public void testPatterns(
Pattern pCorreto, State state, List<RnaResult> results, List<Pattern> patterns, int hidden) {
MultilayerPerceptron perceptron = new EQMFunction(Boolean.FALSE, 1).buildPerceptron(state);
int number = eval(pCorreto, perceptron);
log.fine(number + " de " + pCorreto.getX().length + " sem erro");
results.add(
new RnaResult(-2.0, 2.0, hidden, 0, number, pCorreto.getX().length, 0, state.getValue()));
for (Pattern pattern : patterns) {
number = eval(pattern, perceptron);
log.fine(number + " de " + pCorreto.getX().length + " sem erro");
results.add(
new RnaResult(
-2.0,
2.0,
hidden,
0,
number,
pCorreto.getX().length,
pattern.erro,
state.getValue()));
}
}
public void run() {
try {
List<Pattern> patterns = new ArrayList<Pattern>(4);
Pattern pCorreto =
loadFile120_8(
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_12x10.txt"),
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_8x8_1.txt"));
Pattern p5Porcento =
loadFile120_8(
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_12x10_5.txt"),
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_8x8_1.txt"));
patterns.add(p5Porcento);
p5Porcento.erro = 5;
Pattern p10Porcento =
loadFile120_8(
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_12x10_10.txt"),
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_8x8_1.txt"));
patterns.add(p10Porcento);
p10Porcento.erro = 10;
Pattern p20Porcento =
loadFile120_8(
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_12x10_20.txt"),
Experimento_3.class.getResourceAsStream(
"/br/compnatural/atividade_3/char8_8x8_1.txt"));
patterns.add(p20Porcento);
p20Porcento.erro = 20;
State.x = pCorreto;
double[] weights = {2, -2};
int[] hiddens = {5, 10, 25};
List<RnaResult> results = new ArrayList<RnaResult>(20);
ReportUnit reportUnit = null;
State bestState = null;
for (int hidden : hiddens) {
for (int i = 0; i < weights.length; i += 2) {
List<ReportGraphInfo> graphInfo = new ArrayList<ReportGraphInfo>(GENERATIONS + 1);
List<ReportUnit> ds = new ArrayList<ReportUnit>(MAX_IT);
for (int k = 0; k < GENERATIONS; k++) {
graphInfo.add(new ReportGraphInfo(0d, 0d, 0));
}
RealSpecification specification = new RealSpecification();
EQMFunction function = new EQMFunction(Boolean.TRUE, hidden);
MultilayerPerceptron perceptron =
MultilayerPerceptron.getMultilayerPerceptronTangenteOneHidden(
hidden,
pCorreto.getD().length,
pCorreto.getX()[0].length,
weights[i + 1],
weights[i],
true);
State state = EQMFunction.buildState(perceptron);
for (int j = 0; j < state.getCoordinate().size(); j++) {
specification.addCoordinate("x", weights[i + 1], weights[i]);
}
log.fine("Inicio [" + weights[i + 1] + "," + weights[i] + "] - hidden(" + hidden + ")");
for (int m = 0; i < MAX_IT; i++) {
log.info("Nova iteracao " + i);
reportUnit = new ReportUnit();
GeneticAlgorithm lGenetic = new GeneticAlgorithm(50, 0.75f, 0.1f, Boolean.TRUE);
reportUnit.setAlgorithm(new Experiment.AlgorithmWrapper(lGenetic, null));
reportUnit.setFunction(function);
long ini = System.nanoTime();
specification.pm = new Float(lGenetic.pm);
state = lGenetic.optimize(GENERATIONS, function, specification, reportUnit);
if (bestState != null) {
if (bestState.getValue() < state.getValue()) {
bestState = state;
replace(graphInfo, reportUnit.getReportGraphInfos());
}
} else {
bestState = state;
replace(graphInfo, reportUnit.getReportGraphInfos());
}
reportUnit.setTime(System.nanoTime() - ini);
reportUnit.setTotalIteraction((double) hidden);
ds.add(reportUnit);
testPatterns(pCorreto, state, results, patterns, hidden);
}
Map parameters = new HashMap();
parameters.put("nome", "FInal");
parameters.put("ds", ds);
avg(graphInfo, MAX_IT);
reportUnit.setReportGraphInfo(new JRBeanCollectionDataSource(graphInfo));
ReportManager.saveReport(
"/otimizacao_grafico.jrxml", parameters, "experimento_2GA_" + hidden + ".pdf");
log.fine("Fim");
}
}
Collections.sort(
results,
new Comparator<RnaResult>() {
@Override
public int compare(RnaResult o1, RnaResult o2) {
return o2.getMatchs() - o1.getMatchs();
}
});
String result = "";
for (RnaResult rnaResult : results) {
result += rnaResult.toString();
}
log.info(result);
} catch (FileNotFoundException e) {
log.log(Level.SEVERE, "Arquivo nao encontrado", e);
} catch (IOException e) {
log.log(Level.SEVERE, "Erro ao ler o arquivo", e);
}
}