"""
 This tutorial introduces denoising auto-encoders (dA) using Theano.

 Denoising autoencoders are the building blocks for SdA. 
 They are based on auto-encoders as the ones used in Bengio et al. 2007.
 An autoencoder takes an input x and first maps it to a hidden representation
 y = f_{\theta}(x) = s(Wx+b), parameterized by \theta={W,b}. The resulting 
 latent representation y is then mapped back to a "reconstructed" vector 
 z \in [0,1]^d in input space z = g_{\theta'}(y) = s(W'y + b').  The weight 
 matrix W' can optionally be constrained such that W' = W^T, in which case 
 the autoencoder is said to have tied weights. The network is trained such 
 that to minimize the reconstruction error (the error between x and z).

 For the denosing autoencoder, during training, first x is corrupted into 
 \tilde{x}, where \tilde{x} is a partially destroyed version of x by means 
 of a stochastic mapping. Afterwards y is computed as before (using 
 \tilde{x}), y = s(W\tilde{x} + b) and z as s(W'y + b'). The reconstruction 
 error is now measured between z and the uncorrupted input x, which is 
 computed as the cross-entropy : 
      - \sum_{k=1}^d[ x_k \log z_k + (1-x_k) \log( 1-z_k)]


 References :
   - P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol: Extracting and 
   Composing Robust Features with Denoising Autoencoders, ICML'08, 1096-1103,
   2008
   - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise
   Training of Deep Networks, Advances in Neural Information Processing 
   Systems 19, 2007

"""

import numpy, time, cPickle, gzip 

import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams

from logistic_sgd import load_data
from utils import tile_raster_images

import PIL.Image


class dA(object):
    """Denoising Auto-Encoder class (dA) 

    A denoising autoencoders tries to reconstruct the input from a corrupted 
    version of it by projecting it first in a latent space and reprojecting 
    it afterwards back in the input space. Please refer to Vincent et al.,2008
    for more details. If x is the input then equation (1) computes a partially
    destroyed version of x by means of a stochastic mapping q_D. Equation (2) 
    computes the projection of the input into the latent space. Equation (3) 
    computes the reconstruction of the input, while equation (4) computes the 
    reconstruction error.
  
    .. math::

        \tilde{x} ~ q_D(\tilde{x}|x)                                     (1)

        y = s(W \tilde{x} + b)                                           (2)

        x = s(W' y  + b')                                                (3)

        L(x,z) = -sum_{k=1}^d [x_k \log z_k + (1-x_k) \log( 1-z_k)]      (4)

    """

    def __init__(self, numpy_rng, theano_rng = None, input = None, n_visible= 784, n_hidden= 500, 
               W = None, bhid = None, bvis = None):
        """
        Initialize the dA class by specifying the number of visible units (the 
        dimension d of the input ), the number of hidden units ( the dimension 
        d' of the latent or hidden space ) and the corruption level. The 
        constructor also receives symbolic variables for the input, weights and 
        bias. Such a symbolic variables are useful when, for example the input is 
        the result of some computations, or when weights are shared between the 
        dA and an MLP layer. When dealing with SdAs this always happens,
        the dA on layer 2 gets as input the output of the dA on layer 1, 
        and the weights of the dA are used in the second stage of training 
        to construct an MLP.
   
        :type numpy_rng: numpy.random.RandomState
        :param numpy_rng: number random generator used to generate weights

        :type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
        :param theano_rng: Theano random generator; if None is given one is generated
                     based on a seed drawn from `rng`
    
        :type input: theano.tensor.TensorType
        :paran input: a symbolic description of the input or None for standalone
                      dA

        :type n_visible: int
        :param n_visible: number of visible units

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

        :type W: theano.tensor.TensorType
        :param W: Theano variable pointing to a set of weights that should be 
                  shared belong the dA and another architecture; if dA should 
                  be standalone set this to None
              
        :type bhid: theano.tensor.TensorType
        :param bhid: Theano variable pointing to a set of biases values (for 
                     hidden units) that should be shared belong dA and another 
                     architecture; if dA should be standalone set this to None

        :type bvis: theano.tensor.TensorType
        :param bvis: Theano variable pointing to a set of biases values (for 
                     visible units) that should be shared belong dA and another
                     architecture; if dA should be standalone set this to None
        
    
        """
        self.n_visible = n_visible
        self.n_hidden  = n_hidden
        
        # create a Theano random generator that gives symbolic random values
        if not theano_rng : 
            theano_rng = RandomStreams(rng.randint(2**30))
    
        # note : W' was written as `W_prime` and b' as `b_prime`
        if not W:
            # W is initialized with `initial_W` which is uniformely sampled
            # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible)
            # the output of uniform if converted using asarray to dtype 
            # theano.config.floatX so that the code is runable on GPU
            initial_W = numpy.asarray( numpy_rng.uniform( 
                      low  = -numpy.sqrt(6./(n_hidden+n_visible)), 
                      high = numpy.sqrt(6./(n_hidden+n_visible)), 
                      size = (n_visible, n_hidden)), dtype = theano.config.floatX)
            W = theano.shared(value = initial_W, name ='W')  
    
        if not bvis:
            bvis = theano.shared(value = numpy.zeros(n_visible, 
                                         dtype = theano.config.floatX))

        if not bhid:
            bhid = theano.shared(value = numpy.zeros(n_hidden,
                                              dtype = theano.config.floatX))


