/** Insert the method's description here. Creation date: (16/01/2002 9.53.37) */
public static boolean xor_evaluate(Organism organism) {
Network _net = null;
boolean success = false;
double errorsum = 0.0;
double[] out = new double[4]; // The four outputs
// int numnodes = 0;
int net_depth = 0; // The max depth of the network to be activated
int count = 0;
// The four possible input combinations to xor
// The first number is for biasing
double in[][] = {{1.0, 0.0, 0.0}, {1.0, 0.0, 1.0}, {1.0, 1.0, 0.0}, {1.0, 1.0, 1.0}};
_net = organism.net;
// numnodes = organism.genome.nodes.size();
net_depth = _net.max_depth();
// for each example , 'count', propagate signal .... and compute results
for (count = 0; count <= 3; count++) {
// first activation from sensor to first next levelof neurons
_net.load_sensors(in[count]);
success = _net.activate();
// next activation while last level is reached !
// use depth to ensure relaxation
for (int relax = 0; relax <= net_depth; relax++) success = _net.activate();
// ok : the propagation is completed : repeat until all examples are presented
out[count] = ((NNode) _net.getOutputs().firstElement()).getActivation();
_net.flush();
}
// control the result
if (success) {
errorsum =
(double)
(Math.abs(out[0])
+ Math.abs(1.0 - out[1])
+ Math.abs(1.0 - out[2])
+ Math.abs(out[3]));
organism.setFitness(Math.pow((4.0 - errorsum), 2));
organism.setError(errorsum);
} else {
errorsum = 999.0;
organism.setFitness(0.001);
organism.setError(errorsum);
}
String mask03 = "0.000";
DecimalFormat fmt03 = new DecimalFormat(mask03);
if ((out[0] < 0.5) && (out[1] >= 0.5) && (out[2] >= 0.5) && (out[3] < 0.5)) {
organism.setWinner(true);
return true;
} else {
organism.setWinner(false);
return false;
}
}
