/* * RapidMiner * * Copyright (C) 2001-2008 by Rapid-I and the contributors * * Complete list of developers available at our web site: * * http://rapid-i.com * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU Affero General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Affero General Public License for more details. * * You should have received a copy of the GNU Affero General Public License * along with this program. If not, see http://www.gnu.org/licenses/. */ package com.rapidminer.operator.learner.lazy; import java.util.Iterator; import com.rapidminer.example.Attribute; import com.rapidminer.example.Example; import com.rapidminer.example.ExampleSet; import com.rapidminer.operator.OperatorException; import com.rapidminer.operator.learner.PredictionModel; import com.rapidminer.tools.Tools; /** * The default model sets the prediction of all examples to the mode value in * case of nominal labels and to the average value in case of numerical labels. * * @author Stefan Rueping, Ingo Mierswa * @version $Id: DefaultModel.java,v 1.5 2008/05/09 19:23:24 ingomierswa Exp $ */ public class DefaultModel extends PredictionModel { private static final long serialVersionUID = -1455906287520811107L; /** The default prediction. */ private double value; /** The confidence values for all predictions. */ private double[] confidences; /** Can be used to create a default model for regression tasks. */ public DefaultModel(ExampleSet exampleSet, double value) { this(exampleSet, value, null); } /** * Can be used to create a default model for classification tasks * (confidence values should not be null in this case). */ public DefaultModel(ExampleSet exampleSet, double value, double[] confidences) { super(exampleSet); this.value = value; this.confidences = confidences; } /** Iterates over all examples and applies the model to them. */ public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) throws OperatorException { Attribute label = getLabel(); Iterator<Example> r = exampleSet.iterator(); while (r.hasNext()) { Example example = r.next(); example.setValue(predictedLabelAttribute, value); if (label.isNominal()) { for (int i = 0; i < confidences.length; i++) { example.setConfidence(predictedLabelAttribute.getMapping().mapIndex(i), confidences[i]); } } } return exampleSet; } public String toString() { return super.toString() + Tools.getLineSeparator() + "default value: " + (getLabel().isNominal() ? getLabel().getMapping().mapIndex((int) value) : value + ""); } }