Examples of joshua.discriminative.semiring_parsing.MinRiskDAGradientSemiringParser

    if(useSemiringV2){
      //System.out.println("----------------useSemiringV2");
      this.gradientSemiringParserV2 =  new MinRiskDADenseFeaturesSemiringParser(this.temperature);      
    }else{
      //System.out.println("----------------useSemiringV1");
      this.gradientSemiringParserV1 = new MinRiskDAGradientSemiringParser(1, 0, scalingFactor, temperature);      
        this.funcValSemiringParserV1 =new MinRiskDAFuncValSemiringParser(1, 0, scalingFactor, temperature);      
    }
  }


    
  
    private void reComputeFunctionValueAndGradientHelper(double[] weightsForTheta){
    
      MinRiskDAGradientSemiringParser gradientSemiringParser 
       = new MinRiskDAGradientSemiringParser(1, 0, scalingFactor, temperature);
    
      MinRiskDAFuncValSemiringParser funcValSemiringParser 
     = new MinRiskDAFuncValSemiringParser(1, 0, scalingFactor, temperature);
    
       
        
    for(int sentID=0; sentID < numSentence; sentID ++){
      //System.out.println("#Process sentence " + sent_id);
      
      FeatureForest fForest = hgFactory.nextHG(sentID);
      
      fForest.setFeatureWeights(weightsForTheta);
      fForest.setScale(scalingFactor);
  


      /** Based on a model and a test hypergraph (which provides the topology and feature/risk annotation), 
       *  compute the gradient.
       **/
      
      //@todo: we should check if hg_test is a feature forest or not
      gradientSemiringParser.setHyperGraph(fForest);
      HashMap<Integer, Double> gradients = gradientSemiringParser.computeGradientForTheta();
      
      for(Map.Entry<Integer, Double> feature : gradients.entrySet()){
        gradientsForTheta[feature.getKey()] -= feature.getValue(); //we are maximizing, instead of minizing
      }

Examples of joshua.discriminative.semiring_parsing.MinRiskDAGradientSemiringParser

Related Classes of joshua.discriminative.semiring_parsing.MinRiskDAGradientSemiringParser