        self.W = W
        # b corresponds to the bias of the hidden 
        self.b = bhid
        # b_prime corresponds to the bias of the visible
        self.b_prime = bvis
        # tied weights, therefore W_prime is W transpose
        self.W_prime = self.W.T 
        self.theano_rng = theano_rng
        # if no input is given, generate a variable representing the input
        if input == None : 
            # we use a matrix because we expect a minibatch of several examples,
            # each example being a row
            self.x = T.dmatrix(name = 'input') 
        else:
            self.x = input

        self.params = [self.W, self.b, self.b_prime]

    def get_corrupted_input(self, input, corruption_level):
        """ This function keeps ``1-corruption_level`` entries of the inputs the same 
        and zero-out randomly selected subset of size ``coruption_level`` 
        Note : first argument of theano.rng.binomial is the shape(size) of 
               random numbers that it should produce
               second argument is the number of trials 
               third argument is the probability of success of any trial
        
                this will produce an array of 0s and 1s where 1 has a probability of 
                1 - ``corruption_level`` and 0 with ``corruption_level``
        """
        return  self.theano_rng.binomial( size = input.shape, n = 1, prob =  1 - corruption_level) * input

    
    def get_hidden_values(self, input):
        """ Computes the values of the hidden layer """
        return T.nnet.sigmoid(T.dot(input, self.W) + self.b)

    def get_reconstructed_input(self, hidden ):
        """ Computes the reconstructed input given the values of the hidden layer """
        return  T.nnet.sigmoid(T.dot(hidden, self.W_prime) + self.b_prime)
    
    def get_cost_updates(self, corruption_level, learning_rate):
        """ This function computes the cost and the updates for one trainng
        step of the dA """

        tilde_x = self.get_corrupted_input(self.x, corruption_level)
        y       = self.get_hidden_values( tilde_x)
        z       = self.get_reconstructed_input(y)
        # note : we sum over the size of a datapoint; if we are using minibatches,
        #        L will  be a vector, with one entry per example in minibatch
        L = - T.sum( self.x*T.log(z) + (1-self.x)*T.log(1-z), axis=1 ) 
        # note : L is now a vector, where each element is the cross-entropy cost 
        #        of the reconstruction of the corresponding example of the 
        #        minibatch. We need to compute the average of all these to get 
        #        the cost of the minibatch
        cost = T.mean(L)

        # compute the gradients of the cost of the `dA` with respect
        # to its parameters 
        gparams = T.grad(cost, self.params)
        # generate the list of updates
        updates = {}
        for param, gparam in zip(self.params, gparams):
            updates[param] = param -  learning_rate*gparam
    
        return (cost, updates)




def test_dA( learning_rate = 0.1, training_epochs = 15, dataset ='mnist.pkl.gz' ):

    """
    This demo is tested on MNIST

    :type learning_rate: float
    :param learning_rate: learning rate used for training the DeNosing AutoEncoder

    :type training_epochs: int
    :param training_epochs: number of epochs used for training 

    :type dataset: string
    :param dataset: path to the picked dataset

    """
    datasets = load_data(dataset)
    train_set_x, train_set_y = datasets[0]

    batch_size = 20   # size of the minibatch

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.value.shape[0] / batch_size

    # allocate symbolic variables for the data
    index = T.lscalar()    # index to a [mini]batch 
    x     = T.matrix('x')  # the data is presented as rasterized images

    ####################################
    # BUILDING THE MODEL NO CORRUPTION #
    ####################################

    rng        = numpy.random.RandomState(123)
    theano_rng = RandomStreams( rng.randint(2**30))

    da = dA(numpy_rng = rng, theano_rng = theano_rng, input = x,
            n_visible = 28*28, n_hidden = 500)

    cost, updates = da.get_cost_updates(corruption_level = 0.,
                                learning_rate = learning_rate)

    
    train_da = theano.function([index], cost, updates = updates,
         givens = {x:train_set_x[index*batch_size:(index+1)*batch_size]})

    start_time = time.clock()

    ############
    # TRAINING #
    ############

    # go through training epochs
    for epoch in xrange(training_epochs):
        # go through trainng set
        c = []
        for batch_index in xrange(n_train_batches):
            c.append(train_da(batch_index))

        print 'Training epoch %d, cost '%epoch, numpy.mean(c)

    end_time = time.clock()

    training_time = (end_time - start_time)

    print ('Training took %f minutes' %(training_time/60.))

    image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T,
                 img_shape = (28,28),tile_shape = (10,10), 
                 tile_spacing=(1,1)))
    image.save('filters_corruption_0.png') 
 
    #####################################
    # BUILDING THE MODEL CORRUPTION 30% #
    #####################################

    rng        = numpy.random.RandomState(123)
    theano_rng = RandomStreams( rng.randint(2**30))

    da = dA(numpy_rng = rng, theano_rng = theano_rng, input = x,
            n_visible = 28*28, n_hidden = 500)

    cost, updates = da.get_cost_updates(corruption_level = 0.3,
                                learning_rate = learning_rate)

    
    train_da = theano.function([index], cost, updates = updates,
         givens = {x:train_set_x[index*batch_size:(index+1)*batch_size]})

    start_time = time.clock()

    ############
    # TRAINING #
    ############

    # go through training epochs
    for epoch in xrange(training_epochs):
        # go through trainng set
        c = []
        for batch_index in xrange(n_train_batches):
            c.append(train_da(batch_index))

        print 'Training epoch %d, cost '%epoch, numpy.mean(c)

    end_time = time.clock()

    training_time = (end_time - start_time)

    print ('Training took %f minutes' %(training_time/60.))

    image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T,
                 img_shape = (28,28),tile_shape = (10,10), 
                 tile_spacing=(1,1)))
    image.save('filters_corruption_30.png') 
 


if __name__ == '__main__':
    test_dA()