body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
8bd46412e03f2c40e3ecdce251cb9cf1719c84c8e9a51305d73f8d606f897d54 | def else_scope(self):
'Create an else scope.\n\n This can only be used right after an if scope.\n\n Returns\n -------\n else_scope : WithScope\n The result else scope.\n\n Examples\n --------\n .. code-block:: python\n\n ib = tvm.ir_builder.creat... | Create an else scope.
This can only be used right after an if scope.
Returns
-------
else_scope : WithScope
The result else scope.
Examples
--------
.. code-block:: python
ib = tvm.ir_builder.create()
i = tvm.var("i")
x = ib.pointer("float32")
with ib.if_scope((i % 2) == 0):
x[i] = x[i - ... | third_party/incubator-tvm/python/tvm/ir_builder.py | else_scope | tianjiashuo/akg | 286 | python | def else_scope(self):
'Create an else scope.\n\n This can only be used right after an if scope.\n\n Returns\n -------\n else_scope : WithScope\n The result else scope.\n\n Examples\n --------\n .. code-block:: python\n\n ib = tvm.ir_builder.creat... | def else_scope(self):
'Create an else scope.\n\n This can only be used right after an if scope.\n\n Returns\n -------\n else_scope : WithScope\n The result else scope.\n\n Examples\n --------\n .. code-block:: python\n\n ib = tvm.ir_builder.creat... |
45504d0aef957530ca1e21ddda5b2830c87c149562db00b6e421c498e8935b1e | def new_scope(self):
'Create new scope,\n\n this is useful to set boundary of attr and allocate.\n\n Returns\n -------\n new_scope : WithScope\n The result new scope.\n '
self._seq_stack.append([])
def _exit_cb():
self.emit(self._pop_seq())
return Wi... | Create new scope,
this is useful to set boundary of attr and allocate.
Returns
-------
new_scope : WithScope
The result new scope. | third_party/incubator-tvm/python/tvm/ir_builder.py | new_scope | tianjiashuo/akg | 286 | python | def new_scope(self):
'Create new scope,\n\n this is useful to set boundary of attr and allocate.\n\n Returns\n -------\n new_scope : WithScope\n The result new scope.\n '
self._seq_stack.append([])
def _exit_cb():
self.emit(self._pop_seq())
return Wi... | def new_scope(self):
'Create new scope,\n\n this is useful to set boundary of attr and allocate.\n\n Returns\n -------\n new_scope : WithScope\n The result new scope.\n '
self._seq_stack.append([])
def _exit_cb():
self.emit(self._pop_seq())
return Wi... |
65f8a8361cbb0712bb4166d6eacd48baa50a76db90c16f8a16152b21c6d09675 | def allocate(self, dtype, shape, name='buf', scope=None):
'Create a allocate statement.\n\n Parameters\n ----------\n dtype : str\n The content data type.\n\n shape : tuple of Expr\n The shape of array to be allocated.\n\n name : str, optional\n Th... | Create a allocate statement.
Parameters
----------
dtype : str
The content data type.
shape : tuple of Expr
The shape of array to be allocated.
name : str, optional
The name of the buffer.
scope : str, optional
The scope of the buffer.
Returns
-------
buffer : BufferVar
The buffer var represent... | third_party/incubator-tvm/python/tvm/ir_builder.py | allocate | tianjiashuo/akg | 286 | python | def allocate(self, dtype, shape, name='buf', scope=None):
'Create a allocate statement.\n\n Parameters\n ----------\n dtype : str\n The content data type.\n\n shape : tuple of Expr\n The shape of array to be allocated.\n\n name : str, optional\n Th... | def allocate(self, dtype, shape, name='buf', scope=None):
'Create a allocate statement.\n\n Parameters\n ----------\n dtype : str\n The content data type.\n\n shape : tuple of Expr\n The shape of array to be allocated.\n\n name : str, optional\n Th... |
6180f8bd53da51d688ef403a8404f7a62f194645841057d213bebd5d869b78a0 | def pointer(self, content_type, name='ptr'):
'Create pointer variable with content type.\n\n Parameters\n ----------\n content_type : str\n The content data type.\n\n name : str, optional\n The name of the pointer.\n\n Returns\n -------\n ptr : ... | Create pointer variable with content type.
Parameters
----------
content_type : str
The content data type.
name : str, optional
The name of the pointer.
Returns
-------
ptr : BufferVar
The buffer var representing the buffer. | third_party/incubator-tvm/python/tvm/ir_builder.py | pointer | tianjiashuo/akg | 286 | python | def pointer(self, content_type, name='ptr'):
'Create pointer variable with content type.\n\n Parameters\n ----------\n content_type : str\n The content data type.\n\n name : str, optional\n The name of the pointer.\n\n Returns\n -------\n ptr : ... | def pointer(self, content_type, name='ptr'):
'Create pointer variable with content type.\n\n Parameters\n ----------\n content_type : str\n The content data type.\n\n name : str, optional\n The name of the pointer.\n\n Returns\n -------\n ptr : ... |
a02ac6dd2a394f72c6f860e16305ace59a78b7d6520cfdc9be090d520905c4b0 | def buffer_ptr(self, buf):
'Create pointer variable corresponds to buffer ptr.\n\n Parameters\n ----------\n buf : Buffer\n The buffer to be extracted.\n\n Returns\n -------\n ptr : BufferVar\n The buffer var representing the buffer.\n '
ret... | Create pointer variable corresponds to buffer ptr.
Parameters
----------
buf : Buffer
The buffer to be extracted.
Returns
-------
ptr : BufferVar
The buffer var representing the buffer. | third_party/incubator-tvm/python/tvm/ir_builder.py | buffer_ptr | tianjiashuo/akg | 286 | python | def buffer_ptr(self, buf):
'Create pointer variable corresponds to buffer ptr.\n\n Parameters\n ----------\n buf : Buffer\n The buffer to be extracted.\n\n Returns\n -------\n ptr : BufferVar\n The buffer var representing the buffer.\n '
ret... | def buffer_ptr(self, buf):
'Create pointer variable corresponds to buffer ptr.\n\n Parameters\n ----------\n buf : Buffer\n The buffer to be extracted.\n\n Returns\n -------\n ptr : BufferVar\n The buffer var representing the buffer.\n '
ret... |
6f94160edbabeab2751680b90fbfa58aa0d479ee634dc4b42cf7aea9c3eeb453 | def likely(self, expr):
'Add likely tag for expression.\n Parameters\n ----------\n expr : Expr\n The expression. Usually a condition expression.\n Returns\n -------\n expr : Expr\n The expression will likely tag.\n '
return _make.Call(expr.... | Add likely tag for expression.
Parameters
----------
expr : Expr
The expression. Usually a condition expression.
Returns
-------
expr : Expr
The expression will likely tag. | third_party/incubator-tvm/python/tvm/ir_builder.py | likely | tianjiashuo/akg | 286 | python | def likely(self, expr):
'Add likely tag for expression.\n Parameters\n ----------\n expr : Expr\n The expression. Usually a condition expression.\n Returns\n -------\n expr : Expr\n The expression will likely tag.\n '
return _make.Call(expr.... | def likely(self, expr):
'Add likely tag for expression.\n Parameters\n ----------\n expr : Expr\n The expression. Usually a condition expression.\n Returns\n -------\n expr : Expr\n The expression will likely tag.\n '
return _make.Call(expr.... |
bd1083309733bbb5d80242dd60835f3b42204d662c4ee3672eb7c5257608bdf7 | def get(self):
'Return the builded IR.\n\n Returns\n -------\n stmt : Stmt\n The result statement.\n '
seq = self._pop_seq()
if self._seq_stack:
raise RuntimeError('cannot call get inside construction scope')
return seq | Return the builded IR.
Returns
-------
stmt : Stmt
The result statement. | third_party/incubator-tvm/python/tvm/ir_builder.py | get | tianjiashuo/akg | 286 | python | def get(self):
'Return the builded IR.\n\n Returns\n -------\n stmt : Stmt\n The result statement.\n '
seq = self._pop_seq()
if self._seq_stack:
raise RuntimeError('cannot call get inside construction scope')
return seq | def get(self):
'Return the builded IR.\n\n Returns\n -------\n stmt : Stmt\n The result statement.\n '
seq = self._pop_seq()
if self._seq_stack:
raise RuntimeError('cannot call get inside construction scope')
return seq<|docstring|>Return the builded IR.
Re... |
64885cfb1baee8e81f41d3400e53337333b508e3d39684c059b6f41f9a783f92 | def ret_code_is_suspend(self, ret_code):
'docstring for ret_code_is_suspend'
return (ret_code == self.FCS_STATE_SUSPEND_PROCESS) | docstring for ret_code_is_suspend | code/freecell_solver/__init__.py | ret_code_is_suspend | shlomif/python-freecell_solver | 1 | python | def ret_code_is_suspend(self, ret_code):
return (ret_code == self.FCS_STATE_SUSPEND_PROCESS) | def ret_code_is_suspend(self, ret_code):
return (ret_code == self.FCS_STATE_SUSPEND_PROCESS)<|docstring|>docstring for ret_code_is_suspend<|endoftext|> |
54ae38ce0b93fc2b1ef5df910c5d3c7275d6a855be04a85e2124f04bce416ade | def fastq_generate(num_sequences=100, seq_length=75, gzip_output=True, random_seed=42, target_file=None, alphabet=DEFAULT_NUCLEOTIDE_ALPHABET, probabilities=None):
'\n Generate a random FASTQ file. PHRED scores will be illumina (+33).\n\n :param num_sequences: Number of separate sequence records to include in... | Generate a random FASTQ file. PHRED scores will be illumina (+33).
:param num_sequences: Number of separate sequence records to include in the file. Defaults to 100 records
:type num_sequences: int, optional
:param seq_length: Length in bases of each individual record in the file, Defaults to 75 bases per record
:type... | bio_test_artifacts/generate/fastq.py | fastq_generate | asistradition/bio-test-artifacts | 0 | python | def fastq_generate(num_sequences=100, seq_length=75, gzip_output=True, random_seed=42, target_file=None, alphabet=DEFAULT_NUCLEOTIDE_ALPHABET, probabilities=None):
'\n Generate a random FASTQ file. PHRED scores will be illumina (+33).\n\n :param num_sequences: Number of separate sequence records to include in... | def fastq_generate(num_sequences=100, seq_length=75, gzip_output=True, random_seed=42, target_file=None, alphabet=DEFAULT_NUCLEOTIDE_ALPHABET, probabilities=None):
'\n Generate a random FASTQ file. PHRED scores will be illumina (+33).\n\n :param num_sequences: Number of separate sequence records to include in... |
9b490e18b888df636a441c25a7f333e68ba4752dc6f03cd4f2d64c83804e594e | def generateData(n, N, filename='filename', data_size=256, dtype='float32'):
'\n generateData simulates ftnmr.spectrometer.measure and saves its output as hdf5 files\n \n Parameters\n ----------\n n: int\n n-th data block file index used for hdf5 file naming\n m: int\n Total number o... | generateData simulates ftnmr.spectrometer.measure and saves its output as hdf5 files
Parameters
----------
n: int
n-th data block file index used for hdf5 file naming
m: int
Total number of data blocks
filename: str
Saved data file name without hdf5 extension (default filename)
data_size: int
Mininum f... | scripts/data.py | generateData | sejin8642/projnmr | 0 | python | def generateData(n, N, filename='filename', data_size=256, dtype='float32'):
'\n generateData simulates ftnmr.spectrometer.measure and saves its output as hdf5 files\n \n Parameters\n ----------\n n: int\n n-th data block file index used for hdf5 file naming\n m: int\n Total number o... | def generateData(n, N, filename='filename', data_size=256, dtype='float32'):
'\n generateData simulates ftnmr.spectrometer.measure and saves its output as hdf5 files\n \n Parameters\n ----------\n n: int\n n-th data block file index used for hdf5 file naming\n m: int\n Total number o... |
f684edb2fe6258a137001bf7617bf9ab8afbcb319045cb490f0503d9d702948f | def wordBreak(self, s, words):
'\n :type s: str\n :type wordDict: List[str]\n :rtype: bool\n '
ok = [True]
max_len = max(([0] + map(len, words)))
words = set(words)
for i in range(1, (len(s) + 1)):
ok += (any(((ok[j] and (s[j:i] in words)) for j in range(max(0, (i... | :type s: str
:type wordDict: List[str]
:rtype: bool | leetcode/139. Word Break.py | wordBreak | isaiahnields/algorithms | 0 | python | def wordBreak(self, s, words):
'\n :type s: str\n :type wordDict: List[str]\n :rtype: bool\n '
ok = [True]
max_len = max(([0] + map(len, words)))
words = set(words)
for i in range(1, (len(s) + 1)):
ok += (any(((ok[j] and (s[j:i] in words)) for j in range(max(0, (i... | def wordBreak(self, s, words):
'\n :type s: str\n :type wordDict: List[str]\n :rtype: bool\n '
ok = [True]
max_len = max(([0] + map(len, words)))
words = set(words)
for i in range(1, (len(s) + 1)):
ok += (any(((ok[j] and (s[j:i] in words)) for j in range(max(0, (i... |
d61d06740bda6f8f470c775bf8881469e091d6a47390d630601052711819dea9 | def test_statdist():
'``statdist`` should return the stationary distribution.'
