old_contents: """
  This file tests the MNISTPlus class. majorly concerning the X and y member
  of the dataset and their corresponding sizes, data scales and topological
  views.
  """
  from pylearn2.datasets.mnistplus import MNISTPlus
  from pylearn2.space import IndexSpace, VectorSpace
  import unittest
  from pylearn2.testing.skip import skip_if_no_data
  import numpy as np
  
  
  def test_MNISTPlus():
      """
      Test the MNISTPlus warper.
      Tests the scale of data, the splitting of train, valid, test sets.
      Tests that a topological batch has 4 dimensions.
      Tests that it work well with selected type of augmentation.
      """
      skip_if_no_data()
      train = MNISTPlus(which_set='train')
      valid = MNISTPlus(which_set='valid')
      test = MNISTPlus(which_set='test')
      for ids in [train, valid, test]:
          assert 0.01 >= ids.X.min() >= 0.0
          assert 0.99 <= ids.X.max() <= 1.0
          topo = ids.get_batch_topo(1)
          assert topo.ndim == 4
  
      train_y = MNISTPlus(which_set='train', label_type='label')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert train_y.y.max() == 9
      assert train_y.y.min() == 0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
      train_y = MNISTPlus(which_set='train', label_type='azimuth')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert 0.0 <= train_y.y.max() <= 1.0
      assert 0.0 <= train_y.y.min() <= 1.0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
      train_y = MNISTPlus(which_set='train', label_type='rotation')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert train_y.y.max() == 9
      assert train_y.y.min() == 0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
      train_y = MNISTPlus(which_set='train', label_type='texture_id')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert train_y.y.max() == 9
      assert train_y.y.min() == 0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
new_contents: """
  This file tests the MNISTPlus class. majorly concerning the X and y member
  of the dataset and their corresponding sizes, data scales and topological
  views.
  """
  from pylearn2.datasets.mnistplus import MNISTPlus
  from pylearn2.space import IndexSpace, VectorSpace
  import unittest
  from pylearn2.testing.skip import skip_if_no_data
  import numpy as np
  
  
  def test_MNISTPlus():
      """
      Test the MNISTPlus warper.
      Tests the scale of data, the splitting of train, valid, test sets.
      Tests that a topological batch has 4 dimensions.
      Tests that it work well with selected type of augmentation.
      """
      skip_if_no_data()
      for subset in ['train', 'valid', 'test']:
          ids = MNISTPlus(which_set=subset)
          assert 0.01 >= ids.X.min() >= 0.0
          assert 0.99 <= ids.X.max() <= 1.0
          topo = ids.get_batch_topo(1)
          assert topo.ndim == 4
          del ids
  
      train_y = MNISTPlus(which_set='train', label_type='label')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert train_y.y.max() == 9
      assert train_y.y.min() == 0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
      train_y = MNISTPlus(which_set='train', label_type='azimuth')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert 0.0 <= train_y.y.max() <= 1.0
      assert 0.0 <= train_y.y.min() <= 1.0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
      train_y = MNISTPlus(which_set='train', label_type='rotation')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert train_y.y.max() == 9
      assert train_y.y.min() == 0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
      train_y = MNISTPlus(which_set='train', label_type='texture_id')
      assert 0.99 <= train_y.X.max() <= 1.0
      assert 0.0 <= train_y.X.min() <= 0.01
      assert train_y.y.max() == 9
      assert train_y.y.min() == 0
      assert train_y.y.shape == (train_y.X.shape[0], 1)
  
subject: Load only once MNISTPlus pkl at a time