/** Creates the total selector's set for get all the possible rules */
private Complejo hazSelectores(Dataset train) {
Complejo almacenSelectores;
int nClases = train.getnclases();
almacenSelectores =
new Complejo(nClases); // Aqui voy a almacenar los selectores (numVariable,operador,valor)
Attribute[] atributos = null;
int num_atributos, type;
Vector nominalValues;
atributos = Attributes.getAttributes();
num_atributos = Attributes.getNumAttributes();
Selector s;
for (int i = 0; i < train.getnentradas(); i++) {
type = atributos[i].getType();
switch (type) {
case 0: // NOMINAL
nominalValues = atributos[i].getNominalValuesList();
// System.out.print("{");
for (int j = 0; j < nominalValues.size(); j++) {
// System.out.print ((String)nominalValues.elementAt(j)+" ");
s = new Selector(i, 0, (String) nominalValues.elementAt(j), true); // [atr,op,valor]
// incluimos tb los valores en double para facilitar algunas funciones
s.setValor((double) j);
almacenSelectores.addSelector(s);
// s.print();
}
// System.out.println("}");
break;
}
// System.out.println(num_atributos);
}
return almacenSelectores;
}
/**
* 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();
}
}
/** Method interface for Automatic Branch and Bound */
public void ejecutar() {
String resultado;
int i, numFeatures;
Date d;
d = new Date();
resultado =
"RESULTS generated at "
+ String.valueOf((Date) d)
+ " \n--------------------------------------------------\n";
resultado += "Algorithm Name: " + params.nameAlgorithm + "\n";
/* call of ABB algorithm */
runABB();
resultado += "\nPARTITION Filename: " + params.trainFileNameInput + "\n---------------\n\n";
resultado += "Features selected: \n";
for (i = numFeatures = 0; i < features.length; i++)
if (features[i] == true) {
resultado += Attributes.getInputAttribute(i).getName() + " - ";
numFeatures++;
}
resultado +=
"\n\n"
+ String.valueOf(numFeatures)
+ " features of "
+ Attributes.getInputNumAttributes()
+ "\n\n";
resultado +=
"Error in test (using train for prediction): "
+ String.valueOf(data.validacionCruzada(features))
+ "\n";
resultado +=
"Error in test (using test for prediction): "
+ String.valueOf(data.LVOTest(features))
+ "\n";
resultado += "---------------\n";
System.out.println("Experiment completed successfully");
/* creates the new training and test datasets only with the selected features */
Files.writeFile(params.extraFileNameOutput, resultado);
data.generarFicherosSalida(params.trainFileNameOutput, params.testFileNameOutput, features);
}
/**
* Fill TableVar with the characteristics of the variables and creates characteristics and
* intervals for the fuzzy sets
*
* @param size Number of variables of the dataset
*/
public void Load(int size) {
num_vars = size; // Stores the number of variables of the dataset
var = new TypeVar[num_vars]; // Creates space for the structure
// For each variable of the dataset
for (int i = 0; i < num_vars; i++) {
var[i] = new TypeVar(); // Creates space for the variable chars
var[i].setName(Attributes.getInputAttribute(i).getName());
if (Attributes.getInputAttribute(i).getType() == Attribute.NOMINAL) {
var[i].setType('e');
var[i].setContinuous(false);
var[i].initValues(Attributes.getInputAttribute(i).getNominalValuesList());
var[i].setMin(
0); // Enumerated values are translated into values from 0 to number of elements - 1
var[i].setMax(Attributes.getInputAttribute(i).getNumNominalValues() - 1);
var[i].setNLabels(Attributes.getInputAttribute(i).getNumNominalValues());
// Update max number of values for discrete vars
if (var[i].getNLabels() > MaxValores) MaxValores = var[i].getNLabels();
} else if (Attributes.getInputAttribute(i).getType() == Attribute.REAL) {
// Real: Continuous type
var[i].setType('r');
var[i].setContinuous(true);
var[i].setMin((float) Attributes.getInputAttribute(i).getMinAttribute());
var[i].setMax((float) Attributes.getInputAttribute(i).getMaxAttribute());
var[i].setNLabels(n_etiq);
// Update the max number of labels for cont variables and number of values
if (var[i].getNLabels() > MaxEtiquetas) MaxEtiquetas = var[i].getNLabels();
if (var[i].getNLabels() > MaxValores) MaxValores = var[i].getNLabels();
} else {
// Integer: Continuous type
var[i].setType('i');
var[i].setContinuous(true);
var[i].setMin((float) Attributes.getInputAttribute(i).getMinAttribute());
var[i].setMax((float) Attributes.getInputAttribute(i).getMaxAttribute());
var[i].setNLabels(n_etiq);
// Update the max number of labels for cont variables and number of values
if (var[i].getNLabels() > MaxEtiquetas) MaxEtiquetas = var[i].getNLabels();
if (var[i].getNLabels() > MaxValores) MaxValores = var[i].getNLabels();
}
}
// Creates "Fuzzy" characteristics and intervals
BaseDatos = new Fuzzy[num_vars][MaxValores];
for (int x = 0; x < num_vars; x++)
for (int y = 0; y < MaxValores; y++) BaseDatos[x][y] = new Fuzzy();
}
/**
* Creates a boolean array with all values to true
*
* @return returns a boolean vector with all values to true
*/
private boolean[] startSolution() {
boolean fv[];
fv = new boolean[Attributes.getInputNumAttributes()];
for (int i = 0; i < fv.length; i++) fv[i] = true;
return fv;
}
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();
}
}
}
}
/**
* Computes the distance between two instances (without previous normalization)
*
* @param i First instance
* @param j Second instance
* @return The Euclidean distance between i and j
*/
private double distance(Instance i, Instance j) {
double dist = 0;
int in = 0;
int out = 0;
for (int l = 0; l < nvariables; l++) {
Attribute a = Attributes.getAttribute(l);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !i.getInputMissingValues(in)) {
// real value, apply euclidean distance
dist +=
(i.getInputRealValues(in) - j.getInputRealValues(in))
* (i.getInputRealValues(in) - j.getInputRealValues(in));
} else {
if (!i.getInputMissingValues(in)
&& i.getInputNominalValues(in) != j.getInputNominalValues(in)) dist += 1;
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !i.getOutputMissingValues(out)) {
dist +=
(i.getOutputRealValues(out) - j.getOutputRealValues(out))
* (i.getOutputRealValues(out) - j.getOutputRealValues(out));
} else {
if (!i.getOutputMissingValues(out)
&& i.getOutputNominalValues(out) != j.getOutputNominalValues(out)) dist += 1;
}
out++;
}
}
}
return dist;
}
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
/**
* 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);
}
}
