body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def make_catalog_db(catalogitems):
'Takes an array of catalog items and builds some indexes so we can\n get our common data faster. Returns a dict we can use like a database'
name_table = {}
pkgid_table = {}
itemindex = (- 1)
for item in catalogitems:
itemindex = (itemindex + 1)
n... | 4,497,944,335,075,595,300 | Takes an array of catalog items and builds some indexes so we can
get our common data faster. Returns a dict we can use like a database | code/client/munkilib/updatecheck/catalogs.py | make_catalog_db | Artoria2e5/munki | python | def make_catalog_db(catalogitems):
'Takes an array of catalog items and builds some indexes so we can\n get our common data faster. Returns a dict we can use like a database'
name_table = {}
pkgid_table = {}
itemindex = (- 1)
for item in catalogitems:
itemindex = (itemindex + 1)
n... |
def add_package_ids(catalogitems, itemname_to_pkgid, pkgid_to_itemname):
'Adds packageids from each catalogitem to two dictionaries.\n One maps itemnames to receipt pkgids, the other maps receipt pkgids\n to itemnames'
for item in catalogitems:
name = item.get('name')
if (not name):
... | -4,838,195,185,309,404,000 | Adds packageids from each catalogitem to two dictionaries.
One maps itemnames to receipt pkgids, the other maps receipt pkgids
to itemnames | code/client/munkilib/updatecheck/catalogs.py | add_package_ids | Artoria2e5/munki | python | def add_package_ids(catalogitems, itemname_to_pkgid, pkgid_to_itemname):
'Adds packageids from each catalogitem to two dictionaries.\n One maps itemnames to receipt pkgids, the other maps receipt pkgids\n to itemnames'
for item in catalogitems:
name = item.get('name')
if (not name):
... |
def split_name_and_version(some_string):
"Splits a string into the name and version number.\n\n Name and version must be separated with a hyphen ('-')\n or double hyphen ('--').\n 'TextWrangler-2.3b1' becomes ('TextWrangler', '2.3b1')\n 'AdobePhotoshopCS3--11.2.1' becomes ('AdobePhotoshopCS3', '11.2.1')... | -8,237,331,361,948,013,000 | Splits a string into the name and version number.
Name and version must be separated with a hyphen ('-')
or double hyphen ('--').
'TextWrangler-2.3b1' becomes ('TextWrangler', '2.3b1')
'AdobePhotoshopCS3--11.2.1' becomes ('AdobePhotoshopCS3', '11.2.1')
'MicrosoftOffice2008-12.2.1' becomes ('MicrosoftOffice2008', '12.2... | code/client/munkilib/updatecheck/catalogs.py | split_name_and_version | Artoria2e5/munki | python | def split_name_and_version(some_string):
"Splits a string into the name and version number.\n\n Name and version must be separated with a hyphen ('-')\n or double hyphen ('--').\n 'TextWrangler-2.3b1' becomes ('TextWrangler', '2.3b1')\n 'AdobePhotoshopCS3--11.2.1' becomes ('AdobePhotoshopCS3', '11.2.1')... |
def get_all_items_with_name(name, cataloglist):
'Searches the catalogs in a list for all items matching a given name.\n\n Returns:\n list of pkginfo items; sorted with newest version first. No precedence\n is given to catalog order.\n '
def item_version(item):
'Returns a MunkiLooseVersi... | 6,239,491,405,437,279,000 | Searches the catalogs in a list for all items matching a given name.
Returns:
list of pkginfo items; sorted with newest version first. No precedence
is given to catalog order. | code/client/munkilib/updatecheck/catalogs.py | get_all_items_with_name | Artoria2e5/munki | python | def get_all_items_with_name(name, cataloglist):
'Searches the catalogs in a list for all items matching a given name.\n\n Returns:\n list of pkginfo items; sorted with newest version first. No precedence\n is given to catalog order.\n '
def item_version(item):
'Returns a MunkiLooseVersi... |
def get_auto_removal_items(installinfo, cataloglist):
'Gets a list of items marked for automatic removal from the catalogs\n in cataloglist. Filters those against items in the processed_installs\n list, which should contain everything that is supposed to be installed.\n Then filters against the removals li... | 4,459,911,686,786,282,500 | Gets a list of items marked for automatic removal from the catalogs
in cataloglist. Filters those against items in the processed_installs
list, which should contain everything that is supposed to be installed.
Then filters against the removals list, which contains all the removals
that have already been processed. | code/client/munkilib/updatecheck/catalogs.py | get_auto_removal_items | Artoria2e5/munki | python | def get_auto_removal_items(installinfo, cataloglist):
'Gets a list of items marked for automatic removal from the catalogs\n in cataloglist. Filters those against items in the processed_installs\n list, which should contain everything that is supposed to be installed.\n Then filters against the removals li... |
def look_for_updates(itemname, cataloglist):
"Looks for updates for a given manifest item that is either\n installed or scheduled to be installed or removed. This handles not only\n specific application updates, but also updates that aren't simply\n later versions of the manifest item.\n For example, Ad... | 6,435,376,517,397,081,000 | Looks for updates for a given manifest item that is either
installed or scheduled to be installed or removed. This handles not only
specific application updates, but also updates that aren't simply
later versions of the manifest item.
For example, AdobeCameraRaw is an update for Adobe Photoshop, but
doesn't update the ... | code/client/munkilib/updatecheck/catalogs.py | look_for_updates | Artoria2e5/munki | python | def look_for_updates(itemname, cataloglist):
"Looks for updates for a given manifest item that is either\n installed or scheduled to be installed or removed. This handles not only\n specific application updates, but also updates that aren't simply\n later versions of the manifest item.\n For example, Ad... |
def look_for_updates_for_version(itemname, itemversion, cataloglist):
'Looks for updates for a specific version of an item. Since these\n can appear in manifests and pkginfo as item-version or item--version\n we have to search twice.'
name_and_version = ('%s-%s' % (itemname, itemversion))
alt_name_and... | 7,019,348,813,548,713,000 | Looks for updates for a specific version of an item. Since these
can appear in manifests and pkginfo as item-version or item--version
we have to search twice. | code/client/munkilib/updatecheck/catalogs.py | look_for_updates_for_version | Artoria2e5/munki | python | def look_for_updates_for_version(itemname, itemversion, cataloglist):
'Looks for updates for a specific version of an item. Since these\n can appear in manifests and pkginfo as item-version or item--version\n we have to search twice.'
name_and_version = ('%s-%s' % (itemname, itemversion))
alt_name_and... |
def best_version_match(vers_num, item_dict):
'Attempts to find the best match in item_dict for vers_num'
vers_tuple = vers_num.split('.')
precision = 1
while (precision <= len(vers_tuple)):
test_vers = '.'.join(vers_tuple[0:precision])
match_names = []
for item in item_dict.keys(... | -552,078,823,119,619,400 | Attempts to find the best match in item_dict for vers_num | code/client/munkilib/updatecheck/catalogs.py | best_version_match | Artoria2e5/munki | python | def best_version_match(vers_num, item_dict):
vers_tuple = vers_num.split('.')
precision = 1
while (precision <= len(vers_tuple)):
test_vers = '.'.join(vers_tuple[0:precision])
match_names = []
for item in item_dict.keys():
for item_version in item_dict[item]:
... |
@utils.Memoize
def analyze_installed_pkgs():
'Analyze catalog data and installed packages in an attempt to determine\n what is installed.'
pkgdata = {}
itemname_to_pkgid = {}
pkgid_to_itemname = {}
for catalogname in _CATALOG:
catalogitems = _CATALOG[catalogname]['items']
add_pack... | -5,312,295,383,452,086,000 | Analyze catalog data and installed packages in an attempt to determine
what is installed. | code/client/munkilib/updatecheck/catalogs.py | analyze_installed_pkgs | Artoria2e5/munki | python | @utils.Memoize
def analyze_installed_pkgs():
'Analyze catalog data and installed packages in an attempt to determine\n what is installed.'