gen1 = np.array([[(- 1), (2 / 3), (1 / 3)], [(1 / 3), (- 1), (2 / 3)], [(2 / 3), (1 / 3), (- 1)]])
dist1 = np.array([1.0, 1.0, 1.0])
assert np.allclose(statdist(gen1), dist1)
gen2 = np.array([[((- 2) / 3), (2 / 3), 0], [(... | ``statdist`` should return the stationary distribution. | tests/test_utils.py | test_statdist | dbdr/choix | 117 | python | def test_statdist():
gen1 = np.array([[(- 1), (2 / 3), (1 / 3)], [(1 / 3), (- 1), (2 / 3)], [(2 / 3), (1 / 3), (- 1)]])
dist1 = np.array([1.0, 1.0, 1.0])
assert np.allclose(statdist(gen1), dist1)
gen2 = np.array([[((- 2) / 3), (2 / 3), 0], [(1 / 3), (- 1), (2 / 3)], [0, (1 / 3), ((- 1) / 3)]])
... | def test_statdist():
gen1 = np.array([[(- 1), (2 / 3), (1 / 3)], [(1 / 3), (- 1), (2 / 3)], [(2 / 3), (1 / 3), (- 1)]])
dist1 = np.array([1.0, 1.0, 1.0])
assert np.allclose(statdist(gen1), dist1)
gen2 = np.array([[((- 2) / 3), (2 / 3), 0], [(1 / 3), (- 1), (2 / 3)], [0, (1 / 3), ((- 1) / 3)]])
... |
f90a51d4efad716910a8c41ffbfd1171751b517f6b95e36fad8b9e5cb29c918d | def test_statdist_single_absorbing_class():
'\n ``statdist`` should work when the graph is not strongly connected, but has\n a single absorbing class.\n '
gen = np.array([[(- 1), 1, 0, 0], [1, (- 2), 1, 0], [0, 0, (- 1), 1], [0, 0, 1, (- 1)]], dtype=float)
dist = np.array([0.0, 0.0, 2.0, 2.0])
... | ``statdist`` should work when the graph is not strongly connected, but has
a single absorbing class. | tests/test_utils.py | test_statdist_single_absorbing_class | dbdr/choix | 117 | python | def test_statdist_single_absorbing_class():
'\n ``statdist`` should work when the graph is not strongly connected, but has\n a single absorbing class.\n '
gen = np.array([[(- 1), 1, 0, 0], [1, (- 2), 1, 0], [0, 0, (- 1), 1], [0, 0, 1, (- 1)]], dtype=float)
dist = np.array([0.0, 0.0, 2.0, 2.0])
... | def test_statdist_single_absorbing_class():
'\n ``statdist`` should work when the graph is not strongly connected, but has\n a single absorbing class.\n '
gen = np.array([[(- 1), 1, 0, 0], [1, (- 2), 1, 0], [0, 0, (- 1), 1], [0, 0, 1, (- 1)]], dtype=float)
dist = np.array([0.0, 0.0, 2.0, 2.0])
... |
b51a220cd73f237617b2dea211a48d09bdded001867a5c5c327b304bbf1d4812 | def test_statdist_two_absorbing_classes():
'\n ``statdist`` should fail when the graph is disconnected or has more than\n one absorbing class.\n '
gen1 = np.array([[(- 1), 1, 0, 0, 0], [1, (- 1), 0, 0, 0], [0, 1, (- 2), 1, 0], [0, 0, 0, (- 1), 1], [0, 0, 0, 1, (- 1)]], dtype=float)
with pytest.rais... | ``statdist`` should fail when the graph is disconnected or has more than
one absorbing class. | tests/test_utils.py | test_statdist_two_absorbing_classes | dbdr/choix | 117 | python | def test_statdist_two_absorbing_classes():
'\n ``statdist`` should fail when the graph is disconnected or has more than\n one absorbing class.\n '
gen1 = np.array([[(- 1), 1, 0, 0, 0], [1, (- 1), 0, 0, 0], [0, 1, (- 2), 1, 0], [0, 0, 0, (- 1), 1], [0, 0, 0, 1, (- 1)]], dtype=float)
with pytest.rais... | def test_statdist_two_absorbing_classes():
'\n ``statdist`` should fail when the graph is disconnected or has more than\n one absorbing class.\n '
gen1 = np.array([[(- 1), 1, 0, 0, 0], [1, (- 1), 0, 0, 0], [0, 1, (- 2), 1, 0], [0, 0, 0, (- 1), 1], [0, 0, 0, 1, (- 1)]], dtype=float)
with pytest.rais... |
78eefed258895d0bb20766761db85fc49b618cd0b4f6bd4440407f0ebef25af9 | def test_softmax():
'``softmax`` should work as expected.'
params1 = np.array([0, 0, 0])
params2 = np.array([1000, 1000, 2000])
assert np.allclose(softmax(params1), [(1 / 3), (1 / 3), (1 / 3)])
assert np.allclose(softmax(params2), [0, 0, 1]) | ``softmax`` should work as expected. | tests/test_utils.py | test_softmax | dbdr/choix | 117 | python | def test_softmax():
params1 = np.array([0, 0, 0])
params2 = np.array([1000, 1000, 2000])
assert np.allclose(softmax(params1), [(1 / 3), (1 / 3), (1 / 3)])
assert np.allclose(softmax(params2), [0, 0, 1]) | def test_softmax():
params1 = np.array([0, 0, 0])
params2 = np.array([1000, 1000, 2000])
assert np.allclose(softmax(params1), [(1 / 3), (1 / 3), (1 / 3)])
assert np.allclose(softmax(params2), [0, 0, 1])<|docstring|>``softmax`` should work as expected.<|endoftext|> |
4e6ea4b6ee772b470a2fbc67730ee3316f0281d726bb88095bd7f44366a0a1cf | def test_normal_cdf():
'``normal_cdf`` should return the value of the normal CDF.'
for x in (3 * RND.randn(10)):
np.allclose(normal_cdf(x), sps.norm.cdf(x)) | ``normal_cdf`` should return the value of the normal CDF. | tests/test_utils.py | test_normal_cdf | dbdr/choix | 117 | python | def test_normal_cdf():
for x in (3 * RND.randn(10)):
np.allclose(normal_cdf(x), sps.norm.cdf(x)) | def test_normal_cdf():
for x in (3 * RND.randn(10)):
np.allclose(normal_cdf(x), sps.norm.cdf(x))<|docstring|>``normal_cdf`` should return the value of the normal CDF.<|endoftext|> |
69e8ef5c106bb214696b672475e2a553d945dd0cd51e7c1f466050a34da95215 | def test_normal_pdf():
'``normal_pdf`` should return the value of the normal PDF.'
for x in (3 * RND.randn(10)):
np.allclose(normal_pdf(x), sps.norm.pdf(x)) | ``normal_pdf`` should return the value of the normal PDF. | tests/test_utils.py | test_normal_pdf | dbdr/choix | 117 | python | def test_normal_pdf():
for x in (3 * RND.randn(10)):
np.allclose(normal_pdf(x), sps.norm.pdf(x)) | def test_normal_pdf():
for x in (3 * RND.randn(10)):
np.allclose(normal_pdf(x), sps.norm.pdf(x))<|docstring|>``normal_pdf`` should return the value of the normal PDF.<|endoftext|> |
df2c21cafd0e6c85dceb00c2b608a3dc3a5fca65b8a965eb5e5bf9ab1565fa73 | def test_inv_posdef():
'``inv_posdef`` should return the correct inverse.'
mat = RND.randn(8, 8)
mat = mat.dot(mat.T)
assert np.allclose(inv_posdef(mat), inv(mat)) | ``inv_posdef`` should return the correct inverse. | tests/test_utils.py | test_inv_posdef | dbdr/choix | 117 | python | def test_inv_posdef():
mat = RND.randn(8, 8)
mat = mat.dot(mat.T)
assert np.allclose(inv_posdef(mat), inv(mat)) | def test_inv_posdef():
mat = RND.randn(8, 8)
mat = mat.dot(mat.T)
assert np.allclose(inv_posdef(mat), inv(mat))<|docstring|>``inv_posdef`` should return the correct inverse.<|endoftext|> |
34863a7472bf27b416406e0f0066a5d1d49f32549f06046566ab5b03367574de | def test_generate_params():
'``generate_params`` should work as expected.'
params1 = generate_params(10)
assert (len(params1) == 10)
params2 = generate_params(10, ordered=True)
assert (params2.tolist() == sorted(params2)) | ``generate_params`` should work as expected. | tests/test_utils.py | test_generate_params | dbdr/choix | 117 | python | def test_generate_params():
params1 = generate_params(10)
assert (len(params1) == 10)
params2 = generate_params(10, ordered=True)
assert (params2.tolist() == sorted(params2)) | def test_generate_params():
params1 = generate_params(10)
assert (len(params1) == 10)
params2 = generate_params(10, ordered=True)
assert (params2.tolist() == sorted(params2))<|docstring|>``generate_params`` should work as expected.<|endoftext|> |
288ece5e245bba2411d24163ef2d32d8dff3ca11dd007a367c5e4caa53e8e445 | def test_generate_pairwise():
'``generate_pairwise`` should work as expected.'
params = np.exp(RND.rand(10))
for num in RND.choice(20, size=3, replace=False):
data = generate_pairwise(params, num)
assert (np.array(data).shape == (num, 2)) | ``generate_pairwise`` should work as expected. | tests/test_utils.py | test_generate_pairwise | dbdr/choix | 117 | python | def test_generate_pairwise():
params = np.exp(RND.rand(10))
for num in RND.choice(20, size=3, replace=False):
data = generate_pairwise(params, num)
assert (np.array(data).shape == (num, 2)) | def test_generate_pairwise():
params = np.exp(RND.rand(10))
for num in RND.choice(20, size=3, replace=False):
data = generate_pairwise(params, num)
assert (np.array(data).shape == (num, 2))<|docstring|>``generate_pairwise`` should work as expected.<|endoftext|> |
325ae5717e4b125411972d3b6a008f6815c1a221ec1353536f86503c23c0517d | def test_generate_rankings():
'``generate_rankings`` should work as expected.'
n_items = 10
params = np.exp(RND.rand(n_items))
for num in RND.choice(20, size=3, replace=False):
size = (1 + RND.choice((n_items - 1)))
print(params, num, size)
data = generate_rankings(params, num, s... | ``generate_rankings`` should work as expected. | tests/test_utils.py | test_generate_rankings | dbdr/choix | 117 | python | def test_generate_rankings():
n_items = 10
params = np.exp(RND.rand(n_items))
for num in RND.choice(20, size=3, replace=False):
size = (1 + RND.choice((n_items - 1)))
print(params, num, size)
data = generate_rankings(params, num, size=size)
assert (np.array(data).shape =... | def test_generate_rankings():
n_items = 10
params = np.exp(RND.rand(n_items))
for num in RND.choice(20, size=3, replace=False):
size = (1 + RND.choice((n_items - 1)))
print(params, num, size)
data = generate_rankings(params, num, size=size)
assert (np.array(data).shape =... |
752f04782d86e21e391e8357587a5c9acdac83ece0b9dba6b11bc0086fb4462f | def test_compare_choice():
'``compare`` should work as expected for choices.'
params1 = np.array([0, 100, (- 100), (- 100), (- 100)])
x1 = compare((3, 0, 2, 4), params1)
assert (x1 == 0)
x2 = compare((3, 0, 1, 4), params1)
assert (x2 == 1)
params2 = np.zeros(10)
for _ in range(10):
... | ``compare`` should work as expected for choices. | tests/test_utils.py | test_compare_choice | dbdr/choix | 117 | python | def test_compare_choice():
params1 = np.array([0, 100, (- 100), (- 100), (- 100)])
x1 = compare((3, 0, 2, 4), params1)
assert (x1 == 0)
x2 = compare((3, 0, 1, 4), params1)
assert (x2 == 1)
params2 = np.zeros(10)
for _ in range(10):
items = RND.choice(10, size=3, replace=False)
... | def test_compare_choice():
params1 = np.array([0, 100, (- 100), (- 100), (- 100)])
x1 = compare((3, 0, 2, 4), params1)
assert (x1 == 0)
x2 = compare((3, 0, 1, 4), params1)
assert (x2 == 1)
params2 = np.zeros(10)
for _ in range(10):
items = RND.choice(10, size=3, replace=False)
... |
c68b1f134c6e10ca53938ca3e51597d1896b61f7cdbb69618d2f146aaba5cbfa | def test_compare_rankings():
'``compare`` should work as expected for rankings.'
params = np.array([0, 100, (- 100), (- 100), (- 100)])
x1 = compare((3, 0), params, rank=True)
assert np.array_equal(x1, np.array([0, 3]))
x2 = compare((3, 0, 1), params, rank=True)
assert np.array_equal(x2, np.arra... | ``compare`` should work as expected for rankings. | tests/test_utils.py | test_compare_rankings | dbdr/choix | 117 | python | def test_compare_rankings():
params = np.array([0, 100, (- 100), (- 100), (- 100)])
x1 = compare((3, 0), params, rank=True)
assert np.array_equal(x1, np.array([0, 3]))
x2 = compare((3, 0, 1), params, rank=True)
assert np.array_equal(x2, np.array([1, 0, 3])) | def test_compare_rankings():
params = np.array([0, 100, (- 100), (- 100), (- 100)])
x1 = compare((3, 0), params, rank=True)
assert np.array_equal(x1, np.array([0, 3]))
x2 = compare((3, 0, 1), params, rank=True)
assert np.array_equal(x2, np.array([1, 0, 3]))<|docstring|>``compare`` should work a... |
f6934bc2a649e425eae3565bfe210608624e149349c3b10154b1fb221e4f4dd9 | def test_probabilities():
'``probabilities`` should work as expected.'
params = np.log([1, 2, 3, 4])
assert np.allclose(probabilities([0, 2, 3], params), [(1 / 8), (3 / 8), (4 / 8)])
assert np.allclose(probabilities([1, 0], params), [(2 / 3), (1 / 3)]) | ``probabilities`` should work as expected. | tests/test_utils.py | test_probabilities | dbdr/choix | 117 | python | def test_probabilities():
params = np.log([1, 2, 3, 4])
assert np.allclose(probabilities([0, 2, 3], params), [(1 / 8), (3 / 8), (4 / 8)])
assert np.allclose(probabilities([1, 0], params), [(2 / 3), (1 / 3)]) | def test_probabilities():
params = np.log([1, 2, 3, 4])
assert np.allclose(probabilities([0, 2, 3], params), [(1 / 8), (3 / 8), (4 / 8)])
assert np.allclose(probabilities([1, 0], params), [(2 / 3), (1 / 3)])<|docstring|>``probabilities`` should work as expected.<|endoftext|> |
abeedeef18c37504feaa36379614c22e7bcea478de077ba529387f045afdb485 | def _eval(self, context: RuleContext) -> Optional[LintResult]:
'Do not use special characters in object names.'
