uid stringlengths 24 24 | split stringclasses 1
value | category stringclasses 2
values | content stringlengths 5 482k | signature stringlengths 1 14k | suffix stringlengths 1 482k | prefix stringlengths 9 14k | prefix_token_count int64 3 5.01k | prefix_token_budget int64 64 256 | element_token_count int64 1 292k | signature_token_count int64 1 5.01k | prefix_context_token_count int64 0 255 | repo stringlengths 7 112 | path stringlengths 4 208 | language stringclasses 1
value | name stringlengths 1 218 | qualname stringlengths 1 218 | start_line int64 1 26.7k | end_line int64 1 26.7k | signature_start_line int64 1 26.7k | signature_end_line int64 1 26.7k | source_hash stringlengths 40 40 | source_dataset stringclasses 1
value | source_split stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5502acc3ced49c361d35a7b2 | train | class | class TransformerBuild(Build):
params:dict
model:Model
eval_model:Model
train_model:Model
epoch:Optional[int]
filewriter:SummaryWriter
tbcallback:TensorBoard
subtokenizer:Subtokenizer
| class TransformerBuild(Build):
| params:dict
model:Model
eval_model:Model
train_model:Model
epoch:Optional[int]
filewriter:SummaryWriter
tbcallback:TensorBoard
subtokenizer:Subtokenizer
| .data import eval_ds, predict_ds
from stagedml.types import ( WmtSubtok, TransWmt, Dict, Optional,Any,List,Tuple,Union )
from stagedml.stages.fetchwmt import create_subtokenizer
from official.utils.flags._performance import DTYPE_MAP
class TransformerBuild(Build):
| 64 | 64 | 56 | 5 | 58 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | TransformerBuild | TransformerBuild | 32 | 40 | 32 | 32 | 58f870c6c9db239d982cadda0985de2626b7097f | bigcode/the-stack | train |
e29b9c3769ddd0d267fa9edb | train | function | def build(b:TransformerBuild, instance_idx:int)->None:
c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o)
clear_session()
compute_missing_params(b, instance_idx)
set_session_config(enable_xla=c.enable_xla)
b.train_model = create_train_model(c.params)
b.train_model.compile(create_... | def build(b:TransformerBuild, instance_idx:int)->None:
| c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o)
clear_session()
compute_missing_params(b, instance_idx)
set_session_config(enable_xla=c.enable_xla)
b.train_model = create_train_model(c.params)
b.train_model.compile(create_optimizer(c.params))
# b.train_model.summary()
b.e... | .vocab_refpath.syspath)
assert vocab_contents is not None
c.params["vocab_size"] = len(vocab_contents.split('\n'))
# print(f'Setting vocab_size to {c.params["vocab_size"]}')
def build(b:TransformerBuild, instance_idx:int)->None:
| 63 | 64 | 176 | 13 | 50 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | build | build | 58 | 76 | 58 | 58 | 889353c44a882bd7bfc8ed7d7eeb36e01fc47a75 | bigcode/the-stack | train |
1198b5d20a06b9e8b03cada7 | train | function | def transformer_wmt(m:Manager, wmt:WmtSubtok, num_instances:int=1)->TransWmt:
train_steps = 300000
def _realize(b:TransformerBuild)->None:
build_setoutpaths(b,num_instances)
for instance_idx in range(num_instances):
# print(f'Building Transformer instance {instance_idx+1} of {num_instances}')
b... | def transformer_wmt(m:Manager, wmt:WmtSubtok, num_instances:int=1)->TransWmt:
| train_steps = 300000
def _realize(b:TransformerBuild)->None:
build_setoutpaths(b,num_instances)
for instance_idx in range(num_instances):
# print(f'Building Transformer instance {instance_idx+1} of {num_instances}')
build(b,instance_idx)
train(b,instance_idx)
def _config():
name = ... | b.epoch < nepoches:
history = b.train_model.fit(
train_ds(c.params),
initial_epoch=b.epoch,
epochs=b.epoch+1,
steps_per_epoch=c.steps_between_evals,
callbacks=callbacks,
verbose=True)
ckpt = me.checkpoint_refpath.syspath
print(f"Saving '{ckpt}'")
b.train_model.save_... | 136 | 136 | 456 | 26 | 109 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | transformer_wmt | transformer_wmt | 180 | 226 | 180 | 180 | 1def1654b730f0c103f0326bd611aca12fee9c64 | bigcode/the-stack | train |
2e5dde9fc9199409ba6f776c | train | function | def evaluate(b:TransformerBuild, instance_idx:int)->None:
assert b.train_model is not None
c = build_cattrs(b)
me = mklens(b,o=build_outpaths(b)[instance_idx])
input_txt:Path=me.eval_input_refpath.syspath
target_src_txt:Path=me.eval_target_refpath.syspath
# print('Evaluating')
epoch=b.epoch if b.epoch is ... | def evaluate(b:TransformerBuild, instance_idx:int)->None:
| assert b.train_model is not None
c = build_cattrs(b)
me = mklens(b,o=build_outpaths(b)[instance_idx])
input_txt:Path=me.eval_input_refpath.syspath
target_src_txt:Path=me.eval_target_refpath.syspath
# print('Evaluating')
epoch=b.epoch if b.epoch is not None else 0
ds = eval_ds(b.subtokenizer,
... | klens(b,o=build_outpaths(b)[instance_idx])
ckpt0 = me.checkpoint_init.val
assert ckpt0 is not None
b.train_model.load_weights(ckpt0)
b.epoch = None
def evaluate(b:TransformerBuild, instance_idx:int)->None:
