/*
* RapidMiner
*
* Copyright (C) 2001-2007 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 General Public License as
* published by the Free Software Foundation; either version 2 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
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
* USA.
*/
package com.rapidminer.operator.features.transformation;
import java.util.Iterator;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
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;
/**
* Projects the examples onto the hyperplane using AttributeWeights as the
* normal. Additionally the user can specify a bias of the hyperplane.
*
* @author Daniel Hakenjos, Ingo Mierswa
* @version $Id: HyperplaneProjection.java,v 1.1 2006/04/14 13:07:13 ingomierswa
* Exp $
*/
public class HyperplaneProjection extends Operator {
/** The parameter name for "The bias of the hyperplane" */
public static final String PARAMETER_BIAS = "bias";
private static final Class[] INPUT_CLASSES = new Class[] { ExampleSet.class, AttributeWeights.class };
private static final Class[] OUTPUT_CLASSES = new Class[] { ExampleSet.class };
private int numberOfSamples, numberOfAttributes;
private double[][] samples;
private double[] weights;
private double bias;
public HyperplaneProjection(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = this.getInput(ExampleSet.class);
AttributeWeights attributeWeights = this.getInput(AttributeWeights.class);
this.bias = getParameterAsDouble(PARAMETER_BIAS);
this.numberOfSamples = exampleSet.size();
this.numberOfAttributes = exampleSet.getAttributes().size();
// Create Samples
this.samples = new double[numberOfSamples][numberOfAttributes];
weights = new double[numberOfAttributes];
int w = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
weights[w++] = attributeWeights.getWeight(attribute.getName());
}
Iterator<Example> reader = exampleSet.iterator();
Example example;
for (int sample = 0; sample < numberOfSamples; sample++) {
example = reader.next();
int i = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
samples[sample][i++] = example.getValue(attribute);
}
}
calculateHyperplaneSamples();
reader = exampleSet.iterator();
for (int sample = 0; sample < exampleSet.size(); sample++) {
example = reader.next();
int d = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
example.setValue(attribute, samples[sample][d++]);
}
}
exampleSet.recalculateAllAttributeStatistics();
return new IOObject[] { exampleSet };
}
private void calculateHyperplaneSamples() {
double ww = 0.0d;
for (int i = 0; i < weights.length; i++) {
ww += weights[i] * weights[i];
}
double wx;
double t;
// double planepoint;
for (int s = 0; s < numberOfSamples; s++) {
wx = 0.0d;
for (int i = 0; i < this.numberOfAttributes; i++) {
wx += weights[i] * samples[s][i];
}
t = ((-1.0d) * bias - wx) / ww;
for (int i = 0; i < this.numberOfAttributes; i++) {
samples[s][i] = samples[s][i] + t * weights[i];
}
}
}
public Class[] getInputClasses() {
return INPUT_CLASSES;
}
public Class[] getOutputClasses() {
return OUTPUT_CLASSES;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> list = super.getParameterTypes();
ParameterType type = new ParameterTypeDouble(PARAMETER_BIAS, "The bias of the hyperplane", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0d);
list.add(type);
return list;
}
}