/*
* 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.
*/
/*
* PredictionAppender.java
* Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
*
*/
package weka.gui.beans;
import weka.clusterers.DensityBasedClusterer;
import weka.core.Instance;
import weka.core.DenseInstance;
import weka.core.Instances;
import java.awt.BorderLayout;
import java.beans.EventSetDescriptor;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;
import javax.swing.JPanel;
/**
* Bean that can can accept batch or incremental classifier events
* and produce dataset or instance events which contain instances with
* predictions appended.
*
* @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
* @version $Revision: 6804 $
*/
public class PredictionAppender
extends JPanel
implements DataSource, TrainingSetProducer, TestSetProducer, Visible, BeanCommon,
EventConstraints, BatchClassifierListener,
IncrementalClassifierListener, BatchClustererListener, Serializable {
/** for serialization */
private static final long serialVersionUID = -2987740065058976673L;
/**
* Objects listenening for dataset events
*/
protected Vector m_dataSourceListeners = new Vector();
/**
* Objects listening for instances events
*/
protected Vector m_instanceListeners = new Vector();
/**
* Objects listening for training set events
*/
protected Vector m_trainingSetListeners = new Vector();;
/**
* Objects listening for test set events
*/
protected Vector m_testSetListeners = new Vector();
/**
* Non null if this object is a target for any events.
*/
protected Object m_listenee = null;
/**
* Format of instances to be produced.
*/
protected Instances m_format;
protected BeanVisual m_visual =
new BeanVisual("PredictionAppender",
BeanVisual.ICON_PATH+"PredictionAppender.gif",
BeanVisual.ICON_PATH+"PredictionAppender_animated.gif");
/**
* Append classifier's predicted probabilities (if the class is discrete
* and the classifier is a distribution classifier)
*/
protected boolean m_appendProbabilities;
protected transient weka.gui.Logger m_logger;
/**
* Global description of this bean
*
* @return a <code>String</code> value
*/
public String globalInfo() {
return "Accepts batch or incremental classifier events and "
+"produces a new data set with classifier predictions appended.";
}
/**
* Creates a new <code>PredictionAppender</code> instance.
*/
public PredictionAppender() {
setLayout(new BorderLayout());
add(m_visual, BorderLayout.CENTER);
}
/**
* Set a custom (descriptive) name for this bean
*
* @param name the name to use
*/
public void setCustomName(String name) {
m_visual.setText(name);
}
/**
* Get the custom (descriptive) name for this bean (if one has been set)
*
* @return the custom name (or the default name)
*/
public String getCustomName() {
return m_visual.getText();
}
/**
* Return a tip text suitable for displaying in a GUI
*
* @return a <code>String</code> value
*/
public String appendPredictedProbabilitiesTipText() {
return "append probabilities rather than labels for discrete class "
+"predictions";
}
/**
* Return true if predicted probabilities are to be appended rather
* than class value
*
* @return a <code>boolean</code> value
*/
public boolean getAppendPredictedProbabilities() {
return m_appendProbabilities;
}
/**
* Set whether to append predicted probabilities rather than
* class value (for discrete class data sets)
*
* @param ap a <code>boolean</code> value
*/
public void setAppendPredictedProbabilities(boolean ap) {
m_appendProbabilities = ap;
}
/**
* Add a training set listener
*
* @param tsl a <code>TrainingSetListener</code> value
*/
public void addTrainingSetListener(TrainingSetListener tsl) {
// TODO Auto-generated method stub
m_trainingSetListeners.addElement(tsl);
// pass on any format that we might have determined so far
if (m_format != null) {
TrainingSetEvent e = new TrainingSetEvent(this, m_format);
tsl.acceptTrainingSet(e);
}
}
/**
* Remove a training set listener
*
* @param tsl a <code>TrainingSetListener</code> value
*/
public void removeTrainingSetListener(TrainingSetListener tsl) {
m_trainingSetListeners.removeElement(tsl);
}
/**
* Add a test set listener
*
* @param tsl a <code>TestSetListener</code> value
