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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.

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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 *    ConditionalSufficientStats.java
 *    Copyright (C) 2013 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.ht;

import java.io.Serializable;
import java.util.HashMap;
import java.util.Map;

/**
 * Records sufficient stats for an attribute
 * 
 * @author Richard Kirkby ([email protected])
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision: 9705 $
 */
public abstract class ConditionalSufficientStats implements Serializable {

  /**
   * For serialization
   */
  private static final long serialVersionUID = 8724787722646808376L;

  /** Lookup by class value */
  protected Map m_classLookup = new HashMap();

  /**
   * Update this stat with the supplied attribute value and class value
   * 
   * @param attVal the value of the attribute
   * @param classVal the class value
   * @param weight the weight of this observation
   */
  public abstract void update(double attVal, String classVal, double weight);

  /**
   * Return the probability of an attribute value conditioned on a class value
   * 
   * @param attVal the attribute value to compute the conditional probability
   *          for
   * @param classVal the class value
   * @return the probability
   */
  public abstract double probabilityOfAttValConditionedOnClass(double attVal,
      String classVal);

  /**
   * Return the best split
   * 
   * @param splitMetric the split metric to use
   * @param preSplitDist the distribution of class values prior to splitting
   * @param attName the name of the attribute being considered for splitting
   * @return the best split for the attribute
   */
  public abstract SplitCandidate bestSplit(SplitMetric splitMetric,
      Map preSplitDist, String attName);
}