/**
* computes the cost function which is to be minimized.
*
* @param examples training data
* @return cost
*/
public double computeCost(ArrayList<Instance> examples) {
int m = examples.size();
double cost = 0.0;
for (Instance example : examples) {
double prediction = computeHypothesis(example.getAllInputs());
int label = example.getLabel();
cost += label * Math.log(prediction) + (1.0 - label) * Math.log(1.0 - prediction);
}
return -cost / m;
}
protected void _trainIteration(ArrayList<Instance> examples, double rate) {
double biasGradient = 0.0;
double[] gradients = new double[weights.length];
for (Instance example : examples) {
double error = example.getLabel() - computeHypothesis(example.getAllInputs());
biasGradient += (error);
for (int i = 0; i < gradients.length; i++) {
gradients[i] += (error) * example.getInput(i);
}
}
for (int i = 0; i < weights.length; i++) {
gradients[i] = gradients[i] / examples.size();
}
biasWeight += rate * biasGradient / examples.size();
for (int i = 0; i < weights.length; i++) {
weights[i] += rate * gradients[i] / examples.size();
}
}