/**
* Process a dataset file for a clustering problem.
*
* @param nfexamples Name of the dataset file
* @param train The dataset file is for training or for test
* @throws java.io.IOException if there is any semantical, lexical or sintactical error in the
* input file.
*/
public void processClusterDataset(String nfexamples, boolean train) throws IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(nfexamples, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVariables = nInputs + Attributes.getOutputNumAttributes();
if (Attributes.getOutputNumAttributes() != 0) {
System.out.println("This algorithm can not process datasets with outputs");
System.out.println("All outputs will be removed");
}
// Initialize and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
// Maximum and minimum of inputs
iMaximum = new double[nInputs];
iMinimum = new double[nInputs];
// Maximum and minimum for output data
oMaximum = 0;
oMinimum = 0;
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > iMaximum[j] || i == 0) {
iMaximum[j] = X[i][j];
}
if (X[i][j] < iMinimum[j] || i == 0) {
iMinimum[j] = X[i][j];
}
}
}
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
/**
* Returns the fraction of correct instances of the instance's set for the rule 'regla'
*
* @param i Number of the rule
* @return Fraction of correct instances of the instance's set for the rule 'regla'
*/
private double getAccuracy(int i) {
Instance instancia;
double Accuracy;
num_cubiertas = 0;
num_correctas = 0;
for (int k = 0; k < instancias.getNumInstances(); k++) {
instancia = instancias.getInstance(k);
cubierta = regla.reglaCubreInstancia(instancia);
if (cubierta) {
num_cubiertas++;
clase = instancia.getOutputNominalValuesInt(0);
if (clase == i) num_correctas++;
}
}
Accuracy = (double) num_correctas / (double) num_cubiertas;
if (num_cubiertas == 0) Accuracy = 0;
return Accuracy;
}
/**
* Removes from the instance's set those instances that matches with the rule
*
* @param i Numebr of the rule
*/
private void removeInstancesCovered(int i) {
for (int k = 0; k < instancias.getNumInstances(); k++) {
instancia = instancias.getInstance(k);
/*System.out.print(k+" ");
instancia.print();
System.out.println();*/
cubierta = regla.reglaCubreInstancia(instancia);
if (cubierta) {
// System.out.println("CUBIERTA");
clase = instancia.getOutputNominalValuesInt(0);
// if(clase==i){
instancias.removeInstance(k);
instancia.print();
System.out.println();
k = k - 1;
// }
}
}
}
public LVQ(String ficheroScript) {
super(ficheroScript);
try {
referencia = new InstanceSet();
referencia.readSet(ficheroReferencia, false);
/*Normalize the data*/
normalizarReferencia();
} catch (Exception e) {
System.err.println(e);
System.exit(1);
}
}
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();
}
}
}
}
// Write data matrix X to disk, in KEEL format
private void write_results(String output) {
// File OutputFile = new File(output_train_name.substring(1, output_train_name.length()-1));
try {
FileWriter file_write = new FileWriter(output);
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
for (int i = 0; i < ndatos; i++) {
file_write.write(X[i][0]);
for (int j = 1; j < nvariables; j++) {
file_write.write("," + X[i][j]);
}
file_write.write("\n");
}
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
System.exit(-1);
}
}
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());
}
/**
* Constructor with all the attributes to initialize
*
* @param ficheroTrain Train file
* @param ficheroTest Test file
* @param fSalidaTr Out-put train file
* @param fSalidaTst Out-put test file
* @param fsalida Out-put file
* @param semilla seed
*/
public Prism(
String ficheroTrain,
String ficheroTest,
String fSalidaTr,
String fSalidaTst,
String fsalida,
long semilla) {
ficheroSalida = fsalida;
ficheroSalidaTr = fSalidaTr;
ficheroSalidaTst = fSalidaTst;
seed = semilla;
datosTrain = new ConjDatos(); // datosEval = new ConjDatos();
datosTest = new ConjDatos();
train = new Dataset();
test = new Dataset();
s = new Selector(0, 0, 0.);
conjunto_reglas = new ConjReglas();
try {
Randomize.setSeed(seed);
System.out.println("la semilla es " + seed);
train.leeConjunto(ficheroTrain, true);
test.leeConjunto(ficheroTest, false); //
if (train.hayAtributosContinuos() /*|| train.hayAtributosDiscretos()*/) {
System.err.println("\nPrism may not work properly with real or integer attributes.\n");
// System.exit(-1);
hayContinuos = true;
}
if (!hayContinuos) {
train.calculaMasComunes(); // eval.calculaMasComunes();
test.calculaMasComunes();
datosTrain =
creaConjunto(
train); // Leemos los datos de entrenamiento (todos seguidos como un
// String)//datosEval = creaConjunto(eval);