pkgdata = {}
itemname_to_pkgid = {}
pkgid_to_itemname = {}
for catalogname in _CATALOG:
catalogitems = _CATALOG[catalogname]['items']
add_pack... |
def get_item_detail(name, cataloglist, vers='', skip_min_os_check=False, suppress_warnings=False):
"Searches the catalogs in list for an item matching the given name that\n can be installed on the current hardware/OS (optionally skipping the\n minimum OS check so we can return an item that requires a higher O... | -1,328,711,189,519,149,000 | Searches the catalogs in list for an item matching the given name that
can be installed on the current hardware/OS (optionally skipping the
minimum OS check so we can return an item that requires a higher OS)
If no version is supplied, but the version is appended to the name
('TextWrangler--2.3.0.0.0') that version is... | code/client/munkilib/updatecheck/catalogs.py | get_item_detail | Artoria2e5/munki | python | def get_item_detail(name, cataloglist, vers=, skip_min_os_check=False, suppress_warnings=False):
"Searches the catalogs in list for an item matching the given name that\n can be installed on the current hardware/OS (optionally skipping the\n minimum OS check so we can return an item that requires a higher OS)... |
def get_catalogs(cataloglist):
'Retrieves the catalogs from the server and populates our catalogs\n dictionary.\n '
for catalogname in cataloglist:
if (not (catalogname in _CATALOG)):
catalogpath = download.download_catalog(catalogname)
if catalogpath:
try:
... | 2,477,234,418,475,394,600 | Retrieves the catalogs from the server and populates our catalogs
dictionary. | code/client/munkilib/updatecheck/catalogs.py | get_catalogs | Artoria2e5/munki | python | def get_catalogs(cataloglist):
'Retrieves the catalogs from the server and populates our catalogs\n dictionary.\n '
for catalogname in cataloglist:
if (not (catalogname in _CATALOG)):
catalogpath = download.download_catalog(catalogname)
if catalogpath:
try:
... |
def clean_up():
'Removes any catalog files that are no longer in use by this client'
catalog_dir = os.path.join(prefs.pref('ManagedInstallDir'), 'catalogs')
for item in os.listdir(catalog_dir):
if (item not in _CATALOG):
os.unlink(os.path.join(catalog_dir, item)) | -2,612,927,915,147,749,400 | Removes any catalog files that are no longer in use by this client | code/client/munkilib/updatecheck/catalogs.py | clean_up | Artoria2e5/munki | python | def clean_up():
catalog_dir = os.path.join(prefs.pref('ManagedInstallDir'), 'catalogs')
for item in os.listdir(catalog_dir):
if (item not in _CATALOG):
os.unlink(os.path.join(catalog_dir, item)) |
def catalogs():
'Returns our internal _CATALOG dict'
return _CATALOG | -1,107,014,203,898,371,800 | Returns our internal _CATALOG dict | code/client/munkilib/updatecheck/catalogs.py | catalogs | Artoria2e5/munki | python | def catalogs():
return _CATALOG |
def item_version(item):
'Returns a MunkiLooseVersion for pkginfo item'
return pkgutils.MunkiLooseVersion(item['version']) | 5,069,734,528,680,948 | Returns a MunkiLooseVersion for pkginfo item | code/client/munkilib/updatecheck/catalogs.py | item_version | Artoria2e5/munki | python | def item_version(item):
return pkgutils.MunkiLooseVersion(item['version']) |
def munki_version_ok(item):
'Returns a boolean to indicate if the current Munki version is high\n enough to install this item. If not, also adds the failure reason to\n the rejected_items list.'
if item.get('minimum_munki_version'):
min_munki_vers = item['minimum_munki_version']
di... | -1,973,357,087,770,327,300 | Returns a boolean to indicate if the current Munki version is high
enough to install this item. If not, also adds the failure reason to
the rejected_items list. | code/client/munkilib/updatecheck/catalogs.py | munki_version_ok | Artoria2e5/munki | python | def munki_version_ok(item):
'Returns a boolean to indicate if the current Munki version is high\n enough to install this item. If not, also adds the failure reason to\n the rejected_items list.'
if item.get('minimum_munki_version'):
min_munki_vers = item['minimum_munki_version']
di... |
def os_version_ok(item, skip_min_os_check=False):
'Returns a boolean to indicate if the item is ok to install under\n the current OS. If not, also adds the failure reason to the\n rejected_items list. If skip_min_os_check is True, skips the minimum os\n version check.'
if (item.get('minimum... | 6,567,157,521,926,049,000 | Returns a boolean to indicate if the item is ok to install under
the current OS. If not, also adds the failure reason to the
rejected_items list. If skip_min_os_check is True, skips the minimum os
version check. | code/client/munkilib/updatecheck/catalogs.py | os_version_ok | Artoria2e5/munki | python | def os_version_ok(item, skip_min_os_check=False):
'Returns a boolean to indicate if the item is ok to install under\n the current OS. If not, also adds the failure reason to the\n rejected_items list. If skip_min_os_check is True, skips the minimum os\n version check.'
if (item.get('minimum... |
def cpu_arch_ok(item):
'Returns a boolean to indicate if the item is ok to install under\n the current CPU architecture. If not, also adds the failure reason to\n the rejected_items list.'
if item.get('supported_architectures'):
display.display_debug1('Considering item %s, version %s with ... | -7,022,270,190,569,582,000 | Returns a boolean to indicate if the item is ok to install under
the current CPU architecture. If not, also adds the failure reason to
the rejected_items list. | code/client/munkilib/updatecheck/catalogs.py | cpu_arch_ok | Artoria2e5/munki | python | def cpu_arch_ok(item):
'Returns a boolean to indicate if the item is ok to install under\n the current CPU architecture. If not, also adds the failure reason to\n the rejected_items list.'
if item.get('supported_architectures'):
display.display_debug1('Considering item %s, version %s with ... |
def installable_condition_ok(item):
'Returns a boolean to indicate if an installable_condition predicate\n in the current item passes. If not, also adds the failure reason to\n the rejected_items list.'
if item.get('installable_condition'):
if (not info.predicate_evaluates_as_true(item['in... | -2,771,831,491,757,247,000 | Returns a boolean to indicate if an installable_condition predicate
in the current item passes. If not, also adds the failure reason to
the rejected_items list. | code/client/munkilib/updatecheck/catalogs.py | installable_condition_ok | Artoria2e5/munki | python | def installable_condition_ok(item):
'Returns a boolean to indicate if an installable_condition predicate\n in the current item passes. If not, also adds the failure reason to\n the rejected_items list.'
if item.get('installable_condition'):
if (not info.predicate_evaluates_as_true(item['in... |
@registry.register('A000073')
def tribonacci() -> Iterable[int]:
'Tribonacci numbers.'
(yield 0)
(yield 0)
(yield 1)
p3: int = 0
p2: int = 0
p1: int = 1
while True:
curr: int = ((p1 + p2) + p3)
(yield curr)
(p1, p2, p3) = (curr, p1, p2) | 300,005,145,968,154,100 | Tribonacci numbers. | oeis/tribonacci.py | tribonacci | reidhoch/oeis-seq | python | @registry.register('A000073')
def tribonacci() -> Iterable[int]:
(yield 0)
(yield 0)
(yield 1)
p3: int = 0
p2: int = 0
p1: int = 1
while True:
curr: int = ((p1 + p2) + p3)
(yield curr)
(p1, p2, p3) = (curr, p1, p2) |
def set_line(self, line, membership):
'Set whether a given line is a member of the set.'
self._lines[line] = membership | -6,751,681,664,870,876,000 | Set whether a given line is a member of the set. | pytype/directors.py | set_line | Flameeyes/pytype | python | def set_line(self, line, membership):
self._lines[line] = membership |
def start_range(self, line, membership):
'Start a range of lines that are either included/excluded from the set.\n\n Args:\n line: A line number.\n membership: If True, lines >= line are included in the set (starting\n a range), otherwise they are excluded (ending a range).\n\n Raises:\n ... | 2,535,163,513,588,088,000 | Start a range of lines that are either included/excluded from the set.
Args:
line: A line number.
membership: If True, lines >= line are included in the set (starting
a range), otherwise they are excluded (ending a range).
Raises:
ValueError: if line is less than that of a previous call to start_range(). | pytype/directors.py | start_range | Flameeyes/pytype | python | def start_range(self, line, membership):
'Start a range of lines that are either included/excluded from the set.\n\n Args:\n line: A line number.\n membership: If True, lines >= line are included in the set (starting\n a range), otherwise they are excluded (ending a range).\n\n Raises:\n ... |
def __contains__(self, line):
'Return if a line is a member of the set.'
specific = self._lines.get(line)
if (specific is not None):
return specific
pos = bisect.bisect(self._transitions, line)
return ((pos % 2) == 1) | -5,524,570,454,118,664,000 | Return if a line is a member of the set. | pytype/directors.py | __contains__ | Flameeyes/pytype | python | def __contains__(self, line):
specific = self._lines.get(line)
if (specific is not None):
return specific
pos = bisect.bisect(self._transitions, line)
return ((pos % 2) == 1) |
def get_disable_after(self, lineno):
'Get an unclosed disable, if any, that starts after lineno.'
if (((len(self._transitions) % 2) == 1) and (self._transitions[(- 1)] >= lineno)):
return self._transitions[(- 1)]
return None | -8,653,321,035,775,793,000 | Get an unclosed disable, if any, that starts after lineno. | pytype/directors.py | get_disable_after | Flameeyes/pytype | python | def get_disable_after(self, lineno):
if (((len(self._transitions) % 2) == 1) and (self._transitions[(- 1)] >= lineno)):
return self._transitions[(- 1)]
return None |
def __init__(self, src, errorlog, filename, disable):
'Create a Director for a source file.\n\n Args:\n src: The source text as a string.\n errorlog: An ErrorLog object. Directive errors will be logged to the\n errorlog.\n filename: The name of the source file.\n disable: List of e... | -7,014,119,069,833,995,000 | Create a Director for a source file.
