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def half_mag_amplitude_ratio(self, mag, avg, weight):
"""
Return ratio of amplitude of higher and lower magnitudes.
A ratio of amplitude of higher and lower magnitudes than average,
considering weights. This ratio, by definition, should be higher
for EB than for others.
... | Return ratio of amplitude of higher and lower magnitudes.
A ratio of amplitude of higher and lower magnitudes than average,
considering weights. This ratio, by definition, should be higher
for EB than for others.
Parameters
----------
mag : array_like
An ar... | entailment |
def half_mag_amplitude_ratio2(self, mag, avg):
"""
Return ratio of amplitude of higher and lower magnitudes.
A ratio of amplitude of higher and lower magnitudes than average,
considering weights. This ratio, by definition, should be higher
for EB than for others.
Param... | Return ratio of amplitude of higher and lower magnitudes.
A ratio of amplitude of higher and lower magnitudes than average,
considering weights. This ratio, by definition, should be higher
for EB than for others.
Parameters
----------
mag : array_like
An ar... | entailment |
def get_eta(self, mag, std):
"""
Return Eta feature.
Parameters
----------
mag : array_like
An array of magnitudes.
std : array_like
A standard deviation of magnitudes.
Returns
-------
eta : float
The value of ... | Return Eta feature.
Parameters
----------
mag : array_like
An array of magnitudes.
std : array_like
A standard deviation of magnitudes.
Returns
-------
eta : float
The value of Eta index. | entailment |
def slope_percentile(self, date, mag):
"""
Return 10% and 90% percentile of slope.
Parameters
----------
date : array_like
An array of phase-folded date. Sorted.
mag : array_like
An array of phase-folded magnitudes. Sorted by date.
Return... | Return 10% and 90% percentile of slope.
Parameters
----------
date : array_like
An array of phase-folded date. Sorted.
mag : array_like
An array of phase-folded magnitudes. Sorted by date.
Returns
-------
per_10 : float
10% pe... | entailment |
def get_cusum(self, mag):
"""
Return max - min of cumulative sum.
Parameters
----------
mag : array_like
An array of magnitudes.
Returns
-------
mm_cusum : float
Max - min of cumulative sum.
"""
c = np.cumsum(mag ... | Return max - min of cumulative sum.
Parameters
----------
mag : array_like
An array of magnitudes.
Returns
-------
mm_cusum : float
Max - min of cumulative sum. | entailment |
def get_features2(self):
"""
Return all features with its names.
Returns
-------
names : list
Feature names.
values : list
Feature values
"""
feature_names = []
feature_values = []
# Get all the names of features.... | Return all features with its names.
Returns
-------
names : list
Feature names.
values : list
Feature values | entailment |
def get_features_all(self):
"""
Return all features with its names.
Regardless of being used for train and prediction. Sorted by the names.
Returns
-------
all_features : OrderedDict
Features dictionary.
"""
features = {}
# Get all ... | Return all features with its names.
Regardless of being used for train and prediction. Sorted by the names.
Returns
-------
all_features : OrderedDict
Features dictionary. | entailment |
def init(device_id=None, random_seed=None):
"""Initialize Hebel.
This function creates a CUDA context, CUBLAS context and
initializes and seeds the pseudo-random number generator.
**Parameters:**
device_id : integer, optional
The ID of the GPU device to use. If this is omitted, PyCUDA... | Initialize Hebel.
This function creates a CUDA context, CUBLAS context and
initializes and seeds the pseudo-random number generator.
**Parameters:**
device_id : integer, optional
The ID of the GPU device to use. If this is omitted, PyCUDA's
default context is used, which by defaul... | entailment |
def inflate_context_tuple(ast_rootpath, root_env):
"""Instantiate a Tuple from a TupleNode.
Walking the AST tree upwards, evaluate from the root down again.
"""
with util.LogTime('inflate_context_tuple'):
# We only need to look at tuple members going down.
inflated = ast_rootpath[0].eval(root_env)
... | Instantiate a Tuple from a TupleNode.
Walking the AST tree upwards, evaluate from the root down again. | entailment |
def enumerate_scope(ast_rootpath, root_env=None, include_default_builtins=False):
"""Return a dict of { name => Completions } for the given tuple node.
Enumerates all keys that are in scope in a given tuple. The node
part of the tuple may be None, in case the binding is a built-in.
"""
with util.LogTime('enu... | Return a dict of { name => Completions } for the given tuple node.
Enumerates all keys that are in scope in a given tuple. The node
part of the tuple may be None, in case the binding is a built-in. | entailment |
def find_deref_completions(ast_rootpath, root_env=gcl.default_env):
"""Returns a dict of { name => Completions }."""
with util.LogTime('find_deref_completions'):
tup = inflate_context_tuple(ast_rootpath, root_env)
path = path_until(ast_rootpath, is_deref_node)
if not path:
return {}
deref = pa... | Returns a dict of { name => Completions }. | entailment |
def is_identifier_position(rootpath):
"""Return whether the cursor is in identifier-position in a member declaration."""
if len(rootpath) >= 2 and is_tuple_member_node(rootpath[-2]) and is_identifier(rootpath[-1]):
return True
if len(rootpath) >= 1 and is_tuple_node(rootpath[-1]):
# No deeper node than tu... | Return whether the cursor is in identifier-position in a member declaration. | entailment |
def find_completions_at_cursor(ast_tree, filename, line, col, root_env=gcl.default_env):
"""Find completions at the cursor.
