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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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.
*/
/*
* NNConditionalEstimator.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.estimators;
import java.util.Random;
import java.util.Vector;
import weka.core.matrix.Matrix;
import weka.core.RevisionUtils;
import weka.core.Utils;
/**
* Conditional probability estimator for a numeric domain conditional upon
* a numeric domain (using Mahalanobis distance).
*
* @author Len Trigg ([email protected])
* @version $Revision: 1.8 $
*/
public class NNConditionalEstimator implements ConditionalEstimator {
/** Vector containing all of the values seen */
private Vector m_Values = new Vector();
/** Vector containing all of the conditioning values seen */
private Vector m_CondValues = new Vector();
/** Vector containing the associated weights */
private Vector m_Weights = new Vector();
/** The sum of the weights so far */
private double m_SumOfWeights;
/** Current Conditional mean */
private double m_CondMean;
/** Current Values mean */
private double m_ValueMean;
/** Current covariance matrix */
private Matrix m_Covariance;
/** Whether we can optimise the kernel summation */
private boolean m_AllWeightsOne = true;
/** 2 * PI */
private static double TWO_PI = 2 * Math.PI;
// ===============
// Private methods
// ===============
/**
* Execute a binary search to locate the nearest data value
*
* @param key the data value to locate
* @param secondaryKey the data value to locate
* @return the index of the nearest data value
*/
private int findNearestPair(double key, double secondaryKey) {
int low = 0;
int high = m_CondValues.size();
int middle = 0;
while (low < high) {
middle = (low + high) / 2;
double current = ((Double)m_CondValues.elementAt(middle)).doubleValue();
if (current == key) {
double secondary = ((Double)m_Values.elementAt(middle)).doubleValue();
if (secondary == secondaryKey) {
return middle;
}
if (secondary > secondaryKey) {
high = middle;
} else if (secondary < secondaryKey) {
low = middle + 1;
}
}
if (current > key) {
high = middle;
} else if (current < key) {
low = middle + 1;
}
}
return low;
}
/** Calculate covariance and value means */
private void calculateCovariance() {
double sumValues = 0, sumConds = 0;
for(int i = 0; i < m_Values.size(); i++) {
sumValues += ((Double)m_Values.elementAt(i)).doubleValue()
* ((Double)m_Weights.elementAt(i)).doubleValue();
sumConds += ((Double)m_CondValues.elementAt(i)).doubleValue()
* ((Double)m_Weights.elementAt(i)).doubleValue();
}
m_ValueMean = sumValues / m_SumOfWeights;
m_CondMean = sumConds / m_SumOfWeights;
double c00 = 0, c01 = 0, c10 = 0, c11 = 0;
for(int i = 0; i < m_Values.size(); i++) {
double x = ((Double)m_Values.elementAt(i)).doubleValue();
double y = ((Double)m_CondValues.elementAt(i)).doubleValue();
double weight = ((Double)m_Weights.elementAt(i)).doubleValue();
c00 += (x - m_ValueMean) * (x - m_ValueMean) * weight;
c01 += (x - m_ValueMean) * (y - m_CondMean) * weight;
c11 += (y - m_CondMean) * (y - m_CondMean) * weight;
}
c00 /= (m_SumOfWeights - 1.0);
c01 /= (m_SumOfWeights - 1.0);
c10 = c01;
c11 /= (m_SumOfWeights - 1.0);
m_Covariance = new Matrix(2, 2);
m_Covariance.set(0, 0, c00);
m_Covariance.set(0, 1, c01);
m_Covariance.set(1, 0, c10);
m_Covariance.set(1, 1, c11);
}
/**
* Returns value for normal kernel
*
* @param x the argument to the kernel function
* @param variance the variance
* @return the value for a normal kernel
*/
private double normalKernel(double x, double variance) {
return Math.exp(-x * x / (2 * variance)) / Math.sqrt(variance * TWO_PI);
}
/**
* Add a new data value to the current estimator.
*
* @param data the new data value
* @param given the new value that data is conditional upon
* @param weight the weight assigned to the data value
*/
public void addValue(double data, double given, double weight) {
int insertIndex = findNearestPair(given, data);
if ((m_Values.size() <= insertIndex)
|| (((Double)m_CondValues.elementAt(insertIndex)).doubleValue()
!= given)
|| (((Double)m_Values.elementAt(insertIndex)).doubleValue()
!= data)) {
m_CondValues.insertElementAt(new Double(given), insertIndex);
m_Values.insertElementAt(new Double(data), insertIndex);
m_Weights.insertElementAt(new Double(weight), insertIndex);
if (weight != 1) {
m_AllWeightsOne = false;
}
} else {
double newWeight = ((Double)m_Weights.elementAt(insertIndex))
.doubleValue();
newWeight += weight;
m_Weights.setElementAt(new Double(newWeight), insertIndex);
m_AllWeightsOne = false;
}
m_SumOfWeights += weight;
// Invalidate any previously calculated covariance matrix
m_Covariance = null;
}
/**
* Get a probability estimator for a value
*
* @param given the new value that data is conditional upon
* @return the estimator for the supplied value given the condition
*/
public Estimator getEstimator(double given) {
if (m_Covariance == null) {
calculateCovariance();
}
Estimator result = new MahalanobisEstimator(m_Covariance,
given - m_CondMean,
m_ValueMean);
return result;
}
/**
* Get a probability estimate for a value
*
* @param data the value to estimate the probability of
* @param given the new value that data is conditional upon
* @return the estimated probability of the supplied value
*/
public double getProbability(double data, double given) {
return getEstimator(given).getProbability(data);
}
/** Display a representation of this estimator */
public String toString() {
if (m_Covariance == null) {
calculateCovariance();
}
String result = "NN Conditional Estimator. "
+ m_CondValues.size()
+ " data points. Mean = " + Utils.doubleToString(m_ValueMean, 4, 2)
+ " Conditional mean = " + Utils.doubleToString(m_CondMean, 4, 2);
result += " Covariance Matrix: \n" + m_Covariance;
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.8 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain a sequence of numeric values
*/
public static void main(String [] argv) {
try {
int seed = 42;
if (argv.length > 0) {
seed = Integer.parseInt(argv[0]);
}
NNConditionalEstimator newEst = new NNConditionalEstimator();
// Create 100 random points and add them
Random r = new Random(seed);
int numPoints = 50;
if (argv.length > 2) {
numPoints = Integer.parseInt(argv[2]);
}
for(int i = 0; i < numPoints; i++) {
int x = Math.abs(r.nextInt() % 100);
int y = Math.abs(r.nextInt() % 100);
System.out.println("# " + x + " " + y);
newEst.addValue(x, y, 1);
}
// System.out.println(newEst);
int cond;
if (argv.length > 1) {
cond = Integer.parseInt(argv[1]);
}
else cond = Math.abs(r.nextInt() % 100);
System.out.println("## Conditional = " + cond);
Estimator result = newEst.getEstimator(cond);
for(int i = 0; i <= 100; i+= 5) {
System.out.println(" " + i + " " + result.getProbability(i));
}
} catch (Exception e) {
System.out.println(e.getMessage());
}
}
}
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