@Override
public void algorithm(HttpServletRequest request) {
KNN knn0 = new KNN();
knn0.processAlgorithm();
request.setAttribute("time", knn0.ma.getTime());
request.setAttribute("KNNResult", knn0.result);
request.setAttribute("accuracyRate", knn0.ma.getAccuracyRate());
request.setAttribute("errorRate", knn0.ma.getErrorRate());
double avgAccruacyRate = 0, avgErrorRate = 0;
for (int i = 0; i < 10; i++) {
KNN knn = new KNN(new Data(i));
knn.processAlgorithm();
avgAccruacyRate += knn.ma.getAccuracyRate();
avgErrorRate += knn.ma.getErrorRate();
}
request.setAttribute("avgAccuracyRate", avgAccruacyRate / 10);
request.setAttribute("avgErrorRate", avgErrorRate / 10);
}
/**
* Apply the NNSSLGenerator method.
*
* @return
*/
public Pair<PrototypeSet, PrototypeSet> applyAlgorithm() {
System.out.print("\nThe algorithm SELF TRAINING is starting...\n Computing...\n");
PrototypeSet labeled;
PrototypeSet unlabeled;
labeled =
new PrototypeSet(
trainingDataSet.getAllDifferentFromClass(
this.numberOfClass)); // Selecting labeled prototypes from the training set.
unlabeled = new PrototypeSet(trainingDataSet.getFromClass(this.numberOfClass));
PrototypeSet tranductive = new PrototypeSet(this.transductiveDataSet.clone());
PrototypeSet test = new PrototypeSet(this.testDataSet.clone());
// We have to return the classification done.
for (int i = 0; i < this.transductiveDataSet.size(); i++) {
tranductive
.get(i)
.setFirstOutput((labeled.nearestTo(this.transductiveDataSet.get(i))).getOutput(0));
}
for (int i = 0; i < this.testDataSet.size(); i++) {
test.get(i).setFirstOutput((labeled.nearestTo(this.testDataSet.get(i))).getOutput(0));
}
// Transductive Accuracy
System.out.println(
"AccTrs ="
+ KNN.classficationAccuracy1NN(labeled, this.transductiveDataSet)
* 100.
/ this.transductiveDataSet.size());
// test accuracy
System.out.println(
"AccTst ="
+ KNN.classficationAccuracy1NN(labeled, this.testDataSet)
* 100.
/ this.testDataSet.size());
return new Pair<PrototypeSet, PrototypeSet>(tranductive, test);
}
/**
* General main for all the prototoype generators Arguments: 0: Filename with the training data
* set to be condensed. 1: Filename wich will contain the test data set
*
* @param args Arguments of the main function.
*/
public static void main(String[] args) {
Parameters.setUse("AVG", "");
Parameters.assertBasicArgs(args);
PrototypeSet training = PrototypeGenerationAlgorithm.readPrototypeSet(args[0]);
PrototypeSet test = PrototypeGenerationAlgorithm.readPrototypeSet(args[1]);
AVG generator = new AVG(training);
PrototypeSet resultingSet = generator.execute();
int accuracy1NN = KNN.classficationAccuracy(resultingSet, test);
generator.showResultsOfAccuracy(Parameters.getFileName(), accuracy1NN, test);
}