Structured learning 2

README.md

@@ -1,7 +1,9 @@
 OpenGM 2
 ========
 
-[![Build Status](https://travis-ci.org/opengm/opengm.png?branch=master)](https://travis-ci.org/opengm/opengm)
+[![Build Status](https://travis-ci.org/opengm/opengm.png?branch=master)](https://travis-ci.org/opengm/opengm) (master)
+
+[![Build Status](https://travis-ci.org/opengm/opengm.png?branch=learning-experimental)](https://travis-ci.org/opengm/opengm) (learning-experimental)
 
 
 -----------------------------------------------------------------------------------------------

include/opengm/functions/learnable/lunary.hxx

@@ -101,8 +101,8 @@ protected:
 
     mutable const opengm::learning::Weights<T> *    weights_;
 
-    opengm::UInt16Type numberOfLabels_;
-    std::vector<opengm::UInt16Type> offsets_;
+    IndexType numberOfLabels_;
+    std::vector<IndexType> offsets_;
     std::vector<size_t> weightIds_;
     std::vector<ValueType> features_;
 

include/opengm/functions/learnable/lweightedsum_of_functions.hxx

@@ -77,8 +77,11 @@ LWeightedSumOfFunctions<T, I, L>::LWeightedSumOfFunctions
    :   shape_(shape), weights_(&weights), weightIDs_(weightIDs),feat_(feat)
 {
    OPENGM_ASSERT( weightIDs_.size() == feat_.size() );
-   for(size_t i=0 i<weightIDs_.size();++1)
+   for(size_t i=0; i<weightIDs_.size(); ++i){
       OPENGM_ASSERT( size() == feat_[i].size() );
+      for(size_t j=0; j<dimension(); ++j)
+          OPENGM_ASSERT( shape_[j] == feat_[i].shape(j))
+   }
 }
 
 template <class T, class I, class L>
@@ -99,7 +102,7 @@ LWeightedSumOfFunctions<T, I, L>::weightGradient
    ITERATOR begin
 ) const {
   OPENGM_ASSERT(weightNumber< numberOfWeights());
-  return feat_[weightNumber](*begin);
+  return feat_[weightNumber](begin);
 }
 
 template <class T, class I, class L>

include/opengm/inference/lp_inference_base.hxx

@@ -1842,7 +1842,6 @@ inline LPInferenceBase<LP_INFERENCE_TYPE>::LPInferenceBase(const GraphicalModelT
    if(!opengm::meta::Compare<OperatorType, opengm::Adder>::value) {
       throw RuntimeError("This implementation does only supports Min-Sum-Semiring and Max-Sum-Semiring.");
    }
-
    // sort factors
    sortFactors();
 

include/opengm/inference/lpcplex.hxx

@@ -482,7 +482,7 @@ LPCplex<GM, ACC>::infer
          break; 
       }
 
-      // MIP EMPHASIS 
+      // MIP EMPHASIS
       switch(parameter_.mipEmphasis_) {
       case MIP_EMPHASIS_BALANCED:
          cplex_.setParam(IloCplex::MIPEmphasis, 0);
@@ -503,11 +503,11 @@ LPCplex<GM, ACC>::infer
 
       // verbose options
       if(parameter_.verbose_ == false) {
-	cplex_.setParam(IloCplex::MIPDisplay, 0);
-        cplex_.setParam(IloCplex::BarDisplay, 0);
-	cplex_.setParam(IloCplex::SimDisplay, 0);
-        cplex_.setParam(IloCplex::NetDisplay, 0);
-	cplex_.setParam(IloCplex::SiftDisplay, 0);
+       cplex_.setParam(IloCplex::MIPDisplay, 0);
+       cplex_.setParam(IloCplex::BarDisplay, 0);
+       cplex_.setParam(IloCplex::SimDisplay, 0);
+       cplex_.setParam(IloCplex::NetDisplay, 0);
+       cplex_.setParam(IloCplex::SiftDisplay, 0);
       } 
 
       // tolarance settings
@@ -555,11 +555,12 @@ LPCplex<GM, ACC>::infer
       //cplex_.setParam(IloCplex::MIRCuts, parameter_.MIRCutLevel_);
 
       // solve problem
+
       if(!cplex_.solve()) {
          std::cout << "failed to optimize. " <<cplex_.getStatus() << std::endl;
          return UNKNOWN;
       } 
-      cplex_.getValues(sol_, x_);  
+      cplex_.getValues(sol_, x_);
    }
    catch(IloCplex::Exception e) {
       std::cout << "caught CPLEX exception: " << e << std::endl;

include/opengm/inference/lpcplex2.hxx

@@ -25,6 +25,16 @@ public:
 
