function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def one_drop(self, one_drop):
assert_is_type(one_drop, None, bool)
self._parms["one_drop"] = one_drop | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def skip_drop(self):
"""
For booster=dart only: skip_drop (0..1)
Type: ``float`` (default: ``0``).
"""
return self._parms.get("skip_drop") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def skip_drop(self, skip_drop):
assert_is_type(skip_drop, None, float)
self._parms["skip_drop"] = skip_drop | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def tree_method(self):
"""
Tree method
One of: ``"auto"``, ``"exact"``, ``"approx"``, ``"hist"`` (default: ``"auto"``).
"""
return self._parms.get("tree_method") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def tree_method(self, tree_method):
assert_is_type(tree_method, None, Enum("auto", "exact", "approx", "hist"))
self._parms["tree_method"] = tree_method | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def grow_policy(self):
"""
Grow policy - depthwise is standard GBM, lossguide is LightGBM
One of: ``"depthwise"``, ``"lossguide"`` (default: ``"depthwise"``).
"""
return self._parms.get("grow_policy") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def grow_policy(self, grow_policy):
assert_is_type(grow_policy, None, Enum("depthwise", "lossguide"))
self._parms["grow_policy"] = grow_policy | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def booster(self):
"""
Booster type
One of: ``"gbtree"``, ``"gblinear"``, ``"dart"`` (default: ``"gbtree"``).
"""
return self._parms.get("booster") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def booster(self, booster):
assert_is_type(booster, None, Enum("gbtree", "gblinear", "dart"))
self._parms["booster"] = booster | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def reg_lambda(self):
"""
L2 regularization
Type: ``float`` (default: ``1``).
"""
return self._parms.get("reg_lambda") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def reg_lambda(self, reg_lambda):
assert_is_type(reg_lambda, None, float)
self._parms["reg_lambda"] = reg_lambda | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def reg_alpha(self):
"""
L1 regularization
Type: ``float`` (default: ``0``).
"""
return self._parms.get("reg_alpha") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def reg_alpha(self, reg_alpha):
assert_is_type(reg_alpha, None, float)
self._parms["reg_alpha"] = reg_alpha | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def dmatrix_type(self):
"""
Type of DMatrix. For sparse, NAs and 0 are treated equally.
One of: ``"auto"``, ``"dense"``, ``"sparse"`` (default: ``"auto"``).
"""
return self._parms.get("dmatrix_type") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def dmatrix_type(self, dmatrix_type):
assert_is_type(dmatrix_type, None, Enum("auto", "dense", "sparse"))
self._parms["dmatrix_type"] = dmatrix_type | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def backend(self):
"""
Backend. By default (auto), a GPU is used if available.
One of: ``"auto"``, ``"gpu"``, ``"cpu"`` (default: ``"auto"``).
"""
return self._parms.get("backend") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def backend(self, backend):
assert_is_type(backend, None, Enum("auto", "gpu", "cpu"))
self._parms["backend"] = backend | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def gpu_id(self):
"""
Which GPU to use.
Type: ``int`` (default: ``0``).
"""
return self._parms.get("gpu_id") | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def gpu_id(self, gpu_id):
assert_is_type(gpu_id, None, int)
self._parms["gpu_id"] = gpu_id | h2oai/h2o-dev | [
6169,
1943,
6169,
208,
1393862887
] |
def __init__(
self,
type: OperationType,
create_account_op: CreateAccountOp = None,
payment_op: PaymentOp = None,
path_payment_strict_receive_op: PathPaymentStrictReceiveOp = None,
manage_sell_offer_op: ManageSellOfferOp = None,
create_passive_sell_offer_op: Creat... | StellarCN/py-stellar-base | [
328,
158,
328,
6,
1443187561
] |
def pack(self, packer: Packer) -> None:
self.type.pack(packer)
if self.type == OperationType.CREATE_ACCOUNT:
if self.create_account_op is None:
raise ValueError("create_account_op should not be None.")
