/**
* Save network weights to a file
*
* @param file_name Output file name
* @param header header of the data set for which the network has been adjusted to
*/
protected void printNetworkToFile(String file_name, String header) {
// write the header to the file
Files.writeFile(file_name, header);
Files.addToFile(file_name, "Number of neurons: " + nSel + "\n");
for (int i = 0; i < nSel; i++) {
Files.addToFile(file_name, "\nNeuron " + i + "\n");
for (int j = 0; j < conjS[i].length; j++) {
Files.addToFile(file_name, Double.toString(conjS[i][j]) + " ");
}
Files.addToFile(file_name, " Class = " + clasesS[i] + "\n");
}
}
/**
* Defined to manage de semantics of the linguistic variables Generates the semantics of the
* linguistic variables using a partition consisting of triangle simetrics fuzzy sets. The cut
* points al stored at 0.5 level of the fuzzy sets to be considered in the computation of the gain
* of information. Also writes the semantics of the linguistic variables in the specified file
*
* @param nFile Name of file to write the semantics
*/
public void InitSemantics(String nFile) {
int v, etq;
float marca, valor, p_corte;
float auxX0, auxX1, auxX3, auxY;
String contents;
contents = "\n--------------------------------------------\n";
contents += "| Semantics for the continuous variables |\n";
contents += "--------------------------------------------\n";
for (v = 0; v < num_vars; v++) {
if (var[v].getContinuous() == true) {
marca = (var[v].getMax() - var[v].getMin()) / ((float) (var[v].getNLabels() - 1));
p_corte = var[v].getMin() + marca / 2;
contents += "Fuzzy sets parameters for variable " + var[v].getName() + ":\n";
for (etq = 0; etq < var[v].getNLabels(); etq++) {
valor = var[v].getMin() + marca * (etq - 1);
auxX0 = Round(valor, var[v].getMax());
valor = var[v].getMin() + marca * etq;
auxX1 = Round(valor, var[v].getMax());
valor = var[v].getMin() + marca * (etq + 1);
auxX3 = Round(valor, var[v].getMax());
auxY = 1;
BaseDatos[v][etq].setVal(auxX0, auxX1, auxX3, auxY);
p_corte += marca;
contents +=
"\tLabel "
+ etq
+ ": "
+ BaseDatos[v][etq].getX0()
+ " "
+ BaseDatos[v][etq].getX1()
+ " "
+ BaseDatos[v][etq].getX3()
+ "\n";
}
}
}
contents += "\n";
if (nFile != "") Files.addToFile(nFile, contents);
}
/**
* Computes and stores the information gain of each attribute (variable) of the dataset
*
* @param Examples Set of instances of the dataset
* @param nFile Name of the file
*/
public void GainInit(TableDat Examples, String nFile) {
int i, j, h, v;
boolean encontrado;
float info_gk, suma, suma1, suma2, p_clase, logaritmo;
int num_clase[] = new int[n_clases];
int n_vars = this.getNVars();
int MaxVal = this.getMaxVal();
float p[][] = new float[n_vars][MaxVal];
float p_cond[][][] = new float[n_clases][n_vars][MaxVal];
GI = new float[n_vars];
intervalosGI = new float[n_vars][MaxVal];
String contents;
contents = "\n--------------------------------------------\n";
contents += "| Computation of the info gain |\n";
contents += "--------------------------------------------\n";
contents += "Points for computation of the info gain:\n";
// Loads the values for "intervalosGI"
float marca, p_corte;
for (int v1 = 0; v1 < n_vars; v1++) {
if (this.getContinuous(v1) == true) {
contents += "\tVariable " + var[v1].getName() + ": ";
marca = (this.getMax(v1) - this.getMin(v1)) / ((float) (this.getNLabelVar(v1) - 1));