Return a dict of { name => Completion } objects.
"""
q = gcl.SourceQuery(filename, line, col - 1)
rootpath = ast_tree.find_tokens(q)
if is_identifier_position(rootpath):
return f... | Find completions at the cursor.
Return a dict of { name => Completion } objects. | entailment |
def find_inherited_key_completions(rootpath, root_env):
"""Return completion keys from INHERITED tuples.
Easiest way to get those is to evaluate the tuple, check if it is a CompositeTuple,
then enumerate the keys that are NOT in the rightmost tuple.
"""
tup = inflate_context_tuple(rootpath, root_env)
if is... | Return completion keys from INHERITED tuples.
Easiest way to get those is to evaluate the tuple, check if it is a CompositeTuple,
then enumerate the keys that are NOT in the rightmost tuple. | entailment |
def find_value_at_cursor(ast_tree, filename, line, col, root_env=gcl.default_env):
"""Find the value of the object under the cursor."""
q = gcl.SourceQuery(filename, line, col)
rootpath = ast_tree.find_tokens(q)
rootpath = path_until(rootpath, is_thunk)
if len(rootpath) <= 1:
# Just the file tuple itself... | Find the value of the object under the cursor. | entailment |
def add_vec_to_mat(mat, vec, axis=None, inplace=False,
target=None, substract=False):
""" Add a vector to a matrix
"""
assert mat.flags.c_contiguous
if axis is None:
if vec.shape[0] == mat.shape[0]:
axis = 0
elif vec.shape[0] == mat.shape[1]:
... | Add a vector to a matrix | entailment |
def vector_normalize(mat, max_vec_norm=1.):
""" Normalize each column vector in mat to length
max_vec_norm if it is longer than max_vec_norm
"""
assert mat.flags.c_contiguous
n, m = mat.shape
vector_normalize_kernel.prepared_call(
(m, 1, 1), (32, 1, 1),
mat.gpudata,
np.f... | Normalize each column vector in mat to length
max_vec_norm if it is longer than max_vec_norm | entailment |
def preprocess(string):
"""
Preprocesses a string, by replacing ${VARNAME} with
os.environ['VARNAME']
Parameters
----------
string: the str object to preprocess
Returns
-------
the preprocessed string
"""
split = string.split('${')
rval = [split[0]]
for candidate... | Preprocesses a string, by replacing ${VARNAME} with
os.environ['VARNAME']
Parameters
----------
string: the str object to preprocess
Returns
-------
the preprocessed string | entailment |
def tokenize_by_number(s):
""" splits a string into a list of tokens
each is either a string containing no numbers
or a float """
r = find_number(s)
if r == None:
return [ s ]
else:
tokens = []
if r[0] > 0:
tokens.append(s[0:r[0]])
tokens.app... | splits a string into a list of tokens
each is either a string containing no numbers
or a float | entailment |
def number_aware_alphabetical_cmp(str1, str2):
""" cmp function for sorting a list of strings by alphabetical order, but with
numbers sorted numerically.
i.e., foo1, foo2, foo10, foo11
instead of foo1, foo10
"""
def flatten_tokens(tokens):
l = []
for token in tokens... | cmp function for sorting a list of strings by alphabetical order, but with
numbers sorted numerically.
i.e., foo1, foo2, foo10, foo11
instead of foo1, foo10 | entailment |
def match(wrong, candidates):
"""
wrong: a mispelling
candidates: a set of correct words
returns a guess of which candidate is the right one
This should be used with a small number of candidates and a high potential
edit distance.
ie, use it to correct a wrong filen... | wrong: a mispelling
candidates: a set of correct words
returns a guess of which candidate is the right one
This should be used with a small number of candidates and a high potential
edit distance.
ie, use it to correct a wrong filename in a directory, wrong class name
i... | entailment |
def censor_non_alphanum(s):
"""
Returns s with all non-alphanumeric characters replaced with *
"""
def censor(ch):
if (ch >= 'A' and ch <= 'z') or (ch >= '0' and ch <= '9'):
return ch
return '*'
return ''.join([censor(ch) for ch in s]) | Returns s with all non-alphanumeric characters replaced with * | entailment |
def is_period_alias(period):
"""
Check if a given period is possibly an alias.
Parameters
----------
period : float
A period to test if it is a possible alias or not.
Returns
-------
is_alias : boolean
True if the given period is in a range of period alias.
"""
... | Check if a given period is possibly an alias.
Parameters
----------
period : float
A period to test if it is a possible alias or not.
Returns
-------
is_alias : boolean
True if the given period is in a range of period alias. | entailment |
def save(filepath, obj, on_overwrite = 'ignore'):
"""
Serialize `object` to a file denoted by `filepath`.
