# tensorflow in action: author Huang et al
# chatper 4.2
import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# what's xavier?
# satisty Mean=0 and Var=2/(n_in, n_out)

def xavier_init(fan_in, fan_out, constant=1):
    low = -constant * np.sqrt(6.0 / (fan_in + fan_out))
    high = constant * np.sqrt(6.0 / (fan_in + fan_out))
    return tf.random_uniform((fan_in, fan_out), 
                             minval=low, maxval=high,
                             dtype=tf.float32)

class AdditiveGaussianNoiseAutoencoder(object):
    def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus,
                 optimizer=tf.train.AdamOptimizer(), scale=0.1):
        self.n_input = n_input
        self.n_hidden = n_hidden
        self.transfer = transfer_function
        self.scale = tf.placeholder(tf.float32)
        self.training_scale = scale # noise scale 
        network_weights = self._initialize_weights()
        self.weights = network_weights

        # define the struction of the network
        self.x = tf.placeholder(tf.float32, [None, self.n_input])
        self.hidden = self.transfer(tf.add(tf.matmul(self.x + scale * tf.random_normal((n_input, )), # add noise
                                                     self.weights['w1']), 
                                           self.weights['b1']))
        self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']),
                                    self.weights['b2'])
        
        # optimizer part
        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction,
                                                           self.x),
                                               2.0))
        self.optimizer = optimizer.minimize(self.cost)
        
        # init part
        init = tf.global_variables_initializer()
        self.sess = tf.Session()
        self.sess.run(init)


    def _initialize_weights(self):
        all_weights = {}
        all_weights['w1'] = tf.Variable(xavier_init(self.n_input, self.n_hidden))
        all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32))
        all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden, self.n_input], dtype=tf.float32))
        all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype=tf.float32))
        
        return all_weights

    def partial_fit(self, X):
        cost, opt = self.sess.run((self.cost, self.optimizer),
                                  feed_dict={self.x: X, self.scale: self.training_scale})
        return cost

    def calc_total_cost(self, X):
        return self.sess.run(self.cost, 
                             feed_dict={self.x: X, self.scale: self.training_scale})

    def transform(self, X):
        return self.sess.run(self.hidden, 
                             feed_dict={self.x: X, self.scale: self.training_scale})
                                        
    def generate(self, hidden=None):
        if hidden is None:
            hidden = np.random.normal(size=self.weights['b1'])
        return self.sess.run(self.reconstruction, 
                             feed_dict={self.hidden: hidden})
    
    def reconstruct(self, X):
        return self.sess.run(self.reconstruction, 
                             feed_dict={self.x: X, self.scale: self.training_scale})

    def getWeights(self):
        return self.sess.run(self.weights['w1'])

    def getBiases(self):
        return self.sess.run(self.weights['b1'])


mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
def standard_scale(X_train, X_test):
    preprocessor = prep.StandardScaler().fit(X_train)
    X_train = preprocessor.transform(X_train)
    X_test = preprocessor.transform(X_test)
    return X_train, X_test

def get_random_block_from_data(data, batch_size):
    start_index = np.random.randint(0, len(data) - batch_size)
    return data[start_index: (start_index+ batch_size)]

X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)

n_samples = int(mnist.train.num_examples)
training_epochs = 20
batch_size = 128
display_step = 1

autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = 784,
                                               n_hidden = 200,
                                               transfer_function = tf.nn.softplus,
                                               optimizer = tf.train.AdamOptimizer(learning_rate=0.001),
                                               scale = 0.01)

for epoch in range(training_epochs):
    avg_cost = 0
    total_batch = int(n_samples / batch_size)
    for i in range(total_batch):
        batch_xs = get_random_block_from_data(X_train, batch_size)
        
        cost = autoencoder.partial_fit(batch_xs)
        avg_cost += cost / n_samples * batch_size
    
    if epoch % display_step == 0:
        print("Epoch: ", "%04d" % (epoch + 1), 
              "cost=", "{:.9f}".format(avg_cost))

print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

