/**
* Load ensemble from file_name
*
* @param file_name File name
*/
public void LoadEnsemble(String file_name) {
String name;
// Load networks
for (int i = 0; i < Nnetworks; i++) {
name = file_name + "_net_" + i;
nets[i].LoadNetwork(name);
}
// Load weigths
try {
FileInputStream file = new FileInputStream(file_name);
DataInputStream dataIn = new DataInputStream(file);
// Load weights
for (int i = 0; i < Noutputs; i++) {
for (int j = 0; j < Nnetworks; j++) {
weights[i][j] = dataIn.readDouble();
}
}
dataIn.close();
} catch (FileNotFoundException ex) {
System.err.println("Unable to open ensemble files");
System.exit(1);
} catch (IOException ex) {
System.err.println("IO exception");
System.exit(1);
}
}
/**
* Train Ensemble
*
* @param global Global definition parameters
* @param data Input data
*/
public void TrainEnsemble(EnsembleParameters global, Data data) {
// Test type of sampling
if (global.sampling.compareToIgnoreCase("None") == 0) {
TrainEnsembleNoSampling(global, data);
} else if (global.sampling.compareToIgnoreCase("Bagging") == 0) {
TrainEnsembleBagging(global, data);
} else if (global.sampling.compareToIgnoreCase("Arcing") == 0) {
TrainEnsembleArcing(global, data);
} else if (global.sampling.compareToIgnoreCase("Ada") == 0) {
TrainEnsembleAda(global, data);
} else {
System.err.println("Invalid sampling method");
System.exit(1);
}
}
/**
* Save data in output file
*
* @param file_name File name
* @param data Data to be saved
* @param n No of patterns
* @param problem Type of problem (CLASSIFICATION | REGRESSION)
* @throws IOException
*/
public void SaveOutputFile(
String file_name, double data[][], int n, String problem, double[] a, double[] b) {
String line;
double outputs[] = new double[Noutputs];
try {
// Result file
FileOutputStream file = new FileOutputStream(file_name);
BufferedWriter f = new BufferedWriter(new OutputStreamWriter(file));
// File header
f.write("@relation " + Attributes.getRelationName() + "\n");
f.write(Attributes.getInputAttributesHeader());
f.write(Attributes.getOutputAttributesHeader());
f.write(Attributes.getInputHeader() + "\n");
f.write(Attributes.getOutputHeader() + "\n");
f.write("@data\n");
// For all patterns
for (int i = 0; i < n; i++) {
// Classification
if (problem.compareToIgnoreCase("Classification") == 0) {
// Obtain class
int Class = 0;
for (int j = 1; j < Noutputs; j++) {
if (data[i][Class + Ninputs] < data[i][j + Ninputs]) {
Class = j;
}
}
/*
f.write(Integer.toString(Class) + " ");
f.write(Integer.toString(EnsembleGetClassOfPattern(data[i])));
f.newLine();
*/
f.write(Attributes.getOutputAttributes()[0].getNominalValue(Class) + " ");
f.write(
Attributes.getOutputAttributes()[0].getNominalValue(
EnsembleGetClassOfPattern(data[i])));
f.newLine();
f.flush();
}
// Regression
else {
if (a != null && b != null) {
for (int j = 0; j < Noutputs; j++) {
f.write(Double.toString((data[i][Ninputs + j] - b[j]) / a[j]) + " ");
}
EnsembleOutput(data[i], outputs);
for (int j = 0; j < Noutputs; j++) {
f.write(Double.toString((outputs[j] - b[j]) / a[j]) + " ");
}
f.newLine();
} else {
for (int j = 0; j < Noutputs; j++) {
f.write(Double.toString(data[i][Ninputs + j]) + " ");
}
EnsembleOutput(data[i], outputs);
for (int j = 0; j < Noutputs; j++) {
f.write(Double.toString(outputs[j]) + " ");
}
f.newLine();
}
}
}
f.close();
file.close();
} catch (FileNotFoundException e) {
System.err.println("Training file does not exist");
System.exit(1);
} catch (IOException e) {
e.printStackTrace();
System.exit(-1);
}
}
