private void testSetIt() {
neurNet.set(dataCrosValid, Parameters.dataModifier);
double err = neurNet.calcANN(getBestVector());
try {
PrintWriter pw =
new PrintWriter(new FileWriter(new File(Parameters.CrossValidationErrorFile), true));
pw.println(err);
pw.close();
} catch (Exception e) {
e.printStackTrace();
}
neurNet.set(dataStart, Parameters.dataModifier);
}
@Override
public void set(int numOfWeights, INeuralNetwork neurNet) {
this.dim = numOfWeights;
this.neurNet = neurNet;
this.popNum = Parameters.NumOfUnits;
this.maxGenNoChamdge = Parameters.MaxNumOfGenWithoutChaindge;
this.strategy = Parameters.strategy;
this.F = Parameters.F;
this.Cr = Parameters.Cr;
this.initMax = Parameters.initMax;
this.initMin = Parameters.initMin;
this.rand = new Random();
if (Parameters.enableCrossVal) {
dataStart = neurNet.getData();
dataCrosValid = Parameters.testgSet.getData(Parameters.testDataPath);
Parameters.dataModifier.modyfyData(dataCrosValid);
File f = new File(Parameters.CrossValidationErrorFile);
f.delete();
}
}
private void evalOne(Unit unit) {
unit.fitnes = neurNet.calcANN(unit.vector);
}