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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see {
/** for serialization */
private static final long serialVersionUID = -5526486742612434779L;
/** Hold the counts */
private final double[] m_Counts;
/** Hold the sum of counts */
private double m_SumOfCounts;
/** Initialization for counts */
private double m_FPrior;
/**
* Constructor
*
* @param numSymbols the number of possible symbols (remember to include 0)
* @param laplace if true, counts will be initialised to 1
*/
public DiscreteEstimator(int numSymbols, boolean laplace) {
m_Counts = new double[numSymbols];
m_SumOfCounts = 0;
if (laplace) {
m_FPrior = 1;
for (int i = 0; i < numSymbols; i++) {
m_Counts[i] = 1;
}
m_SumOfCounts = numSymbols;
}
}
/**
* Constructor
*
* @param nSymbols the number of possible symbols (remember to include 0)
* @param fPrior value with which counts will be initialised
*/
public DiscreteEstimator(int nSymbols, double fPrior) {
m_Counts = new double[nSymbols];
m_FPrior = fPrior;
for (int iSymbol = 0; iSymbol < nSymbols; iSymbol++) {
m_Counts[iSymbol] = fPrior;
}
m_SumOfCounts = fPrior * nSymbols;
}
/**
* Add a new data value to the current estimator.
*
* @param data the new data value
* @param weight the weight assigned to the data value
*/
@Override
public void addValue(double data, double weight) {
m_Counts[(int) data] += weight;
m_SumOfCounts += weight;
}
/**
* Get a probability estimate for a value
*
* @param data the value to estimate the probability of
* @return the estimated probability of the supplied value
*/
@Override
public double getProbability(double data) {
if (m_SumOfCounts == 0) {
return 0;
}
return m_Counts[(int) data] / m_SumOfCounts;
}
/**
* Gets the number of symbols this estimator operates with
*
* @return the number of estimator symbols
*/
public int getNumSymbols() {
return (m_Counts == null) ? 0 : m_Counts.length;
}
/**
* Get the count for a value
*
* @param data the value to get the count of
* @return the count of the supplied value
*/
public double getCount(double data) {
if (m_SumOfCounts == 0) {
return 0;
}
return m_Counts[(int) data];
}
/**
* Get the sum of all the counts
*
* @return the total sum of counts
*/
public double getSumOfCounts() {
return m_SumOfCounts;
}
/**
* Display a representation of this estimator
*/
@Override
public String toString() {
StringBuffer result = new StringBuffer("Discrete Estimator. Counts = ");
if (m_SumOfCounts > 1) {
for (int i = 0; i < m_Counts.length; i++) {
result.append(" ").append(Utils.doubleToString(m_Counts[i], 2));
}
result.append(" (Total = ").append(
Utils.doubleToString(m_SumOfCounts, 2));
result.append(")\n");
} else {
for (int i = 0; i < m_Counts.length; i++) {
result.append(" ").append(m_Counts[i]);
}
result.append(" (Total = ").append(m_SumOfCounts).append(")\n");
}
return result.toString();
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
@Override
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// class
if (!m_noClass) {
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
} else {
result.enable(Capability.NO_CLASS);
}
// attributes
result.enable(Capability.NUMERIC_ATTRIBUTES);
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 11247 $");
}
@Override
public DiscreteEstimator aggregate(DiscreteEstimator toAggregate)
throws Exception {
if (toAggregate.m_Counts.length != m_Counts.length) {
throw new Exception("DiscreteEstimator to aggregate has a different "
+ "number of symbols");
}
m_SumOfCounts += toAggregate.m_SumOfCounts;
for (int i = 0; i < m_Counts.length; i++) {
m_Counts[i] += (toAggregate.m_Counts[i] - toAggregate.m_FPrior);
}
m_SumOfCounts -= (toAggregate.m_FPrior * m_Counts.length);
return this;
}
@Override
public void finalizeAggregation() throws Exception {
// nothing to do
}
protected static void testAggregation() {
DiscreteEstimator df = new DiscreteEstimator(5, true);
DiscreteEstimator one = new DiscreteEstimator(5, true);
DiscreteEstimator two = new DiscreteEstimator(5, true);
java.util.Random r = new java.util.Random(1);
for (int i = 0; i < 100; i++) {
int z = r.nextInt(5);
df.addValue(z, 1);
if (i < 50) {
one.addValue(z, 1);
} else {
two.addValue(z, 1);
}
}
try {
System.out.println("\n\nFull\n");
System.out.println(df.toString());
System.out.println("Prob (0): " + df.getProbability(0));
System.out.println("\nOne\n" + one.toString());
System.out.println("Prob (0): " + one.getProbability(0));
System.out.println("\nTwo\n" + two.toString());
System.out.println("Prob (0): " + two.getProbability(0));
one = one.aggregate(two);
System.out.println("\nAggregated\n");
System.out.println(one.toString());
System.out.println("Prob (0): " + one.getProbability(0));
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Main method for testing this class.
*
* @param argv should contain a sequence of integers which will be treated as
* symbolic.
*/
public static void main(String[] argv) {
try {
if (argv.length == 0) {
System.out.println("Please specify a set of instances.");
return;
}
int current = Integer.parseInt(argv[0]);
int max = current;
for (int i = 1; i < argv.length; i++) {
current = Integer.parseInt(argv[i]);
if (current > max) {
max = current;
}
}
DiscreteEstimator newEst = new DiscreteEstimator(max + 1, true);
for (int i = 0; i < argv.length; i++) {
current = Integer.parseInt(argv[i]);
System.out.println(newEst);
System.out.println("Prediction for " + current + " = "
+ newEst.getProbability(current));
newEst.addValue(current, 1);
}
DiscreteEstimator.testAggregation();
} catch (Exception e) {
System.out.println(e.getMessage());
}
}
}