Args:
src: The source text as a string.
errorlog: An ErrorLog object. Directive errors will be logged to the
errorlog.
filename: The name of the source file.
disable: List of error messages to always ignore. | pytype/directors.py | __init__ | Flameeyes/pytype | python | def __init__(self, src, errorlog, filename, disable):
'Create a Director for a source file.\n\n Args:\n src: The source text as a string.\n errorlog: An ErrorLog object. Directive errors will be logged to the\n errorlog.\n filename: The name of the source file.\n disable: List of e... |
def _adjust_type_comments(self, closing_bracket_lines, whitespace_lines):
'Adjust any type comments affected by closing bracket lines.\n\n Lines that contain nothing but closing brackets don\'t appear in the\n bytecode, so for, e.g.,\n v = [\n "hello",\n "world",\n ] # line 4\n lin... | -3,983,367,050,733,600,300 | Adjust any type comments affected by closing bracket lines.
Lines that contain nothing but closing brackets don't appear in the
bytecode, so for, e.g.,
v = [
"hello",
"world",
] # line 4
line 4 is where any type comment for 'v' should be put, but the
STORE_NAME opcode for 'v' is at line 3. If we find a ty... | pytype/directors.py | _adjust_type_comments | Flameeyes/pytype | python | def _adjust_type_comments(self, closing_bracket_lines, whitespace_lines):
'Adjust any type comments affected by closing bracket lines.\n\n Lines that contain nothing but closing brackets don\'t appear in the\n bytecode, so for, e.g.,\n v = [\n "hello",\n "world",\n ] # line 4\n lin... |
def _parse_source(self, src):
'Parse a source file, extracting directives from comments.'
f = moves.StringIO(src)
defs_start = None
closing_bracket_lines = set()
whitespace_lines = set()
for (tok, _, start, _, line) in tokenize.generate_tokens(f.readline):
(lineno, col) = start
i... | -4,893,756,587,152,604,000 | Parse a source file, extracting directives from comments. | pytype/directors.py | _parse_source | Flameeyes/pytype | python | def _parse_source(self, src):
f = moves.StringIO(src)
defs_start = None
closing_bracket_lines = set()
whitespace_lines = set()
for (tok, _, start, _, line) in tokenize.generate_tokens(f.readline):
(lineno, col) = start
if ((defs_start is None) and _CLASS_OR_FUNC_RE.match(line)):... |
def _process_type(self, lineno, code, data, is_nested):
'Process a type: comment.'
if ((not code) and is_nested):
return
if (lineno in self._type_comments):
self._errorlog.invalid_directive(self._filename, lineno, 'Multiple type comments on the same line.')
if (data == 'ignore'):
... | 2,161,644,576,464,778,500 | Process a type: comment. | pytype/directors.py | _process_type | Flameeyes/pytype | python | def _process_type(self, lineno, code, data, is_nested):
if ((not code) and is_nested):
return
if (lineno in self._type_comments):
self._errorlog.invalid_directive(self._filename, lineno, 'Multiple type comments on the same line.')
if (data == 'ignore'):
if (not code):
... |
def _process_pytype(self, lineno, data, open_ended):
'Process a pytype: comment.'
if (not data):
raise _DirectiveError('Invalid directive syntax.')
for option in data.split():
if (option == 'skip-file'):
raise SkipFile()
try:
(command, values) = option.split('... | 821,426,886,601,486,200 | Process a pytype: comment. | pytype/directors.py | _process_pytype | Flameeyes/pytype | python | def _process_pytype(self, lineno, data, open_ended):
if (not data):
raise _DirectiveError('Invalid directive syntax.')
for option in data.split():
if (option == 'skip-file'):
raise SkipFile()
try:
(command, values) = option.split('=', 1)
values = ... |
def should_report_error(self, error):
'Return whether the error should be logged.\n\n This method is suitable for use as an error filter.\n\n Args:\n error: An error._Error object.\n\n Returns:\n True iff the error should be included in the log.\n '
if ((error.filename != self._filename) o... | -4,967,645,690,361,252,000 | Return whether the error should be logged.
This method is suitable for use as an error filter.
Args:
error: An error._Error object.
Returns:
True iff the error should be included in the log. | pytype/directors.py | should_report_error | Flameeyes/pytype | python | def should_report_error(self, error):
'Return whether the error should be logged.\n\n This method is suitable for use as an error filter.\n\n Args:\n error: An error._Error object.\n\n Returns:\n True iff the error should be included in the log.\n '
if ((error.filename != self._filename) o... |
def _create_label(self, kg: KG, vertex: Vertex, n: int) -> str:
'Creates a label according to a vertex and its neighbors.\n\n kg: The Knowledge Graph.\n\n The graph from which the neighborhoods are extracted for the\n provided entities.\n vertex: The vertex to get its neighbors t... | -926,188,340,973,383,600 | Creates a label according to a vertex and its neighbors.
kg: The Knowledge Graph.
The graph from which the neighborhoods are extracted for the
provided entities.
vertex: The vertex to get its neighbors to create the suffix.
n: The index of the neighbor
Returns:
the label created for the vertex. | pyrdf2vec/walkers/weisfeiler_lehman.py | _create_label | vishalbelsare/pyRDF2Vec | python | def _create_label(self, kg: KG, vertex: Vertex, n: int) -> str:
'Creates a label according to a vertex and its neighbors.\n\n kg: The Knowledge Graph.\n\n The graph from which the neighborhoods are extracted for the\n provided entities.\n vertex: The vertex to get its neighbors t... |
def _weisfeiler_lehman(self, kg: KG) -> None:
'Performs Weisfeiler-Lehman relabeling of the vertices.\n\n Args:\n kg: The Knowledge Graph.\n\n The graph from which the neighborhoods are extracted for the\n provided entities.\n\n '
for vertex in kg._vertices... | 3,049,563,729,577,228,000 | Performs Weisfeiler-Lehman relabeling of the vertices.
Args:
kg: The Knowledge Graph.
The graph from which the neighborhoods are extracted for the
provided entities. | pyrdf2vec/walkers/weisfeiler_lehman.py | _weisfeiler_lehman | vishalbelsare/pyRDF2Vec | python | def _weisfeiler_lehman(self, kg: KG) -> None:
'Performs Weisfeiler-Lehman relabeling of the vertices.\n\n Args:\n kg: The Knowledge Graph.\n\n The graph from which the neighborhoods are extracted for the\n provided entities.\n\n '
for vertex in kg._vertices... |
def extract(self, kg: KG, entities: Entities, verbose: int=0) -> List[List[SWalk]]:
'Fits the provided sampling strategy and then calls the\n private _extract method that is implemented for each of the\n walking strategies.\n\n Args:\n kg: The Knowledge Graph.\n entities: ... | 8,135,408,716,254,422,000 | Fits the provided sampling strategy and then calls the
private _extract method that is implemented for each of the
walking strategies.
Args:
kg: The Knowledge Graph.
entities: The entities to be extracted from the Knowledge Graph.
verbose: The verbosity level.
0: does not display anything;
... | pyrdf2vec/walkers/weisfeiler_lehman.py | extract | vishalbelsare/pyRDF2Vec | python | def extract(self, kg: KG, entities: Entities, verbose: int=0) -> List[List[SWalk]]:
'Fits the provided sampling strategy and then calls the\n private _extract method that is implemented for each of the\n walking strategies.\n\n Args:\n kg: The Knowledge Graph.\n entities: ... |
def _map_wl(self, entity: Vertex, pos: int, n: int) -> str:
'Maps certain vertices to MD5 hashes to save memory. For entities of\n interest (provided by the user to the extract function) and predicates,\n the string representation is kept.\n\n Args:\n entity: The entity to be mapped.... | 2,031,195,953,491,032,300 | Maps certain vertices to MD5 hashes to save memory. For entities of
interest (provided by the user to the extract function) and predicates,
the string representation is kept.
Args:
entity: The entity to be mapped.
pos: The position of the entity in the walk.
n: The iteration number of the WL algorithm.
Re... | pyrdf2vec/walkers/weisfeiler_lehman.py | _map_wl | vishalbelsare/pyRDF2Vec | python | def _map_wl(self, entity: Vertex, pos: int, n: int) -> str:
'Maps certain vertices to MD5 hashes to save memory. For entities of\n interest (provided by the user to the extract function) and predicates,\n the string representation is kept.\n\n Args:\n entity: The entity to be mapped.... |
def _extract(self, kg: KG, entity: Vertex) -> EntityWalks:
'Extracts random walks for an entity based on a Knowledge Graph.\n\n Args:\n kg: The Knowledge Graph.\n entity: The root node to extract walks.\n\n Returns:\n A dictionary having the entity as key and a list of... | -9,107,139,105,087,989,000 | Extracts random walks for an entity based on a Knowledge Graph.
Args:
kg: The Knowledge Graph.
entity: The root node to extract walks.