Parameters
----------
filepath : str
A filename. If the suffix is `.joblib` and joblib can be
imported, `joblib.dump` is used in place of the regular
pickling mechanisms... | Serialize `object` to a file denoted by `filepath`.
Parameters
----------
filepath : str
A filename. If the suffix is `.joblib` and joblib can be
imported, `joblib.dump` is used in place of the regular
pickling mechanisms; this results in much faster saves by
saving arrays a... | entailment |
def get_pickle_protocol():
"""
Allow configuration of the pickle protocol on a per-machine basis.
This way, if you use multiple platforms with different versions of
pickle, you can configure each of them to use the highest protocol
supported by all of the machines that you want to be able to
com... | Allow configuration of the pickle protocol on a per-machine basis.
This way, if you use multiple platforms with different versions of
pickle, you can configure each of them to use the highest protocol
supported by all of the machines that you want to be able to
communicate. | entailment |
def load_train_file(config_file_path):
"""Loads and parses a yaml file for a Train object.
Publishes the relevant training environment variables"""
from pylearn2.config import yaml_parse
suffix_to_strip = '.yaml'
# publish environment variables related to file name
if config_file_path.endswith... | Loads and parses a yaml file for a Train object.
Publishes the relevant training environment variables | entailment |
def feed_forward(self, input_data, prediction=False):
"""Propagate forward through the layer
**Parameters:**
input_data : ``GPUArray``
Inpute data to perform dropout on.
prediction : bool, optional
Whether to use prediction model. If true, then the data is
... | Propagate forward through the layer
**Parameters:**
input_data : ``GPUArray``
Inpute data to perform dropout on.
prediction : bool, optional
Whether to use prediction model. If true, then the data is
scaled by ``1 - dropout_probability`` uses dropout.
... | entailment |
def backprop(self, input_data, df_output, cache=None):
""" Backpropagate through the hidden layer
**Parameters:**
input_data : ``GPUArray``
Inpute data to perform dropout on.
df_output : ``GPUArray``
Gradients with respect to the output of this layer
... | Backpropagate through the hidden layer
**Parameters:**
input_data : ``GPUArray``
Inpute data to perform dropout on.
df_output : ``GPUArray``
Gradients with respect to the output of this layer
(received from the layer above).
cache : list of ``GPUAr... | entailment |
def POINTER(obj):
"""
Create ctypes pointer to object.
Notes
-----
This function converts None to a real NULL pointer because of bug
in how ctypes handles None on 64-bit platforms.
"""
p = ctypes.POINTER(obj)
if not isinstance(p.from_param, classmethod):
def from_param(cls... | Create ctypes pointer to object.
Notes
-----
This function converts None to a real NULL pointer because of bug
in how ctypes handles None on 64-bit platforms. | entailment |
def gpuarray_ptr(g):
"""
Return ctypes pointer to data in GPUAarray object.
"""
addr = int(g.gpudata)
if g.dtype == np.int8:
return ctypes.cast(addr, POINTER(ctypes.c_byte))
if g.dtype == np.uint8:
return ctypes.cast(addr, POINTER(ctypes.c_ubyte))
if g.dtype == np.int16:
... | Return ctypes pointer to data in GPUAarray object. | entailment |
def cudaMalloc(count, ctype=None):
"""
Allocate device memory.
Allocate memory on the device associated with the current active
context.
Parameters
----------
count : int
Number of bytes of memory to allocate
ctype : _ctypes.SimpleType, optional
ctypes type to cast retu... | Allocate device memory.
Allocate memory on the device associated with the current active
context.
Parameters
----------
count : int
Number of bytes of memory to allocate
ctype : _ctypes.SimpleType, optional
ctypes type to cast returned pointer.
Returns
-------
ptr ... | entailment |
def cudaMallocPitch(pitch, rows, cols, elesize):
"""
Allocate pitched device memory.
Allocate pitched memory on the device associated with the current active
context.
Parameters
----------
pitch : int
Pitch for allocation.
rows : int
Requested pitched allocation height.... | Allocate pitched device memory.
Allocate pitched memory on the device associated with the current active
context.
Parameters
----------
pitch : int
Pitch for allocation.
rows : int
Requested pitched allocation height.
cols : int
Requested pitched allocation width.
... | entailment |
def cudaMemcpy_htod(dst, src, count):
"""
Copy memory from host to device.
Copy data from host memory to device memory.
Parameters
----------
dst : ctypes pointer
Device memory pointer.
src : ctypes pointer
Host memory pointer.
count : int
Number of bytes to cop... | Copy memory from host to device.
Copy data from host memory to device memory.
Parameters
----------
dst : ctypes pointer
Device memory pointer.
src : ctypes pointer
Host memory pointer.
count : int
Number of bytes to copy. | entailment |
def cudaMemcpy_dtoh(dst, src, count):
"""
Copy memory from device to host.
Copy data from device memory to host memory.
Parameters
----------
dst : ctypes pointer
Host memory pointer.
src : ctypes pointer
Device memory pointer.
count : int
Number of bytes to cop... | Copy memory from device to host.
