/*
* 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.
*/
/*
* NaiveBayesSimple.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes;
import weka.classifiers.Classifier;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import java.util.Enumeration;
import weka.classifiers.AbstractClassifier;
/**
<!-- globalinfo-start -->
* Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.<br/>
* <br/>
* For more information, see<br/>
* <br/>
* Richard Duda, Peter Hart (1973). Pattern Classification and Scene Analysis. Wiley, New York.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @book{Duda1973,
* address = {New York},
* author = {Richard Duda and Peter Hart},
* publisher = {Wiley},
* title = {Pattern Classification and Scene Analysis},
* year = {1973}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 5516 $
*/
public class NaiveBayesSimple
extends AbstractClassifier implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -1478242251770381214L;
/** All the counts for nominal attributes. */
protected double [][][] m_Counts;
/** The means for numeric attributes. */
protected double [][] m_Means;
/** The standard deviations for numeric attributes. */
protected double [][] m_Devs;
/** The prior probabilities of the classes. */
protected double [] m_Priors;
/** The instances used for training. */
protected Instances m_Instances;
/** Constant for normal distribution. */
protected static double NORM_CONST = Math.sqrt(2 * Math.PI);
/**
* Returns a string describing this classifier
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Class for building and using a simple Naive Bayes classifier."
+ "Numeric attributes are modelled by a normal distribution.\n\n"
+ "For more information, see\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.BOOK);
result.setValue(Field.AUTHOR, "Richard Duda and Peter Hart");
result.setValue(Field.YEAR, "1973");
result.setValue(Field.TITLE, "Pattern Classification and Scene Analysis");
result.setValue(Field.PUBLISHER, "Wiley");
result.setValue(Field.ADDRESS, "New York");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
return result;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @exception Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
int attIndex = 0;
double sum;
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
m_Instances = new Instances(instances, 0);
// Reserve space
m_Counts = new double[instances.numClasses()]
[instances.numAttributes() - 1][0];
m_Means = new double[instances.numClasses()]
[instances.numAttributes() - 1];
m_Devs = new double[instances.numClasses()]
[instances.numAttributes() - 1];
m_Priors = new double[instances.numClasses()];
Enumeration enu = instances.enumerateAttributes();
while (enu.hasMoreElements()) {
Attribute attribute = (Attribute) enu.nextElement();
if (attribute.isNominal()) {
for (int j = 0; j < instances.numClasses(); j++) {
m_Counts[j][attIndex] = new double[attribute.numValues()];
}
} else {
for (int j = 0; j < instances.numClasses(); j++) {
m_Counts[j][attIndex] = new double[1];
}
}
attIndex++;
}
// Compute counts and sums
Enumeration enumInsts = instances.enumerateInstances();
while (enumInsts.hasMoreElements()) {
Instance instance = (Instance) enumInsts.nextElement();
if (!instance.classIsMissing()) {
Enumeration enumAtts = instances.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = (Attribute) enumAtts.nextElement();
if (!instance.isMissing(attribute)) {
if (attribute.isNominal()) {
m_Counts[(int)instance.classValue()][attIndex]
[(int)instance.value(attribute)]++;
} else {
m_Means[(int)instance.classValue()][attIndex] +=
instance.value(attribute);
m_Counts[(int)instance.classValue()][attIndex][0]++;
}
}
attIndex++;
}
m_Priors[(int)instance.classValue()]++;
}
}
// Compute means
Enumeration enumAtts = instances.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = (Attribute) enumAtts.nextElement();
if (attribute.isNumeric()) {
for (int j = 0; j < instances.numClasses(); j++) {
if (m_Counts[j][attIndex][0] < 2) {
throw new Exception("attribute " + attribute.name() +
": less than two values for class " +
instances.classAttribute().value(j));
}
m_Means[j][attIndex] /= m_Counts[j][attIndex][0];
}
}
attIndex++;
}
