private static IList<IList<IAgent>> clusteringUsingWeka(
final IScope scope,
final Clusterer clusterer,
final IList<String> attributes,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents)
throws GamaRuntimeException {
Instances dataset = convertToInstances(scope, attributes, agents);
try {
clusterer.buildClusterer(dataset);
IList<IList<IAgent>> groupes = GamaListFactory.create(Types.LIST.of(Types.AGENT));
for (int i = 0; i < clusterer.numberOfClusters(); i++) {
groupes.add(GamaListFactory.<IAgent>create(Types.AGENT));
}
for (int i = 0; i < dataset.numInstances(); i++) {
Instance inst = dataset.instance(i);
int clusterIndex = -1;
clusterIndex = clusterer.clusterInstance(inst);
IList<IAgent> groupe = groupes.get(clusterIndex);
groupe.add(agents.get(scope, i));
}
return groupes;
} catch (Exception e) {
return null;
}
}
/**
* Print the cluster assignments for either the training or the testing data.
*
* @param clusterer the clusterer to use for cluster assignments
* @return a string containing the instance indexes and cluster assigns.
* @exception if cluster assignments can't be printed
*/
private static String printClusterings(
Clusterer clusterer, Instances train, String testFileName, Range attributesToOutput)
throws Exception {
StringBuffer text = new StringBuffer();
int i = 0;
int cnum;
if (testFileName.length() != 0) {
BufferedReader testStream = null;
try {
testStream = new BufferedReader(new FileReader(testFileName));
} catch (Exception e) {
throw new Exception("Can't open file " + e.getMessage() + '.');
}
Instances test = new Instances(testStream, 1);
while (test.readInstance(testStream)) {
try {
cnum = clusterer.clusterInstance(test.instance(0));
text.append(
i
+ " "
+ cnum
+ " "
+ attributeValuesString(test.instance(0), attributesToOutput)
+ "\n");
} catch (Exception e) {
/* throw new Exception('\n' + "Unable to cluster instance\n"
+ e.getMessage()); */
text.append(
i
+ " Unclustered "
+ attributeValuesString(test.instance(0), attributesToOutput)
+ "\n");
}
test.delete(0);
i++;
}
} else // output for training data
{
for (i = 0; i < train.numInstances(); i++) {
try {
cnum = clusterer.clusterInstance(train.instance(i));
text.append(
i
+ " "
+ cnum
+ " "
+ attributeValuesString(train.instance(i), attributesToOutput)
+ "\n");
} catch (Exception e) {
/* throw new Exception('\n'
+ "Unable to cluster instance\n"
+ e.getMessage()); */
text.append(
i
+ " Unclustered "
+ attributeValuesString(train.instance(i), attributesToOutput)
+ "\n");
}
}
}
return text.toString();
}
/**
* Print the cluster statistics for either the training or the testing data.
*
* @param clusterer the clusterer to use for generating statistics.
* @return a string containing cluster statistics.
* @exception if statistics can't be generated.
*/
private static String printClusterStats(Clusterer clusterer, String fileName) throws Exception {
StringBuffer text = new StringBuffer();
int i = 0;
int cnum;
double loglk = 0.0;
double[] dist;
double temp;
int cc = clusterer.numberOfClusters();
double[] instanceStats = new double[cc];
int unclusteredInstances = 0;
if (fileName.length() != 0) {
BufferedReader inStream = null;
try {
inStream = new BufferedReader(new FileReader(fileName));
} catch (Exception e) {
throw new Exception("Can't open file " + e.getMessage() + '.');
}
Instances inst = new Instances(inStream, 1);
while (inst.readInstance(inStream)) {
try {
cnum = clusterer.clusterInstance(inst.instance(0));
if (clusterer instanceof DensityBasedClusterer) {
loglk += ((DensityBasedClusterer) clusterer).logDensityForInstance(inst.instance(0));
// temp = Utils.sum(dist);
}
instanceStats[cnum]++;
} catch (Exception e) {
unclusteredInstances++;
}
inst.delete(0);
i++;
}
/*
// count the actual number of used clusters
int count = 0;
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
count++;
}
}
if (count > 0) {
double [] tempStats = new double [count];
count=0;
for (i=0;i<cc;i++) {
if (instanceStats[i] > 0) {
tempStats[count++] = instanceStats[i];
