# Path: nowtrade/logger.py
# CRITICAL = logging.CRITICAL
# ERROR = logging.ERROR
# WARNING = logging.WARNING
# INFO = logging.INFO
# DEBUG = logging.DEBUG
# class Logger(object):
#     def __init__(self, name, log_file=configuration.LOG_FILE, \
#                  console_lvl=configuration.LOGGING_DEFAULT_CONSOLE, \
#                  file_lvl=configuration.LOGGING_DEFAULT_FILE):
#     def get_logger_level(self, name, default, out):
#     def get_console_level(self, name, default):
#     def get_file_level(self, name, default):
#     def set_file_level(self, lvl):
#     def set_console_level(self, lvl):
#     def debug(self, message):
#     def info(self, message):
#     def warning(self, message):
#     def error(self, message):
#     def critical(self, message):
#     def exception(self, message):
#     def __getstate__(self):
#     def __setstate__(self, data):


import cPickle
import numpy as np
from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets.supervised import SupervisedDataSet
from pybrain.supervised.trainers.backprop import BackpropTrainer
from nowtrade import logger
    pass
class InvalidDataset(Exception):
    """
    Exception raised when a invalid dataset is specified.
    """
    pass

class NeuralNetwork(object):
    """
    The neural network class does all the heavy lifting to incorporate pybrain
    neural networks into the NowTrade ecosystem.
    """
    def __init__(self, train_data, prediction_data, network_type=FEED_FORWARD_NETWORK,
                 network_dataset_type=SUPERVISED_DATASET,
                 trainer_type=BACKPROP_TRAINER):
        self.train_data = train_data
        self.prediction_data = prediction_data
        self.network_type = network_type
        self.network_dataset_type = network_dataset_type
        self.trainer_type = trainer_type
        self.network = None
        self.network_dataset = None
        self.dataset = None
        self.trainer = None
        self.trained_iterations = 0
        self.momentum = None
        self.learning_rate = None
        self.hidden_layers = None
        self.prediction_window = None
        self.logger = logger.Logger(self.__class__.__name__)

# Path: nowtrade/logger.py
# CRITICAL = logging.CRITICAL
# ERROR = logging.ERROR
# WARNING = logging.WARNING
# INFO = logging.INFO
# DEBUG = logging.DEBUG
# class Logger(object):
#     def __init__(self, name, log_file=configuration.LOG_FILE, \
#                  console_lvl=configuration.LOGGING_DEFAULT_CONSOLE, \
#                  file_lvl=configuration.LOGGING_DEFAULT_FILE):
#     def get_logger_level(self, name, default, out):
#     def get_console_level(self, name, default):
#     def get_file_level(self, name, default):
#     def set_file_level(self, lvl):
#     def set_console_level(self, lvl):
#     def debug(self, message):
#     def info(self, message):
#     def warning(self, message):
#     def error(self, message):
#     def critical(self, message):
#     def exception(self, message):
#     def __getstate__(self):
#     def __setstate__(self, data):


# Path: nowtrade/neural_network.py
import cPickle
import numpy as np
from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets.supervised import SupervisedDataSet
from pybrain.supervised.trainers.backprop import BackpropTrainer
from nowtrade import logger

    pass
class InvalidDataset(Exception):
    """
    Exception raised when a invalid dataset is specified.
    """
    pass

class NeuralNetwork(object):
    """
    The neural network class does all the heavy lifting to incorporate pybrain
    neural networks into the NowTrade ecosystem.
    """
    def __init__(self, train_data, prediction_data, network_type=FEED_FORWARD_NETWORK,
                 network_dataset_type=SUPERVISED_DATASET,
                 trainer_type=BACKPROP_TRAINER):
        self.train_data = train_data
        self.prediction_data = prediction_data
        self.network_type = network_type
        self.network_dataset_type = network_dataset_type
        self.trainer_type = trainer_type
        self.network = None
        self.network_dataset = None
        self.dataset = None
        self.trainer = None
        self.trained_iterations = 0
        self.momentum = None
        self.learning_rate = None
        self.hidden_layers = None
        self.prediction_window = None
        self.logger = logger.Logger(self.__class__.__name__)

        self.logger.info('train_data: %s  prediction_data: %s, network_type: %s, \