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、knn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86行目のk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。

以下コード；

import pickle
import numpy as np
import os

def unpickle(file):
    fo = open(file, 'rb')
    dict = pickle.load(fo)
    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.sqrt(np.sum(np.square(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"

    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)
    # assume we have Xtr_rows, Ytr, Xte_rows, Yte as before
    # recall Xtr_rows is 50,000 x 3072 matrix
    Xval_rows = Xtr_rows[:1000, :]  # take first 1000 for validation
    Yval = Ytr[:1000]
    Xtr_rows = Xtr_rows[1000:, :]  # keep last 49,000 for train
    Ytr = Ytr[1000:]

    # find hyperparameters that work best on the validation set
    validation_accuracies = []

    for k in [1, 3, 5, 10, 20, 50, 100]:
        # use a particular value of k and evaluation on validation data
        nn = NearestNeighbor()
        nn.train(Xtr_rows, Ytr)
        # here we assume a modified NearestNeighbor class that can take a k as input
        Yval_predict = nn.predict(Xval_rows, k=k)
        acc = np.mean(Yval_predict == Yval)
        print 'accuracy: %f' % (acc,)

        # keep track of what works on the validation set
        validation_accuracies.append((k, acc))

以下エラー

Traceback (most recent call last): File
"C:/…/PycharmProjects/Convolutional Neural Networks for Visual
Recognition/knn.py", line 86, in
Yval_predict = nn.predict(Xval_rows, k=k) TypeError: predict() got an unexpected keyword argument 'k'