| 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); |
| } |
| } |
| } |