| 64 | 64 | 198 | 13 | 50 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | evaluate | evaluate | 87 | 104 | 87 | 87 | 41e0c06d5c9034a43f2edf19c425a3fe1754bca4 | bigcode/the-stack | train |
f1099449138f0feaaa4c8a30 | train | function | def predict(b:TransformerBuild, instance_idx:int)->None:
assert b.eval_model is not None
c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o=o)
input_txt:Path=me.eval_input_refpath.syspath
target_src_txt:Path=me.eval_target_refpath.syspath
target_txt=join(o,'targets.txt')
epoch=b.epo... | def predict(b:TransformerBuild, instance_idx:int)->None:
| assert b.eval_model is not None
c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o=o)
input_txt:Path=me.eval_input_refpath.syspath
target_src_txt:Path=me.eval_target_refpath.syspath
target_txt=join(o,'targets.txt')
epoch=b.epoch if b.epoch is not None else 0
output_txt:Path=Path(j... | spath
# print('Evaluating')
epoch=b.epoch if b.epoch is not None else 0
ds = eval_ds(b.subtokenizer,
input_txt,
target_src_txt,
batch_size=me.eval_batch_size.val,
params=me.params.val,
take=me.eval_steps.val)
h=b.train_model.evaluate(ds,... | 137 | 137 | 459 | 13 | 124 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | predict | predict | 107 | 145 | 107 | 107 | 6d4bd9d22ba819a78a4e6f5a3ee475d80cf7e94d | bigcode/the-stack | train |
ac9e837a227c0f3bf680f4ad | train | function | def compute_missing_params(b, instance_idx:int):
c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o)
c.params["model_dir"] = o
c.params["dtype"] = DTYPE_MAP[c.dtype]
c.params["data_dir"] = me.data_dir.syspath
vocab_contents = tryread(me.vocab_refpath.syspath)
assert vocab_contents i... | def compute_missing_params(b, instance_idx:int):
| c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o)
c.params["model_dir"] = o
c.params["dtype"] = DTYPE_MAP[c.dtype]
c.params["data_dir"] = me.data_dir.syspath
vocab_contents = tryread(me.vocab_refpath.syspath)
assert vocab_contents is not None
c.params["vocab_size"] = len(vocab_c... | filewriter:SummaryWriter
tbcallback:TensorBoard
subtokenizer:Subtokenizer
# def cont(b:TransformerBuild)->None:
# copy_tree(rref2path(build_cattrs(b).continue_from),build_outpath(b))
def compute_missing_params(b, instance_idx:int):
| 64 | 64 | 114 | 10 | 54 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | compute_missing_params | compute_missing_params | 45 | 54 | 45 | 45 | 93b557e7c28daddbfad67dbad6bebb67d0ffb97f | bigcode/the-stack | train |
16025ea6ec9d8a3263a93775 | train | function | def loadcp(b:TransformerBuild, instance_idx):
me = mklens(b,o=build_outpaths(b)[instance_idx])
ckpt0 = me.checkpoint_init.val
assert ckpt0 is not None
b.train_model.load_weights(ckpt0)
b.epoch = None
| def loadcp(b:TransformerBuild, instance_idx):
| me = mklens(b,o=build_outpaths(b)[instance_idx])
ckpt0 = me.checkpoint_init.val
assert ckpt0 is not None
b.train_model.load_weights(ckpt0)
b.epoch = None
| = create_file_writer(join(o,'eval'))
b.tbcallback = TensorBoard(log_dir=o, profile_batch=0, write_graph=False)
b.subtokenizer = create_subtokenizer(WmtSubtok(me.wmt.dref), build_context(b))
def loadcp(b:TransformerBuild, instance_idx):
| 64 | 64 | 66 | 11 | 53 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | loadcp | loadcp | 79 | 84 | 79 | 79 | c30a4bf55d80ea797b39ff80da90366eaee03b45 | bigcode/the-stack | train |
08c256fb52d35932295789cf | train | function | def train(b:TransformerBuild, instance_idx:int=0):
assert b.train_model is not None
c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o)
callbacks = [
b.tbcallback,
LearningRateScheduler(
LearningRateFn(c.params["learning_rate"],
c.params["hidden_size... | def train(b:TransformerBuild, instance_idx:int=0):
| assert b.train_model is not None
c = build_cattrs(b)
o = build_outpaths(b)[instance_idx]
me = mklens(b,o)
callbacks = [
b.tbcallback,
LearningRateScheduler(
LearningRateFn(c.params["learning_rate"],
c.params["hidden_size"],
c.params["learning_rate_... | step=epoch)
tf.summary.scalar('bleu_uncased',bleu_uncased,step=epoch)
with open(me.bleu_refpath.syspath,'w') as f:
f.write(f"{(bleu_cased + bleu_uncased)/2.0}\n")
def train(b:TransformerBuild, instance_idx:int=0):
| 74 | 74 | 249 | 13 | 61 | stagedml/stagedml | src/stagedml/stages/transformer_wmt.py | Python | train | train | 148 | 177 | 148 | 148 | 32ce7a807f5ae11f191f291fe102582285678cdf | bigcode/the-stack | train |
67445fa991aee1fa6045467c | train | class | class ID3NoHeaderError(error, ValueError):
pass
| class ID3NoHeaderError(error, ValueError):
| pass
| Christoph Reiter
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of version 2 of the GNU General Public License as
# published by the Free Software Foundation.
class error(Exception):
pass
class ID3NoHeaderError(error, ValueError):
| 64 | 64 | 14 | 11 | 52 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3NoHeaderError | ID3NoHeaderError | 13 | 14 | 13 | 13 | ced21196c4f63f1ff59a2976068c6a0e72b80327 | bigcode/the-stack | train |
5a5a69a2561725ac278b5512 | train | class | class ID3BadCompressedData(error, ValueError):
pass
| class ID3BadCompressedData(error, ValueError):