self.quoted_identifiers_policy: str
self.unquoted_identifiers_policy: str
self.allow_space_in_identifier: bool
self.additional_allowed_characters: str
self.ignore_words: str
self.i... | Do not use special characters in object names. | src/sqlfluff/rules/L057.py | _eval | R7L208/sqlfluff | 173 | python | def _eval(self, context: RuleContext) -> Optional[LintResult]:
self.quoted_identifiers_policy: str
self.unquoted_identifiers_policy: str
self.allow_space_in_identifier: bool
self.additional_allowed_characters: str
self.ignore_words: str
self.ignore_words_regex: str
if (context.segment.n... | def _eval(self, context: RuleContext) -> Optional[LintResult]:
self.quoted_identifiers_policy: str
self.unquoted_identifiers_policy: str
self.allow_space_in_identifier: bool
self.additional_allowed_characters: str
self.ignore_words: str
self.ignore_words_regex: str
if (context.segment.n... |
9f9e475560ac19f7c70cb03abd7df8a8c1381439ea92d8982db3ae93c1455a2b | def _init_ignore_words_list(self):
'Called first time rule is evaluated to fetch & cache the policy.'
ignore_words_config: str = str(getattr(self, 'ignore_words'))
if (ignore_words_config and (ignore_words_config != 'None')):
self.ignore_words_list = self.split_comma_separated_string(ignore_words_co... | Called first time rule is evaluated to fetch & cache the policy. | src/sqlfluff/rules/L057.py | _init_ignore_words_list | R7L208/sqlfluff | 173 | python | def _init_ignore_words_list(self):
ignore_words_config: str = str(getattr(self, 'ignore_words'))
if (ignore_words_config and (ignore_words_config != 'None')):
self.ignore_words_list = self.split_comma_separated_string(ignore_words_config.lower())
else:
self.ignore_words_list = []
re... | def _init_ignore_words_list(self):
ignore_words_config: str = str(getattr(self, 'ignore_words'))
if (ignore_words_config and (ignore_words_config != 'None')):
self.ignore_words_list = self.split_comma_separated_string(ignore_words_config.lower())
else:
self.ignore_words_list = []
re... |
a5882d181bdbfaa8121bbbc997a3bf6e700b30ffe37d33472b36bf2bceeb23cf | def load_commute_volume(filename, date_range):
'Loads commute data and clips or extends date range'
commute_raw = pd.read_csv(filename, index_col='date')
commute_raw.index = pd.to_datetime(commute_raw.index, format='%Y-%m-%d')
commute_raw.sort_index(axis=0, inplace=True)
commute = pd.DataFrame(index... | Loads commute data and clips or extends date range | covid/pydata.py | load_commute_volume | claudiofronterre/covid19uk | 0 | python | def load_commute_volume(filename, date_range):
commute_raw = pd.read_csv(filename, index_col='date')
commute_raw.index = pd.to_datetime(commute_raw.index, format='%Y-%m-%d')
commute_raw.sort_index(axis=0, inplace=True)
commute = pd.DataFrame(index=np.arange(date_range[0], date_range[1], np.timedelt... | def load_commute_volume(filename, date_range):
commute_raw = pd.read_csv(filename, index_col='date')
commute_raw.index = pd.to_datetime(commute_raw.index, format='%Y-%m-%d')
commute_raw.sort_index(axis=0, inplace=True)
commute = pd.DataFrame(index=np.arange(date_range[0], date_range[1], np.timedelt... |
37d5a6d85e269f60b3c4ffd48d0daf77f53d646ed3792d4700c91e215b4b9ece | def group_ages(df):
'\n Sums age groups\n :param df: a dataframe with columns 0,1,2,...,90\n :return: a dataframe with 5-year age groups\n '
ages = np.arange(90).reshape([(90 // 5), 5]).astype(np.str)
grouped_ages = pd.DataFrame()
for age_group in ages:
grouped_ages[f'[{age_group[0]}... | Sums age groups
:param df: a dataframe with columns 0,1,2,...,90
:return: a dataframe with 5-year age groups | covid/pydata.py | group_ages | claudiofronterre/covid19uk | 0 | python | def group_ages(df):
'\n Sums age groups\n :param df: a dataframe with columns 0,1,2,...,90\n :return: a dataframe with 5-year age groups\n '
ages = np.arange(90).reshape([(90 // 5), 5]).astype(np.str)
grouped_ages = pd.DataFrame()
for age_group in ages:
grouped_ages[f'[{age_group[0]}... | def group_ages(df):
'\n Sums age groups\n :param df: a dataframe with columns 0,1,2,...,90\n :return: a dataframe with 5-year age groups\n '
ages = np.arange(90).reshape([(90 // 5), 5]).astype(np.str)
grouped_ages = pd.DataFrame()
for age_group in ages:
grouped_ages[f'[{age_group[0]}... |
0e5556e75cac08b339d5753fed74ea6e740bf716f856446045e98bc47e72639f | def collapse_commute_data(flow_file):
'Collapses LTLA-based commuting data in England to UTLA areas.\n\n Merges commuting data at LTLA areal basis onto modified UTLA Dec 2019 area.\n\n Modifications:\n E06000052, E06000053 combined\n E09000001, E09000033 combined\n '
filedir = os.path.dirname(os.... | Collapses LTLA-based commuting data in England to UTLA areas.
Merges commuting data at LTLA areal basis onto modified UTLA Dec 2019 area.
Modifications:
E06000052, E06000053 combined
E09000001, E09000033 combined | covid/pydata.py | collapse_commute_data | claudiofronterre/covid19uk | 0 | python | def collapse_commute_data(flow_file):
'Collapses LTLA-based commuting data in England to UTLA areas.\n\n Merges commuting data at LTLA areal basis onto modified UTLA Dec 2019 area.\n\n Modifications:\n E06000052, E06000053 combined\n E09000001, E09000033 combined\n '
filedir = os.path.dirname(os.... | def collapse_commute_data(flow_file):
'Collapses LTLA-based commuting data in England to UTLA areas.\n\n Merges commuting data at LTLA areal basis onto modified UTLA Dec 2019 area.\n\n Modifications:\n E06000052, E06000053 combined\n E09000001, E09000033 combined\n '
filedir = os.path.dirname(os.... |
363e0a1a535a90ed987bd6e9fadcba7d99a685fb52405020968623e51c525f25 | def collapse_pop(pop_file):
'Aggregates LTLA2019 population data to UTLA2019 and 5-year age groups to 80+'
filedir = os.path.dirname(os.path.abspath(__file__))
pop = pd.read_csv(pop_file)
pop = pop[pop['lad19cd'].str.startswith('E')]
lt_map = pd.read_csv((filedir + '/../data/Lower_Tier_Local_Authori... | Aggregates LTLA2019 population data to UTLA2019 and 5-year age groups to 80+ | covid/pydata.py | collapse_pop | claudiofronterre/covid19uk | 0 | python | def collapse_pop(pop_file):
filedir = os.path.dirname(os.path.abspath(__file__))
pop = pd.read_csv(pop_file)
pop = pop[pop['lad19cd'].str.startswith('E')]
lt_map = pd.read_csv((filedir + '/../data/Lower_Tier_Local_Authority_to_Upper_Tier_Local_Authority_April_2019_Lookup_in_England_and_Wales.csv'))... | def collapse_pop(pop_file):
filedir = os.path.dirname(os.path.abspath(__file__))
pop = pd.read_csv(pop_file)
pop = pop[pop['lad19cd'].str.startswith('E')]
lt_map = pd.read_csv((filedir + '/../data/Lower_Tier_Local_Authority_to_Upper_Tier_Local_Authority_April_2019_Lookup_in_England_and_Wales.csv'))... |
213c264c2ce32af05619f4bf9ce89ece3f96fc9e3720eac8d1e5a1971b061051 | def main(cfg):
'Runs main training procedure.'
seed_everything(seed=cfg['seed'])
neptune.init(project_qualified_name=cfg['neptune_project_name'], api_token=cfg['neptune_api_token'])
neptune.create_experiment(name=cfg['neptune_experiment'], params=cfg)
print('Preparing model and data...')
print('... | Runs main training procedure. | src/train.py | main | DIAGNijmegen/pathology-artifact-detection | 6 | python | def main(cfg):
seed_everything(seed=cfg['seed'])
neptune.init(project_qualified_name=cfg['neptune_project_name'], api_token=cfg['neptune_api_token'])
neptune.create_experiment(name=cfg['neptune_experiment'], params=cfg)
print('Preparing model and data...')
print('Using SMP version:', smp.__vers... | def main(cfg):
seed_everything(seed=cfg['seed'])
neptune.init(project_qualified_name=cfg['neptune_project_name'], api_token=cfg['neptune_api_token'])
neptune.create_experiment(name=cfg['neptune_experiment'], params=cfg)
print('Preparing model and data...')
print('Using SMP version:', smp.__vers... |
3e10afe97415905e982c04a94b1d8b1d701fec39443b14db6e91356042dad08d | def parse_servers(result: Sequence[Tuple[(str, str, List[str])]]) -> Dict[(str, dict)]:
'Convert servers list (from protocol method "server.peers.subscribe") into dict format.\n Also validate values, such as IP addresses and ports.\n '
servers = {}
for item in result:
host = item[1]
ou... | Convert servers list (from protocol method "server.peers.subscribe") into dict format.
Also validate values, such as IP addresses and ports. | electrum/network.py | parse_servers | Jesusown/electrum | 5,905 | python | def parse_servers(result: Sequence[Tuple[(str, str, List[str])]]) -> Dict[(str, dict)]:
'Convert servers list (from protocol method "server.peers.subscribe") into dict format.\n Also validate values, such as IP addresses and ports.\n '
servers = {}
for item in result:
host = item[1]
ou... | def parse_servers(result: Sequence[Tuple[(str, str, List[str])]]) -> Dict[(str, dict)]:
'Convert servers list (from protocol method "server.peers.subscribe") into dict format.\n Also validate values, such as IP addresses and ports.\n '
servers = {}
for item in result:
host = item[1]
ou... |
112d77a8d19e39f7f2863d590d435447e3318a9ecbc071722ff378435976eff0 | def filter_protocol(hostmap, *, allowed_protocols: Iterable[str]=None) -> Sequence[ServerAddr]:
'Filters the hostmap for those implementing protocol.'
if (allowed_protocols is None):
allowed_protocols = {PREFERRED_NETWORK_PROTOCOL}
eligible = []
for (host, portmap) in hostmap.items():
fo... | Filters the hostmap for those implementing protocol. | electrum/network.py | filter_protocol | Jesusown/electrum | 5,905 | python | def filter_protocol(hostmap, *, allowed_protocols: Iterable[str]=None) -> Sequence[ServerAddr]:
if (allowed_protocols is None):
allowed_protocols = {PREFERRED_NETWORK_PROTOCOL}
eligible = []
for (host, portmap) in hostmap.items():
for protocol in allowed_protocols:
port = po... | def filter_protocol(hostmap, *, allowed_protocols: Iterable[str]=None) -> Sequence[ServerAddr]:
if (allowed_protocols is None):
allowed_protocols = {PREFERRED_NETWORK_PROTOCOL}
eligible = []
for (host, portmap) in hostmap.items():
for protocol in allowed_protocols:
port = po... |
b4796b7e3d639790a3062896b6889b14994d422cede45d2aaab9d8867ab2669b | def has_internet_connection(self) -> bool:
'Our guess whether the device has Internet-connectivity.'
return self._has_ever_managed_to_connect_to_server | Our guess whether the device has Internet-connectivity. | electrum/network.py | has_internet_connection | Jesusown/electrum | 5,905 | python | def has_internet_connection(self) -> bool:
return self._has_ever_managed_to_connect_to_server | def has_internet_connection(self) -> bool:
return self._has_ever_managed_to_connect_to_server<|docstring|>Our guess whether the device has Internet-connectivity.<|endoftext|> |
51e47406c9e50266a5a18ea01e2e657547146f0fe4e8f113c4f26a92726c043f | def get_interfaces(self) -> List[ServerAddr]:
'The list of servers for the connected interfaces.'
with self.interfaces_lock:
return list(self.interfaces) | The list of servers for the connected interfaces. | electrum/network.py | get_interfaces | Jesusown/electrum | 5,905 | python | def get_interfaces(self) -> List[ServerAddr]:
with self.interfaces_lock:
return list(self.interfaces) | def get_interfaces(self) -> List[ServerAddr]:
with self.interfaces_lock:
return list(self.interfaces)<|docstring|>The list of servers for the connected interfaces.<|endoftext|> |
501a9dc75e591d8f95ac17540b9a94495ff3c6593cf2d8547df7a2f11d7c482a | async def _switch_to_random_interface(self):
'Switch to a random connected server other than the current one'
servers = self.get_interfaces()
if (self.default_server in servers):
servers.remove(self.default_server)
if servers:
(await self.switch_to_interface(random.choice(servers))) | Switch to a random connected server other than the current one | electrum/network.py | _switch_to_random_interface | Jesusown/electrum | 5,905 | python | async def _switch_to_random_interface(self):
servers = self.get_interfaces()
if (self.default_server in servers):
servers.remove(self.default_server)
if servers:
(await self.switch_to_interface(random.choice(servers))) | async def _switch_to_random_interface(self):
servers = self.get_interfaces()
if (self.default_server in servers):
servers.remove(self.default_server)
if servers:
(await self.switch_to_interface(random.choice(servers)))<|docstring|>Switch to a random connected server other than the curre... |
1747cedc5d43829c2a8d0bea62f0ecd19569208d07c48b09a828c955bf84005d | async def switch_lagging_interface(self):
'If auto_connect and lagging, switch interface (only within fork).'
