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import cPickle
import data
import model.dest_simple_mlp_tgtcls_alexandre as model
n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
n_end_pts = 5
n_valid = 1000
with open(data.DATA_PATH + "/arrival-clusters.pkl") as f: tgtcls = cPickle.load(f)
dim_embeddings = [
('origin_call', data.n_train_clients+1, 10),
('origin_stand', data.n_stands+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),
]
dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
dim_hidden = [500]
dim_output = tgtcls.shape[0]
learning_rate = 0.01
momentum = 0.9
batch_size = 200
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