/**
* This is a test for compute depth of genome and trace all debug information for viewing all
* signal flowing is not necessary for network simulation
*/
public static void Experiment2(String xFileName) {
StringTokenizer st;
String curword;
String xline;
String fnamebuf;
IOseq xFile;
int id;
Genome g1 = null;
Network net = null;
System.out.println("------ Start experiment 2 -------");
xFile = new IOseq(xFileName);
boolean ret = xFile.IOseqOpenR();
if (ret) {
try {
System.out.println(" Start experiment 2");
System.out.println(" Read start genome..");
xline = xFile.IOseqRead();
st = new StringTokenizer(xline);
// skip
curword = st.nextToken();
// id of genome can be readed
curword = st.nextToken();
id = Integer.parseInt(curword);
g1 = new Genome(id, xFile);
// generate a link mutation
g1.mutate_link_weight(Neat.p_weight_mut_power, 1.0, NeatConstant.GAUSSIAN);
// generate from genome the phenotype
g1.genesis(id);
// view genotype
g1.op_view();
// assign reference to genotype
net = g1.phenotype;
// compute first the 'teorical' depth
int lx = net.max_depth();
// compute . after, the 'pratical' depth passing
// the virtual depth;
int dx = net.is_stabilized(lx);
// after reset all value of net
net.flush();
System.out.print("\n For genome : " + xFileName + " : max depth virtuale=" + lx);
System.out.print(", max depth reale=" + dx);
if (dx != lx) System.out.print("\n *ALERT* This net is NOT S T A B L E ");
net.flush();
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;
boolean success = false;
double in[] = {1.0, 1.0, 1.0};
count = 0;
// first activation from sensor to first next level of neurons
net.load_sensors(in);
// first activation....
success = net.activate();
// next activation while last level is reached !
// use depth to ensure relaxation
for (int relax = 1; relax <= dx; relax++) {
success = net.activate();
// System.out.print("\n -----TIME <"+relax+"> -----");
}
// ok : the propagation is completed
} catch (Throwable e) {
System.err.println(e + " : error during open " + xFileName);
}
xFile.IOseqCloseR();
} else System.err.print("\n : error during open " + xFileName);
System.out.println("\n\n End of experiment");
}
/** 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;
}
}
