/*
* inferece of HMM parameters based on variational Bayes method
*/
int TrainVBHMM::train(const Array2D<Real>& music_spec_td, const HyperparameterVBHMM& hyper_param){
const int T = music_spec_td.rows(), D = music_spec_td.cols();
Array2D<Array2D<PN> > prev_on_s_td_kj; // 4D-array for transition probability
// prev_on_s_td_kj(k,j)(t,d): probability of s_{t,d} being j where s_{t-1,d} = k and k = {0,1}
// p_on_s_td_(t,d): probability of s_{t,d} being 1. 1-p_on_s_td_(t,d) is the probability of s_{t,d}=0.
// 1. initialize
Real active_proportion;
if(is_silent_)
active_proportion = init_1st_time(music_spec_td, hyper_param.m0_, hyper_param.init_active_probability_, p_on_s_td_, prev_on_s_td_kj);
else
active_proportion = init(music_spec_td, p_on_s_td_, prev_on_s_td_kj);
if(active_proportion < hyper_param.active_proportion_){ // no inference for now
// the observation is too biased to silence
return 1;
}
is_silent_ = false;
// initialize the suffcient statistics
std::vector<Real> suff_1_j(2,0), suff_x_j(2,0), suff_xx_j(2,0);
update_suff_stat(music_spec_td, p_on_s_td_, suff_1_j, suff_x_j, suff_xx_j);
update_normal_gamma(music_spec_td, hyper_param, suff_1_j, suff_x_j, suff_xx_j); // hat_*** variables are updated
// 2. iteration
const Real convergence_threshold = 0.001;
Array2D<PN> before_p_on_s_td = p_on_s_td_;
for(int iter = 0; iter < hyper_param.max_iteration_; ++iter){
forward_backward(music_spec_td, p_on_s_td_, prev_on_s_td_kj);
update_alpha(hyper_param, prev_on_s_td_kj);
update_suff_stat(music_spec_td, p_on_s_td_, suff_1_j, suff_x_j, suff_xx_j);
update_normal_gamma(music_spec_td, hyper_param, suff_1_j, suff_x_j, suff_xx_j);
// check convergence
if(iter == 0)
continue;
Real diff_p=0;
for(int t = 0; t < T; ++t){
for(int d = 0; d < D; ++d){
const Real diff_td = static_cast<Real>(p_on_s_td_(t,d) - before_p_on_s_td(t,d));
diff_p += diff_td > 0 ? diff_td : -diff_td;
}
}
diff_p /= (T*D);
//printf("iter (%02d): diff_p = %f\n",iter,diff_p);
if(diff_p < convergence_threshold){
break; // end the iteration after converged
}
before_p_on_s_td = p_on_s_td_;
}
return 0;
}
int main(int argc, char *argv[])
{
char *configfile = NULL;
FILE *fin, *bin;
char *linebuf = NULL;
size_t buflen = 0;
int iterations = 3;
int mode = 3;
int c;
float d;
float *loglik;
float p;
int i, j, k;
opterr = 0;
while ((c = getopt(argc, argv, "c:n:hp:")) != -1) {
switch (c) {
case 'c':
configfile = optarg;
break;
case 'h':
usage();
exit(EXIT_SUCCESS);
case 'n':
iterations = atoi(optarg);
break;
case 'p':
mode = atoi(optarg);
if (mode != 1 && mode != 2 && mode != 3) {
fprintf(stderr, "illegal mode: %d\n", mode);
exit(EXIT_FAILURE);
}
break;
case '?':
fprintf(stderr, "illegal options\n");
exit(EXIT_FAILURE);
default:
abort();
}
}
if (configfile == NULL) {
fin = stdin;
} else {
fin = fopen(configfile, "r");
if (fin == NULL) {
handle_error("fopen");
}
}
i = 0;
while ((c = getline(&linebuf, &buflen, fin)) != -1) {
if (c <= 1 || linebuf[0] == '#')
continue;
if (i == 0) {
if (sscanf(linebuf, "%d", &nstates) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
prior = (float *) malloc(sizeof(float) * nstates);
if (prior == NULL) handle_error("malloc");
trans = (float *) malloc(sizeof(float) * nstates * nstates);