| pass
| 2 of the GNU General Public License as
# published by the Free Software Foundation.
class error(Exception):
pass
class ID3NoHeaderError(error, ValueError):
pass
class ID3BadUnsynchData(error, ValueError):
pass
class ID3BadCompressedData(error, ValueError):
| 64 | 64 | 14 | 11 | 52 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3BadCompressedData | ID3BadCompressedData | 21 | 22 | 21 | 21 | 17c4c589e07cf02e869fc8b9d72f00773664c74c | bigcode/the-stack | train |
86e95c896b1ec673a23277df | train | class | class BitPaddedInt(int, _BitPaddedMixin):
def __new__(cls, value, bits=7, bigendian=True):
mask = (1 << (bits)) - 1
numeric_value = 0
shift = 0
if isinstance(value, (int, long)):
while value:
numeric_value += (value & mask) << shift
valu... | class BitPaddedInt(int, _BitPaddedMixin):
| def __new__(cls, value, bits=7, bigendian=True):
mask = (1 << (bits)) - 1
numeric_value = 0
shift = 0
if isinstance(value, (int, long)):
while value:
numeric_value += (value & mask) << shift
value >>= 8
shift += bits
... | value & mask:
return False
value >>= 8
elif isinstance(value, str):
for byte in value:
if ord(byte) & mask:
return False
else:
raise TypeError
return True
class BitPaddedInt(int, _BitPaddedMixin):
| 64 | 64 | 208 | 13 | 50 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | BitPaddedInt | BitPaddedInt | 143 | 172 | 143 | 144 | a539c5782da96562caf7a089c73d38c17cc15de9 | bigcode/the-stack | train |
cd656615320d70b33c66005f | train | class | class ID3EncryptionUnsupportedError(error, NotImplementedError):
pass
| class ID3EncryptionUnsupportedError(error, NotImplementedError):
| pass
| ynchData(error, ValueError):
pass
class ID3BadCompressedData(error, ValueError):
pass
class ID3TagError(error, ValueError):
pass
class ID3UnsupportedVersionError(error, NotImplementedError):
pass
class ID3EncryptionUnsupportedError(error, NotImplementedError):
| 64 | 64 | 15 | 12 | 51 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3EncryptionUnsupportedError | ID3EncryptionUnsupportedError | 33 | 34 | 33 | 33 | f7d9e5f84b8f97a0101a856b95c3abe24e194356 | bigcode/the-stack | train |
9896e27b0b7fddbcce3108ba | train | class | class ID3TagError(error, ValueError):
pass
| class ID3TagError(error, ValueError):
| pass
| Free Software Foundation.
class error(Exception):
pass
class ID3NoHeaderError(error, ValueError):
pass
class ID3BadUnsynchData(error, ValueError):
pass
class ID3BadCompressedData(error, ValueError):
pass
class ID3TagError(error, ValueError):
| 64 | 64 | 13 | 10 | 53 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3TagError | ID3TagError | 25 | 26 | 25 | 25 | 9cab8b9cd13e0e4567923a9dda0b6a9c31279913 | bigcode/the-stack | train |
a1d09558dc80c7b506353782 | train | class | class ID3JunkFrameError(error, ValueError):
pass
| class ID3JunkFrameError(error, ValueError):
| pass
| Data(error, ValueError):
pass
class ID3TagError(error, ValueError):
pass
class ID3UnsupportedVersionError(error, NotImplementedError):
pass
class ID3EncryptionUnsupportedError(error, NotImplementedError):
pass
class ID3JunkFrameError(error, ValueError):
| 64 | 64 | 15 | 12 | 51 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3JunkFrameError | ID3JunkFrameError | 37 | 38 | 37 | 37 | 1db1a6303b5757354238e33634198c47ff8e1a32 | bigcode/the-stack | train |
8a8e789b698795a5758687ed | train | class | class error(Exception):
pass
| class error(Exception):
| pass
| ) 2005 Michael Urman
# 2013 Christoph Reiter
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of version 2 of the GNU General Public License as
# published by the Free Software Foundation.
class error(Exception):
| 64 | 64 | 7 | 4 | 60 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | error | error | 9 | 10 | 9 | 9 | 1d7ff5edbc7d9e7d9e9055f6cab0637fb51786c0 | bigcode/the-stack | train |
4640a8ca6c09ee759b781ca2 | train | class | class unsynch(object):
@staticmethod
def decode(value):
output = []
safe = True
append = output.append
for val in value:
if safe:
append(val)
safe = val != '\xFF'
else:
if val >= '\xE0':
r... | class unsynch(object):
@staticmethod
| def decode(value):
output = []
safe = True
append = output.append
for val in value:
if safe:
append(val)
safe = val != '\xFF'
else:
if val >= '\xE0':
raise ValueError('invalid sync-safe string... |
class ID3UnsupportedVersionError(error, NotImplementedError):
pass
class ID3EncryptionUnsupportedError(error, NotImplementedError):
pass
class ID3JunkFrameError(error, ValueError):
pass
class ID3Warning(error, UserWarning):
pass
class unsynch(object):
@staticmethod
| 67 | 67 | 226 | 9 | 57 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | unsynch | unsynch | 45 | 84 | 45 | 46 | 74e62a8b7dcd02d1e9b8622cd571cea92adfe86b | bigcode/the-stack | train |
1e44e6787e9e77ddd45b5283 | train | class | class ID3Warning(error, UserWarning):
pass
| class ID3Warning(error, UserWarning):
| pass
| TagError(error, ValueError):
pass
class ID3UnsupportedVersionError(error, NotImplementedError):
pass
class ID3EncryptionUnsupportedError(error, NotImplementedError):
pass
class ID3JunkFrameError(error, ValueError):
pass
class ID3Warning(error, UserWarning):
| 64 | 64 | 12 | 9 | 54 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3Warning | ID3Warning | 41 | 42 | 41 | 41 | 682c76ddc229c288fdf8c1fbbd2504e1ae744509 | bigcode/the-stack | train |
314bf9fb6583638d4baa94d2 | train | class | class _BitPaddedMixin(object):
def as_str(self, width=4, minwidth=4):
return self.to_str(self, self.bits, self.bigendian, width, minwidth)
@staticmethod
def to_str(value, bits=7, bigendian=True, width=4, minwidth=4):
mask = (1 << bits) - 1
if width != -1:
index = 0
... | class _BitPaddedMixin(object):
| def as_str(self, width=4, minwidth=4):
return self.to_str(self, self.bits, self.bigendian, width, minwidth)
@staticmethod
def to_str(value, bits=7, bigendian=True, width=4, minwidth=4):
mask = (1 << bits) - 1
if width != -1:
index = 0
bytes_ = bytearray(widt... | = True
append = output.append
for val in value:
if safe:
append(val)
if val == '\xFF':
safe = False
elif val == '\x00' or val >= '\xE0':
append('\x00')
append(val)
safe = val != '... | 108 | 108 | 360 | 8 | 100 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | _BitPaddedMixin | _BitPaddedMixin | 87 | 140 | 87 | 88 | f664b89b20d4675b7497cd45316880208b1ffa2b | bigcode/the-stack | train |
3c91c0e059d19e7dfcfef7b8 | train | class | class ID3BadUnsynchData(error, ValueError):
pass
| class ID3BadUnsynchData(error, ValueError):