*/
public void addTestSetListener(TestSetListener tsl) {
m_testSetListeners.addElement(tsl);
// pass on any format that we might have determined so far
if (m_format != null) {
TestSetEvent e = new TestSetEvent(this, m_format);
tsl.acceptTestSet(e);
}
}
/**
* Remove a test set listener
*
* @param tsl a <code>TestSetListener</code> value
*/
public void removeTestSetListener(TestSetListener tsl) {
m_testSetListeners.removeElement(tsl);
}
/**
* Add a datasource listener
*
* @param dsl a <code>DataSourceListener</code> value
*/
public synchronized void addDataSourceListener(DataSourceListener dsl) {
m_dataSourceListeners.addElement(dsl);
// pass on any format that we might have determined so far
if (m_format != null) {
DataSetEvent e = new DataSetEvent(this, m_format);
dsl.acceptDataSet(e);
}
}
/**
* Remove a datasource listener
*
* @param dsl a <code>DataSourceListener</code> value
*/
public synchronized void removeDataSourceListener(DataSourceListener dsl) {
m_dataSourceListeners.remove(dsl);
}
/**
* Add an instance listener
*
* @param dsl a <code>InstanceListener</code> value
*/
public synchronized void addInstanceListener(InstanceListener dsl) {
m_instanceListeners.addElement(dsl);
// pass on any format that we might have determined so far
if (m_format != null) {
InstanceEvent e = new InstanceEvent(this, m_format);
dsl.acceptInstance(e);
}
}
/**
* Remove an instance listener
*
* @param dsl a <code>InstanceListener</code> value
*/
public synchronized void removeInstanceListener(InstanceListener dsl) {
m_instanceListeners.remove(dsl);
}
/**
* Set the visual for this data source
*
* @param newVisual a <code>BeanVisual</code> value
*/
public void setVisual(BeanVisual newVisual) {
m_visual = newVisual;
}
/**
* Get the visual being used by this data source.
*
*/
public BeanVisual getVisual() {
return m_visual;
}
/**
* Use the default images for a data source
*
*/
public void useDefaultVisual() {
m_visual.loadIcons(BeanVisual.ICON_PATH+"PredictionAppender.gif",
BeanVisual.ICON_PATH+"PredictionAppender_animated.gif");
}
protected InstanceEvent m_instanceEvent;
/**
* Accept and process an incremental classifier event
*
* @param e an <code>IncrementalClassifierEvent</code> value
*/
public void acceptClassifier(IncrementalClassifierEvent e) {
weka.classifiers.Classifier classifier = e.getClassifier();
Instance currentI = e.getCurrentInstance();
int status = e.getStatus();
int oldNumAtts = 0;
if (status == IncrementalClassifierEvent.NEW_BATCH) {
oldNumAtts = e.getStructure().numAttributes();
} else {
oldNumAtts = currentI.dataset().numAttributes();
}
if (status == IncrementalClassifierEvent.NEW_BATCH) {
m_instanceEvent = new InstanceEvent(this, null, 0);
// create new header structure
Instances oldStructure = new Instances(e.getStructure(), 0);
//String relationNameModifier = oldStructure.relationName()
//+"_with predictions";
String relationNameModifier = "_with predictions";
//+"_with predictions";
if (!m_appendProbabilities
|| oldStructure.classAttribute().isNumeric()) {
try {
m_format = makeDataSetClass(oldStructure, oldStructure, classifier,
relationNameModifier);
} catch (Exception ex) {
ex.printStackTrace();
return;
}
} else if (m_appendProbabilities) {
try {
m_format =
makeDataSetProbabilities(oldStructure, oldStructure, classifier,
relationNameModifier);
} catch (Exception ex) {
ex.printStackTrace();
return;
}
}
// Pass on the structure
m_instanceEvent.setStructure(m_format);
notifyInstanceAvailable(m_instanceEvent);
return;
}
double[] instanceVals = new double [m_format.numAttributes()];
Instance newInst = null;
try {
// process the actual instance
for (int i = 0; i < oldNumAtts; i++) {
instanceVals[i] = currentI.value(i);
}
if (!m_appendProbabilities
|| currentI.dataset().classAttribute().isNumeric()) {
double predClass =
classifier.classifyInstance(currentI);
instanceVals[instanceVals.length - 1] = predClass;
} else if (m_appendProbabilities) {
double [] preds = classifier.distributionForInstance(currentI);
for (int i = oldNumAtts; i < instanceVals.length; i++) {
instanceVals[i] = preds[i-oldNumAtts];
}
}
} catch (Exception ex) {
ex.printStackTrace();
