Examples of joshua.discriminative.training.learning_algorithm.DefaultPerceptron

        perceptronModel.put(baselineFeatName, 1.0);
        System.out.println("feature set size is " + perceptronModel.size());
      }else{
        System.out.println("In perceptron, should specify feature set");        
      }
      optimizer = new DefaultPerceptron(perceptron_sum_model, perceptron_avg_model,train_size, batch_update_size, converge_pass, init_gain, sigma, is_minimize_score);
      hgdl = new HGDiscriminativeLearner(optimizer,  new HashSet<String>(perceptronModel.keySet()));
      hgdl.reset_baseline_feat();
    }    
        
    //#####begin to do training

        DiscriminativeSupport.loadModel(initModelFile, perceptronModel, null);
        perceptronModel.put(baselineFeatName, 1.0);
      }else{
        System.out.println("In perceptron, should specify feature set");        
      }
      optimizer = new DefaultPerceptron(perceptronSumModel, perceptronAvgModel,trainSize, batchUpdateSize, convergePass, initGain, sigma, isMinimizeScore);
      ndl = new NBESTDiscriminativeLearner(optimizer,  new HashSet<String>(perceptronModel.keySet()));
      ndl.resetBaselineFeat();
    }  
    
    //TODO

Related Classes of joshua.discriminative.training.learning_algorithm.DefaultPerceptron

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