/**
* Main method for ABB, that explores the search space by pruning nodes and checking their
* inconsistency ratio.
*/
private void runABB() {
boolean[] root = startSolution();
System.arraycopy(root, 0, features, 0, root.length);
abb(root);
/* checks if a subset satisfies the condition (more than 0 selected features) */
if (features == null) {
System.err.println("ERROR: It couldn't be possible to find any solution.");
System.exit(0);
}
}
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 generateModel() {
String salida = new String("");
double max_auc = 0;
ArrayList<String> solutions = this.getAllSolutions();
models = new ArrayList<Farchd>();
int nEjemplos = train.getnData();
if (this.instances == this.MAJ) {
nEjemplos = train.getMajority();
}
boolean[] variables = new boolean[train.getnInputs()];
boolean[] ejemplos = new boolean[nEjemplos];
this.weightsAUC = new double[solutions.size() / 2]; // Hay 2 soluciones FS e IS
for (int i = 0, j = 0; i < solutions.size(); i += 2, j++) {
int vars, ejs;
vars = ejs = 0;
variables = decode(solutions.get(i));
ejemplos = decode(solutions.get(i + 1));
for (int l = 0; l < variables.length; l++) {
// variables[j] = solution[j];
if (variables[l]) vars++;
}
for (int l = 0; l < ejemplos.length; l++) {
if (ejemplos[l]) ejs++;
}
try {
Farchd model = new Farchd(train, val, test, variables, ejemplos);
/** ******** */
// double fit = model.getAUCTr();
double auc_tr = model.execute(true);
double auc_tst = model.getAUCTst();
if (auc_tr > max_auc) {
max_auc = auc_tr;
indexBest = j;
}
this.weightsAUC[j] = auc_tr;
salida +=
"Solution[" + j + "]:\t" + vars + "\t" + ejs + "\t" + auc_tr + "\t" + auc_tst + "\n";
/** ******** */
models.add(model);
} catch (Exception e) {
System.err.println("Liada maxima al generar modelo ");
e.printStackTrace(System.err);
System.exit(-1);
}
}
System.out.print(salida);
Files.writeFile(header + "_AUC.txt", salida);
}
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);
}
}
/**
* Constructor of the Class Parametros
*
* @param nombreFileParametros is the pathname of input parameter file
*/
Parametros(String nombreFileParametros) {
try {
int i;
String fichero, linea, tok;
StringTokenizer lineasFile, tokens;
/* read the parameter file using Files class */
fichero = Files.readFile(nombreFileParametros);
fichero += "\n";
/* remove all \r characters. it is neccesary for a correst use in Windows and UNIX */
fichero = fichero.replace('\r', ' ');
/* extracts the differents tokens of the file */
lineasFile = new StringTokenizer(fichero, "\n");
i = 0;
while (lineasFile.hasMoreTokens()) {
linea = lineasFile.nextToken();
i++;
tokens = new StringTokenizer(linea, " ,\t");
if (tokens.hasMoreTokens()) {
tok = tokens.nextToken();
if (tok.equalsIgnoreCase("algorithm")) nameAlgorithm = getParamString(tokens);
else if (tok.equalsIgnoreCase("inputdata")) getInputFiles(tokens);
else if (tok.equalsIgnoreCase("outputdata")) getOutputFiles(tokens);
else if (tok.equalsIgnoreCase("seed")) seed = getParamLong(tokens);
else throw new java.io.IOException("Syntax error on line " + i + ": [" + tok + "]\n");
}
}
} catch (java.io.FileNotFoundException e) {
System.err.println(e + "Parameter file");
} catch (java.io.IOException e) {
System.err.println(e + "Aborting program");
System.exit(-1);
}
/** show the read parameter in the standard output */
String contents = "-- Parameters echo --- \n";
contents += "Algorithm name: " + nameAlgorithm + "\n";
contents += "Input Train File: " + trainFileNameInput + "\n";
contents += "Input Test File: " + testFileNameInput + "\n";
contents += "Output Train File: " + trainFileNameOutput + "\n";
contents += "Output Test File: " + testFileNameOutput + "\n";
System.out.println(contents);
}
/**
* Removes one feature at a time, starting from the furthest on the right
*
* @param featuresVector solution to generate its neighbor
* @param which number of the feature to remove starting from the right
* @return next neighbor of the given solution with one less feature
*/
private static boolean[] removeOne(boolean featuresVector[], int which) {
boolean[] fv = new boolean[featuresVector.length];
System.arraycopy(featuresVector, 0, fv, 0, fv.length);
boolean stop = false;
int count = 0;
for (int i = fv.length - 1; i >= 0 && !stop; i--) {
if (fv[i]) {
count++;
if (count == which) {
fv[i] = false;
stop = true;
}
}
}
return fv;
}
/** It launches the algorithm */
public void execute() {
if (this.somethingWrong) { // We do not execute the program
System.err.println("An error was found, either the data-set has missing values.");
System.err.println(
"Please remove the examples with missing data or apply a MV preprocessing.");
System.err.println("Aborting the program");
// We should not use the statement: System.exit(-1);
} else {
// We do here the algorithm's operations
int nClasses = train.getnClasses();
aprioriClassDistribution = new double[nClasses];
for (int i = 0; i < nClasses; i++) {
aprioriClassDistribution[i] = 1.0 * val.numberInstances(i) / val.size();
}
if (model) { // el modelo no esta generado en fichero previamente
NSGA2 search =
new NSGA2(
train, seed, populationSize, maxTrials, crossover, mutation, instances, fitness);
