/*
* 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 <http://www.gnu.org/licenses/>.
*/
/*
* AbstractClassifier.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.CapabilitiesHandler;
import weka.core.Instance;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.SerializedObject;
import weka.core.Utils;
/**
* Abstract classifier. All schemes for numeric or nominal prediction in
* Weka extend this class. Note that a classifier MUST either implement
* distributionForInstance() or classifyInstance().
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public abstract class AbstractClassifier
implements Classifier, Cloneable, Serializable, OptionHandler,
CapabilitiesHandler, RevisionHandler {
/** for serialization */
private static final long serialVersionUID = 6502780192411755341L;
/** Whether the classifier is run in debug mode. */
protected boolean m_Debug = false;
/**
* Classifies the given test instance. The instance has to belong to a
* dataset when it's being classified. Note that a classifier MUST
* implement either this or distributionForInstance().
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or
* Utils.missingValue() if no prediction is made
* @exception Exception if an error occurred during the prediction
*/
public double classifyInstance(Instance instance) throws Exception {
double [] dist = distributionForInstance(instance);
if (dist == null) {
throw new Exception("Null distribution predicted");
}
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
double max = 0;
int maxIndex = 0;
for (int i = 0; i < dist.length; i++) {
if (dist[i] > max) {
maxIndex = i;
max = dist[i];
}
}
if (max > 0) {
return maxIndex;
} else {
return Utils.missingValue();
}
case Attribute.NUMERIC:
return dist[0];
default:
return Utils.missingValue();
}
}
/**
* Predicts the class memberships for a given instance. If
* an instance is unclassified, the returned array elements
* must be all zero. If the class is numeric, the array
* must consist of only one element, which contains the
* predicted value. Note that a classifier MUST implement
* either this or classifyInstance().
*
* @param instance the instance to be classified
* @return an array containing the estimated membership
* probabilities of the test instance in each class
* or the numeric prediction
* @exception Exception if distribution could not be
* computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double[] dist = new double[instance.numClasses()];
switch (instance.classAttribute().type()) {
case Attribute.NOMINAL:
double classification = classifyInstance(instance);
if (Utils.isMissingValue(classification)) {
return dist;
} else {
dist[(int)classification] = 1.0;
}
return dist;
case Attribute.NUMERIC:
dist[0] = classifyInstance(instance);
return dist;
default:
return dist;
}
}
/**
* Creates a new instance of a classifier given it's class name and
* (optional) arguments to pass to it's setOptions method. If the
* classifier implements OptionHandler and the options parameter is
* non-null, the classifier will have it's options set.
*
* @param classifierName the fully qualified class name of the classifier
* @param options an array of options suitable for passing to setOptions. May
* be null.
* @return the newly created classifier, ready for use.
* @exception Exception if the classifier name is invalid, or the options
* supplied are not acceptable to the classifier
*/
public static Classifier forName(String classifierName,
String [] options) throws Exception {
return ((AbstractClassifier)Utils.forName(Classifier.class,
classifierName,
options));
}
/**
* Creates a deep copy of the given classifier using serialization.
*
* @param model the classifier to copy
* @return a deep copy of the classifier
* @exception Exception if an error occurs
*/
public static Classifier makeCopy(Classifier model) throws Exception {
return (Classifier)new SerializedObject(model).getObject();
}
/**
* Creates a given number of deep copies of the given classifier using serialization.
*
* @param model the classifier to copy
* @param num the number of classifier copies to create.
* @return an array of classifiers.
* @exception Exception if an error occurs
*/
public static Classifier [] makeCopies(Classifier model, int num) throws Exception {
if (model == null) {
throw new Exception("No model classifier set");
}
Classifier [] classifiers = new Classifier [num];
SerializedObject so = new SerializedObject(model);
for(int i = 0; i < classifiers.length; i++) {
classifiers[i] = (Classifier) so.getObject();
}
return classifiers;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(1);
newVector.addElement(new Option(
"\tIf set, classifier is run in debug mode and\n"
+ "\tmay output additional info to the console",
"D", 0, "-D"));
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -D <br>
* If set, classifier is run in debug mode and
* may output additional info to the console.<p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setDebug(Utils.getFlag('D', options));
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] options;
if (getDebug()) {
options = new String[1];
options[0] = "-D";
} else {
options = new String[0];
}
return options;
}
/**
* Set debugging mode.
*
* @param debug true if debug output should be printed
*/
public void setDebug(boolean debug) {
m_Debug = debug;
}
/**
* Get whether debugging is turned on.
*
* @return true if debugging output is on
*/
public boolean getDebug() {
return m_Debug;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String debugTipText() {
return "If set to true, classifier may output additional info to " +
"the console.";
}
/**
* Returns the Capabilities of this classifier. Maximally permissive
* capabilities are allowed by default. Derived classifiers should
* override this method and first disable all capabilities and then
* enable just those capabilities that make sense for the scheme.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result = new Capabilities(this);
result.enableAll();
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* runs the classifier instance with the given options.
*
* @param classifier the classifier to run
* @param options the commandline options
*/
public static void runClassifier(Classifier classifier, String[] options) {
try {
System.out.println(Evaluation.evaluateModel(classifier, options));
}
catch (Exception e) {
if ( ((e.getMessage() != null) && (e.getMessage().indexOf("General options") == -1))
|| (e.getMessage() == null) )
e.printStackTrace();
else
System.err.println(e.getMessage());
}
}
}