#!/usr/bin/env/python
#-*python-*-
from paddle.trainer_config_helpers import *


################################### DATA Configuration #############################################
is_predict = get_config_arg('is_predict', bool, False)
trn = './data/train.list' if not is_predict else None
tst = './data/test.list' if not is_predict else './data/pred.list'
process = 'process' if not is_predict else 'process_predict'
define_py_data_sources2(train_list=trn,
                        test_list=tst,
                        module="dataprovider",
                        obj=process)
################################### Parameter Configuaration #######################################
TERM_NUM=24
FORECASTING_NUM= 25
emb_size=16
batch_size=128 if not is_predict else 1
settings(
    batch_size = batch_size,
    learning_rate = 1e-3,
    learning_method = RMSPropOptimizer()
)
################################### Algorithm Configuration ########################################

output_label = []

link_encode = data_layer(name='link_encode', size=TERM_NUM)
for i in xrange(FORECASTING_NUM):
    # Each task share same weight.
    link_param = ParamAttr(name='_link_vec.w', initial_max=1.0, initial_min=-1.0)
    link_vec = fc_layer(input=link_encode,size=emb_size, param_attr=link_param)
    score = fc_layer(input=link_vec, size=4, act=SoftmaxActivation())
    if is_predict:
        maxid = maxid_layer(score)
        output_label.append(maxid)
    else:
        # Multi-task training.
        label = data_layer(name='label_%dmin'%((i+1)*5), size=4)
        cls = classification_cost(input=score,name="cost_%dmin"%((i+1)*5), label=label)
        output_label.append(cls)
outputs(output_label)