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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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/*
 *    AdaptiveMultiTargetRegressor.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.functions;

import moa.classifiers.AbstractMultiLabelLearner;
import moa.classifiers.MultiTargetRegressor;
import moa.classifiers.rules.multilabel.errormeasurers.AbstractMultiTargetErrorMeasurer;
import moa.classifiers.rules.multilabel.errormeasurers.MeanAbsoluteDeviationMT;
import moa.classifiers.rules.multilabel.errormeasurers.MultiTargetErrorMeasurer;
import moa.core.Measurement;
import moa.options.ClassOption;

import com.github.javacliparser.IntOption;
import com.yahoo.labs.samoa.instances.MultiLabelInstance;
import com.yahoo.labs.samoa.instances.Prediction;

/**
 * Adaptive MultiTarget Regressor uses two learner
 * The first is used in first stage when high error are produced(e.g. Target mean)
 * The second is used in a second stage when low error are produced(e.g perceptron)
 * 	baseLearnerOption1- Learner one ;
 *  baseLearnerOption2- learner two
 */


public class AdaptiveMultiTargetRegressor extends AbstractMultiLabelLearner
implements MultiTargetRegressor, AMRulesFunction {

	/**
	 * 
	 */
	private static final long serialVersionUID = 1L;

	private static final int NUM_LEARNERS=2;

	public ClassOption baseLearnerOption1;
	public ClassOption baseLearnerOption2;

	public ClassOption errorMeasurerOption;
	
	public IntOption randomSeedOption = new IntOption("randomSeedOption",
			'r', "randomSeedOption", 
			1,Integer.MIN_VALUE, Integer.MAX_VALUE);

	protected boolean hasStarted;

	protected MultiTargetRegressor baseLearner[] ;


	protected MultiTargetErrorMeasurer [] errorMeasurer;

	public AdaptiveMultiTargetRegressor(){
		super.randomSeedOption=randomSeedOption;
		baseLearnerOption1 = new ClassOption("baseLearner1", 'l',
				"First base learner.", AMRulesFunction.class, MultiTargetMeanRegressor.class.getName()) ;
		baseLearnerOption2= new ClassOption("baseLearner2", 'm',
				"Second base learner.", AMRulesFunction.class, MultiTargetPerceptronRegressor.class.getName()) ;
		errorMeasurerOption = new ClassOption("errorMeasurer", 'e',
				"Measure of error for deciding which learner should predict.", AbstractMultiTargetErrorMeasurer.class, MeanAbsoluteDeviationMT.class.getName()) ;

	}
	@Override
	public void trainOnInstanceImpl(MultiLabelInstance instance) {
		if (!this.hasStarted){	
			baseLearner=new MultiTargetRegressor[NUM_LEARNERS];
			errorMeasurer= new MultiTargetErrorMeasurer[NUM_LEARNERS];
			baseLearner[0]=(MultiTargetRegressor) getPreparedClassOption(this.baseLearnerOption1);
			baseLearner[1]=(MultiTargetRegressor) getPreparedClassOption(this.baseLearnerOption2);
			for (int i=0; i