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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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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 .
 */

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

package weka.classifiers.trees.ht;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.TreeSet;

import weka.core.Utils;
import weka.estimators.UnivariateNormalEstimator;

/**
 * Maintains sufficient stats for a Gaussian distribution for a numeric
 * attribute
 * 
 * @author Richard Kirkby ([email protected])
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision: 9705 $
 */
public class GaussianConditionalSufficientStats extends
    ConditionalSufficientStats implements Serializable {

  /**
   * For serialization
   */
  private static final long serialVersionUID = -1527915607201784762L;

  /**
   * Inner class that implements a Gaussian estimator
   * 
   * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
   */
  protected class GaussianEstimator extends UnivariateNormalEstimator implements
      Serializable {

    /**
     * For serialization
     */
    private static final long serialVersionUID = 4756032800685001315L;

    public double getSumOfWeights() {
      return m_SumOfWeights;
    }

    public double probabilityDensity(double value) {
      updateMeanAndVariance();

      if (m_SumOfWeights > 0) {
        double stdDev = Math.sqrt(m_Variance);
        if (stdDev > 0) {
          double diff = value - m_Mean;
          return (1.0 / (CONST * stdDev))
              * Math.exp(-(diff * diff / (2.0 * m_Variance)));
        }
        return value == m_Mean ? 1.0 : 0.0;
      }

      return 0.0;
    }

    public double[] weightLessThanEqualAndGreaterThan(double value) {
      double stdDev = Math.sqrt(m_Variance);
      double equalW = probabilityDensity(value) * m_SumOfWeights;

      double lessW = (stdDev > 0) ? weka.core.Statistics
          .normalProbability((value - m_Mean) / stdDev)
          * m_SumOfWeights
          - equalW : (value < m_Mean) ? m_SumOfWeights - equalW : 0.0;
      double greaterW = m_SumOfWeights - equalW - lessW;

      return new double[] { lessW, equalW, greaterW };
    }
  }

  protected Map m_minValObservedPerClass = new HashMap();
  protected Map m_maxValObservedPerClass = new HashMap();

  protected int m_numBins = 10;

  public void setNumBins(int b) {
    m_numBins = b;
  }

  public int getNumBins() {
    return m_numBins;
  }

  @Override
  public void update(double attVal, String classVal, double weight) {
    if (!Utils.isMissingValue(attVal)) {
      GaussianEstimator norm = (GaussianEstimator) m_classLookup.get(classVal);
      if (norm == null) {
        norm = new GaussianEstimator();
        m_classLookup.put(classVal, norm);
        m_minValObservedPerClass.put(classVal, attVal);
        m_maxValObservedPerClass.put(classVal, attVal);
      } else {
        if (attVal < m_minValObservedPerClass.get(classVal)) {
          m_minValObservedPerClass.put(classVal, attVal);
        }

        if (attVal > m_maxValObservedPerClass.get(classVal)) {
          m_maxValObservedPerClass.put(classVal, attVal);
        }
      }
      norm.addValue(attVal, weight);
    }
  }

  @Override
  public double probabilityOfAttValConditionedOnClass(double attVal,
      String classVal) {
    GaussianEstimator norm = (GaussianEstimator) m_classLookup.get(classVal);
    if (norm == null) {
      return 0;
    }

    // return Utils.lo
    return norm.probabilityDensity(attVal);
  }

  protected TreeSet getSplitPointCandidates() {
    TreeSet splits = new TreeSet();
    double min = Double.POSITIVE_INFINITY;
    double max = Double.NEGATIVE_INFINITY;

    for (String classVal : m_classLookup.keySet()) {
      if (m_minValObservedPerClass.containsKey(classVal)) {
        if (m_minValObservedPerClass.get(classVal) < min) {
          min = m_minValObservedPerClass.get(classVal);
        }

        if (m_maxValObservedPerClass.get(classVal) > max) {
          max = m_maxValObservedPerClass.get(classVal);
        }
      }
    }

    if (min < Double.POSITIVE_INFINITY) {
      double bin = max - min;
      bin /= (m_numBins + 1);
      for (int i = 0; i < m_numBins; i++) {
        double split = min + (bin * (i + 1));

        if (split > min && split < max) {
          splits.add(split);
        }
      }
    }
    return splits;
  }

  protected List> classDistsAfterSplit(double splitVal) {
    Map lhsDist = new HashMap();
    Map rhsDist = new HashMap();

    for (Map.Entry e : m_classLookup.entrySet()) {
      String classVal = e.getKey();
      GaussianEstimator attEst = (GaussianEstimator) e.getValue();

      if (attEst != null) {
        if (splitVal < m_minValObservedPerClass.get(classVal)) {
          WeightMass mass = rhsDist.get(classVal);
          if (mass == null) {
            mass = new WeightMass();
            rhsDist.put(classVal, mass);
          }
          mass.m_weight += attEst.getSumOfWeights();
        } else if (splitVal > m_maxValObservedPerClass.get(classVal)) {
          WeightMass mass = lhsDist.get(classVal);
          if (mass == null) {
            mass = new WeightMass();
            lhsDist.put(classVal, mass);
          }
          mass.m_weight += attEst.getSumOfWeights();
        } else {
          double[] weights = attEst.weightLessThanEqualAndGreaterThan(splitVal);
          WeightMass mass = lhsDist.get(classVal);
          if (mass == null) {
            mass = new WeightMass();
            lhsDist.put(classVal, mass);
          }
          mass.m_weight += weights[0] + weights[1]; // <=

          mass = rhsDist.get(classVal);
          if (mass == null) {
            mass = new WeightMass();
            rhsDist.put(classVal, mass);
          }
          mass.m_weight += weights[2]; // >
        }
      }
    }

    List> dists = new ArrayList>();
    dists.add(lhsDist);
    dists.add(rhsDist);

    return dists;
  }

  @Override
  public SplitCandidate bestSplit(SplitMetric splitMetric,
      Map preSplitDist, String attName) {

    SplitCandidate best = null;

    TreeSet candidates = getSplitPointCandidates();
    for (Double s : candidates) {
      List> postSplitDists = classDistsAfterSplit(s);

      double splitMerit = splitMetric.evaluateSplit(preSplitDist,
          postSplitDists);

      if (best == null || splitMerit > best.m_splitMerit) {
        Split split = new UnivariateNumericBinarySplit(attName, s);
        best = new SplitCandidate(split, postSplitDists, splitMerit);
      }
    }

    return best;
  }
}