public void playCard(CardType card) {
// if (cards.contains(card)) {
cards.removeFirstOccurrence(card);
used.push(card);
// } else {
// throw new RuntimeException();
// }
}
// 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;
}
public static Tuple pivot(Tuple t, int l, int e) { // Tuple contains all values N B A b c v
LinkedList<Integer> N = t.getN(); // Initialize N from the tuple
LinkedList<Integer> B = t.getB(); // Initialize B from the tuple
double[][] A = t.getA(); // Initialize A from the tuple
double[] b = t.getb(); // Initialize b from the tuple
double[] c = t.getc(); // Initialize c from the tuple
double v = t.getv(); // Initialize v from the tuple
double[] bhat = new double[b.length];
double[] chat = new double[c.length];
for (int i = 0; i < b.length; i++) {
bhat[i] = 0;
}
for (int i = 0; i < c.length; i++) {
chat[i] = 0;
}
System.out.println("Starting A:");
for (int i = 0; i < A.length; i++) {
for (int j = 0; j < A[0].length; j++) {
System.out.print(A[i][j] + " ");
}
System.out.print("\n");
}
/*System.out.println("Start b:");
for(int i=0; i<b.length;i++){
System.out.println(b[i]);
}
System.out.println("Start c:");
for(int i=0; i<c.length;i++){
System.out.println(c[i]);
}*/
/*System.out.println("Ending N:");
for(int i=0; i<N.size();i++){
System.out.println(N.get(i));
}
System.out.println("Ending B:");
for(int i=0; i<B.size();i++){
System.out.println(B.get(i));
}*/
int dim = N.size() + B.size();
double[][] Ahat = new double[dim][dim]; // initialize Ahat dim * dim matrix
for (int i = 0; i < dim; i++) { // initialize all Ahat to be 0
for (int j = 0; j < dim; j++) {
Ahat[i][j] = 0;
}
}
/*
* Compute the coefficient of the equation for the new basic variable xe
*/
bhat[e] = b[l] / A[l][e]; // Line 3 code
for (int i = 0; i < dim; i++) {
if (N.contains(i) && i != e) {
// System.out.println("Iterating for j = "+i);
Ahat[e][i] = A[l][i] / A[l][e];
}
}
Ahat[e][l] = 1 / A[l][e];
/*
* Compute the coefficient of the equation for the remaining constants
*/
for (int i = 0; i < dim; i++) {
if (B.contains(i) && i != l) {
System.out.println("2Iterating for i = " + i);
bhat[i] = b[i] - A[i][e] * bhat[e];
for (int j = 0; j < dim; j++) {
if (N.contains(j) && j != e) Ahat[i][j] = A[i][j] - A[i][e] * Ahat[e][j];
Ahat[i][l] = -A[i][e] * Ahat[e][l];
}
}
}
/*
* Compute the objective function
*/
v = v + c[e] * bhat[e]; // Line 14
for (int j = 0; j < dim; j++) {
if (N.contains(j) && j != e) {
chat[j] = c[j] - c[e] * Ahat[e][j];
}
}
chat[l] = -c[e] * Ahat[e][l];
/*
* Compute the new sets of basic and non-basic variables
*/
N.removeFirstOccurrence(e);
N.add(l);
B.removeFirstOccurrence(l);
B.add(e);
/*
* Print check
*/
/*System.out.println("Ending A:");
for(int i=0; i<Ahat.length;i++){
for(int j=0; j<Ahat[0].length;j++){
System.out.print(Ahat[i][j]+" ");
}
System.out.print("\n");
}
System.out.println("Ending N:");
for(int i=0; i<N.size();i++){
System.out.println(N.get(i));
}
System.out.println("Ending B:");
for(int i=0; i<B.size();i++){
System.out.println(B.get(i));
}
/*System.out.println("Ending b:");
for(int i=0; i<b.length;i++){
System.out.println(bhat[i]);
}
System.out.println("Ending c:");
for(int i=0; i<c.length;i++){
System.out.println(chat[i]);
}*/
System.out.println("Final value v: " + v);
Tuple t2 = new Tuple(N, B, Ahat, bhat, chat, v);
return t2;
}