Copy data from device memory to host memory.
Parameters
----------
dst : ctypes pointer
Host memory pointer.
src : ctypes pointer
Device memory pointer.
count : int
Number of bytes to copy. | entailment |
def cudaMemGetInfo():
"""
Return the amount of free and total device memory.
Returns
-------
free : long
Free memory in bytes.
total : long
Total memory in bytes.
"""
free = ctypes.c_size_t()
total = ctypes.c_size_t()
status = _libcudart.cudaMemGetInfo(ctypes.b... | Return the amount of free and total device memory.
Returns
-------
free : long
Free memory in bytes.
total : long
Total memory in bytes. | entailment |
def cudaGetDevice():
"""
Get current CUDA device.
Return the identifying number of the device currently used to
process CUDA operations.
Returns
-------
dev : int
Device number.
"""
dev = ctypes.c_int()
status = _libcudart.cudaGetDevice(ctypes.byref(dev))
cudaChec... | Get current CUDA device.
Return the identifying number of the device currently used to
process CUDA operations.
Returns
-------
dev : int
Device number. | entailment |
def cudaDriverGetVersion():
"""
Get installed CUDA driver version.
Return the version of the installed CUDA driver as an integer. If
no driver is detected, 0 is returned.
Returns
-------
version : int
Driver version.
"""
version = ctypes.c_int()
status = _libcudart.cu... | Get installed CUDA driver version.
Return the version of the installed CUDA driver as an integer. If
no driver is detected, 0 is returned.
Returns
-------
version : int
Driver version. | entailment |
def cudaPointerGetAttributes(ptr):
"""
Get memory pointer attributes.
Returns attributes of the specified pointer.
Parameters
----------
ptr : ctypes pointer
Memory pointer to examine.
Returns
-------
memory_type : int
Memory type; 1 indicates host memory, 2 indica... | Get memory pointer attributes.
Returns attributes of the specified pointer.
Parameters
----------
ptr : ctypes pointer
Memory pointer to examine.
Returns
-------
memory_type : int
Memory type; 1 indicates host memory, 2 indicates device
memory.
device : int
... | entailment |
def eval(thunk, env):
"""Evaluate a thunk in an environment.
Will defer the actual evaluation to the thunk itself, but adds two things:
caching and recursion detection.
Since we have to use a global evaluation stack (because there is a variety of functions that may
be invoked, not just eval() but also __get... | Evaluate a thunk in an environment.
Will defer the actual evaluation to the thunk itself, but adds two things:
caching and recursion detection.
Since we have to use a global evaluation stack (because there is a variety of functions that may
be invoked, not just eval() but also __getitem__, and not all of them... | entailment |
def get_node(self, key):
"""Delegate to our current "value provider" for the node belonging to this key."""
if key in self.names:
return self.values.get_member_node(key) if hasattr(self.values, 'get_member_node') else None
return self.parent.get_node(key) | Delegate to our current "value provider" for the node belonging to this key. | entailment |
def create_table(cls):
"""
create_table
Manually create a temporary table for model in test data base.
:return:
"""
schema_editor = getattr(connection, 'schema_editor', None)
if schema_editor:
with schema_editor() as schema_editor:
sch... | create_table
Manually create a temporary table for model in test data base.
:return: | entailment |
def delete_table(cls):
"""
delete_table
Manually delete a temporary table for model in test data base.
:return:
"""
schema_editor = getattr(connection, 'schema_editor', None)
if schema_editor:
with connection.schema_editor() as schema_editor:
... | delete_table
Manually delete a temporary table for model in test data base.
:return: | entailment |
def fake_me(cls, source):
"""
fake_me
Class or method decorator
Class decorator: create temporary table for all tests in SimpleTestCase.
Method decorator: create temporary model only for given test method.
:param source: SimpleTestCase or test function
:return:
... | fake_me
Class or method decorator
Class decorator: create temporary table for all tests in SimpleTestCase.
Method decorator: create temporary model only for given test method.
:param source: SimpleTestCase or test function
:return: | entailment |
def vcr(decorated_func=None, debug=False, overwrite=False, disabled=False,
playback_only=False, tape_name=None):
"""
Decorator for capturing and simulating network communication
``debug`` : bool, optional
Enables debug mode.
``overwrite`` : bool, optional
Will run vcr in recordi... | Decorator for capturing and simulating network communication
``debug`` : bool, optional
Enables debug mode.
``overwrite`` : bool, optional
Will run vcr in recording mode - overwrites any existing vcrtapes.
``playback_only`` : bool, optional
Will run vcr in playback mode - will not c... | entailment |
def reset(cls):
"""
Reset to default settings
"""
cls.debug = False
cls.disabled = False
cls.overwrite = False
cls.playback_only = False
cls.recv_timeout = 5
cls.recv_endmarkers = []
cls.recv_size = None | Reset to default settings | entailment |
def to_python(value, seen=None):
"""Reify values to their Python equivalents.
Does recursion detection, failing when that happens.
"""
seen = seen or set()
if isinstance(value, framework.TupleLike):
if value.ident in seen:
raise RecursionException('to_python: infinite recursion while evaluating %r'... | Reify values to their Python equivalents.
