/**
* Compute P(O|Theta), the probability of the observation sequence given the model, by forward
* recursion with scaling.
*
* @param O an observation sequence
* @return P(O|Theta)
*/
public double evaluate(int[] O) {
// Forward Recursion with Scaling
int T = O.length;
double[] c = allocateVector(T);
double[] alpha_hat_t = allocateVector(N);
double[] alpha_hat_t_plus_1 = allocateVector(N);
double[] temp_alpha = null;
double log_likelihood = 0;
for (int t = 0; t < T; t++) {
if (t == 0) {
for (int i = 0; i < N; i++) {
alpha_hat_t[i] = pi[i] * B[i][O[0]];
}
} else {
clearVector(alpha_hat_t_plus_1);
for (int j = 0; j < N; j++) {
for (int i = 0; i < N; i++) {
alpha_hat_t_plus_1[j] += alpha_hat_t[i] * A[i][j] * B[j][O[t]];
}
}
temp_alpha = alpha_hat_t;
alpha_hat_t = alpha_hat_t_plus_1;
alpha_hat_t_plus_1 = temp_alpha;
}
c[t] = 1.0 / sum(alpha_hat_t);
timesAssign(alpha_hat_t, c[t]);
log_likelihood -= Math.log(c[t]);
}
return Math.exp(log_likelihood);
}
/**
* Inference the basic HMM with scaling. Memory complexity is O(TN) + O(N^2) + O(NM), and
* computation complexity is O(tDTN^2), where t is the number of outer iterations.
*/
public void train() {
int D = Os.length;
int T_n = 0;
double log_likelihood = 0;
double log_likelihood_new = 0;
double epsilon = this.epsilon;
int maxIter = this.maxIter;
// Initialization
clearVector(pi);
clearMatrix(A);
clearMatrix(B);
double[] a = allocateVector(N);
double[] b = allocateVector(N);
int[] Q_n = null;
int[] O_n = null;
if (Qs == null) {
pi = initializePi();
A = initializeA();
B = initializeB();
} else {
for (int n = 0; n < D; n++) {
Q_n = Qs[n];
O_n = Os[n];
T_n = Os[n].length;
for (int t = 0; t < T_n; t++) {
if (t < T_n - 1) {
A[Q_n[t]][Q_n[t + 1]] += 1;
a[Q_n[t]] += 1;
if (t == 0) {
pi[Q_n[0]] += 1;
}
}
B[Q_n[t]][O_n[t]] += 1;
b[Q_n[t]] += 1;
}
}
divideAssign(pi, D);
for (int i = 0; i < N; i++) {
divideAssign(A[i], a[i]);
divideAssign(B[i], b[i]);
}
}
int s = 0;
double[] pi_new = allocateVector(N);
double[][] A_new = allocateMatrix(N, N);
double[][] B_new = allocateMatrix(N, M);
double[] temp_pi = null;
double[][] temp_A = null;
double[][] temp_B = null;
double[][] alpha_hat = null;
double[][] beta_hat = null;
double[] c_n = null;
double[][] xi = allocateMatrix(N, N);
double[] gamma = allocateVector(N);
do {
// Clearance
clearVector(pi_new);
clearMatrix(A_new);
clearMatrix(B_new);
clearVector(a);
clearVector(b);
/*clearMatrix(xi);
clearVector(gamma);*/
log_likelihood_new = 0;
for (int n = 0; n < D; n++) {
// Q_n = Qs[n];
O_n = Os[n];
T_n = Os[n].length;
c_n = allocateVector(T_n);
alpha_hat = allocateMatrix(T_n, N);
beta_hat = allocateMatrix(T_n, N);
// Forward Recursion with Scaling
for (int t = 0; t <= T_n - 1; t++) {
if (t == 0) {
for (int i = 0; i < N; i++) {
alpha_hat[0][i] = pi[i] * B[i][O_n[0]];
}
} else {
for (int j = 0; j < N; j++) {
for (int i = 0; i < N; i++) {
alpha_hat[t][j] += alpha_hat[t - 1][i] * A[i][j] * B[j][O_n[t]];
}
}
}
c_n[t] = 1.0 / sum(alpha_hat[t]);
timesAssign(alpha_hat[t], c_n[t]);
}
// Backward Recursion with Scaling
for (int t = T_n + 1; t >= 2; t--) {
if (t == T_n + 1) {
for (int i = 0; i < N; i++) {
beta_hat[t - 2][i] = 1;
}
}
if (t <= T_n) {
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
beta_hat[t - 2][i] += A[i][j] * B[j][O_n[t - 1]] * beta_hat[t - 1][j];
}
}
}
timesAssign(beta_hat[t - 2], c_n[t - 2]);
}
// Expectation Variables and Updating Model Parameters
for (int t = 0; t <= T_n - 1; t++) {
if (t < T_n - 1) {
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
xi[i][j] = alpha_hat[t][i] * A[i][j] * B[j][O_n[t + 1]] * beta_hat[t + 1][j];
// A_new[i][j] += xi[i][j];
}
plusAssign(A_new[i], xi[i]);
gamma[i] = sum(xi[i]);
}
if (t == 0) {
plusAssign(pi_new, gamma);
}
plusAssign(a, gamma);
} else {
assignVector(gamma, alpha_hat[t]);
}
for (int j = 0; j < N; j++) {
B_new[j][O_n[t]] += gamma[j];
}
plusAssign(b, gamma);
log_likelihood_new += -Math.log(c_n[t]);
}
}
// Normalization (Sum to One)
sum2one(pi_new);
for (int i = 0; i < N; i++) {
divideAssign(A_new[i], a[i]);
}
for (int j = 0; j < N; j++) {
divideAssign(B_new[j], b[j]);
}
temp_pi = pi;
pi = pi_new;
pi_new = temp_pi;
temp_A = A;
A = A_new;
A_new = temp_A;
temp_B = B;
B = B_new;
B_new = temp_B;
// display(B);
s = s + 1;
if (s > 1) {
if (Math.abs((log_likelihood_new - log_likelihood) / log_likelihood) < epsilon) {
fprintf("log[P(O|Theta)] does not increase.\n\n");
break;
}
}
log_likelihood = log_likelihood_new;
fprintf("Iter: %d, log[P(O|Theta)]: %f\n", s, log_likelihood);
} while (s < maxIter);
}
