/*
* 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., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* SoftClassifiedInstance.java
* Copyright (C) 2003 Ray Mooney
*
*/
package weka.core;
import java.util.*;
import java.io.*;
/**
* An Instance that has a probability distribution across class values.
* Particularly useful for EM using a SoftClassifier
*
* @author Ray Mooney (mooney@cs.utexas.edu)
*/
public class SoftClassifiedSparseInstance extends SparseInstance implements SoftClassifiedInstance{
/** An array of probabilities giving the probability of each class
* for this Instance */
protected double[] m_ClassDistribution;
/**
* Constructor that copies the attribute values and the weight from
* the given instance and gives SoftInstance random class probabilities
* generated by the given randomizer.
*
*/
public SoftClassifiedSparseInstance(SparseInstance instance, Random randomizer) {
super(instance);
m_Dataset = instance.m_Dataset;
m_ClassDistribution = randomClassDistribution(randomizer);
}
/**
* Constructor that copies the attribute values and the weight from
* the given instance and gives SoftInstance random class probabilities
* that assign all probability (1) to the instance's given class
*/
public SoftClassifiedSparseInstance(SparseInstance instance) {
super(instance);
m_Dataset = instance.m_Dataset;
m_ClassDistribution = new double[classAttribute().numValues()];
if(instance.classIsMissing())
for (int i = 0; i < classAttribute().numValues(); i++) {
m_ClassDistribution[i] = 1.0/classAttribute().numValues();
}
else
m_ClassDistribution[(int)classValue()] = 1.0;
}
public SoftClassifiedSparseInstance() {
};
/** Return a random class distribution */
protected double[] randomClassDistribution(Random randomizer) {
double[] dist = new double[classAttribute().numValues()];
for (int i = 0; i < classAttribute().numValues(); i++) {
dist[i] = randomizer.nextDouble();
}
Utils.normalize(dist);
return dist;
}
/** Return the probability the instance is in the given class */
public double getClassProbability (int classNum) {
return m_ClassDistribution[classNum];
}
/** Set the probability the instance is in the given class */
public void setClassProbability (int classNum, double prob) {
m_ClassDistribution[classNum] = prob;
}
/** Get the class distribution for this instance */
public double[] getClassDistribution () {
return m_ClassDistribution;
}
/** Set the class distribution for this instance */
public void setClassDistribution (double[] dist) throws Exception {
if (dist.length != numClasses())
throw new Exception("Class distribution of incorrect length: " + dist);
m_ClassDistribution = dist;
}
/**
* Produces a shallow copy of this instance. The copy has
* access to the same dataset. (if you want to make a copy
* that doesn't have access to the dataset, use
* <code>new Instance(instance)</code>
*
* @return the shallow copy
*/
public Object copy() {
SoftClassifiedSparseInstance result = new SoftClassifiedSparseInstance();
result.m_AttValues = m_AttValues;
result.m_Weight = m_Weight;
result.m_Dataset = m_Dataset;
result.m_Indices = m_Indices;
result.m_NumAttributes = m_NumAttributes;
result.m_ClassDistribution = m_ClassDistribution;
return result;
}
}