    // public member functions
    virtual std::string name() const;
+
+   template<class _GM>
+   struct RebindGm{
+       typedef LPCplex2<_GM, ACC_TYPE> type;
+   };
+
+   template<class _GM,class _ACC>
+   struct RebindGmAndAcc{
+       typedef LPCplex2<_GM, _ACC > type;
+   };
 };
 
 template<class GM_TYPE, class ACC_TYPE>

include/opengm/inference/lpgurobi2.hxx

@@ -25,6 +25,16 @@ public:
 
    // public member functions
    virtual std::string name() const;
+
+   template<class _GM>
+   struct RebindGm{
+       typedef LPGurobi2<_GM, ACC_TYPE> type;
+   };
+
+   template<class _GM,class _ACC>
+   struct RebindGmAndAcc{
+       typedef LPGurobi2<_GM, _ACC > type;
+   };
 };
 
 template<class GM_TYPE, class ACC_TYPE>

include/opengm/learning/bundle-optimizer.hxx

@@ -12,14 +12,14 @@ namespace learning {
 //template <typename T>
 //std::ostream& operator<<(std::ostream& out, Weights<T>& w) {
 
-	//out << "[";
-	//for (int i = 0; i < w.numberOfWeights(); i++) {
-
-		//if (i > 0)
-			//out << ", ";
-		//out << w[i];
-	//}
-	//out << "]";
+//    out << "[";
+//    for (int i = 0; i < w.numberOfWeights(); i++) {
+
+//        if (i > 0)
+//            out << ", ";
+//        out << w[i];
+//    }
+//    out << "]";
 //}
 
 enum OptimizerResult {
@@ -153,15 +153,21 @@ BundleOptimizer<T>::optimize(Oracle& oracle, Weights& w) {
 
 	unsigned int t = 0;
 
-	while (true) {
+    while (true) {
 
 		t++;
 
-		std::cout << std::endl << "----------------- iteration " << t << std::endl;
+        if(oracle.getInfParam().verbose_ )
+            std::cout << std::endl << "----------------- iteration      " << t << std::endl;
 
         Weights w_tm1 = w;
 
-		//std::cout << "current w is " << w_tm1 << std::endl;
+        if(oracle.getInfParam().verbose_ ){
+            std::cout << "w: ";
+            for(size_t i=0; i<w_tm1.size(); ++i)
+                std::cout << w_tm1[i] << " ";
+            std::cout << std::endl;
+        }
 
 		// value of L at current w
 		T L_w_tm1 = 0.0;
@@ -172,30 +178,55 @@ BundleOptimizer<T>::optimize(Oracle& oracle, Weights& w) {
 		// get current value and gradient
 		oracle(w_tm1, L_w_tm1, a_t);
 
-		std::cout << "       L(w)              is: " << L_w_tm1 << std::endl;
-		//std::cout << "      ∂L(w)/∂            is: " << a_t << std::endl;
+        if(oracle.getInfParam().verbose_ ){
+            std::cout << "       L(w)              is: " << L_w_tm1 << std::endl;
+            std::cout << "∂L(w)/∂:  (";
+            for(size_t i=0; i<a_t.size(); ++i)
+                std::cout << a_t[i] << " ";
+            std::cout << ")" << std::endl;
+        }
 
 		// update smallest observed value of regularized L
 		minValue = std::min(minValue, L_w_tm1 + _parameter.lambda*0.5*dot(w_tm1, w_tm1));
 
-		std::cout << " min_i L(w_i) + ½λ|w_i|² is: " << minValue << std::endl;
+        if(oracle.getInfParam().verbose_ )
+            std::cout << " min_i L(w_i) + ½λ|w_i|² is: " << minValue << std::endl;
 
 		// compute hyperplane offset
 		T b_t = L_w_tm1 - dot(w_tm1, a_t);
 
-		//std::cout << "adding hyperplane " << a_t << "*w + " << b_t << std::endl;
+        if(oracle.getInfParam().verbose_ ){
+            std::cout << "adding hyperplane: ( ";
+            for(size_t i=0; i<a_t.size(); ++i)
+                std::cout << a_t[i] << " ";
+            std::cout << ")*w + " << b_t << std::endl;
+        }
 
 		// update lower bound
 		_bundleCollector.addHyperplane(a_t, b_t);
 
 		// minimal value of lower bound
 		T minLower;
 
-		// update w and get minimal value
+        // update w and get minimal value
 		findMinLowerBound(w, minLower);
 