datosTest = creaConjunto(test);
valores = train.getX2(); // obtengo los valores nominales
clases = train.getC2();
clasitas = train.getC();
/*System.out.println(train.getndatos());
System.out.println(train.getnentradas());
for(int i=0;i<train.getndatos();i++){
for(int j=0;j<train.getnentradas();j++)
System.out.print(valores[i][j]);
System.out.print(clases[i]);System.out.println(clasitas[i]);}*/
// COMENZAMOS EL ALGORITMO PRISM
// FOR EACH CLASS C
clases = train.dameClases();
int unavez = 0, candidato;
for (int i = 0; i < train.getnclases(); i++) {
System.out.println("CLASE :" + clases[i] + "\n");
// initialize E to the instance set
/*Cuando haya que inicializar de nuevo el conjunto de instancias no es necesario insertar aquellas que se eliminaron, sino que nos va a bastar con inicializar otra vez el conjunto mediante el fichero de entrenamiento. Por eso hay un metodo para insertar una instancia*/
train.leeConjunto(ficheroTrain, false);
nombre_atributos = train.dameNombres();
instancias = train.getInstanceSet();
// While E contains instances in class C
while (train.hayInstanciasDeClaseC(i)) {
// Create a rule R with an empty left-hand side that predicts class C
regla = new Complejo(train.getnclases());
regla.setClase(i);
regla.adjuntaNombreAtributos(nombre_atributos);
// esto lo hacemos solo aqui pq luego vamos quitando selectores del almacen
almacen = hazSelectores(train);
almacen.adjuntaNombreAtributos(nombre_atributos);
do {
// FOR EACH ATTRIBUTE A NOT MENTIONED IN R, AND EACH VALUE V
accuracy_ant = -1.;
p = 0;
int seleccionados[] = new int[almacen.size()];
for (int jj = 0; jj < almacen.size(); jj++) seleccionados[jj] = 0;
System.out.println();
for (int j = 0; j < almacen.size(); j++) {
// tenemos que quitar el selector anterior
if (j > 0) regla.removeSelector(s);
s = almacen.getSelector(j);
// if(i==0)
s.print();
// CONSIDER ADDING THE CONDITION A=V TO THE LHS OF R
regla.addSelector(s);
accuracy = getAccuracy(i);
// if(i==0) {
System.out.println("correctas " + num_correctas + " cubiertas " + num_cubiertas);
System.out.println("Acurracy " + accuracy);
// }
if ((accuracy > accuracy_ant)
|| ((accuracy == accuracy_ant) && (num_correctas > p))) {
// if((accuracy==accuracy_ant) &&(num_correctas>p)){
// System.out.println("atn "+accuracy_ant);
// System.out.println("ahora "+accuracy);
accuracy_ant = accuracy;
seleccionado = j;
p = num_correctas;
// si se encuentra un superior hay que quitar
// todo lo q se hay ido almacenando en esta iteracion
for (int jj = 0; jj < almacen.size(); jj++) seleccionados[jj] = 0;
// }
} else {
if ((accuracy == accuracy_ant) && (num_correctas == p)) {
seleccionados[seleccionado] = 1;
seleccionados[j] = 1;
}
}
}
// seleccionamos uno de los seleccionados en el caso de empate
int contador = 0;
for (int jj = 0; jj < almacen.size(); jj++) {
if (seleccionados[jj] == 1) {
contador++;
System.out.println("OPCION " + jj);
}
}
if (contador > 0) {
candidato = Randomize.RandintClosed(1, contador);
contador = 0;
for (int jj = 0; jj < almacen.size(); jj++) {
if (seleccionados[jj] == 1) {
contador++;
if (contador == candidato) seleccionado = jj;
}
}
}
System.out.println("ELEGIDO " + seleccionado);
// antes hay que quitar el q metimos
regla.removeSelector(s);
s = almacen.getSelector(seleccionado);
s.print();
// ADD A=V TO R
regla.addSelector(s);
/*AHORA HAY QUE QUITAR DEL ALMACEN SE SELECTORES AQUELLOS QUE
HACEN REFERENCIA AL ATRIBUTO SELECCIONADO*/
// obtener el atributo del selector ganador
atributo = s.getAtributo();
// se borran todos los q tengan ese atributo
// System.out.println("ALMACEN");almacen.print();
almacen.removeSelectorAtributo(atributo);
reglaPerfecta = perfectRule(regla, train);
} while (!reglaPerfecta && (regla.size() < train.getnentradas()));
System.out.println("\n");
System.out.println("\nREGLA............................................");
regla.print();
System.out.println("\n");
/*necesitamos evaluar la regla para obtener la salida del metodo
para compararla con la salida esperada*/
evaluarComplejo(regla, datosTrain);
// INCLUIMOS ESTA REGLA YA PARA EL CONJUNTO FINAL DE REGLAS
conjunto_reglas.addRegla(regla);
// REMOVE THE INSTANCES COVERED BY R FROM E
// Instance instancia;
/*for(int k=0;k<instancias.getNumInstances();k++){
instancia=instancias.getInstance(k);
System.out.print(k+" ");
instancia.print();
System.out.println();
}*/
removeInstancesCovered(i);
for (int k = 0; k < instancias.getNumInstances(); k++) {
instancia = instancias.getInstance(k);
clase = instancia.getOutputNominalValuesInt(0);
if (clase == i) {
System.out.print(k + " ");
instancia.print();
System.out.println();
}
}
// instancias.print();
