def test_shuffle(self):
input_data = np.arange(10)
shuffeled_data = shuffle(input_data, input_data)
np.testing.assert_array_equal(*shuffeled_data)
np.testing.assert_array_equal(tuple(), shuffle())
with self.assertRaises(ValueError):
shuffle(input_data, input_data[:len(input_data) - 1])
def test_shuffle(self):
input_data = np.arange(10)
shuffeled_data = shuffle(input_data, input_data)
np.testing.assert_array_equal(*shuffeled_data)
np.testing.assert_array_equal(tuple(), shuffle())
with self.assertRaises(ValueError):
shuffle(input_data, input_data[:len(input_data) - 1])
def make_reber_classification(n_samples, invalid_size=0.5):
"""
Generate random dataset for Reber grammar classification.
Invalid words contains the same letters as at Reber grammar, but
they are build whithout grammar rules.
Parameters
----------
n_samples : int
Number of samples in dataset.
invalid_size : float
Proportion of invalid words in dataset, defaults to `0.5`. Value
must be between 0 and 1, but not equal to them.
Returns
-------
tuple
Return two lists. First contains words and second - labels for them.
Examples
--------
>>> from neupy.datasets import make_reber_classification
>>>
>>> data, labels = make_reber_classification(10, invalid_size=0.5)
>>> data
array(['SXSXVSXXVX', 'VVPS', 'VVPSXTTS', 'VVS', 'VXVS', 'VVS',
'PPTTTXPSPTV', 'VTTSXVPTXVXT', 'VSSXSTX', 'TTXVS'],
dtype='<U12')
>>> labels
array([0, 1, 0, 1, 1, 1, 0, 0, 0, 1])
"""
if n_samples < 2:
raise ValueError("There are must be at least 2 samples.")
if invalid_size <= 0 or invalid_size >= 1:
raise ValueError("`invalid_size` property must be between zero and"
"one, but not equal.")
n_valid_words = int(math.ceil(n_samples * invalid_size))
n_invalid_words = n_samples - n_valid_words
valid_words = make_reber(n_valid_words)
valid_labels = [1] * n_valid_words
invalid_words = []
invalid_labels = [0] * n_valid_words
for i in range(n_invalid_words):
word_length = randint(3, 14)
word = [choice(avaliable_letters) for _ in range(word_length)]
invalid_words.append(''.join(word))
return shuffle(np.array(valid_words + invalid_words),
np.array(valid_labels + invalid_labels))
def make_reber_classification(n_samples, invalid_size=0.5):
""" Generate random dataset for Reber grammar classification.
Invalid words contains the same letters as at Reber grammar, but
they are build whithout grammar rules.
Parameters
----------
n_samples : int
Number of samples in dataset.
invalid_size : float
Proportion of invalid words in dataset, defaults to `0.5`. Value
must be between 0 and 1, but not equal to them.
Returns
-------
tuple
Return two lists. First contains words and second - labels for them.
Examples
--------
>>> from neupy.datasets import make_reber_classification
>>>
>>> data, labels = make_reber_classification(10, invalid_size=0.5)
>>> data
array(['SXSXVSXXVX', 'VVPS', 'VVPSXTTS', 'VVS', 'VXVS', 'VVS',
'PPTTTXPSPTV', 'VTTSXVPTXVXT', 'VSSXSTX', 'TTXVS'],
dtype='<U12')
>>> labels
array([0, 1, 0, 1, 1, 1, 0, 0, 0, 1])
"""
if n_samples < 2:
raise ValueError("There are must be at least 2 samples.")
if invalid_size <= 0 or invalid_size >= 1:
raise ValueError("`invalid_size` property must be between zero and" "one, but not equal.")
n_valid_words = int(math.ceil(n_samples * invalid_size))
n_invalid_words = n_samples - n_valid_words
valid_words = make_reber(n_valid_words)
valid_labels = [1] * n_valid_words
invalid_words = []
invalid_labels = [0] * n_valid_words
for i in range(n_invalid_words):
word_length = randint(3, 14)
word = [choice(avaliable_letters) for _ in range(word_length)]
invalid_words.append("".join(word))
return shuffle(np.array(valid_words + invalid_words), np.array(valid_labels + invalid_labels))