try {
search.execute();
} catch (Exception e) {
e.printStackTrace(System.err);
}
}
// Finally we should fill the training and test output files
this.generateModel();
double avgTr = this.doOutput(val, this.outputTr, false);
double aucTr = getAUC(val);
double avgTst = this.doOutput(test, this.outputTst, true);
double aucTst = getAUC(test);
System.out.print("AUC Train: " + aucTr);
System.out.println("; AvgAcc Train: " + avgTr);
System.out.print("AUC Test: " + aucTst);
System.out.println("; AvgAcc Test: " + avgTst);
totalTime = System.currentTimeMillis() - startTime;
System.out.println("Algorithm Finished: " + totalTime);
}
}
/** Recursive method for ABB */
private void abb(boolean feat[]) {
boolean[] child;
double measure;
threshold = data.measureIEP(feat);
for (int i = 0; i < cardinalidadCto(feat); i++) {
child = removeOne(feat, i);
measure = data.measureIEP(child);
if (legitimate(child) && measure < threshold) {
if (measure < data.measureIEP(features)) {
// we keep the best found in 'features'
System.arraycopy(child, 0, features, 0, child.length);
}
abb(child);
} else { // we prune this node
pruned.add(child);
}
}
}
// 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, o;
int nClases;
int claseObt;
boolean marcas[];
double conjS[][];
int clasesS[];
int eleS[], eleT[];
int bestAc, aciertos;
int temp[];
int pos, tmp;
long tiempo = System.currentTimeMillis();
/*Getting the number of different classes*/
nClases = 0;
for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];
nClases++;
/*Inicialization of the flagged instance vector of the S set*/
marcas = new boolean[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) marcas[i] = false;
/*Allocate memory for the random selection*/
m = (int) ((porcentaje * datosTrain.length) / 100.0);
eleS = new int[m];
eleT = new int[datosTrain.length - m];
temp = new int[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) temp[i] = i;
/** Random distribution of elements in each set */
Randomize.setSeed(semilla);
for (i = 0; i < eleS.length; i++) {
pos = Randomize.Randint(i, datosTrain.length - 1);
tmp = temp[i];
temp[i] = temp[pos];
temp[pos] = tmp;
eleS[i] = temp[i];
}
for (i = 0; i < eleT.length; i++) {
pos = Randomize.Randint(m + i, datosTrain.length - 1);
tmp = temp[m + i];
temp[m + i] = temp[pos];
temp[pos] = tmp;
eleT[i] = temp[m + i];
}
for (i = 0; i < eleS.length; i++) marcas[eleS[i]] = true;
/*Building of the S set from the flags*/
conjS = new double[m][datosTrain[0].length];
clasesS = new int[m];
for (o = 0, l = 0; o < datosTrain.length; o++) {
if (marcas[o]) { // the instance will be evaluated
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
/*Evaluation of the S set*/
bestAc = 0;
for (i = 0; i < datosTrain.length; i++) {
claseObt = KNN.evaluacionKNN2(k, conjS, clasesS, datosTrain[i], nClases);
if (claseObt == clasesTrain[i]) // correct clasification
bestAc++;
}
/*Body of the ENNRS algorithm. Change the S set in each iteration for instances
of the T set until get a complete sustitution*/
for (i = 0; i < n; i++) {
/*Preparation the set to interchange*/
for (j = 0; j < eleS.length; j++) {
pos = Randomize.Randint(j, eleT.length - 1);
tmp = eleT[j];
eleT[j] = eleT[pos];
eleT[pos] = tmp;
}
/*Interchange of instances*/
for (j = 0; j < eleS.length; j++) {
tmp = eleS[j];
eleS[j] = eleT[j];
eleT[j] = tmp;
marcas[eleS[j]] = true;
marcas[eleT[j]] = false;
}
/*Building of the S set from the flags*/
for (o = 0, l = 0; o < datosTrain.length; o++) {
if (marcas[o]) { // the instance will evaluate
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
/*Evaluation of the S set*/
aciertos = 0;
for (j = 0; j < datosTrain.length; j++) {
claseObt = KNN.evaluacionKNN2(k, conjS, clasesS, datosTrain[j], nClases);
if (claseObt == clasesTrain[j]) // correct clasification
aciertos++;
}
if (aciertos > bestAc) { // keep S
bestAc = aciertos;
} else { // undo changes
for (j = 0; j < eleS.length; j++) {
tmp = eleS[j];
eleS[j] = eleT[j];
eleT[j] = tmp;
marcas[eleS[j]] = true;
marcas[eleT[j]] = false;
}
}
}
/*Building of the S set from the flags*/
/*Building of the S set from the flags*/
for (o = 0, l = 0; o < datosTrain.length; o++) {
if (marcas[o]) { // the instance will evaluate
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
System.out.println(
"ENNRS " + relation + " " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
// COn conjS me vale.
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
// Working on training
for (i = 0; i < datosTrain.length; i++) {
trainRealClass[i][0] = clasesTrain[i];
trainPrediction[i][0] = KNN.evaluate(datosTrain[i], conjS, nClases, clasesS, this.k);
}
KNN.writeOutput(ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
// Working on test
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
// Check time
for (i = 0; i < realClass.length; i++) {
realClass[i][0] = clasesTest[i];
prediction[i][0] = KNN.evaluate(datosTest[i], conjS, nClases, clasesS, this.k);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}
/**
* It reads the data from the input files (training, validation and test) and parse all the
* parameters from the parameters array.