System.out.println("\n");
} // del while
} // del for de las clases
// EVALUAMOS LA CALIDAD DE LAS REGLAS
int[] clasesEval;
clasesEval = train.getC();
muestPorClaseEval = new int[train.getnclases()];
for (int j = 0; j < train.getnclases(); j++) {
muestPorClaseEval[j] = 0;
for (int i = 0; i < datosTrain.size(); i++) {
if (
/*valorClases[j]*/ j == clasesEval[i]) {
muestPorClaseEval[j]++;
}
}
}
conjunto_reglas.eliminaRepetidos(1);
evReg =
new EvaluaCalidadReglas(
conjunto_reglas,
datosTrain,
datosTest,
muestPorClaseEval,
muestPorClaseEval,
clases);
// GENERAMOS LA SALIDA
generaSalida();
System.out.println("la semilla es " + seed);
} // del if
} catch (IOException e) {
System.err.println("There was a problem while trying to read the dataset files:");
System.err.println("-> " + e);
// System.exit(0);
}
}
/**
* Returns the header of the data set with the attributes' information
*
* @return The header of the data set
*/
public String getHeader() {
return IS.getHeader();
}
/**
* Process a dataset file for a classification problem.
*
* @param nfejemplos Name of the dataset file
* @param train The dataset file is for training or for test
* @throws java.io.IOException if there is any semantical, lexical or sintactical error in the
* input file.
*/
public void processClassifierDataset(String nfejemplos, boolean train) throws IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(nfejemplos, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVariables = nInputs + Attributes.getOutputNumAttributes();
// Check that there is only one output variable and
// it is nominal
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");
}
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;
}
// Initialize and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
C = new int[nData];
// Maximum and minimum of inputs
iMaximum = new double[nInputs];
iMinimum = new double[nInputs];
// Maximum and minimum for output data
oMaximum = 0;
oMinimum = 0;
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > iMaximum[j] || i == 0) {
iMaximum[j] = X[i][j];
}
if (X[i][j] < iMinimum[j] || i == 0) {
iMinimum[j] = X[i][j];
}
}
if (noOutputs) {
C[i] = 0;
} else {
C[i] = (int) IS.getOutputNumericValue(i, 0);
}
if (C[i] > nClasses) {
nClasses = C[i];
}
}
nClasses++;
System.out.println("Number of classes=" + nClasses);
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
// declarations
double[] outputs;
double[] outputs2;
Instance neighbor;
double dist, mean;
int actual;
Randomize rnd = new Randomize();
Instance ex;
gCenter kmeans = null;
int iterations = 0;
double E;
double prevE;
int totalMissing = 0;
boolean allMissing = true;
rnd.setSeed(semilla);
// PROCESS
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][nvariables]; // matrix with transformed data
kmeans = new gCenter(K, ndatos, nvariables);
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
// first, we choose k 'means' randomly from all
// instances
totalMissing = 0;
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
if (inst.existsAnyMissingValue()) totalMissing++;
}
if (totalMissing == ndatos) allMissing = true;
else allMissing = false;
for (int numMeans = 0; numMeans < K; numMeans++) {
do {
actual = (int) (ndatos * rnd.Rand());
ex = IS.getInstance(actual);
} while (ex.existsAnyMissingValue() && !allMissing);
kmeans.copyCenter(ex, numMeans);
}
// now, iterate adjusting clusters' centers and
// instances to them
prevE = 0;
iterations = 0;
do {
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
kmeans.setClusterOf(inst, i);
}
// set new centers
kmeans.recalculateCenters(IS);
// compute RMSE
E = 0;
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
E += kmeans.distance(inst, kmeans.getClusterOf(i));
}
iterations++;
// System.out.println(iterations+"\t"+E);
if (Math.abs(prevE - E) == 0) iterations = maxIter;
else prevE = E;
} while (E > minError && iterations < maxIter);
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
out++;
}
}
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_train_name);
/** ************************************************************************************ */
// does a test file associated exist?
if (input_train_name.compareTo(input_test_name) != 0) {
try {
// Load in memory a dataset that contains a classification problem
IStest.readSet(input_test_name, false);
int in = 0;
int out = 0;
ndatos = IStest.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
for (int i = 0; i < ndatos; i++) {
Instance inst = IStest.getInstance(i);
in = 0;
out = 0;
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
out++;
}
}
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_test_name);
}
}
