public void UI() {
for (int normalisation = 0; normalisation <= 0; normalisation++) {
tw = new TraceWriter("UI2LettersStochasticLevenshtein6Norm" + normalisation + ".txt");
tw.println(
"-------------------------------------------------- User Dependent -------------------------------------------------");
tw.println();
for (int numberTrainingExamples = 6; numberTrainingExamples <= 6; numberTrainingExamples++) {
confusionMatrixes.clear();
for (int knn = 0; knn < numberTrainingExamples; knn++)
confusionMatrixes.add(new int[26][26]);
tw.println(
"------------------------------------------- numberTrainingExamples="
+ numberTrainingExamples
+ " ---------------------------------------");
tw.println();
userRecognitionRates = new double[10][9];
for (int user = 1; user <= 10; user++) {
UI(user, numberTrainingExamples, normalisation);
}
for (int knn = 0; knn < confusionMatrixes.size(); knn++) {
tw.println(
"normalisation="
+ normalisation
+ " numberTrainingExamples="
+ numberTrainingExamples
+ " knn="
+ (knn + 1)
+ " :");
tw.println();
tw.println(Utils.matrixToString(confusionMatrixes.get(knn)));
tw.println(Utils.matrixToStringForLatex(confusionMatrixes.get(knn)));
double[] informations = Utils.informations(confusionMatrixes.get(knn));
tw.println();
tw.println(
"normalisation="
+ normalisation
+ " numberTrainingExamples="
+ numberTrainingExamples
+ " knn="
+ (knn + 1)
+ " :");
tw.println();
for (int user = 0; user < 10; user++)
tw.println(
"Recognition rate for user" + (user + 1) + " = " + userRecognitionRates[user][knn]);
tw.println();
tw.println("Goodclass examples = " + informations[0]);
tw.println("Badclass examples = " + informations[1]);
tw.println("total examples = " + informations[2]);
tw.println("Recognition rate = " + informations[3]);
tw.println(
"----------------------------------------------------------------------------------------------------------------");
tw.println();
tw.println();
tw.println();
}
tw.println(
"================================================================================================================");
tw.println();
}
tw.close();
}
}
