public Vector getRangos() {
Vector salida = new Vector();
double[][] rangos = this.devuelveRangos();
for (int i = 0; i < nVars; i++) {
Vector rango = new Vector();
if (this.getTiposIndex2(i).equals("real")) {
// if (this.getTiposIndex(i).equals("real")) {
rango.add(new Double(rangos[i][0]));
rango.add(new Double(rangos[i][1]));
/*}else if(this.getTiposIndex2(i).equals("integer")){
rango.add(new Integer((int)rangos[i][0]));
rango.add(new Integer((int)rangos[i][1]));
*/
} else {
if (i == nVars - 1) {
for (int j = 0; j < Attributes.getOutputAttribute(0).getNumNominalValues(); j++) {
rango.add(Attributes.getOutputAttribute(0).getNominalValue(j));
}
} else {
for (int j = 0; j < Attributes.getAttribute(i).getNumNominalValues(); j++) {
rango.add(Attributes.getAttribute(i).getNominalValue(j));
}
}
}
salida.add(rango);
}
return salida;
}
/**
* Function to read an itemset and appends it to the dataset.
*
* @return True if the itemset was readed succesfully.
*/
private boolean getItemsetFull() {
// fill itemset
for (int j = 0; j < IS.getNumInstances(); j++) {
double[] itemset = new double[Attributes.getNumAttributes()];
int index;
// Get values for all input attributes.
for (int i = 0; i < Attributes.getInputNumAttributes(); i++) {
// check type and if there is null
if (IS.getInstance(j).getInputMissingValues(i)) itemset[i] = Itemset.getMissingValue();
else {
if (Attributes.getInputAttribute(i).getType() == 0) // nominal
{
for (int k = 0; k < Attributes.getInputAttribute(i).getNumNominalValues(); k++)
if (Attributes.getInputAttribute(i)
.getNominalValue(k)
.equals(IS.getInstance(j).getInputNominalValues(i))) itemset[i] = (double) k;
} else // real and integer
{
itemset[i] = IS.getInstance(j).getInputRealValues(i);
}
} // else
} // for
// Get values for output attribute.
int i = Attributes.getInputNumAttributes();
// check type and if there is null
if (IS.getInstance(j).getOutputMissingValues(0)) itemset[i] = Itemset.getMissingValue();
else {
if (Attributes.getOutputAttribute(0).getType() == 0) // nominal
{
for (int k = 0; k < Attributes.getOutputAttribute(0).getNumNominalValues(); k++)
if (Attributes.getOutputAttribute(0)
.getNominalValue(k)
.equals(IS.getInstance(j).getOutputNominalValues(0))) itemset[i] = (double) k;
} else // real and integer
{
itemset[i] = IS.getInstance(j).getOutputRealValues(0);
}
} // else
// Add itemset to dataset
addItemset(new Itemset(1, itemset));
} // for
return true;
}
/**
* Devuelve el universo de discuros de las variables de entrada y salida
*
* @return double[][] El rango minimo y maximo de cada variable
*/
public double[][] devuelveRangos() {
double[][] rangos = new double[this.getnVars()][2];
for (int i = 0; i < this.getnInputs(); i++) {
if (Attributes.getInputAttribute(i).getNumNominalValues() > 0) {
rangos[i][0] = 0;
rangos[i][1] = Attributes.getInputAttribute(i).getNumNominalValues() - 1;
} else {
rangos[i][0] = Attributes.getInputAttribute(i).getMinAttribute();
rangos[i][1] = Attributes.getInputAttribute(i).getMaxAttribute();
}
}
rangos[this.getnVars() - 1][0] = Attributes.getOutputAttribute(0).getMinAttribute();
rangos[this.getnVars() - 1][1] = Attributes.getOutputAttribute(0).getMaxAttribute();
return rangos;
}
/**
* Generates output file for a clasification problem @ param Foutput Name of the output file @
* param real Vector of outputs instances @ param obtained Vector of net outputs
*
* @return Nothing
*/
public void generateResultsClasification(String Foutput, int[] real, int[] obtained) {
// Output file, classification problems
FileOutputStream out;
PrintStream p;
Attribute at = Attributes.getOutputAttribute(0);
// Check whether the output value is nominal or integer
boolean isNominal = (at.getType() == at.NOMINAL);
try {
out = new FileOutputStream(Foutput);
p = new PrintStream(out);
CopyHeaderTest(p);
// System.out.println("Longitudes "+real.length+" "+obtained.length);
for (int i = 0; i < real.length; i++) {