if (self.auto_connect and (await self._server_is_lagging())):
best_header = self.blockchain().header_at_tip()
with self.interfaces_lock:
interfaces = list(self.interfaces.val... | If auto_connect and lagging, switch interface (only within fork). | electrum/network.py | switch_lagging_interface | Jesusown/electrum | 5,905 | python | async def switch_lagging_interface(self):
if (self.auto_connect and (await self._server_is_lagging())):
best_header = self.blockchain().header_at_tip()
with self.interfaces_lock:
interfaces = list(self.interfaces.values())
filtered = list(filter((lambda iface: (iface.tip_hea... | async def switch_lagging_interface(self):
if (self.auto_connect and (await self._server_is_lagging())):
best_header = self.blockchain().header_at_tip()
with self.interfaces_lock:
interfaces = list(self.interfaces.values())
filtered = list(filter((lambda iface: (iface.tip_hea... |
2b10fc6cd65c14fbbf27d04b6a4886e64cc52fec064aefbd402848d02fb44cff | async def switch_unwanted_fork_interface(self) -> None:
'If auto_connect, maybe switch to another fork/chain.'
if ((not self.auto_connect) or (not self.interface)):
return
with self.interfaces_lock:
interfaces = list(self.interfaces.values())
pref_height = self._blockchain_preferred_bloc... | If auto_connect, maybe switch to another fork/chain. | electrum/network.py | switch_unwanted_fork_interface | Jesusown/electrum | 5,905 | python | async def switch_unwanted_fork_interface(self) -> None:
if ((not self.auto_connect) or (not self.interface)):
return
with self.interfaces_lock:
interfaces = list(self.interfaces.values())
pref_height = self._blockchain_preferred_block['height']
pref_hash = self._blockchain_preferred... | async def switch_unwanted_fork_interface(self) -> None:
if ((not self.auto_connect) or (not self.interface)):
return
with self.interfaces_lock:
interfaces = list(self.interfaces.values())
pref_height = self._blockchain_preferred_block['height']
pref_hash = self._blockchain_preferred... |
61dd56058c5daf6114799d63dbb253e341da76213a9a3110ec3ee470f2b964f5 | async def switch_to_interface(self, server: ServerAddr):
'Switch to server as our main interface. If no connection exists,\n queue interface to be started. The actual switch will\n happen when the interface becomes ready.\n '
self.default_server = server
old_interface = self.interface
... | Switch to server as our main interface. If no connection exists,
queue interface to be started. The actual switch will
happen when the interface becomes ready. | electrum/network.py | switch_to_interface | Jesusown/electrum | 5,905 | python | async def switch_to_interface(self, server: ServerAddr):
'Switch to server as our main interface. If no connection exists,\n queue interface to be started. The actual switch will\n happen when the interface becomes ready.\n '
self.default_server = server
old_interface = self.interface
... | async def switch_to_interface(self, server: ServerAddr):
'Switch to server as our main interface. If no connection exists,\n queue interface to be started. The actual switch will\n happen when the interface becomes ready.\n '
self.default_server = server
old_interface = self.interface
... |
a14d62b01839a055b27fdfa2a97cd755d7cdb033b120b7189b2ece0f5cd31638 | async def connection_down(self, interface: Interface):
'A connection to server either went down, or was never made.\n We distinguish by whether it is in self.interfaces.'
if (not interface):
return
if (interface.server == self.default_server):
self._set_status('disconnected')
(awa... | A connection to server either went down, or was never made.
We distinguish by whether it is in self.interfaces. | electrum/network.py | connection_down | Jesusown/electrum | 5,905 | python | async def connection_down(self, interface: Interface):
'A connection to server either went down, or was never made.\n We distinguish by whether it is in self.interfaces.'
if (not interface):
return
if (interface.server == self.default_server):
self._set_status('disconnected')
(awa... | async def connection_down(self, interface: Interface):
'A connection to server either went down, or was never made.\n We distinguish by whether it is in self.interfaces.'
if (not interface):
return
if (interface.server == self.default_server):
self._set_status('disconnected')
(awa... |
487487eda4ecdf4b346b57e52fa07c3aa7f21a8ec06d6a34b2785be14b5eed51 | def get_server_height(self) -> int:
'Length of header chain, as claimed by main interface.'
interface = self.interface
return (interface.tip if interface else 0) | Length of header chain, as claimed by main interface. | electrum/network.py | get_server_height | Jesusown/electrum | 5,905 | python | def get_server_height(self) -> int:
interface = self.interface
return (interface.tip if interface else 0) | def get_server_height(self) -> int:
interface = self.interface
return (interface.tip if interface else 0)<|docstring|>Length of header chain, as claimed by main interface.<|endoftext|> |
db33a6c105a1ab3f7e6d7472eec61e983d43044e299ae997efa88dc8fdf42e33 | def get_local_height(self):
'Length of header chain, POW-verified.\n In case of a chain split, this is for the branch the main interface is on,\n but it is the tip of that branch (even if main interface is behind).\n '
return self.blockchain().height() | Length of header chain, POW-verified.
In case of a chain split, this is for the branch the main interface is on,
but it is the tip of that branch (even if main interface is behind). | electrum/network.py | get_local_height | Jesusown/electrum | 5,905 | python | def get_local_height(self):
'Length of header chain, POW-verified.\n In case of a chain split, this is for the branch the main interface is on,\n but it is the tip of that branch (even if main interface is behind).\n '
return self.blockchain().height() | def get_local_height(self):
'Length of header chain, POW-verified.\n In case of a chain split, this is for the branch the main interface is on,\n but it is the tip of that branch (even if main interface is behind).\n '
return self.blockchain().height()<|docstring|>Length of header chain, PO... |
d1d8c5095a6cf1b73daf3ef93a197756da179079a18da0d3a8fcfb5c8871f700 | def export_checkpoints(self, path):
'Run manually to generate blockchain checkpoints.\n Kept for console use only.\n '
cp = self.blockchain().get_checkpoints()
with open(path, 'w', encoding='utf-8') as f:
f.write(json.dumps(cp, indent=4)) | Run manually to generate blockchain checkpoints.
Kept for console use only. | electrum/network.py | export_checkpoints | Jesusown/electrum | 5,905 | python | def export_checkpoints(self, path):
'Run manually to generate blockchain checkpoints.\n Kept for console use only.\n '
cp = self.blockchain().get_checkpoints()
with open(path, 'w', encoding='utf-8') as f:
f.write(json.dumps(cp, indent=4)) | def export_checkpoints(self, path):
'Run manually to generate blockchain checkpoints.\n Kept for console use only.\n '
cp = self.blockchain().get_checkpoints()
with open(path, 'w', encoding='utf-8') as f:
f.write(json.dumps(cp, indent=4))<|docstring|>Run manually to generate blockchain... |
83b709fded9e1538a436dea6c5d4fb321835520da95649a25584eb8a3e55e783 | def start(self, jobs: Iterable=None):
'Schedule starting the network, along with the given job co-routines.\n\n Note: the jobs will *restart* every time the network restarts, e.g. on proxy\n setting changes.\n '
self._jobs = (jobs or [])
asyncio.run_coroutine_threadsafe(self._start(), s... | Schedule starting the network, along with the given job co-routines.
Note: the jobs will *restart* every time the network restarts, e.g. on proxy
setting changes. | electrum/network.py | start | Jesusown/electrum | 5,905 | python | def start(self, jobs: Iterable=None):
'Schedule starting the network, along with the given job co-routines.\n\n Note: the jobs will *restart* every time the network restarts, e.g. on proxy\n setting changes.\n '
self._jobs = (jobs or [])
asyncio.run_coroutine_threadsafe(self._start(), s... | def start(self, jobs: Iterable=None):
'Schedule starting the network, along with the given job co-routines.\n\n Note: the jobs will *restart* every time the network restarts, e.g. on proxy\n setting changes.\n '
self._jobs = (jobs or [])
asyncio.run_coroutine_threadsafe(self._start(), s... |
32bde37d01af77f9ba86ff20605caa085f5fbde678def981d0f0a13eebe0643c | def perform_destroy(self, instance):
'\n perform_destroy is used to performance a logic delete\n '
instance.is_active = (not instance.is_active)
instance.save() | perform_destroy is used to performance a logic delete | apps/inventory/viewsets/batch_viewset.py | perform_destroy | luishgranja/superdrogas | 8 | python | def perform_destroy(self, instance):
'\n \n '
instance.is_active = (not instance.is_active)
instance.save() | def perform_destroy(self, instance):
'\n \n '
instance.is_active = (not instance.is_active)
instance.save()<|docstring|>perform_destroy is used to performance a logic delete<|endoftext|> |
0f1c790a82724e92ab7ed9e208b3e6f17397e69b11c090ef39b2a7657ae7ff3f | @pytest.mark.parametrize('linter_name, input_msg, output_error, output_warning, output_other', MSG)
def test_split_warnings_errors(linter_name, input_msg, output_error, output_warning, output_other):
'\n Given:\n - linter name releated to input_msg which was returned from this specific linter.\n\n... | Given:
- linter name releated to input_msg which was returned from this specific linter.
When:
- Running split_warnings_errors on the given inupt.
Then:
- Ensure that the error, warning, other return values equal to expected. | demisto_sdk/commands/lint/tests/helper_test.py | test_split_warnings_errors | guiguitodelperuu/demisto-sdk | 42 | python | @pytest.mark.parametrize('linter_name, input_msg, output_error, output_warning, output_other', MSG)
def test_split_warnings_errors(linter_name, input_msg, output_error, output_warning, output_other):
'\n Given:\n - linter name releated to input_msg which was returned from this specific linter.\n\n... | @pytest.mark.parametrize('linter_name, input_msg, output_error, output_warning, output_other', MSG)
def test_split_warnings_errors(linter_name, input_msg, output_error, output_warning, output_other):
'\n Given:\n - linter name releated to input_msg which was returned from this specific linter.\n\n... |
9abc369714c276cdc1fcb96cc303e5aca54df43344277e8c3201d44ccae34464 | def download_blob(source_blob_name: str, destination_file_name: str, project_id: str, bucket_name: str) -> None:
'Downloads a blob from the bucket.'
storage_client = storage.Client(project=project_id)
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_f... | Downloads a blob from the bucket. | torchlit/cloud/gcloud.py | download_blob | himanshu-dutta/torchlit | 1 | python | def download_blob(source_blob_name: str, destination_file_name: str, project_id: str, bucket_name: str) -> None:
storage_client = storage.Client(project=project_id)
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_filename(destination_file_name)
... | def download_blob(source_blob_name: str, destination_file_name: str, project_id: str, bucket_name: str) -> None:
storage_client = storage.Client(project=project_id)
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_filename(destination_file_name)
... |
dc5cfa01ec8877a79e3b45be06efd5b936d13a91014c5cf198d5959bd9a50375 | def download_gcs_to_local_directory(source_path: str, project_id: str, bucket_name: str) -> None:
'Downloads a blob from the bucket.'
storage_client = storage.Client(project=project_id)
bucket = storage_client.get_bucket(bucket_name)
blobs = bucket.list_blobs(prefix=source_path)
for blob in tqdm(blo... | Downloads a blob from the bucket. | torchlit/cloud/gcloud.py | download_gcs_to_local_directory | himanshu-dutta/torchlit | 1 | python | def download_gcs_to_local_directory(source_path: str, project_id: str, bucket_name: str) -> None:
storage_client = storage.Client(project=project_id)
bucket = storage_client.get_bucket(bucket_name)
blobs = bucket.list_blobs(prefix=source_path)
for blob in tqdm(blobs):
if blob.name.endswith(... | def download_gcs_to_local_directory(source_path: str, project_id: str, bucket_name: str) -> None:
storage_client = storage.Client(project=project_id)
bucket = storage_client.get_bucket(bucket_name)
blobs = bucket.list_blobs(prefix=source_path)
for blob in tqdm(blobs):
if blob.name.endswith(... |
08250fcfb54049137f89dd4ae457325e9820005ce588d6101b00d30b9b7e32a8 | def list_blobs(source_path: str, project_id: str, bucket_name: str) -> None:
'Downloads a blob from the bucket.'
storage_client = storage.Client(project=project_id)
bucket = storage_client.get_bucket(bucket_name)
blobs = bucket.list_blobs(prefix=source_path)
for blob in blobs:
print(blob.nam... | Downloads a blob from the bucket. | torchlit/cloud/gcloud.py | list_blobs | himanshu-dutta/torchlit | 1 | python | def list_blobs(source_path: str, project_id: str, bucket_name: str) -> None:
storage_client = storage.Client(project=project_id)
bucket = storage_client.get_bucket(bucket_name)
blobs = bucket.list_blobs(prefix=source_path)
for blob in blobs:
print(blob.name) | def list_blobs(source_path: str, project_id: str, bucket_name: str) -> None:
storage_client = storage.Client(project=project_id)
bucket = storage_client.get_bucket(bucket_name)
blobs = bucket.list_blobs(prefix=source_path)
for blob in blobs:
print(blob.name)<|docstring|>Downloads a blob fro... |
0dfe53e5ea3bcf79a81650c9d5a1c5822bc49e431fe6201b4e301c9528b479e4 | def max_element(l: list):
'Return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n '
m = l[0]
for e in l:
if (e > m):
m = e
return m | Return maximum element in the list.
>>> max_element([1, 2, 3])
3
>>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])
123 | human_eval/max.py | max_element | LaudateCorpus1/code-align-evals-data | 3 | python | def max_element(l: list):
'Return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n '
m = l[0]
for e in l:
if (e > m):
m = e
return m | def max_element(l: list):
'Return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n '
m = l[0]
for e in l:
if (e > m):
m = e
return m<|docstring|>Return maximum element in the list.
>>> max_... |
5811ae805357900f7fde7fb8d5a75314a5deaa9dc3ca42575c5ff797c8b09cfd | def ParseAmInstrumentRawOutput(raw_output):
'Parses the output of an |am instrument -r| call.\n\n Args:\n raw_output: the output of an |am instrument -r| call as a list of lines\n Returns:\n A 3-tuple containing:\n - the instrumentation code as an integer\n - the instrumentation result as a list o... | Parses the output of an |am instrument -r| call.
