@Override
public ClusterModel generateClusterModel(ExampleSet exampleSet) throws OperatorException {
// checking and creating ids if necessary
Tools.checkAndCreateIds(exampleSet);
// generating assignment
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
int clusterAssignments[] = new int[exampleSet.size()];
int k = getParameterAsInt(PARAMETER_NUMBER_OF_CLUSTERS);
for (int i = 0; i < exampleSet.size(); i++) {
clusterAssignments[i] = random.nextInt(k);
}
ClusterModel model =
new ClusterModel(
exampleSet,
k,
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_ADD_AS_LABEL),
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_REMOVE_UNLABELED));
model.setClusterAssignments(clusterAssignments, exampleSet);
// generating cluster attribute
if (addsClusterAttribute()) {
Attribute cluster = AttributeFactory.createAttribute("cluster", Ontology.NOMINAL);
exampleSet.getExampleTable().addAttribute(cluster);
exampleSet.getAttributes().setCluster(cluster);
int i = 0;
for (Example example : exampleSet) {
example.setValue(cluster, "cluster_" + clusterAssignments[i]);
i++;
}
}
return model;
}
@Override
public ClusterModel generateClusterModel(ExampleSet exampleSet) throws OperatorException {
int k = getParameterAsInt(PARAMETER_K);
int maxOptimizationSteps = getParameterAsInt(PARAMETER_MAX_OPTIMIZATION_STEPS);
boolean useExampleWeights = getParameterAsBoolean(PARAMETER_USE_WEIGHTS);
Kernel kernel = Kernel.createKernel(this);
// init operator progress
getProgress().setTotal(maxOptimizationSteps);
// checking and creating ids if necessary
Tools.checkAndCreateIds(exampleSet);
// additional checks
Tools.onlyNonMissingValues(exampleSet, getOperatorClassName(), this, new String[0]);
if (exampleSet.size() < k) {
throw new UserError(this, 142, k);
}
// extracting attribute names
Attributes attributes = exampleSet.getAttributes();
ArrayList<String> attributeNames = new ArrayList<String>(attributes.size());
for (Attribute attribute : attributes) {
attributeNames.add(attribute.getName());
}
Attribute weightAttribute = attributes.getWeight();
RandomGenerator generator = RandomGenerator.getRandomGenerator(this);
ClusterModel model =
new ClusterModel(
exampleSet,
k,
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_ADD_AS_LABEL),
getParameterAsBoolean(RMAbstractClusterer.PARAMETER_REMOVE_UNLABELED));
// init centroids
int[] clusterAssignments = new int[exampleSet.size()];
for (int i = 0; i < exampleSet.size(); i++) {
clusterAssignments[i] = generator.nextIntInRange(0, k);
}
// run optimization steps
boolean stable = false;
for (int step = 0; step < maxOptimizationSteps && !stable; step++) {
// calculating cluster kernel properties
double[] clusterWeights = new double[k];
double[] clusterKernelCorrection = new double[k];
int i = 0;
for (Example firstExample : exampleSet) {
double firstExampleWeight = useExampleWeights ? firstExample.getValue(weightAttribute) : 1d;
double[] firstExampleValues = getAsDoubleArray(firstExample, attributes);
clusterWeights[clusterAssignments[i]] += firstExampleWeight;
int j = 0;
for (Example secondExample : exampleSet) {
if (clusterAssignments[i] == clusterAssignments[j]) {
double secondExampleWeight =
useExampleWeights ? secondExample.getValue(weightAttribute) : 1d;
clusterKernelCorrection[clusterAssignments[i]] +=
firstExampleWeight
* secondExampleWeight
* kernel.calculateDistance(
firstExampleValues, getAsDoubleArray(secondExample, attributes));
}
j++;
}
i++;
}
for (int z = 0; z < k; z++) {
clusterKernelCorrection[z] /= clusterWeights[z] * clusterWeights[z];
}
// assign examples to new centroids
int[] newClusterAssignments = new int[exampleSet.size()];
i = 0;
for (Example example : exampleSet) {
double[] exampleValues = getAsDoubleArray(example, attributes);
double exampleKernelValue = kernel.calculateDistance(exampleValues, exampleValues);
double nearestDistance = Double.POSITIVE_INFINITY;
int nearestIndex = 0;
for (int clusterIndex = 0; clusterIndex < k; clusterIndex++) {
double distance = 0;
// iterating over all examples in cluster to get kernel distance
int j = 0;
for (Example clusterExample : exampleSet) {
if (clusterAssignments[j] == clusterIndex) {
distance +=
(useExampleWeights ? clusterExample.getValue(weightAttribute) : 1d)
* kernel.calculateDistance(
getAsDoubleArray(clusterExample, attributes), exampleValues);
}
j++;
}
distance *= -2d / clusterWeights[clusterIndex];
// copy in outer loop
distance += exampleKernelValue;
distance += clusterKernelCorrection[clusterIndex];
if (distance < nearestDistance) {
nearestDistance = distance;
nearestIndex = clusterIndex;
}
}
newClusterAssignments[i] = nearestIndex;
i++;
}
// finishing assignment
stable = true;
for (int j = 0; j < exampleSet.size() && stable; j++) {
stable &= newClusterAssignments[j] == clusterAssignments[j];
}
clusterAssignments = newClusterAssignments;
// trigger operator progress
getProgress().step();
}
// setting last clustering into model