// Write the label associated to the class number,
// when the output is nominal
if (isNominal)
p.print(at.getNominalValue(real[i]) + " " + at.getNominalValue(obtained[i]) + "\n");
else p.print(real[i] + " " + obtained[i] + "\n");
}
p.close();
} catch (Exception e) {
System.err.println("Error building file for results: " + Foutput);
}
}
/**
* The main method of the class
*
* @param script Name of the configuration script
*/
public KNN(Instance[] trainI, Instance[] testI) {
// set parameters
trainInst = trainI;
testInst = testI;
trainData = new double[trainInst.length][Parameters.numAttributes];
for (int i = 0; i < trainInst.length; ++i)
System.arraycopy(
trainInst[i].getAllInputValues(), 0, trainData[i], 0, Parameters.numAttributes);
testData = new double[testInst.length][Parameters.numAttributes];
for (int i = 0; i < testInst.length; ++i)
System.arraycopy(
testInst[i].getAllInputValues(), 0, testData[i], 0, Parameters.numAttributes);
k = Parameters.numNeighbors;
if (Parameters.distanceType.equalsIgnoreCase("euclidean")) distanceType = EUCLIDEAN;
else if (Parameters.distanceType.equalsIgnoreCase("manhattan")) distanceType = MANHATTAN;
else if (Parameters.distanceType.equalsIgnoreCase("hvdm")) distanceType = HVDM;
// read Data Files -----------------------
inputAtt = Attributes.getInputNumAttributes();
inputs = Attributes.getInputAttributes();
output = Attributes.getOutputAttribute(0);
// Normalize the datasets
normalizeTrain();
normalizeTest();
// Get the number of classes
nClasses = Attributes.getOutputAttribute(0).getNumNominalValues();
// And the number of instances on each class
nInstances = new int[nClasses];
Arrays.fill(nInstances, 0);
for (int i = 0; i < trainOutput.length; i++) nInstances[trainOutput[i]]++;
// Initialization of auxiliary structures
if (distanceType == HVDM) {
stdDev = new double[inputAtt];
calculateHVDM();
}
}
private void normalizarTest() {
int i, j, cont = 0, k;
Instance temp;
boolean hecho;
double caja[];
StringTokenizer tokens;
boolean nulls[];
/* Check if dataset corresponding with a classification problem */
if (Attributes.getOutputNumAttributes() < 1) {
System.err.println(
"This dataset haven´t outputs, so it not corresponding to a classification problem.");
System.exit(-1);
} else if (Attributes.getOutputNumAttributes() > 1) {
System.err.println("This dataset have more of one output.");
System.exit(-1);
}
if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
System.err.println(
"This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
System.exit(-1);
}
datosTest = new double[test.getNumInstances()][Attributes.getInputNumAttributes()];
clasesTest = new int[test.getNumInstances()];
caja = new double[1];
for (i = 0; i < test.getNumInstances(); i++) {
temp = test.getInstance(i);
nulls = temp.getInputMissingValues();
datosTest[i] = test.getInstance(i).getAllInputValues();
for (j = 0; j < nulls.length; j++) if (nulls[j]) datosTest[i][j] = 0.0;
caja = test.getInstance(i).getAllOutputValues();
clasesTest[i] = (int) caja[0];
for (k = 0; k < datosTest[i].length; k++) {
if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
datosTest[i][k] /= Attributes.getInputAttribute(k).getNominalValuesList().size() - 1;
} else {
datosTest[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
datosTest[i][k] /=
Attributes.getInputAttribute(k).getMaxAttribute()
- Attributes.getInputAttribute(k).getMinAttribute();
}
}
}
}
private void normalizarReferencia() throws CheckException {
int i, j, cont = 0, k;
Instance temp;
boolean hecho;
double caja[];
StringTokenizer tokens;
boolean nulls[];
/*Check if dataset corresponding with a classification problem*/
if (Attributes.getOutputNumAttributes() < 1) {
throw new CheckException(
"This dataset haven´t outputs, so it not corresponding to a classification problem.");
} else if (Attributes.getOutputNumAttributes() > 1) {
throw new CheckException("This dataset have more of one output.");
}
