/*
* 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.features.construction;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeString;
import com.rapidminer.tools.Ontology;
/**
* Creates a gaussian function based on a given attribute and a specified mean and standard deviation sigma.
*
* @author Ingo Mierswa
* @version $Id: GaussFeatureConstructionOperator.java,v 1.1 2008/09/04 17:54:08 ingomierswa Exp $
*/
public class GaussFeatureConstructionOperator extends Operator {
public static final String PARAMETER_ATTRIBUTE_NAME = "attribute_name";
public static final String PARAMETER_MEAN = "mean";
public static final String PARAMETER_SIGMA = "sigma";
public GaussFeatureConstructionOperator(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = getInput(ExampleSet.class);
String attributeName = getParameterAsString(PARAMETER_ATTRIBUTE_NAME);
double mean = getParameterAsDouble(PARAMETER_MEAN);
double sigma = getParameterAsDouble(PARAMETER_SIGMA);
List<Attribute> newAttributes = new LinkedList<Attribute>();
for (Attribute attribute : exampleSet.getAttributes()) {
if (attribute.isNumerical()) {
if (attribute.getName().matches(attributeName)) {
newAttributes.add(createAttribute(exampleSet, attribute, mean, sigma));
}
}
}
for (Attribute attribute : newAttributes) {
exampleSet.getAttributes().addRegular(attribute);
}
return new IOObject[] { exampleSet };
}
private Attribute createAttribute(ExampleSet exampleSet, Attribute base, double mean, double sigma) {
Attribute newAttribute = AttributeFactory.createAttribute("gauss(" + base.getName() + ", " + mean + ", " + sigma + ")", Ontology.REAL);
exampleSet.getExampleTable().addAttribute(newAttribute);
for (Example example : exampleSet) {
double value = example.getValue(base);
double gaussValue = Math.exp((-1) * ((value - mean) * (value - mean)) / (sigma * sigma));
example.setValue(newAttribute, gaussValue);
}
return newAttribute;
}
public Class<?>[] getInputClasses() {
return new Class[] { ExampleSet.class };
}
public Class<?>[] getOutputClasses() {
return new Class[] { ExampleSet.class };
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeString(PARAMETER_ATTRIBUTE_NAME, "Indicates on which attribute(s) the gaussian construction should be applied (regular expression possible)", false));
types.add(new ParameterTypeDouble(PARAMETER_MEAN, "The mean value for the gaussian function.", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0d));
types.add(new ParameterTypeDouble(PARAMETER_SIGMA, "The sigma value for the gaussian function.", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 1.0d));
return types;
}
}