/*
* RapidMiner
*
* Copyright (C) 2001-2011 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;
import com.rapidminer.operator.performance.PerformanceVector;
/**
* Individuals contain all necessary informations for feature selection or weighting
* of example sets for population based search heuristics, including the performance. Each
* individiual can also handle a crowding distance for multi-objecitve
* optimization approaches.
*
* @author Ingo Mierswa
*/
public class Individual {
/** The weight mask. */
private double[] weights;
/**
* The performance this example set has achieved during evaluation. Null if
* no evaluation has been performed so far.
*/
private PerformanceVector performanceVector = null;
/** The crowding distance can used for multiobjective optimization schemes. */
private double crowdingDistance = Double.NaN;
/** Creates a new individual by cloning the given values. */
public Individual(double[] weights) {
this.weights = weights;
}
public double[] getWeights() {
return this.weights;
}
public double[] getWeightsClone() {
double[] clone = new double[weights.length];
System.arraycopy(this.weights, 0, clone, 0, this.weights.length);
return clone;
}
public int getNumberOfUsedAttributes() {
int count = 0;
for (double d : this.weights)
if (d > 0)
count++;
return count;
}
public PerformanceVector getPerformance() {
return performanceVector;
}
public void setPerformance(PerformanceVector performanceVector) {
this.performanceVector = performanceVector;
}
public double getCrowdingDistance() {
return this.crowdingDistance;
}
public void setCrowdingDistance(double crowdingDistance) {
this.crowdingDistance = crowdingDistance;
}
}