以下のリンクにあるCIFAR-10(ラベル付されたサイズが32x32のカラー画像8000万枚のデータセット)を読み取り、Nearest Neighbor Classifierによりクラス分けしその精度を%で出力させたいのですが以下のエラー出てしまいました。問題は58行目のxrangeにあるようですが解決方法がみつからず、何かアドバイス頂けると幸いです。

以下データ元:
http://www.cs.toronto.edu/~kriz/cifar.html

以下コードです:

import pickle
import numpy as np
import os

def unpickle(file):
    fo = open(file, 'rb')
    u = pickle._Unpickler(fo)
    u.encoding = 'latin1'
    dict = u.load()
    fo.close()
    return dict

def conv_data2image(data):
    return np.rollaxis(data.reshape((3,32,32)),0,3)

def get_cifar10(folder):
    tr_data = np.empty((0,32*32*3))
    tr_labels = np.empty(1)
    '''
    32x32x3
    '''
    for i in range(1,6):
        fname = os.path.join(folder, "%s%d" % ("data_batch_", i))
        data_dict = unpickle(fname)
        if i == 1:
            tr_data = data_dict['data']
            tr_labels = data_dict['labels']
        else:
            tr_data = np.vstack((tr_data, data_dict['data']))
            tr_labels = np.hstack((tr_labels, data_dict['labels']))

    data_dict = unpickle(os.path.join(folder, 'test_batch'))
    te_data = data_dict['data']
    te_labels = np.array(data_dict['labels'])

    bm = unpickle(os.path.join(folder, 'batches.meta'))
    label_names = bm['label_names']
    return tr_data, tr_labels, te_data, te_labels, label_names


class NearestNeighbor(object):
  def __init__(self):
    pass

  def train(self, X, y):
    """ X is N x D where each row is an example. Y is 1-dimension of size N """
    # the nearest neighbor classifier simply remembers all the training data
    self.Xtr = X
    self.ytr = y

  def predict(self, X):
    """ X is N x D where each row is an example we wish to predict label for """
    num_test = X.shape[0]
    # lets make sure that the output type matches the input type
    Ypred = np.zeros(num_test, dtype = self.ytr.dtype)

    # loop over all test rows
    for i in xrange(num_test):
      # find the nearest training image to the i'th test image
      # using the L1 distance (sum of absolute value differences)
      distances = np.sum(np.abs(self.Xtr - X[i,:]), axis = 1)
      min_index = np.argmin(distances) # get the index with smallest distance
      Ypred[i] = self.ytr[min_index] # predict the label of the nearest example

    return Ypred

if __name__ == '__main__':
    datapath = "./data/cifar-10-batches-py"

    tr_data10, tr_labels10, te_data10, te_labels10, label_names10 = get_cifar10(datapath)
    Xtr, Ytr, Xte, Yte, label_names10 = get_cifar10(datapath)
    Xtr_rows = Xtr.reshape(Xtr.shape[0], 32 * 32 * 3)
    Xte_rows = Xte.reshape(Xte.shape[0], 32 * 32 * 3)
    nn = NearestNeighbor()  # create a Nearest Neighbor classifier class
    nn.train(Xtr_rows, Ytr)  # train the classifier on the training images and labels
    Yte_predict = nn.predict(Xte_rows)  # predict labels on the test images
    # and now print the classification accuracy, which is the average number
    # of examples that are correctly predicted (i.e. label matches)
    print(Yte_predict)

以下エラー

"C:\…\AppData\Local\Programs\Python\Python35\python.exe"
"C:/…/PycharmProjects/Convolutional Neural
Networks for Visual Recognition/input_cifar.py"
Traceback (most recent
call last): File "C:/…/PycharmProjects/Convolutional Neural Networks for Visual
Recognition/input_cifar.py", line 76, in
Yte_predict = nn.predict(Xte_rows) # predict labels on the test images File "C:/Users/Naoki Ishibashi/PycharmProjects/Convolutional
Neural Networks for Visual Recognition/input_cifar.py", line 58, in
predict
for i in xrange(num_test): NameError: name 'xrange' is not defined