*
* @param parameters parseParameters It contains the input files, output files and parameters
*/
public Wrapper(parseParameters parameters) {
this.startTime = System.currentTimeMillis();
this.train = new myDataset();
this.val = new myDataset();
this.test = new myDataset();
try {
System.out.println("\nReading the training set: " + parameters.getTrainingInputFile());
this.train.readClassificationSet(parameters.getTrainingInputFile(), true);
System.out.println("\nReading the validation set: " + parameters.getValidationInputFile());
this.val.readClassificationSet(parameters.getValidationInputFile(), false);
System.out.println("\nReading the test set: " + parameters.getTestInputFile());
this.test.readClassificationSet(parameters.getTestInputFile(), false);
} catch (IOException e) {
System.err.println("There was a problem while reading the input data-sets: " + e);
this.somethingWrong = true;
}
// We may check if there are some numerical attributes, because our algorithm may not handle
// them:
// somethingWrong = somethingWrong || train.hasNumericalAttributes();
this.somethingWrong = this.somethingWrong || this.train.hasMissingAttributes();
this.outputTr = parameters.getTrainingOutputFile();
this.outputTst = parameters.getTestOutputFile();
this.fileRB = parameters.getOutputFile(0);
this.data = parameters.getTrainingInputFile();
// Now we parse the parameters
seed = Long.parseLong(parameters.getParameter(0));
this.populationSize = Integer.parseInt(parameters.getParameter(1));
this.maxTrials = Integer.parseInt(parameters.getParameter(2));
if (this.populationSize % 2 > 0) this.populationSize++;
this.crossover = Double.parseDouble(parameters.getParameter(3)); // crossover probability
this.mutation = Double.parseDouble(parameters.getParameter(4));
String aux2 = parameters.getParameter(5);
this.fitness = this.aucVal;
if (aux2.equalsIgnoreCase("AUC_TR")) {
fitness = this.aucTrain;
} else if (aux2.equalsIgnoreCase("GM_VAL")) {
fitness = this.gmVal;
}
aux2 = parameters.getParameter(6);
this.instances = this.ALL;
if (aux2.equalsIgnoreCase("MAJ")) {
instances = this.MAJ;
}
aux2 = parameters.getParameter(7);
this.ensemble = this.NONE;
if (aux2.equalsIgnoreCase("WV")) {
ensemble = this.WV;
} else if (aux2.equalsIgnoreCase("WTA")) {
ensemble = this.WTA;
} else if (aux2.equalsIgnoreCase("VOTE")) {
ensemble = this.VOTE;
}
// ensemble = aux2.equalsIgnoreCase("true");
aux2 = parameters.getParameter(8);
model = aux2.equalsIgnoreCase("true");
aux2 = parameters.getParameter(9);
this.weighting = aux2.equalsIgnoreCase("true");
header = parameters.getTestInputFile();
String[] aux = null;
aux = header.split("\\.");
header = aux[aux.length - 2]; // aux.length-1 is the extension
aux = header.split("/");
header = aux[aux.length - 1]; // To be run in SGE
Randomize.setSeed(seed);
}
/**
* The main method of the class that includes the operations of the algorithm. It includes all the
* operations that the algorithm has and finishes when it writes the output information into
* files.
*/
public void run() {
int nPos = 0;
int nNeg = 0;
int i, j, l, m;
int tmp;
int posID;
int positives[];
int overs[];
double conjS[][];
int clasesS[];
int tamS;
long tiempo = System.currentTimeMillis();
/*Count of number of positive and negative examples*/
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == 0) nPos++;
else nNeg++;
}
if (nPos > nNeg) {
tmp = nPos;
nPos = nNeg;
nNeg = tmp;
posID = 1;
} else {
posID = 0;
}
/*Localize the positive instances*/
positives = new int[nPos];
for (i = 0, j = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == posID) {
positives[j] = i;
j++;
}
}
/*Obtain the oversampling array taking account the previous array*/
overs = new int[nNeg - nPos];
Randomize.setSeed(semilla);
for (i = 0; i < overs.length; i++) {
tmp = Randomize.Randint(0, nPos - 1);
overs[i] = positives[tmp];
}
tamS = 2 * nNeg;
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
clasesS = new int[tamS];
for (j = 0; j < datosTrain.length; j++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[j][l];
clasesS[j] = clasesTrain[j];
}
for (m = 0; j < tamS; j++, m++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[overs[m]][l];
clasesS[j] = clasesTrain[overs[m]];
}
System.out.println(
"RandomOverSampling "
+ relation
+ " "
+ (double) (System.currentTimeMillis() - tiempo) / 1000.0
+ "s");
OutputIS.escribeSalida(ficheroSalida[0], conjS, clasesS, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
}
/**
* The main method of the class that includes the operations of the algorithm. It includes all the
* operations that the algorithm has and finishes when it writes the output information into
* files.
*/
public void run() {
int S[];
int i, j, l, m;
int nPos = 0, nNeg = 0;
int posID;
int nClases;
int pos;
int baraje[];
int tmp;
double conjS[][];
int clasesS[];
int tamS = 0;
int claseObt;
int cont;
int busq;
boolean marcas[];
int nSel;
double conjS2[][];
int clasesS2[];
double minDist, dist;
long tiempo = System.currentTimeMillis();
/*CNN PART*/
/*Count of number of positive and negative examples*/
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == 0) nPos++;
else nNeg++;
}
if (nPos > nNeg) {
tmp = nPos;
nPos = nNeg;
nNeg = tmp;
posID = 1;
} else {
posID = 0;
}
/*Inicialization of the candidates set*/
S = new int[datosTrain.length];
for (i = 0; i < S.length; i++) S[i] = Integer.MAX_VALUE;
/*Inserting an element of mayority class*/
Randomize.setSeed(semilla);
pos = Randomize.Randint(0, clasesTrain.length - 1);
while (clasesTrain[pos] == posID) pos = (pos + 1) % clasesTrain.length;
S[tamS] = pos;
tamS++;
/*Insert all subset of minority class*/
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == posID) {
S[tamS] = i;
tamS++;
}
}
/*Algorithm body. We resort randomly the instances of T and compare with the rest of S.
