rowvec rand_dirichlet(const rowvec &alpha) {
rowvec ret(alpha.size());
for (unsigned int i = 0; i < ret.size(); ++i) {
gamma_distribution<> g(alpha[i], 1);
ret[i] = g(GENERATOR);
}
normalize_simplex(ret);
return ret;
}
void normalize_simplex(rowvec &x) {
double sum = 0;
for (auto &xi : x) {
assert(xi >= 0);
sum += xi;
}
if (sum < numeric_limits<double>::epsilon()) {
x.fill(1.0 / x.size());
} else {
x /= sum;
}
}
void assignWinners(mat bids, rowvec prices, umat & assignments) {
uword
winnerIdx = 0,
nItems = prices.size();
double winningBid;
for(int item = 0; item < nItems; item++) {
vec winner = getMaxItemBid(item, bids);
winnerIdx = winner(0);
winningBid = winner(1);
if(winningBid < 0.0)
continue;
prices(item) += winningBid;
assignments.col(item).fill(0);
assignments(winnerIdx, item) = 1;
}
}
mat compute_hessian_theta1_delta_weighted(uint i, mat station_data, uint wdclat1_col, uint wdclon1_col,
double pointslat1_i, double pointslon1_i, double beta1, double sigma0, colvec xdeltain,
uvec st_point_list_uvec, rowvec deltain_row, urowvec mat_st_state_row,
NumericVector xv0_vec, uint focal_station_index, uint xtheta1_size,
double point_density_i, rowvec points_den_covariates) {
rowvec station_data_dis_vIdx = conv_to< rowvec >::from(latlondistance(station_data.col(wdclat1_col),
station_data.col(wdclon1_col), pointslat1_i, pointslon1_i));
rowvec util = exp(beta1*station_data_dis_vIdx + deltain_row)% (mat_st_state_row==0);
double den_util = sum(util);
uint no_t_st = util.size();
//rowvec lambda_st_t(no_t_st,fill::zeros);
mat hessian_beta1_delta_t(1,no_t_st,fill::zeros);
mat hessian_theta1_delta_t(xtheta1_size,no_t_st,fill::zeros);
rowvec grad_delta(no_t_st,fill::zeros);
uvec no_focal_indexes(no_t_st,fill::zeros);
//fill no_focal_indexes with index sequence
//find more efficient way to do this
for(uint m=0; m<no_focal_indexes.size(); ++m) {
no_focal_indexes(m)=m;
}
no_focal_indexes.shed_row(focal_station_index);
for(int m=0; m<xv0_vec.size(); ++m) {
double out = exp(-xv0_vec(m)*sigma0);
double denutil_t = den_util+out;
rowvec util_prob_t = util/denutil_t;
rowvec disP = util_prob_t%station_data_dis_vIdx;
double disP_sum = sum( disP);
// rowvec disP_sum_vec(no_t_st);
// disP_sum_vec.fill(disP_sum);
vec val1(no_t_st,fill::zeros);
val1 = station_data_dis_vIdx
val1 += station_data_dis_vIdx(focal_station_index) - 2*disP_sum_vec;
val1 = val1 % util_prob_t;
val1 *= -util_prob_t(focal_station_index);
//remove focal_station_index from val1 as it is incorrect.
val1.shed_row(focal_station_index);
hessian_beta1_delta_t(0,no_focal_indexes) += val1;
hessian_beta1_delta_t(0,focal_station_index) += util_prob_t(focal_station_index) * (1-2*util_prob_t(focal_station_index))\
(station_data_dis_vIdx(focal_station_index)-disP_sum);
grad_delta -= util_prob_t(focal_station_index)*util_prob_t;
grad_delta(focal_station_index) += util_prob_t(focal_station_index);
}
grad_delta *= (1/xv0_vec.size());
hessian_beta1_delta_t *= (1/xv0_vec.size())* point_density_i;
mat hessian_thetaden_delta_t = points_den_covariates.t() * grad_delta;
assert(hessian_thetaden_delta_t.n_rows==points_den_covariates.size());
assert(hessian_thetaden_delta_t.n_cols==grad_delta.size());
hessian_theta1_delta_t.row(0)=hessian_beta1_delta_t;
hessian_theta1_delta_t.rows(span(2,xtheta1_size-1))=hessian_thetaden_delta_t;
return((hessian_theta1_delta_t));
}