void fgmm_m_step(struct gmm * GMM,
const _fgmm_real * data,
int data_len,
_fgmm_real * pix,
int * reestimate_flag,
enum COVARIANCE_TYPE covar_t)
{
int state_i,k;
int random_point = 0;
for(state_i=0; state_i<GMM->nstates; state_i++)
{
GMM->gauss[state_i].prior = 0;
for(k=0; k<GMM->dim; k++)
GMM->gauss[state_i].mean[k] = 0;
switch(covar_t)
{
case COVARIANCE_DIAG :
GMM->gauss[state_i].prior = smat_covariance_diag(GMM->gauss[state_i].covar,
data_len,
&pix[state_i*data_len],
data,
GMM->gauss[state_i].mean);
break;
case COVARIANCE_SPHERE :
GMM->gauss[state_i].prior = smat_covariance_single(GMM->gauss[state_i].covar,
data_len,
&pix[state_i*data_len],
data,
GMM->gauss[state_i].mean);
break;
default :
GMM->gauss[state_i].prior = smat_covariance(GMM->gauss[state_i].covar,
data_len,
&pix[state_i*data_len],
data,
GMM->gauss[state_i].mean);
break;
}
// If no point belong to us, reassign to a random one ..
if(GMM->gauss[state_i].prior == 0)
{
random_point = rand()%data_len;
for(k=0; k<GMM->dim; k++)
GMM->gauss[state_i].mean[k] = data[random_point*GMM->dim + k];
*reestimate_flag = 1; // then we shall restimate mean/covar of this cluster
}
else
{
GMM->gauss[state_i].prior /= data_len;
invert_covar(&GMM->gauss[state_i]);
}
}
}
int main(int argc,char ** argv)
{
int i;
float vn=0;
int sig_perc=0, sig3_perc=0;
struct gaussian g;
float v[] = {1.,1.,1.};
float pdf=0;
_fgmm_real samp[3];
_fgmm_real * data;
_fgmm_real * weights;
int k=0;
struct smat * cv=NULL;
_fgmm_real mean[3];
_fgmm_real * pcv;
srand(time(NULL));
printf("gaussian test suite, each test uses %d samples \n",randn_samples);
printf("box_muller testing :\n");
for(i=0;i<randn_samples;i++)
{
vn = randn_boxmuller();
if( fabs(vn) < 1.)
sig_perc++;
else if( fabs(vn) >= 2.)
sig3_perc++;
}
//printf("%d %d\n",sig_perc,sig3_perc);
assert( abs(sig_perc - one_sigma*randn_samples) < randn_samples / 400);
assert( abs(sig3_perc - three_sigma*randn_samples) < randn_samples / 400);
printf("..pass \n");
/* ----------------------------------------------- */
printf("simple gaussian pdf test :\n");
gaussian_init(&g,3);
invert_covar(&g);
assert(g.dim == 3);
pdf = gaussian_pdf(&g,v);
assert(fabs(pdf - value) < 1e-5);
printf("..pass \n");
/* ----------------------------------------------- */
printf("drawing sample from gaussian test :\n");
data = (_fgmm_real *) malloc( sizeof(_fgmm_real) * randn_samples * 3);
weights = (_fgmm_real *) malloc(sizeof(_fgmm_real) * randn_samples);
for(i=0;i<randn_samples;i++)
{
weights[i] = 1.;
gaussian_draw(&g,samp);
//printf("%f %f %f\n",samp[0],samp[1],samp[2]);
for(k=0;k<3;k++)
{
data[3*i+k] = samp[k];
}
}
smat_zero(&cv,3);
smat_covariance(cv,randn_samples,weights,data,mean);
// smat_pmatrix(cv);
/* checking covariance is identity */
pcv = cv->_;
for(i=0;i<3;i++)
{
assert(fabs(mean[i]) < 1e-2);
assert( fabs(*(pcv++)- 1.) < 2e-2);
for(k=i+1;k<3;k++)
assert(fabs(*pcv++) < 1e-2);
}
printf("..pass\n");
return EXIT_SUCCESS;
}