If an instance doesn´t classified correctly, it is inserted in S*/
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, clasesTrain.length - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
for (i = 0; i < datosTrain.length; i++) {
if (clasesTrain[i] != posID) { // only for mayority class instances
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
clasesS = new int[tamS];
for (j = 0; j < tamS; j++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[S[j]][l];
clasesS[j] = clasesTrain[S[j]];
}
/*Do KNN to the instance*/
claseObt = KNN.evaluacionKNN(k, conjS, clasesS, datosTrain[baraje[i]], 2);
if (claseObt != clasesTrain[baraje[i]]) { // fail in the class, it is included in S
Arrays.sort(S);
busq = Arrays.binarySearch(S, baraje[i]);
if (busq < 0) {
S[tamS] = baraje[i];
tamS++;
}
}
}
}
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
clasesS = new int[tamS];
for (j = 0; j < tamS; j++) {
for (l = 0; l < datosTrain[0].length; l++) conjS[j][l] = datosTrain[S[j]][l];
clasesS[j] = clasesTrain[S[j]];
}
/*TOMEK LINKS PART*/
/*Inicialization of the instance flagged vector of the S set*/
marcas = new boolean[conjS.length];
for (i = 0; i < conjS.length; i++) {
marcas[i] = true;
}
nSel = conjS.length;
for (i = 0; i < conjS.length; i++) {
minDist = Double.POSITIVE_INFINITY;
pos = 0;
for (j = 0; j < conjS.length; j++) {
if (i != j) {
dist = KNN.distancia(conjS[i], conjS[j]);
if (dist < minDist) {
minDist = dist;
pos = j;
}
}
}
if (clasesS[i] != clasesS[pos]) {
if (clasesS[i] != posID) {
if (marcas[i] == true) {
marcas[i] = false;
nSel--;
}
} else {
if (marcas[pos] == true) {
marcas[pos] = false;
nSel--;
}
}
}
}
/*Construction of the S set from the flags*/
conjS2 = new double[nSel][conjS[0].length];
clasesS2 = new int[nSel];
for (m = 0, l = 0; m < conjS.length; m++) {
if (marcas[m]) { // the instance will evaluate
for (j = 0; j < conjS[0].length; j++) {
conjS2[l][j] = conjS[m][j];
}
clasesS2[l] = clasesS[m];
l++;
}
}
System.out.println(
"CNN_TomekLinks "
+ relation
+ " "
+ (double) (System.currentTimeMillis() - tiempo) / 1000.0
+ "s");
OutputIS.escribeSalida(
ficheroSalida[0], conjS2, clasesS2, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
}
/** It runs the Qstatistic */
public void runAlgorithm() {
int i, j, l, h;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Chromosome poblacion[];
int ev = 0;
Chromosome C[];
int baraje[];
int pos, tmp;
Chromosome newPob[];
int d;
int tamC;
Chromosome pobTemp[];
int nPos = 0, nNeg = 0, posID, negID;
double datosArt[][];
double realArt[][];
int nominalArt[][];
boolean nulosArt[][];
int clasesArt[];
int tamS;
long tiempo = System.currentTimeMillis();
// Randomize.setSeed (semilla);
posID = clasesTrain[0];
negID = -1;
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] != posID) {
negID = clasesTrain[i];
break;
}
}
/* Count of number of positive and negative examples */
for (i = 0; i < clasesTrain.length; i++) {
if (clasesTrain[i] == posID) nPos++;
else nNeg++;
}
if (nPos > nNeg) {
tmp = nPos;
nPos = nNeg;
nNeg = tmp;
tmp = posID;
posID = negID;
negID = tmp;
} else {
/*
* tmp = posID; posID = negID; negID = tmp;
*/
}
if (hybrid.equalsIgnoreCase("smote + eus")) {
if (balance) {
tamS = 2 * nNeg;
} else {
tamS = nNeg + nPos + (int) (nPos * smoting);
}
datosArt = new double[tamS][datosTrain[0].length];
realArt = new double[tamS][datosTrain[0].length];
nominalArt = new int[tamS][datosTrain[0].length];
nulosArt = new boolean[tamS][datosTrain[0].length];
clasesArt = new int[tamS];
SMOTE(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
kSMOTE,
ASMO,
smoting,
balance,
nPos,
posID,
nNeg,
negID,
distanceEu);
} else {
datosArt = new double[datosTrain.length][datosTrain[0].length];
realArt = new double[datosTrain.length][datosTrain[0].length];
nominalArt = new int[datosTrain.length][datosTrain[0].length];
nulosArt = new boolean[datosTrain.length][datosTrain[0].length];
clasesArt = new int[clasesTrain.length];
for (i = 0; i < datosTrain.length; i++) {
for (j = 0; j < datosTrain[i].length; j++) {
datosArt[i][j] = datosTrain[i][j];
realArt[i][j] = realTrain[i][j];
nominalArt[i][j] = nominalTrain[i][j];
nulosArt[i][j] = nulosTrain[i][j];
}
clasesArt[i] = clasesTrain[i];
}
}
/* Count of number of positive and negative examples */
nPos = nNeg = 0;
for (i = 0; i < clasesArt.length; i++) {
if (clasesArt[i] == posID) nPos++;
else nNeg++;
}
if (majSelection) d = nNeg / 4;
else d = datosArt.length / 4;
/* Random initialization of the population */
poblacion = new Chromosome[popSize];
baraje = new int[popSize];
for (i = 0; i < popSize; i++)
if (majSelection) poblacion[i] = new Chromosome(nNeg);
else poblacion[i] = new Chromosome(datosArt.length);
/* Initial evaluation of the population */
for (i = 0; i < popSize; i++)
poblacion[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
wrapper,
k,
evMeas,
majSelection,
pFactor,
P,
posID,
nPos,
distanceEu,
entradas,
anteriores,
salidasAnteriores);
/* Until stop condition */
while (ev < nEval) {
C = new Chromosome[popSize];
/* Selection(r) of C(t) from P(t) */
for (i = 0; i < popSize; i++) baraje[i] = i;
for (i = 0; i < popSize; i++) {
pos = Randomize.Randint(i, popSize - 1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
for (i = 0; i < popSize; i++)
if (majSelection) C[i] = new Chromosome(nNeg, poblacion[baraje[i]]);
else C[i] = new Chromosome(datosArt.length, poblacion[baraje[i]]);
/* Structure recombination in C(t) constructing C'(t) */
tamC = recombinar(C, d, nNeg, nPos, majSelection);
newPob = new Chromosome[tamC];
for (i = 0, l = 0; i < C.length; i++) {
if (C[i].esValido()) { // the cromosome must be copied to the
// new poblation C'(t)
if (majSelection) newPob[l] = new Chromosome(nNeg, C[i]);
else newPob[l] = new Chromosome(datosArt.length, C[i]);
