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Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

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/*
 *    LearningPerformanceEvaluator.java
 *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet ([email protected])
 *
 *    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 .
 *    
 */
package moa.evaluation;

import com.yahoo.labs.samoa.instances.InstanceData;
import com.yahoo.labs.samoa.instances.Prediction;

import moa.MOAObject;
import moa.core.Example;
import moa.core.Measurement;

/**
 * Interface implemented by learner evaluators to monitor
 * the results of the learning process.
 *
 * @author Richard Kirkby ([email protected])
 * @version $Revision: 7 $
 */
public interface LearningPerformanceEvaluator extends MOAObject {

    /**
     * Resets this evaluator. It must be similar to
     * starting a new evaluator from scratch.
     *
     */
	public void reset();

   /**
     * Adds a learning result to this evaluator.
     *
     * @param example the example to be classified
     * @param classVotes an array containing the estimated membership
     * probabilities of the test instance in each class
     * @return an array of measurements monitored in this evaluator
     */
	public void addResult(E testInst, Prediction prediction);
    public void addResult(E example, double[] classVotes);

    /**
     * Gets the current measurements monitored by this evaluator.
     *
     * @return an array of measurements monitored by this evaluator
     */
	public Measurement[] getPerformanceMeasurements();

}