import pickle
import gzip

import numpy as np

def load_data():
  f = gzip.open('./authors/data/mnist.pkl.gz', 'rb')
  u = pickle._Unpickler(f)
  u.encoding = 'latin1'
  training_data, validation_data, test_data = u.load()
  f.close()
  return (training_data, validation_data, test_data)

def load_data_wrapper():
  tr_d, va_d, te_d = load_data()
  training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]
  training_results = [vectorised_result(y) for y in tr_d[1]]
  training_data = list(zip(training_inputs, training_results))
  validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]
  validation_data = list(zip(validation_inputs, va_d[1]))
  test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]
  test_data = list(zip(test_inputs, te_d[1]))
  return (training_data, validation_data, test_data)

def vectorised_result(j):
  e = np.zeros((10, 1))
  e[j] = 1.0
  return e