public static float calcFitness(int[] particle, LinkedList<PatternList> l) throws IOException {
PatternList pl1 = l.getFirst();
PatternList pl2 = l.getLast();
float fit;
int[] pagesArray = pl1.getPageArray();
float f1 = 0;
float f2 = 0;
for (int i = 0; i < particle.length - 2 + 1; i++) {
int[] duo = new int[2];
for (int j = 0; j < 2; j++) {
// System.out.println("\t\t"+this.dimensions[i + j]);
duo[j] = pagesArray[particle[i + j]];
}
Pattern current = pl2.seek(duo);
if (current != null) {
f2 += (current.getFreq() * (1000 * current.getConfidence()));
} else if (current == null) {
// System.out.print("\t\t duo : ");
// new Pattern(duo).print();
fit = (float) -6.0;
return fit;
}
}
f2 = (f2 / ((particle.length - 2 + 1) * pl2.getTotalFreq() * pl2.getDistribution()));
for (int i = 0; i < particle.length; i++) {
Pattern current = pl1.seek(particle[i]);
if (current == null) {
fit = (float) 0.0;
return (float) -6.0;
} else {
f1 += current.getFreq();
}
}
f1 = (f1 / (particle.length * pl1.getTotalFreq() * pl1.getDistribution()));
// System.out.println("\t\t\tf2 freq : "+ f2);
return fit = 1000 * ((1 * f1 + 3 * f2) / 4);
// fitness da partícula 8,8,8 é :0.468331
// fitness da partícula 1,1,1 é :0.35263315
// fitness da partícula 6,6,7 é :0.050931655
// fitness da partícula 14,13,16 é :3.627465E-4
// fitness da partícula 8,8,8 é :0.039101
// fitness da partícula 1,1,1 é :0.029390793
// fitness da partícula 6,6,7 é :0.0042792875
// fitness da partícula 14,13,16 é :1.3224661E-4
}
/**
* Train this network using the supplied pattern list.
*
* @param patternz list of training patterns
* @param qerr quadratic error
* @param max_cycles maximum training cycles, -1 = no limit
* @param threshold limit near zero or one to count as zero or one
* @param verbose write progress messages
* @return quantity of trained patterns
*/
public int trainNetwork(
PatternList patternz, double qerr, int max_cycles, double threshold, boolean verbose) {
int limit = patternz.size();
int counter = 0;
int success;
int max_success = 0;
long tot_train = 0, tot_run = 0;
long train_start, train_end, run_start, run_end;
do {
success = 0;
_network.resetQError();
_network.resetAggOutputError();
for (int ii = 0; ii < limit; ii++) {
Pattern pattern = patternz.get(ii);
run_start = System.currentTimeMillis();
_network.runNetWork(pattern.getInput());
run_end = System.currentTimeMillis();
tot_run += (run_end - run_start);
train_start = System.currentTimeMillis();
double[] raw_results = _network.trainNetWork(pattern.getOutput());
train_end = System.currentTimeMillis();
tot_train += (train_end - train_start);
int[] truth = Mathz.thresholdArray(threshold, pattern.getOutput());
int[] results = Mathz.thresholdArray(threshold, raw_results);
pattern.setTrained(true);
for (int jj = 0; jj < raw_results.length; jj++) {
if (results[jj] != truth[jj]) {
pattern.setTrained(false);
break;
}
}
if (pattern.isTrained()) {
++success;
}
}
// Batch mode
_network.updateWeights();
if (max_success < success) {
max_success = success;
}
if ((++counter % 100) == 0) {
if (verbose) {
System.out.println("Fwd time: " + tot_run);
System.out.println("Bwd time: " + tot_train);
System.out.println("Netw qerror: " + _network.getQError());
System.out.println(counter + " success:" + success + " best run:" + max_success);
}
}
if (max_cycles != -1) {
if (counter >= max_cycles) {
break;
}
}
} while (_network.getQError() > qerr);
if (verbose) {
System.out.println("Fwd time: " + tot_run);
System.out.println("Bwd time: " + tot_train);
System.out.println("Netw qerror: " + _network.getQError());
System.out.println("Training complete in " + counter + " cycles");
}
return (success);
}
