public static Vector<Double> trainNode(
String filename,
int outputRepresentation,
String inputRepresentation,
int filter,
double learningRate,
int epochs) {
Vector<Double> node = null; // initialize node to null
double error = 1; // initialize error
double output = 0; // initialize output
Vector<Double> trainError = new Vector<Double>();
double meansq = 0;
String binTarget = "";
String binaryRep = "";
int target;
openFile(filename); // open our file for training
firstRead = true;
int targetCounter = 0;
while ((target = readFileIntoInputVector(inputRepresentation))
!= -2) { // while we have not reached out end of file error
if (outputRepresentation == 4) {
binTarget = Integer.toBinaryString(target); // convert target to binary
int[] targetArray = new int[4]; // array to hold binary ints
for (int h = 0; h < targetArray.length; h++) { // add binary elements to int array
try {
targetArray[targetArray.length - h - 1] =
Character.digit(binTarget.charAt(binTarget.length() - h - 1), 10);
} catch (IndexOutOfBoundsException e) { // if there is empty spaces, fill with zeros
targetArray[targetArray.length - h - 1] = 0;
}
}
target = targetArray[filter];
binTarget = "";
for (int i = 0; i < targetArray.length; i++) {
binTarget = binTarget + targetArray[i];
}
}
if ((filter == target && outputRepresentation == 10) || outputRepresentation != 10) {
if (node == null) {
node = new Vector<Double>();
for (int i = 0; i < inputs.size(); i++) { // for each input
double randWeight = 1; // initialize random weight
while (randWeight > 0.15) // while out random isn't less than 0.15
randWeight = random.nextDouble(); // get a new random double
if (random.nextBoolean()) randWeight = randWeight * -1; // randomly set to negative
node.addElement(new Double(randWeight)); // store random weight
}
}
targetCounter++;
// train for number of epochs
for (int i = 0; i < epochs; i++) { // for each epoch
double weightedSum = 0; // set/reset the weighted sum to 0
for (int j = 0; j < inputs.size(); j++) {
weightedSum += (inputs.elementAt(j) * node.elementAt(j)); // calculate weighted sum
}
output = activation(weightedSum); // retrieve output
if (outputRepresentation == 1 || outputRepresentation == 10) output = output * 10;
else if (outputRepresentation == 4) { // each node must have a whole number
if (output > 0.5) output = 1;
else output = 0;
}
error = target - output; // calculate error
if (i == epochs - 1) {
if (outputRepresentation == 4) {
if (filter == 0) {
allOutputs.ensureCapacity(targetCounter);
allOutputs.add(Integer.toString((int) output));
} else {
allOutputs.set(
targetCounter - 1, allOutputs.get(targetCounter - 1) + ((int) output));
}
}
trainError.add(error);
if (Math.round(error) == 0 && outputRepresentation != 4) {
trainCountCorrect++;
} else if (outputRepresentation == 4 && filter == 3) {
if (allOutputs.get(targetCounter - 1).equals(binTarget)) {
trainCountCorrect++;
}
}
}
for (int k = 0; k < inputs.size(); k++) { // for each input
double derivative =
(1.64872 * Math.exp(weightedSum))
/ (Math.pow(1.64872 + Math.exp(weightedSum), 2)); // calculate new weights
double newWeight =
node.elementAt(k)
+ (learningRate * inputs.elementAt(k) * error * derivative); // cont.
node.setElementAt(newWeight, k); // update weights
}
}
}
inputs.clear();
}
errorVect.addElement((Vector<Double>) trainError.clone());
trainError.clear();
if (outputRepresentation == 4 && filter == 3)
System.out.println(
"Training Percent: " + (100 * (double) trainCountCorrect / (double) targetCounter));
if (outputRepresentation == 10 && filter == 9)
System.out.println(
"Training Percent: " + (10 * (double) trainCountCorrect / (double) targetCounter));
if (outputRepresentation == 1)
System.out.println(
"Training Percent: " + (100 * (double) trainCountCorrect / (double) targetCounter));
closeFile();
return node; // node is now trained for an epoch
}