text: from sklearn.model_selection import RepeatedKFold
  from sklearn.neural_network import MLPClassifier
  from sklearn import metrics
  from scipy import stats
  import pandas as pd
  import numpy
  import training
  
  training.createdata()
  dataset = pd.read_csv('dir/hog.csv')
  dataset = dataset[(numpy.abs(stats.zscore(dataset)) < 5.04).all(axis=1)]
  random_state = 12883823
  rkf = RepeatedKFold(n_splits=5, n_repeats=30, random_state=random_state)
  result = next(rkf.split(dataset), None)
  
  data_train = dataset.iloc[result[0]]
  data_test = dataset.iloc[result[1]]
  
  data = data_train.iloc[:, [0, 3780]]
  target = data_train.iloc[:, [3781]]
  
  classifier = MLPClassifier(random_state=30, hidden_layer_sizes=8, learning_rate_init=0.1, momentum=0.9)
  classifier.fit(data, target)
  
  dataset_test = pd.read_csv('dir/test_hog.csv')
  
  predicted = classifier.predict(dataset_test.iloc[:, [0, 3780]])
  print(metrics.classification_report(dataset_test.iloc[:, [3781]], predicted))
  print("Confusion matrix:\n%s" % metrics.confusion_matrix(dataset_test.iloc[:, [3781]], predicted))
  print(classifier.score(dataset_test.iloc[:, [0, 3780]], dataset_test.iloc[:, [3781]]))
  
  