import cPickle
import model.joint_simple_mlp_tgtcls as model
from blocks.initialization import IsotropicGaussian, Constant
import data
n_begin_end_pts = 10 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 10
n_valid = 1000
with open("%s/arrival-clusters.pkl" % data.path) as f:
dest_tgtcls = cPickle.load(f)
# generate target classes for time prediction as a Fibonacci sequence
time_tgtcls = [1, 2]
for i in range(21):
time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2])
dim_embeddings = [
('origin_call', data.origin_call_size+1, 15),
('origin_stand', data.stands_size+1, 10),
('week_of_year', 52, 10),
('day_of_week', 7, 10),
('qhour_of_day', 24 * 4, 10),
('day_type', 3, 10),
('taxi_id', 448, 10),
]
# Common network part
dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
dim_hidden = [1000]
# Destination prediction part
dim_hidden_dest = [400]
dim_output_dest = dest_tgtcls.shape[0]
# Time prediction part
dim_hidden_time = [400]
dim_output_time = len(time_tgtcls)
# Cost ratio between distance cost and time cost
time_cost_factor = 4
embed_weights_init = IsotropicGaussian(0.01)
mlp_weights_init = IsotropicGaussian(0.1)
mlp_biases_init = Constant(0.01)
learning_rate = 0.000001
momentum = 0.99
batch_size = 200
valid_set = 'cuts/test_times_0'
