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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
* Instances train = ... // from somewhere
* Instances test = ... // from somewhere
* Clusterer cls = ... // from somewhere
* // build and evaluate clusterer
* cls.buildClusterer(train);
* ClusterEvaluation eval = new ClusterEvaluation();
* eval.setClusterer(cls);
* eval.evaluateClusterer(test);
* // generate plot instances
* ClustererPlotInstances plotInstances = new ClustererPlotInstances();
* plotInstances.setClusterer(cls);
* plotInstances.setInstances(test);
* plotInstances.setClusterer(cls);
* plotInstances.setClusterEvaluation(eval);
* plotInstances.setUp();
* // generate visualization
* VisualizePanel visPanel = new VisualizePanel();
* visPanel.addPlot(plotInstances.getPlotData("plot name"));
* // clean up
* plotInstances.cleanUp();
*
*
* @author fracpete (fracpete at waikato dot ac dot nz)
* @version $Revision: 10222 $
*/
public class ClustererAssignmentsPlotInstances extends AbstractPlotInstances {
/** for serialization. */
private static final long serialVersionUID = -4748134272046520423L;
/** for storing the plot shapes. */
protected int[] m_PlotShapes;
/** the clusterer being used. */
protected Clusterer m_Clusterer;
/** the cluster evaluation to use. */
protected ClusterEvaluation m_Evaluation;
/**
* Initializes the members.
*/
@Override
protected void initialize() {
super.initialize();
m_PlotShapes = null;
m_Clusterer = null;
m_Evaluation = null;
}
/**
* Sets the classifier used for making the predictions.
*
* @param value the clusterer to use
*/
public void setClusterer(Clusterer value) {
m_Clusterer = value;
}
/**
* Returns the currently set clusterer.
*
* @return the clusterer in use
*/
public Clusterer getClusterer() {
return m_Clusterer;
}
/**
* Sets the cluster evaluation object to use.
*
* @param value the evaluation object
*/
public void setClusterEvaluation(ClusterEvaluation value) {
m_Evaluation = value;
}
/**
* Returns the cluster evaluation object in use.
*
* @return the evaluation object
*/
public ClusterEvaluation getClusterEvaluation() {
return m_Evaluation;
}
/**
* Checks whether clusterer and evaluation are provided.
*/
@Override
protected void check() {
super.check();
if (m_Clusterer == null) {
throw new IllegalStateException("No clusterer set!");
}
if (m_Evaluation == null) {
throw new IllegalStateException("No cluster evaluation set!");
}
}
/**
* Sets up the structure for the plot instances.
*/
@Override
protected void determineFormat() {
int numClusters;
ArrayList hv;
Attribute predictedCluster;
ArrayList clustVals;
int i;
numClusters = m_Evaluation.getNumClusters();
hv = new ArrayList();
clustVals = new ArrayList();
for (i = 0; i < numClusters; i++) {
clustVals.add("cluster" + /* (i+1) */i);
}
predictedCluster = new Attribute("Cluster", clustVals);
for (i = 0; i < m_Instances.numAttributes(); i++) {
hv.add((Attribute) m_Instances.attribute(i).copy());
}
hv.add(predictedCluster);
m_PlotInstances = new Instances(m_Instances.relationName() + "_clustered",
hv, m_Instances.numInstances());
}
/**
* Generates the cluster assignments.
*
* @see #m_PlotShapes
* @see #m_PlotSizes
* @see #m_PlotInstances
*/
protected void process() {
double[] clusterAssignments;
int i;
double[] values;
int j;
int[] classAssignments;
clusterAssignments = m_Evaluation.getClusterAssignments();
classAssignments = null;
if (m_Instances.classIndex() >= 0) {
classAssignments = m_Evaluation.getClassesToClusters();
m_PlotShapes = new int[m_Instances.numInstances()];
for (i = 0; i < m_Instances.numInstances(); i++) {
m_PlotShapes[i] = Plot2D.CONST_AUTOMATIC_SHAPE;
}
}
for (i = 0; i < m_Instances.numInstances(); i++) {
values = new double[m_PlotInstances.numAttributes()];
for (j = 0; j < m_Instances.numAttributes(); j++) {
values[j] = m_Instances.instance(i).value(j);
}
if (clusterAssignments[i] < 0) {
values[j] = Utils.missingValue();
} else {
values[j] = clusterAssignments[i];
}
m_PlotInstances.add(new DenseInstance(1.0, values));
if (m_PlotShapes != null) {
if (clusterAssignments[i] >= 0) {
if ((int) m_Instances.instance(i).classValue() != classAssignments[(int) clusterAssignments[i]]) {
m_PlotShapes[i] = Plot2D.ERROR_SHAPE;
}
} else {
m_PlotShapes[i] = Plot2D.MISSING_SHAPE;
}
}
}
}
/**
* Performs optional post-processing.
*/
@Override
protected void finishUp() {
super.finishUp();
process();
}
/**
* Assembles and returns the plot. The relation name of the dataset gets added
* automatically.
*
* @param name the name of the plot
* @return the plot
* @throws Exception if plot generation fails
*/
@Override
protected PlotData2D createPlotData(String name) throws Exception {
PlotData2D result;
result = new PlotData2D(m_PlotInstances);
if (m_PlotShapes != null) {
result.setShapeType(m_PlotShapes);
}
result.addInstanceNumberAttribute();
result.setPlotName(name + " (" + m_Instances.relationName() + ")");
return result;
}
/**
* For freeing up memory. Plot data cannot be generated after this call!
*/
@Override
public void cleanUp() {
super.cleanUp();
m_Clusterer = null;
m_Evaluation = null;
m_PlotShapes = null;
}
}