return;
} finally {
newInst = new DenseInstance(currentI.weight(), instanceVals);
newInst.setDataset(m_format);
m_instanceEvent.setInstance(newInst);
m_instanceEvent.setStatus(status);
// notify listeners
notifyInstanceAvailable(m_instanceEvent);
}
if (status == IncrementalClassifierEvent.BATCH_FINISHED) {
// clean up
// m_incrementalStructure = null;
m_instanceEvent = null;
}
}
/**
* Accept and process a batch classifier event
*
* @param e a <code>BatchClassifierEvent</code> value
*/
public void acceptClassifier(BatchClassifierEvent e) {
if (m_dataSourceListeners.size() > 0
|| m_trainingSetListeners.size() > 0
|| m_testSetListeners.size() > 0) {
if (e.getTestSet() == null) {
// can't append predictions
return;
}
Instances testSet = e.getTestSet().getDataSet();
Instances trainSet = e.getTrainSet().getDataSet();
int setNum = e.getSetNumber();
int maxNum = e.getMaxSetNumber();
weka.classifiers.Classifier classifier = e.getClassifier();
String relationNameModifier = "_set_"+e.getSetNumber()+"_of_"
+e.getMaxSetNumber();
if (!m_appendProbabilities || testSet.classAttribute().isNumeric()) {
try {
Instances newTestSetInstances = makeDataSetClass(testSet, trainSet,
classifier, relationNameModifier);
Instances newTrainingSetInstances = makeDataSetClass(trainSet, trainSet,
classifier, relationNameModifier);
if (m_trainingSetListeners.size() > 0) {
TrainingSetEvent tse = new TrainingSetEvent(this,
new Instances(newTrainingSetInstances, 0));
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTrainingSetAvailable(tse);
// fill in predicted values
for (int i = 0; i < trainSet.numInstances(); i++) {
double predClass =
classifier.classifyInstance(trainSet.instance(i));
newTrainingSetInstances.instance(i).setValue(newTrainingSetInstances.numAttributes()-1,
predClass);
}
tse = new TrainingSetEvent(this,
newTrainingSetInstances);
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTrainingSetAvailable(tse);
}
if (m_testSetListeners.size() > 0) {
TestSetEvent tse = new TestSetEvent(this,
new Instances(newTestSetInstances, 0));
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTestSetAvailable(tse);
}
if (m_dataSourceListeners.size() > 0) {
notifyDataSetAvailable(new DataSetEvent(this, new Instances(newTestSetInstances,0)));
}
if (e.getTestSet().isStructureOnly()) {
m_format = newTestSetInstances;
}
if (m_dataSourceListeners.size() > 0 || m_testSetListeners.size() > 0) {
// fill in predicted values
for (int i = 0; i < testSet.numInstances(); i++) {
Instance tempInst = testSet.instance(i);
// if the class value is missing, then copy the instance
// and set the data set to the training data. This is
// just in case this test data was loaded from a CSV file
// with all missing values for a nominal class (in this
// case we have no information on the legal class values
// in the test data)
if (tempInst.isMissing(tempInst.classIndex()) &&
!(classifier instanceof weka.classifiers.misc.InputMappedClassifier)) {
tempInst = (Instance)testSet.instance(i).copy();
tempInst.setDataset(trainSet);
}
double predClass =
classifier.classifyInstance(tempInst);
newTestSetInstances.instance(i).setValue(newTestSetInstances.numAttributes()-1,
predClass);
}
}
// notify listeners
if (m_testSetListeners.size() > 0) {
TestSetEvent tse = new TestSetEvent(this, newTestSetInstances);
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTestSetAvailable(tse);
}
if (m_dataSourceListeners.size() > 0) {
notifyDataSetAvailable(new DataSetEvent(this, newTestSetInstances));
}
return;
} catch (Exception ex) {
ex.printStackTrace();
}
}
if (m_appendProbabilities) {
try {
Instances newTestSetInstances =
makeDataSetProbabilities(testSet, trainSet,
classifier,relationNameModifier);
Instances newTrainingSetInstances =
makeDataSetProbabilities(trainSet, trainSet,
classifier,relationNameModifier);
if (m_trainingSetListeners.size() > 0) {
TrainingSetEvent tse = new TrainingSetEvent(this,
new Instances(newTrainingSetInstances, 0));
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTrainingSetAvailable(tse);
// fill in predicted probabilities
for (int i = 0; i < trainSet.numInstances(); i++) {
double [] preds = classifier.
