/**
* 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;
}
/** @param args the command line arguments */
public static void main(String[] args) {
ParserParameters.doParse(args[0]);
LogManager.initLogManager();
InstanceSet is = new InstanceSet();
try {
is.readSet(Parameters.trainInputFile, true);
} catch (Exception e) {
LogManager.printErr(e.toString());
System.exit(1);
}
checkDataset();
Discretizer dis;
String name = Parameters.algorithmName;
dis = new FayyadDiscretizer();
dis.buildCutPoints(is);
dis.applyDiscretization(Parameters.trainInputFile, Parameters.trainOutputFile);
dis.applyDiscretization(Parameters.testInputFile, Parameters.testOutputFile);
LogManager.closeLog();
}
/**
* 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;
// }
}
}
}
// 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);
}
}
/**
* 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);
}
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
double[] outputs;
double[] outputs2;
Instance neighbor;
double dist, mean;
int actual;
int[] N = new int[nneigh];
double[] Ndist = new double[nneigh];
boolean allNull;
svm_problem SVMp = null;
svm_parameter SVMparam = new svm_parameter();
svm_model svr = null;
svm_node SVMn[];
double[] outputsCandidate = null;
boolean same = true;
Vector instancesSelected = new Vector();
Vector instancesSelected2 = new Vector();
// SVM PARAMETERS
SVMparam.C = C;
SVMparam.cache_size = 10; // 10MB of cache
SVMparam.degree = degree;
SVMparam.eps = eps;
SVMparam.gamma = gamma;
SVMparam.nr_weight = 0;
SVMparam.nu = nu;
SVMparam.p = p;
SVMparam.shrinking = shrinking;
SVMparam.probability = 0;
if (kernelType.compareTo("LINEAR") == 0) {
SVMparam.kernel_type = svm_parameter.LINEAR;
} else if (kernelType.compareTo("POLY") == 0) {
SVMparam.kernel_type = svm_parameter.POLY;
} else if (kernelType.compareTo("RBF") == 0) {
SVMparam.kernel_type = svm_parameter.RBF;
} else if (kernelType.compareTo("SIGMOID") == 0) {
SVMparam.kernel_type = svm_parameter.SIGMOID;
}
SVMparam.svm_type = svm_parameter.EPSILON_SVR;
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][2]; // matrix with transformed data
mostCommon = new String[nvariables];
SVMp = new svm_problem();
SVMp.l = ndatos;
SVMp.y = new double[SVMp.l];
SVMp.x = new svm_node[SVMp.l][nentradas + 1];
for (int l = 0; l < SVMp.l; l++) {
for (int n = 0; n < Attributes.getInputNumAttributes() + 1; n++) {
SVMp.x[l][n] = new svm_node();
}
}
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
SVMp.y[i] = inst.getAllOutputValues()[0];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMp.x[i][n].index = n;
SVMp.x[i][n].value = inst.getAllInputValues()[n];
SVMp.y[i] = inst.getAllOutputValues()[0];
}
// end of instance
SVMp.x[i][nentradas].index = -1;
}
if (svm.svm_check_parameter(SVMp, SVMparam) != null) {
System.out.println("SVM parameter error in training:");
System.out.println(svm.svm_check_parameter(SVMp, SVMparam));
System.exit(-1);
}
// train the SVM
if (ndatos > 0) {
svr = svm.svm_train(SVMp, SVMparam);
}
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));
// the values used for regression
SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMn[n] = new svm_node();
SVMn[n].index = n;
SVMn[n].value = inst.getAllInputValues()[n];
}
SVMn[nentradas] = new svm_node();
SVMn[nentradas].index = -1;
// pedict the class
X[i][1] = new String(String.valueOf((svm.svm_predict(svr, SVMn))));
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_train_name);
/** ************************************************************************************ */
try {
// Load in memory a dataset that contains a classification
// problem
IS.readSet(input_test_name, false);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][2]; // matrix with transformed data
// data
mostCommon = new String[nvariables];
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));
SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
SVMn[n] = new svm_node();
SVMn[n].index = n;
SVMn[n].value = inst.getAllInputValues()[n];
}
SVMn[nentradas] = new svm_node();
SVMn[nentradas].index = -1;
// pedict the class
X[i][1] = new String(String.valueOf(svm.svm_predict(svr, SVMn)));
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
System.out.println("escribiendo test");
write_results(output_test_name);
}
/** 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);
}
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void 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
boolean[] isMissed =
new boolean[ndatos]; // vector which points out instances with missed data
try {
FileWriter file_write = new FileWriter(output_train_name);
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
// file_write.close();
PrintWriter pw = new PrintWriter(file_write);
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
if (!inst.existsAnyMissingValue()) {
inst.printAsOriginal(pw);
// file_write.write(inst.toString()); // DOES NOT WRITE BACK NON-DEF DIRECTION
// ATTRIBUTES!!!!