Does recursion detection, failing when that happens. | entailment |
def walk(value, walker, path=None, seen=None):
"""Walks the _evaluated_ tree of the given GCL tuple.
The appropriate methods of walker will be invoked for every element in the
tree.
"""
seen = seen or set()
path = path or []
# Recursion
if id(value) in seen:
walker.visitRecursion(path)
return
... | Walks the _evaluated_ tree of the given GCL tuple.
The appropriate methods of walker will be invoked for every element in the
tree. | entailment |
def fingerprint(value):
"""Return a hash value that uniquely identifies the GCL value."""
h = hashlib.sha256()
_digest(value, h)
return h.digest().encode('hex') | Return a hash value that uniquely identifies the GCL value. | entailment |
def compact_error(err):
"""Return the the last 2 error messages from an error stack.
These error messages turns out to be the most descriptive.
"""
def err2(e):
if isinstance(e, exceptions.EvaluationError) and e.inner:
message, i = err2(e.inner)
if i == 1:
return ', '.join([e.args[0], s... | Return the the last 2 error messages from an error stack.
These error messages turns out to be the most descriptive. | entailment |
def backprop(self, input_data, targets,
cache=None):
""" Backpropagate through the logistic layer.
**Parameters:**
input_data : ``GPUArray``
Inpute data to compute activations for.
targets : ``GPUArray``
The target values of the units.
... | Backpropagate through the logistic layer.
**Parameters:**
input_data : ``GPUArray``
Inpute data to compute activations for.
targets : ``GPUArray``
The target values of the units.
cache : list of ``GPUArray``
Cache obtained from forward pass. If the... | entailment |
def cross_entropy_error(self, input_data, targets, average=True,
cache=None, prediction=False):
""" Return the cross entropy error
"""
if cache is not None:
activations = cache
else:
activations = \
self.feed_forward(inpu... | Return the cross entropy error | entailment |
def stylize_comment_block(lines):
"""Parse comment lines and make subsequent indented lines into a code block
block.
"""
normal, sep, in_code = range(3)
state = normal
for line in lines:
indented = line.startswith(' ')
empty_line = line.strip() == ''
if state == normal and empty_line:
... | Parse comment lines and make subsequent indented lines into a code block
block. | entailment |
def sort_members(tup, names):
"""Return two pairs of members, scalar and tuple members.
The scalars will be sorted s.t. the unbound members are at the top.
"""
scalars, tuples = partition(lambda x: not is_tuple_node(tup.member[x].value), names)
unbound, bound = partition(lambda x: tup.member[x].value.is_unbo... | Return two pairs of members, scalar and tuple members.
The scalars will be sorted s.t. the unbound members are at the top. | entailment |
def resolve_file(fname, paths):
"""Resolve filename relatively against one of the given paths, if possible."""
fpath = path.abspath(fname)
for p in paths:
spath = path.abspath(p)
if fpath.startswith(spath):
return fpath[len(spath) + 1:]
return fname | Resolve filename relatively against one of the given paths, if possible. | entailment |
def generate(self):
"""Generate a list of strings representing the table in RST format."""
header = ' '.join('=' * self.width[i] for i in range(self.w))
lines = [
' '.join(row[i].ljust(self.width[i]) for i in range(self.w))
for row in self.rows]
return [header] + lines + [header] | Generate a list of strings representing the table in RST format. | entailment |
def partition(pred, iterable):
'Use a predicate to partition entries into false entries and true entries'
# partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9
t1, t2 = itertools.tee(iterable)
return list(filter(negate(pred), t1)), list(filter(pred, t2)) | Use a predicate to partition entries into false entries and true entries | entailment |
def select(self, model):
"""Select nodes according to the input selector.
This can ALWAYS return multiple root elements.
"""
res = []
def doSelect(value, pre, remaining):
if not remaining:
res.append((pre, value))
else:
# For the other selectors to work, value must be a... | Select nodes according to the input selector.
This can ALWAYS return multiple root elements. | entailment |
def deep(self):
"""Return a deep dict of the values selected.
The leaf values may still be gcl Tuples. Use util.to_python() if you want
to reify everything to real Python values.
"""
self.lists = {}
ret = {}
for path, value in self.paths_values():
self.recursiveSet(ret, path, value)
... | Return a deep dict of the values selected.
The leaf values may still be gcl Tuples. Use util.to_python() if you want
to reify everything to real Python values. | entailment |
def ldSet(self, what, key, value):
"""List/dictionary-aware set."""
if isListKey(key):
# Make sure we keep the indexes consistent, insert missing_values
# as necessary. We do remember the lists, so that we can remove
# missing values after inserting all values from all selectors.
self.li... | List/dictionary-aware set. | entailment |
def ldGet(self, what, key):
"""List-aware get."""
if isListKey(key):
return what[listKeyIndex(key)]
else:
return what[key] | List-aware get. | entailment |
def ldContains(self, what, key):
"""List/dictinary/missing-aware contains.
