# Path: hidden.py
# class HiddenLayer(object):
#     def __init__(self, rng, input, n_in, n_out, W=None, b=None,
#                  activation=T.tanh):
#         """
#         Typical hidden layer of a MLP: units are fully-connected and have
#         sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
#         and the bias vector b is of shape (n_out,).
# 
#         NOTE : The nonlinearity used here is tanh
# 
#         Hidden unit activation is given by: tanh(dot(input,W) + b)
# 
#         :type rng: numpy.random.RandomState
#         :param rng: a random number generator used to initialize weights
# 
#         :type input: theano.tensor.dmatrix
#         :param input: a symbolic tensor of shape (n_examples, n_in)
# 
#         :type n_in: int
#         :param n_in: dimensionality of input
# 
#         :type n_out: int
#         :param n_out: number of hidden units
# 
#         :type activation: theano.Op or function
#         :param activation: Non linearity to be applied in the hidden
#                            layer
#         """
#         self.input = input
#         # end-snippet-1
# 
#         # `W` is initialized with `W_values` which is uniformely sampled
#         # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))
#         # for tanh activation function
#         # the output of uniform if converted using asarray to dtype
#         # theano.config.floatX so that the code is runable on GPU
#         # Note : optimal initialization of weights is dependent on the
#         #        activation function used (among other things).
#         #        For example, results presented in [Xavier10] suggest that you
#         #        should use 4 times larger initial weights for sigmoid
#         #        compared to tanh
#         #        We have no info for other function, so we use the same as
#         #        tanh.
#         if W is None:
#             W_values = numpy.asarray(
#                 rng.uniform(
#                     low=-numpy.sqrt(6. / (n_in + n_out)),
#                     high=numpy.sqrt(6. / (n_in + n_out)),
#                     size=(n_in, n_out)
#                 ),
#                 dtype=theano.config.floatX
#             )
#             if activation == theano.tensor.nnet.sigmoid:
#                 W_values *= 4
# 
#             W = theano.shared(value=W_values, name='W', borrow=True)
# 
#         if b is None:
#             b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
#             b = theano.shared(value=b_values, name='b', borrow=True)
# 
#         self.W = W
#         self.b = b
# 
#         lin_output = T.dot(input, self.W) + self.b
#         self.output = (
#             lin_output if activation is None
#             else activation(lin_output)
#         )
#         # parameters of the model
#         self.params = [self.W, self.b]


import numpy
import theano
import theano.tensor as T
from hidden import HiddenLayer
    class).
    """

    def __init__(self, rng, input, n_in, n_hidden, n_out):
        """Initialize the parameters for the multilayer perceptron

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
        architecture (one minibatch)

        :type n_in: int
        :param n_in: number of input units, the dimension of the space in
        which the datapoints lie

        :type n_hidden: int
        :param n_hidden: number of hidden units

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
        which the labels lie

        """

        # Since we are dealing with a one hidden layer MLP, this will translate
        # into a HiddenLayer with a tanh activation function connected to the
        # LogisticRegression layer; the activation function can be replaced by
        # sigmoid or any other nonlinear function

# Path: hidden.py
# class HiddenLayer(object):
#     def __init__(self, rng, input, n_in, n_out, W=None, b=None,
#                  activation=T.tanh):
#         """
#         Typical hidden layer of a MLP: units are fully-connected and have
#         sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
#         and the bias vector b is of shape (n_out,).
# 
#         NOTE : The nonlinearity used here is tanh
# 
#         Hidden unit activation is given by: tanh(dot(input,W) + b)
# 
#         :type rng: numpy.random.RandomState
#         :param rng: a random number generator used to initialize weights
# 
#         :type input: theano.tensor.dmatrix
#         :param input: a symbolic tensor of shape (n_examples, n_in)
# 
#         :type n_in: int
#         :param n_in: dimensionality of input
# 
#         :type n_out: int
#         :param n_out: number of hidden units
# 
#         :type activation: theano.Op or function
#         :param activation: Non linearity to be applied in the hidden
#                            layer
#         """
#         self.input = input
#         # end-snippet-1
# 
#         # `W` is initialized with `W_values` which is uniformely sampled
#         # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))
#         # for tanh activation function
#         # the output of uniform if converted using asarray to dtype
#         # theano.config.floatX so that the code is runable on GPU
#         # Note : optimal initialization of weights is dependent on the
#         #        activation function used (among other things).
#         #        For example, results presented in [Xavier10] suggest that you
#         #        should use 4 times larger initial weights for sigmoid
#         #        compared to tanh
#         #        We have no info for other function, so we use the same as
#         #        tanh.
#         if W is None:
#             W_values = numpy.asarray(
#                 rng.uniform(
#                     low=-numpy.sqrt(6. / (n_in + n_out)),
#                     high=numpy.sqrt(6. / (n_in + n_out)),
#                     size=(n_in, n_out)
#                 ),
#                 dtype=theano.config.floatX
#             )
#             if activation == theano.tensor.nnet.sigmoid:
#                 W_values *= 4
# 
#             W = theano.shared(value=W_values, name='W', borrow=True)
# 
#         if b is None:
#             b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
#             b = theano.shared(value=b_values, name='b', borrow=True)
# 
#         self.W = W
#         self.b = b
# 
#         lin_output = T.dot(input, self.W) + self.b
#         self.output = (
#             lin_output if activation is None
#             else activation(lin_output)
#         )
#         # parameters of the model
#         self.params = [self.W, self.b]


# Path: mlp.py
import numpy
import theano
import theano.tensor as T
from hidden import HiddenLayer

    class).
    """

    def __init__(self, rng, input, n_in, n_hidden, n_out):
        """Initialize the parameters for the multilayer perceptron

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
        architecture (one minibatch)

        :type n_in: int
        :param n_in: number of input units, the dimension of the space in
        which the datapoints lie

        :type n_hidden: int
        :param n_hidden: number of hidden units

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
        which the labels lie

        """

        # Since we are dealing with a one hidden layer MLP, this will translate
        # into a HiddenLayer with a tanh activation function connected to the
        # LogisticRegression layer; the activation function can be replaced by
        # sigmoid or any other nonlinear function

        self.hiddenLayer = HiddenLayer(