Class GaussianConditionalSufficientStats

java.lang.Object
weka.classifiers.trees.ht.ConditionalSufficientStats
weka.classifiers.trees.ht.GaussianConditionalSufficientStats
All Implemented Interfaces:
Serializable

public class GaussianConditionalSufficientStats extends ConditionalSufficientStats implements Serializable
Maintains sufficient stats for a Gaussian distribution for a numeric attribute
Version:
$Revision: 9705 $
Author:
Richard Kirkby (rkirkby@cs.waikato.ac.nz), Mark Hall (mhall{[at]}pentaho{[dot]}com)
See Also:
  • Constructor Details

    • GaussianConditionalSufficientStats

      public GaussianConditionalSufficientStats()
  • Method Details

    • setNumBins

      public void setNumBins(int b)
    • getNumBins

      public int getNumBins()
    • update

      public void update(double attVal, String classVal, double weight)
      Description copied from class: ConditionalSufficientStats
      Update this stat with the supplied attribute value and class value
      Specified by:
      update in class ConditionalSufficientStats
      Parameters:
      attVal - the value of the attribute
      classVal - the class value
      weight - the weight of this observation
    • probabilityOfAttValConditionedOnClass

      public double probabilityOfAttValConditionedOnClass(double attVal, String classVal)
      Description copied from class: ConditionalSufficientStats
      Return the probability of an attribute value conditioned on a class value
      Specified by:
      probabilityOfAttValConditionedOnClass in class ConditionalSufficientStats
      Parameters:
      attVal - the attribute value to compute the conditional probability for
      classVal - the class value
      Returns:
      the probability
    • bestSplit

      public SplitCandidate bestSplit(SplitMetric splitMetric, Map<String,WeightMass> preSplitDist, String attName)
      Description copied from class: ConditionalSufficientStats
      Return the best split
      Specified by:
      bestSplit in class ConditionalSufficientStats
      Parameters:
      splitMetric - the split metric to use
      preSplitDist - the distribution of class values prior to splitting
      attName - the name of the attribute being considered for splitting
      Returns:
      the best split for the attribute