import numpy as np
import cPickle
import gzip
import os
import sys
import time
from collections import OrderedDict

import theano
import theano.tensor as T
from theano.ifelse import ifelse
import theano.printing
import theano.tensor.shared_randomstreams

from logistic_sgd import LogisticRegression


##################################
## Various activation functions ##
##################################
#### rectified linear unit
def ReLU(x):
    y = T.maximum(0.0, x)
    return(y)
#### sigmoid
def Sigmoid(x):
    y = T.nnet.sigmoid(x)
    return(y)
#### tanh
def Tanh(x):
    y = T.tanh(x)
    return(y)
    
class HiddenLayer(object):
    def __init__(self, rng, input, n_in, n_out,
                 activation, W=None, b=None,
                 use_bias=False):

        self.input = input
        
        if W is None:
            W_values = np.asarray(
                rng.uniform(
                    low=-np.sqrt(6. / (n_in + n_out)),
                    high=np.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 = np.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
        if use_bias:
            self.params = [self.W, self.b]
        else:
            self.params = [self.W]

def _dropout_from_layer(rng, layer, p):
    """p is the probablity of dropping a unit
    """
    srng = theano.tensor.shared_randomstreams.RandomStreams(
            rng.randint(999999))
    # p=1-p because 1's indicate keep and p is prob of dropping
    mask = srng.binomial(n=1, p=1.0-p, size=layer.shape)
    # The cast is important because
    # int * float32 = float64 which pulls things off the gpu
    output = layer * T.cast(mask, theano.config.floatX)
    return output

class DropoutHiddenLayer(HiddenLayer):
    def __init__(self, rng, input, n_in, n_out,
                 activation, dropout_rate, use_bias, W=None, b=None):

        dropout_input = _dropout_from_layer(rng, input, p=0.5)
        super(DropoutHiddenLayer, self).__init__(
                rng=rng, input=dropout_input, n_in=n_in, n_out=n_out, W=W, b=b,
                activation=activation, use_bias=use_bias)
       
        self.output = _dropout_from_layer(rng, self.output, p=dropout_rate)


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def Tanh(x):
    y = T.tanh(x)
    return y


class HiddenLayer(object):

    def __init__(self, rng, input, n_in, n_out, activation, W=None, b=None,
        use_bias=False):
        self.input = input
        if W is None:
            W_values = np.asarray(rng.uniform(low=-np.sqrt(6.0 / (n_in +
                n_out)), high=np.sqrt(6.0 / (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 = np.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)
        if use_bias:
            self.params = [self.W, self.b]
        else:
            self.params = [self.W]


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class DropoutHiddenLayer(HiddenLayer):

    def __init__(self, rng, input, n_in, n_out, activation, dropout_rate,
        use_bias, W=None, b=None):
        dropout_input = _dropout_from_layer(rng, input, p=0.5)
        super(DropoutHiddenLayer, self).__init__(rng=rng, input=
            dropout_input, n_in=n_in, n_out=n_out, W=W, b=b, activation=
            activation, use_bias=use_bias)
        self.output = _dropout_from_layer(rng, self.output, p=dropout_rate)

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def Sigmoid(x):
    y = T.nnet.sigmoid(x)
    return y


def Tanh(x):
    y = T.tanh(x)
    return y


class HiddenLayer(object):

    def __init__(self, rng, input, n_in, n_out, activation, W=None, b=None,
        use_bias=False):
        self.input = input
        if W is None:
            W_values = np.asarray(rng.uniform(low=-np.sqrt(6.0 / (n_in +
                n_out)), high=np.sqrt(6.0 / (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 = np.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)
        if use_bias:
            self.params = [self.W, self.b]
        else:
            self.params = [self.W]


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class DropoutHiddenLayer(HiddenLayer):

    def __init__(self, rng, input, n_in, n_out, activation, dropout_rate,
        use_bias, W=None, b=None):
        dropout_input = _dropout_from_layer(rng, input, p=0.5)
        super(DropoutHiddenLayer, self).__init__(rng=rng, input=
            dropout_input, n_in=n_in, n_out=n_out, W=W, b=b, activation=
            activation, use_bias=use_bias)
        self.output = _dropout_from_layer(rng, self.output, p=dropout_rate)