// Compute standard deviations
enumInsts = instances.enumerateInstances();
while (enumInsts.hasMoreElements()) {
Instance instance =
(Instance) enumInsts.nextElement();
if (!instance.classIsMissing()) {
enumAtts = instances.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = (Attribute) enumAtts.nextElement();
if (!instance.isMissing(attribute)) {
if (attribute.isNumeric()) {
m_Devs[(int)instance.classValue()][attIndex] +=
(m_Means[(int)instance.classValue()][attIndex]-
instance.value(attribute))*
(m_Means[(int)instance.classValue()][attIndex]-
instance.value(attribute));
}
}
attIndex++;
}
}
}
enumAtts = instances.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = (Attribute) enumAtts.nextElement();
if (attribute.isNumeric()) {
for (int j = 0; j < instances.numClasses(); j++) {
if (m_Devs[j][attIndex] <= 0) {
throw new Exception("attribute " + attribute.name() +
": standard deviation is 0 for class " +
instances.classAttribute().value(j));
}
else {
m_Devs[j][attIndex] /= m_Counts[j][attIndex][0] - 1;
m_Devs[j][attIndex] = Math.sqrt(m_Devs[j][attIndex]);
}
}
}
attIndex++;
}
// Normalize counts
enumAtts = instances.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = (Attribute) enumAtts.nextElement();
if (attribute.isNominal()) {
for (int j = 0; j < instances.numClasses(); j++) {
sum = Utils.sum(m_Counts[j][attIndex]);
for (int i = 0; i < attribute.numValues(); i++) {
m_Counts[j][attIndex][i] =
(m_Counts[j][attIndex][i] + 1)
/ (sum + (double)attribute.numValues());
}
}
}
attIndex++;
}
// Normalize priors
sum = Utils.sum(m_Priors);
for (int j = 0; j < instances.numClasses(); j++)
m_Priors[j] = (m_Priors[j] + 1)
/ (sum + (double)instances.numClasses());
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @exception Exception if distribution can't be computed
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double [] probs = new double[instance.numClasses()];
int attIndex;
for (int j = 0; j < instance.numClasses(); j++) {
probs[j] = 1;
Enumeration enumAtts = instance.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = (Attribute) enumAtts.nextElement();
if (!instance.isMissing(attribute)) {
if (attribute.isNominal()) {
probs[j] *= m_Counts[j][attIndex][(int)instance.value(attribute)];
} else {
probs[j] *= normalDens(instance.value(attribute),
m_Means[j][attIndex],
m_Devs[j][attIndex]);}
}
attIndex++;
}
probs[j] *= m_Priors[j];
}
// Normalize probabilities
Utils.normalize(probs);
return probs;
}
/**
* Returns a description of the classifier.
*
* @return a description of the classifier as a string.
*/
public String toString() {
if (m_Instances == null) {
return "Naive Bayes (simple): No model built yet.";
}
try {
StringBuffer text = new StringBuffer("Naive Bayes (simple)");
int attIndex;
for (int i = 0; i < m_Instances.numClasses(); i++) {
text.append("\n\nClass " + m_Instances.classAttribute().value(i)
+ ": P(C) = "
+ Utils.doubleToString(m_Priors[i], 10, 8)
+ "\n\n");
Enumeration enumAtts = m_Instances.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = (Attribute) enumAtts.nextElement();
text.append("Attribute " + attribute.name() + "\n");
if (attribute.isNominal()) {
for (int j = 0; j < attribute.numValues(); j++) {
text.append(attribute.value(j) + "\t");
}
text.append("\n");
for (int j = 0; j < attribute.numValues(); j++)
text.append(Utils.
doubleToString(m_Counts[i][attIndex][j], 10, 8)
+ "\t");
} else {
text.append("Mean: " + Utils.
doubleToString(m_Means[i][attIndex], 10, 8) + "\t");
text.append("Standard Deviation: "
+ Utils.doubleToString(m_Devs[i][attIndex], 10, 8));
}
text.append("\n\n");
attIndex++;
}
}
return text.toString();
} catch (Exception e) {
return "Can't print Naive Bayes classifier!";
}
}
/**
* Density function of normal distribution.
*
* @param x the value to get the density for
* @param mean the mean
* @param stdDev the standard deviation
* @return the density
*/
protected double normalDens(double x, double mean, double stdDev) {
double diff = x - mean;
return (1 / (NORM_CONST * stdDev))
* Math.exp(-(diff * diff / (2 * stdDev * stdDev)));
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5516 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new NaiveBayesSimple(), argv);
}
}