//
// Find fitness of this particle for all training data
// - This is the summed squared error
public double GetFitness(double[][] inputArray, double[][] outputArray)
{
double error = 0; // Initialize error to 0
// Get error
int row = 0;
foreach (double[] line in inputArray)
{
double[] output = currNetwork.Think(line);
int col = 0;
foreach (double val in output)
{
error += Math.Abs((val - outputArray[row][col]) * (val - outputArray[row][col]));
col++;
}
row++;
}
ParticleFitness = error;
// Check for personal best
if (ParticleFitness < PersonalBest)
{
PersonalBest = ParticleFitness;
bestNetwork = currNetwork.SetWeights();
}
return(ParticleFitness);
}
//
// Constructor
public Particle(int[] LayerInfo, int[] inputStats, int[] outputStats, bool[] inUseOpponent, bool[] inUseOffense, Random random)
{
currNetwork = new Neural_Network(LayerInfo, inputStats, outputStats, inUseOpponent, inUseOffense, random);
bestNetwork = currNetwork.SetWeights();
stepNetwork = new Neural_Network(LayerInfo, inputStats, outputStats, inUseOpponent, inUseOffense, 0);
}
