import os
import cPickle
from blocks.algorithms import Momentum
from blocks.initialization import IsotropicGaussian, Constant
import data
from model.bidirectional_tgtcls import Model, Stream
with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f)
dim_embeddings = [
('origin_call', data.origin_call_train_size, 10),
('origin_stand', data.stands_size, 10),
('week_of_year', 52, 10),
('day_of_week', 7, 10),
('qhour_of_day', 24 * 4, 10),
('taxi_id', data.taxi_id_size, 10),
]
hidden_state_dim = 100
dim_hidden = [500, 500]
embed_weights_init = IsotropicGaussian(0.01)
weights_init = IsotropicGaussian(0.1)
biases_init = Constant(0.01)
batch_size = 400
batch_sort_size = 20
max_splits = 100
train_max_len = 500
window_size = 5
# monitor_freq = 10000 # temporary, for finding good learning rate
step_rule= Momentum(learning_rate=0.001, momentum=0.9)