if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
throw new CheckException(
"This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
}
datosReferencia = new double[referencia.getNumInstances()][Attributes.getInputNumAttributes()];
clasesReferencia = new int[referencia.getNumInstances()];
caja = new double[1];
/*Get the number of instances that have a null value*/
for (i = 0; i < referencia.getNumInstances(); i++) {
temp = referencia.getInstance(i);
nulls = temp.getInputMissingValues();
datosReferencia[i] = referencia.getInstance(i).getAllInputValues();
for (j = 0; j < nulls.length; j++) if (nulls[j]) datosReferencia[i][j] = 0.0;
caja = referencia.getInstance(i).getAllOutputValues();
clasesReferencia[i] = (int) caja[0];
for (k = 0; k < datosReferencia[i].length; k++) {
if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
datosReferencia[i][k] /=
Attributes.getInputAttribute(k).getNominalValuesList().size() - 1;
} else {
datosReferencia[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
datosReferencia[i][k] /=
Attributes.getInputAttribute(k).getMaxAttribute()
- Attributes.getInputAttribute(k).getMinAttribute();
}
}
}
}
public void ejecutar() {
int i, j, l, m;
double alfai;
int nClases;
int claseObt;
boolean marcas[];
boolean notFound;
int init;
int clasSel[];
int baraje[];
int pos, tmp;
String instanciasIN[];
String instanciasOUT[];
long tiempo = System.currentTimeMillis();
/* Getting the number of differents classes */
nClases = 0;
for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];
nClases++;
/* Shuffle the train set */
baraje = new int[datosTrain.length];
Randomize.setSeed(semilla);
for (i = 0; i < datosTrain.length; i++) baraje[i] = i;
for (i = 0; i < datosTrain.length; i++) {
pos = Randomize.Randint(i, datosTrain.length - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
/*
* Inicialization of the flagged instaces vector for a posterior
* elimination
*/
marcas = new boolean[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) marcas[i] = false;
if (datosTrain.length > 0) {
// marcas[baraje[0]] = true; //the first instance is included always
nSel = n_p;
if (nSel < nClases) nSel = nClases;
} else {
System.err.println("Input dataset is empty");
nSel = 0;
}
clasSel = new int[nClases];
System.out.print("Selecting initial neurons... ");
// at least, there must be 1 neuron of each class at the beginning
init = nClases;
for (i = 0; i < nClases && i < datosTrain.length; i++) {
pos = Randomize.Randint(0, datosTrain.length - 1);
tmp = 0;
while ((clasesTrain[pos] != i || marcas[pos]) && tmp < datosTrain.length) {
pos = (pos + 1) % datosTrain.length;
tmp++;
}
if (tmp < datosTrain.length) marcas[pos] = true;
else init--;
// clasSel[i] = i;
}
for (i = init; i < Math.min(nSel, datosTrain.length); i++) {
tmp = 0;
pos = Randomize.Randint(0, datosTrain.length - 1);
while (marcas[pos]) {
pos = (pos + 1) % datosTrain.length;
tmp++;
}
// if(i<nClases){
// notFound = true;
// do{
// for(j=i-1;j>=0 && notFound;j--){
// if(clasSel[j] == clasesTrain[pos])
// notFound = false;
// }
// if(!notFound)
// pos = Randomize.Randint (0, datosTrain.length-1);
// }while(!notFound);
// }
// clasSel[i] = clasesTrain[pos];
marcas[pos] = true;
init++;
}
nSel = init;
System.out.println("Initial neurons selected: " + nSel);
/* Building of the S set from the flags */
conjS = new double[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (m = 0, l = 0; m < datosTrain.length; m++) {
if (marcas[m]) { // the instance must be copied to the solution
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
alfai = alpha;
boolean change = true;
/* Body of the LVQ algorithm. */
// Train the network
for (int it = 0; it < T && change; it++) {
change = false;
alpha = alfai;
for (i = 1; i < datosTrain.length; i++) {
// search for the nearest neuron to training instance
pos = NN(nSel, conjS, datosTrain[baraje[i]]);
// nearest neuron labels correctly the class of training
// instance?
