"""This tutorial introduces the LeNet5 neural network architecture
using Theano.  LeNet5 is a convolutional neural network, good for
classifying images. This tutorial shows how to build the architecture,
and comes with all the hyper-parameters you need to reproduce the
paper's MNIST results.


This implementation simplifies the model in the following ways:

 - LeNetConvPool doesn't implement location-specific gain and bias parameters
 - LeNetConvPool doesn't implement pooling by average, it implements pooling
   by max.
 - Digit classification is implemented with a logistic regression rather than
   an RBF network
 - LeNet5 was not fully-connected convolutions at second layer

References:
 - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner:
   Gradient-Based Learning Applied to Document
   Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
   http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf

"""

from __future__ import print_function

import os
import sys
import timeit

import numpy

import theano
import theano.tensor as T
from theano.tensor.signal import pool
from theano.tensor.nnet import conv2d
from sklearn.cluster import KMeans
from sklearn import metrics

from logistic_sgd import LogisticRegression, load_data
from mlp import HiddenLayer


class LeNetConvPoolLayer(object):
    """Pool Layer of a convolutional network """

    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
        """
        Allocate a LeNetConvPoolLayer with shared variable internal parameters.

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

        :type input: theano.tensor.dtensor4
        :param input: symbolic image tensor, of shape image_shape

        :type filter_shape: tuple or list of length 4
        :param filter_shape: (number of filters, num input feature maps,
                              filter height, filter width)

        :type image_shape: tuple or list of length 4
        :param image_shape: (batch size, num input feature maps,
                             image height, image width)

        :type poolsize: tuple or list of length 2
        :param poolsize: the downsampling (pooling) factor (#rows, #cols)
        """

        assert image_shape[1] == filter_shape[1]
        self.input = input

        # there are "num input feature maps * filter height * filter width"
        # inputs to each hidden unit
        fan_in = numpy.prod(filter_shape[1:])
        # each unit in the lower layer receives a gradient from:
        # "num output feature maps * filter height * filter width" /
        #   pooling size
        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) //
                   numpy.prod(poolsize))
        # initialize weights with random weights
        W_bound = numpy.sqrt(6. / (fan_in + fan_out))
        self.W = theano.shared(
            numpy.asarray(
                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
                dtype=theano.config.floatX
            ),
            borrow=True
        )

        # the bias is a 1D tensor -- one bias per output feature map
        b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, borrow=True)

        # convolve input feature maps with filters
        conv_out = conv2d(
            input=input,
            filters=self.W,
            filter_shape=filter_shape,
            input_shape=image_shape
        )

        # pool each feature map individually, using maxpooling
        pooled_out = pool.pool_2d(
            input=conv_out,
            ds=poolsize,
            ignore_border=True
        )

        # add the bias term. Since the bias is a vector (1D array), we first
        # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
        # thus be broadcasted across mini-batches and feature map
        # width & height
        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))

        # store parameters of this layer
        self.params = [self.W, self.b]

        # keep track of model input
        self.input = input


#def evaluate_lenet5(learning_rate=0.1, n_epochs=100,
#                    dataset='mnist.pkl.gz',
#                    nkerns=[20, 50], batch_size=500):
    """ Demonstrates lenet on MNIST dataset

    :type learning_rate: float
    :param learning_rate: learning rate used (factor for the stochastic
                          gradient)

    :type n_epochs: int
    :param n_epochs: maximal number of epochs to run the optimizer

    :type dataset: string
    :param dataset: path to the dataset used for training /testing (MNIST here)

    :type nkerns: list of ints
    :param nkerns: number of kernels on each layer
    """
    
    
learning_rate=0.01
n_epochs=100
nClass = 10
dataset='mnist.pkl.gz'
nkerns=[20, 50]
batch_size=500
rng = numpy.random.RandomState(23455)

datasets = load_data(dataset)

train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]

label_true = train_set_y.eval()

# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches //= batch_size
n_valid_batches //= batch_size
n_test_batches //= batch_size

# allocate symbolic variables for the data
index = T.lscalar()  # index to a [mini]batch

# start-snippet-1
x = T.matrix('x')   # the data is presented as rasterized images
y = T.ivector('y')  # the labels are presented as 1D vector of
                    # [int] labels

######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')

# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 28, 28))

# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
    rng,
    input=layer0_input,
    image_shape=(batch_size, 1, 28, 28),
    filter_shape=(nkerns[0], 1, 5, 5),
    poolsize=(2, 2)
)

# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(
    rng,
    input=layer0.output,
    image_shape=(batch_size, nkerns[0], 12, 12),
    filter_shape=(nkerns[1], nkerns[0], 5, 5),
    poolsize=(2, 2)
)

# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_input = layer1.output.flatten(2)

# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
    rng,
    input=layer2_input,
    n_in=nkerns[1] * 4 * 4,
    n_out=12,
    activation=T.nnet.sigmoid
)

layer3 = HiddenLayer(
    rng,
    input=layer2.output,
    n_in = 12,
    n_out= 28*28,
    activation=T.nnet.sigmoid
)

z = layer3.output
cost = -T.mean(T.sum(x*T.log(z) + (1-x)*T.log(1-z), axis = 1))

# classify the values of the fully-connected sigmoidal layer
#layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
#
## the cost we minimize during training is the NLL of the model
#cost = layer3.negative_log_likelihood(y)

# create a function to compute the mistakes that are made by the model

#test_model = theano.function(
#    [index],
#    layer3.errors(y),
#    givens={
#        x: test_set_x[index * batch_size: (index + 1) * batch_size],
#        y: test_set_y[index * batch_size: (index + 1) * batch_size]
#    }
#)
#
#validate_model = theano.function(
#    [index],
#    layer3.errors(y),
#    givens={
#        x: valid_set_x[index * batch_size: (index + 1) * batch_size],
#        y: valid_set_y[index * batch_size: (index + 1) * batch_size]
#    }
#)
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params

# create a list of gradients for all model parameters
grads = T.grad(cost, params)

# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
updates = [
    (param_i, param_i - learning_rate * grad_i)
    for param_i, grad_i in zip(params, grads)
]

train_model = theano.function(
    [index],
    cost,
    updates=updates,
    givens={
        x: train_set_x[index * batch_size: (index + 1) * batch_size]
    }
)
# end-snippet-1

###############
# TRAIN MODEL #
###############
print('... training')
# early-stopping parameters
patience = 10000  # look as this many examples regardless
patience_increase = 2  # wait this much longer when a new best is
                       # found
improvement_threshold = 0.995  # a relative improvement of this much is
                               # considered significant
validation_frequency = min(n_train_batches, patience // 2)
                              # go through this many
                              # minibatche before checking the network
                              # on the validation set; in this case we
                              # check every epoch

best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()

epoch = 0
done_looping = False

out = layer2.output
get_hidden = theano.function(
    [index],
    outputs = out,
    givens = {x: train_set_x[index * batch_size: (index + 1) * batch_size]}
)   
km = KMeans(n_clusters = nClass)   

while (epoch < n_epochs) and (not done_looping):
    epoch = epoch + 1
    for minibatch_index in range(n_train_batches):

        iter = (epoch - 1) * n_train_batches + minibatch_index

        if iter % 100 == 0:
            print('training @ iter = ', iter)
        cost_ij = train_model(minibatch_index)
        
        print('Mini-batch cost: %.4f'%cost_ij)

#        if (iter + 1) % validation_frequency == 0:
#
#            # compute zero-one loss on validation set
#            validation_losses = [validate_model(i) for i
#                                 in range(n_valid_batches)]
#            this_validation_loss = numpy.mean(validation_losses)
#            print('epoch %i, minibatch %i/%i, validation error %f %%' %
#                  (epoch, minibatch_index + 1, n_train_batches,
#                   this_validation_loss * 100.))
#
#            # if we got the best validation score until now
#            if this_validation_loss < best_validation_loss:
#
#                #improve patience if loss improvement is good enough
#                if this_validation_loss < best_validation_loss *  \
#                   improvement_threshold:
#                    patience = max(patience, iter * patience_increase)
#
#                # save best validation score and iteration number
#                best_validation_loss = this_validation_loss
#                best_iter = iter
#
#                # test it on the test set
#                test_losses = [
#                    test_model(i)
#                    for i in range(n_test_batches)
#                ]
#                test_score = numpy.mean(test_losses)
#                print(('     epoch %i, minibatch %i/%i, test error of '
#                       'best model %f %%') %
#                      (epoch, minibatch_index + 1, n_train_batches,
#                       test_score * 100.))
#
#        if patience <= iter:
#            done_looping = True
#            break


hidden_val = [] 
for batch_index in xrange(n_train_batches):
     hidden_val.append(get_hidden(batch_index))    
hidden_array  = numpy.asarray(hidden_val)
hidden_size = hidden_array.shape        
hidden_array = numpy.reshape(hidden_array, (hidden_size[0] * hidden_size[1], hidden_size[2] ))

km_idx = km.fit_predict(hidden_array)
nmi = metrics.adjusted_mutual_info_score(label_true, km_idx)

end_time = timeit.default_timer()
print('Optimization complete.')
print('NMI is: %.4f'%nmi)
#print('Best validation score of %f %% obtained at iteration %i, '
#      'with test performance %f %%' %
#      (best_validation_loss * 100., best_iter + 1, test_score * 100.))
#    print(('The code for file ' +
#           os.path.split(__file__)[1] +
#           ' ran for %.2fm' % ((end_time - start_time) / 60.)), file=sys.stderr)

#if __name__ == '__main__':
#    evaluate_lenet5()


def experiment(state, channel):
    evaluate_lenet5(state.learning_rate, dataset=state.dataset)
