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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 .
 */

/*
 *    SplitEvaluator.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */


package weka.experiment;

import java.io.Serializable;

import weka.core.Instances;

/**
 * Interface to objects able to generate a fixed set of results for
 * a particular split of a dataset. The set of results should contain
 * fields related to any settings of the SplitEvaluator (not including
 * the dataset name. For example, one field for the classifier used to
 * get the results, another for the classifier options, etc). 

* * Possible implementations of SplitEvaluator:
*

    *
  • StdClassification results *
  • StdRegression results *
* * @author Len Trigg ([email protected]) * @version $Revision: 8034 $ */ public interface SplitEvaluator extends Serializable { /** * Sets a list of method names for additional measures to look for * in SplitEvaluators. * @param additionalMeasures a list of method names */ void setAdditionalMeasures(String [] additionalMeasures); /** * Gets the names of each of the key columns produced for a single run. * The names should not contain spaces (use '_' instead for easy * translation.) The number of key fields must be constant for a given * SplitEvaluator. * * @return an array containing the name of each key column */ String [] getKeyNames(); /** * Gets the data types of each of the key columns produced for a single run. * The number of key fields must be constant * for a given SplitEvaluator. * * @return an array containing objects of the type of each key column. The * objects should be Strings, or Doubles. */ Object [] getKeyTypes(); /** * Gets the names of each of the result columns produced for a single run. * The names should not contain spaces (use '_' instead for easy * translation.) The number of result fields must be constant * for a given SplitEvaluator. * * @return an array containing the name of each result column */ String [] getResultNames(); /** * Gets the data types of each of the result columns produced for a * single run. The number of result fields must be constant * for a given SplitEvaluator. * * @return an array containing objects of the type of each result column. * The objects should be Strings, or Doubles. */ Object [] getResultTypes(); /** * Gets the key describing the current SplitEvaluator. For example * This may contain the name of the classifier used for classifier * predictive evaluation. The number of key fields must be constant * for a given SplitEvaluator. * * @return a value of type 'Object' */ Object [] getKey(); /** * Gets the results for the supplied train and test datasets. * * @param train the training Instances. * @param test the testing Instances. * @return the results stored in an array. The objects stored in * the array may be Strings, Doubles, or null (for the missing value). * @exception Exception if a problem occurs while getting the results */ Object [] getResult(Instances train, Instances test) throws Exception; /** * Returns the raw output for the most recent call to getResult. Useful * for debugging splitEvaluators. * * @return the raw output corresponding to the most recent call * to getResut */ String getRawResultOutput(); } // SplitEvaluator




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