/**
* Name: createClassifier Goal: Evolves a fuzzy system according to given options and dataset
*
* @param train_data: the data used to compute performance of a system and provide data
* information
* @param options: the options used to control the genetic algorithm
* @return boolean: indicates if creation was successful or not
*/
public boolean createClassifier(Object train_data, String options) {
try {
this.setOptions(weka.core.Utils.splitOptions(options));
} catch (Exception e) {
this.appendError(this.getClass().getName(), "Option not recognised: " + e.getMessage());
System.out.println("option non reconnue:");
System.out.println(e.getMessage());
return false;
}
// construction du classificateur
// les options sont separee pour plus de lisibilite en cas d'erreur
Instances data = null;
if (train_data instanceof String)
data = DataLoader.loadData(null, null, (String) train_data, true, -1);
else if (train_data instanceof Instances) data = (Instances) train_data;
else {
System.out.println("train data not supported!");
this.appendError(this.getClass().getName(), "Training data format not supported");
return false;
}
this.data = data;
Coevolution ce =
new Coevolution(
data,
mutation_rate,
crossover_rate,
selection_rate,
pop_size,
num_generations,
is_binary,
selection_algorithm,
error_algorithm,
elitism_rate,
tournament_size,
classification_weight,
error_weight,
rule_number_weight,
var_per_rule_weight,
rule_count);
fs = ce.evolveSystem();
fs.setData(data);
best_fitnesses = ce.getBestFitnesses();
if (getLogLevel() > 2)
this.appendInfo(this.getClass().getName(), "Classifier was successfully created and trained");
return true;
} /*end createClassifier*/
/**
* Name: createClassifier Goal: Allows creation, option setting and training of a J48 classifier
*
* @param options: the options of the classifier to be set
* @param train_data: training data for the classifier. May be the path to the file, the file or a
* set of instances
* @return Classifier: returns the trained classifier or null if creation was unsuccessful
*/
public boolean createClassifier(String options, Object train_data) {
tree = new J48();
// set des options
try {
String[] opts = weka.core.Utils.splitOptions(options);
tree.setOptions(opts);
} catch (Exception e) {
this.appendError(this.getClass().getName(), "Option not recognised: " + e.getMessage());
System.out.println("option non reconnue:");
System.out.println(e.getMessage());
return false;
}
// construction du classificateur
// les options sont separee pour plus de lisibilite en cas d'erreur
Instances data = null;
if (train_data instanceof String)
data = DataLoader.loadData(null, null, (String) train_data, true, -1);
else if (train_data instanceof Instances) data = (Instances) train_data;
else {
System.out.println("train data not supported!");
this.appendError(this.getClass().getName(), "Training data format not supported");
return false;
}
try {
tree.buildClassifier(data);
} catch (Exception e) {
this.appendError(
this.getClass().getName(), "Classifier could not be created: " + e.getMessage());
System.out.println("le classificateur n'a pas pu etre construit:");
System.out.println(e.getMessage());
return false;
}
if (getLogLevel() > 2)
this.appendInfo(this.getClass().getName(), "Classifier was successfully created and trained");
return true;
} /*end createClassifier*/
/**
* 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*/