def monteCarloIndicator(self, estim_param, estimate, space_dict, assg_map):
'''
Calculates indicators from the MC runs
'''
start_index = estim_param.tint_eval_start * len(space_dict)
end_index = (estim_param.tint_eval_start +
len(estim_param.tint_eval_dict)) * len(space_dict)
if estim_param.MC_iterations > 1:
IndDist = np.zeros((len(estim_param.tint_dict) * len(space_dict),
estim_param.MC_iterations))
for iteration in range(estim_param.MC_iterations):
IndDist[:, iteration] = np.asarray(
DNL.apply_assg(assg_map, estimate.demand_distr[:,
iteration]))
ind_mean = np.mean(IndDist[start_index:end_index, :], axis=1)
ind_std = np.std(IndDist[start_index:end_index, :], axis=1)
ind_dist = IndDist[start_index:end_index, :]
else:
ind_mean = np.asarray(
DNL.apply_assg(np.asarray(assg_map),
estimate.demand_distr))[start_index:end_index]
ind_std = None
ind_dist = ind_mean
return ind_mean, ind_std, ind_dist
def monteCarloIndicator(self, estim_param, estimate, space_dict, assg_map):
"""
Calculates indicators from the MC runs
"""
start_index = estim_param.tint_eval_start * len(space_dict)
end_index = (estim_param.tint_eval_start + len(estim_param.tint_eval_dict)) * len(space_dict)
if estim_param.MC_iterations > 1:
IndDist = np.zeros((len(estim_param.tint_dict) * len(space_dict), estim_param.MC_iterations))
for iteration in range(estim_param.MC_iterations):
IndDist[:, iteration] = np.asarray(DNL.apply_assg(assg_map, estimate.demand_distr[:, iteration]))
ind_mean = np.mean(IndDist[start_index:end_index, :], axis=1)
ind_std = np.std(IndDist[start_index:end_index, :], axis=1)
ind_dist = IndDist[start_index:end_index, :]
else:
ind_mean = np.asarray(DNL.apply_assg(np.asarray(assg_map), estimate.demand_distr))[start_index:end_index]
ind_std = None
ind_dist = ind_mean
return ind_mean, ind_std, ind_dist