| pass
| redistribute it and/or modify
# it under the terms of version 2 of the GNU General Public License as
# published by the Free Software Foundation.
class error(Exception):
pass
class ID3NoHeaderError(error, ValueError):
pass
class ID3BadUnsynchData(error, ValueError):
| 64 | 64 | 15 | 12 | 51 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3BadUnsynchData | ID3BadUnsynchData | 17 | 18 | 17 | 17 | de1247f47549b956d3e8c9440b703e9287d060a1 | bigcode/the-stack | train |
3f44727fedc25dcc8a4ceb4d | train | class | class BitPaddedLong(long, _BitPaddedMixin):
pass
| class BitPaddedLong(long, _BitPaddedMixin):
| pass
| = long.__new__(BitPaddedLong, numeric_value)
else:
self = int.__new__(BitPaddedInt, numeric_value)
self.bits = bits
self.bigendian = bigendian
return self
class BitPaddedLong(long, _BitPaddedMixin):
| 64 | 64 | 16 | 13 | 50 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | BitPaddedLong | BitPaddedLong | 175 | 176 | 175 | 175 | 5f3057f32b01410b614211afbc5642ed670b3c4f | bigcode/the-stack | train |
47986c6e17f2f64e908f79f4 | train | class | class ID3UnsupportedVersionError(error, NotImplementedError):
pass
| class ID3UnsupportedVersionError(error, NotImplementedError):
| pass
| HeaderError(error, ValueError):
pass
class ID3BadUnsynchData(error, ValueError):
pass
class ID3BadCompressedData(error, ValueError):
pass
class ID3TagError(error, ValueError):
pass
class ID3UnsupportedVersionError(error, NotImplementedError):
| 64 | 64 | 15 | 12 | 51 | Jianwei-Wang/python2.7_lib | dist-packages/mutagen/_id3util.py | Python | ID3UnsupportedVersionError | ID3UnsupportedVersionError | 29 | 30 | 29 | 29 | 016cdb4b352abc5503b34345d188868c7e04646e | bigcode/the-stack | train |
bf57e5c8733c4ab0988cb152 | train | function | def entry_point():
"""Zero-argument entry point for use with setuptools/distribute."""
raise SystemExit(main(sys.argv))
| def entry_point():
| """Zero-argument entry point for use with setuptools/distribute."""
raise SystemExit(main(sys.argv))
| #!/usr/bin/env /usr/bin/python3
import sys
from luxon.core.handlers.cmd import Cmd
import tradius.cmd
def main(argv):
tradius = Cmd('tradius', path='/tmp')
tradius()
def entry_point():
| 52 | 64 | 26 | 4 | 48 | Vuader/tradius | tradius/main.py | Python | entry_point | entry_point | 11 | 13 | 11 | 11 | 1af93eb768bfe8148f0c26db43bba44ac2b60554 | bigcode/the-stack | train |
e57af44a2bf71f6754c8a2f8 | train | function | def main(argv):
tradius = Cmd('tradius', path='/tmp')
tradius()
| def main(argv):
| tradius = Cmd('tradius', path='/tmp')
tradius()
| #!/usr/bin/env /usr/bin/python3
import sys
from luxon.core.handlers.cmd import Cmd
import tradius.cmd
def main(argv):
| 31 | 64 | 21 | 4 | 26 | Vuader/tradius | tradius/main.py | Python | main | main | 7 | 9 | 7 | 7 | 879caf34653a102b2b2209697d47b394859299af | bigcode/the-stack | train |
03cbaac7db70b8e54ad7aff9 | train | class | class Player(object):
def __init__(self):
self.name = None
self.results = dict()
self.rolls = []
self.scores = dict()
#
# set pins scores for a frame.
#
def set_result(self, frame, result):
self.results[frame] = result
def calculate_score(... | class Player(object):
| def __init__(self):
self.name = None
self.results = dict()
self.rolls = []
self.scores = dict()
