/** @param args */
public static void main(String[] args) {
int numStates = 3;
int numObservations = 2;
double epsilon = 1e-8;
int maxIter = 10;
double[] pi = new double[] {0.33, 0.33, 0.34};
double[][] A =
new double[][] {
{0.5, 0.3, 0.2},
{0.3, 0.5, 0.2},
{0.2, 0.4, 0.4}
};
double[][] B =
new double[][] {
{0.7, 0.3},
{0.5, 0.5},
{0.4, 0.6}
};
// Generate the data sequences for training
int D = 10000;
int T_min = 5;
int T_max = 10;
int[][][] data = HMM.generateDataSequences(D, T_min, T_max, pi, A, B);
int[][] Os = data[0];
int[][] Qs = data[1];
/*int[][] Os = new int[][] {
{0, 0, 1, 0, 1},
{0, 1, 1, 0, 1},
{1, 0, 0, 1, 0},
{0, 1, 1, 1, 0},
{0, 1, 1, 0},
{0, 1, 0, 1, 1}
};
int[][] Qs = new int[][] {
{0, 0, 1, 0, 1},
{0, 1, 1, 0, 1},
{1, 0, 0, 0, 0},
{0, 0, 1, 1, 1},
{1, 1, 1, 0},
{0, 1, 1, 1, 1}
};*/
boolean trainHMM = !false;
if (trainHMM) {
HMM HMM = new HMM(numStates, numObservations, epsilon, maxIter);
HMM.feedData(Os);
HMM.feedLabels(Qs);
HMM.train();
fprintf("True Model Parameters: \n");
fprintf("Initial State Distribution: \n");
display(pi);
fprintf("State Transition Probability Matrix: \n");
display(A);
fprintf("Observation Probability Matrix: \n");
display(B);
fprintf("Trained Model Parameters: \n");
fprintf("Initial State Distribution: \n");
display(HMM.pi);
fprintf("State Transition Probability Matrix: \n");
display(HMM.A);
fprintf("Observation Probability Matrix: \n");
display(HMM.B);
String HMMModelFilePath = "HMMModel.dat";
HMM.saveModel(HMMModelFilePath);
}
/*HMM.setPi(pi);
HMM.setA(A);
HMM.setB(B);*/
// Predict the single best state path
// int[] O = new int[] {1, 0, 1, 1, 1, 0, 0, 1};
int ID = new Random().nextInt(D);
int[] O = Os[ID];
HMM HMMt = new HMM();
HMMt.loadModel("HMMModel.dat");
int[] Q = HMMt.predict(O);
fprintf("Observation sequence: \n");
HMMt.showObservationSequence(O);
fprintf("True state sequence: \n");
HMMt.showStateSequence(Qs[ID]);
fprintf("Predicted state sequence: \n");
HMMt.showStateSequence(Q);
double p = HMMt.evaluate(O);
System.out.format("P(O|Theta) = %f\n", p);
}