l++;
}
}
/* Structure evaluation in C'(t) */
for (i = 0; i < newPob.length; i++) {
newPob[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
wrapper,
k,
evMeas,
majSelection,
pFactor,
P,
posID,
nPos,
distanceEu,
entradas,
anteriores,
salidasAnteriores);
ev++;
}
/* Selection(s) of P(t) from C'(t) and P(t-1) */
Arrays.sort(poblacion);
Arrays.sort(newPob);
/*
* If the best of C' is worse than the worst of P(t-1), then there
* will no changes
*/
if (tamC == 0 || newPob[0].getCalidad() < poblacion[popSize - 1].getCalidad()) {
d--;
} else {
pobTemp = new Chromosome[popSize];
for (i = 0, j = 0, l = 0; i < popSize && l < tamC; i++) {
if (poblacion[j].getCalidad() > newPob[l].getCalidad()) {
if (majSelection) pobTemp[i] = new Chromosome(nNeg, poblacion[j]);
else pobTemp[i] = new Chromosome(datosArt.length, poblacion[j]);
j++;
} else {
if (majSelection) pobTemp[i] = new Chromosome(nNeg, newPob[l]);
else pobTemp[i] = new Chromosome(datosArt.length, newPob[l]);
l++;
}
}
if (l == tamC) { // there are cromosomes for copying
for (; i < popSize; i++) {
if (majSelection) pobTemp[i] = new Chromosome(nNeg, poblacion[j]);
else pobTemp[i] = new Chromosome(datosArt.length, poblacion[j]);
j++;
}
}
poblacion = pobTemp;
}
/* Last step of the algorithm */
if (d <= 0) {
for (i = 1; i < popSize; i++) {
poblacion[i].divergeCHC(r, poblacion[0], prob0to1Div);
}
for (i = 0; i < popSize; i++)
if (!(poblacion[i].estaEvaluado())) {
poblacion[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
wrapper,
k,
evMeas,
majSelection,
pFactor,
P,
posID,
nPos,
distanceEu,
entradas,
anteriores,
salidasAnteriores);
ev++;
}
/* Reinicialization of d value */
if (majSelection) d = (int) (r * (1.0 - r) * (double) nNeg);
else d = (int) (r * (1.0 - r) * (double) datosArt.length);
}
}
Arrays.sort(poblacion);
if (majSelection) {
nSel = poblacion[0].genesActivos() + nPos;
/* Construction of S set from the best cromosome */
conjS = new double[nSel][datosArt[0].length];
conjR = new double[nSel][datosArt[0].length];
conjN = new int[nSel][datosArt[0].length];
conjM = new boolean[nSel][datosArt[0].length];
clasesS = new int[nSel];
h = 0;
for (i = 0, l = 0; i < nNeg; i++, h++) {
for (; clasesArt[h] == posID && h < clasesArt.length; h++) ;
if (poblacion[0].getGen(i)) { // the instance must be copied to
// the solution
for (j = 0; j < datosArt[h].length; j++) {
conjS[l][j] = datosArt[h][j];
conjR[l][j] = realArt[h][j];
conjN[l][j] = nominalArt[h][j];
conjM[l][j] = nulosArt[h][j];
}
clasesS[l] = clasesArt[h];
l++;
}
}
for (i = 0; i < datosArt.length; i++) {
if (clasesArt[i] == posID) {
for (j = 0; j < datosArt[i].length; j++) {
conjS[l][j] = datosArt[i][j];
conjR[l][j] = realArt[i][j];
conjN[l][j] = nominalArt[i][j];
conjM[l][j] = nulosArt[i][j];
}
clasesS[l] = clasesArt[i];
l++;
}
}
} else {
nSel = poblacion[0].genesActivos();
/* Construction of S set from the best cromosome */
conjS = new double[nSel][datosArt[0].length];
conjR = new double[nSel][datosArt[0].length];
conjN = new int[nSel][datosArt[0].length];
conjM = new boolean[nSel][datosArt[0].length];
clasesS = new int[nSel];
for (i = 0, l = 0; i < datosArt.length; i++) {
if (poblacion[0].getGen(i)) { // the instance must be copied to
// the solution
for (j = 0; j < datosArt[i].length; j++) {
conjS[l][j] = datosArt[i][j];
conjR[l][j] = realArt[i][j];
conjN[l][j] = nominalArt[i][j];
conjM[l][j] = nulosArt[i][j];
}
clasesS[l] = clasesArt[i];
l++;
}
}
}
if (hybrid.equalsIgnoreCase("eus + smote")) {
nPos = nNeg = 0;
for (i = 0; i < clasesS.length; i++) {
if (clasesS[i] == posID) nPos++;
else nNeg++;
}
if (nPos < nNeg) {
if (balance) {
tamS = 2 * nNeg;
} else {
tamS = nNeg + nPos + (int) (nPos * smoting);
}
datosArt = new double[tamS][datosTrain[0].length];
realArt = new double[tamS][datosTrain[0].length];
nominalArt = new int[tamS][datosTrain[0].length];
nulosArt = new boolean[tamS][datosTrain[0].length];
clasesArt = new int[tamS];
SMOTE(
conjS,
conjR,
conjN,
conjM,
clasesS,
datosArt,
realArt,
nominalArt,
nulosArt,
clasesArt,
kSMOTE,
ASMO,
smoting,
balance,
nPos,
posID,
nNeg,
negID,
distanceEu);
nSel = datosArt.length;
/* Construction of S set from the best cromosome */
conjS = new double[nSel][datosArt[0].length];
conjR = new double[nSel][datosArt[0].length];
conjN = new int[nSel][datosArt[0].length];
conjM = new boolean[nSel][datosArt[0].length];
clasesS = new int[nSel];
for (i = 0; i < datosArt.length; i++) {
for (j = 0; j < datosArt[i].length; j++) {
conjS[i][j] = datosArt[i][j];
conjR[i][j] = realArt[i][j];
conjN[i][j] = nominalArt[i][j];
conjM[i][j] = nulosArt[i][j];
}
clasesS[i] = clasesArt[i];
}
}
}
/*
* for (i = 0; i < poblacion.length; i++){ for (j = 0; j <
* poblacion[0].cuerpo.length; j++){
* System.out.print((poblacion[i].cuerpo[j] ? 1 : 0)); }
* System.out.println(" Calidad: " + poblacion[i].calidad); }
*/
best = poblacion[0].cuerpo.clone();
bestOutputs = poblacion[0].prediction.clone();
System.out.println(
"QstatEUSCHC "
+ relation
+ " "
+ (double) (System.currentTimeMillis() - tiempo) / 1000.0
+ "s");
OutputIS.escribeSalida(
ficheroSalida[0], conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
// OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida,
// nEntradas, relation);
}
public void ejecutar() {
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int S[]; /* Binary Vector, to decide if the instance will be included*/
int i, j, l, cont;
int nClases;
int tamS;
int transformations;
int claseObt[];
int clasePredominante;
long tiempo = System.currentTimeMillis();
transformations = 0;
/*Getting the number of different classes*/
nClases = 0;
for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];
nClases++;
if (nClases < 2) {
System.err.println("Input dataset is empty");
nClases = 0;
}
/*Algorithm body.