#
# set pins scores for a frame.
#
def set_result(self, frame, result):
self.results[frame] = result
def calculate_score(self):
running... | class Player(object):
| 4 | 83 | 278 | 4 | 0 | ask5/bowling | player.py | Python | Player | Player | 2 | 35 | 2 | 2 | 83a17262372d4b6d298b3ab8eff7a4ac799b4bd4 | bigcode/the-stack | train |
e20a17b2d05c43881ad777ad | train | function | def search(code: str):
log = []
# DNA SEQUENCE
log.append('-'.join(splitThree(code)))
# tRNA SEQUENCE
swapped = ''
for i in code:
swapped += swap(i, False)
log.append('-'.join(splitThree(swapped)))
# RNA SEQUENCE
swapped = ''
for i in code:
swapped += swap(i, True)
log.append('-'.join(splitThree(swa... | def search(code: str):
| log = []
# DNA SEQUENCE
log.append('-'.join(splitThree(code)))
# tRNA SEQUENCE
swapped = ''
for i in code:
swapped += swap(i, False)
log.append('-'.join(splitThree(swapped)))
# RNA SEQUENCE
swapped = ''
for i in code:
swapped += swap(i, True)
log.append('-'.join(splitThree(swapped)))
# HIDDEN MESS... | t':
return 'u'
return value
# Split string into array of strings for every 3 letters
def splitThree(str: str):
n = 3
arr = [str[i:i+n] for i in range(0, len(str), n)]
return arr
def search(code: str):
| 64 | 64 | 215 | 6 | 57 | TenType/dna-decoder | main.py | Python | search | search | 17 | 59 | 17 | 17 | b8a470a90a7a9ae10d05ecf11a7823b83c797922 | bigcode/the-stack | train |
3996e37cc72150bc60344395 | train | function | def splitThree(str: str):
n = 3
arr = [str[i:i+n] for i in range(0, len(str), n)]
return arr
| def splitThree(str: str):
| n = 3
arr = [str[i:i+n] for i in range(0, len(str), n)]
return arr
| inputs import inputs
# Swap pairs
def swap(letter: str, rna: bool):
value = bases[letter]
if rna is True and value == 't':
return 'u'
return value
# Split string into array of strings for every 3 letters
def splitThree(str: str):
| 64 | 64 | 35 | 7 | 56 | TenType/dna-decoder | main.py | Python | splitThree | splitThree | 12 | 15 | 12 | 12 | ecadc39185cfcf7762941c2c9fc07ec54c95e037 | bigcode/the-stack | train |
5610d8134f508c9e56a35062 | train | function | def swap(letter: str, rna: bool):
value = bases[letter]
if rna is True and value == 't':
return 'u'
return value
| def swap(letter: str, rna: bool):
| value = bases[letter]
if rna is True and value == 't':
return 'u'
return value
| from data import bases, decoderKey, acids
from inputs import inputs
# Swap pairs
def swap(letter: str, rna: bool):
| 30 | 64 | 36 | 11 | 18 | TenType/dna-decoder | main.py | Python | swap | swap | 5 | 9 | 5 | 5 | 5330255b969ec2bb646dbf7d7251f88ef6e1f2fd | bigcode/the-stack | train |
f5717b900934be6f981567a3 | train | class | class Order(models.Model):
order_date = models.DateField(null=False)
shipped_date = models.DateField(null=True)
delivered_date = models.DateField(null=True)
coupon_code = models.CharField(max_length=50, null=True)
customer = models.ForeignKey(Customer, on_delete=models.CASCADE, null=False)
produ... | class Order(models.Model):
| order_date = models.DateField(null=False)
shipped_date = models.DateField(null=True)
delivered_date = models.DateField(null=True)
coupon_code = models.CharField(max_length=50, null=True)
customer = models.ForeignKey(Customer, on_delete=models.CASCADE, null=False)
products = models.ManyToManyFiel... | models.CharField(max_length=50, null=False)
email = models.CharField(max_length=50, null=False, unique=True)
class Product(models.Model):
name = models.CharField(max_length=50, null=False, unique=True)
price = models.IntegerField(null=False)
class Order(models.Model):
| 64 | 64 | 81 | 5 | 59 | Ujjawal-Rajput/yes | 2021/07/28/How to Use Select Related and Prefetch Related in Django/django_related/related_example/example/models.py | Python | Order | Order | 15 | 21 | 15 | 15 | baa3cd0f55bb37294dd9c49afad69010f5e80870 | bigcode/the-stack | train |
1809ddf39d53cfbe43a8aa63 | train | class | class Product(models.Model):
name = models.CharField(max_length=50, null=False, unique=True)
price = models.IntegerField(null=False)
| class Product(models.Model):
| name = models.CharField(max_length=50, null=False, unique=True)
price = models.IntegerField(null=False)
| address = models.CharField(max_length=500, null=False)
city = models.CharField(max_length=50, null=False)
postcode = models.CharField(max_length=50, null=False)
email = models.CharField(max_length=50, null=False, unique=True)
class Product(models.Model):
| 64 | 64 | 31 | 5 | 59 | Ujjawal-Rajput/yes | 2021/07/28/How to Use Select Related and Prefetch Related in Django/django_related/related_example/example/models.py | Python | Product | Product | 11 | 13 | 11 | 11 | 60f749443936703aac3b50a1cd72c0115587ccf3 | bigcode/the-stack | train |
ec88dbaf9952e7aebf71d8f4 | train | class | class LineItem(models.Model):
order = models.ForeignKey(Order, on_delete=models.CASCADE, null=False)
product = models.ForeignKey(Product, on_delete=models.CASCADE, null=False)
quantity = models.IntegerField(null=False) | class LineItem(models.Model):
| order = models.ForeignKey(Order, on_delete=models.CASCADE, null=False)
product = models.ForeignKey(Product, on_delete=models.CASCADE, null=False)
quantity = models.IntegerField(null=False) | =True)
delivered_date = models.DateField(null=True)
coupon_code = models.CharField(max_length=50, null=True)
customer = models.ForeignKey(Customer, on_delete=models.CASCADE, null=False)
products = models.ManyToManyField(Product, through='LineItem')
class LineItem(models.Model):
| 64 | 64 | 47 | 6 | 58 | Ujjawal-Rajput/yes | 2021/07/28/How to Use Select Related and Prefetch Related in Django/django_related/related_example/example/models.py | Python | LineItem | LineItem | 23 | 26 | 23 | 23 | b09babe3a69dbe8063708954afa246527a5005b2 | bigcode/the-stack | train |
34dbdd896a2d45a733001f15 | train | class | class Customer(models.Model):
first_name = models.CharField(max_length=50, null=False)
last_name = models.CharField(max_length=50, null=False)
address = models.CharField(max_length=500, null=False)
city = models.CharField(max_length=50, null=False)
postcode = models.CharField(max_length=50, null=Fal... | class Customer(models.Model):
| first_name = models.CharField(max_length=50, null=False)
last_name = models.CharField(max_length=50, null=False)
address = models.CharField(max_length=500, null=False)
city = models.CharField(max_length=50, null=False)
postcode = models.CharField(max_length=50, null=False)
email = models.CharFie... | from django.db import models
class Customer(models.Model):
| 11 | 64 | 94 | 5 | 5 | Ujjawal-Rajput/yes | 2021/07/28/How to Use Select Related and Prefetch Related in Django/django_related/related_example/example/models.py | Python | Customer | Customer | 3 | 9 | 3 | 3 | d28d7e2234fb4aed6fcb2e7634dbf80d0954eef3 | bigcode/the-stack | train |
b8257170e2f75afe1db7ead7 | train | class | class PassaroVermelho(Passaro):
_caracter_ativo = 'V'
_caracter_destruido = 'v'
velocidade_escalar = 20
| class PassaroVermelho(Passaro):
| _caracter_ativo = 'V'
_caracter_destruido = 'v'
velocidade_escalar = 20
| return self.foi_lancado() and self.status == ATIVO
class PassaroAmarelo(Passaro):
_caracter_ativo = 'A'
_caracter_destruido = 'a'
velocidade_escalar = 30
class PassaroVermelho(Passaro):
| 64 | 64 | 39 | 10 | 53 | Arthurregismais/pythonbirds | atores.py | Python | PassaroVermelho | PassaroVermelho | 175 | 178 | 175 | 175 | 2f47f399c95ae5055dd10551a02a01de562de3ac | bigcode/the-stack | train |
a403651ac0de12be14ab0aab | train | class | class Ator():
"""
Classe que representa um ator. Ele representa um ponto cartesiano na tela.
