/*
* 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.transformation;
import java.util.ArrayList;
import Jama.Matrix;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.AbstractModel;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.learner.functions.kernel.functions.Kernel;
import com.rapidminer.tools.Ontology;
/**
* The model for the Kernel-PCA.
*
* @author Sebastian Land
* @version $Id: KernelPCAModel.java,v 1.3 2008/07/13 16:39:42 ingomierswa Exp $
*/
public class KernelPCAModel extends AbstractModel {
private static final long serialVersionUID = -6699248775014738833L;
private Matrix eigenVectors;
private ArrayList<double[]> exampleValues;
private Kernel kernel;
private ArrayList<String> attributeNames;
protected KernelPCAModel(ExampleSet exampleSet) {
super(exampleSet);
}
public KernelPCAModel(ExampleSet exampleSet, Matrix eigenVectors, ArrayList<double[]> exampleValues, Kernel kernel) {
super(exampleSet);
this.eigenVectors = eigenVectors;
this.exampleValues = exampleValues;
this.kernel = kernel;
this.attributeNames = new ArrayList<String>();
for (Attribute attribute: exampleSet.getAttributes()) {
attributeNames.add(attribute.getName());
}
}
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
Attributes attributes = exampleSet.getAttributes();
checkNames(attributes);
log("Adding new the derived features...");
Attribute[] pcatts = new Attribute[exampleValues.size()];
for (int i = 0; i < exampleValues.size(); i++) {
pcatts[i] = AttributeFactory.createAttribute("kpc_" + (i + 1), Ontology.REAL);
exampleSet.getExampleTable().addAttribute(pcatts[i]);
exampleSet.getAttributes().addRegular(pcatts[i]);
}
log("Calculating new features");
Matrix distanceValues = new Matrix(1, exampleValues.size());
for (Example example: exampleSet) {
int i = 0;
for (double[] trainValue: exampleValues) {
distanceValues.set(0, i++, kernel.calculateDistance(trainValue, getAttributeValues(example, attributes)));
}
Matrix resultValues = eigenVectors.times(distanceValues.transpose());
for (int j = 0; j < exampleValues.size(); j++) {
example.setValue(pcatts[j], resultValues.get(j, 0));
}
}
// removing old attributes
attributes.clearRegular();
for(Attribute attribute: pcatts) {
attributes.addRegular(attribute);
}
return exampleSet;
}
private void checkNames(Attributes attributes) throws UserError {
int i = 0;
for (Attribute attribute: attributes) {
if (attribute.getName() != attributeNames.get(i++)) {
throw new UserError(null, 141);
}
}
}
private double[] getAttributeValues(Example example, Attributes attributes) {
double[] values = new double[attributes.size()];
int x = 0;
for (Attribute attribute : attributes)
values[x++] = example.getValue(attribute);
return values;
}
}