RulePassiveRegressionNode.java

package com.yahoo.labs.samoa.learners.classifiers.rules.common;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2013 - 2014 Yahoo! Inc.
 * %%
 * 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.
 * #L%
 */

import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.moa.core.DoubleVector;

/**
 * LearningNode for regression rule that does not update
 * statistics for expanding rule. It only updates statistics for
 * computing predictions.
 * 
 * @author Anh Thu Vu
 *
 */
public class RulePassiveRegressionNode extends RuleRegressionNode implements RulePassiveLearningNode {

	/**
	 * 
	 */
	private static final long serialVersionUID = 3720878438856489690L;
	
	public RulePassiveRegressionNode (double[] statistics) {
		super(statistics);
	}
	
	public RulePassiveRegressionNode() {
		super();
	}
	
	public RulePassiveRegressionNode(RuleRegressionNode activeLearningNode) {
		this.predictionFunction = activeLearningNode.predictionFunction;
		this.ruleNumberID = activeLearningNode.ruleNumberID;
		this.nodeStatistics = new DoubleVector(activeLearningNode.nodeStatistics);
		this.learningRatio = activeLearningNode.learningRatio;
		this.perceptron = new Perceptron(activeLearningNode.perceptron, true);
		this.targetMean = new TargetMean(activeLearningNode.targetMean);
	}
	
	/*
	 * Update with input instance
	 */
	@Override
	public void updateStatistics(Instance inst) {
		// Update the statistics for this node
		// number of instances passing through the node
		nodeStatistics.addToValue(0, 1);
		// sum of y values
		nodeStatistics.addToValue(1, inst.classValue());
		// sum of squared y values
		nodeStatistics.addToValue(2, inst.classValue()*inst.classValue());
				
		this.perceptron.trainOnInstance(inst);
		if (this.predictionFunction != 1) { //Train target mean if prediction function is not Perceptron
			this.targetMean.trainOnInstance(inst);
		}
	}
}