If the value is a "missing_value", we'll treat it as non-existent
so it will be overwritten by an empty list/dict when necessary to
assign child keys.
"""
if isListKey(key):
i = listKeyIndex(key)
return i < l... | List/dictinary/missing-aware contains.
If the value is a "missing_value", we'll treat it as non-existent
so it will be overwritten by an empty list/dict when necessary to
assign child keys. | entailment |
def find_recursive_dependency(self):
"""Return a list of nodes that have a recursive dependency."""
nodes_on_path = []
def helper(nodes):
for node in nodes:
cycle = node in nodes_on_path
nodes_on_path.append(node)
if cycle or helper(self.deps.get(node, [])):
return T... | Return a list of nodes that have a recursive dependency. | entailment |
def enterTuple(self, tuple, path):
"""Called for every tuple.
If this returns False, the elements of the tuple will not be recursed over
and leaveTuple() will not be called.
"""
if skip_name(path):
return False
node = Node(path, tuple)
if self.condition.matches(node):
self.unord... | Called for every tuple.
If this returns False, the elements of the tuple will not be recursed over
and leaveTuple() will not be called. | entailment |
def convertAndMake(converter, handler):
"""Convert with location."""
def convertAction(loc, value):
return handler(loc, converter(value))
return convertAction | Convert with location. | entailment |
def mkApplications(location, *atoms):
"""Make a sequence of applications from a list of tokens.
atoms is a list of atoms, which will be handled left-associatively. E.g:
['foo', [], []] == foo()() ==> Application(Application('foo', []), [])
"""
atoms = list(atoms)
while len(atoms) > 1:
atoms[0:2] =... | Make a sequence of applications from a list of tokens.
atoms is a list of atoms, which will be handled left-associatively. E.g:
['foo', [], []] == foo()() ==> Application(Application('foo', []), []) | entailment |
def call_fn(fn, arglist, env):
"""Call a function, respecting all the various types of functions that exist."""
if isinstance(fn, framework.LazyFunction):
# The following looks complicated, but this is necessary because you can't
# construct closures over the loop variable directly.
thunks = [(lambda th... | Call a function, respecting all the various types of functions that exist. | entailment |
def schema_spec_from_tuple(tup):
"""Return the schema spec from a run-time tuple."""
if hasattr(tup, 'get_schema_spec'):
# Tuples have a TupleSchema field that contains a model of the schema
return schema.from_spec({
'fields': TupleSchemaAccess(tup),
'required': tup.get_required_fields()})
... | Return the schema spec from a run-time tuple. | entailment |
def make_schema_from(value, env):
"""Make a Schema object from the given spec.
The input and output types of this function are super unclear, and are held together by ponies,
wishes, duct tape, and a load of tests. See the comments for horrific entertainment.
"""
# So this thing may not need to evaluate any... | Make a Schema object from the given spec.
The input and output types of this function are super unclear, and are held together by ponies,
wishes, duct tape, and a load of tests. See the comments for horrific entertainment. | entailment |
def bracketedList(l, r, sep, expr, allow_missing_close=False):
"""Parse bracketed list.
Empty list is possible, as is a trailing separator.
"""
# We may need to backtrack for lists, because of list comprehension, but not for
# any of the other lists
strict = l != '['
closer = sym(r) if not allow_missing_... | Parse bracketed list.
Empty list is possible, as is a trailing separator. | entailment |
def unquote(s):
"""Unquote the indicated string."""
# Ignore the left- and rightmost chars (which should be quotes).
# Use the Python engine to decode the escape sequence
i, N = 1, len(s) - 1
ret = []
while i < N:
if s[i] == '\\' and i < N - 1:
ret.append(UNQUOTE_MAP.get(s[i+1], s[i+1]))
i +... | Unquote the indicated string. | entailment |
def pattern(name, pattern):
"""Function to put a name on a pyparsing pattern.
Just for ease of debugging/tracing parse errors.
"""
pattern.setName(name)
astracing.maybe_trace(pattern)
return pattern | Function to put a name on a pyparsing pattern.
Just for ease of debugging/tracing parse errors. | entailment |
def make_grammar(allow_errors):
"""Make the part of the grammar that depends on whether we swallow errors or not."""
if allow_errors in GRAMMAR_CACHE:
return GRAMMAR_CACHE[allow_errors]
tuple = p.Forward()
catch_errors = p.Forward()
catch_errors << (p.Regex('[^{};]*') - p.Optional(tuple) - p.Regex('[^;}]... | Make the part of the grammar that depends on whether we swallow errors or not. | entailment |
def reads(s, filename, loader, implicit_tuple, allow_errors):
"""Load but don't evaluate a GCL expression from a string."""
try:
the_context.filename = filename
the_context.loader = loader
grammar = make_grammar(allow_errors=allow_errors)
root = grammar.start_tuple if implicit_tuple else grammar.st... | Load but don't evaluate a GCL expression from a string. | entailment |
def find_tokens(self, q):
"""Find all AST nodes at the given filename, line and column."""