self.create_account_op.pack(packer)
return
... | StellarCN/py-stellar-base | [
328,
158,
328,
6,
1443187561
] |
def unpack(cls, unpacker: Unpacker) -> "OperationBody":
type = OperationType.unpack(unpacker)
if type == OperationType.CREATE_ACCOUNT:
create_account_op = CreateAccountOp.unpack(unpacker)
return cls(type=type, create_account_op=create_account_op)
if type == OperationType.... | StellarCN/py-stellar-base | [
328,
158,
328,
6,
1443187561
] |
def from_xdr_bytes(cls, xdr: bytes) -> "OperationBody":
unpacker = Unpacker(xdr)
return cls.unpack(unpacker) | StellarCN/py-stellar-base | [
328,
158,
328,
6,
1443187561
] |
def from_xdr(cls, xdr: str) -> "OperationBody":
xdr_bytes = base64.b64decode(xdr.encode())
return cls.from_xdr_bytes(xdr_bytes) | StellarCN/py-stellar-base | [
328,
158,
328,
6,
1443187561
] |
def expression(symbolAction, nextState):
#expression ::= | pathitem pathtail
#pathitem ::= | "(" pathlist ")"
# | "[" propertylist "]"
# | "{" formulacontent "}"
# | boolean
# | literal
# | numericliteral
# ... | gniezen/n3pygments | [
22,
6,
22,
3,
1327326868
] |
def series_rolling_median():
series = pd.Series([4, 3, 5, 2, 6]) # Series of 4, 3, 5, 2, 6
out_series = series.rolling(3).median()
return out_series # Expect series of NaN, NaN, 4.0, 3.0, 5.0 | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def Xval_on_single_patient(predictor_cls, feature_extractor, patient_name="Dog_1",preprocess=True):
"""
Single patient cross validation
Returns 2 lists of cross validation performances
:param predictor_cls:
:param feature_extractor
:param patient_name:
:return:
"""
# predictor_cls is... | vincentadam87/gatsby-hackathon-seizure | [
3,
1,
3,
5,
1403950691
] |
def main():
# code run at script launch
#patient_name = sys.argv[1]
# There are Dog_[1-4] and Patient_[1-8]
patients_list = ["Dog_%d" % i for i in range(1, 5)] + ["Patient_%d" % i for i in range(1, 9)]
patients_list = ["Dog_%d" % i for i in [1]] #["Patient_%d" % i for i in range(1, 9)]#++ | vincentadam87/gatsby-hackathon-seizure | [
3,
1,
3,
5,
1403950691
] |
def generate(env):
"""Add Builders and construction variables for ar to an Environment."""
SCons.Tool.createStaticLibBuilder(env) | kerwinxu/barcodeManager | [
4,
1,
4,
3,
1447294107
] |
def exists(env):
return env.Detect('CC') or env.Detect('ar') | kerwinxu/barcodeManager | [
4,
1,
4,
3,
1447294107
] |
def test_install_packages():
d = dun.CreateDummy()
d()
package.install_package('./dummy/dummytest_1.0.0.tar.gz', verbose=True)
d._clean() | biokit/biokit | [
47,
22,
47,
13,
1410258271
] |
def test_get_r_version():
package.get_R_version() | biokit/biokit | [
47,
22,
47,
13,
1410258271
] |
def forwards(self, orm):
# Changing field 'Student.student_id'
db.alter_column('publications_student', 'student_id', self.gf('django.db.models.fields.CharField')(unique=True, max_length=12)) | evildmp/arkestra-publications | [
2,
3,
2,
6,
1320326961
] |
def forwards(self, orm):
# Adding field 'Face.district_id'
db.add_column(u'faces_face', 'district_id',
self.gf('django.db.models.fields.related.ForeignKey')(to=orm['faces.District'], null=True),
keep_default=False) | RuralIndia/pari | [
22,
9,
22,
39,
1360646573
] |
def __init__(self, input=None, n_visible=784, n_hidden=500, \
W=None, hbias=None, vbias=None, numpy_rng=None,
theano_rng=None):
"""
RBM constructor. Defines the parameters of the model along with
basic operations for inferring hidden from visible (and vice-versa),
as well... | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def propup(self, vis):
'''This function propagates the visible units activation upwards to
the hidden units
Note that we return also the pre-sigmoid activation of the
layer. As it will turn out later, due to how Theano deals with
optimizations, this symbolic variable will be nee... | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def propdown(self, hid):
'''This function propagates the hidden units activation downwards to
the visible units
Note that we return also the pre_sigmoid_activation of the
layer. As it will turn out later, due to how Theano deals with
optimizations, this symbolic variable will be... | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def gibbs_hvh(self, h0_sample):
''' This function implements one step of Gibbs sampling,
starting from the hidden state'''
pre_sigmoid_v1, v1_mean, v1_sample = self.sample_v_given_h(h0_sample)
pre_sigmoid_h1, h1_mean, h1_sample = self.sample_h_given_v(v1_sample)