file_write.write("\n");
}
}
pw.close();
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
System.exit(-1);
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
// 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
IS.readSet(input_test_name, false);
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
boolean[] isMissed =
new boolean[ndatos]; // vector which points out instances with missed data
try {
FileWriter file_write = new FileWriter(output_test_name);
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
PrintWriter pw = new PrintWriter(file_write);
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
if (!inst.existsAnyMissingValue()) {
inst.printAsOriginal(pw);
file_write.write("\n");
}
}
pw.close();
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
System.exit(-1);
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
}
// write_results(); / since there ins't any data transformation, is not needed
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
double[] outputs;
double[] outputs2;
try {
FileWriter file_write = new FileWriter(output_train_name);
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
int in2 = 0;
int out2 = 0;
int lastMissing = -1;
boolean fin = false;
boolean stepNext = false;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
String[] row = null;
X = new Vector[ndatos]; // matrix with transformed data
for (int i = 0; i < ndatos; i++) X[i] = new Vector();
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
// now, search for missed data, and replace them with
// the most common value
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
row = new String[nvariables];
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.existsAnyMissingValue()) {
row[j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.existsAnyMissingValue()) row[j] = inst.getInputNominalValues(in);
else {
// missing data
outputs = inst.getAllOutputValues();
in2 = 0;
out2 = 0;
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in2)) {
row[attr] = new String(String.valueOf(inst.getInputRealValues(in2)));
} else {
if (!inst.getInputMissingValues(in2))
row[attr] = inst.getInputNominalValues(in2);
}
in2++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out2)) {
row[attr] = new String(String.valueOf(inst.getOutputRealValues(out2)));
} else {
if (!inst.getOutputMissingValues(out2))
row[attr] = inst.getOutputNominalValues(out2);
}
out2++;
}
}
}
// make frecuencies for each attribute
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
lastMissing = attr;
timesSeen[attr] = new FreqList();
for (int m = 0; m < ndatos; m++) {
Instance inst2 = IS.getInstance(m);
outputs2 = inst2.getAllOutputValues();
boolean sameClass = true;
// are they same concept instances??
for (int k = 0; k < nsalidas && sameClass; k++)
if (outputs[k] != outputs2[k]) sameClass = false;
if (sameClass) {
if (tipo != Attribute.NOMINAL && !inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(
new String(String.valueOf(inst2.getInputRealValues(attr))));
} else {
if (!inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(inst2.getInputNominalValues(attr));
}
}
}
}
}
}
for (int attr = 0; attr < nvariables; attr++) {
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
timesSeen[attr].reset();
}
}
fin = false;
stepNext = false;
while (!fin) {
in2 = 0;
for (int attr = 0; attr < nvariables && !fin; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(in2)) {
if (stepNext) {
timesSeen[attr].iterate();
stepNext = false;
}
if (timesSeen[attr].outOfBounds()) {
stepNext = true;
if (attr == lastMissing) fin = true;
timesSeen[attr].reset();
}
if (!fin)
row[attr] =
((ValueFreq) timesSeen[attr].getCurrent())
.getValue(); // replace missing data
}
in2++;
}
if (!fin) {
stepNext = true;
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
// X[i].addElement(row);
// row = (String[])row.clone();
}
}
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
row[j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) row[j] = inst.getOutputNominalValues(out);
else row[j] = new String("?");
}
out++;
}
}
}
if (!inst.existsAnyMissingValue()) {
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
e.printStackTrace();
System.exit(-1);
}
/** ************************************************************************************ */
// does a test file associated exist?
