//------------------------------------------------------------
double MLB::logp(const ChoiceData & dp)const{
// for right now... assumes all choices are available to everyone
// uint n = dp->n_avail();
wsp.resize(nch_);
fill_eta(dp, wsp);
uint y = dp.value();
double ans = wsp[y] - lse(wsp);
return ans;
}
void MLVSS::update(const ChoiceData &dp, const Vec & wgts, const Vec &u){
const Mat & X(dp.X(false)); // 'false' means omit columns
xtwx_.add_inner(X, wgts, false); // corresponding to subject X's at
xtwu_ += X.Tmult(wgts*u); // choice level 0.
sym_ = false;
for (int i = 0; i < wgts.size(); ++i) {
weighted_sum_of_squares_ += wgts[i] * square(u[i]);
}
}
void MlvsDataImputer::impute_latent_data_point(const ChoiceData &dp,
SufficientStatistics *suf,
RNG &rng) {
model_->fill_eta(dp, eta); // eta+= downsampling_logprob
if (downsampling_) eta += log_sampling_probs_; //
uint M = model_->Nchoices();
uint y = dp.value();
assert(y < M);
double loglam = lse(eta);
double logzmin = rlexp_mt(rng, loglam);
u[y] = -logzmin;
for (uint m = 0; m < M; ++m) {
if (m != y) {
double tmp = rlexp_mt(rng, eta[m]);
double logz = lse2(logzmin, tmp);
u[m] = -logz;
}
uint k = unmix(rng, u[m] - eta[m]);
u[m] -= mu_[k];
wgts[m] = sigsq_inv_[k];
}
suf->update(dp, wgts, u);
}
