opennlp-ml/src/main/java/org/apache/opennlp/ml/perceptron/PerceptronModel.java

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 * 
 *   http://www.apache.org/licenses/LICENSE-2.0
 * 
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */

package org.apache.opennlp.ml.perceptron;

import java.io.BufferedReader;
import java.io.File;
import java.io.InputStreamReader;
import java.text.DecimalFormat;
import java.util.Map;

import org.apache.opennlp.ml.model.AbstractModel;
import org.apache.opennlp.ml.model.Context;
import org.apache.opennlp.ml.model.EvalParameters;
import org.apache.opennlp.ml.model.IndexHashTable;

public class PerceptronModel extends AbstractModel {

  public PerceptronModel(Context[] params, String[] predLabels, IndexHashTable<String> pmap, String[] outcomeNames) {
    super(params,predLabels,pmap,outcomeNames);
    modelType = ModelType.Perceptron;
  }
  
  /**
   * @deprecated use the constructor with the {@link IndexHashTable} instead!
   */
  @Deprecated
  public PerceptronModel(Context[] params, String[] predLabels, Map<String,Integer> pmap, String[] outcomeNames) {
    super(params,predLabels,outcomeNames);
    modelType = ModelType.Perceptron;
  }
  
  public PerceptronModel(Context[] params, String[] predLabels, String[] outcomeNames) {
    super(params,predLabels,outcomeNames);
    modelType = ModelType.Perceptron;
  }
  
  public double[] eval(String[] context) {
    return eval(context,new double[evalParams.getNumOutcomes()]);
  }
  
  public double[] eval(String[] context, float[] values) {
    return eval(context,values,new double[evalParams.getNumOutcomes()]);
  }

  public double[] eval(String[] context, double[] probs) {
    return eval(context,null,probs);
  }

  public double[] eval(String[] context, float[] values,double[] outsums) {
    int[] scontexts = new int[context.length];
    java.util.Arrays.fill(outsums, 0);
    for (int i=0; i<context.length; i++) {
      Integer ci = pmap.get(context[i]);
      scontexts[i] = ci == null ? -1 : ci;
    }
    return eval(scontexts,values,outsums,evalParams,true);
  }
  
  public static double[] eval(int[] context, double[] prior, EvalParameters model) {
    return eval(context,null,prior,model,true);
  }
  
  public static double[] eval(int[] context, float[] values, double[] prior, EvalParameters model, boolean normalize) {
    Context[] params = model.getParams();
    double[] activeParameters;
    int[] activeOutcomes;
    double value = 1;
    for (int ci = 0; ci < context.length; ci++) {
      if (context[ci] >= 0) {
        Context predParams = params[context[ci]];
        activeOutcomes = predParams.getOutcomes();
        activeParameters = predParams.getParameters();
        if (values != null) {
          value = values[ci];
        }
        for (int ai = 0; ai < activeOutcomes.length; ai++) {
          int oid = activeOutcomes[ai];
          prior[oid] += activeParameters[ai] * value;
        }
      }
    }    
    if (normalize) {
      int numOutcomes = model.getNumOutcomes();
      
      double maxPrior = 1;
      
      for (int oid = 0; oid < numOutcomes; oid++) {
        if (maxPrior < Math.abs(prior[oid]))
          maxPrior = Math.abs(prior[oid]);
      }
      
      double normal = 0.0;
      for (int oid = 0; oid < numOutcomes; oid++) {
        prior[oid] = Math.exp(prior[oid]/maxPrior);
        normal += prior[oid];
      }

      for (int oid = 0; oid < numOutcomes; oid++)
        prior[oid] /= normal;
    }
    return prior;
  }
  
  public static void main(String[] args) throws java.io.IOException {
    if (args.length == 0) {
      System.err.println("Usage: PerceptronModel modelname < contexts");
      System.exit(1);
    }
    AbstractModel m = new PerceptronModelReader(new File(args[0])).getModel();
    BufferedReader in = new BufferedReader(new InputStreamReader(System.in));
    DecimalFormat df = new java.text.DecimalFormat(".###");
    for (String line = in.readLine(); line != null; line = in.readLine()) {
      String[] context = line.split(" ");
      double[] dist = m.eval(context);
      for (int oi=0;oi<dist.length;oi++) {
        System.out.print("["+m.getOutcome(oi)+" "+df.format(dist[oi])+"] ");
      }
      System.out.println();
    }
  }
}