if (input_train_name.compareTo(input_test_name) != 0) {
try {
FileWriter file_write = new FileWriter(output_test_name);
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_test_name, false);
int in = 0;
int out = 0;
int in2 = 0;
int out2 = 0;
int lastMissing = -1;
boolean fin = false;
boolean stepNext = false;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
String[] row = null;
X = new Vector[ndatos]; // matrix with transformed data
for (int i = 0; i < ndatos; i++) X[i] = new Vector();
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
file_write.write(IS.getHeader());
// now, print the normalized data
file_write.write("@data\n");
// now, search for missed data, and replace them with
// the most common value
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
row = new String[nvariables];
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.existsAnyMissingValue()) {
row[j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.existsAnyMissingValue()) row[j] = inst.getInputNominalValues(in);
else {
// missing data
outputs = inst.getAllOutputValues();
in2 = 0;
out2 = 0;
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in2)) {
row[attr] = new String(String.valueOf(inst.getInputRealValues(in2)));
} else {
if (!inst.getInputMissingValues(in2))
row[attr] = inst.getInputNominalValues(in2);
}
in2++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out2)) {
row[attr] = new String(String.valueOf(inst.getOutputRealValues(out2)));
} else {
if (!inst.getOutputMissingValues(out2))
row[attr] = inst.getOutputNominalValues(out2);
}
out2++;
}
}
}
// make frecuencies for each attribute
for (int attr = 0; attr < nvariables; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
lastMissing = attr;
timesSeen[attr] = new FreqList();
for (int m = 0; m < ndatos; m++) {
Instance inst2 = IS.getInstance(m);
outputs2 = inst2.getAllOutputValues();
boolean sameClass = true;
// are they same concept instances??
for (int k = 0; k < nsalidas && sameClass; k++)
if (outputs[k] != outputs2[k]) sameClass = false;
if (sameClass) {
if (tipo != Attribute.NOMINAL && !inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(
new String(String.valueOf(inst2.getInputRealValues(attr))));
} else {
if (!inst2.getInputMissingValues(attr)) {
timesSeen[attr].AddElement(inst2.getInputNominalValues(attr));
}
}
}
}
}
}
for (int attr = 0; attr < nvariables; attr++) {
if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
timesSeen[attr].reset();
}
}
fin = false;
stepNext = false;
while (!fin) {
in2 = 0;
for (int attr = 0; attr < nvariables && !fin; attr++) {
Attribute b = Attributes.getAttribute(attr);
direccion = b.getDirectionAttribute();
tipo = b.getType();
if (direccion == Attribute.INPUT && inst.getInputMissingValues(in2)) {
if (stepNext) {
timesSeen[attr].iterate();
stepNext = false;
}
if (timesSeen[attr].outOfBounds()) {
stepNext = true;
if (attr == lastMissing) fin = true;
timesSeen[attr].reset();
}
if (!fin)
row[attr] =
((ValueFreq) timesSeen[attr].getCurrent())
.getValue(); // replace missing data
}
in2++;
}
if (!fin) {
stepNext = true;
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
// X[i].addElement(row);
// row = (String[])row.clone();
}
}
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
row[j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out))
row[j] = inst.getOutputNominalValues(out);
else row[j] = new String("?");
}
out++;
}
}
}
if (!inst.existsAnyMissingValue()) {
file_write.write(row[0]);
for (int y = 1; y < nvariables; y++) {
file_write.write("," + row[y]);
}
file_write.write("\n");
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
e.printStackTrace();
System.exit(-1);
}
}
}