return [pre_s... | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def get_cost_updates(self, lr=0.1, persistent=None, k=1):
"""This functions implements one step of CD-k or PCD-k
:param lr: learning rate used to train the RBM
:param persistent: None for CD. For PCD, shared variable
containing old state of Gibbs chain. This must be a shared
... | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def get_reconstruction_cost(self, updates, pre_sigmoid_nv):
"""Approximation to the reconstruction error
Note that this function requires the pre-sigmoid activation as
input. To understand why this is so you need to understand a
bit about how Theano works. Whenever you compile a Theano... | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def test_model(model_trained,test_set_x_org=None):
"""
Get the reduced data using the model learned. | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def sample_model(rng,model_trained,test_set_x_org=None,n_chains=20,n_samples=10,sample_gap=1000):
"""
Sample from the trained RBM given some actual examples to initialize the algorithm. | yifeng-li/DECRES | [
33,
13,
33,
5,
1434585720
] |
def is_valid(self, raise_exception=True):
result = super().is_valid(raise_exception=raise_exception)
if result:
if not self.phone_number or PHONE_NUMBER_REGEXP and not re.match(PHONE_NUMBER_REGEXP, self.phone_number):
if not raise_exception:
return False
... | thorgate/django-esteid | [
19,
2,
19,
3,
1445964219
] |
def format(self, value):
self._format = value
return self | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def input(self, text):
self._input = self._params["input"] = text
return self | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def categories(self, tree_key=None, additionals=None):
params = dict(additionals or [])
if tree_key is not None:
params.update(dict(tree_key=tree_key)) | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def keywords(self):
self._functions["keywords"] = True
return self | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def entities(self):
self._functions["entities"] = True
return self | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def language(self):
self._functions["language"] = True
return self | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def meaningfy(self):
fs = []
for k,v in self._functions.items():
kk = k
if isinstance(v, dict):
if v.has_key("additionals"):
for a in v["additionals"]:
kk = "%s+%s" % (kk, a)
... | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def _reset(self):
self._functions = {}
self._params = {} | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def get_builder(self):
if self.__builder__ is None:
self.__builder__ = NeocortexRestClient.Builder(self.BASE_URL, self.api_key).format(ResponseFormats.JSON) | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def categories(self, input, tree_key=None, additionals=None):
builder = self.get_builder()
return builder.format(ResponseFormats.JSON).input(input).categories(tree_key, additionals).meaningfy().payload["categories"] | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def entities(self, input):
builder = self.get_builder()
return builder.format(ResponseFormats.JSON).input(input).entities().meaningfy().payload["entities"] | popego/neocortex-api-python | [
8,
2,
8,
1,
1262800862
] |
def setUp(self):
super(BcryptTests, self).setUp()
User.objects.create_user('john', 'johndoe@example.com',
password='123456')
User.objects.create_user('jane', 'janedoe@example.com',
password='abc')
User.objects.create_user(... | fwenzel/django-sha2 | [
109,
18,
109,
3,
1293582874
] |
def test_bcrypt_auth(self):
"""Try authenticating."""
assert authenticate(username='john', password='123456')
assert authenticate(username='jane', password='abc')
assert not authenticate(username='jane', password='123456')
assert authenticate(username='jude', password=u'abcéäêëôø... | fwenzel/django-sha2 | [
109,
18,
109,
3,
1293582874
] |
def test_nokey(self):
"""With no HMAC key, no dice."""
assert not authenticate(username='john', password='123456')
assert not authenticate(username='jane', password='abc')
assert not authenticate(username='jane', password='123456')
assert not authenticate(username='jude', passwor... | fwenzel/django-sha2 | [
109,
18,
109,
3,
1293582874
] |
def test_hmac_autoupdate(self):
"""Auto-update HMAC key if hash in DB is outdated."""