Args:
raw_output: the output of an |am instrument -r| call as a list of lines
Returns:
A 3-tuple containing:
- the instrumentation code as an integer
- the instrumentation result as a list of lines
- the instrumentation statuses received as a list of 2-t... | build/android/pylib/instrumentation/instrumentation_test_instance.py | ParseAmInstrumentRawOutput | Cela-Inc/WebARonARCore | 777 | python | def ParseAmInstrumentRawOutput(raw_output):
'Parses the output of an |am instrument -r| call.\n\n Args:\n raw_output: the output of an |am instrument -r| call as a list of lines\n Returns:\n A 3-tuple containing:\n - the instrumentation code as an integer\n - the instrumentation result as a list o... | def ParseAmInstrumentRawOutput(raw_output):
'Parses the output of an |am instrument -r| call.\n\n Args:\n raw_output: the output of an |am instrument -r| call as a list of lines\n Returns:\n A 3-tuple containing:\n - the instrumentation code as an integer\n - the instrumentation result as a list o... |
046b1504ab4282e296e6bb530ac9ab1c461fd01a939da1dbae54aba1027c1702 | def GenerateTestResults(result_code, result_bundle, statuses, start_ms, duration_ms):
'Generate test results from |statuses|.\n\n Args:\n result_code: The overall status code as an integer.\n result_bundle: The summary bundle dump as a dict.\n statuses: A list of 2-tuples containing:\n - the status c... | Generate test results from |statuses|.
Args:
result_code: The overall status code as an integer.
result_bundle: The summary bundle dump as a dict.
statuses: A list of 2-tuples containing:
- the status code as an integer
- the bundle dump as a dict mapping string keys to string values
Note that this i... | build/android/pylib/instrumentation/instrumentation_test_instance.py | GenerateTestResults | Cela-Inc/WebARonARCore | 777 | python | def GenerateTestResults(result_code, result_bundle, statuses, start_ms, duration_ms):
'Generate test results from |statuses|.\n\n Args:\n result_code: The overall status code as an integer.\n result_bundle: The summary bundle dump as a dict.\n statuses: A list of 2-tuples containing:\n - the status c... | def GenerateTestResults(result_code, result_bundle, statuses, start_ms, duration_ms):
'Generate test results from |statuses|.\n\n Args:\n result_code: The overall status code as an integer.\n result_bundle: The summary bundle dump as a dict.\n statuses: A list of 2-tuples containing:\n - the status c... |
6b9bc8b6a609f5383b6cca6a91fd08d57f2fb47ab836565cc0908d98a4092439 | def ParseCommandLineFlagParameters(annotations):
'Determines whether the test is parameterized to be run with different\n command-line flags.\n\n Args:\n annotations: The annotations of the test.\n\n Returns:\n If the test is parameterized, returns a list of named tuples\n with lists of flags, e.g.:\... | Determines whether the test is parameterized to be run with different
command-line flags.
Args:
annotations: The annotations of the test.
Returns:
If the test is parameterized, returns a list of named tuples
with lists of flags, e.g.:
[(add=['--flag-to-add']), (remove=['--flag-to-remove']), ()]
That ... | build/android/pylib/instrumentation/instrumentation_test_instance.py | ParseCommandLineFlagParameters | Cela-Inc/WebARonARCore | 777 | python | def ParseCommandLineFlagParameters(annotations):
'Determines whether the test is parameterized to be run with different\n command-line flags.\n\n Args:\n annotations: The annotations of the test.\n\n Returns:\n If the test is parameterized, returns a list of named tuples\n with lists of flags, e.g.:\... | def ParseCommandLineFlagParameters(annotations):
'Determines whether the test is parameterized to be run with different\n command-line flags.\n\n Args:\n annotations: The annotations of the test.\n\n Returns:\n If the test is parameterized, returns a list of named tuples\n with lists of flags, e.g.:\... |
e4602128d0ab4ab953a56a9984486c0d7c3ce1084d0ef9aa9648176212721d0d | def FilterTests(tests, test_filter=None, annotations=None, excluded_annotations=None):
'Filter a list of tests\n\n Args:\n tests: a list of tests. e.g. [\n {\'annotations": {}, \'class\': \'com.example.TestA\', \'methods\':[]},\n {\'annotations": {}, \'class\': \'com.example.TestB\', \'metho... | Filter a list of tests
Args:
tests: a list of tests. e.g. [
{'annotations": {}, 'class': 'com.example.TestA', 'methods':[]},
{'annotations": {}, 'class': 'com.example.TestB', 'methods':[]}]
test_filter: googletest-style filter string.
annotations: a dict of wanted annotations for test methods.
... | build/android/pylib/instrumentation/instrumentation_test_instance.py | FilterTests | Cela-Inc/WebARonARCore | 777 | python | def FilterTests(tests, test_filter=None, annotations=None, excluded_annotations=None):
'Filter a list of tests\n\n Args:\n tests: a list of tests. e.g. [\n {\'annotations": {}, \'class\': \'com.example.TestA\', \'methods\':[]},\n {\'annotations": {}, \'class\': \'com.example.TestB\', \'metho... | def FilterTests(tests, test_filter=None, annotations=None, excluded_annotations=None):
'Filter a list of tests\n\n Args:\n tests: a list of tests. e.g. [\n {\'annotations": {}, \'class\': \'com.example.TestA\', \'methods\':[]},\n {\'annotations": {}, \'class\': \'com.example.TestB\', \'metho... |
d59a5204c6b1fe8337a35b38798c6c0c59e0b0c706ed71e88479a0a53a92a7ae | def GetTestName(test, sep='#'):
'Gets the name of the given test.\n\n Note that this may return the same name for more than one test, e.g. if a\n test is being run multiple times with different parameters.\n\n Args:\n test: the instrumentation test dict.\n sep: the character(s) that should join the class n... | Gets the name of the given test.
Note that this may return the same name for more than one test, e.g. if a
test is being run multiple times with different parameters.
Args:
test: the instrumentation test dict.
sep: the character(s) that should join the class name and the method name.
Returns:
The test name as a... | build/android/pylib/instrumentation/instrumentation_test_instance.py | GetTestName | Cela-Inc/WebARonARCore | 777 | python | def GetTestName(test, sep='#'):
'Gets the name of the given test.\n\n Note that this may return the same name for more than one test, e.g. if a\n test is being run multiple times with different parameters.\n\n Args:\n test: the instrumentation test dict.\n sep: the character(s) that should join the class n... | def GetTestName(test, sep='#'):
'Gets the name of the given test.\n\n Note that this may return the same name for more than one test, e.g. if a\n test is being run multiple times with different parameters.\n\n Args:\n test: the instrumentation test dict.\n sep: the character(s) that should join the class n... |
1891ace1eafd530e7e6f1a4d3b1661876a2f267ae151192b5e2c9919abb387bb | def GetUniqueTestName(test, sep='#'):
'Gets the unique name of the given test.\n\n This will include text to disambiguate between tests for which GetTestName\n would return the same name.\n\n Args:\n test: the instrumentation test dict.\n sep: the character(s) that should join the class name and the method... | Gets the unique name of the given test.
This will include text to disambiguate between tests for which GetTestName
would return the same name.
Args:
test: the instrumentation test dict.
sep: the character(s) that should join the class name and the method name.
Returns:
The unique test name as a string. | build/android/pylib/instrumentation/instrumentation_test_instance.py | GetUniqueTestName | Cela-Inc/WebARonARCore | 777 | python | def GetUniqueTestName(test, sep='#'):
'Gets the unique name of the given test.\n\n This will include text to disambiguate between tests for which GetTestName\n would return the same name.\n\n Args:\n test: the instrumentation test dict.\n sep: the character(s) that should join the class name and the method... | def GetUniqueTestName(test, sep='#'):
'Gets the unique name of the given test.\n\n This will include text to disambiguate between tests for which GetTestName\n would return the same name.\n\n Args:\n test: the instrumentation test dict.\n sep: the character(s) that should join the class name and the method... |
72efb1887c58b38e2aebc715b15d4efa56805f88d0c064bd1f91a4417cbcb3ce | def generate_cache_key(observable: 'ace.analysis.Observable', amt: 'ace.analysis.AnalysisModuleType') -> str:
'Returns the key that should be used for caching the result of the\n analysis generated by this analysis module type against this observable.'
if (observable is None):
return None
if (amt... | Returns the key that should be used for caching the result of the
analysis generated by this analysis module type against this observable. | ace/system/caching.py | generate_cache_key | ace-ecosystem/ace2-core | 0 | python | def generate_cache_key(observable: 'ace.analysis.Observable', amt: 'ace.analysis.AnalysisModuleType') -> str:
'Returns the key that should be used for caching the result of the\n analysis generated by this analysis module type against this observable.'
if (observable is None):
return None
if (amt... | def generate_cache_key(observable: 'ace.analysis.Observable', amt: 'ace.analysis.AnalysisModuleType') -> str:
'Returns the key that should be used for caching the result of the\n analysis generated by this analysis module type against this observable.'
if (observable is None):
return None
if (amt... |
1f7fb20d0aa3f48c3b4447a642c289eac034cc073b73877d7c60bdcb094b95f4 | def __init__(self, data_url, citation, url, **kwargs):
'\n Args:\n data_url: `string`, url to download the zip file from.\n citation: `string`, citation for the data set.\n url: `string`, url for information about the data set.\n **kwargs: keyword arguments forwarded to super.\n ... | Args:
data_url: `string`, url to download the zip file from.
citation: `string`, citation for the data set.
url: `string`, url for information about the data set.
**kwargs: keyword arguments forwarded to super. | datasets/bnl_newspapers/bnl_newspapers.py | __init__ | Jebrankhan/datasets | 2 | python | def __init__(self, data_url, citation, url, **kwargs):
'\n Args:\n data_url: `string`, url to download the zip file from.\n citation: `string`, citation for the data set.\n url: `string`, url for information about the data set.\n **kwargs: keyword arguments forwarded to super.\n ... | def __init__(self, data_url, citation, url, **kwargs):
'\n Args:\n data_url: `string`, url to download the zip file from.\n citation: `string`, citation for the data set.\n url: `string`, url for information about the data set.\n **kwargs: keyword arguments forwarded to super.\n ... |
ba659e418a7f2dbb7734e7aa3c29f9007fe88a310aa73c2b645e04435ae61f00 | def _split_generators(self, dl_manager):
'Returns SplitGenerators.'
_URL = self.config.data_url
data_dir = dl_manager.download_and_extract(_URL)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'dirpath': data_dir})] | Returns SplitGenerators. | datasets/bnl_newspapers/bnl_newspapers.py | _split_generators | Jebrankhan/datasets | 2 | python | def _split_generators(self, dl_manager):
_URL = self.config.data_url
data_dir = dl_manager.download_and_extract(_URL)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'dirpath': data_dir})] | def _split_generators(self, dl_manager):
_URL = self.config.data_url
data_dir = dl_manager.download_and_extract(_URL)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'dirpath': data_dir})]<|docstring|>Returns SplitGenerators.<|endoftext|> |
c13fbb3c9101386e4a0a4d1ce4e11180b064ae18d8f66fed2c0956566060f433 | def _generate_examples(self, dirpath):
'Yields examples as (key, example) tuples.'
ns = {'': 'http://www.openarchives.org/OAI/2.0/', 'xsi': 'http://www.w3.org/2001/XMLSchema-instance', 'oai_dc': 'http://www.openarchives.org/OAI/2.0/oai_dc/', 'dc': 'http://purl.org/dc/elements/1.1/', 'dcterms': 'http://purl.org/... | Yields examples as (key, example) tuples. | datasets/bnl_newspapers/bnl_newspapers.py | _generate_examples | Jebrankhan/datasets | 2 | python | def _generate_examples(self, dirpath):
ns = {: 'http://www.openarchives.org/OAI/2.0/', 'xsi': 'http://www.w3.org/2001/XMLSchema-instance', 'oai_dc': 'http://www.openarchives.org/OAI/2.0/oai_dc/', 'dc': 'http://purl.org/dc/elements/1.1/', 'dcterms': 'http://purl.org/dc/terms/'}
for (id_, xml) in enumerate(P... | def _generate_examples(self, dirpath):
ns = {: 'http://www.openarchives.org/OAI/2.0/', 'xsi': 'http://www.w3.org/2001/XMLSchema-instance', 'oai_dc': 'http://www.openarchives.org/OAI/2.0/oai_dc/', 'dc': 'http://purl.org/dc/elements/1.1/', 'dcterms': 'http://purl.org/dc/terms/'}
for (id_, xml) in enumerate(P... |
d3ecd98f20b28a718624a0f30e2088f27ac25c2cac20aa9c6e68214202be5e72 | def apk(actual, predicted, k=10):
"\n Computes the average precision at k.\n This function computes the average prescision at k between two lists of\n items.\n Parameters\n ----------\n actual : list\n A list of elements that are to be predicted (order doesn't matter)\n predicted : ... | Computes the average precision at k.
This function computes the average prescision at k between two lists of
items.
Parameters
----------
actual : list
A list of elements that are to be predicted (order doesn't matter)
predicted : list
A list of predicted elements (order does matter)
k : int, optio... | src/metrics.py | apk | ivallesp/corporacionfavorita | 0 | python | def apk(actual, predicted, k=10):
"\n Computes the average precision at k.\n This function computes the average prescision at k between two lists of\n items.\n Parameters\n ----------\n actual : list\n A list of elements that are to be predicted (order doesn't matter)\n predicted : ... | def apk(actual, predicted, k=10):
"\n Computes the average precision at k.\n This function computes the average prescision at k between two lists of\n items.\n Parameters\n ----------\n actual : list\n A list of elements that are to be predicted (order doesn't matter)\n predicted : ... |
570529bb77278b46631009031e0298a779d9527571985a31b1dcd2cb289b8526 | def mapk(actual, predicted, k=10):
"\n Computes the mean average precision at k.\n This function computes the mean average prescision at k between two lists\n of lists of items.\n Parameters\n ----------\n actual : list\n A list of lists of elements that are to be predicted\n ... | Computes the mean average precision at k.
This function computes the mean average prescision at k between two lists
of lists of items.