Returns:
A dictionary having the entity as key and a list of tuples as value
corresponding to the extracted walks. | pyrdf2vec/walkers/weisfeiler_lehman.py | _extract | vishalbelsare/pyRDF2Vec | python | def _extract(self, kg: KG, entity: Vertex) -> EntityWalks:
'Extracts random walks for an entity based on a Knowledge Graph.\n\n Args:\n kg: The Knowledge Graph.\n entity: The root node to extract walks.\n\n Returns:\n A dictionary having the entity as key and a list of... |
def csbal_process():
'\n This method is run when the `csbal` script is called.\n can be used to check a single file (check balance state after adjusting)\n args are file stem, freq (Hz [rpm/60] float), samp_rate (data collector)\n '
args = sys.argv[1:]
stem = args[0]
freq = float(args[1])
... | 4,217,561,701,050,841,000 | This method is run when the `csbal` script is called.
can be used to check a single file (check balance state after adjusting)
args are file stem, freq (Hz [rpm/60] float), samp_rate (data collector) | cheapskate_bal/cheapskate_bal/cli.py | csbal_process | kevinpowell/balancer | python | def csbal_process():
'\n This method is run when the `csbal` script is called.\n can be used to check a single file (check balance state after adjusting)\n args are file stem, freq (Hz [rpm/60] float), samp_rate (data collector)\n '
args = sys.argv[1:]
stem = args[0]
freq = float(args[1])
... |
def csbal_single():
'\n This method performs the whole process for a single plane balance\n Four data files are captured, and the results are emitted\n args are file stem, freq(Hz), shift angle of test mass (deg), test mass '
args = sys.argv[1:]
if (len(args) < 4):
print('args are stem, fre... | -2,981,180,016,032,641,000 | This method performs the whole process for a single plane balance
Four data files are captured, and the results are emitted
args are file stem, freq(Hz), shift angle of test mass (deg), test mass | cheapskate_bal/cheapskate_bal/cli.py | csbal_single | kevinpowell/balancer | python | def csbal_single():
'\n This method performs the whole process for a single plane balance\n Four data files are captured, and the results are emitted\n args are file stem, freq(Hz), shift angle of test mass (deg), test mass '
args = sys.argv[1:]
if (len(args) < 4):
print('args are stem, fre... |
def csbal_dual_init():
'\n THis method performs the whole process for a dual plane balance\n Three files are captured and the results are emitted\n args are file stem, freq(Hz), shift angle of test mass (deg), test mass '
args = sys.argv[1:]
if (len(args) < 4):
print('args are stem, freq, s... | 4,723,339,305,924,539,000 | THis method performs the whole process for a dual plane balance
Three files are captured and the results are emitted
args are file stem, freq(Hz), shift angle of test mass (deg), test mass | cheapskate_bal/cheapskate_bal/cli.py | csbal_dual_init | kevinpowell/balancer | python | def csbal_dual_init():
'\n THis method performs the whole process for a dual plane balance\n Three files are captured and the results are emitted\n args are file stem, freq(Hz), shift angle of test mass (deg), test mass '
args = sys.argv[1:]
if (len(args) < 4):
print('args are stem, freq, s... |
def csbal_dual_iter():
'\n This method performs an iteration of dual plane balance, once the\n influence params are known. One file is captured and the results\n are emitted\n args are file stem, tag, freq\n '
args = sys.argv[1:]
if (len(args) < 3):
print('args are: filestem, tag, fr... | 8,211,429,376,707,480,000 | This method performs an iteration of dual plane balance, once the
influence params are known. One file is captured and the results
are emitted
args are file stem, tag, freq | cheapskate_bal/cheapskate_bal/cli.py | csbal_dual_iter | kevinpowell/balancer | python | def csbal_dual_iter():
'\n This method performs an iteration of dual plane balance, once the\n influence params are known. One file is captured and the results\n are emitted\n args are file stem, tag, freq\n '
args = sys.argv[1:]
if (len(args) < 3):
print('args are: filestem, tag, fr... |
def prepare_package(err, path, expectation=0, for_appversions=None, timeout=(- 1)):
'Prepares a file-based package for validation.\n\n timeout is the number of seconds before validation is aborted.\n If timeout is -1 then no timeout checking code will run.\n '
package = None
try:
if (not os... | 4,124,246,953,156,577,000 | Prepares a file-based package for validation.
timeout is the number of seconds before validation is aborted.
If timeout is -1 then no timeout checking code will run. | validator/submain.py | prepare_package | kumar303/amo-validator | python | def prepare_package(err, path, expectation=0, for_appversions=None, timeout=(- 1)):
'Prepares a file-based package for validation.\n\n timeout is the number of seconds before validation is aborted.\n If timeout is -1 then no timeout checking code will run.\n '
package = None
try:
if (not os... |
def test_search(err, package, expectation=0):
'Tests the package to see if it is a search provider.'
expected_search_provider = (expectation in (PACKAGE_ANY, PACKAGE_SEARCHPROV))
if (not expected_search_provider):
return err.warning(('main', 'test_search', 'extension'), 'Unexpected file extension.')... | 3,807,156,908,103,187,000 | Tests the package to see if it is a search provider. | validator/submain.py | test_search | kumar303/amo-validator | python | def test_search(err, package, expectation=0):
expected_search_provider = (expectation in (PACKAGE_ANY, PACKAGE_SEARCHPROV))
if (not expected_search_provider):
return err.warning(('main', 'test_search', 'extension'), 'Unexpected file extension.')
detect_opensearch(err, package, listed=err.get_re... |
def test_package(err, file_, name, expectation=PACKAGE_ANY, for_appversions=None):
'Begins tests for the package.'
try:
package = XPIManager(file_, mode='r', name=name)
has_package_json = ('package.json' in package)
has_manifest_json = ('manifest.json' in package)
has_install_rdf... | -6,450,757,682,280,588,000 | Begins tests for the package. | validator/submain.py | test_package | kumar303/amo-validator | python | def test_package(err, file_, name, expectation=PACKAGE_ANY, for_appversions=None):
try:
package = XPIManager(file_, mode='r', name=name)
has_package_json = ('package.json' in package)
has_manifest_json = ('manifest.json' in package)
has_install_rdf = ('install.rdf' in package)
... |
def populate_chrome_manifest(err, xpi_package):
"Loads the chrome.manifest if it's present"
if ('chrome.manifest' in xpi_package):
chrome_data = xpi_package.read('chrome.manifest')
chrome = ChromeManifest(chrome_data, 'chrome.manifest')
chrome_recursion_buster = set()
def get_li... | 3,416,004,744,526,102,000 | Loads the chrome.manifest if it's present | validator/submain.py | populate_chrome_manifest | kumar303/amo-validator | python | def populate_chrome_manifest(err, xpi_package):
if ('chrome.manifest' in xpi_package):
chrome_data = xpi_package.read('chrome.manifest')
chrome = ChromeManifest(chrome_data, 'chrome.manifest')
chrome_recursion_buster = set()
def get_linked_manifest(path, from_path, from_chrome,... |
def test_inner_package(err, xpi_package, for_appversions=None):
"Tests a package's inner content."
populate_chrome_manifest(err, xpi_package)
for tier in sorted(decorator.get_tiers()):
err.set_tier(tier)
for test in decorator.get_tests(tier, err.detected_type):
if (test['versions... | 3,624,565,051,179,548,000 | Tests a package's inner content. | validator/submain.py | test_inner_package | kumar303/amo-validator | python | def test_inner_package(err, xpi_package, for_appversions=None):
populate_chrome_manifest(err, xpi_package)
for tier in sorted(decorator.get_tiers()):
err.set_tier(tier)
for test in decorator.get_tests(tier, err.detected_type):
if (test['versions'] is not None):
i... |
def Main():
'The main program function.\n\n Returns:\n bool: True if successful or False if not.\n '
argument_parser = argparse.ArgumentParser(description='Extracts information from BSM event auditing files.')
argument_parser.add_argument('-d', '--debug', dest='debug', action='store_true', default=Fals... | -7,459,887,251,937,730,000 | The main program function.
Returns:
bool: True if successful or False if not. | scripts/bsm.py | Main | jleaniz/dtformats | python | def Main():
'The main program function.\n\n Returns:\n bool: True if successful or False if not.\n '
argument_parser = argparse.ArgumentParser(description='Extracts information from BSM event auditing files.')
argument_parser.add_argument('-d', '--debug', dest='debug', action='store_true', default=Fals... |
def annuli_around(region, inner_factor, outer_factor, header, x_size, y_size):
'\n This function ...\n :param region:\n :param inner_factor:\n :param outer_factor:\n :param header:\n :param x_size:\n :param y_size:\n :return:\n '
inner_region = regions.expand(region, inner_factor)
... | -1,261,848,470,547,287,000 | This function ...
:param region:
:param inner_factor:
:param outer_factor:
:param header:
:param x_size:
:param y_size:
:return: | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/tools/masks.py | annuli_around | Stargrazer82301/CAAPR | python | def annuli_around(region, inner_factor, outer_factor, header, x_size, y_size):
'\n This function ...\n :param region:\n :param inner_factor:\n :param outer_factor:\n :param header:\n :param x_size:\n :param y_size:\n :return:\n '
inner_region = regions.expand(region, inner_factor)
... |
def masked_outside(region, header, x_size, y_size, expand_factor=1.0):
'\n This function ...\n :param region:\n :param header:\n :param x_size:\n :param y_size:\n :param expand_factor:\n :return:\n '
region = regions.expand(region, factor=expand_factor)
mask = np.logical_not(regions.... | 1,645,701,534,307,375,000 | This function ...
