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.
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")
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.
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")
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 test_shuffle_with_nones(self):
input_with_nones = (None, None)
actual_output = shuffle(*input_with_nones)
self.assertEqual(input_with_nones, actual_output)
def test_shuffle_invalid_shapes_exception(self):
input_data = np.arange(10)
with self.assertRaisesRegexp(ValueError, r'\(10,\), \(9,\)'):
shuffle(input_data, input_data[:len(input_data) - 1])
def test_shuffle_single_input(self):
input_data = np.ones(10)
shuffeled_data = shuffle(input_data)
# Output suppose to be a shuffled array, but
# not a tuple with shuffled array
np.testing.assert_array_equal(input_data, shuffeled_data)
def test_shuffle_empty_input(self):
np.testing.assert_array_equal(tuple(), shuffle())
def test_shuffle_basic(self):
input_data = np.arange(10)
shuffeled_data = shuffle(input_data, input_data)
np.testing.assert_array_equal(*shuffeled_data)
def test_shuffle_with_nones(self):
input_with_nones = (None, None)
actual_output = shuffle(*input_with_nones)
self.assertEqual(input_with_nones, actual_output)
def test_shuffle_invalid_shapes_exception(self):
input_data = np.arange(10)
with self.assertRaises(ValueError):
shuffle(input_data, input_data[:len(input_data) - 1])
def test_shuffle_single_input(self):
input_data = np.ones(10)
shuffeled_data = shuffle(input_data)
# Output suppose to be a shuffled array, but
# not a tuple with shuffled array
np.testing.assert_array_equal(input_data, shuffeled_data)
def test_shuffle_empty_input(self):
np.testing.assert_array_equal(tuple(), shuffle())
def test_shuffle_basic(self):
input_data = np.arange(10)
shuffeled_data = shuffle(input_data, input_data)
np.testing.assert_array_equal(*shuffeled_data)
def make_reber_classification(n_samples,
invalid_size=0.5,
return_indeces=False):
"""
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``.
return_indeces : bool
If ``True``, each word will be converted to array where each
letter converted to the index. Defaults to ``False``.
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])
>>>
>>> data, labels = make_reber_classification(
... 4, invalid_size=0.5, return_indeces=True)
>>> data
array([array([1, 3, 1, 4]),
array([0, 3, 0, 3, 0, 4, 3, 0, 4, 4]),
array([1, 3, 1, 2, 3, 1, 2, 4]),
array([0, 3, 0, 0, 3, 0, 4, 2, 4, 1, 0, 4, 0])], dtype=object)
"""
if n_samples < 2:
raise ValueError("There are must be at least 2 samples")
if not 0 < invalid_size < 1:
raise ValueError("`invalid_size` argument value must be between "
"zero and one, got {}".format(invalid_size))
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))
samples, labels = shuffle(np.array(valid_words + invalid_words),
np.array(valid_labels + invalid_labels))
if return_indeces:
samples = convert_letters_to_indeces(samples)
return samples, labels