)
print(obs)
print("\nPath in terms of arcs:")
for path in obs:
string = "Orig: "
f = "Empty Path, should not happen"
for arc_index in path:
string += f"-{arc_index + 1}- => "
string += ": Dest"
print(string)
obs_fname = "my_networks_obs2.json"
write_obs_to_json(obs_fname, obs, allow_rewrite=True)
# np.core.arrayprint._line_width = 500
# obs_fname = 'my_networks_obs.json'
obs_lil = load_obs_from_json(obs_fname)
obs_ak = ak.from_json(obs_fname)
print("len ", len(obs_ak))
# silly levels of inefficiency but will fix later
# obs = np.array(obs_lil)
# obs = scipy.sparse.dok_matrix(obs_lil)
#
# DATA
distances = np.array(
string += f"({s}) -> "
string += ": Dest"
print(string)
print("\nreport arcs and nodes")
for path in obs:
string = "Orig: "
f = "Empty Path, should not happen"
for arc_index in path:
if arc_index == len(index_node_pair_map):
# pass
continue # we are at the dummy dest arc
s, f = index_node_pair_map[arc_index]
string += f"({s}) -{arc_index + 1}- => "
string += ": Dest"
print(string)
print("\nPath in terms of arcs:")
for path in obs:
string = "Orig: "
f = "Empty Path, should not happen"
for arc_index in path:
string += f"-{arc_index + 1}- => "
string += ": Dest"
print(string)
print(obs)
write_obs_to_json("sioux_out_obs.json", obs, allow_rewrite=True)
mu=1)
# obs_indices = [i for i in range(8)]
# obs = model.generate_observations(origin_indices=obs_indices,
# dest_indices=obs_indices,
# num_obs_per_pair=1, iter_cap=2000, rng_seed=1,
# )
obs_indices = [0, 1, 2, 3, 4, 5, 6, 7]
dest_indices = [0, 1, 2, 3, 4, 5, 6, 7]
obs = model.generate_observations(
origin_indices=obs_indices,
dest_indices=dest_indices,
num_obs_per_pair=2,
iter_cap=2000,
rng_seed=1,
)
print(obs)
print("\nPath in terms of arcs:")
for path in obs:
string = "Orig: "
f = "Empty Path, should not happen"
for arc_index in path:
string += f"-{arc_index + 1}- => "
string += ": Dest"
print(string)
print(obs)
write_obs_to_json("my_networks_obs.json", obs, allow_rewrite=True)
# network_struct = ModelDataStruct(data_list, incidence_mat,
# data_array_names_debug=("distances", "capacity"))
#
# beta_vec = np.array([-0.7, -0.0001])
network_struct = ModelDataStruct(data_list, incidence_mat,
data_array_names_debug=("distances",))
beta_vec = np.array([-0.4])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec, mu=1)
# orig_indices = np.arange(1, 70, 50)
orig_indices = [12]
# dest_indices = np.arange(1, 8, 2)
dest_indices = [5]
obs_per_pair = 1000
obs = model.generate_observations(origin_indices=orig_indices, dest_indices=dest_indices,
num_obs_per_pair=obs_per_pair, iter_cap=8000, rng_seed=1,
)
# print(obs)
# Print paths in terms of nodes
print("\nNode Paths")
for path in obs:
print([i +1 for i in path[1:]])
print(obs)
from recursiveRouteChoice.data_loading import write_obs_to_json
write_obs_to_json(f"beta{beta_vec[0]}obs.json", obs, allow_rewrite=False)
#
# print(obs)
# write_obs_to_json("sioux_out_obs.json", obs, allow_rewrite=True)