if (clasesS[pos] != clasesTrain[baraje[i]]) { // NO - repel
// the neuron
for (j = 0; j < conjS[pos].length; j++) {
conjS[pos][j] = conjS[pos][j] - alpha * (datosTrain[baraje[i]][j] - conjS[pos][j]);
}
change = true;
} else { // YES - migrate the neuron towards the input vector
for (j = 0; j < conjS[pos].length; j++) {
conjS[pos][j] = conjS[pos][j] + alpha * (datosTrain[baraje[i]][j] - conjS[pos][j]);
}
}
alpha = nu * alpha;
}
// Shuffle again the training partition
baraje = new int[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) baraje[i] = i;
for (i = 0; i < datosTrain.length; i++) {
pos = Randomize.Randint(i, datosTrain.length - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
}
System.out.println(
"LVQ " + relation + " " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
// Classify the train data set
instanciasIN = new String[datosReferencia.length];
instanciasOUT = new String[datosReferencia.length];
for (i = 0; i < datosReferencia.length; i++) {
/* Classify the instance selected in this iteration */
Attribute a = Attributes.getOutputAttribute(0);
int tipo = a.getType();
claseObt = KNN.evaluacionKNN2(1, conjS, clasesS, datosReferencia[i], nClases);
if (tipo != Attribute.NOMINAL) {
instanciasIN[i] = new String(String.valueOf(clasesReferencia[i]));
instanciasOUT[i] = new String(String.valueOf(claseObt));
} else {
instanciasIN[i] = new String(a.getNominalValue(clasesReferencia[i]));
instanciasOUT[i] = new String(a.getNominalValue(claseObt));
}
}
escribeSalida(
ficheroSalida[0], instanciasIN, instanciasOUT, entradas, salida, nEntradas, relation);
// Classify the test data set
normalizarTest();
instanciasIN = new String[datosTest.length];
instanciasOUT = new String[datosTest.length];
for (i = 0; i < datosTest.length; i++) {
/* Classify the instance selected in this iteration */
Attribute a = Attributes.getOutputAttribute(0);
int tipo = a.getType();
claseObt = KNN.evaluacionKNN2(1, conjS, clasesS, datosTest[i], nClases);
if (tipo != Attribute.NOMINAL) {
instanciasIN[i] = new String(String.valueOf(clasesTest[i]));
instanciasOUT[i] = new String(String.valueOf(claseObt));
} else {
instanciasIN[i] = new String(a.getNominalValue(clasesTest[i]));
instanciasOUT[i] = new String(a.getNominalValue(claseObt));
}
}
escribeSalida(
ficheroSalida[1], instanciasIN, instanciasOUT, entradas, salida, nEntradas, relation);
// Print the network to a file
printNetworkToFile(ficheroSalida[2], referencia.getHeader());
}
/**
* This function builds the data matrix for reference data and normalizes inputs values
*
* @throws keel.Algorithms.Preprocess.Basic.CheckException Can not be normalized
*/
protected void normalizar() throws CheckException {
int i, j, k;
Instance temp;
double caja[];
StringTokenizer tokens;
boolean nulls[];
/*Check if dataset corresponding with a classification problem*/