}
}
instanceStats = tempStats;
cc = instanceStats.length;
} */
int clustFieldWidth = (int) ((Math.log(cc) / Math.log(10)) + 1);
int numInstFieldWidth = (int) ((Math.log(i) / Math.log(10)) + 1);
double sum = Utils.sum(instanceStats);
loglk /= sum;
text.append("Clustered Instances\n");
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
text.append(
Utils.doubleToString((double) i, clustFieldWidth, 0)
+ " "
+ Utils.doubleToString(instanceStats[i], numInstFieldWidth, 0)
+ " ("
+ Utils.doubleToString((instanceStats[i] / sum * 100.0), 3, 0)
+ "%)\n");
}
}
if (unclusteredInstances > 0) {
text.append("\nUnclustered Instances : " + unclusteredInstances);
}
if (clusterer instanceof DensityBasedClusterer) {
text.append("\n\nLog likelihood: " + Utils.doubleToString(loglk, 1, 5) + "\n");
}
}
return text.toString();
}
/**
* Evaluate the clusterer on a set of instances. Calculates clustering statistics and stores
* cluster assigments for the instances in m_clusterAssignments
*
* @param test the set of instances to cluster
* @exception Exception if something goes wrong
*/
public void evaluateClusterer(Instances test) throws Exception {
int i = 0;
int cnum;
double loglk = 0.0;
double[] dist;
double temp;
int cc = m_Clusterer.numberOfClusters();
m_numClusters = cc;
int numInstFieldWidth = (int) ((Math.log(test.numInstances()) / Math.log(10)) + 1);
double[] instanceStats = new double[cc];
m_clusterAssignments = new double[test.numInstances()];
Instances testCopy = test;
boolean hasClass = (testCopy.classIndex() >= 0);
int unclusteredInstances = 0;
// If class is set then do class based evaluation as well
if (hasClass) {
if (testCopy.classAttribute().isNumeric()) {
throw new Exception("ClusterEvaluation: Class must be nominal!");
}
Remove removeClass = new Remove();
removeClass.setAttributeIndices("" + (testCopy.classIndex() + 1));
removeClass.setInvertSelection(false);
removeClass.setInputFormat(testCopy);
testCopy = Filter.useFilter(testCopy, removeClass);
}
for (i = 0; i < testCopy.numInstances(); i++) {
cnum = -1;
try {
if (m_Clusterer instanceof DensityBasedClusterer) {
loglk +=
((DensityBasedClusterer) m_Clusterer).logDensityForInstance(testCopy.instance(i));
// temp = Utils.sum(dist);
// Utils.normalize(dist);
cnum = m_Clusterer.clusterInstance(testCopy.instance(i));
// Utils.maxIndex(dist);
m_clusterAssignments[i] = (double) cnum;
} else {
cnum = m_Clusterer.clusterInstance(testCopy.instance(i));
m_clusterAssignments[i] = (double) cnum;
}
} catch (Exception e) {
unclusteredInstances++;
}
if (cnum != -1) {
instanceStats[cnum]++;
}
}
/* // count the actual number of used clusters
int count = 0;
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
count++;
}
}
if (count > 0) {
double [] tempStats = new double [count];
double [] map = new double [m_clusterAssignments.length];
count=0;
for (i=0;i<cc;i++) {
if (instanceStats[i] > 0) {
tempStats[count] = instanceStats[i];
map[i] = count;
count++;
}
}
instanceStats = tempStats;
cc = instanceStats.length;
for (i=0;i<m_clusterAssignments.length;i++) {
m_clusterAssignments[i] = map[(int)m_clusterAssignments[i]];
}
} */
double sum = Utils.sum(instanceStats);
loglk /= sum;
m_logL = loglk;
m_clusteringResults.append(m_Clusterer.toString());
m_clusteringResults.append("Clustered Instances\n\n");
int clustFieldWidth = (int) ((Math.log(cc) / Math.log(10)) + 1);
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
m_clusteringResults.append(
Utils.doubleToString((double) i, clustFieldWidth, 0)
+ " "
+ Utils.doubleToString(instanceStats[i], numInstFieldWidth, 0)
+ " ("
+ Utils.doubleToString((instanceStats[i] / sum * 100.0), 3, 0)
+ "%)\n");
}
}
if (unclusteredInstances > 0) {
m_clusteringResults.append("\nUnclustered instances : " + unclusteredInstances);
}
if (m_Clusterer instanceof DensityBasedClusterer) {
m_clusteringResults.append("\n\nLog likelihood: " + Utils.doubleToString(loglk, 1, 5) + "\n");
}
if (hasClass) {
evaluateClustersWithRespectToClass(test);
}
}