Parameters
----------
actual : list
A list of lists of elements that are to be predicted
(order doesn't matter in the lists)
predicted : list
A list of lis... | src/metrics.py | mapk | ivallesp/corporacionfavorita | 0 | python | def mapk(actual, predicted, k=10):
"\n Computes the mean average precision at k.\n This function computes the mean average prescision at k between two lists\n of lists of items.\n Parameters\n ----------\n actual : list\n A list of lists of elements that are to be predicted\n ... | def mapk(actual, predicted, k=10):
"\n Computes the mean average precision at k.\n This function computes the mean average prescision at k between two lists\n of lists of items.\n Parameters\n ----------\n actual : list\n A list of lists of elements that are to be predicted\n ... |
7d7576580a3d0339491546bbacf5daeda569e58fb858c0d1b62fe38e78efc245 | def in_top_k(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k on the right and calculates the accuracies\n to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of predictions to consider (int)... | Trims the y_pred to a length of k on the right and calculates the accuracies
to the y_true.
:y_true: list of actual values to be predicted (list)
:y_pred: list of predicted values (ordered by propensity) (list)
:k: number of predictions to consider (int)
:return: a list of top_k accuracies (list) | src/metrics.py | in_top_k | ivallesp/corporacionfavorita | 0 | python | def in_top_k(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k on the right and calculates the accuracies\n to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of predictions to consider (int)... | def in_top_k(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k on the right and calculates the accuracies\n to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of predictions to consider (int)... |
5c0cffd2d43fac4a787e387d7d6da8b7c16b2c7865b7ac7186f881cf08f8d0a0 | def top_k_categorical_accuracy(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k by the right and calculates the topK categorical accuracy to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of p... | Trims the y_pred to a length of k by the right and calculates the topK categorical accuracy to the y_true.
:y_true: list of actual values to be predicted (list)
:y_pred: list of predicted values (ordered by propensity) (list)
:k: number of predictions to consider (int)
:return: the average of the accuracies (float) | src/metrics.py | top_k_categorical_accuracy | ivallesp/corporacionfavorita | 0 | python | def top_k_categorical_accuracy(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k by the right and calculates the topK categorical accuracy to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of p... | def top_k_categorical_accuracy(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k by the right and calculates the topK categorical accuracy to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of p... |
810879830d60f3de3e19c64bda76f0f3bffeb16f716c8569e85761578a82be72 | def top_k_hit_ratio(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k on the right and calculates the hit ratio at k\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of predictions to consider (int)\n :re... | Trims the y_pred to a length of k on the right and calculates the hit ratio at k
:y_true: list of actual values to be predicted (list)
:y_pred: list of predicted values (ordered by propensity) (list)
:k: number of predictions to consider (int)
:return: the average of the git ratios(float) | src/metrics.py | top_k_hit_ratio | ivallesp/corporacionfavorita | 0 | python | def top_k_hit_ratio(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k on the right and calculates the hit ratio at k\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of predictions to consider (int)\n :re... | def top_k_hit_ratio(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k on the right and calculates the hit ratio at k\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k: number of predictions to consider (int)\n :re... |
661ec83513d92463e17a2fc985acc4fae755028fa9b68569414aed683ee54a43 | def rank_precision_recall_fscore_support_at_k(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k by the right and calculates the average\n accuracies to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k... | Trims the y_pred to a length of k by the right and calculates the average
accuracies to the y_true.
:y_true: list of actual values to be predicted (list)
:y_pred: list of predicted values (ordered by propensity) (list)
:k: number of predictions to consider (int)
:return: the average of the accuracies (float) | src/metrics.py | rank_precision_recall_fscore_support_at_k | ivallesp/corporacionfavorita | 0 | python | def rank_precision_recall_fscore_support_at_k(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k by the right and calculates the average\n accuracies to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k... | def rank_precision_recall_fscore_support_at_k(y_true, y_pred, k=5):
'\n Trims the y_pred to a length of k by the right and calculates the average\n accuracies to the y_true.\n :y_true: list of actual values to be predicted (list)\n :y_pred: list of predicted values (ordered by propensity) (list)\n :k... |
309c3f7e29ae09149bb15f4970e590d8e341fb05400f4ffc3a6334aa8ab60309 | def generate_rank_reports(y_true, y_pred, k_range=None):
'\n Given the true values and the predicted ones, it generates a dataframe containing \n the map@k, the topKcategoricalAccuracy and the hitsRatio@K, the precision and recall\n @k by product.\n :y_true: list of actual values to be predicted (list)\... | Given the true values and the predicted ones, it generates a dataframe containing
the map@k, the topKcategoricalAccuracy and the hitsRatio@K, the precision and recall
@k by product.
:y_true: list of actual values to be predicted (list)
:y_pred: list of predicted values (ordered by propensity) (list)
:k_range: range nu... | src/metrics.py | generate_rank_reports | ivallesp/corporacionfavorita | 0 | python | def generate_rank_reports(y_true, y_pred, k_range=None):
'\n Given the true values and the predicted ones, it generates a dataframe containing \n the map@k, the topKcategoricalAccuracy and the hitsRatio@K, the precision and recall\n @k by product.\n :y_true: list of actual values to be predicted (list)\... | def generate_rank_reports(y_true, y_pred, k_range=None):
'\n Given the true values and the predicted ones, it generates a dataframe containing \n the map@k, the topKcategoricalAccuracy and the hitsRatio@K, the precision and recall\n @k by product.\n :y_true: list of actual values to be predicted (list)\... |
e0a3045a356557f466cb9e0e0fc2c0f226e41a0b6f89e72b384b66989cc944b2 | def mae(truth, preds):
'\n Calculates the Mean average error\n :param truth: list of actual values to be predicted (list)\n :param preds: list of predicted values (ordered by propensity) (list)\n :return: the mean absolute error (float)\n '
return np.abs((truth - preds)).mean() | Calculates the Mean average error
:param truth: list of actual values to be predicted (list)
:param preds: list of predicted values (ordered by propensity) (list)
:return: the mean absolute error (float) | src/metrics.py | mae | ivallesp/corporacionfavorita | 0 | python | def mae(truth, preds):
'\n Calculates the Mean average error\n :param truth: list of actual values to be predicted (list)\n :param preds: list of predicted values (ordered by propensity) (list)\n :return: the mean absolute error (float)\n '
return np.abs((truth - preds)).mean() | def mae(truth, preds):
'\n Calculates the Mean average error\n :param truth: list of actual values to be predicted (list)\n :param preds: list of predicted values (ordered by propensity) (list)\n :return: the mean absolute error (float)\n '
return np.abs((truth - preds)).mean()<|docstring|>Calcul... |
13a639dcd11b5c669e3348cd5af919bf791ac97d73cb402ae12cb566e4802acd | def generate_binary_reports(y_true, y_pred, path, alias='', uplift_bins=100):
'\n Given a target variable and a set of predictions, calculates a set of standard metrics to measure the performance\n :param y_true: list of actual values to be predicted (list)\n :param y_pred: list of predicted values (ordere... | Given a target variable and a set of predictions, calculates a set of standard metrics to measure the performance
:param y_true: list of actual values to be predicted (list)
:param y_pred: list of predicted values (ordered by propensity) (list)
:param path: path to the folder where the reports must be saved (str|unicod... | src/metrics.py | generate_binary_reports | ivallesp/corporacionfavorita | 0 | python | def generate_binary_reports(y_true, y_pred, path, alias=, uplift_bins=100):
'\n Given a target variable and a set of predictions, calculates a set of standard metrics to measure the performance\n :param y_true: list of actual values to be predicted (list)\n :param y_pred: list of predicted values (ordered ... | def generate_binary_reports(y_true, y_pred, path, alias=, uplift_bins=100):
'\n Given a target variable and a set of predictions, calculates a set of standard metrics to measure the performance\n :param y_true: list of actual values to be predicted (list)\n :param y_pred: list of predicted values (ordered ... |
eb82281faf0285b0d88ed935d67e9d5eeaa7d5a432e49d16f433613070b95090 | def testSuccess(self):
'test successfully writing a layer'
path = os.path.join(unitTestDataPath(), 'raster', 'with_color_table.tif')
raster_layer = QgsRasterLayer(path, 'test')
self.assertTrue(raster_layer.isValid())
pipe = QgsRasterPipe()
self.assertTrue(pipe.set(raster_layer.dataProvider().clo... | test successfully writing a layer | tests/src/python/test_qgsrasterfilewritertask.py | testSuccess | dyna-mis/Hilabeling | 0 | python | def testSuccess(self):
path = os.path.join(unitTestDataPath(), 'raster', 'with_color_table.tif')
raster_layer = QgsRasterLayer(path, 'test')
self.assertTrue(raster_layer.isValid())
pipe = QgsRasterPipe()
self.assertTrue(pipe.set(raster_layer.dataProvider().clone()))
tmp = create_temp_filena... | def testSuccess(self):
path = os.path.join(unitTestDataPath(), 'raster', 'with_color_table.tif')
raster_layer = QgsRasterLayer(path, 'test')
self.assertTrue(raster_layer.isValid())
pipe = QgsRasterPipe()
self.assertTrue(pipe.set(raster_layer.dataProvider().clone()))
tmp = create_temp_filena... |
c1e84d243bfe48942fa4bd7fd41dfab8c44ddc57ed7be96ff8a08abb8e853f30 | def testLayerRemovalBeforeRun(self):
'test behavior when layer is removed before task begins'
path = os.path.join(unitTestDataPath(), 'raster', 'with_color_table.tif')
raster_layer = QgsRasterLayer(path, 'test')
self.assertTrue(raster_layer.isValid())
pipe = QgsRasterPipe()
self.assertTrue(pipe.... | test behavior when layer is removed before task begins | tests/src/python/test_qgsrasterfilewritertask.py | testLayerRemovalBeforeRun | dyna-mis/Hilabeling | 0 | python | def testLayerRemovalBeforeRun(self):
path = os.path.join(unitTestDataPath(), 'raster', 'with_color_table.tif')
raster_layer = QgsRasterLayer(path, 'test')
self.assertTrue(raster_layer.isValid())
pipe = QgsRasterPipe()
self.assertTrue(pipe.set(raster_layer.dataProvider().clone()))
tmp = crea... | def testLayerRemovalBeforeRun(self):
path = os.path.join(unitTestDataPath(), 'raster', 'with_color_table.tif')
raster_layer = QgsRasterLayer(path, 'test')
self.assertTrue(raster_layer.isValid())
pipe = QgsRasterPipe()
self.assertTrue(pipe.set(raster_layer.dataProvider().clone()))
tmp = crea... |
db691091155e33e8a9ded51fc987d571a4121d936cedc4e45cc3169f6d49b709 | @plugin.rule('hello', 'hi', 'hey', 'hello|hi')
@plugin.example('hello')
def handler(wrapped, trigger):
'This is the doc you are looking for.'
wrapped.reply('Hi!') | This is the doc you are looking for. | test/plugins/test_plugins_rules.py | handler | FahimFBA/sopel | 555 | python | @plugin.rule('hello', 'hi', 'hey', 'hello|hi')
@plugin.example('hello')
def handler(wrapped, trigger):
wrapped.reply('Hi!') | @plugin.rule('hello', 'hi', 'hey', 'hello|hi')
@plugin.example('hello')
def handler(wrapped, trigger):
wrapped.reply('Hi!')<|docstring|>This is the doc you are looking for.<|endoftext|> |
c9a0e71b3263797a110a28056ee10fb9d58d891c1892ea07e94d031191255f82 | @plugin.rule('hello', 'hi', 'hey', 'hello|hi')
@plugin.example('hello')
def handler(wrapped, trigger):
'This is the doc you are looking for.\n\n And now with extended text, for testing purpose only.\n '
wrapped.reply('Hi!') | This is the doc you are looking for.
And now with extended text, for testing purpose only. | test/plugins/test_plugins_rules.py | handler | FahimFBA/sopel | 555 | python | @plugin.rule('hello', 'hi', 'hey', 'hello|hi')
@plugin.example('hello')
def handler(wrapped, trigger):
'This is the doc you are looking for.\n\n And now with extended text, for testing purpose only.\n '
wrapped.reply('Hi!') | @plugin.rule('hello', 'hi', 'hey', 'hello|hi')
@plugin.example('hello')
def handler(wrapped, trigger):
'This is the doc you are looking for.\n\n And now with extended text, for testing purpose only.\n '
wrapped.reply('Hi!')<|docstring|>This is the doc you are looking for.
And now with extended te... |
3f62eb4dae80d64ec4a969e29fed6b854cd8828c5f0288b6d2bbe6e1c218d8bc | @pytest.fixture
def bobster_columnar_table_multi_batch_normal_mean_5000_stdev_1000_data_context(tmp_path_factory, monkeypatch) -> DataContext:
"\n This fixture generates three years' worth (36 months; i.e., 36 batches) of taxi trip data with the number of rows\n of a batch sampled from a normal distribution w... | This fixture generates three years' worth (36 months; i.e., 36 batches) of taxi trip data with the number of rows
of a batch sampled from a normal distribution with the mean of 5,000 rows and the standard deviation of 1,000 rows. | tests/rule_based_profiler/conftest.py | bobster_columnar_table_multi_batch_normal_mean_5000_stdev_1000_data_context | cn-karan-mudaliar/great_expectations | 6,451 | python | @pytest.fixture
def bobster_columnar_table_multi_batch_normal_mean_5000_stdev_1000_data_context(tmp_path_factory, monkeypatch) -> DataContext:
"\n This fixture generates three years' worth (36 months; i.e., 36 batches) of taxi trip data with the number of rows\n of a batch sampled from a normal distribution w... | @pytest.fixture
def bobster_columnar_table_multi_batch_normal_mean_5000_stdev_1000_data_context(tmp_path_factory, monkeypatch) -> DataContext:
"\n This fixture generates three years' worth (36 months; i.e., 36 batches) of taxi trip data with the number of rows\n of a batch sampled from a normal distribution w... |
37f6217a4e35c09065bf44f6ec91b69333099747b01509b4b5007a51a81e4b51 | @pytest.fixture
def multi_part_name_parameter_container():
'\n $parameter.date_strings.yyyy_mm_dd_hh_mm_ss_tz_date_format\n $parameter.date_strings.yyyy_mm_dd_date_format\n $parameter.date_strings.mm_yyyy_dd_hh_mm_ss_tz_date_format\n $parameter.date_strings.mm_yyyy_dd_date_format\n $parameter.date_st... | $parameter.date_strings.yyyy_mm_dd_hh_mm_ss_tz_date_format
$parameter.date_strings.yyyy_mm_dd_date_format
$parameter.date_strings.mm_yyyy_dd_hh_mm_ss_tz_date_format
$parameter.date_strings.mm_yyyy_dd_date_format
$parameter.date_strings.tolerances.max_abs_error_time_milliseconds
$parameter.date_strings.tolerances.max_nu... | tests/rule_based_profiler/conftest.py | multi_part_name_parameter_container | cn-karan-mudaliar/great_expectations | 6,451 | python | @pytest.fixture
def multi_part_name_parameter_container():
'\n $parameter.date_strings.yyyy_mm_dd_hh_mm_ss_tz_date_format\n $parameter.date_strings.yyyy_mm_dd_date_format\n $parameter.date_strings.mm_yyyy_dd_hh_mm_ss_tz_date_format\n $parameter.date_strings.mm_yyyy_dd_date_format\n $parameter.date_st... | @pytest.fixture
def multi_part_name_parameter_container():
'\n $parameter.date_strings.yyyy_mm_dd_hh_mm_ss_tz_date_format\n $parameter.date_strings.yyyy_mm_dd_date_format\n $parameter.date_strings.mm_yyyy_dd_hh_mm_ss_tz_date_format\n $parameter.date_strings.mm_yyyy_dd_date_format\n $parameter.date_st... |
4b725b08187e07df6e4bb5235519b200d30e59a4bfcf6dab2397bdf66f5a60d4 | def merge_config(config):
'\n Merge config into global config.\n Args:\n config (dict): Config to be merged.\n Returns: global config\n '
for (key, value) in config.items():
if ('.' not in key):
if (isinstance(value, dict) and (key in global_config)):
globa... | Merge config into global config.
