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)