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(); } }