if (trans == NULL) handle_error("malloc");
xi = (float *) malloc(sizeof(float) * nstates * nstates);
if (xi == NULL) handle_error("malloc");
pi = (float *) malloc(sizeof(float) * nstates);
if (pi == NULL) handle_error("malloc");
} else if (i == 1) {
if (sscanf(linebuf, "%d", &nobvs) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
obvs = (float *) malloc(sizeof(float) * nstates * nobvs);
if (obvs == NULL) handle_error("malloc");
gmm = (float *) malloc(sizeof(float) * nstates * nobvs);
if (gmm == NULL) handle_error("malloc");
} else if (i == 2) {
/* read initial state probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nstates; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
prior[j] = logf(d);
}
fclose(bin);
} else if (i <= 2 + nstates) {
/* read state transition probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nstates; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
trans[IDX((i - 3),j,nstates)] = logf(d);
}
fclose(bin);
} else if (i <= 2 + nstates * 2) {
/* read output probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nobvs; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
obvs[IDX((i - 3 - nstates),j,nobvs)] = logf(d);
}
fclose(bin);
} else if (i == 3 + nstates * 2) {
if (sscanf(linebuf, "%d %d", &nseq, &length) != 2) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
data = (int *) malloc (sizeof(int) * nseq * length);
if (data == NULL) handle_error("malloc");
} else if (i <= 3 + nstates * 2 + nseq) {
/* read data */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < length; j++) {
if (fscanf(bin, "%d", &k) != 1 || k < 0 || k >= nobvs) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
data[(i - 4 - nstates * 2) * length + j] = k;
}
fclose(bin);
}
i++;
}
fclose(fin);
if (linebuf) free(linebuf);
if (i < 4 + nstates * 2 + nseq) {
fprintf(stderr, "configuration incomplete.\n");
freeall();
exit(EXIT_FAILURE);
}
if (mode == 3) {
loglik = (float *) malloc(sizeof(float) * nseq);
if (loglik == NULL) handle_error("malloc");
for (i = 0; i < iterations; i++) {
init_count();
for (j = 0; j < nseq; j++) {
loglik[j] = forward_backward(data + length * j, length, 1);
}
p = sumf(loglik, nseq);
update_prob();
printf("iteration %d log-likelihood: %.4f\n", i + 1, p);
printf("updated parameters:\n");
printf("# initial state probability\n");
for (j = 0; j < nstates; j++) {
printf(" %.4f", exp(prior[j]));
}
printf("\n");
printf("# state transition probability\n");
for (j = 0; j < nstates; j++) {
for (k = 0; k < nstates; k++) {
printf(" %.4f", exp(trans[IDX(j,k,nstates)]));
}
printf("\n");
}
printf("# state output probility\n");
for (j = 0; j < nstates; j++) {
for (k = 0; k < nobvs; k++) {
printf(" %.4f", exp(obvs[IDX(j,k,nobvs)]));
}
printf("\n");
}
printf("\n");
}
free(loglik);
} else if (mode == 2) {
for (i = 0; i < nseq; i++) {
viterbi(data + length * i, length);
}
} else if (mode == 1) {
loglik = (float *) malloc(sizeof(float) * nseq);
if (loglik == NULL) handle_error("malloc");
for (i = 0; i < nseq; i++) {
loglik[i] = forward_backward(data + length * i, length, 0);
}
p = sumf(loglik, nseq);
for (i = 0; i < nseq; i++)
printf("%.4f\n", loglik[i]);
printf("total: %.4f\n", p);
free(loglik);
}
freeall();
return 0;
}
