gmm_t * gmm_read(FILE *f) {
int d,k;
READANDCHECK(&d,1);
READANDCHECK(&k,1);
gmm_t *g=gmm_new(d,k);
READANDCHECK(g->w,g->k);
READANDCHECK(g->mu,g->k*g->d);
READANDCHECK(g->sigma,g->k*g->d);
return g;
}
int main()
{
const char *data_filenm = "sample_data.txt";
const int gmm_num_components = 3;
// Load data from file
FILE *fp = fopen(data_filenm, "r");
if (fp == NULL)
{
printf("ERROR: File 'sample_data.txt' not found.\nRun scripts/generate_data.py to generate sample data.\n");
exit(1);
}
int N = 0, D = 0;
size_t bytes_read, len = 0;
char *line = NULL;
while ((bytes_read = getline(&line, &len, fp)) != -1)
{
if (bytes_read > 0)
N++;
}
rewind(fp);
len = 0;
getline(&line, &len, fp);
char *token = strtok(line, " \n");
while (token != NULL)
{
D++;
token = strtok(NULL, " \n");
}
double *X = malloc(N*D*sizeof(double));
rewind(fp);
line = NULL; len = 0;
for (int t=0; t<N; t++)
{
getline(&line, &len, fp);
token = strtok(line, " \n");
X[D*t+0] = atof(token);
for (int i=1; i<D; i++)
{
token = strtok(NULL, " \n");
X[D*t+i] = atof(token);
}
}
fclose(fp);
// Train the SGMM
GMM *gmm = gmm_new(gmm_num_components, D, "diagonal");
gmm_set_convergence_tol(gmm, 1e-6);
gmm_set_regularization_value(gmm, 1e-6);
gmm_set_initialization_method(gmm, "random");
struct timeval st, en;
gettimeofday(&st, NULL);
gmm_fit(gmm, X, N);
gettimeofday(&en, NULL);
printf("Time elapsed = %lf s\n", (en.tv_sec-st.tv_sec) + (1e-6)*(en.tv_usec-st.tv_usec));
gmm_print_params(gmm);
double llh = gmm_score(gmm, X, N);
printf("Score (LLH) = %lf\n", llh);
gmm_free(gmm);
// Free data
free(X);
return 0;
}
gmm_t * gmm_learn (int di, int ni, int ki, int niter,
const float * v, int nt, int seed, int nredo,
int flags)
{
long d=di,k=ki,n=ni;
int iter, iter_tot = 0;
double old_key, key = 666;
niter = (niter == 0 ? 10000 : niter);
/* the GMM parameters */
float * p = fvec_new_0 (n * k); /* p(ci|x) for all i */
gmm_t * g = gmm_new (d, k);
/* initialize the GMM: k-means + variance estimation */
int * nassign = ivec_new (n); /* not useful -> to be removed when debugged */
float * dis = fvec_new (n);
kmeans (d, n, k, niter, v, nt, seed, nredo, g->mu, dis, NULL, nassign);
fflush (stderr);
fprintf (stderr, "assign = ");
ivec_print (nassign, k);
fprintf (stderr, "\n");
free (nassign);
/* initialization of the GMM parameters assuming a diagonal matrix */
fvec_set (g->w, k, 1.0 / k);
double sig = fvec_sum (dis, n) / n;
printf ("sigma at initialization = %.3f\n", sig);
fvec_set (g->sigma, k * d, sig);
free (dis);
/* start the EM algorithm */
fprintf (stdout, "<><><><> GMM <><><><><>\n");
if(flags & GMM_FLAGS_PURE_KMEANS) niter=0;
for (iter = 1 ; iter <= niter ; iter++) {
gmm_compute_p_thread (n, v, g, p, flags, nt);
fflush(stdout);
gmm_handle_empty(n, v, g, p);
gmm_compute_params (n, v, p, g, flags, nt);
fflush(stdout);
iter_tot++;
/* convergence reached -> leave */
old_key = key;
key = fvec_sum (g->mu, k * d);
printf ("keys %5d: %.6f -> %.6f\n", iter, old_key, key);
fflush(stdout);
if (key == old_key)
break;
}
fprintf (stderr, "\n");
free(p);
return g;
}