# Get HMAC key IDs to compare
old_key_id = max(settings.HMAC_KEYS.keys())
new_key_id = '2020-01-01'
# Add a new HMAC key
new_keys = settings.HMAC_KEYS.copy()
new_keys[new_key_id... | fwenzel/django-sha2 | [
109,
18,
109,
3,
1293582874
] |
def extractNotoriousOnlineBlogspotCom(item):
'''
Parser for 'notorious-online.blogspot.com'
'''
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol) or "preview" in item['title'].lower():
return None
tagmap = [
('PRC', 'PRC', 'translated'),... | fake-name/ReadableWebProxy | [
191,
16,
191,
3,
1437712243
] |
def __init__(self, config, name):
"""
:type name: str
"""
self.config = config
self.name = name | ThomasGerstenberg/serial_monitor | [
9,
5,
9,
4,
1440644224
] |
def open(self):
raise NotImplementedError | ThomasGerstenberg/serial_monitor | [
9,
5,
9,
4,
1440644224
] |
def close(self):
raise NotImplementedError | ThomasGerstenberg/serial_monitor | [
9,
5,
9,
4,
1440644224
] |
def read(self, num_bytes=1):
raise NotImplementedError | ThomasGerstenberg/serial_monitor | [
9,
5,
9,
4,
1440644224
] |
def write(self, data):
raise NotImplementedError | ThomasGerstenberg/serial_monitor | [
9,
5,
9,
4,
1440644224
] |
def command_oraakkeli(bot, user, channel, args):
"""Asks a question from the oracle (http://www.lintukoto.net/viihde/oraakkeli/)"""
if not args: return
args = urllib.quote_plus(args)
answer = getUrl("http://www.lintukoto.net/viihde/oraakkeli/index.php?kysymys=%s&html=0" % args).getContent()
answe... | nigeljonez/newpyfibot | [
8,
3,
8,
1,
1285336020
] |
def __unicode__(self):
return unicode(self.user) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def get_absolute_url(self):
return reverse('desktop.user', args=[self.user.username]) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def generic_sharing_url(self):
url = urlparams(django_reverse('desktop.user', args=[self.user.username]))
return absolute_url(url) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def _social_sharing_url(self, service):
# django_reverse used instead of reverse because we don't want a locale preprended to sharing links.
url = urlparams(django_reverse('desktop.user', args=[self.user.username]),
f=service)
retu... | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def twitter_sharing_url(self):
return self._social_sharing_url('t') | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def facebook_sharing_url(self):
return self._social_sharing_url('fb') | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def poster_sharing_url(self):
return self._social_sharing_url('p') | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def badges(self):
"""Returns a list of dicts used for badge list rendering.
They represent all badges earned by the user in the Spark game.
"""
badges = []
completed_challenges = CompletedChallenge.objects.filter(profile=self,
... | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def has_badge(self, badge_id):
"""Returns whether this user has earned the given badge."""
if badge_id:
return CompletedChallenge.objects.filter(profile=self, challenge__pk=badge_id,
date_badge_earned__isnull=False).count() == 1
else:
... | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def total_badges_earned(self):
"""Returns the total number of badges earned by the user.
Doesn't include hidden unlocked badges from an upper level.
"""
return CompletedChallenge.objects.filter(profile=self, date_badge_earned__isnull=False).count() | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def spark_started_with(self):
if self.parent_username is not None:
return self.parent_username
return '' | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def most_recent_share(self):
"""Most recent share stat displayed on desktop dashboard/user pages."""
from stats.models import SharingHistory | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def shares_over_time(self):
"""Aggregate data of Spark shares since the start of the campaign.
Used by the 'shares over time' diagram in the user dashboard.
"""
from stats.models import SharingHistory
return SharingHistory.get_shares_over_time(self) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def sparked_countries(self):
"""List of countries this user has shared their Spark with."""
from .utils import user_node
countries = set()
node = user_node(self.user)
for child in node.get_children():
cc = child.user.profile.country_code
if cc:
... | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def total_shares(self):
"""Total shares stat displayed on desktop dashboard/user pages."""
from stats.models import SharingHistory | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def challenge_info(self):
"""Returns a list of dicts containing level/challenge completion information.