distributionForInstance(trainSet.instance(i));
for (int j = 0; j < trainSet.classAttribute().numValues(); j++) {
newTrainingSetInstances.instance(i).setValue(trainSet.numAttributes()+j,
preds[j]);
}
}
tse = new TrainingSetEvent(this,
newTrainingSetInstances);
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTrainingSetAvailable(tse);
}
if (m_testSetListeners.size() > 0) {
TestSetEvent tse = new TestSetEvent(this,
new Instances(newTestSetInstances, 0));
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTestSetAvailable(tse);
}
if (m_dataSourceListeners.size() > 0) {
notifyDataSetAvailable(new DataSetEvent(this, new Instances(newTestSetInstances,0)));
}
if (e.getTestSet().isStructureOnly()) {
m_format = newTestSetInstances;
}
if (m_dataSourceListeners.size() > 0 || m_testSetListeners.size() > 0) {
// fill in predicted probabilities
for (int i = 0; i < testSet.numInstances(); i++) {
Instance tempInst = testSet.instance(i);
// if the class value is missing, then copy the instance
// and set the data set to the training data. This is
// just in case this test data was loaded from a CSV file
// with all missing values for a nominal class (in this
// case we have no information on the legal class values
// in the test data)
if (tempInst.isMissing(tempInst.classIndex()) &&
!(classifier instanceof weka.classifiers.misc.InputMappedClassifier)) {
tempInst = (Instance)testSet.instance(i).copy();
tempInst.setDataset(trainSet);
}
double [] preds = classifier.
distributionForInstance(tempInst);
for (int j = 0; j < tempInst.classAttribute().numValues(); j++) {
newTestSetInstances.instance(i).setValue(testSet.numAttributes()+j,
preds[j]);
}
}
}
// notify listeners
if (m_testSetListeners.size() > 0) {
TestSetEvent tse = new TestSetEvent(this, newTestSetInstances);
tse.m_setNumber = setNum;
tse.m_maxSetNumber = maxNum;
notifyTestSetAvailable(tse);
}
if (m_dataSourceListeners.size() > 0) {
notifyDataSetAvailable(new DataSetEvent(this, newTestSetInstances));
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
}
}
/**
* Accept and process a batch clusterer event
*
* @param e a <code>BatchClassifierEvent</code> value
*/
public void acceptClusterer(BatchClustererEvent e) {
if (m_dataSourceListeners.size() > 0
|| m_trainingSetListeners.size() > 0
|| m_testSetListeners.size() > 0) {
if(e.getTestSet().isStructureOnly()) {
return;
}
Instances testSet = e.getTestSet().getDataSet();
weka.clusterers.Clusterer clusterer = e.getClusterer();
String test;
if(e.getTestOrTrain() == 0) {
test = "test";
} else {
test = "training";
}
String relationNameModifier = "_"+test+"_"+e.getSetNumber()+"_of_"
+e.getMaxSetNumber();
if (!m_appendProbabilities || !(clusterer instanceof DensityBasedClusterer)) {
if(m_appendProbabilities && !(clusterer instanceof DensityBasedClusterer)){
System.err.println("Only density based clusterers can append probabilities. Instead cluster will be assigned for each instance.");
if (m_logger != null) {
m_logger.logMessage("[PredictionAppender] "
+ statusMessagePrefix() + " Only density based clusterers can "