if (Attributes.getOutputNumAttributes() < 1) {
throw new CheckException(
"This dataset haven?t outputs, so it not corresponding to a classification problem.");
} else if (Attributes.getOutputNumAttributes() > 1) {
throw new CheckException("This dataset have more of one output.");
}
if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
throw new CheckException(
"This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
}
entradas = Attributes.getInputAttributes();
salida = Attributes.getOutputAttribute(0);
nEntradas = Attributes.getInputNumAttributes();
tokens = new StringTokenizer(training.getHeader(), " \n\r");
tokens.nextToken();
relation = tokens.nextToken();
datosTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];
clasesTrain = new int[training.getNumInstances()];
caja = new double[1];
nulosTrain = new boolean[training.getNumInstances()][Attributes.getInputNumAttributes()];
nominalTrain = new int[training.getNumInstances()][Attributes.getInputNumAttributes()];
realTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];
for (i = 0; i < training.getNumInstances(); i++) {
temp = training.getInstance(i);
nulls = temp.getInputMissingValues();
datosTrain[i] = training.getInstance(i).getAllInputValues();
for (j = 0; j < nulls.length; j++)
if (nulls[j]) {
datosTrain[i][j] = 0.0;
nulosTrain[i][j] = true;
}
caja = training.getInstance(i).getAllOutputValues();
clasesTrain[i] = (int) caja[0];
for (k = 0; k < datosTrain[i].length; k++) {
if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
nominalTrain[i][k] = (int) datosTrain[i][k];
datosTrain[i][k] /= Attributes.getInputAttribute(k).getNominalValuesList().size() - 1;
} else {
realTrain[i][k] = datosTrain[i][k];
datosTrain[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
datosTrain[i][k] /=
Attributes.getInputAttribute(k).getMaxAttribute()
- Attributes.getInputAttribute(k).getMinAttribute();
if (Double.isNaN(datosTrain[i][k])) {
datosTrain[i][k] = realTrain[i][k];
}
}
}
}
datosTest = new double[test.getNumInstances()][Attributes.getInputNumAttributes()];
clasesTest = new int[test.getNumInstances()];
caja = new double[1];
for (i = 0; i < test.getNumInstances(); i++) {
temp = test.getInstance(i);
nulls = temp.getInputMissingValues();
datosTest[i] = test.getInstance(i).getAllInputValues();
for (j = 0; j < nulls.length; j++)
if (nulls[j]) {
datosTest[i][j] = 0.0;
}
caja = test.getInstance(i).getAllOutputValues();
clasesTest[i] = (int) caja[0];
}
} // end-method
public String getOutputValue(int intValue) {
return Attributes.getOutputAttribute(0).getNominalValue(intValue);
}
/** Function to stores header of a data file. */
private void readHeader() {
String attributeName;
Vector attributeValues;
int i;
name = Attributes.getRelationName();
// Create vectors to hold information temporarily.
