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A collection of multi-instance learning classifiers. Includes the Citation KNN method, several variants of the diverse density method, support vector machines for multi-instance learning, simple wrappers for applying standard propositional learners to multi-instance data, decision tree and rule learners, and some other methods.

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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 .
 */

/*
 *    IBestSplitMeasure.java
 *    Copyright (C) 2011 University of Waikato, Hamilton, New Zealand
 *
 */
package weka.classifiers.mi.miti;

/**
 * Interface to be implemented by split selection measures.
 *
 * @author Luke Bjerring
 * @version $Revision: 8109 $
 */
public interface IBestSplitMeasure {

  /**
   * Returns a purity score of the two groups after the split - larger is better
   */
  public double getScore(SufficientStatistics ss, int kBEPPConstant, boolean unbiasedEstimate);
	
	
  /**
   * Returns a purity score for the N groups after a nominal split - larger is better
   */
  public double getScore(double[] totalCounts, double[] positiveCounts, int kBEPPConstant, boolean unbiasedEstimate);
}