First, S=TS.
Then, for each instance of TS, the first step is to repeat the aplication of the k-nn, and then
we decide if we need to change the label of the instance or we don't need it.
*/
/*Inicialization of the candidates set, S=X, where X is the original Training Set*/
S = new int[datosTrain.length];
for (i = 0; i < S.length; i++) S[i] = 1; /* All included*/
tamS = datosTrain.length;
System.out.print("K= " + k + "\n");
System.out.print("K'= " + k2 + "\n");
for (i = 0; i < datosTrain.length; i++) {
/* I need find the k-nn of i in X - {i}, so I make conjS without i*/
conjS = new double[datosTrain.length - 1][datosTrain[0].length];
conjR = new double[datosTrain.length - 1][datosTrain[0].length];
conjN = new int[datosTrain.length - 1][datosTrain[0].length];
conjM = new boolean[datosTrain.length - 1][datosTrain[0].length];
clasesS = new int[datosTrain.length - 1];
cont = 0;
for (j = 0; j < datosTrain.length; j++) {
if (i != j) {
for (l = 0; l < datosTrain[0].length; l++) {
conjS[cont][l] = datosTrain[j][l];
conjR[cont][l] = realTrain[j][l];
conjN[cont][l] = nominalTrain[j][l];
conjM[cont][l] = nulosTrain[j][l];
}
clasesS[cont] = clasesTrain[j];
cont++;
}
}
/*Do KNN to the instance*/
claseObt =
KNN.evaluacionKNN3(
k,
conjS,
conjR,
conjN,
conjM,
clasesS,
datosTrain[i],
realTrain[i],
nominalTrain[i],
nulosTrain[i],
nClases,
distanceEu);
/*
System.out.print("Las clases de los k vecinos m�s cercanos son\n");
for(int m=0;m<k;m++){
System.out.print(claseObt[m]+ " ");
}
System.out.print("\n-----------------------------------------------\n");
*/
/*Now, we must check that we have at least k2 neighboors with the same class. */
int max = 0;
clasePredominante = 0;
for (int m = 0; m < claseObt.length; m++) {
int claseDeInstancia = claseObt[m]; // Select one class.
int iguales = 0;
for (j = 0; j < claseObt.length; j++) { // Check numbers of instances with this class
if (j != m) { // I can't count the same.
if (claseObt[j] == claseDeInstancia) {
iguales++;
}
}
}
// I must check if there is another class with more instances.
if (iguales > max) {
max = iguales;
clasePredominante = claseObt[m];
}
}
// System.out.print("max " + max +"\n");
// System.out.print("Clase Predominante: "+clasePredominante+"\n");
/* Max+1 = number of neighbours with the same class*/
if ((max) >= k2) {
/* if there are at least k2 neighbour, we change the class in S, */
if (clasePredominante != clasesTrain[i]) transformations++;
clasesTrain[i] = clasePredominante;
S[i] = 1;
} else {
/* Discard.*/
tamS--;
S[i] = 0;
}
}
System.out.print("S size resultante= " + tamS + "\n");
System.out.print("Transformations = " + transformations + "\n");
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
conjR = new double[tamS][datosTrain[0].length];
conjN = new int[tamS][datosTrain[0].length];
conjM = new boolean[tamS][datosTrain[0].length];
clasesS = new int[tamS];
cont = 0; /* To establish the sets' sizes */
for (j = 0; j < datosTrain.length; j++) {
if (S[j] == 1) {
/* Checking the instance is included*/
for (l = 0; l < datosTrain[0].length; l++) {
conjS[cont][l] = datosTrain[j][l];
conjR[cont][l] = realTrain[j][l];
conjN[cont][l] = nominalTrain[j][l];
conjM[cont][l] = nulosTrain[j][l];
}
clasesS[cont] = clasesTrain[j];
cont++;
}
}
System.out.println(
"Time elapse: " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
OutputIS.escribeSalida(
ficheroSalida[0], conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
}
public void ejecutar() {
int i, j, l;
boolean marcas[];
boolean marcas2[];
boolean marcastmp[];
boolean incorrect[];
int nSel;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
Vector<Integer> vecinos[];
int next;
int maxneigh;
int pos;
int borrado;
int claseObt;
int nClases;
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++;
/*Inicialization of the flagged instances vector for a posterior copy*/
marcas = new boolean[datosTrain.length];
marcas2 = new boolean[datosTrain.length];
incorrect = new boolean[datosTrain.length];
marcastmp = new boolean[datosTrain.length];
Arrays.fill(marcas, true);
Arrays.fill(marcas2, true);
Arrays.fill(incorrect, false);
Arrays.fill(marcastmp, true);
vecinos = new Vector[datosTrain.length];
for (i = 0; i < datosTrain.length; i++) vecinos[i] = new Vector<Integer>();
for (i = 0; i < datosTrain.length; i++) {
next = nextNeighbour(marcas, datosTrain, i, vecinos[i]);
for (j = 0; j < datosTrain.length; j++) marcastmp[j] = marcas[j];
while (next >= 0 && clasesTrain[next] == clasesTrain[i]) {
vecinos[i].add(new Integer(next));
marcastmp[next] = false;
next = nextNeighbour(marcastmp, datosTrain, i, vecinos[i]);
}
}
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
while (maxneigh > 0) {
for (i = 0; i < vecinos[pos].size(); i++) {
borrado = vecinos[pos].elementAt(i).intValue();
marcas[borrado] = false;
