/**
* Classifies the input.
*
* @param input the value for each feature.
* @return the probability associated with each classification.
*/
public HashMap<Integer, Double> classify(double[] input) {
HashMap<Integer, Double> p = new HashMap<Integer, Double>();
double tProb = 0;
for (Hypothesis h : hypothesis) {
double prob = h.predict(input);
tProb += prob;
p.put(h.getClassification(), prob);
}
for (Integer c : p.keySet()) p.put(c, p.get(c) / tProb);
return p;
}
/**
* Calculates the cost of the <code>trainingSet</code>.
*
* @param hyp the hypothesis to use in calculating the cost.
* @return the cost associated with the hypothesis.
*/
public double defaultCostFunction(Hypothesis hyp) {
double error = 0;
double h;
int answer;
for (TrainingExample t : trainingSet) {
try {
h = (Double) hyp.predict(t.getInput());
} catch (Exception e) {
e.printStackTrace();
continue;
}
answer = t.getAnswer();
error -= answer * log(h) + (1 - answer) * log(1 - h);
}
double regError = 0;
for (int i = 0; i < hyp.getNumFeatures(); ++i) {
regError += pow(hyp.getParameter(i), 2);
}
error += regError / regularizationParam;
return error / (2 * trainingSet.length);
}