Args:
config (dict): Config to be merged.
Returns: global config | sugar/tools/program.py | merge_config | mechanicalsea/sugar | 4 | python | def merge_config(config):
'\n Merge config into global config.\n Args:\n config (dict): Config to be merged.\n Returns: global config\n '
for (key, value) in config.items():
if ('.' not in key):
if (isinstance(value, dict) and (key in global_config)):
globa... | def merge_config(config):
'\n Merge config into global config.\n Args:\n config (dict): Config to be merged.\n Returns: global config\n '
for (key, value) in config.items():
if ('.' not in key):
if (isinstance(value, dict) and (key in global_config)):
globa... |
354f0e0b43857b3d2abbad139bad2ec8cea69ac4eb32d6b746b7c470502fb7fa | def load_config(file_path):
'\n Load config from yml/yaml file.\n Args:\n file_path (str): Path of the config file to be loaded.\n Returns: global config\n '
(_, ext) = os.path.splitext(file_path)
assert (ext in ['.yml', '.yaml']), 'only support yaml files for now'
merge_config(yaml.l... | Load config from yml/yaml file.
Args:
file_path (str): Path of the config file to be loaded.
Returns: global config | sugar/tools/program.py | load_config | mechanicalsea/sugar | 4 | python | def load_config(file_path):
'\n Load config from yml/yaml file.\n Args:\n file_path (str): Path of the config file to be loaded.\n Returns: global config\n '
(_, ext) = os.path.splitext(file_path)
assert (ext in ['.yml', '.yaml']), 'only support yaml files for now'
merge_config(yaml.l... | def load_config(file_path):
'\n Load config from yml/yaml file.\n Args:\n file_path (str): Path of the config file to be loaded.\n Returns: global config\n '
(_, ext) = os.path.splitext(file_path)
assert (ext in ['.yml', '.yaml']), 'only support yaml files for now'
merge_config(yaml.l... |
bfe08fb8cb522e482a8b02eda517e554c1bd5c67e60d0bd4467c8a444e49e91d | def check_gpu(use_gpu):
'\n Log error and exit when set use_gpu=true in paddlepaddle\n cpu version.\n '
err = 'Config use_gpu cannot be set as true while you are using pytorch cpu version ! \nPlease try: \n\t1. Install pytorch-gpu to run model on GPU \n\t2. Set use_gpu as false in config file to run mo... | Log error and exit when set use_gpu=true in paddlepaddle
cpu version. | sugar/tools/program.py | check_gpu | mechanicalsea/sugar | 4 | python | def check_gpu(use_gpu):
'\n Log error and exit when set use_gpu=true in paddlepaddle\n cpu version.\n '
err = 'Config use_gpu cannot be set as true while you are using pytorch cpu version ! \nPlease try: \n\t1. Install pytorch-gpu to run model on GPU \n\t2. Set use_gpu as false in config file to run mo... | def check_gpu(use_gpu):
'\n Log error and exit when set use_gpu=true in paddlepaddle\n cpu version.\n '
err = 'Config use_gpu cannot be set as true while you are using pytorch cpu version ! \nPlease try: \n\t1. Install pytorch-gpu to run model on GPU \n\t2. Set use_gpu as false in config file to run mo... |
2eb6868a9e6f7132404ec2979ea953289fcbf7ef8d475a776d8453e3a465dbd6 | def load_external_lengths(path):
'Loads a length distribution from a plain text file. The file\n must contain blank separated <length>:<score> pairs in each line.\n \n Args:\n path (string): Path to the length file.\n \n Returns:\n list of dicts mapping a length to its scores, one dict ... | Loads a length distribution from a plain text file. The file
must contain blank separated <length>:<score> pairs in each line.
Args:
path (string): Path to the length file.
Returns:
list of dicts mapping a length to its scores, one dict for each
sentence. | cam/sgnmt/predictors/structure.py | load_external_lengths | cimeister/sgnmt | 59 | python | def load_external_lengths(path):
'Loads a length distribution from a plain text file. The file\n must contain blank separated <length>:<score> pairs in each line.\n \n Args:\n path (string): Path to the length file.\n \n Returns:\n list of dicts mapping a length to its scores, one dict ... | def load_external_lengths(path):
'Loads a length distribution from a plain text file. The file\n must contain blank separated <length>:<score> pairs in each line.\n \n Args:\n path (string): Path to the length file.\n \n Returns:\n list of dicts mapping a length to its scores, one dict ... |
4fe9a549807d2c86b181b5e4743fdb9f9611144ee34109bbc6856a82314a2c1a | def update_trg_osm_ids(wmap_path):
'Update the OSM_*_ID variables using a target word map.\n\n Args:\n wmap_path (string): Path to the wmap file.\n '
global OSM_SRC_POP_ID, OSM_SET_MARKER_ID, OSM_JUMP_FWD_ID, OSM_JUMP_BWD_ID, OSM_SRC_POP2_ID, OSM_COPY_ID, OSM_SRC_UNPOP_ID
if (not wmap_path):
... | Update the OSM_*_ID variables using a target word map.
Args:
wmap_path (string): Path to the wmap file. | cam/sgnmt/predictors/structure.py | update_trg_osm_ids | cimeister/sgnmt | 59 | python | def update_trg_osm_ids(wmap_path):
'Update the OSM_*_ID variables using a target word map.\n\n Args:\n wmap_path (string): Path to the wmap file.\n '
global OSM_SRC_POP_ID, OSM_SET_MARKER_ID, OSM_JUMP_FWD_ID, OSM_JUMP_BWD_ID, OSM_SRC_POP2_ID, OSM_COPY_ID, OSM_SRC_UNPOP_ID
if (not wmap_path):
... | def update_trg_osm_ids(wmap_path):
'Update the OSM_*_ID variables using a target word map.\n\n Args:\n wmap_path (string): Path to the wmap file.\n '
global OSM_SRC_POP_ID, OSM_SET_MARKER_ID, OSM_JUMP_FWD_ID, OSM_JUMP_BWD_ID, OSM_SRC_POP2_ID, OSM_COPY_ID, OSM_SRC_UNPOP_ID
if (not wmap_path):
... |
abc931b1ac28268b015370471e207de9b365e44eb36ceef1ca1a55a7f515254d | def update_src_osm_ids(wmap_path):
'Update the OSM_*_ID variables using a source word map.\n\n Args:\n wmap_path (string): Path to the wmap file.\n '
global OSM_EOP_ID
if (not wmap_path):
return
with open(wmap_path) as f:
for line in f:
(word, word_id) = line.str... | Update the OSM_*_ID variables using a source word map.
Args:
wmap_path (string): Path to the wmap file. | cam/sgnmt/predictors/structure.py | update_src_osm_ids | cimeister/sgnmt | 59 | python | def update_src_osm_ids(wmap_path):
'Update the OSM_*_ID variables using a source word map.\n\n Args:\n wmap_path (string): Path to the wmap file.\n '
global OSM_EOP_ID
if (not wmap_path):
return
with open(wmap_path) as f:
for line in f:
(word, word_id) = line.str... | def update_src_osm_ids(wmap_path):
'Update the OSM_*_ID variables using a source word map.\n\n Args:\n wmap_path (string): Path to the wmap file.\n '
global OSM_EOP_ID
if (not wmap_path):
return
with open(wmap_path) as f:
for line in f:
(word, word_id) = line.str... |
a26febbbf1989670650ee2841eebc9f91a16578bf8deab849bd65f70de528c24 | def __init__(self, src_wmap, trg_wmap, use_jumps=True, use_auto_pop=False, use_unpop=False, use_pop2=False, use_src_eop=False, use_copy=False):
'Creates a new osm predictor.\n\n Args:\n src_wmap (string): Path to the source wmap. Used to grap\n EOP id.\n tr... | Creates a new osm predictor.
Args:
src_wmap (string): Path to the source wmap. Used to grap
EOP id.
trg_wmap (string): Path to the target wmap. Used to update
IDs of operations.
use_jumps (bool): If true, use SET_MARKER, JUMP_FWD and
JUMP_... | cam/sgnmt/predictors/structure.py | __init__ | cimeister/sgnmt | 59 | python | def __init__(self, src_wmap, trg_wmap, use_jumps=True, use_auto_pop=False, use_unpop=False, use_pop2=False, use_src_eop=False, use_copy=False):
'Creates a new osm predictor.\n\n Args:\n src_wmap (string): Path to the source wmap. Used to grap\n EOP id.\n tr... | def __init__(self, src_wmap, trg_wmap, use_jumps=True, use_auto_pop=False, use_unpop=False, use_pop2=False, use_src_eop=False, use_copy=False):
'Creates a new osm predictor.\n\n Args:\n src_wmap (string): Path to the source wmap. Used to grap\n EOP id.\n tr... |
60dd6a18350e1b3608cb5cb85cbb7ef25e9eb47156051eba1d21510ba079c8c2 | def initialize(self, src_sentence):
'Sets the number of source tokens.\n \n Args:\n src_sentence (list): Not used\n '
if self.use_src_eop:
self.src_len = (src_sentence.count(OSM_EOP_ID) + 1)
else:
self.src_len = len(src_sentence)
self.n_holes = 0
self.... | Sets the number of source tokens.
Args:
src_sentence (list): Not used | cam/sgnmt/predictors/structure.py | initialize | cimeister/sgnmt | 59 | python | def initialize(self, src_sentence):
'Sets the number of source tokens.\n \n Args:\n src_sentence (list): Not used\n '
if self.use_src_eop:
self.src_len = (src_sentence.count(OSM_EOP_ID) + 1)
else:
self.src_len = len(src_sentence)
self.n_holes = 0
self.... | def initialize(self, src_sentence):
'Sets the number of source tokens.\n \n Args:\n src_sentence (list): Not used\n '
if self.use_src_eop:
self.src_len = (src_sentence.count(OSM_EOP_ID) + 1)
else:
self.src_len = len(src_sentence)
self.n_holes = 0
self.... |
8809de341be38e51533cc301bd02c9b302accdc90e54fd7252047e03c9ac543d | def predict_next(self):
'Apply OSM constraints.\n \n Returns:\n dict.\n '
ret = {}
if (self.n_pop >= self.src_len):
return {utils.EOS_ID: 0.0}
else:
ret[utils.EOS_ID] = utils.NEG_INF
if (self.use_unpop and (self.n_pop <= 0)):
ret[OSM_SRC_UNPOP_... | Apply OSM constraints.
Returns:
dict. | cam/sgnmt/predictors/structure.py | predict_next | cimeister/sgnmt | 59 | python | def predict_next(self):
'Apply OSM constraints.\n \n Returns:\n dict.\n '
ret = {}
if (self.n_pop >= self.src_len):
return {utils.EOS_ID: 0.0}
else:
ret[utils.EOS_ID] = utils.NEG_INF
if (self.use_unpop and (self.n_pop <= 0)):
ret[OSM_SRC_UNPOP_... | def predict_next(self):
'Apply OSM constraints.\n \n Returns:\n dict.\n '
ret = {}
if (self.n_pop >= self.src_len):
return {utils.EOS_ID: 0.0}
else:
ret[utils.EOS_ID] = utils.NEG_INF
if (self.use_unpop and (self.n_pop <= 0)):
ret[OSM_SRC_UNPOP_... |
efd3163e0028445a9fccb08dff5045227a97c7ca9816823f783e275cfd569eff | def consume(self, word):
'Updates the number of holes, EOPs, and the head position.'
if (not self._is_pop(word)):
if (self.use_unpop and (word == OSM_SRC_UNPOP_ID)):
self.n_pop -= 1
else:
self.history.append(word)
else:
self.n_pop += 1
if self.use_jumps:
... | Updates the number of holes, EOPs, and the head position. | cam/sgnmt/predictors/structure.py | consume | cimeister/sgnmt | 59 | python | def consume(self, word):
if (not self._is_pop(word)):
if (self.use_unpop and (word == OSM_SRC_UNPOP_ID)):
self.n_pop -= 1
else:
self.history.append(word)
else:
self.n_pop += 1
if self.use_jumps:
if (word == OSM_SET_MARKER_ID):
self.n_h... | def consume(self, word):
if (not self._is_pop(word)):
if (self.use_unpop and (word == OSM_SRC_UNPOP_ID)):
self.n_pop -= 1
else:
self.history.append(word)
else:
self.n_pop += 1
if self.use_jumps:
if (word == OSM_SET_MARKER_ID):
self.n_h... |
5d70dc6e08cc7c1257b31a4fef397266af96ba0ea6560443c3c2c88a10ca046b | def is_equal(self, state1, state2):
'Trivial implementation'
return (state1 == state2) | Trivial implementation | cam/sgnmt/predictors/structure.py | is_equal | cimeister/sgnmt | 59 | python | def is_equal(self, state1, state2):
return (state1 == state2) | def is_equal(self, state1, state2):
return (state1 == state2)<|docstring|>Trivial implementation<|endoftext|> |
973dad8dfca650cb5a40479629ebc9f1cc089316656b2891e23c25bbfab9b52d | def __init__(self, trg_wmap, trg_test_file):
'Creates a new forcedosm predictor.\n\n Args:\n trg_wmap (string): Path to the target wmap file. Used to\n grap OSM operation IDs.\n trg_test_file (string): Path to the plain text file with \n ... | Creates a new forcedosm predictor.