:param region:
:param header:
:param x_size:
:param y_size:
:param expand_factor:
:return: | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/tools/masks.py | masked_outside | Stargrazer82301/CAAPR | python | def masked_outside(region, header, x_size, y_size, expand_factor=1.0):
'\n This function ...\n :param region:\n :param header:\n :param x_size:\n :param y_size:\n :param expand_factor:\n :return:\n '
region = regions.expand(region, factor=expand_factor)
mask = np.logical_not(regions.... |
def create_disk_mask(x_size, y_size, x_center, y_center, radius):
'\n This function ...\n :param x_size:\n :param y_size:\n :param x_center:\n :param y_center:\n :param radius:\n :return:\n '
(y, x) = np.ogrid[(- y_center):(y_size - y_center), (- x_center):(x_size - x_center)]
mask =... | -2,308,099,002,475,903,500 | This function ...
:param x_size:
:param y_size:
:param x_center:
:param y_center:
:param radius:
:return: | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/tools/masks.py | create_disk_mask | Stargrazer82301/CAAPR | python | def create_disk_mask(x_size, y_size, x_center, y_center, radius):
'\n This function ...\n :param x_size:\n :param y_size:\n :param x_center:\n :param y_center:\n :param radius:\n :return:\n '
(y, x) = np.ogrid[(- y_center):(y_size - y_center), (- x_center):(x_size - x_center)]
mask =... |
def union(mask_a, mask_b):
'\n This function ...\n :param args:\n :return:\n '
return (mask_a + mask_b) | -4,128,617,534,399,295,500 | This function ...
:param args:
:return: | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/tools/masks.py | union | Stargrazer82301/CAAPR | python | def union(mask_a, mask_b):
'\n This function ...\n :param args:\n :return:\n '
return (mask_a + mask_b) |
def intersection(mask_a, mask_b):
'\n This function ...\n :param args:\n :return:\n '
return (mask_a * mask_b) | -3,610,174,022,581,261,300 | This function ...
:param args:
:return: | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/tools/masks.py | intersection | Stargrazer82301/CAAPR | python | def intersection(mask_a, mask_b):
'\n This function ...\n :param args:\n :return:\n '
return (mask_a * mask_b) |
def overlap(mask_a, mask_b):
'\n This function ...\n :param mask_a:\n :param mask_b:\n :return:\n '
return np.any(intersection(mask_a, mask_b)) | -6,524,343,050,101,109,000 | This function ...
:param mask_a:
:param mask_b:
:return: | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/tools/masks.py | overlap | Stargrazer82301/CAAPR | python | def overlap(mask_a, mask_b):
'\n This function ...\n :param mask_a:\n :param mask_b:\n :return:\n '
return np.any(intersection(mask_a, mask_b)) |
def split_overlap(base_mask, test_mask, return_segments=False):
'\n This function takes all blobs in the base_mask and checks whether they overlap with the test_mask.\n The function returns two new masks, one mask with all the blobs that overlapped, and another with the blobs\n that did not overlap.\n :... | 8,212,150,702,368,379,000 | This function takes all blobs in the base_mask and checks whether they overlap with the test_mask.
The function returns two new masks, one mask with all the blobs that overlapped, and another with the blobs
that did not overlap.
:param base_mask:
:param test_mask:
:return: | CAAPR/CAAPR_AstroMagic/PTS/pts/magic/tools/masks.py | split_overlap | Stargrazer82301/CAAPR | python | def split_overlap(base_mask, test_mask, return_segments=False):
'\n This function takes all blobs in the base_mask and checks whether they overlap with the test_mask.\n The function returns two new masks, one mask with all the blobs that overlapped, and another with the blobs\n that did not overlap.\n :... |
def wrap_check_policy(func):
'Check policy corresponding to the wrapped methods prior to execution\n\n This decorator requires the first 3 args of the wrapped function\n to be (self, context, volume)\n '
@functools.wraps(func)
def wrapped(self, context, target_obj, *args, **kwargs):
check_... | 2,356,297,874,592,871,000 | Check policy corresponding to the wrapped methods prior to execution
This decorator requires the first 3 args of the wrapped function
to be (self, context, volume) | cinder/volume/api.py | wrap_check_policy | CiscoSystems/cinder-old | python | def wrap_check_policy(func):
'Check policy corresponding to the wrapped methods prior to execution\n\n This decorator requires the first 3 args of the wrapped function\n to be (self, context, volume)\n '
@functools.wraps(func)
def wrapped(self, context, target_obj, *args, **kwargs):
check_... |
def remove_from_compute(self, context, volume, instance_id, host):
'Remove volume from specified compute host.'
rpc.call(context, rpc.queue_get_for(context, FLAGS.compute_topic, host), {'method': 'remove_volume_connection', 'args': {'instance_id': instance_id, 'volume_id': volume['id']}}) | -6,526,975,400,008,303,000 | Remove volume from specified compute host. | cinder/volume/api.py | remove_from_compute | CiscoSystems/cinder-old | python | def remove_from_compute(self, context, volume, instance_id, host):
rpc.call(context, rpc.queue_get_for(context, FLAGS.compute_topic, host), {'method': 'remove_volume_connection', 'args': {'instance_id': instance_id, 'volume_id': volume['id']}}) |
@wrap_check_policy
def get_volume_metadata(self, context, volume):
'Get all metadata associated with a volume.'
rv = self.db.volume_metadata_get(context, volume['id'])
return dict(rv.iteritems()) | 5,805,609,976,959,036,000 | Get all metadata associated with a volume. | cinder/volume/api.py | get_volume_metadata | CiscoSystems/cinder-old | python | @wrap_check_policy
def get_volume_metadata(self, context, volume):
rv = self.db.volume_metadata_get(context, volume['id'])
return dict(rv.iteritems()) |
@wrap_check_policy
def delete_volume_metadata(self, context, volume, key):
'Delete the given metadata item from an volume.'
self.db.volume_metadata_delete(context, volume['id'], key) | -1,655,074,085,884,326,100 | Delete the given metadata item from an volume. | cinder/volume/api.py | delete_volume_metadata | CiscoSystems/cinder-old | python | @wrap_check_policy
def delete_volume_metadata(self, context, volume, key):
self.db.volume_metadata_delete(context, volume['id'], key) |
@wrap_check_policy
def update_volume_metadata(self, context, volume, metadata, delete=False):
'Updates or creates volume metadata.\n\n If delete is True, metadata items that are not specified in the\n `metadata` argument will be deleted.\n\n '
if delete:
_metadata = metadata
els... | -3,966,998,166,392,626,000 | Updates or creates volume metadata.
If delete is True, metadata items that are not specified in the
`metadata` argument will be deleted. | cinder/volume/api.py | update_volume_metadata | CiscoSystems/cinder-old | python | @wrap_check_policy
def update_volume_metadata(self, context, volume, metadata, delete=False):
'Updates or creates volume metadata.\n\n If delete is True, metadata items that are not specified in the\n `metadata` argument will be deleted.\n\n '
if delete:
_metadata = metadata
els... |
def get_volume_metadata_value(self, volume, key):
'Get value of particular metadata key.'
metadata = volume.get('volume_metadata')
if metadata:
for i in volume['volume_metadata']:
if (i['key'] == key):
return i['value']
return None | -8,077,658,148,052,221,000 | Get value of particular metadata key. | cinder/volume/api.py | get_volume_metadata_value | CiscoSystems/cinder-old | python | def get_volume_metadata_value(self, volume, key):
metadata = volume.get('volume_metadata')
if metadata:
for i in volume['volume_metadata']:
if (i['key'] == key):
return i['value']
return None |
def _check_volume_availability(self, context, volume, force):
'Check if the volume can be used.'
if (volume['status'] not in ['available', 'in-use']):
msg = _('Volume status must be available/in-use.')
raise exception.InvalidVolume(reason=msg)
if ((not force) and ('in-use' == volume['status'... | 5,035,103,438,389,566,000 | Check if the volume can be used. | cinder/volume/api.py | _check_volume_availability | CiscoSystems/cinder-old | python | def _check_volume_availability(self, context, volume, force):
if (volume['status'] not in ['available', 'in-use']):
msg = _('Volume status must be available/in-use.')
raise exception.InvalidVolume(reason=msg)
if ((not force) and ('in-use' == volume['status'])):
msg = _('Volume statu... |
@wrap_check_policy
def copy_volume_to_image(self, context, volume, metadata, force):
'Create a new image from the specified volume.'