p_corte = this.getMin(v1) + marca / 2;
for (int et = 0; et < this.getNLabelVar(v1); et++) {
intervalosGI[v1][et] = p_corte;
contents += intervalosGI[v1][et] + " ";
p_corte += marca;
}
contents += "\n";
}
}
// Structure initialization
for (i = 0; i < n_clases; i++) num_clase[i] = 0;
for (i = 0; i < n_vars; i++)
for (j = 0; j < MaxVal; j++) {
p[i][j] = 0; // Simple probabilities matrix
for (h = 0; h < n_clases; h++) p_cond[h][i][j] = 0; // Conditional probabilities matrix
}
// Computation of the Simple and Conditional probabilities matrixs
for (i = 0; i < Examples.getNEx(); i++) {
num_clase[Examples.getClass(i)]++; // distribution by classes
for (j = 0; j < n_vars; j++) { // distribution by values
if (!this.getContinuous(j)) { // Discrete variable
if (!Examples.getLost(this, i, j)) {
// if the value is not a lost one
p[j][(int) Examples.getDat(i, j)]++;
p_cond[(int) Examples.getClass(i)][j][(int) Examples.getDat(i, j)]++;
}
} else { // Continuous variable
encontrado = false;
h = 0;
while (!encontrado && h < this.getNLabelVar(j)) {
if (Examples.getDat(i, j) <= intervalosGI[j][h]) encontrado = true;
else h++;
}
if (encontrado == true) {
p[j][h]++;
p_cond[(int) Examples.getClass(i)][j][h]++;
} else {
if (!Examples.getLost(this, i, j)) {
// Lost value
System.out.println(
"Fallo al calcular la ganancia de infor, Variable " + j + " Ejemplo " + i);
return;
}
}
}
}
}
for (h = 0; h < n_clases; h++)
for (i = 0; i < n_vars; i++) {
if (!this.getContinuous(i)) // Discrete variable
for (j = (int) this.getMin(i); j <= (int) this.getMax(i); j++)
p_cond[h][i][j] = p_cond[h][i][j] / Examples.getNEx();
else // Continuous variable
for (j = 0; j < this.getNLabelVar(i); j++)
p_cond[h][i][j] = p_cond[h][i][j] / Examples.getNEx();
}
for (i = 0; i < n_vars; i++) {
if (!this.getContinuous(i)) // Discrete variable
for (j = (int) this.getMin(i); j <= (int) this.getMax(i); j++)
p[i][j] = p[i][j] / Examples.getNEx();
else // Continuous variable
for (j = 0; j < this.getNLabelVar(i); j++) p[i][j] = p[i][j] / Examples.getNEx();
}
// Info Gk computation
suma = 0;
for (i = 0; i < n_clases; i++) {
p_clase = ((float) num_clase[i]) / Examples.getNEx();
if (p_clase > 0) {
logaritmo = (float) (Math.log((double) p_clase) / Math.log(2));
suma += p_clase * logaritmo;
}
}
info_gk = (-1) * suma;
// Information gain computation for each attibute
for (v = 0; v < n_vars; v++) {
suma = info_gk;
suma1 = 0;
if (!this.getContinuous(v)) { // Discrete Variable
for (i = (int) this.getMin(v); i <= (int) this.getMax(v); i++) {
suma2 = 0;
for (j = 0; j < n_clases; j++)
if (p_cond[j][v][i] > 0) {
logaritmo = (float) (Math.log(p_cond[j][v][i]) / Math.log(2));
suma2 += p_cond[j][v][i] * logaritmo;
}
suma1 += p[v][i] * (-1) * suma2;
}
} else { // Continuous Variable
for (i = 0; i < this.getNLabelVar(v); i++) {
suma2 = 0;
for (j = 0; j < n_clases; j++)
if (p_cond[j][v][i] > 0) {
logaritmo = (float) (Math.log(p_cond[j][v][i]) / Math.log(2));
suma2 += p_cond[j][v][i] * logaritmo;
}
suma1 += p[v][i] * (-1) * suma2;
}
}
GI[v] = suma + (-1) * suma1;
}
contents += "Information Gain of the variables:\n";
for (v = 0; v < n_vars; v++) {
if (this.getContinuous(v) == true)
contents += "\tVariable " + var[v].getName() + ": " + GI[v] + "\n";
}
if (nFile != "") Files.addToFile(nFile, contents);
}