"""
_caracter_ativo = 'A'
_caracter_destruido = ' '
def __init__(self, x=0, y=0):
"""
Método de inicialização da classe. Deve inicializar os parâmetros x, y, caracter e status
:... | class Ator():
| """
Classe que representa um ator. Ele representa um ponto cartesiano na tela.
"""
_caracter_ativo = 'A'
_caracter_destruido = ' '
def __init__(self, x=0, y=0):
"""
Método de inicialização da classe. Deve inicializar os parâmetros x, y, caracter e status
:param x: Posiç... | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import math
DESTRUIDO = 'Destruido'
ATIVO = 'Ativo'
GRAVIDADE = 10 # m/s^2
class Ator():
| 53 | 132 | 443 | 4 | 48 | Arthurregismais/pythonbirds | atores.py | Python | Ator | Ator | 12 | 60 | 12 | 12 | 95457413a3834091bb842b2bd6c80cdee550178d | bigcode/the-stack | train |
8eb1d19d783195b90a8250e8 | train | class | class Obstaculo(Ator):
_caracter_ativo = 'O'
_caracter_destruido = ' '
| class Obstaculo(Ator):
| _caracter_ativo = 'O'
_caracter_destruido = ' '
| _x = abs(self.x - outro_ator.x)
delta_y = abs(self.y - outro_ator.y)
if delta_x <= intervalo and delta_y <= intervalo:
self.status = DESTRUIDO
outro_ator.status = DESTRUIDO
class Obstaculo(Ator):
| 64 | 64 | 28 | 8 | 55 | Arthurregismais/pythonbirds | atores.py | Python | Obstaculo | Obstaculo | 64 | 66 | 64 | 64 | 4ab6ebf7afbe3c1065e7ea310dfdda7c95682693 | bigcode/the-stack | train |
bd0a4af0597bbb7f6f24c087 | train | class | class Passaro(Ator):
velocidade_escalar = 10
def __init__(self, x=0, y=0):
"""
Método de inicialização de pássaro.
Deve chamar a inicialização de ator. Além disso, deve armazenar a posição inicial e incializar o tempo de
lançamento e angulo de lançamento
:param x:
... | class Passaro(Ator):
| velocidade_escalar = 10
def __init__(self, x=0, y=0):
"""
Método de inicialização de pássaro.
Deve chamar a inicialização de ator. Além disso, deve armazenar a posição inicial e incializar o tempo de
lançamento e angulo de lançamento
:param x:
:param y:
... | igual ao parâmetro intervalo, em volta do ponto onde se
encontra o ator. Se os atores estiverem dentro desse mesmo quadrado, seus status devem ser alterados para
destruido, seus caracteres para destruido também.
:param outro_ator: Ator a ser considerado na colisão
:param intervalo: Int... | 230 | 230 | 767 | 7 | 222 | Arthurregismais/pythonbirds | atores.py | Python | Passaro | Passaro | 79 | 166 | 79 | 79 | c20a93c02c9d2a0256237a7b6329dc4cdd866a26 | bigcode/the-stack | train |
c7b05a4bf41fc0a3410bbf69 | train | class | class Porco(Ator):
_caracter_ativo = '@'
_caracter_destruido = '+'
| class Porco(Ator):
| _caracter_ativo = '@'
_caracter_destruido = '+'
| <= intervalo and delta_y <= intervalo:
self.status = DESTRUIDO
outro_ator.status = DESTRUIDO
class Obstaculo(Ator):
_caracter_ativo = 'O'
_caracter_destruido = ' '
class Porco(Ator):
| 63 | 64 | 26 | 7 | 56 | Arthurregismais/pythonbirds | atores.py | Python | Porco | Porco | 70 | 72 | 70 | 70 | 31598e5a246e4bb9cd7c2971be49c25792558cac | bigcode/the-stack | train |
ff5fd2385f172212e5b6fa8d | train | class | class PassaroAmarelo(Passaro):
_caracter_ativo = 'A'
_caracter_destruido = 'a'
velocidade_escalar = 30
| class PassaroAmarelo(Passaro):
| _caracter_ativo = 'A'
_caracter_destruido = 'a'
velocidade_escalar = 30
| x_atual += self.velocidade_escalar * delta_t * math.cos(angulo_radianos)
self.x = x_atual
def _esta_voando(self):
return self.foi_lancado() and self.status == ATIVO
class PassaroAmarelo(Passaro):
| 64 | 64 | 39 | 10 | 53 | Arthurregismais/pythonbirds | atores.py | Python | PassaroAmarelo | PassaroAmarelo | 169 | 172 | 169 | 169 | feab6c1a7cc47872daf214d3141787871e0a3db0 | bigcode/the-stack | train |
281ad7ddd70c6b8e20094c90 | train | class | class DuploLancamentoExcecao(Exception):
pass
| class DuploLancamentoExcecao(Exception):
| pass
| Obstaculo(Ator):
_caracter_ativo = 'O'
_caracter_destruido = ' '
class Porco(Ator):
_caracter_ativo = '@'
_caracter_destruido = '+'
class DuploLancamentoExcecao(Exception):
| 64 | 64 | 13 | 10 | 53 | Arthurregismais/pythonbirds | atores.py | Python | DuploLancamentoExcecao | DuploLancamentoExcecao | 75 | 76 | 75 | 75 | 7e4a1496f7347bf8b7e97b659c0bc76665283bd9 | bigcode/the-stack | train |
84d87e7438a2137715810be7 | train | function | @event('plugin.register')
def register_plugin():
plugin.register(TorrentFiles, 'torrent_files', builtin=True, api_ver=2)
| @event('plugin.register')
def register_plugin():
| plugin.register(TorrentFiles, 'torrent_files', builtin=True, api_ver=2)
| .join(item['path'], item['name'])
for item in entry['torrent'].get_filelist()
]
if files:
log.debug('%s files: %s' % (entry['title'], files))
entry['content_files'] = files
@event('plugin.register')
def register_plugin():
| 64 | 64 | 29 | 10 | 53 | guillaumelamirand/Flexget | flexget/components/bittorrent/torrent_files.py | Python | register_plugin | register_plugin | 26 | 28 | 26 | 27 | f8c38c4a0dccf3c03f6357028c2319a357278f07 | bigcode/the-stack | train |
dbbd98fdf14828a9e65da4aa | train | class | class TorrentFiles:
"""Provides content files information when dealing with torrents."""