Used to render both desktop and mobile collapsing challenge lists.
"""
return utils.get_profile_levels(self) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def new_challenge_count(self):
"""Returns the number of newly available challenges in the user's current level."""
if self.new_challenges:
challenge_count = utils.CHALLENGE_COUNT_PER_LVL[self.level-1]
completed_challenge_count = len(CompletedChallenge.objects.filter(profile=self,... | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def new_badge_count(self):
"""Returns the number of recently earned badges."""
return len([b for b in self.badges if b['new']]) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def qr_code_download(self):
"""Returns the URL of a QR code which, when scanned, points to: https://[domain]/download?f=qr&user=[username]
"""
url = absolute_url(urlparams(django_reverse('sharing.download'), user=self.user.username))
return sharing_utils.url2qr(url) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def continent_code(self):
from geo.continents import countries_continents
code = ''
if self.country_code:
code = countries_continents[self.country_code] | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def total_countries_sparked(self):
"""Returns the total number of countries where the user's children are located."""
return len(self.sparked_countries) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def total_continents_sparked(self):
"""Returns the total number of continents where the user's children are located."""
from geo.continents import countries_continents
from .utils import user_node | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def children_profiles(self):
"""Returns a list of profiles of the user's children in the user tree."""
from .utils import user_node | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def clear_new_badges(self):
"""Clears notifications of recently earned badges."""
CompletedChallenge.objects.filter(profile=self, new_badge=True).update(new_badge=False) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def clear_new_challenges(self):
"""Clears notifications of new available challenges."""
self.new_challenges = False
self.save() | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def trigger_multisparker_badge(self):
from challenges.tasks import update_completed_challenges | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def update_ancestors_longest_chain(self):
"""Updates 'longest chain' stat of all ancestors of this user when relevant.
Used after Boost step 2 confirmation so that all users involved have their longest chain stat updated.
"""
from .utils import user_node
ancestors = user_node... | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def add_city_shares_for_children(self):
"""Creates city shares in the CitySharingHistory for the global visualization.
This is useful when a user already has children when he completes boost 1 (geolocation).
As soon as it's completed, city shares are created for all geolocated children.
... | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def __unicode__(self):
return "%s <-> %s" % (self.profile, self.challenge) | mozilla/spark | [
5,
6,
5,
4,
1297724745
] |
def binarize_vector(u):
return u > 0 | mikekestemont/PyStyl | [
55,
13,
55,
6,
1409592784
] |
def cosine_distance_binary(u, v):
u = binarize_vector(u)
v = binarize_vector(v)
return (1.0 * (u * v).sum()) / numpy.sqrt((u.sum() * v.sum())) | mikekestemont/PyStyl | [
55,
13,
55,
6,
1409592784
] |
def cityblock_distance(u, v):
"""Return the Manhattan/City Block distance between two vectors."""
return abs(u-v).sum() | mikekestemont/PyStyl | [
55,
13,
55,
6,
1409592784
] |
def correlation(u, v):
"""Return the correlation distance between two vectors."""
u_var = u - u.mean()
v_var = v - v.mean()
return 1.0 - dot(u_var, v_var) / (sqrt(dot(u_var, u_var)) *
sqrt(dot(v_var, v_var))) | mikekestemont/PyStyl | [
55,
13,
55,
6,
1409592784
] |
def jaccard_distance(u, v):
"""return jaccard distance"""
u = numpy.asarray(u)
v = numpy.asarray(v)
return (numpy.double(numpy.bitwise_and((u != v),
numpy.bitwise_or(u != 0, v != 0)).sum())
/ numpy.double(numpy.bitwise_or(u != 0, v != 0).sum())) | mikekestemont/PyStyl | [
55,
13,
55,
6,
1409592784
] |
def setUp(self):
mock_client = mock.MagicMock()
mock_client.user.return_value = 'mocked user'
self.request = Request('http://a.b/path')
self.request.grant_type = 'password'
self.request.username = 'john'
self.request.password = 'doe'
self.request.client = mock_cli... | idan/oauthlib | [
2555,
477,
2555,
82,
1321744131
] |
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