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def Sigmoid(x):
    y = T.nnet.sigmoid(x)
    return y


def Tanh(x):
    y = T.tanh(x)
    return y


class HiddenLayer(object):

    def __init__(self, rng, input, n_in, n_out, activation, W=None, b=None,
        use_bias=False):
        self.input = input
        if W is None:
            W_values = np.asarray(rng.uniform(low=-np.sqrt(6.0 / (n_in +
                n_out)), high=np.sqrt(6.0 / (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 = np.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)
        if use_bias:
            self.params = [self.W, self.b]
        else:
            self.params = [self.W]


def _dropout_from_layer(rng, layer, p):
    """p is the probablity of dropping a unit
    """
    srng = theano.tensor.shared_randomstreams.RandomStreams(rng.randint(999999)
        )
    mask = srng.binomial(n=1, p=1.0 - p, size=layer.shape)
    output = layer * T.cast(mask, theano.config.floatX)
    return output


class DropoutHiddenLayer(HiddenLayer):

    def __init__(self, rng, input, n_in, n_out, activation, dropout_rate,
        use_bias, W=None, b=None):
        dropout_input = _dropout_from_layer(rng, input, p=0.5)
        super(DropoutHiddenLayer, self).__init__(rng=rng, input=
            dropout_input, n_in=n_in, n_out=n_out, W=W, b=b, activation=
            activation, use_bias=use_bias)
        self.output = _dropout_from_layer(rng, self.output, p=dropout_rate)

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def ReLU(x):
    y = T.maximum(0.0, x)
    return y


def Sigmoid(x):
    y = T.nnet.sigmoid(x)
    return y


def Tanh(x):
    y = T.tanh(x)
    return y


class HiddenLayer(object):

    def __init__(self, rng, input, n_in, n_out, activation, W=None, b=None,
        use_bias=False):
        self.input = input
        if W is None:
            W_values = np.asarray(rng.uniform(low=-np.sqrt(6.0 / (n_in +
                n_out)), high=np.sqrt(6.0 / (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 = np.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)
        if use_bias:
            self.params = [self.W, self.b]
        else:
            self.params = [self.W]


def _dropout_from_layer(rng, layer, p):
    """p is the probablity of dropping a unit
    """
    srng = theano.tensor.shared_randomstreams.RandomStreams(rng.randint(999999)
        )
    mask = srng.binomial(n=1, p=1.0 - p, size=layer.shape)
    output = layer * T.cast(mask, theano.config.floatX)
    return output


class DropoutHiddenLayer(HiddenLayer):

    def __init__(self, rng, input, n_in, n_out, activation, dropout_rate,
        use_bias, W=None, b=None):
        dropout_input = _dropout_from_layer(rng, input, p=0.5)
        super(DropoutHiddenLayer, self).__init__(rng=rng, input=
            dropout_input, n_in=n_in, n_out=n_out, W=W, b=b, activation=
            activation, use_bias=use_bias)
        self.output = _dropout_from_layer(rng, self.output, p=dropout_rate)

import numpy as np
import cPickle
import gzip
import os
import sys
import time
from collections import OrderedDict

import theano
import theano.tensor as T
from theano.ifelse import ifelse
import theano.printing
import theano.tensor.shared_randomstreams

from logistic_sgd import LogisticRegression


##################################
## Various activation functions ##
##################################
#### rectified linear unit
def ReLU(x):
    y = T.maximum(0.0, x)
    return(y)
#### sigmoid
def Sigmoid(x):
    y = T.nnet.sigmoid(x)
    return(y)
#### tanh
def Tanh(x):
    y = T.tanh(x)
    return(y)
    
class HiddenLayer(object):
    def __init__(self, rng, input, n_in, n_out,
                 activation, W=None, b=None,
                 use_bias=False):

        self.input = input
        
        if W is None:
            W_values = np.asarray(
                rng.uniform(
                    low=-np.sqrt(6. / (n_in + n_out)),
                    high=np.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 = np.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
        if use_bias:
            self.params = [self.W, self.b]
        else:
            self.params = [self.W]

def _dropout_from_layer(rng, layer, p):
    """p is the probablity of dropping a unit
    """
    srng = theano.tensor.shared_randomstreams.RandomStreams(
            rng.randint(999999))
    # p=1-p because 1's indicate keep and p is prob of dropping
    mask = srng.binomial(n=1, p=1.0-p, size=layer.shape)
    # The cast is important because
    # int * float32 = float64 which pulls things off the gpu
    output = layer * T.cast(mask, theano.config.floatX)
    return output

class DropoutHiddenLayer(HiddenLayer):
    def __init__(self, rng, input, n_in, n_out,
                 activation, dropout_rate, use_bias, W=None, b=None):

        dropout_input = _dropout_from_layer(rng, input, p=0.5)
        super(DropoutHiddenLayer, self).__init__(
                rng=rng, input=dropout_input, n_in=n_in, n_out=n_out, W=W, b=b,
                activation=activation, use_bias=use_bias)
       
        self.output = _dropout_from_layer(rng, self.output, p=dropout_rate)