self._check_volume_availability(context, volume, force)
recv_metadata = self.image_service.create(context, metadata)
self.update(context, volume, {'status': 'uploading'})
... | -7,635,538,943,194,196,000 | Create a new image from the specified volume. | cinder/volume/api.py | copy_volume_to_image | CiscoSystems/cinder-old | python | @wrap_check_policy
def copy_volume_to_image(self, context, volume, metadata, force):
self._check_volume_availability(context, volume, force)
recv_metadata = self.image_service.create(context, metadata)
self.update(context, volume, {'status': 'uploading'})
rpc.cast(context, rpc.queue_get_for(context... |
def show_mri_sample(sample, pred_mask=None, pred_lbl=None, seg_downsample=None, save_fn=None):
' Plot sample in three projections '
plt.close('all')
alpha = 0.5
image_alpha = 1.0
ims = sample['image'].numpy()
means = sample['mean'].numpy()
stds = sample['std'].numpy()
segs = (sample['seg... | 4,095,470,176,055,205,000 | Plot sample in three projections | src/seg_model_utils/visualization.py | show_mri_sample | jpjuvo/RSNA-MICCAI-Brain-Tumor-Classification | python | def show_mri_sample(sample, pred_mask=None, pred_lbl=None, seg_downsample=None, save_fn=None):
' '
plt.close('all')
alpha = 0.5
image_alpha = 1.0
ims = sample['image'].numpy()
means = sample['mean'].numpy()
stds = sample['std'].numpy()
segs = (sample['segmentation'].numpy() if ('segment... |
def date_to_int(self, dates):
"\n calculates number of days between 01/01/0001 and each date in dates\n date has format '%m/%d/%Y'\n\n :param dates: Pandas Series\n :return: list\n "
ret = []
for date in dates:
date0 = datetime.datetime(year=1, month=1, day=1)
... | -6,971,423,626,463,422,000 | calculates number of days between 01/01/0001 and each date in dates
date has format '%m/%d/%Y'
:param dates: Pandas Series
:return: list | backend/data_merge.py | date_to_int | repeating/stock-analyzer | python | def date_to_int(self, dates):
"\n calculates number of days between 01/01/0001 and each date in dates\n date has format '%m/%d/%Y'\n\n :param dates: Pandas Series\n :return: list\n "
ret = []
for date in dates:
date0 = datetime.datetime(year=1, month=1, day=1)
... |
def _get_args(info):
'Return the list of args & kwds for building the __init__ function'
required = set()
kwds = set()
invalid_kwds = set()
if info.is_allOf():
arginfo = [_get_args(child) for child in info.allOf]
nonkeyword = all((args[0] for args in arginfo))
required = set.... | 962,504,644,605,657,200 | Return the list of args & kwds for building the __init__ function | tools/schemapi/codegen.py | _get_args | aladdingsw/altair | python | def _get_args(info):
required = set()
kwds = set()
invalid_kwds = set()
if info.is_allOf():
arginfo = [_get_args(child) for child in info.allOf]
nonkeyword = all((args[0] for args in arginfo))
required = set.union(set(), *(args[1] for args in arginfo))
kwds = set.uni... |
def schema_class(self):
'Generate code for a schema class'
rootschema = (self.rootschema if (self.rootschema is not None) else self.schema)
schemarepr = (self.schemarepr if (self.schemarepr is not None) else self.schema)
rootschemarepr = self.rootschemarepr
if (rootschemarepr is None):
if (r... | 2,319,359,880,158,826,000 | Generate code for a schema class | tools/schemapi/codegen.py | schema_class | aladdingsw/altair | python | def schema_class(self):
rootschema = (self.rootschema if (self.rootschema is not None) else self.schema)
schemarepr = (self.schemarepr if (self.schemarepr is not None) else self.schema)
rootschemarepr = self.rootschemarepr
if (rootschemarepr is None):
if (rootschema is self.schema):
... |
def init_code(self, indent=0):
'Return code suitablde for the __init__ function of a Schema class'
info = SchemaInfo(self.schema, rootschema=self.rootschema)
(nonkeyword, required, kwds, invalid_kwds, additional) = _get_args(info)
nodefault = set(self.nodefault)
required -= nodefault
kwds -= nod... | 7,375,621,453,165,764,000 | Return code suitablde for the __init__ function of a Schema class | tools/schemapi/codegen.py | init_code | aladdingsw/altair | python | def init_code(self, indent=0):
info = SchemaInfo(self.schema, rootschema=self.rootschema)
(nonkeyword, required, kwds, invalid_kwds, additional) = _get_args(info)
nodefault = set(self.nodefault)
required -= nodefault
kwds -= nodefault
args = ['self']
super_args = []
if nodefault:
... |
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n ... | 3,202,055,015,200,675,300 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type. | datameta_client_lib/model/staged_meta_data_sets.py | openapi_types | ghga-de/datameta-client-lib | python | @cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n ... |
@convert_js_args_to_python_args
def __init__(self, metadataset_ids, *args, **kwargs):
'StagedMetaDataSets - a model defined in OpenAPI\n\n Args:\n metadataset_ids ([str]):\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n ... | -4,712,968,294,148,478,000 | StagedMetaDataSets - a model defined in OpenAPI
Args:
metadataset_ids ([str]):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
... | datameta_client_lib/model/staged_meta_data_sets.py | __init__ | ghga-de/datameta-client-lib | python | @convert_js_args_to_python_args
def __init__(self, metadataset_ids, *args, **kwargs):
'StagedMetaDataSets - a model defined in OpenAPI\n\n Args:\n metadataset_ids ([str]):\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n ... |
def __init__(self, alpha, beta, validate_args=True, allow_nan_stats=False, name='Gamma'):
'Construct Gamma distributions with parameters `alpha` and `beta`.\n\n The parameters `alpha` and `beta` must be shaped in a way that supports\n broadcasting (e.g. `alpha + beta` is a valid operation).\n\n Args:\n ... | 8,342,424,512,551,853,000 | Construct Gamma distributions with parameters `alpha` and `beta`.
The parameters `alpha` and `beta` must be shaped in a way that supports
broadcasting (e.g. `alpha + beta` is a valid operation).
Args:
alpha: Floating point tensor, the shape params of the
distribution(s).
alpha must contain only positive val... | tensorflow/contrib/distributions/python/ops/gamma.py | __init__ | enrewen1/tf | python | def __init__(self, alpha, beta, validate_args=True, allow_nan_stats=False, name='Gamma'):
'Construct Gamma distributions with parameters `alpha` and `beta`.\n\n The parameters `alpha` and `beta` must be shaped in a way that supports\n broadcasting (e.g. `alpha + beta` is a valid operation).\n\n Args:\n ... |
@property
def allow_nan_stats(self):
'Boolean describing behavior when a stat is undefined for batch member.'
return self._allow_nan_stats | -6,998,151,567,754,223,000 | Boolean describing behavior when a stat is undefined for batch member. | tensorflow/contrib/distributions/python/ops/gamma.py | allow_nan_stats | enrewen1/tf | python | @property
def allow_nan_stats(self):
return self._allow_nan_stats |
@property
def validate_args(self):
'Boolean describing behavior on invalid input.'
return self._validate_args | -1,579,648,302,353,013,800 | Boolean describing behavior on invalid input. | tensorflow/contrib/distributions/python/ops/gamma.py | validate_args | enrewen1/tf | python | @property
def validate_args(self):
return self._validate_args |
@property
def name(self):
'Name to prepend to all ops.'
return self._name | -1,989,245,888,842,757,000 | Name to prepend to all ops. | tensorflow/contrib/distributions/python/ops/gamma.py | name | enrewen1/tf | python | @property
def name(self):
return self._name |
@property
def dtype(self):
'dtype of samples from this distribution.'
return self._alpha.dtype | -6,171,087,007,865,193,000 | dtype of samples from this distribution. | tensorflow/contrib/distributions/python/ops/gamma.py | dtype | enrewen1/tf | python | @property
def dtype(self):
return self._alpha.dtype |
@property
def alpha(self):
'Shape parameter.'
return self._alpha | -6,876,081,743,250,618,000 | Shape parameter. | tensorflow/contrib/distributions/python/ops/gamma.py | alpha | enrewen1/tf | python | @property
def alpha(self):
return self._alpha |
@property
def beta(self):
'Inverse scale parameter.'
return self._beta | -8,770,863,598,163,808,000 | Inverse scale parameter. | tensorflow/contrib/distributions/python/ops/gamma.py | beta | enrewen1/tf | python | @property
def beta(self):
return self._beta |
def batch_shape(self, name='batch_shape'):
'Batch dimensions of this instance as a 1-D int32 `Tensor`.\n\n The product of the dimensions of the `batch_shape` is the number of\n independent distributions of this kind the instance represents.\n\n Args:\n name: name to give to the op\n\n Returns:\n ... | -794,722,025,407,041,500 | Batch dimensions of this instance as a 1-D int32 `Tensor`.
The product of the dimensions of the `batch_shape` is the number of
independent distributions of this kind the instance represents.
Args:
name: name to give to the op
Returns:
`Tensor` `batch_shape` | tensorflow/contrib/distributions/python/ops/gamma.py | batch_shape | enrewen1/tf | python | def batch_shape(self, name='batch_shape'):
'Batch dimensions of this instance as a 1-D int32 `Tensor`.\n\n The product of the dimensions of the `batch_shape` is the number of\n independent distributions of this kind the instance represents.\n\n Args:\n name: name to give to the op\n\n Returns:\n ... |
def get_batch_shape(self):
'`TensorShape` available at graph construction time.\n\n Same meaning as `batch_shape`. May be only partially defined.\n\n Returns:\n `TensorShape` object.\n '
return self._get_batch_shape | -6,757,097,947,968,199,000 | `TensorShape` available at graph construction time.
Same meaning as `batch_shape`. May be only partially defined.