model.setClusterAssignments(clusterAssignments, exampleSet);
getProgress().complete();
if (addsClusterAttribute()) {
Attribute cluster = AttributeFactory.createAttribute("cluster", Ontology.NOMINAL);
exampleSet.getExampleTable().addAttribute(cluster);
exampleSet.getAttributes().setCluster(cluster);
int i = 0;
for (Example example : exampleSet) {
example.setValue(cluster, "cluster_" + clusterAssignments[i]);
i++;
}
}
return model;
}
// private IOObject getValidations( WhiBoCentroidClusterModel
// centroidClusterModel, ExampleSet exampleSet) {
private IOObject getValidations(ClusterModel centroidClusterModel, ExampleSet exampleSet) {
CentroidClusterModel centroidClusterModelRapid = null;
WhiBoCentroidClusterModel centroidClusterModelWhiBo = null;
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class)) {
centroidClusterModelRapid = (CentroidClusterModel) centroidClusterModel;
} else {
centroidClusterModelWhiBo = (WhiBoCentroidClusterModel) centroidClusterModel;
}
// Gets distance measure from the component repository on the basis of
// selected parameter
String className = getParameterType("Distance_Measure").toString(0);
rs.fon.whibo.GC.component.DistanceMeasure.DistanceMeasure distance = null;
Constructor c = null;
try {
c = Class.forName(className).getConstructor(new Class[] {List.class});
distance =
(rs.fon.whibo.GC.component.DistanceMeasure.DistanceMeasure)
c.newInstance(new Object[] {new LinkedList<SubproblemParameter>()});
} catch (Exception e) {
}
Evaluation e = null;
double evaluation = 0;
MyPerformanceCriterion mpc = new MyPerformanceCriterion();
LinkedList<SubproblemParameter> ll = new LinkedList<SubproblemParameter>();
// Instantiates sub-problem parameter class for validation measures that
// need parameters
SubproblemParameter sp = new SubproblemParameter();
sp.setParametertType(Integer.class);
sp.setMinValue("1");
sp.setMaxValue("1000");
// Gets and executes evaluation components from the component repository
// on the basis of selected parameter
if (getParameterAsBoolean("Intra_Cluster_Distance")) {
e = new IntraClusterDistance(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("Intra_Cluster_Distance", evaluation);
}
if (getParameterAsBoolean("Connectivity")) {
try {
sp.setXenteredValue(getParameter("NN_Connectivity").toString());
} catch (UndefinedParameterError e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
} catch (IllegalArgumentException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
ll.add(sp);
e = new Connectivity(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("Connectivity", evaluation);
}
if (getParameterAsBoolean("Global_Silhouette_Index")) {
e = new GlobalSilhouetteIndex(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("Global_Silhouette_Index", evaluation);
}
if (getParameterAsBoolean("XB_Index")) {
e = new XBIndex(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("XB_Index", evaluation);
}
if (getParameterAsBoolean("Min_Max_Cut")) {
e = new MInMaxCut(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("Min_Max_Cut", evaluation);
}
if (getParameterAsBoolean("Symmetry")) {
try {
sp.setXenteredValue(getParameter("NN_Symmetry").toString());
} catch (UndefinedParameterError e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
} catch (IllegalArgumentException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
ll.removeFirstOccurrence(sp);
ll.add(sp);
// e=new Symmetry(ll);
// if
// (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
// evaluation = e.Evaluate(distance, centroidClusterModelRapid,
// exampleSet);
// else
// evaluation = e.Evaluate(distance, centroidClusterModelWhiBo,
// exampleSet);
// mpc.addPerformance("Symmetry", evaluation);
// }
// if(getParameterAsBoolean("BIC")){
// e=new BIC(ll);
// if
// (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
// evaluation = e.Evaluate(distance, centroidClusterModelRapid,
// exampleSet);
// else
// evaluation = e.Evaluate(distance, centroidClusterModelWhiBo,
// exampleSet);
// mpc.addPerformance("BIC", evaluation);
}
if (getParameterAsBoolean("Fowlkes_Mallows_Index")) {
e = new FowlkesMallowsIndex(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("Fowlkes_Mallows_Index", evaluation);
}
if (getParameterAsBoolean("Jaccard_Index")) {
e = new JaccardIndex(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("Jaccard_Index", evaluation);
}
if (getParameterAsBoolean("Rand_Index")) {
e = new RandIndex(ll);
evaluation = e.Evaluate(distance, centroidClusterModel, exampleSet);
mpc.addPerformance("Rand_Index", evaluation);
}
if (getParameterAsBoolean("Adjusted_Rand_Index")) {
e = new AdjustedRandIndex(ll);
if (centroidClusterModel.getClass().equals(CentroidClusterModel.class))
evaluation = e.Evaluate(distance, centroidClusterModelRapid, exampleSet);
else evaluation = e.Evaluate(distance, centroidClusterModelWhiBo, exampleSet);
mpc.addPerformance("Adjusted_Rand_Index", evaluation);
}
return mpc;
}