attributes = new Vector();
Attribute at;
// store attribute inputs and of the header
for (int j = 0; j < Attributes.getInputNumAttributes(); j++) {
at = Attributes.getInputAttribute(j);
attributeName = at.getName();
// check if it is real
if (at.getType() == 2) {
float min = (float) at.getMinAttribute();
float max = (float) at.getMinAttribute();
attributes.addElement(new MyAttribute(attributeName, j));
MyAttribute att = (MyAttribute) attributes.elementAt(j);
att.setRange(min, max);
att.activate();
} else {
if (at.getType() == 1) // check if it is integer
{
int min = (int) at.getMinAttribute();
int max = (int) at.getMinAttribute();
attributes.addElement(new MyAttribute(attributeName, j));
MyAttribute att = (MyAttribute) attributes.elementAt(j);
att.setRange(min, max);
att.activate();
} else // it is nominal
{
attributeValues = new Vector();
for (int k = 0; k < at.getNumNominalValues(); k++) {
attributeValues.addElement(at.getNominalValue(k));
}
attributes.addElement(new MyAttribute(attributeName, attributeValues, j));
MyAttribute att = (MyAttribute) attributes.elementAt(j);
att.activate();
}
}
} // for
// store outputs of the header
at = Attributes.getOutputAttribute(0);
attributeName = at.getName();
int j = Attributes.getNumAttributes() - 1;
// check if it is real
if (at.getType() == 2) {
float min = (float) at.getMinAttribute();
float max = (float) at.getMinAttribute();
attributes.addElement(new MyAttribute(attributeName, j));
MyAttribute att = (MyAttribute) attributes.elementAt(j);
att.setRange(min, max);
att.activate();
} else {
if (at.getType() == 1) // check if it is integer
{
int min = (int) at.getMinAttribute();
int max = (int) at.getMinAttribute();
attributes.addElement(new MyAttribute(attributeName, j));
MyAttribute att = (MyAttribute) attributes.elementAt(j);
att.setRange(min, max);
att.activate();
} else // it is nominal
{
attributeValues = new Vector();
for (int k = 0; k < at.getNumNominalValues(); k++) {
attributeValues.addElement(at.getNominalValue(k));
}
attributes.addElement(new MyAttribute(attributeName, attributeValues, j));
MyAttribute att = (MyAttribute) attributes.elementAt(j);
att.activate();
}
}
// set the index of the output class
classIndex = Attributes.getNumAttributes() - 1;
}
/**
* It reads the whole input data-set and it stores each example and its associated output value in
* local arrays to ease their use.
*
* @param datasetFile String name of the file containing the dataset
* @param train boolean It must have the value "true" if we are reading the training data-set
* @throws IOException If there ocurs any problem with the reading of the data-set
*/
public void readClassificationSet(String datasetFile, boolean train) throws IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(datasetFile, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVars = nInputs + Attributes.getOutputNumAttributes();
// outputIntegerheck that there is only one output variable
if (Attributes.getOutputNumAttributes() > 1) {
System.out.println("This algorithm can not process MIMO datasets");
System.out.println("All outputs but the first one will be removed");
System.exit(1);
}
boolean noOutputs = false;
if (Attributes.getOutputNumAttributes() < 1) {
System.out.println("This algorithm can not process datasets without outputs");
System.out.println("Zero-valued output generated");
noOutputs = true;
System.exit(1);
}
// Initialice and fill our own tables
X = new double[nData][nInputs];
Nominal = new String[nData][nVars];
missing = new boolean[nData][nVars];
outputInteger = new int[nData];
outputReal = new double[nData];
output = new String[nData];
// Maximum and minimum of inputs
emax = new double[nInputs];
emin = new double[nInputs];
for (int i = 0; i < nInputs; i++) {
emax[i] = Attributes.getAttribute(i).getMaxAttribute();
emin[i] = Attributes.getAttribute(i).getMinAttribute();
}
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < nData; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j); // inst.getInputRealValues(j);
Nominal[i][j] = "" + IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (missing[i][j]) {
X[i][j] = emin[j] - 1;
}
}
if (noOutputs) {
outputInteger[i] = 0;
output[i] = "";
} else {
outputInteger[i] = (int) IS.getOutputNumericValue(i, 0);
output[i] = IS.getOutputNominalValue(i, 0);
Nominal[i][nInputs] = output[i];
missing[i][nInputs] = false;
}
if (outputInteger[i] > nClasses) {
nClasses = outputInteger[i];
}
}
nClasses++;
System.out.println("Number of classes=" + nClasses);
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
computeStatistics();
this.computeInstancesPerClass();
Attribute[] atts = Attributes.getAttributes();
for (indexClass = 0;
(indexClass < atts.length)
&& (!(Attributes.getOutputAttribute(0)
.getName()
.equalsIgnoreCase(atts[indexClass].getName())));
indexClass++) {;
}
}