@plugin.priority(200)
def on_task_modify(self, task, config):
for entry in task.entries:
if 'torrent' in entry:
files = [
posixpath.join(item['path'], item['name'... | class TorrentFiles:
| """Provides content files information when dealing with torrents."""
@plugin.priority(200)
def on_task_modify(self, task, config):
for entry in task.entries:
if 'torrent' in entry:
files = [
posixpath.join(item['path'], item['name'])
... | import logging
import posixpath
from flexget import plugin
from flexget.event import event
log = logging.getLogger('torrent_files')
class TorrentFiles:
| 34 | 64 | 110 | 4 | 30 | guillaumelamirand/Flexget | flexget/components/bittorrent/torrent_files.py | Python | TorrentFiles | TorrentFiles | 10 | 23 | 10 | 10 | 9034c55b9c2e975dc9189d8d1e0ed93d9ad82b5e | bigcode/the-stack | train |
2c8e1e3f7e7fe393bf988067 | train | function | def f(x):
return np.sin(2*x*math.pi)
| def f(x):
| return np.sin(2*x*math.pi)
| import numpy as np
import matplotlib.pyplot as plt
import math
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error
def f(x):
| 50 | 64 | 15 | 4 | 45 | him1411/machine-learning-assignments | assignment1/task4-3.py | Python | f | f | 10 | 11 | 10 | 10 | 412157ff9378e7c6f1ac267abff9ea6198033891 | bigcode/the-stack | train |
28eed04a84371e8698d68cb9 | train | class | class SynonymMapsOperations:
"""SynonymMapsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:typ... | class SynonymMapsOperations:
| """SynonymMapsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.search.docum... | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may ... | 246 | 256 | 3,201 | 6 | 240 | praveenkuttappan/azure-sdk-for-python | sdk/search/azure-search-documents/azure/search/documents/indexes/_generated/aio/operations/_synonym_maps_operations.py | Python | SynonymMapsOperations | SynonymMapsOperations | 25 | 363 | 25 | 25 | f577b11aebc6c3d2aa02e2a5cd53baaeff2afbd4 | bigcode/the-stack | train |
575ef27cefe311678b62ce8a | train | function | def provide_trigger_dict():
"""Provide a dictionnary mapping str names to byte values."""
trigger_dict = OrderedDict()
# At the beginning and end of the experiment ... take these triggers to
# crop the meaningful EEG data. Make sure to include some time BEFORE and
# AFTER the triggers so that filte... | def provide_trigger_dict():
| """Provide a dictionnary mapping str names to byte values."""
trigger_dict = OrderedDict()
# At the beginning and end of the experiment ... take these triggers to
# crop the meaningful EEG data. Make sure to include some time BEFORE and
# AFTER the triggers so that filtering does not introduce arti... | """Definitions for the TTL triggers to be sent.
main file: sp.py
For more information, see also the "event_value" key within the
define_variable_meanings.make_events_json_dict.
"""
from collections import OrderedDict
def provide_trigger_dict():
| 52 | 180 | 601 | 5 | 46 | sappelhoff/sp_psychopy | sp_experiment/define_ttl_triggers.py | Python | provide_trigger_dict | provide_trigger_dict | 12 | 70 | 12 | 12 | 21a434d747d1c9fc47fb7242a8f56ae107670930 | bigcode/the-stack | train |
f028aa66a50559714c75cb79 | train | function | def optimize(fn=None, *, data=None):
if fn is None:
return partial(optimize, data=data)
return Optimizer(fn, data)
| def optimize(fn=None, *, data=None):
| if fn is None:
return partial(optimize, data=data)
return Optimizer(fn, data)
| joblib.load(self.model_path)
else:
self.model_ = self.optimize()
predicted_probas = self.model_.predict_proba(X)
if as_proba:
return predicted_probas[0, 1]
return predicted_probas.argmax()
def optimize(fn=None, *, data=None):
| 64 | 64 | 32 | 9 | 55 | cosmicBboy/software20 | software20/optimizers.py | Python | optimize | optimize | 109 | 112 | 109 | 109 | 5182e4b97868a65e6b0c30d9de78a497bc4449c9 | bigcode/the-stack | train |
4868e7ba1edec36a609fe6ff | train | class | class Optimizer:
def __init__(self, opt_fn, data=None, batch_size=3, test_size=0.2):
self._opt_fn = opt_fn
self._data = data
self._batch_size = batch_size
self._test_size = test_size
self.model_ = None
self.model_dir = Path(os.path.dirname(self.module.__file__)) / "m... | class Optimizer:
| def __init__(self, opt_fn, data=None, batch_size=3, test_size=0.2):
self._opt_fn = opt_fn
self._data = data
self._batch_size = batch_size
self._test_size = test_size
self.model_ = None
self.model_dir = Path(os.path.dirname(self.module.__file__)) / "models"
sel... | """Optimizer decorators."""