found_me = []
if hasattr(self, 'location'):
if self.location.contains(q):
found_me = [self]
elif self._found_by(q):
found_me = [self]
cs = [n.find_tokens(q) for n in self._children()]
... | Find all AST nodes at the given filename, line and column. | entailment |
def _make_tuple(self, env):
"""Instantiate the Tuple based on this TupleNode."""
t = runtime.Tuple(self, env, dict2tuple)
# A tuple also provides its own schema spec
schema = schema_spec_from_tuple(t)
t.attach_schema(schema)
return t | Instantiate the Tuple based on this TupleNode. | entailment |
def applyTuple(self, tuple, right, env):
"""Apply a tuple to something else."""
if len(right) != 1:
raise exceptions.EvaluationError('Tuple (%r) can only be applied to one argument, got %r' % (self.left, self.right))
right = right[0]
return tuple(right) | Apply a tuple to something else. | entailment |
def applyIndex(self, lst, right):
"""Apply a list to something else."""
if len(right) != 1:
raise exceptions.EvaluationError('%r can only be applied to one argument, got %r' % (self.left, self.right))
right = right[0]
if isinstance(right, int):
return lst[right]
raise exceptions.Evalua... | Apply a list to something else. | entailment |
def pre_gradient_update(self):
""" First step of Nesterov momentum method:
take step in direction of accumulated gradient
"""
updates = zip(self.velocity, self.model.n_parameters * [1.])
self.model.update_parameters(updates) | First step of Nesterov momentum method:
take step in direction of accumulated gradient | entailment |
def class_error(self, input_data, targets, average=True,
cache=None, prediction=False):
""" Return the classification error rate
"""
if cache is not None:
activations = cache
else:
activations = \
self.feed_forward(input_data, pr... | Return the classification error rate | entailment |
def kl_error(self, input_data, targets, average=True,
cache=None, prediction=True):
""" The KL divergence error
"""
if cache is not None:
activations = cache
else:
activations = \
self.feed_forward(input_data, prediction=prediction)... | The KL divergence error | entailment |
def dot(x_gpu, y_gpu, transa='N', transb='N', handle=None, target=None):
"""
Dot product of two arrays.
For 1D arrays, this function computes the inner product. For 2D
arrays of shapes `(m, k)` and `(k, n)`, it computes the matrix
product; the result has shape `(m, n)`.
Parameters
--------... | Dot product of two arrays.
For 1D arrays, this function computes the inner product. For 2D
arrays of shapes `(m, k)` and `(k, n)`, it computes the matrix
product; the result has shape `(m, n)`.
Parameters
----------
x_gpu : pycuda.gpuarray.GPUArray
Input array.
y_gpu : pycuda.gpuar... | entailment |
def make_tempfile(data=None):
"Create a temp file, write our PID into it."
with tempfile.NamedTemporaryFile(mode='w', delete=False) as temp:
temp.write(six.text_type(data if data is not None else os.getpid()))
return temp.name | Create a temp file, write our PID into it. | entailment |
def parameters(self):
"""Return a list where each element contains the parameters for a task.
"""
parameters = []
for task in self.tasks:
parameters.extend(task.parameters)
return parameters | Return a list where each element contains the parameters for a task. | entailment |
def parameters(self, value):
"""Update the parameters.
``value`` must be a list/tuple of length
``MultitaskTopLayer.n_tasks``, each element of which must have
the correct number of parameters for the task.
"""
assert len(value) == self.n_parameters
i = 0
... | Update the parameters.
``value`` must be a list/tuple of length
``MultitaskTopLayer.n_tasks``, each element of which must have
the correct number of parameters for the task. | entailment |
def feed_forward(self, input_data, prediction=False):
"""Call ``feed_forward`` for each task and combine the activations.
Passes ``input_data`` to all tasks and returns the activations
as a list.
**Parameters:**
input_data : ``GPUArray``
Inpute data to compute ... | Call ``feed_forward`` for each task and combine the activations.
Passes ``input_data`` to all tasks and returns the activations
as a list.
**Parameters:**
input_data : ``GPUArray``
Inpute data to compute activations for.
prediction : bool, optional
... | entailment |
def backprop(self, input_data, targets, cache=None):
"""Compute gradients for each task and combine the results.
**Parameters:**
input_data : ``GPUArray``
Inpute data to compute activations for.
targets : ``GPUArray``
The target values of the units.
ca... | Compute gradients for each task and combine the results.
**Parameters:**
input_data : ``GPUArray``
Inpute data to compute activations for.
targets : ``GPUArray``
The target values of the units.
cache : list of ``GPUArray``
Cache obtained from forwa... | entailment |
def cross_entropy_error(self, input_data, targets, average=True,
cache=None, prediction=False,
sum_errors=True):
""" Computes the cross-entropy error for all tasks.
