::endl;
 	  
 	  
-	  opengm::learning::StructMaxMargin<DS2,LOSS>::Parameter para;
-	  opengm::learning::StructMaxMargin<DS2,LOSS> learner(dataset,para);
+      opengm::learning::StructMaxMargin<DS2>::Parameter para;
+      opengm::learning::StructMaxMargin<DS2> learner(dataset,para);
 	  
 	  
 	  INF::Parameter infPara;

-- 
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/opengm.git