/**
* 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;
}
/**
* Runs gradient decent to tune the parameters of each hypothesis.
*
* @param iterations the number of times to run gradient decent
*/
public void tune(int iterations) {
for (Hypothesis h : hypothesis) {
// construct a new training set using One vs. Rest
// if the training example has the same value as the
// hypothesis then set the answer to 1
// otherwise set the answer to 0.
TrainingExample[] tSet = new TrainingExample[trainingSet.length];
int answer;
int i = 0;
for (TrainingExample t : trainingSet) {
if (t.getAnswer() == h.getClassification()) answer = 1;
else answer = 0;
tSet[i] = new TrainingExample(t.getInput(), answer);
++i;
}
for (i = 0; i < iterations; ++i) {
h.gradientDecent(tSet);
}
}
}
/** Prints each hypothesis along with its classification to stdout. */
public void show() {
for (Hypothesis h : hypothesis) {
System.out.print(h.getClassification() + ": ");
h.show();
}
}
