public void UD(int nbUsers, int numberTrainingExamples, int normalisation) {
List<List<int[][]>> usersConfusionMatrixes = new ArrayList<List<int[][]>>();
for (int user = 1; user <= nbUsers; user++) {
List<int[][]> userConfusionMatrixes = new ArrayList<int[][]>();
for (int knn = 0; knn < numberTrainingExamples; knn++)
userConfusionMatrixes.add(new int[10][10]);
usersConfusionMatrixes.add(userConfusionMatrixes);
}
String[] alphabet = {"0", "1", "2", "3", "4", "5", "6", "7"};
ConditionalRecognizer cr = new ConditionalRecognizer(alphabet);
for (int user = 1; user <= nbUsers; user++)
for (int teIndex = 1; teIndex <= numberTrainingExamples; teIndex++)
cr.addTemplate(user + "", strings.get("" + user + "signature" + teIndex));
cr.compile(normalisation);
for (int knn = numberTrainingExamples; knn <= numberTrainingExamples; knn++) {
for (int user = 1; user <= nbUsers; user++)
for (int teIndex = numberTrainingExamples + 1; teIndex <= 30; teIndex++) {
String[] foundSigs =
cr.recognizeForAllKnn(strings.get(user + "signature" + teIndex), normalisation, knn);
for (int i = 0; i < foundSigs.length; i++) {
String foundSig = foundSigs[i];
confusionMatrixes.get(i)[user - 1][Integer.parseInt(foundSig) - 1]++;
usersConfusionMatrixes.get(user - 1).get(i)[user - 1][Integer.parseInt(foundSig) - 1]++;
}
}
System.out.println(
"norm="
+ normalisation
+ "|nte="
+ numberTrainingExamples
+ "|nbUsers="
+ nbUsers
+ " recognized");
}
for (int user = 1; user <= nbUsers; user++)
for (int knn = 0; knn < numberTrainingExamples; knn++)
userRecognitionRates[user - 1][knn] =
Utils.recognitionRate(usersConfusionMatrixes.get(user - 1).get(knn));
// tw.println("norm="+normalisation+"|nte="+numberTrainingExamples+"|user="******": recognition
// rate = "+Utils.recognitionRate(userConfusionMatrix));
}
public void UD() {
for (int normalisation = 0; normalisation <= 0; normalisation++) {
for (int nbUsers = 2; nbUsers <= 2; nbUsers++) {
for (int numberTrainingExamples = 4;
numberTrainingExamples <= 4;
numberTrainingExamples++) {
tw =
new TraceWriter(
"SignaturesStochasticLevenshteinNorm"
+ normalisation
+ "Nte"
+ numberTrainingExamples
+ ".txt");
confusionMatrixes.clear();
for (int knn = 0; knn < numberTrainingExamples; knn++)
confusionMatrixes.add(new int[10][10]);
tw.println(
"------------------------------------------- numberTrainingExamples="
+ numberTrainingExamples
+ " ---------------------------------------");
tw.println();
userRecognitionRates = new double[10][29];
UD(nbUsers, 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();
}
}
}
}