# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):
# 
# Path: code/nn/initialization.py
# def set_default_rng_seed(seed):
#     global default_rng, default_srng
#     random.seed(seed)
#     default_rng = np.random.RandomState(random.randint(0,9999))
#     default_srng = T.shared_randomstreams.RandomStreams(default_rng.randint(9999))
# 
# def random_init(size, rng=None, rng_type=None):
#     if rng is None: rng = default_rng
#     if rng_type is None:
#         #vals = rng.standard_normal(size)
#         vals = rng.uniform(low=-0.05, high=0.05, size=size)
# 
#     elif rng_type == "normal":
#         vals = rng.standard_normal(size)
# 
#     elif rng_type == "uniform":
#         vals = rng.uniform(low=-3.0**0.5, high=3.0**0.5, size=size)
# 
#     else:
#         raise Exception(
#             "unknown random inittype: {}".format(rng_type)
#           )
# 
#     return vals.astype(theano.config.floatX)
# 
# def create_shared(vals, name=None):
#     return theano.shared(vals, name=name)
# 
# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):


import numpy as np
import theano
import theano.tensor as T
from utils import say
from .initialization import default_srng, default_rng, USE_XAVIER_INIT
from .initialization import set_default_rng_seed, random_init, create_shared
from .initialization import ReLU, sigmoid, tanh, softmax, linear, get_activation_by_name

    @params.setter
    def params(self, param_list):
        start = 0
        for layer in self.internal_layers:
            end = start + len(layer.params)
            layer.params = param_list[start:end]
            start = end


class CNN(Layer):
    '''
        CNN implementation. Return feature maps over time. No pooling is used.

        Inputs
        ------

            order       : feature filter width
    '''
    def __init__(self, n_in, n_out, activation=tanh,
            order=1, clip_gradients=False):

        self.n_in = n_in
        self.n_out = n_out
        self.activation = activation
        self.order = order
        self.clip_gradients = clip_gradients

        internal_layers = self.internal_layers = [ ]
        for i in range(order):

# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):
# 
# Path: code/nn/initialization.py
# def set_default_rng_seed(seed):
#     global default_rng, default_srng
#     random.seed(seed)
#     default_rng = np.random.RandomState(random.randint(0,9999))
#     default_srng = T.shared_randomstreams.RandomStreams(default_rng.randint(9999))
# 
# def random_init(size, rng=None, rng_type=None):
#     if rng is None: rng = default_rng
#     if rng_type is None:
#         #vals = rng.standard_normal(size)
#         vals = rng.uniform(low=-0.05, high=0.05, size=size)
# 
#     elif rng_type == "normal":
#         vals = rng.standard_normal(size)
# 
#     elif rng_type == "uniform":
#         vals = rng.uniform(low=-3.0**0.5, high=3.0**0.5, size=size)
# 
#     else:
#         raise Exception(
#             "unknown random inittype: {}".format(rng_type)
#           )
# 
#     return vals.astype(theano.config.floatX)
# 
# def create_shared(vals, name=None):
#     return theano.shared(vals, name=name)
# 
# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):


# Path: code/nn/basic.py
import numpy as np
import theano
import theano.tensor as T
from utils import say
from .initialization import default_srng, default_rng, USE_XAVIER_INIT
from .initialization import set_default_rng_seed, random_init, create_shared
from .initialization import ReLU, sigmoid, tanh, softmax, linear, get_activation_by_name


    @params.setter
    def params(self, param_list):
        start = 0
        for layer in self.internal_layers:
            end = start + len(layer.params)
            layer.params = param_list[start:end]
            start = end


class CNN(Layer):
    '''
        CNN implementation. Return feature maps over time. No pooling is used.

        Inputs
        ------

            order       : feature filter width
    '''
    def __init__(self, n_in, n_out, activation=tanh,
            order=1, clip_gradients=False):

        self.n_in = n_in
        self.n_out = n_out
        self.activation = activation
        self.order = order
        self.clip_gradients = clip_gradients

        internal_layers = self.internal_layers = [ ]
        for i in range(order):

            input_layer = Layer(n_in, n_out, linear, has_bias=False, \