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/*
 * 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 opennlp.perceptron;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import opennlp.model.AbstractModel;
import opennlp.model.AbstractModelWriter;
import opennlp.model.ComparablePredicate;
import opennlp.model.Context;
import opennlp.model.IndexHashTable;

/**
 * Abstract parent class for Perceptron writers.  It provides the persist method
 * which takes care of the structure of a stored document, and requires an
 * extending class to define precisely how the data should be stored.
 *
 */
public abstract class PerceptronModelWriter extends AbstractModelWriter {
    protected Context[] PARAMS;
    protected String[] OUTCOME_LABELS;
    protected String[] PRED_LABELS;
    int numOutcomes;

    public PerceptronModelWriter (AbstractModel model) {
      
      Object[] data = model.getDataStructures();
      this.numOutcomes = model.getNumOutcomes();
      PARAMS = (Context[]) data[0];
      IndexHashTable pmap = (IndexHashTable) data[1];
      OUTCOME_LABELS = (String[])data[2];
      
      PRED_LABELS = new String[pmap.size()];
      pmap.toArray(PRED_LABELS);
    }

    protected ComparablePredicate[] sortValues () {
      ComparablePredicate[] sortPreds;
      ComparablePredicate[] tmpPreds = new ComparablePredicate[PARAMS.length];
      int[] tmpOutcomes = new int[numOutcomes];
      double[] tmpParams = new double[numOutcomes];
      int numPreds = 0;
      //remove parameters with 0 weight and predicates with no parameters 
      for (int pid=0; pid> computeOutcomePatterns(ComparablePredicate[] sorted) {
      ComparablePredicate cp = sorted[0];
      List> outcomePatterns = new ArrayList>();
      List newGroup = new ArrayList();
      for (ComparablePredicate predicate : sorted) {
        if (cp.compareTo(predicate) == 0) {
          newGroup.add(predicate);
        } else {
          cp = predicate;
          outcomePatterns.add(newGroup);
          newGroup = new ArrayList();
          newGroup.add(predicate);
        }
      }
      outcomePatterns.add(newGroup);
      System.err.println(outcomePatterns.size()+" outcome patterns");
      return outcomePatterns;
    }

    /**
     * Writes the model to disk, using the writeX() methods
     * provided by extending classes.
     *
     * 

If you wish to create a PerceptronModelWriter which uses a different * structure, it will be necessary to override the persist method in * addition to implementing the writeX() methods. */ public void persist() throws IOException { // the type of model (Perceptron) writeUTF("Perceptron"); // the mapping from outcomes to their integer indexes writeInt(OUTCOME_LABELS.length); for (String label : OUTCOME_LABELS) { writeUTF(label); } // the mapping from predicates to the outcomes they contributed to. // The sorting is done so that we actually can write this out more // compactly than as the entire list. ComparablePredicate[] sorted = sortValues(); List> compressed = computeOutcomePatterns(sorted); writeInt(compressed.size()); for (List a : compressed) { writeUTF(a.size() + a.get(0).toString()); } // the mapping from predicate names to their integer indexes writeInt(sorted.length); for (ComparablePredicate s : sorted) { writeUTF(s.name); } // write out the parameters for (int i=0; i





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