"""
loss = []
if cache is None:
cache = self.n_tasks *... | Computes the cross-entropy error for all tasks. | entailment |
def parameters(self, value):
"""Update the parameters. ``value`` must have the shape
``(weights, biases)``"""
self.W = value[0] if isinstance(value[0], GPUArray) else \
gpuarray.to_gpu(value[0])
self.b = value[1] if isinstance(value[0], GPUArray) else \
gpuarray.to_gp... | Update the parameters. ``value`` must have the shape
``(weights, biases)`` | entailment |
def architecture(self):
"""Returns a dictionary describing the architecture of the layer."""
arch = {'class': self.__class__,
'n_in': self.n_in,
'n_units': self.n_units,
'activation_function': self.activation_function
if hasattr(self, 'acti... | Returns a dictionary describing the architecture of the layer. | entailment |
def feed_forward(self, input_data, prediction=False):
"""Propagate forward through the layer
**Parameters:**
input_data : ``GPUArray``
Input data to compute activations for.
prediction : bool, optional
Whether to use prediction model. Only relevant when using
... | Propagate forward through the layer
**Parameters:**
input_data : ``GPUArray``
Input data to compute activations for.
prediction : bool, optional
Whether to use prediction model. Only relevant when using
dropout. If true, then weights are multiplied by
... | entailment |
def backprop(self, input_data, df_output, cache=None):
""" Backpropagate through the hidden layer
**Parameters:**
input_data : ``GPUArray``
Input data to compute activations for.
df_output : ``GPUArray``
Gradients with respect to the activations of this layer
... | Backpropagate through the hidden layer
**Parameters:**
input_data : ``GPUArray``
Input data to compute activations for.
df_output : ``GPUArray``
Gradients with respect to the activations of this layer
(received from the layer above).
cache : list o... | entailment |
def cublasCreate():
"""
Initialize CUBLAS.
Initializes CUBLAS and creates a handle to a structure holding
the CUBLAS library context.
Returns
-------
handle : void_p
CUBLAS context.
"""
handle = ctypes.c_void_p()
status = _libcublas.cublasCreate_v2(ctypes.... | Initialize CUBLAS.
Initializes CUBLAS and creates a handle to a structure holding
the CUBLAS library context.
Returns
-------
handle : void_p
CUBLAS context. | entailment |
def cublasDestroy(handle):
"""
Release CUBLAS resources.
Releases hardware resources used by CUBLAS.
Parameters
----------
handle : void_p
CUBLAS context.
"""
status = _libcublas.cublasDestroy_v2(ctypes.c_void_p(handle))
cublasCheckStatus(status) | Release CUBLAS resources.
Releases hardware resources used by CUBLAS.
Parameters
----------
handle : void_p
CUBLAS context. | entailment |
def cublasGetVersion(handle):
"""
Get CUBLAS version.
Returns version number of installed CUBLAS libraries.
Parameters
----------
handle : void_p
CUBLAS context.
Returns
-------
version : int
CUBLAS version.
"""
version = ctypes.c_int()
status = _... | Get CUBLAS version.
Returns version number of installed CUBLAS libraries.
Parameters
----------
handle : void_p
CUBLAS context.
Returns
-------
version : int
CUBLAS version. | entailment |
def cublasSetStream(handle, id):
"""
Set current CUBLAS library stream.
Parameters
----------
handle : id
CUBLAS context.
id : int
Stream ID.
"""
status = _libcublas.cublasSetStream_v2(handle, id)
cublasCheckStatus(status) | Set current CUBLAS library stream.
Parameters
----------
handle : id
CUBLAS context.
id : int
Stream ID. | entailment |
def cublasGetStream(handle):
"""
Set current CUBLAS library stream.
Parameters
----------
handle : void_p
CUBLAS context.
Returns
-------
id : int
Stream ID.
"""
id = ctypes.c_int()
status = _libcublas.cublasGetStream_v2(handle, ctypes.byref(id))
... | Set current CUBLAS library stream.
Parameters
----------
handle : void_p
CUBLAS context.
Returns
-------
id : int
Stream ID. | entailment |
def cublasSgbmv(handle, trans, m, n, kl, ku, alpha, A, lda,
x, incx, beta, y, incy):
"""
Matrix-vector product for real general banded matrix.
"""
status = _libcublas.cublasSgbmv_v2(handle,
trans, m, n, kl, ku,
... | Matrix-vector product for real general banded matrix. | entailment |
def cublasCgbmv(handle, trans, m, n, kl, ku, alpha, A, lda,
x, incx, beta, y, incy):
"""
Matrix-vector product for complex general banded matrix.
"""
status = _libcublas.cublasCgbmv_v2(handle,
trans, m, n, kl, ku,
... | Matrix-vector product for complex general banded matrix. | entailment |
def cublasZgbmv(handle, trans, m, n, kl, ku, alpha, A, lda,
x, incx, beta, y, incy):
"""
Matrix-vector product for complex general banded matrix.
"""
status = _libcublas.cublasZgbmv_v2(handle,
trans, m, n, kl, ku,
... | Matrix-vector product for complex general banded matrix. | entailment |
def cublasSgemv(handle, trans, m, n, alpha, A, lda, x, incx, beta, y, incy):
"""
Matrix-vector product for real general matrix.
"""
status = _libcublas.cublasSgemv_v2(handle,
_CUBLAS_OP[trans], m, n,
ctypes.byref(ctypes.c_fl... | Matrix-vector product for real general matrix. | entailment |
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