/**
* Name: main Goal: example of JFuge's usage
*
* @param args: not used
*/
public static void main(String[] args) {
Instances train_data =
DataLoader.loadData(
null,
null,
"/Users/numa/NUMA/Dossier Ecole/HEIG/Semestre 7/PDB/code/PDB_Trezzini/src/org/cheminfo/scripting/data/iris_train.arff",
true,
-1);
Instances test_data =
DataLoader.loadData(
null,
null,
"/Users/numa/NUMA/Dossier Ecole/HEIG/Semestre 7/PDB/code/PDB_Trezzini/src/org/cheminfo/scripting/data/iris_test.arff",
true,
-1);
double mutation_rate = 0.1;
double crossover_rate = 0.2;
double selection_rate = -1;
int num_generations = 100;
String selection_algorithm = TOURNAMENT_SELECTION;
String error_algorithm = ERROR_MSE;
double elitism_rate = 0.1;
int tournament_size = 10;
int pop = 200;
double classification_weight = 1;
double error_weight = 0;
double rule_number_weight = 0;
double var_per_rule_weight = 0.1;
int rule_count = 5;
FuzzySystem fs = null;
double max = 0;
for (int i = 0; i < 1; i++) {
Coevolution ce =
new Coevolution(
train_data,
mutation_rate,
crossover_rate,
selection_rate,
pop,
num_generations,
false,
selection_algorithm,
error_algorithm,
elitism_rate,
tournament_size,
classification_weight,
error_weight,
rule_number_weight,
var_per_rule_weight,
rule_count);
FuzzySystem best = ce.evolveSystem();
System.out.println(best);
if (best.getFitness() > max) {
fs = best;
max = best.getFitness();
}
// System.out.println(best);
System.out.println(i + ": " + best.getFitness());
}
System.out.println(fs);
System.out.println(max);
double[] classif = fs.classifyInstances(test_data);
double[][] distrib = fs.distributionForInstances(test_data);
for (int i = 0; i < classif.length; i++) {
System.out.println(classif[i]);
}
for (int i = 0; i < distrib.length; i++) {
for (int j = 0; j < distrib[i].length; j++) {
System.out.print(distrib[i][j] + " ");
}
System.out.println();
}
} /*end main*/
/**
* Name: distributionForInstances
*
* @param test_data: the data to classify
* @return double[][]: the distribution for each instance
*/
public double[][] distributionForInstances(Instances test_data) {
return fs.distributionForInstances(test_data);
}