void updateNu(int *seed,
int *nAccept,
double *nu,
const int *Q,
const int *G,
const int *S,
const double *x,
const int *psi,
const int *delta,
const double *Delta,
const double *gamma2,
const double *rho,
const double *sigma2,
const double *phi,
const double *tau2Rho,
const double *a) {
unsigned int seedU = (unsigned int) *seed;
updateNu(&seedU,nAccept,nu,*Q,*G,S,x,psi,delta,Delta,*gamma2,
rho,sigma2,phi,tau2Rho,a);
*seed = (int) seedU;
return;
}
void LogConCenPH::moveCovOrBase(int i, double delta){
int numCov = cov_b.size();
if(i < numCov){
cov_b[i] = cov_b[i] + delta;
updateNu();
return;
}
int ind = actIndex[i - numCov];
move_act_b(ind, delta);
}
void IBCC::train(int noItr) {
initialize();
for(int i=0; i < noItr; ++i) {
cerr << "VB Iteration " << (i+1) << "..." << endl;
// E-step
lblCnts(i==0); // get class counts
baseCnts(i==0); // get base classifiers counts
// M-step
updateNu(); // update class priors
expctKappa(); // compute class expectations
updateAlpha(); // update base classifiers priors
expctPi(); // compute base classifiers expectations
// E-step
expctRho();
}
}
double LogConCenPH::nullk(){
// int k = x.size();
updateNu();
// double scaleValue = (1 - augLeft - augRight) / s[k-1];
double logScale = log(scaleValue);
// for(int i = 0; i < k; i++)
// s[i] = s[i] * scaleValue + augLeft;
int n_row = Lindex.size();
// double tot_rvec = 0;
double log_sum = 0;
double p_ob = 0;
int hi_ind;
int lo_ind;
for(int i = 0; i < n_row; i++){
hi_ind = Rindex[i];
lo_ind = Lindex[i];
if(hi_ind == lo_ind){
if( abs(nu[i] - 1) > 0.000000001 )
log_sum += log(nu[i]) + (b[lo_ind] + logScale) + (nu[i]-1) * log(1-s[hi_ind]);
else
log_sum += (b[lo_ind] + logScale);
continue;
}
p_ob = pow(1-s[lo_ind], nu[i]) - pow(1-s[hi_ind], nu[i]);
if(!(p_ob > 0) ) {
return(R_NegInf);
}
log_sum += log(p_ob);
}
if(log_sum == R_PosInf || log_sum == R_NegInf){
return(R_NegInf);
}
if(log_sum != log_sum){
return(R_NegInf);
}
return (log_sum);
}