Returns:
`TensorShape` object. | tensorflow/contrib/distributions/python/ops/gamma.py | get_batch_shape | enrewen1/tf | python | def get_batch_shape(self):
'`TensorShape` available at graph construction time.\n\n Same meaning as `batch_shape`. May be only partially defined.\n\n Returns:\n `TensorShape` object.\n '
return self._get_batch_shape |
def event_shape(self, name='event_shape'):
'Shape of a sample from a single distribution as a 1-D int32 `Tensor`.\n\n Args:\n name: name to give to the op\n\n Returns:\n `Tensor` `event_shape`\n '
with ops.name_scope(self.name):
with ops.name_scope(name):
return constant_o... | 8,889,442,052,272,346,000 | Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
Args:
name: name to give to the op
Returns:
`Tensor` `event_shape` | tensorflow/contrib/distributions/python/ops/gamma.py | event_shape | enrewen1/tf | python | def event_shape(self, name='event_shape'):
'Shape of a sample from a single distribution as a 1-D int32 `Tensor`.\n\n Args:\n name: name to give to the op\n\n Returns:\n `Tensor` `event_shape`\n '
with ops.name_scope(self.name):
with ops.name_scope(name):
return constant_o... |
def get_event_shape(self):
'`TensorShape` available at graph construction time.\n\n Same meaning as `event_shape`. May be only partially defined.\n\n Returns:\n `TensorShape` object.\n '
return self._get_event_shape | -1,408,605,194,796,173,800 | `TensorShape` available at graph construction time.
Same meaning as `event_shape`. May be only partially defined.
Returns:
`TensorShape` object. | tensorflow/contrib/distributions/python/ops/gamma.py | get_event_shape | enrewen1/tf | python | def get_event_shape(self):
'`TensorShape` available at graph construction time.\n\n Same meaning as `event_shape`. May be only partially defined.\n\n Returns:\n `TensorShape` object.\n '
return self._get_event_shape |
def mean(self, name='mean'):
'Mean of each batch member.'
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._alpha, self._beta]):
return (self._alpha / self._beta) | 2,590,676,959,716,852,000 | Mean of each batch member. | tensorflow/contrib/distributions/python/ops/gamma.py | mean | enrewen1/tf | python | def mean(self, name='mean'):
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._alpha, self._beta]):
return (self._alpha / self._beta) |
def mode(self, name='mode'):
'Mode of each batch member.\n\n The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,\n and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception\n will be raised rather than returning `NaN`.\n\n Args:\n name: A name to give this op.... | -3,134,000,186,075,014,700 | Mode of each batch member.
The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception
will be raised rather than returning `NaN`.
Args:
name: A name to give this op.
Returns:
The mode for every batch member, a `Tensor` with... | tensorflow/contrib/distributions/python/ops/gamma.py | mode | enrewen1/tf | python | def mode(self, name='mode'):
'Mode of each batch member.\n\n The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,\n and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception\n will be raised rather than returning `NaN`.\n\n Args:\n name: A name to give this op.... |
def variance(self, name='variance'):
'Variance of each batch member.'
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._alpha, self._beta]):
return (self._alpha / math_ops.square(self._beta)) | 3,112,165,319,384,282,600 | Variance of each batch member. | tensorflow/contrib/distributions/python/ops/gamma.py | variance | enrewen1/tf | python | def variance(self, name='variance'):
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._alpha, self._beta]):
return (self._alpha / math_ops.square(self._beta)) |
def std(self, name='std'):
'Standard deviation of this distribution.'
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._alpha, self._beta]):
return (math_ops.sqrt(self._alpha) / self._beta) | -6,884,819,005,825,626,000 | Standard deviation of this distribution. | tensorflow/contrib/distributions/python/ops/gamma.py | std | enrewen1/tf | python | def std(self, name='std'):
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._alpha, self._beta]):
return (math_ops.sqrt(self._alpha) / self._beta) |
def log_prob(self, x, name='log_prob'):
'Log prob of observations in `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n log_prob: tensor of dtype `dtype`, the log-PDFs of ... | -2,444,142,182,391,518,700 | Log prob of observations in `x` under these Gamma distribution(s).
Args:
x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
name: The name to give this op.
Returns:
log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
Raises:
TypeError: if `x` and `alpha` are different dtypes. | tensorflow/contrib/distributions/python/ops/gamma.py | log_prob | enrewen1/tf | python | def log_prob(self, x, name='log_prob'):
'Log prob of observations in `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n log_prob: tensor of dtype `dtype`, the log-PDFs of ... |
def prob(self, x, name='prob'):
'Pdf of observations in `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n prob: tensor of dtype `dtype`, the PDFs of `x`\n\n Raises:\n ... | 3,286,626,708,954,102,000 | Pdf of observations in `x` under these Gamma distribution(s).
Args:
x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
name: The name to give this op.
Returns:
prob: tensor of dtype `dtype`, the PDFs of `x`
Raises:
TypeError: if `x` and `alpha` are different dtypes. | tensorflow/contrib/distributions/python/ops/gamma.py | prob | enrewen1/tf | python | def prob(self, x, name='prob'):
'Pdf of observations in `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n prob: tensor of dtype `dtype`, the PDFs of `x`\n\n Raises:\n ... |
def log_cdf(self, x, name='log_cdf'):
'Log CDF of observations `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n log_cdf: tensor of dtype `dtype`, the log-CDFs of `x`.\n ... | -2,546,260,761,848,574,000 | Log CDF of observations `x` under these Gamma distribution(s).
Args:
x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
name: The name to give this op.
Returns:
log_cdf: tensor of dtype `dtype`, the log-CDFs of `x`. | tensorflow/contrib/distributions/python/ops/gamma.py | log_cdf | enrewen1/tf | python | def log_cdf(self, x, name='log_cdf'):
'Log CDF of observations `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n log_cdf: tensor of dtype `dtype`, the log-CDFs of `x`.\n ... |
def cdf(self, x, name='cdf'):
'CDF of observations `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n cdf: tensor of dtype `dtype`, the CDFs of `x`.\n '
with ops.na... | -6,876,127,372,610,292,000 | CDF of observations `x` under these Gamma distribution(s).
Args:
x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
name: The name to give this op.
Returns:
cdf: tensor of dtype `dtype`, the CDFs of `x`. | tensorflow/contrib/distributions/python/ops/gamma.py | cdf | enrewen1/tf | python | def cdf(self, x, name='cdf'):
'CDF of observations `x` under these Gamma distribution(s).\n\n Args:\n x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.\n name: The name to give this op.\n\n Returns:\n cdf: tensor of dtype `dtype`, the CDFs of `x`.\n '
with ops.na... |
def entropy(self, name='entropy'):
'The entropy of Gamma distribution(s).\n\n This is defined to be\n\n ```\n entropy = alpha - log(beta) + log(Gamma(alpha))\n + (1-alpha)digamma(alpha)\n ```\n\n where digamma(alpha) is the digamma function.\n\n Args:\n name: The name to give ... | 9,167,662,546,117,315,000 | The entropy of Gamma distribution(s).
This is defined to be
```
entropy = alpha - log(beta) + log(Gamma(alpha))
+ (1-alpha)digamma(alpha)
```
where digamma(alpha) is the digamma function.
Args:
name: The name to give this op.
Returns:
entropy: tensor of dtype `dtype`, the entropy. | tensorflow/contrib/distributions/python/ops/gamma.py | entropy | enrewen1/tf | python | def entropy(self, name='entropy'):
'The entropy of Gamma distribution(s).\n\n This is defined to be\n\n ```\n entropy = alpha - log(beta) + log(Gamma(alpha))\n + (1-alpha)digamma(alpha)\n ```\n\n where digamma(alpha) is the digamma function.\n\n Args:\n name: The name to give ... |
def sample_n(self, n, seed=None, name='sample_n'):
'Draws `n` samples from the Gamma distribution(s).\n\n See the doc for tf.random_gamma for further detail.\n\n Args:\n n: Python integer, the number of observations to sample from each\n distribution.\n seed: Python integer, the random seed f... | 6,028,801,741,464,078,000 | Draws `n` samples from the Gamma distribution(s).
See the doc for tf.random_gamma for further detail.
Args:
n: Python integer, the number of observations to sample from each
distribution.
seed: Python integer, the random seed for this operation.
name: Optional name for the operation.
Returns:
samples: a ... | tensorflow/contrib/distributions/python/ops/gamma.py | sample_n | enrewen1/tf | python | def sample_n(self, n, seed=None, name='sample_n'):
'Draws `n` samples from the Gamma distribution(s).\n\n See the doc for tf.random_gamma for further detail.\n\n Args:\n n: Python integer, the number of observations to sample from each\n distribution.\n seed: Python integer, the random seed f... |
@abstractmethod
def has_valid_padding(self, ciphertext: bytes) -> bool:
'\n Override this method and send off the ciphertext to check for valid padding.\n\n :param bytes ciphertext: The ciphertext to check, send this to your padding oracle.\n :rtype: True for valid padding, False otherwise.\n ... | -3,526,876,157,925,465,000 | Override this method and send off the ciphertext to check for valid padding.
:param bytes ciphertext: The ciphertext to check, send this to your padding oracle.
