import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
import scipy
import tensorflow as tf

# The competition datafiles are in the directory ../input
# Read competition data files:
train = pd.read_csv("../input/train.csv")
test  = pd.read_csv("../input/test.csv")

# Write to the log:
print("Training set has {0[0]} rows and {0[1]} columns".format(train.shape))
print("Test set has {0[0]} rows and {0[1]} columns".format(test.shape))
# Any files you write to the current directory get shown as outputs

learning_rate = 0.01
training_iteration = 30
batch_size = 300
display_step = 2


trainfv = train.drop(['label'], axis=1).values.astype(dtype=np.float32)
trainLabels = train['label'].tolist()
ohtrainLabels = tf.one_hot(trainLabels, depth=10)
ohtrainLabelsNdarray = tf.Session().run(ohtrainLabels).astype(dtype=np.float64)
trainfv = np.multiply(trainfv, 1.0 / 255.0) 

testData = test.values
testData = np.multiply(testData, 1.0 / 255.0) 
#train_x,train_y = make_the_data_ready_conv(train_x,train_y)
#valid_x,valid_y = make_the_data_ready_conv(valid_x,valid_y)

x = tf.placeholder("float",[None,784])
y = tf.placeholder("float",[None,10])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

with tf.name_scope("Wx_b") as scope:
    model = tf.nn.softmax(tf.matmul(x,W) + b)
    
w_h = tf.summary.histogram("weights",W)
b_h = tf.summary.histogram("biases",b)

with tf.name_scope("cost_function") as scope:
    cost_function = -tf.reduce_sum(y * tf.log(model))
    tf.summary.scalar("cost_function",cost_function)

with tf.name_scope("train") as scope:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)
    

init = tf.initialize_all_variables()

merged_summary_op = tf.summary.merge_all()


import math

# A mildly random version....simply pick one of the (consecutive) slices of size "size"
from random import randint

# This is used below, for simplicity and for faster convergence ;)
def random_batch(data,labels,size):
    value = math.floor(len(data) / size)    
    intervall = randint(0,value-1)
    return data[intervall*size:intervall*(size+1)],labels[intervall*size:intervall*(size+1)]



with tf.Session() as sess:
    sess.run(init)
    
    #on local machine setup TF and open the commented lines to view the stuff on tensorboard
    #summary_writer = tf.summary.FileWriter("../input/logs",graph_def=sess.graph_def)
    
    for iteration in range(training_iteration):
        avg_cost = 0
        total_batch = int(trainfv.shape[0]/batch_size)
               
        for i in range(total_batch):
            
            batch_xs,batch_ys = random_batch(trainfv,ohtrainLabelsNdarray,total_batch) 
            #print(batch_xs.shape)
            #print(batch_ys.shape)
            sess.run(optimizer,feed_dict={x: batch_xs, y: batch_ys})
            avg_cost += sess.run(cost_function,feed_dict={x: batch_xs, y: batch_ys}) / total_batch
            #summary_str = sess.run(merged_summary_op,feed_dict={x: batch_xs, y: batch_ys})
            #summary_writer.add_summary(summary_str,iteration*total_batch + i)
                        
            if iteration % display_step == 0:
                print ("Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost))
    print("Tuning Completed")

    correct_prediction = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("\nAccuracy of the current model: ",sess.run(accuracy, feed_dict={x: trainfv[0:10000], y: ohtrainLabelsNdarray[0:10000]}))    
    
    prob = sess.run(tf.argmax(model,1), feed_dict = {x: testData})
    which = 1
    print('predicted labe: {}'.format(str(prob[which])))
    
    print(prob)
    #import csv
    #outputFile_dir = '../input/output.csv'
    #header = ['ImageID','Label']
    #with open(outputFile_dir, 'w', newline='') as csvFile:
    #    writer = csv.writer(csvFile, delimiter = ',')
    #    writer.writerow(header)
    #    for i, p in enumerate(prob):
    #        writer.writerow([str(i+1), str(p)])