//---------------------------------------------------------------------- void BLCSSS::rwm_draw_chunk(int chunk){ clock_t start = clock(); const Selector &inc(m_->coef().inc()); int nvars = inc.nvars(); Vec full_nonzero_beta = m_->beta(); // only nonzero components // Compute information matrix for proposal distribution. For // efficiency, also compute the log-posterior of the current beta. Vec mu(inc.select(pri_->mu())); Spd siginv(inc.select(pri_->siginv())); double original_logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true); const std::vector<Ptr<BinomialRegressionData> > &data(m_->dat()); int nobs = data.size(); int full_chunk_size = compute_chunk_size(); int chunk_start = chunk * full_chunk_size; int elements_remaining = nvars - chunk_start; int this_chunk_size = std::min(elements_remaining, full_chunk_size); Selector chunk_selector(nvars, false); for(int i = chunk_start; i< chunk_start + this_chunk_size; ++i) { chunk_selector.add(i); } Spd proposal_ivar = chunk_selector.select(siginv); for(int i = 0; i < nobs; ++i){ Vec x = inc.select(data[i]->x()); double eta = x.dot(full_nonzero_beta); double prob = plogis(eta); double weight = prob * (1-prob); VectorView x_chunk(x, chunk_start, this_chunk_size); // Only upper triangle is accessed. Need to reflect at end of loop. proposal_ivar.add_outer(x_chunk, weight, false); int yi = data[i]->y(); int ni = data[i]->n(); original_logpost += dbinom(yi, ni, prob, true); } proposal_ivar.reflect(); VectorView beta_chunk(full_nonzero_beta, chunk_start, this_chunk_size); if(tdf_ > 0){ beta_chunk = rmvt_ivar_mt( rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_, tdf_); }else{ beta_chunk = rmvn_ivar_mt( rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_); } double logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true); Vec full_beta(inc.expand(full_nonzero_beta)); logpost += m_->log_likelihood(full_beta, 0, 0, false); double log_alpha = logpost - original_logpost; double logu = log(runif_mt(rng())); ++rwm_chunk_attempts_; if(logu < log_alpha){ m_->set_beta(full_nonzero_beta); ++rwm_chunk_successes_; } clock_t end = clock(); rwm_chunk_times_ += double(end - start) / CLOCKS_PER_SEC; }
//---------------------------------------------------------------------- void BLCSSS::rwm_draw_chunk(int chunk){ const Selector &inc(m_->coef().inc()); int nvars = inc.nvars(); Vector full_nonzero_beta = m_->included_coefficients(); // Compute information matrix for proposal distribution. For // efficiency, also compute the log-posterior of the current beta. Vector mu(inc.select(pri_->mu())); SpdMatrix siginv(inc.select(pri_->siginv())); double original_logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true); const std::vector<Ptr<BinomialRegressionData> > &data(m_->dat()); int nobs = data.size(); int full_chunk_size = compute_chunk_size(max_rwm_chunk_size_); int chunk_start = chunk * full_chunk_size; int elements_remaining = nvars - chunk_start; int this_chunk_size = std::min(elements_remaining, full_chunk_size); Selector chunk_selector(nvars, false); for(int i = chunk_start; i< chunk_start + this_chunk_size; ++i) { chunk_selector.add(i); } SpdMatrix proposal_ivar = chunk_selector.select(siginv); for(int i = 0; i < nobs; ++i){ Vector x = inc.select(data[i]->x()); double eta = x.dot(full_nonzero_beta); double prob = plogis(eta); double weight = prob * (1-prob); VectorView x_chunk(x, chunk_start, this_chunk_size); // Only upper triangle is accessed. Need to reflect at end of loop. proposal_ivar.add_outer(x_chunk, weight, false); original_logpost += dbinom(data[i]->y(), data[i]->n(), prob, true); } proposal_ivar.reflect(); VectorView beta_chunk(full_nonzero_beta, chunk_start, this_chunk_size); if(tdf_ > 0){ beta_chunk = rmvt_ivar_mt( rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_, tdf_); }else{ beta_chunk = rmvn_ivar_mt( rng(), beta_chunk, proposal_ivar / rwm_variance_scale_factor_); } double logpost = dmvn(full_nonzero_beta, mu, siginv, 0, true); Vector full_beta(inc.expand(full_nonzero_beta)); logpost += m_->log_likelihood(full_beta, 0, 0, false); double log_alpha = logpost - original_logpost; double logu = log(runif_mt(rng())); if (logu < log_alpha) { m_->set_included_coefficients(full_nonzero_beta); move_accounting_.record_acceptance("rwm_chunk"); } else { move_accounting_.record_rejection("rwm_chunk"); } }
//---------------------------------------------------------------------- double BinomialLogitLogPostChunk::operator()( const Vector &beta_chunk, Vector &grad, Matrix &hess, int nd)const{ Vector nonzero_beta = m_->included_coefficients(); VectorView nonzero_beta_chunk(nonzero_beta, start_, chunk_size_); nonzero_beta_chunk = beta_chunk; const std::vector<Ptr<BinomialRegressionData> > &data(m_->dat()); const Selector &inc(m_->coef().inc()); const SpdMatrix siginv(inc.select(pri_->siginv())); const Vector mu(inc.select(pri_->mu())); double ans = dmvn(nonzero_beta, mu, siginv, 0.0, true); if(nd > 0){ Selector chunk_selector(nonzero_beta.size(), false); for(int i = start_; i < start_ + chunk_size_; ++i) chunk_selector.add(i); grad = -1*chunk_selector.select(siginv * (nonzero_beta - mu)); if(nd > 1) { hess = chunk_selector.select(siginv); hess *= -1; } } int nobs = data.size(); for(int i = 0; i < nobs; ++i){ double yi = data[i]->y(); double ni = data[i]->n(); Vector x = inc.select(data[i]->x()); double eta = nonzero_beta.dot(x); double prob = plogis(eta); ans += dbinom(yi, ni, prob, true); if(nd > 0){ const ConstVectorView x_chunk(x, start_, chunk_size_); grad.axpy(x_chunk, yi - ni*prob); if(nd > 1){ hess.add_outer(x_chunk, x_chunk, -ni * prob * (1-prob)); } } } return ans; }