opennlp.model.AbstractModel Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of opennlp-maxent Show documentation
The newest version!
/*
 * 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.model;

import java.text.DecimalFormat;

public abstract class AbstractModel implements MaxentModel {

  /** Mapping between predicates/contexts and an integer representing them. */
  protected IndexHashTable pmap;
  /** The names of the outcomes. */
  protected String[] outcomeNames;
  /** Parameters for the model. */
  protected EvalParameters evalParams;
  /** Prior distribution for this model. */
  protected Prior prior;
  
  public enum ModelType {Maxent,Perceptron,MaxentQn};
  
  /** The type of the model. */
  protected ModelType modelType;
  
  public AbstractModel(Context[] params, String[] predLabels, IndexHashTable pmap, String[] outcomeNames) {
    this.pmap = pmap;
    this.outcomeNames =  outcomeNames;
    this.evalParams = new EvalParameters(params,outcomeNames.length);
  }

  public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) {
    init(predLabels,outcomeNames);
    this.evalParams = new EvalParameters(params,outcomeNames.length);
  }

  public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames, int correctionConstant,double correctionParam) {
    init(predLabels,outcomeNames);
    this.evalParams = new EvalParameters(params,correctionParam,correctionConstant,outcomeNames.length);
  }
  
  private void init(String[] predLabels, String[] outcomeNames){
    this.pmap = new IndexHashTable(predLabels, 0.7d);
    this.outcomeNames =  outcomeNames;
  }


  /**
   * Return the name of the outcome corresponding to the highest likelihood
   * in the parameter ocs.
   *
   * @param ocs A double[] as returned by the eval(String[] context)
   *            method.
   * @return    The name of the most likely outcome.
   */
  public final String getBestOutcome(double[] ocs) {
      int best = 0;
      for (int i = 1; i ocs[best]) best = i;
      return outcomeNames[best];
  }
  
  public ModelType getModelType(){
    return modelType;
  }

  /**
   * Return a string matching all the outcome names with all the
   * probabilities produced by the eval(String[] context)
   * method.
   *
   * @param ocs A double[] as returned by the
   *            eval(String[] context)
   *            method.
   * @return    String containing outcome names paired with the normalized
   *            probability (contained in the double[] ocs)
   *            for each one.
   */
  public final String getAllOutcomes(double[] ocs) {
      if (ocs.length != outcomeNames.length) {
          return "The double array sent as a parameter to GISModel.getAllOutcomes() must not have been produced by this model.";
      }
      else {
        DecimalFormat df =  new DecimalFormat("0.0000");
        StringBuilder sb = new StringBuilder(ocs.length * 2);
        sb.append(outcomeNames[0]).append("[").append(df.format(ocs[0])).append("]");
        for (int i = 1; iindex 0: opennlp.maxent.Context[] containing the model
   *            parameters  
   * index 1: java.util.Map containing the mapping of model predicates
   *            to unique integers
   * 
index 2: java.lang.String[] containing the names of the outcomes,
   *            stored in the index of the array which represents their
   * 	          unique ids in the model.
   * 
index 3: java.lang.Integer containing the value of the models
   *            correction constant
   * index 4: java.lang.Double containing the value of the models
   *            correction parameter
   *
   * @return An Object[] with the values as described above.
   */
  public final Object[] getDataStructures() {
      Object[] data = new Object[5];
      data[0] = evalParams.getParams();
      data[1] = pmap;
      data[2] = outcomeNames;
      data[3] = (int) evalParams.getCorrectionConstant();
      data[4] = evalParams.getCorrectionParam();
      return data;
  }
}