:rtype: True for valid padding, False otherwise. | paddown.py | has_valid_padding | MarvinKweyu/PadDown | python | @abstractmethod
def has_valid_padding(self, ciphertext: bytes) -> bool:
'\n Override this method and send off the ciphertext to check for valid padding.\n\n :param bytes ciphertext: The ciphertext to check, send this to your padding oracle.\n :rtype: True for valid padding, False otherwise.\n ... |
def boosted_trees_calculate_best_gains_per_feature(node_id_range, stats_summary_list, l1, l2, tree_complexity, min_node_weight, max_splits, name=None):
'Calculates gains for each feature and returns the best possible split information for the feature.\n\n The split information is the best threshold (bucket id), ga... | -1,721,589,663,908,158,700 | Calculates gains for each feature and returns the best possible split information for the feature.
The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature.
It is possible that not all nodes can be split on each feature. Hence, the list of possible nod... | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_calculate_best_gains_per_feature | Con-Mi/lambda-packs | python | def boosted_trees_calculate_best_gains_per_feature(node_id_range, stats_summary_list, l1, l2, tree_complexity, min_node_weight, max_splits, name=None):
'Calculates gains for each feature and returns the best possible split information for the feature.\n\n The split information is the best threshold (bucket id), ga... |
def boosted_trees_calculate_best_gains_per_feature_eager_fallback(node_id_range, stats_summary_list, l1, l2, tree_complexity, min_node_weight, max_splits, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_calculate_best_gains_per_feature\n '
_ctx = (ctx ... | -7,513,891,186,051,138,000 | This is the slowpath function for Eager mode.
This is for function boosted_trees_calculate_best_gains_per_feature | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_calculate_best_gains_per_feature_eager_fallback | Con-Mi/lambda-packs | python | def boosted_trees_calculate_best_gains_per_feature_eager_fallback(node_id_range, stats_summary_list, l1, l2, tree_complexity, min_node_weight, max_splits, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_calculate_best_gains_per_feature\n '
_ctx = (ctx ... |
def boosted_trees_create_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None):
'Creates a tree ensemble model and returns a handle to it.\n\n Args:\n tree_ensemble_handle: A `Tensor` of type `resource`.\n Handle to the tree ensemble resource to be created.\n stamp_token: A `T... | 2,120,951,892,225,411,000 | Creates a tree ensemble model and returns a handle to it.
Args:
tree_ensemble_handle: A `Tensor` of type `resource`.
Handle to the tree ensemble resource to be created.
stamp_token: A `Tensor` of type `int64`.
Token to use as the initial value of the resource stamp.
tree_ensemble_serialized: A `Tensor` o... | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_create_ensemble | Con-Mi/lambda-packs | python | def boosted_trees_create_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None):
'Creates a tree ensemble model and returns a handle to it.\n\n Args:\n tree_ensemble_handle: A `Tensor` of type `resource`.\n Handle to the tree ensemble resource to be created.\n stamp_token: A `T... |
def boosted_trees_create_ensemble_eager_fallback(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_create_ensemble\n '
_ctx = (ctx if ctx else _context.context())
tree_ensemble_handle = _op... | -7,537,204,663,431,566,000 | This is the slowpath function for Eager mode.
This is for function boosted_trees_create_ensemble | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_create_ensemble_eager_fallback | Con-Mi/lambda-packs | python | def boosted_trees_create_ensemble_eager_fallback(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_create_ensemble\n '
_ctx = (ctx if ctx else _context.context())
tree_ensemble_handle = _op... |
def boosted_trees_deserialize_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None):
'Deserializes a serialized tree ensemble config and replaces current tree\n\n ensemble.\n\n Args:\n tree_ensemble_handle: A `Tensor` of type `resource`.\n Handle to the tree ensemble.\n stamp... | -8,351,597,545,228,423,000 | Deserializes a serialized tree ensemble config and replaces current tree
ensemble.
Args:
tree_ensemble_handle: A `Tensor` of type `resource`.
Handle to the tree ensemble.
stamp_token: A `Tensor` of type `int64`.
Token to use as the new value of the resource stamp.
tree_ensemble_serialized: A `Tensor` of... | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_deserialize_ensemble | Con-Mi/lambda-packs | python | def boosted_trees_deserialize_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None):
'Deserializes a serialized tree ensemble config and replaces current tree\n\n ensemble.\n\n Args:\n tree_ensemble_handle: A `Tensor` of type `resource`.\n Handle to the tree ensemble.\n stamp... |
def boosted_trees_deserialize_ensemble_eager_fallback(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_deserialize_ensemble\n '
_ctx = (ctx if ctx else _context.context())
tree_ensemble_ha... | -7,604,356,604,721,799,000 | This is the slowpath function for Eager mode.
This is for function boosted_trees_deserialize_ensemble | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_deserialize_ensemble_eager_fallback | Con-Mi/lambda-packs | python | def boosted_trees_deserialize_ensemble_eager_fallback(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_deserialize_ensemble\n '
_ctx = (ctx if ctx else _context.context())
tree_ensemble_ha... |
def boosted_trees_ensemble_resource_handle_op(container='', shared_name='', name=None):
'Creates a handle to a BoostedTreesEnsembleResource\n\n Args:\n container: An optional `string`. Defaults to `""`.\n shared_name: An optional `string`. Defaults to `""`.\n name: A name for the operation (optional).\n\n... | -1,176,348,563,963,926,300 | Creates a handle to a BoostedTreesEnsembleResource
Args:
container: An optional `string`. Defaults to `""`.
shared_name: An optional `string`. Defaults to `""`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `resource`. | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_ensemble_resource_handle_op | Con-Mi/lambda-packs | python | def boosted_trees_ensemble_resource_handle_op(container=, shared_name=, name=None):
'Creates a handle to a BoostedTreesEnsembleResource\n\n Args:\n container: An optional `string`. Defaults to ``.\n shared_name: An optional `string`. Defaults to ``.\n name: A name for the operation (optional).\n\n Return... |
def boosted_trees_ensemble_resource_handle_op_eager_fallback(container='', shared_name='', name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_ensemble_resource_handle_op\n '
_ctx = (ctx if ctx else _context.context())
if (container is None):
c... | -9,014,903,550,404,713,000 | This is the slowpath function for Eager mode.
This is for function boosted_trees_ensemble_resource_handle_op | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_ensemble_resource_handle_op_eager_fallback | Con-Mi/lambda-packs | python | def boosted_trees_ensemble_resource_handle_op_eager_fallback(container=, shared_name=, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_ensemble_resource_handle_op\n '
_ctx = (ctx if ctx else _context.context())
if (container is None):
conta... |
def boosted_trees_get_ensemble_states(tree_ensemble_handle, name=None):
'Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.\n\n Args:\n tree_ensemble_handle: A `Tensor` of type `resource`.\n Handle to the tree ensemble.\n name: A name for the operation (optional).\... | 7,128,226,942,901,964,000 | Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
Args:
tree_ensemble_handle: A `Tensor` of type `resource`.
Handle to the tree ensemble.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (stamp_token, num_trees, num_finalized_trees, num_... | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_get_ensemble_states | Con-Mi/lambda-packs | python | def boosted_trees_get_ensemble_states(tree_ensemble_handle, name=None):
'Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.\n\n Args:\n tree_ensemble_handle: A `Tensor` of type `resource`.\n Handle to the tree ensemble.\n name: A name for the operation (optional).\... |
def boosted_trees_get_ensemble_states_eager_fallback(tree_ensemble_handle, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_get_ensemble_states\n '
_ctx = (ctx if ctx else _context.context())
tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemb... | 5,859,933,895,684,794,000 | This is the slowpath function for Eager mode.
This is for function boosted_trees_get_ensemble_states | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_get_ensemble_states_eager_fallback | Con-Mi/lambda-packs | python | def boosted_trees_get_ensemble_states_eager_fallback(tree_ensemble_handle, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_get_ensemble_states\n '
_ctx = (ctx if ctx else _context.context())
tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemb... |
def boosted_trees_make_stats_summary(node_ids, gradients, hessians, bucketized_features_list, max_splits, num_buckets, name=None):
'Makes the summary of accumulated stats for the batch.\n\n The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example.\n\n A... | -8,921,132,410,623,024,000 | Makes the summary of accumulated stats for the batch.
The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example.
Args:
node_ids: A `Tensor` of type `int32`.
int32 Rank 1 Tensor containing node ids, which each example falls into for the requested layer.... | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_make_stats_summary | Con-Mi/lambda-packs | python | def boosted_trees_make_stats_summary(node_ids, gradients, hessians, bucketized_features_list, max_splits, num_buckets, name=None):
'Makes the summary of accumulated stats for the batch.\n\n The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example.\n\n A... |
def boosted_trees_make_stats_summary_eager_fallback(node_ids, gradients, hessians, bucketized_features_list, max_splits, num_buckets, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_make_stats_summary\n '
_ctx = (ctx if ctx else _context.context())
... | -6,318,355,599,477,688,000 | This is the slowpath function for Eager mode.
This is for function boosted_trees_make_stats_summary | Keras_tensorflow_nightly/source2.7/tensorflow/python/ops/gen_boosted_trees_ops.py | boosted_trees_make_stats_summary_eager_fallback | Con-Mi/lambda-packs | python | def boosted_trees_make_stats_summary_eager_fallback(node_ids, gradients, hessians, bucketized_features_list, max_splits, num_buckets, name=None, ctx=None):
'This is the slowpath function for Eager mode.\n This is for function boosted_trees_make_stats_summary\n '
_ctx = (ctx if ctx else _context.context())
... |
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