for (j = 0; j < datosTrain.length; j++) {
vecinos[j].removeElement(new Integer(borrado));
}
vecinos[borrado].clear();
}
vecinos[pos].clear();
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
}
/*Building of the S set from the flags*/
nSel = 0;
for (i = 0; i < datosTrain.length; i++) if (marcas[i]) nSel++;
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i = 0, l = 0; i < datosTrain.length; i++) {
if (marcas[i]) { // the instance will be copied to the solution
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
for (i = 0; i < datosTrain.length; i++) {
/*Apply 1-NN to the instance*/
claseObt =
KNN.evaluacionKNN2(
1,
conjS,
conjR,
conjN,
conjM,
clasesTrain,
datosTrain[i],
realTrain[i],
nominalTrain[i],
nulosTrain[i],
nClases,
true);
if (claseObt != clasesTrain[i]) {
incorrect[i] = true;
}
}
for (i = 0; i < datosTrain.length; i++) vecinos[i] = new Vector<Integer>();
for (i = 0; i < datosTrain.length; i++) {
if (incorrect[i]) {
next = nextNeighbour(marcas2, datosTrain, i, vecinos[i]);
for (j = 0; j < datosTrain.length; j++) marcastmp[j] = marcas2[j];
while (next >= 0 && clasesTrain[next] == clasesTrain[i]) {
vecinos[i].add(new Integer(next));
marcastmp[next] = false;
next = nextNeighbour(marcastmp, datosTrain, i, vecinos[i]);
}
}
}
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
while (maxneigh > 0) {
for (i = 0; i < vecinos[pos].size(); i++) {
borrado = vecinos[pos].elementAt(i).intValue();
marcas2[borrado] = false;
for (j = 0; j < datosTrain.length; j++) {
vecinos[j].removeElement(new Integer(borrado));
}
vecinos[borrado].clear();
}
vecinos[pos].clear();
maxneigh = vecinos[0].size();
pos = 0;
for (i = 1; i < datosTrain.length; i++) {
if (vecinos[i].size() > maxneigh) {
maxneigh = vecinos[i].size();
pos = i;
}
}
}
for (i = 0; i < marcas.length; i++) marcas[i] |= (marcas2[i] & incorrect[i]);
/*Building of the S set from the flags*/
nSel = 0;
for (i = 0; i < datosTrain.length; i++) if (marcas[i]) nSel++;
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i = 0, l = 0; i < datosTrain.length; i++) {
if (marcas[i]) { // the instance will be copied to the solution
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
System.out.println(
"Reconsistent "
+ relation
+ " "
+ (double) (System.currentTimeMillis() - tiempo) / 1000.0
+ "s");
// COn conjS me vale.
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
// Working on training
for (i = 0; i < datosTrain.length; i++) {
trainRealClass[i][0] = clasesTrain[i];
trainPrediction[i][0] = KNN.evaluate(datosTrain[i], conjS, nClases, clasesS, 1);
}
KNN.writeOutput(ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
// Working on test
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
// Check time
for (i = 0; i < realClass.length; i++) {
realClass[i][0] = clasesTest[i];
prediction[i][0] = KNN.evaluate(datosTest[i], conjS, nClases, clasesS, 1);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}
/** 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);
}
}
// Read the pattern file, and parse data into strings
private void config_read(String fileParam) {
File inputFile = new File(fileParam);
if (inputFile == null || !inputFile.exists()) {
System.out.println("parameter " + fileParam + " file doesn't exists!");
System.exit(-1);
}
// begin the configuration read from file
try {
FileReader file_reader = new FileReader(inputFile);
BufferedReader buf_reader = new BufferedReader(file_reader);
// FileWriter file_write = new FileWriter(outputFile);
String line;
do {
line = buf_reader.readLine();
} while (line.length() == 0); // avoid empty lines for processing -> produce exec failure
String out[] = line.split("algorithm = ");
// alg_name = new String(out[1]); //catch the algorithm name
// input & output filenames
do {
line = buf_reader.readLine();
} while (line.length() == 0);
out = line.split("inputData = ");
out = out[1].split("\\s\"");
input_train_name = new String(out[0].substring(1, out[0].length() - 1));
input_test_name = new String(out[1].substring(0, out[1].length() - 1));
if (input_test_name.charAt(input_test_name.length() - 1) == '"')
input_test_name = input_test_name.substring(0, input_test_name.length() - 1);
do {
line = buf_reader.readLine();
} while (line.length() == 0);
out = line.split("outputData = ");
out = out[1].split("\\s\"");
output_train_name = new String(out[0].substring(1, out[0].length() - 1));
output_test_name = new String(out[1].substring(0, out[1].length() - 1));
if (output_test_name.charAt(output_test_name.length() - 1) == '"')
output_test_name = output_test_name.substring(0, output_test_name.length() - 1);
// parameters
do {
line = buf_reader.readLine();
} while (line.length() == 0);
out = line.split("seed = ");
semilla = (new Long(out[1])).longValue(); // parse the string into a integer
do {
line = buf_reader.readLine();