+"append probabilities. Instead cluster will be assigned for each "
+"instance.");
m_logger.statusMessage(statusMessagePrefix()
+"WARNING: Only density based clusterers can append probabilities. "
+"Instead cluster will be assigned for each instance.");
}
}
try {
Instances newInstances = makeClusterDataSetClass(testSet, clusterer,
relationNameModifier);
// data source listeners get both train and test sets
if (m_dataSourceListeners.size() > 0) {
notifyDataSetAvailable(new DataSetEvent(this, new Instances(newInstances,0)));
}
if (m_trainingSetListeners.size() > 0 && e.getTestOrTrain() > 0) {
TrainingSetEvent tse =
new TrainingSetEvent(this, new Instances(newInstances, 0));
tse.m_setNumber = e.getSetNumber();
tse.m_maxSetNumber = e.getMaxSetNumber();
notifyTrainingSetAvailable(tse);
}
if (m_testSetListeners.size() > 0 && e.getTestOrTrain() == 0) {
TestSetEvent tse =
new TestSetEvent(this, new Instances(newInstances, 0));
tse.m_setNumber = e.getSetNumber();
tse.m_maxSetNumber = e.getMaxSetNumber();
notifyTestSetAvailable(tse);
}
// fill in predicted values
for (int i = 0; i < testSet.numInstances(); i++) {
double predCluster =
clusterer.clusterInstance(testSet.instance(i));
newInstances.instance(i).setValue(newInstances.numAttributes()-1,
predCluster);
}
// notify listeners
if (m_dataSourceListeners.size() > 0) {
notifyDataSetAvailable(new DataSetEvent(this, newInstances));
}
if (m_trainingSetListeners.size() > 0 && e.getTestOrTrain() > 0) {
TrainingSetEvent tse =
new TrainingSetEvent(this, newInstances);
tse.m_setNumber = e.getSetNumber();
tse.m_maxSetNumber = e.getMaxSetNumber();
notifyTrainingSetAvailable(tse);
}
if (m_testSetListeners.size() > 0 && e.getTestOrTrain() == 0) {
TestSetEvent tse =
new TestSetEvent(this, newInstances);
tse.m_setNumber = e.getSetNumber();
tse.m_maxSetNumber = e.getMaxSetNumber();
notifyTestSetAvailable(tse);
}
return;
} catch (Exception ex) {
ex.printStackTrace();
}
}
else{
try {
Instances newInstances =
makeClusterDataSetProbabilities(testSet,
clusterer,relationNameModifier);
notifyDataSetAvailable(new DataSetEvent(this, new Instances(newInstances,0)));
// fill in predicted probabilities
for (int i = 0; i < testSet.numInstances(); i++) {
double [] probs = clusterer.
distributionForInstance(testSet.instance(i));
for (int j = 0; j < clusterer.numberOfClusters(); j++) {
newInstances.instance(i).setValue(testSet.numAttributes()+j,
probs[j]);
}
}
// notify listeners
notifyDataSetAvailable(new DataSetEvent(this, newInstances));
} catch (Exception ex) {
ex.printStackTrace();
}
}
}
}
private Instances
makeDataSetProbabilities(Instances insts, Instances format,
weka.classifiers.Classifier classifier,
String relationNameModifier)
throws Exception {
// adjust structure for InputMappedClassifier (if necessary)
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
format =
((weka.classifiers.misc.InputMappedClassifier)classifier).
getModelHeader(new Instances(format, 0));
}
String classifierName = classifier.getClass().getName();
classifierName = classifierName.
