@Before
public void setUp() throws Exception {
List<SubproblemParameter> parameters = new LinkedList<SubproblemParameter>();
SubproblemParameter subproblemParameter1 = new SubproblemParameter();
// TODO Object or generics?
subproblemParameter1.setParametertType(Integer.class);
subproblemParameter1.setDefaultValue("10000");
subproblemParameter1.setMinValue("1");
subproblemParameter1.setMaxValue("10000");
subproblemParameter1.setXenteredValue("10000");
parameters.add(0, subproblemParameter1);
treeDepth = new TreeDepth(parameters);
}
// 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;
}