} while (line.length() == 0);
out = line.split("k = ");
K = (new Integer(out[1])).intValue(); // parse the string into a integer
do {
line = buf_reader.readLine();
} while (line.length() == 0);
out = line.split("error = ");
minError = (new Double(out[1])).doubleValue(); // parse the string into a double
do {
line = buf_reader.readLine();
} while (line.length() == 0);
out = line.split("iterations = ");
maxIter = (new Integer(out[1])).intValue(); // parse the string into a double
file_reader.close();
} catch (IOException e) {
System.out.println("IO exception = " + e);
e.printStackTrace();
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());
}
/** Executes the algorithm */
public void ejecutar() {
int i, j, l;
int nClases;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Cromosoma poblacion[];
int ev = 0;
double prob[];
double NUmax = 1.5;
double NUmin = 0.5; // used for lineal ranking
double aux;
double pos1, pos2;
int sel1, sel2, comp1, comp2;
Cromosoma newPob[];
long tiempo = System.currentTimeMillis();
/*Getting the number of different clases*/
nClases = 0;
for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];
nClases++;
/*Random inicialization of the population*/
Randomize.setSeed(semilla);
poblacion = new Cromosoma[tamPoblacion];
for (i = 0; i < tamPoblacion; i++) poblacion[i] = new Cromosoma(datosTrain.length);
/*Initial evaluation of the population*/
for (i = 0; i < tamPoblacion; i++)
poblacion[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
alfa,
kNeigh,
nClases,
distanceEu);
if (torneo) {
while (ev < nEval) {
newPob = new Cromosoma[2];
/*Binary tournament selection*/
comp1 = Randomize.Randint(0, tamPoblacion - 1);
do {
comp2 = Randomize.Randint(0, tamPoblacion - 1);
} while (comp2 == comp1);
if (poblacion[comp1].getCalidad() > poblacion[comp2].getCalidad()) sel1 = comp1;
else sel1 = comp2;
comp1 = Randomize.Randint(0, tamPoblacion - 1);
do {
comp2 = Randomize.Randint(0, tamPoblacion - 1);
} while (comp2 == comp1);
if (poblacion[comp1].getCalidad() > poblacion[comp2].getCalidad()) sel2 = comp1;
else sel2 = comp2;
if (Randomize.Rand() < pCruce) { // there is cross
crucePMX(poblacion, newPob, sel1, sel2);
} else { // there is not cross
newPob[0] = new Cromosoma(datosTrain.length, poblacion[sel1]);
newPob[1] = new Cromosoma(datosTrain.length, poblacion[sel2]);
}
/*Mutation of the cromosomes*/
for (i = 0; i < 2; i++) newPob[i].mutacion(pMutacion1to0, pMutacion0to1);
/*Evaluation of the population*/
for (i = 0; i < 2; i++)
if (!(newPob[i].estaEvaluado())) {
newPob[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
alfa,
kNeigh,
nClases,
distanceEu);
ev++;
}
/*Replace the two worst*/
Arrays.sort(poblacion);
poblacion[tamPoblacion - 2] = new Cromosoma(datosTrain.length, newPob[0]);
poblacion[tamPoblacion - 1] = new Cromosoma(datosTrain.length, newPob[1]);
}
} else {
/*Get the probabilities of lineal ranking in case of not use binary tournament*/
prob = new double[tamPoblacion];
for (i = 0; i < tamPoblacion; i++) {
aux = (double) (NUmax - NUmin) * ((double) i / (tamPoblacion - 1));
prob[i] = (double) (1.0 / (tamPoblacion)) * (NUmax - aux);
}
for (i = 1; i < tamPoblacion; i++) prob[i] = prob[i] + prob[i - 1];
while (ev < nEval) {
/*Sort the population by quality criterion*/
Arrays.sort(poblacion);
newPob = new Cromosoma[2];
pos1 = Randomize.Rand();
pos2 = Randomize.Rand();
for (j = 0; j < tamPoblacion && prob[j] < pos1; j++) ;
sel1 = j;
for (j = 0; j < tamPoblacion && prob[j] < pos2; j++) ;
sel2 = j;
if (Randomize.Rand() < pCruce) { // there is cross
crucePMX(poblacion, newPob, sel1, sel2);
} else { // there is not cross
newPob[0] = new Cromosoma(datosTrain.length, poblacion[sel1]);
newPob[1] = new Cromosoma(datosTrain.length, poblacion[sel2]);
}
/*Mutation of the cromosomes*/
for (i = 0; i < 2; i++) newPob[i].mutacion(pMutacion1to0, pMutacion0to1);
/*Evaluation of the population*/
for (i = 0; i < 2; i++)
if (!(newPob[i].estaEvaluado())) {
newPob[i].evalua(
datosTrain,
realTrain,
nominalTrain,
nulosTrain,
clasesTrain,
alfa,
kNeigh,
nClases,
distanceEu);
ev++;
}
/*Replace the two worst*/
poblacion[tamPoblacion - 2] = new Cromosoma(datosTrain.length, newPob[0]);
poblacion[tamPoblacion - 1] = new Cromosoma(datosTrain.length, newPob[1]);
}
}
nSel = poblacion[0].genesActivos();
/*Building of S set from the best cromosome obtained*/
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i = 0, l = 0; i < datosTrain.length; i++) {
if (poblacion[0].getGen(i)) { // the instance must be copied to the solution
for (j = 0; j < datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
System.out.println(
"SGA " + relation + " " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
OutputIS.escribeSalida(
ficheroSalida[0], conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
} // end-method
