def LA_local_non_routed_costs(alphas, input, output):
net, demand, node, features = load_LA_3()
net2, small_capacity = multiply_cognitive_cost(net, features, 1000., 3000.)
net_local = np.copy(net)
for row in range(net.shape[0]):
if small_capacity[row] == 0.0:
net_local[row, 3:] = net_local[row, 3:] * 0.
OD_non_routed_costs(alphas,
net_local,
net2,
demand,
input,
output,
verbose=1)
def LA_non_routed_costs(alphas, input, output):
net, demand, node, features = load_LA_3()
net2, small_capacity = multiply_cognitive_cost(net, features, 1000., 3000.)
OD_non_routed_costs(alphas, net, net2, demand, input, output, verbose=1)
def chicago_non_routed_costs(alphas):
net, demand, node, features = load_chicago()
net2, small_capacity = multiply_cognitive_cost(net, features, 2000., 1000.)
OD_non_routed_costs(alphas, net, net2, demand, 'data/chicago/test_{}.csv',
'data/chicago/non_routed_costs.csv')