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Simple learning schemes for educational purposes (Prism, Id3, IB1 and NaiveBayesSimple).
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/*
* 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 more information, see
*
* D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
*
*
* BibTeX:
*
* @article{Aha1991,
* author = {D. Aha and D. Kibler},
* journal = {Machine Learning},
* pages = {37-66},
* title = {Instance-based learning algorithms},
* volume = {6},
* year = {1991}
* }
*
*
*
* Valid options are:
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
* @author Stuart Inglis ([email protected])
* @author Len Trigg ([email protected])
* @author Eibe Frank ([email protected])
* @version $Revision: 8109 $
*/
public class IB1
extends AbstractClassifier
implements UpdateableClassifier, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -6152184127304895851L;
/** The training instances used for classification. */
private Instances m_Train;
/** The minimum values for numeric attributes. */
private double [] m_MinArray;
/** The maximum values for numeric attributes. */
private double [] m_MaxArray;
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Nearest-neighbour classifier. Uses normalized Euclidean distance to "
+ "find the training instance closest to the given test instance, and predicts "
+ "the same class as this training instance. If multiple instances have "
+ "the same (smallest) distance to the test instance, the first one found is "
+ "used.\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.ARTICLE);
result.setValue(Field.AUTHOR, "D. Aha and D. Kibler");
result.setValue(Field.YEAR, "1991");
result.setValue(Field.TITLE, "Instance-based learning algorithms");
result.setValue(Field.JOURNAL, "Machine Learning");
result.setValue(Field.VOLUME, "6");
result.setValue(Field.PAGES, "37-66");
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);
// instances
result.setMinimumNumberInstances(0);
return result;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
m_Train = new Instances(instances, 0, instances.numInstances());
m_MinArray = new double [m_Train.numAttributes()];
m_MaxArray = new double [m_Train.numAttributes()];
for (int i = 0; i < m_Train.numAttributes(); i++) {
m_MinArray[i] = m_MaxArray[i] = Double.NaN;
}
Enumeration enu = m_Train.enumerateInstances();
while (enu.hasMoreElements()) {
updateMinMax((Instance) enu.nextElement());
}
}
/**
* Updates the classifier.
*
* @param instance the instance to be put into the classifier
* @throws Exception if the instance could not be included successfully
*/
public void updateClassifier(Instance instance) throws Exception {
if (m_Train.equalHeaders(instance.dataset()) == false) {
throw new Exception("Incompatible instance types\n" + m_Train.equalHeadersMsg(instance.dataset()));
}
if (instance.classIsMissing()) {
return;
}
m_Train.add(instance);
updateMinMax(instance);
}
/**
* Classifies the given test instance.
*
* @param instance the instance to be classified
* @return the predicted class for the instance
* @throws Exception if the instance can't be classified
*/
public double classifyInstance(Instance instance) throws Exception {
if (m_Train.numInstances() == 0) {
throw new Exception("No training instances!");
}
double distance, minDistance = Double.MAX_VALUE, classValue = 0;
updateMinMax(instance);
Enumeration enu = m_Train.enumerateInstances();
while (enu.hasMoreElements()) {
Instance trainInstance = (Instance) enu.nextElement();
if (!trainInstance.classIsMissing()) {
distance = distance(instance, trainInstance);
if (distance < minDistance) {
minDistance = distance;
classValue = trainInstance.classValue();
}
}
}
return classValue;
}
/**
* Returns a description of this classifier.
*
* @return a description of this classifier as a string.
*/
public String toString() {
return ("IB1 classifier");
}
/**
* Calculates the distance between two instances
*
* @param first the first instance
* @param second the second instance
* @return the distance between the two given instances
*/
private double distance(Instance first, Instance second) {
double diff, distance = 0;
for(int i = 0; i < m_Train.numAttributes(); i++) {
if (i == m_Train.classIndex()) {
continue;
}
if (m_Train.attribute(i).isNominal()) {
// If attribute is nominal
if (first.isMissing(i) || second.isMissing(i) ||
((int)first.value(i) != (int)second.value(i))) {
distance += 1;
}
} else {
// If attribute is numeric
if (first.isMissing(i) || second.isMissing(i)){
if (first.isMissing(i) && second.isMissing(i)) {
diff = 1;
} else {
if (second.isMissing(i)) {
diff = norm(first.value(i), i);
} else {
diff = norm(second.value(i), i);
}
if (diff < 0.5) {
diff = 1.0 - diff;
}
}
} else {
diff = norm(first.value(i), i) - norm(second.value(i), i);
}
distance += diff * diff;
}
}
return distance;
}
/**
* Normalizes a given value of a numeric attribute.
*
* @param x the value to be normalized
* @param i the attribute's index
* @return the normalized value
*/
private double norm(double x,int i) {
if (Double.isNaN(m_MinArray[i])
|| Utils.eq(m_MaxArray[i], m_MinArray[i])) {
return 0;
} else {
return (x - m_MinArray[i]) / (m_MaxArray[i] - m_MinArray[i]);
}
}
/**
* Updates the minimum and maximum values for all the attributes
* based on a new instance.
*
* @param instance the new instance
*/
private void updateMinMax(Instance instance) {
for (int j = 0;j < m_Train.numAttributes(); j++) {
if ((m_Train.attribute(j).isNumeric()) && (!instance.isMissing(j))) {
if (Double.isNaN(m_MinArray[j])) {
m_MinArray[j] = instance.value(j);
m_MaxArray[j] = instance.value(j);
} else {
if (instance.value(j) < m_MinArray[j]) {
m_MinArray[j] = instance.value(j);
} else {
if (instance.value(j) > m_MaxArray[j]) {
m_MaxArray[j] = instance.value(j);
}
}
}
}
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8109 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain command line arguments for evaluation
* (see Evaluation).
*/
public static void main(String [] argv) {
runClassifier(new IB1(), argv);
}
}