Args:
trg_wmap (string): Path to the target wmap file. Used to
grap OSM operation IDs.
trg_test_file (string): Path to the plain text file with
the target sentences. Must have the
same number of l... | cam/sgnmt/predictors/structure.py | __init__ | cimeister/sgnmt | 59 | python | def __init__(self, trg_wmap, trg_test_file):
'Creates a new forcedosm predictor.\n\n Args:\n trg_wmap (string): Path to the target wmap file. Used to\n grap OSM operation IDs.\n trg_test_file (string): Path to the plain text file with \n ... | def __init__(self, trg_wmap, trg_test_file):
'Creates a new forcedosm predictor.\n\n Args:\n trg_wmap (string): Path to the target wmap file. Used to\n grap OSM operation IDs.\n trg_test_file (string): Path to the plain text file with \n ... |
904ac38d0ed34fadbc59d6217571b2e4f41a399f5c24f8c0f61ab99e57fd473e | def initialize(self, src_sentence):
'Resets compiled and head.\n \n Args:\n src_sentence (list): Not used\n '
self.compiled = ['X']
self.head = 0
self.cur_trg_sentence = self.trg_sentences[self.current_sen_id] | Resets compiled and head.
Args:
src_sentence (list): Not used | cam/sgnmt/predictors/structure.py | initialize | cimeister/sgnmt | 59 | python | def initialize(self, src_sentence):
'Resets compiled and head.\n \n Args:\n src_sentence (list): Not used\n '
self.compiled = ['X']
self.head = 0
self.cur_trg_sentence = self.trg_sentences[self.current_sen_id] | def initialize(self, src_sentence):
'Resets compiled and head.\n \n Args:\n src_sentence (list): Not used\n '
self.compiled = ['X']
self.head = 0
self.cur_trg_sentence = self.trg_sentences[self.current_sen_id]<|docstring|>Resets compiled and head.
Args:
src_sentence ... |
4afc0b46c1f81c9572a02847768a6c9321f686ac1f8beec2363f3dec14c6daf0 | def _is_complete(self):
'Returns true if the compiled sentence contains the right\n number of terminals.\n '
n_terminals = len([s for s in self.compiled if (s != 'X')])
return (n_terminals == len(self.cur_trg_sentence)) | Returns true if the compiled sentence contains the right
number of terminals. | cam/sgnmt/predictors/structure.py | _is_complete | cimeister/sgnmt | 59 | python | def _is_complete(self):
'Returns true if the compiled sentence contains the right\n number of terminals.\n '
n_terminals = len([s for s in self.compiled if (s != 'X')])
return (n_terminals == len(self.cur_trg_sentence)) | def _is_complete(self):
'Returns true if the compiled sentence contains the right\n number of terminals.\n '
n_terminals = len([s for s in self.compiled if (s != 'X')])
return (n_terminals == len(self.cur_trg_sentence))<|docstring|>Returns true if the compiled sentence contains the right
numbe... |
cc1b6c7d0657d9d5bff8acf29b7f9f538aa472208583cd4476f81bc58dc121f3 | def predict_next(self):
'Apply word reference constraints.\n \n Returns:\n dict.\n '
ret = {OSM_SRC_POP_ID: 0.0}
possible_words = self._align()
if possible_words[self.head]:
ret[OSM_SET_MARKER_ID] = 0.0
if any(possible_words[:self.head]):
ret[OSM_JUMP_... | Apply word reference constraints.
Returns:
dict. | cam/sgnmt/predictors/structure.py | predict_next | cimeister/sgnmt | 59 | python | def predict_next(self):
'Apply word reference constraints.\n \n Returns:\n dict.\n '
ret = {OSM_SRC_POP_ID: 0.0}
possible_words = self._align()
if possible_words[self.head]:
ret[OSM_SET_MARKER_ID] = 0.0
if any(possible_words[:self.head]):
ret[OSM_JUMP_... | def predict_next(self):
'Apply word reference constraints.\n \n Returns:\n dict.\n '
ret = {OSM_SRC_POP_ID: 0.0}
possible_words = self._align()
if possible_words[self.head]:
ret[OSM_SET_MARKER_ID] = 0.0
if any(possible_words[:self.head]):
ret[OSM_JUMP_... |
aadaa9618b1e4991829622593aa1b51e7a130fa10821769c004117cfc8b41842 | def get_unk_probability(self, posterior):
'Always returns -inf.'
return utils.NEG_INF | Always returns -inf. | cam/sgnmt/predictors/structure.py | get_unk_probability | cimeister/sgnmt | 59 | python | def get_unk_probability(self, posterior):
return utils.NEG_INF | def get_unk_probability(self, posterior):
return utils.NEG_INF<|docstring|>Always returns -inf.<|endoftext|> |
af89108dfc80f9da79093deb2b264f812eb76fe9620f7695833a884b6f579512 | def consume(self, word):
'Updates the compiled string and the head position.'
if (word == OSM_SET_MARKER_ID):
self._insert_op('X')
elif (word == OSM_JUMP_FWD_ID):
self._jump_op(1)
elif (word == OSM_JUMP_BWD_ID):
self._jump_op((- 1))
elif (word != OSM_SRC_POP_ID):
self... | Updates the compiled string and the head position. | cam/sgnmt/predictors/structure.py | consume | cimeister/sgnmt | 59 | python | def consume(self, word):
if (word == OSM_SET_MARKER_ID):
self._insert_op('X')
elif (word == OSM_JUMP_FWD_ID):
self._jump_op(1)
elif (word == OSM_JUMP_BWD_ID):
self._jump_op((- 1))
elif (word != OSM_SRC_POP_ID):
self._insert_op(str(word)) | def consume(self, word):
if (word == OSM_SET_MARKER_ID):
self._insert_op('X')
elif (word == OSM_JUMP_FWD_ID):
self._jump_op(1)
elif (word == OSM_JUMP_BWD_ID):
self._jump_op((- 1))
elif (word != OSM_SRC_POP_ID):
self._insert_op(str(word))<|docstring|>Updates the compi... |
5d70dc6e08cc7c1257b31a4fef397266af96ba0ea6560443c3c2c88a10ca046b | def is_equal(self, state1, state2):
'Trivial implementation'
return (state1 == state2) | Trivial implementation | cam/sgnmt/predictors/structure.py | is_equal | cimeister/sgnmt | 59 | python | def is_equal(self, state1, state2):
return (state1 == state2) | def is_equal(self, state1, state2):
return (state1 == state2)<|docstring|>Trivial implementation<|endoftext|> |
fbba0303c790a7a123ddd42e49a481a43d9ad7877d7e87bec5c8044896db06ba | def __init__(self, max_terminal_id, closing_bracket_id, max_depth=(- 1), extlength_path=''):
'Creates a new bracket predictor.\n \n Args:\n max_terminal_id (int): All IDs greater than this are \n brackets\n closing_bracket_id (string): All brackets except these one... | Creates a new bracket predictor.
Args:
max_terminal_id (int): All IDs greater than this are
brackets
closing_bracket_id (string): All brackets except these ones are
opening. Comma-separated list of integers.
max_depth (int): If positive, restrict the maximum depth
extlength_path (stri... | cam/sgnmt/predictors/structure.py | __init__ | cimeister/sgnmt | 59 | python | def __init__(self, max_terminal_id, closing_bracket_id, max_depth=(- 1), extlength_path=):
'Creates a new bracket predictor.\n \n Args:\n max_terminal_id (int): All IDs greater than this are \n brackets\n closing_bracket_id (string): All brackets except these ones ... | def __init__(self, max_terminal_id, closing_bracket_id, max_depth=(- 1), extlength_path=):
'Creates a new bracket predictor.\n \n Args:\n max_terminal_id (int): All IDs greater than this are \n brackets\n closing_bracket_id (string): All brackets except these ones ... |
783b2e365f88e93ec8f74c208f306f27c478f833e7690208bf0a676ecbba93b4 | def initialize(self, src_sentence):
'Sets the current depth to 0.\n \n Args:\n src_sentence (list): Not used\n '
self.cur_depth = 0
self.ends_with_opening = True
self.n_terminals = 0
if self.length_scores:
self.cur_length_scores = self.length_scores[self.curre... | Sets the current depth to 0.
Args:
src_sentence (list): Not used | cam/sgnmt/predictors/structure.py | initialize | cimeister/sgnmt | 59 | python | def initialize(self, src_sentence):
'Sets the current depth to 0.\n \n Args:\n src_sentence (list): Not used\n '
self.cur_depth = 0
self.ends_with_opening = True
self.n_terminals = 0
if self.length_scores:
self.cur_length_scores = self.length_scores[self.curre... | def initialize(self, src_sentence):
'Sets the current depth to 0.\n \n Args:\n src_sentence (list): Not used\n '
self.cur_depth = 0
self.ends_with_opening = True
self.n_terminals = 0
if self.length_scores:
self.cur_length_scores = self.length_scores[self.curre... |
22cab50275fbe7fbe5dfb265212e6b724d0fef7362f2617632a927160564ecfb | def predict_next(self, words):
'If the maximum depth is reached, exclude all opening\n brackets. If history is not balanced, exclude EOS. If the\n current depth is zero, exclude closing brackets.\n \n Args:\n words (list): Set of words to score\n Returns:\n d... | If the maximum depth is reached, exclude all opening
brackets. If history is not balanced, exclude EOS. If the
current depth is zero, exclude closing brackets.
Args:
words (list): Set of words to score
Returns:
dict. | cam/sgnmt/predictors/structure.py | predict_next | cimeister/sgnmt | 59 | python | def predict_next(self, words):
'If the maximum depth is reached, exclude all opening\n brackets. If history is not balanced, exclude EOS. If the\n current depth is zero, exclude closing brackets.\n \n Args:\n words (list): Set of words to score\n Returns:\n d... | def predict_next(self, words):
'If the maximum depth is reached, exclude all opening\n brackets. If history is not balanced, exclude EOS. If the\n current depth is zero, exclude closing brackets.\n \n Args:\n words (list): Set of words to score\n Returns:\n d... |
7c466c50ce008d360e164dde66488f12e33d530e370ecc6f8d964fa5b49e8406 | def get_unk_probability(self, posterior):
'Always returns 0.0'
if ((self.cur_depth == 0) and (not self.ends_with_opening)):
return utils.NEG_INF
return 0.0 | Always returns 0.0 | cam/sgnmt/predictors/structure.py | get_unk_probability | cimeister/sgnmt | 59 | python | def get_unk_probability(self, posterior):
if ((self.cur_depth == 0) and (not self.ends_with_opening)):
return utils.NEG_INF
return 0.0 | def get_unk_probability(self, posterior):
if ((self.cur_depth == 0) and (not self.ends_with_opening)):
return utils.NEG_INF
return 0.0<|docstring|>Always returns 0.0<|endoftext|> |
6bd30068f206213116cccb90ebaf3cfde2389d58a8be60855f1540e44cd44ab3 | def consume(self, word):
'Updates current depth and the number of consumed terminals.'
if (word in self.closing_bracket_ids):
if self.ends_with_opening:
self.n_terminals += 1
self.cur_depth -= 1
self.ends_with_opening = False
elif (word > self.max_terminal_id):
se... | Updates current depth and the number of consumed terminals. | cam/sgnmt/predictors/structure.py | consume | cimeister/sgnmt | 59 | python | def consume(self, word):
if (word in self.closing_bracket_ids):
if self.ends_with_opening:
self.n_terminals += 1
self.cur_depth -= 1
self.ends_with_opening = False
elif (word > self.max_terminal_id):
self.cur_depth += 1
self.ends_with_opening = True | def consume(self, word):
if (word in self.closing_bracket_ids):
if self.ends_with_opening:
self.n_terminals += 1
self.cur_depth -= 1
self.ends_with_opening = False
elif (word > self.max_terminal_id):
self.cur_depth += 1
self.ends_with_opening = True<|docs... |
da8db1fa9da12e154f7db4a95454922709f9d6223988c31fb1c8bc5f7fd36d44 | def get_state(self):
'Returns the current depth and number of consumed terminals'
return (self.cur_depth, self.n_terminals, self.ends_with_opening) | Returns the current depth and number of consumed terminals | cam/sgnmt/predictors/structure.py | get_state | cimeister/sgnmt | 59 | python | def get_state(self):
return (self.cur_depth, self.n_terminals, self.ends_with_opening) | def get_state(self):
return (self.cur_depth, self.n_terminals, self.ends_with_opening)<|docstring|>Returns the current depth and number of consumed terminals<|endoftext|> |
7d860c8df482ad80d6c31d5e81bcf25b2b6cccabe27da66f720c831048f9dcd9 | def set_state(self, state):
'Sets the current depth and number of consumed terminals'
(self.cur_depth, self.n_terminals, self.ends_with_opening) = state | Sets the current depth and number of consumed terminals | cam/sgnmt/predictors/structure.py | set_state | cimeister/sgnmt | 59 | python | def set_state(self, state):
(self.cur_depth, self.n_terminals, self.ends_with_opening) = state | def set_state(self, state):
(self.cur_depth, self.n_terminals, self.ends_with_opening) = state<|docstring|>Sets the current depth and number of consumed terminals<|endoftext|> |
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