public void UI() {
tw.println(
"-------------------------------------------------- User Independent -------------------------------------------------");
tw.println();
for (int numberTrainingExamples = 2; numberTrainingExamples <= 9; numberTrainingExamples++) {
tw.println(
"------------------------------------------- numberTrainingExamples="
+ numberTrainingExamples
+ " ---------------------------------------");
tw.println();
confusionMatrix = new int[26][26];
usersTrainingRecognitionRate = new double[10];
usersRecognitionRate = new double[10];
for (int user = 1; user <= 5; user++) {
UI(user, numberTrainingExamples);
}
tw.println("numberTrainingExamples=" + numberTrainingExamples + " :");
tw.println();
tw.println(Utils.matrixToString(confusionMatrix));
tw.println(Utils.matrixToStringForLatex(confusionMatrix));
double[] informations = Utils.informations(confusionMatrix);
tw.println();
tw.println("umberTrainingExamples=" + numberTrainingExamples + " :");
tw.println();
for (int user = 0; user < 5; user++)
tw.println(
"Recognition rate for training whith user"
+ (user + 1)
+ " = "
+ usersTrainingRecognitionRate[user]);
tw.println();
for (int user = 0; user < 5; user++)
tw.println("Recognition rate for user" + (user + 1) + " = " + usersRecognitionRate[user]);
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();
}
public void UI(int user, int numberTrainingExamples) {
int[][] userTrainingConfusionMatrix = new int[26][26];
List<int[][]> usersConfusionMatrixes = new ArrayList<int[][]>();
for (int i = 0; i < 5; i++) usersConfusionMatrixes.add(new int[26][26]);
for (int firstTeIndex = 1; firstTeIndex <= 10; firstTeIndex++) {
Classifier classifier = new Classifier();
for (int characterIndex = 0; characterIndex < 26; characterIndex++) {
GestureClass gc = new GestureClass(Utils.intToChar(characterIndex) + "");
int mod = 0;
for (int teIndex = firstTeIndex;
mod * 10 + teIndex
< firstTeIndex
+ numberTrainingExamples /*&& (teIndex != firstTeIndex || firstTeIndexPassage)*/;
teIndex++) {
Gesture gest = new Gesture();
// List<Dot> dotSet = (new
// RecordParser("records\\"+user+"\\"+Utils.intToChar(characterIndex)+"\\"+Utils.intToChar(characterIndex)+teIndex+".txt")).parse();
List<Dot> dotSet =
(new RecordParser(
"records/"
+ user
+ "/"
+ Utils.intToChar(characterIndex)
+ "/"
+ Utils.intToChar(characterIndex)
+ teIndex
+ ".txt"))
.parse();
gest.addGesture(dotSet, false, false, false, false);
gc.addExample(gest);
if (teIndex == 10) {
teIndex = 0;
mod = 1;
}
}
classifier.addClass(gc);
}
classifier.compile();
for (int characterIndex = 0; characterIndex < 26; characterIndex++) {
int tmp = (firstTeIndex + numberTrainingExamples) % 10;
if (tmp == 0) tmp = 10;
for (int teIndex = tmp; teIndex != firstTeIndex; teIndex = (teIndex % 10) + 1) {
for (int otherUser = 1; otherUser <= 5; otherUser++)
if (otherUser != user) {
Gesture toRecognize = new Gesture();
// List<Dot> dotSet = (new
// RecordParser("records\\"+user+"\\"+Utils.intToChar(characterIndex)+"\\"+Utils.intToChar(characterIndex)+teIndex+".txt")).parse();
List<Dot> dotSet =
(new RecordParser(
"records/"
+ otherUser
+ "/"
+ Utils.intToChar(characterIndex)
+ "/"
+ Utils.intToChar(characterIndex)
+ teIndex
+ ".txt"))
.parse();
toRecognize.addGesture(dotSet, false, false, false, false);
char foundChar = classifier.classify(toRecognize).getName().charAt(0);
confusionMatrix[characterIndex][Utils.charToInt(foundChar)]++;
userTrainingConfusionMatrix[characterIndex][Utils.charToInt(foundChar)]++;
usersConfusionMatrixes
.get(otherUser - 1)[characterIndex][Utils.charToInt(foundChar)]++;
}
}
}
System.out.println(
"nte="
+ numberTrainingExamples
+ "|user="******"|ftei="
+ firstTeIndex
+ " recognized");
}
usersTrainingRecognitionRate[user - 1] = Utils.recognitionRate(userTrainingConfusionMatrix);
for (int i = 0; i < 5; i++)
if (i != user - 1)
usersRecognitionRate[i] += Utils.recognitionRate(usersConfusionMatrixes.get(i)) / 4;
}
public void UIRubine() {
for (int i = 1; i <= 15; i += 3) {
Classifier classifier = new Classifier();
for (int j = 0; j < 26; j++) {
GestureClass gc = new GestureClass(Utils.intToChar(j) + "");
for (int k = 1; k <= 15; k++) {
if (!(k >= i && k < i + 3)) {
for (int l = 1; l <= 1; l++) {
Gesture gest = new Gesture();
List<Dot> dotSet =
(new RecordParser(
"records\\"
+ k
+ "\\"
+ Utils.intToChar(j)
+ "\\"
+ Utils.intToChar(j)
+ l
+ ".txt"))
.parse();
Iterator<Dot> it = dotSet.iterator();
while (it.hasNext()) {
Dot dot = it.next();
if (dot.valid) gest.addPoint(dot.posX, dot.posY, dot.time_millis);
}
gc.addExample(gest);
}
}
}
classifier.addClass(gc);
System.out.println(i + gc.getName());
}
classifier.compile();
for (int j = i; j < i + 3; j++) {
for (int k = 0; k < 26; k++) {
for (int l = 1; l <= 10; l++) {
Gesture toRecognize = new Gesture();
List<Dot> dotSet =
(new RecordParser(
"records\\"
+ j
+ "\\"
+ Utils.intToChar(k)
+ "\\"
+ Utils.intToChar(k)
+ l
+ ".txt"))
.parse();
Iterator<Dot> it = dotSet.iterator();
while (it.hasNext()) {
Dot dot = it.next();
if (dot.valid) toRecognize.addPoint(dot.posX, dot.posY, dot.time_millis);
}
char foundChar = classifier.classify(toRecognize).getName().charAt(0);
confusionMatrix[k][Utils.charToInt(foundChar)]++;
}
}
}
System.out.println("check :" + i);
}
}
