def test_returns_positions_three(self):
self.assertEquals(chop(5, [1, 3, 5]), 2)
self.assertEquals(chop(1, [1, 2, 3, 4, 5]), 0)
self.assertEquals(chop(2, [1, 2, 3, 4, 5]), 1)
self.assertEquals(chop(3, [1, 2, 3, 4, 5]), 2)
self.assertEquals(chop(4, [1, 2, 3, 4, 5]), 3)
self.assertEquals(chop(5, [1, 2, 3, 4, 5]), 4)
self.assertEquals(chop(3, [1, 2, 3, 4, 5, 6]), 2)
self.assertEquals(chop(5, [1, 2, 3, 4, 5, 6]), 4)
def digits(wild_image): import crop import chop import numpy as np cropped = crop.crop(wild_image) chopped = chop.chop(cropped) n_images = chopped.shape[0] flattened = np.reshape(n_images, -1) return flattened
def test_chop(self):
self.assertEqual(-1, chop(3, []))
self.assertEqual(-1, chop(3, [1]))
self.assertEqual(0, chop(1, [1]))
self.assertEqual(0, chop(1, [1, 3, 5]))
self.assertEqual(1, chop(3, [1, 3, 5]))
self.assertEqual(2, chop(5, [1, 3, 5]))
self.assertEqual(-1, chop(0, [1, 3, 5]))
self.assertEqual(-1, chop(2, [1, 3, 5]))
self.assertEqual(-1, chop(4, [1, 3, 5]))
self.assertEqual(-1, chop(6, [1, 3, 5]))
self.assertEqual(0, chop(1, [1, 3, 5, 7]))
self.assertEqual(1, chop(3, [1, 3, 5, 7]))
self.assertEqual(2, chop(5, [1, 3, 5, 7]))
self.assertEqual(3, chop(7, [1, 3, 5, 7]))
self.assertEqual(-1, chop(0, [1, 3, 5, 7]))
self.assertEqual(-1, chop(2, [1, 3, 5, 7]))
self.assertEqual(-1, chop(4, [1, 3, 5, 7]))
self.assertEqual(-1, chop(6, [1, 3, 5, 7]))
self.assertEqual(-1, chop(8, [1, 3, 5, 7]))
def test_returns_minus_one_when_not_single_element(self):
self.assertEquals(chop(2, [3]), -1)
def test_returns_zero_when_only_number(self):
self.assertEquals(chop(2,[2]),0)
def test_returns_minus_one_when_array_empty(self):
self.assertEquals(chop(2,[]),-1)
def test_returns_positions_two(self):
self.assertEquals(chop(3, [1, 3, 5]), 1)
def test_returns_positions_one(self):
self.assertEquals(chop(1, [1, 3, 5]), 0)
def test_returns_position_when_prestn(self):
self.assertEquals(chop(2, [1,2]), 1)
def test_returns_minus_one_when_not_present(self):
self.assertEquals(chop(2, [1,3,4,5,5,5,5,5,56]), -1)
'word_freq_1999',
'word_freq_parts',
'word_freq_pm',
'word_freq_direct',
'word_freq_cs',
'word_freq_meeting',
'word_freq_original',
'word_freq_project',
'word_freq_re',
'word_freq_edu',
'word_freq_table',
'word_freq_conference',
'char_freq_semicolon',
'char_freq_(',
'char_freq_[',
'char_freq_!',
'char_freq_$',
'char_freq_#',
'capital_run_length_average',
'capital_run_length_longest',
'capital_run_length_total']
params['split_ratio'] = 0.7
params['delimiter_in'] = ','
params['delimiter_out'] = ','
params['history_steps'] = 0
params['history_column'] = ''
params['predict_column'] = ''
params['shuffle'] = True
params['shuffleFirst'] = True
chop(params)
