samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/common/RulePassiveRegressionNode.java

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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 org.apache.samoa.learners.classifiers.rules.common;

import org.apache.samoa.instances.Instance;
import org.apache.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);
    }
  }
}