from PIL import Image

import matplotlib.pyplot as plt
import NN
import numpy
data = open('/home/aaron/Eric workspace/Untitled Folder/mnist_train.csv','r')

data_r = data.read()
raw_data_list = data_r.split()
data.close()

pixel_tuple = [numpy.asfarray(x.split(',')[1:]) for x in raw_data_list]

#get MNIST data, form array actual_numbers to store the target values of each image
#form array scaled_array as a 28X28 array of greyscale pixels corresponding to MNIST images.
#Rescale greyscale values in scaled_array to lie between 0.1 and 1

actual_numbers = [int(x.split(',',  1)[0]) for x in raw_data_list]
scaled_array = [(0.99/255)*x  + 0.01 for x in pixel_tuple]

#turn actual_numbers into an array of lists of length 10 which are 0 everywhere except the index

def listify(x,  onodes):
    #turns the number n from 0 to onodes-1 into the list of length onodes which is supported at index n
    l = [0]*onodes
    l[x] = 1
    l = numpy.asfarray(l)
    return l + 0.1

#create a neural network object:
input_nodes = 784
hidden_nodes = 200
output_nodes = 10
learning_rate = 0.08
n = NN.neuralNetwork(input_nodes,hidden_nodes,output_nodes, learning_rate)
target_outputs = [listify(x,  output_nodes) for x in actual_numbers]

#Train the neural network
count = 0
for item in scaled_array:
    n.train(item,  target_outputs[count])
    count+=1

#load testing data
data = open('/home/aaron/Eric workspace/Untitled Folder/mnist_test.csv','r')
data_r = data.read()
raw_data_list = data_r.split()
data.close()

pixel_tuple = [numpy.asfarray(x.split(',')[1:]) for x in raw_data_list]

#get MNIST data, form array actual_numbers to store the target values of each image
#form array scaled_array as a 1x784 of greyscale pixels corresponding to MNIST images.
#Rescale greyscale values in scaled_array to lie between 0.1 and 1

actual_numbers = [int(x.split(',',  1)[0]) for x in raw_data_list]
scaled_array = [(0.99/255)*x  + 0.01 for x in pixel_tuple]

i = 0
while i < 10:
    op = n.query(scaled_array[i])
    op_list = [list(x)[0] for x in op]
    guess = op_list.index(max(op_list))
    print("Guess:",  guess,  "Actual:",  actual_numbers[i])
    i +=1

scorecard = []
count = 0
for item in scaled_array:
    op = n.query(item)
    op_list = [list(x)[0] for x in op]
    guess = op_list.index(max(op_list))
    scorecard.append(guess == actual_numbers[count])
    count +=1