-		std::cout << " min_w ℒ(w)   + ½λ|w|²   is: " << minLower << std::endl;
-		//std::cout << " w* of ℒ(w)   + ½λ|w|²   is: "  << w << std::endl;
+        // norm of w
+        double norm = 0.0;
+        for(size_t i=0; i<w.size(); ++i)
+            norm += w[i]*w[i];
+        norm = std::sqrt(norm);
+
+        if(oracle.getInfParam().verbose_ ){
+            std::cout << " min_w ℒ(w)   + ½λ|w|²   is: " << minLower << std::endl;
+            std::cout << " w* of ℒ(w)   + ½λ|w|²   is: (";
+            for(size_t i=0; i<w.size(); ++i)
+                std::cout << w[i] << " ";
+            std::cout << ")              normalized: (";
+            for(size_t i=0; i<w.size(); ++i)
+                std::cout << w[i]/norm << " ";
+            std::cout << ")" << std::endl;
+        }
 
 		// compute gap
 		T eps_t;
@@ -206,7 +237,8 @@ BundleOptimizer<T>::optimize(Oracle& oracle, Weights& w) {
 
 		lastMinLower = minLower;
 
-		std::cout  << "          ε   is: " << eps_t << std::endl;
+        if(oracle.getInfParam().verbose_ )
+            std::cout  << "          ε   is: " << eps_t << std::endl;
 
 		// converged?
 		if (eps_t <= _parameter.min_eps)

include/opengm/learning/gradient-accumulator.hxx

@@ -53,9 +53,7 @@ public:
         for (int i = 0; i < function.numberOfWeights(); i++) {
 
             int index = function.weightIndex(i);
-
             double g = function.weightGradient(i, Iter(accessor, 0));
-
             if (_mode == Add)
                 _gradient[index] += g;
             else
@@ -99,8 +97,6 @@ struct FeatureAccumulator{
     }
 
     void resetWeights(){
-        //accFeaturesGt_ = 0.0;
-        //accWeights_ = 0.0;
         for(size_t i=0; i<accWeights_.size(); ++i){
             accWeights_[i] = 0.0;
         }
@@ -117,7 +113,6 @@ struct FeatureAccumulator{
         typedef opengm::SubsetAccessor<Iter, LABEL_ITER> Accessor;
         typedef opengm::AccessorIterator<Accessor, true> AccessorIter;
 
-
         // get the number of weights_
         const size_t nWeights = f.numberOfWeights();
         if(nWeights>0){
@@ -129,7 +124,6 @@ struct FeatureAccumulator{
 
                 const Accessor accessorGt(begin, end, gtLabel_);
                 const Accessor accessorMap(begin, end, mapLabel_);
-
 
                 if(add_){
                     // for gt label
@@ -175,9 +169,6 @@ struct FeatureAccumulator{
     double weight_;
 };
 
-
-
-
 }} // namespace opengm::learning
 
 #endif // OPENGM_LEARNING_GRADIENT_ACCUMULATOR_H__

include/opengm/learning/solver/CplexBackend.h

@@ -330,14 +330,14 @@ CplexBackend::solve(Solution& x, double& value, std::string& msg) {
             std::cerr << "Invalid value for MIP focus!" << std::endl;
 
         setNumThreads(_parameter.numThreads);
-
         if(!cplex_.solve()) {
            std::cout << "failed to optimize. " << cplex_.getStatus() << std::endl;
            msg = "Optimal solution *NOT* found";
            return false;
         }
         else
-            msg = "Optimal solution found";
+            if(_parameter.verbose == true)
+                msg = "Optimal solution found";
 
         // extract solution
         cplex_.getValues(sol_, x_);
@@ -413,12 +413,16 @@ CplexBackend::setVerbose(bool verbose) {
         cplex_.setParam(IloCplex::MIPDisplay, 1);
         cplex_.setParam(IloCplex::SimDisplay, 1);
         cplex_.setParam(IloCplex::SiftDisplay, 1);
+        cplex_.setParam(IloCplex::BarDisplay, 1);
+        cplex_.setParam(IloCplex::NetDisplay, 1);
     }
     else
     {
         cplex_.setParam(IloCplex::MIPDisplay, 0);
         cplex_.setParam(IloCplex::SimDisplay, 0);
         cplex_.setParam(IloCplex::SiftDisplay, 0);
+        cplex_.setParam(IloCplex::BarDisplay, 0);
+        cplex_.setParam(IloCplex::NetDisplay, 0);
     }
 }
 

include/opengm/learning/solver/QuadraticSolverFactory.h

@@ -3,7 +3,7 @@
 
 #ifdef WITH_GUROBI
 #include "GurobiBackend.h"
-#elif WITH_CPLEX
+#elif defined(WITH_CPLEX)
 #include "CplexBackend.h"
 #endif
 
@@ -19,7 +19,7 @@ class QuadraticSolverFactory {
 
 #ifdef WITH_GUROBI
 	return new GurobiBackend();
-#elif WITH_CPLEX
+#elif defined(WITH_CPLEX)
         return new CplexBackend();
 #endif