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 __init__(self, estim_param, network):
'''
Constructor
'''
# create precomputed directory if it does not exist
if not os.path.exists(estim_param.path_precomp):
os.makedirs(estim_param.path_precomp)
'''-----------------------------------------------------------------------------------------------------------------
Loads the link flow assignment matrix if is exists, otherwise calculates it
-----------------------------------------------------------------------------------------------------------------'''
if os.path.isfile(estim_param.path_precomputed_asg + "_linkFlowAssgn_data.csv") and estim_param.forceRecompAssgnment is False:
print "Loading pre-computed flow assignment matrix",
data = np.loadtxt(estim_param.path_precomputed_asg + "_linkFlowAssgn_data.csv", delimiter=",")
indices = np.loadtxt(estim_param.path_precomputed_asg + "_linkFlowAssgn_indices.csv", dtype='int_', delimiter=",")
indptr = np.loadtxt(estim_param.path_precomputed_asg + "_linkFlowAssgn_indptr.csv", dtype='int_', delimiter=",")
self.linkFlowAssgn = sps.csr_matrix((data, indices, indptr)).todense()
else:
print "Computing flow assignment matrix",
self.linkFlowAssgn = np.asmatrix(DNL.compute_assg_mat(network, estim_param))
linkFlowAssgn_csr = sps.csr_matrix(self.linkFlowAssgn)
np.savetxt(estim_param.path_precomputed_asg + "_linkFlowAssgn_data.csv", linkFlowAssgn_csr.data, delimiter=",")
np.savetxt(estim_param.path_precomputed_asg + "_linkFlowAssgn_indices.csv", linkFlowAssgn_csr.indices, fmt='%i', delimiter=",")
np.savetxt(estim_param.path_precomputed_asg + "_linkFlowAssgn_indptr.csv", linkFlowAssgn_csr.indptr, fmt='%i', delimiter=",")
self.linkFlowAssgn_prime = DNL.build_flow_assg_mat(self.linkFlowAssgn, network, estim_param, network.edges_ASE_dict)
self.subRouteAssgn_prime = DNL.build_ODflow_assg_mat(self.linkFlowAssgn, network, estim_param, network.subroutes_VS_dict)
self.accAssgn_prime = DNL.build_flow_assg_mat(self.linkFlowAssgn, network, estim_param, network.edges_TINF_dict)
estim_param.print_incr_runtime()
'''-----------------------------------------------------------------------------------------------------------------
Loads the occupation flow assignment matrix if is exists, otherwise calculates it
-----------------------------------------------------------------------------------------------------------------'''
if os.path.isfile(estim_param.path_precomputed_asg + "_acc_data.csv") and estim_param.forceRecompAssgnment is False:
print "Loading pre-computed accumulation assignment matrix",
data = np.loadtxt(estim_param.path_precomputed_asg + "_acc_data.csv", delimiter=",")
indices = np.loadtxt(estim_param.path_precomputed_asg + "_acc_indices.csv", dtype='int_', delimiter=",")
indptr = np.loadtxt(estim_param.path_precomputed_asg + "_acc_indptr.csv", dtype='int_', delimiter=",")
self.acc = sps.csr_matrix((data, indices, indptr)).todense()
#print "Occupation assignment matrix accumulation loaded."
else:
print "Computing accumulation assignment matrix",
with warnings.catch_warnings():
warnings.simplefilter("ignore") # Prevent some annoying numerical warnings from appearing
self.acc = np.asmatrix(DNL.compute_assg_mat_accumulation(network, estim_param))
acc_csr = sps.csr_matrix(self.acc)
np.savetxt(estim_param.path_precomputed_asg + "_acc_data.csv", acc_csr.data, delimiter=",")
np.savetxt(estim_param.path_precomputed_asg + "_acc_indices.csv", acc_csr.indices, fmt='%i', delimiter=",")
np.savetxt(estim_param.path_precomputed_asg + "_acc_indptr.csv", acc_csr.indptr, fmt='%i', delimiter=",")
estim_param.print_incr_runtime()
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