substring(classifierName.lastIndexOf('.')+1, classifierName.length());
int numOrigAtts = insts.numAttributes();
Instances newInstances = new Instances(insts);
for (int i = 0; i < format.classAttribute().numValues(); i++) {
weka.filters.unsupervised.attribute.Add addF = new
weka.filters.unsupervised.attribute.Add();
addF.setAttributeIndex("last");
addF.setAttributeName(classifierName+"_prob_"+format.classAttribute().value(i));
addF.setInputFormat(newInstances);
newInstances = weka.filters.Filter.useFilter(newInstances, addF);
}
newInstances.setRelationName(insts.relationName()+relationNameModifier);
return newInstances;
}
private Instances makeDataSetClass(Instances insts, Instances structure,
weka.classifiers.Classifier classifier,
String relationNameModifier)
throws Exception {
// adjust structure for InputMappedClassifier (if necessary)
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
structure =
((weka.classifiers.misc.InputMappedClassifier)classifier).
getModelHeader(new Instances(structure, 0));
}
weka.filters.unsupervised.attribute.Add addF = new
weka.filters.unsupervised.attribute.Add();
addF.setAttributeIndex("last");
String classifierName = classifier.getClass().getName();
classifierName = classifierName.
substring(classifierName.lastIndexOf('.')+1, classifierName.length());
addF.setAttributeName("class_predicted_by: "+classifierName);
if (structure.classAttribute().isNominal()) {
String classLabels = "";
Enumeration enu = structure.classAttribute().enumerateValues();
classLabels += (String)enu.nextElement();
while (enu.hasMoreElements()) {
classLabels += ","+(String)enu.nextElement();
}
addF.setNominalLabels(classLabels);
}
addF.setInputFormat(insts);
Instances newInstances =
weka.filters.Filter.useFilter(insts, addF);
newInstances.setRelationName(insts.relationName()+relationNameModifier);
return newInstances;
}
private Instances
makeClusterDataSetProbabilities(Instances format,
weka.clusterers.Clusterer clusterer,
String relationNameModifier)
throws Exception {
int numOrigAtts = format.numAttributes();
Instances newInstances = new Instances(format);
for (int i = 0; i < clusterer.numberOfClusters(); i++) {
weka.filters.unsupervised.attribute.Add addF = new
weka.filters.unsupervised.attribute.Add();
addF.setAttributeIndex("last");
addF.setAttributeName("prob_cluster"+i);
addF.setInputFormat(newInstances);
newInstances = weka.filters.Filter.useFilter(newInstances, addF);
}
newInstances.setRelationName(format.relationName()+relationNameModifier);
return newInstances;
}
private Instances makeClusterDataSetClass(Instances format,
weka.clusterers.Clusterer clusterer,
String relationNameModifier)
throws Exception {
weka.filters.unsupervised.attribute.Add addF = new
weka.filters.unsupervised.attribute.Add();
addF.setAttributeIndex("last");
String clustererName = clusterer.getClass().getName();
clustererName = clustererName.
