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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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/*
 *    BasicRegressionPerformanceEvaluator.java
 *    Copyright (C) 2011 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 *
 *    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 moa.AbstractMOAObject;
import moa.core.Example;
import moa.core.Measurement;

import com.yahoo.labs.samoa.instances.DenseInstance;
import com.yahoo.labs.samoa.instances.DenseInstanceData;
import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.instances.InstanceData;
import com.yahoo.labs.samoa.instances.MultiLabelInstance;
import com.yahoo.labs.samoa.instances.Prediction;

/**
 * Regression evaluator that performs basic incremental evaluation.
 *
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @version $Revision: 7 $
 */
public class BasicMultiTargetPerformanceEvaluator extends AbstractMOAObject
        implements MultiTargetPerformanceEvaluator, RegressionPerformanceEvaluator{

    private static final long serialVersionUID = 1L;

    protected double weightObserved;

    protected double squareError;

    protected double averageError;
    
    protected int numberOutputs;

    @Override
    public void reset() {
        this.weightObserved = 0.0;
        this.squareError = 0.0;
        this.averageError = 0.0;
    }

    @Override
    public void addResult(Example example, Prediction prediction) {

    MultiLabelInstance inst = (MultiLabelInstance) example.getData();
    if (numberOutputs == 0) {
    	numberOutputs = inst.numberOutputTargets();
    }
        if (inst.weight() > 0.0) {
            this.weightObserved += inst.weight();
            if (prediction != null ) {
            	for (int i = 0; i< numberOutputs;i++){
            		double err = inst.classValue(i) - ((prediction.numOutputAttributes()==0) ? 0.0 : prediction.getVote(i,0));
	                this.squareError += (err) * (err);
	                this.averageError += Math.abs(err);
            	}
            }
            //System.out.println(inst.classValue()+", "+prediction);
        }
    }

    @Override
    public Measurement[] getPerformanceMeasurements() {
        return new Measurement[]{
                    new Measurement("classified instances",
                    getTotalWeightObserved()),
                    new Measurement("mean absolute error",
                    getMeanError()),
                    new Measurement("root mean squared error",
                    getSquareError())};
    }

    public double getTotalWeightObserved() {
        return this.weightObserved;
    }

    public double getMeanError() {
        return this.weightObserved > 0.0 ? this.averageError
                / (this.weightObserved * this.numberOutputs) : 0.0;
    }

    public double getSquareError() {
        return Math.sqrt(this.weightObserved > 0.0 ? this.squareError
                / (this.weightObserved * this.numberOutputs): 0.0);
    }

    @Override
    public void getDescription(StringBuilder sb, int indent) {
        Measurement.getMeasurementsDescription(getPerformanceMeasurements(),
                sb, indent);
    }

	@Override
	public void addResult(Example example, double[] classVotes) {
		// TODO Auto-generated method stub
		
	}

}