moa.classifiers.rules.multilabel.errormeasurers.RelativeRootMeanSquaredErrorMT Maven / Gradle / Ivy

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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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/*
 *    RelativeRootMeanSquaredErrorMT.java
 *    Copyright (C) 2017 University of Porto, Portugal
 *    @author J. Duarte, J. Gama
 *
 *    Licensed under the Apache License, Version 2.0 (the "License");
 *    you may not use this file except in compliance with the License.
 *    You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 *    Unless required by applicable law or agreed to in writing, software
 *    distributed under the License is distributed on an "AS IS" BASIS,
 *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *    See the License for the specific language governing permissions and
 *    limitations under the License.
 *
 *
 */
package moa.classifiers.rules.multilabel.errormeasurers;

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

/**
 * Relative Root Mean Squared Error for multitarget and with fading factor
 */




public class RelativeRootMeanSquaredErrorMT extends AbstractMultiTargetErrorMeasurer {

	/**
	 * 
	 */
	protected double weightSeen;
	protected double [] sumY;
	protected double [] sumSquaredError;
	protected double [] sumSquaredErrorToTargetMean;
	protected static final long serialVersionUID = 1L;
	protected boolean hasStarted;
	protected int numLearnedOutputs;

	@Override
	public void addPrediction(Prediction prediction, Prediction trueClass, double weight) {
		int numOutputs=prediction.numOutputAttributes();
		if (!hasStarted){
			sumSquaredError=new double[numOutputs];
			sumY=new double[numOutputs];
			sumSquaredErrorToTargetMean=new double[numOutputs];
			hasStarted=true;
			for(int i=0; i