substring(clustererName.lastIndexOf('.')+1, clustererName.length());
addF.setAttributeName("assigned_cluster: "+clustererName);
//if (format.classAttribute().isNominal()) {
String clusterLabels = "0";
/*Enumeration enu = format.classAttribute().enumerateValues();
clusterLabels += (String)enu.nextElement();
while (enu.hasMoreElements()) {
clusterLabels += ","+(String)enu.nextElement();
}*/
for(int i = 1; i <= clusterer.numberOfClusters()-1; i++)
clusterLabels += ","+i;
addF.setNominalLabels(clusterLabels);
//}
addF.setInputFormat(format);
Instances newInstances =
weka.filters.Filter.useFilter(format, addF);
newInstances.setRelationName(format.relationName()+relationNameModifier);
return newInstances;
}
/**
* Notify all instance listeners that an instance is available
*
* @param e an <code>InstanceEvent</code> value
*/
protected void notifyInstanceAvailable(InstanceEvent e) {
Vector l;
synchronized (this) {
l = (Vector)m_instanceListeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
((InstanceListener)l.elementAt(i)).acceptInstance(e);
}
}
}
/**
* Notify all Data source listeners that a data set is available
*
* @param e a <code>DataSetEvent</code> value
*/
protected void notifyDataSetAvailable(DataSetEvent e) {
Vector l;
synchronized (this) {
l = (Vector)m_dataSourceListeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
((DataSourceListener)l.elementAt(i)).acceptDataSet(e);
}
}
}
/**
* Notify all test set listeners that a test set is available
*
* @param e a <code>TestSetEvent</code> value
*/
protected void notifyTestSetAvailable(TestSetEvent e) {
Vector l;
synchronized (this) {
l = (Vector)m_testSetListeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
((TestSetListener)l.elementAt(i)).acceptTestSet(e);
}
}
}
/**
* Notify all test set listeners that a test set is available
*
* @param e a <code>TestSetEvent</code> value
*/
protected void notifyTrainingSetAvailable(TrainingSetEvent e) {
Vector l;
synchronized (this) {
l = (Vector)m_trainingSetListeners.clone();
}
if (l.size() > 0) {
for(int i = 0; i < l.size(); i++) {
((TrainingSetListener)l.elementAt(i)).acceptTrainingSet(e);
}
}
}
/**
* Set a logger
*
* @param logger a <code>weka.gui.Logger</code> value
*/
public void setLog(weka.gui.Logger logger) {
m_logger = logger;
}
public void stop() {
// tell the listenee (upstream bean) to stop
if (m_listenee instanceof BeanCommon) {
((BeanCommon)m_listenee).stop();
}
}
/**
* Returns true if. at this time, the bean is busy with some
* (i.e. perhaps a worker thread is performing some calculation).
*
* @return true if the bean is busy.
*/
public boolean isBusy() {
return false;
}
/**
* Returns true if, at this time,
* the object will accept a connection according to the supplied
* event name
*
* @param eventName the event
* @return true if the object will accept a connection
*/
public boolean connectionAllowed(String eventName) {
return (m_listenee == null);
}
/**
* Returns true if, at this time,
* the object will accept a connection according to the supplied
* EventSetDescriptor
*
* @param esd the EventSetDescriptor
* @return true if the object will accept a connection
*/
public boolean connectionAllowed(EventSetDescriptor esd) {
return connectionAllowed(esd.getName());
}
/**
* Notify this object that it has been registered as a listener with
* a source with respect to the supplied event name
*
* @param eventName
* @param source the source with which this object has been registered as
* a listener
*/
public synchronized void connectionNotification(String eventName,
Object source) {
if (connectionAllowed(eventName)) {
m_listenee = source;
}
}
/**
* Notify this object that it has been deregistered as a listener with
* a source with respect to the supplied event name
*
* @param eventName the event name
* @param source the source with which this object has been registered as
* a listener
*/
public synchronized void disconnectionNotification(String eventName,
Object source) {
if (m_listenee == source) {
m_listenee = null;
m_format = null; // assume any calculated instance format if now invalid
}
}
/**
* Returns true, if at the current time, the named event could
* be generated. Assumes that supplied event names are names of
* events that could be generated by this bean.
*
* @param eventName the name of the event in question
* @return true if the named event could be generated at this point in
* time
*/
public boolean eventGeneratable(String eventName) {
if (m_listenee == null) {
return false;
}
if (m_listenee instanceof EventConstraints) {
if (eventName.equals("instance")) {
if (!((EventConstraints)m_listenee).
eventGeneratable("incrementalClassifier")) {
return false;
}
}
if (eventName.equals("dataSet")
|| eventName.equals("trainingSet")
|| eventName.equals("testSet")) {
if (((EventConstraints)m_listenee).
eventGeneratable("batchClassifier")) {
return true;
}
if (((EventConstraints)m_listenee).eventGeneratable("batchClusterer")) {
return true;
}
return false;
}
}
return true;
}
private String statusMessagePrefix() {
return getCustomName() + "$" + hashCode() + "|";
}
}