import importlib
import os
from functools import partial
from pathlib import Path
from typing import Any
import pandas as pd
import numpy as np
import joblib
from sklearn.metrics import SCORERS
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import... | 98 | 196 | 654 | 4 | 93 | cosmicBboy/software20 | software20/optimizers.py | Python | Optimizer | Optimizer | 20 | 106 | 20 | 21 | fe0b98e6ea79022a83eaa9db5d34d4801c9306de | bigcode/the-stack | train |
7bcd9af5bbe9d67bb5e3c3aa | train | class | class TestDialign(unittest.TestCase):
def setUp(self):
aln_seqs = {
"HTL2": "ldtapC-LFSDGS------PQKAAYVL-------WDQTILQQDITPLPSHethSAQKGELLALICGLRAak------------",
"MMLV": "pdadhtw-YTDGSSLLQEGQRKAGAAVtteteviWa----KALDAG---T---SAQRAELIALTQALKm--------------",
"HEPB": "rpgl-... | class TestDialign(unittest.TestCase):
| def setUp(self):
aln_seqs = {
"HTL2": "ldtapC-LFSDGS------PQKAAYVL-------WDQTILQQDITPLPSHethSAQKGELLALICGLRAak------------",
"MMLV": "pdadhtw-YTDGSSLLQEGQRKAGAAVtteteviWa----KALDAG---T---SAQRAELIALTQALKm--------------",
"HEPB": "rpgl-CQVFADAT------PTGWGLVM-------GHQRMRGTF... | 43 ---SAQRAEL IALTQALKm- ---------- ---
HEPB 37 t------AEL LAA-CFARSr sganiigtdn svv
ECOL 43 ---TNNRMEL MAAIv----- ---------- ---
0003333455 5533333300 0000000000 000
Sequence tree:
==============
Tree constructed using UPGMAbased on D... | 163 | 163 | 546 | 8 | 155 | rahulghangas/cogent3 | tests/test_parse/test_dialign.py | Python | TestDialign | TestDialign | 90 | 128 | 90 | 90 | 6ac9d43d4d68a266f38cecf2e999b8c139a7f7a9 | bigcode/the-stack | train |
27b8411835bc52338d019754 | train | class | class Api:
"""
Api class
Class to deal with the foreman API v2
"""
maxHistory = 16
def __init__(self, password, login='admin', ip='127.0.0.1',
printErrors=False, ca_cert=None):
""" Function __init__
Init the API with the connection params
@param passwor... | class Api:
| """
Api class
Class to deal with the foreman API v2
"""
maxHistory = 16
def __init__(self, password, login='admin', ip='127.0.0.1',
printErrors=False, ca_cert=None):
""" Function __init__
Init the API with the connection params
@param password: authenti... | #!/usr/bin/python3
# -*- coding: utf-8 -*-
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 202 | 256 | 1,780 | 3 | 198 | ckxng/foreman | foreman/api.py | Python | Api | Api | 25 | 250 | 25 | 25 | 0f623f11f786563b3f6a320513ed0ab18a92cbd1 | bigcode/the-stack | train |
1d5797b0d99086e9d104df42 | train | function | def rename_and_move(folder_name, file_name):
print(file_name)
return folder_name.replace("ui/", "ui_generated/"), "ui_" + file_name
| def rename_and_move(folder_name, file_name):
| print(file_name)
return folder_name.replace("ui/", "ui_generated/"), "ui_" + file_name
| from PyQt5 import uic
def rename_and_move(folder_name, file_name):
| 18 | 64 | 35 | 10 | 7 | LLNL/RASE | build-ui.py | Python | rename_and_move | rename_and_move | 4 | 6 | 4 | 4 | 67592970687694bff14914bef6a4263616539313 | bigcode/the-stack | train |
d2c58881ebc8a4a2d1beabfe | train | function | def download_energy(out_dir):
energy_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx"
with tempfile.TemporaryDirectory() as tmp_dir:
energy_file = os.path.join(tmp_dir, 'energy.xlsx')
r = requests.get(energy_url, allow_redirects=True)
with open(en... | def download_energy(out_dir):
| energy_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx"
with tempfile.TemporaryDirectory() as tmp_dir:
energy_file = os.path.join(tmp_dir, 'energy.xlsx')
r = requests.get(energy_url, allow_redirects=True)
with open(energy_file, 'wb') as fh:
... | (506, 1)
boston_hdf_file = os.path.join(out_dir, 'boston.hdf5')
with h5py.File(boston_hdf_file, 'w') as hf:
hf.create_dataset("X", data=X, dtype=np.float64, compression='gzip')
hf.create_dataset("Y", data=Y, dtype=np.float64, compression='gzip')
def download_energy(out_dir):
| 89 | 89 | 298 | 6 | 83 | mohamad-amin/falkon | notebooks/uci_datasets_download.py | Python | download_energy | download_energy | 51 | 70 | 51 | 51 | e4705553444c947ddd77fefedb77490ad90fc9f9 | bigcode/the-stack | train |
063103d5c28825fce6de9fe0 | train | function | def download_boston(out_dir):
boston_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
with tempfile.TemporaryDirectory() as tmp_dir:
boston_file = os.path.join(tmp_dir, 'boston.tsv')
r = requests.get(boston_url, allow_redirects=True)
with open(boston... | def download_boston(out_dir):
| boston_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
with tempfile.TemporaryDirectory() as tmp_dir:
boston_file = os.path.join(tmp_dir, 'boston.tsv')
r = requests.get(boston_url, allow_redirects=True)
with open(boston_file, 'wb') as fh:
... | os.path.join(out_dir, 'protein.hdf5')
with h5py.File(protein_hdf_file, 'w') as hf:
hf.create_dataset("X", data=X, dtype=np.float64, compression='gzip')
hf.create_dataset("Y", data=Y, dtype=np.float64, compression='gzip')
def download_boston(out_dir):
| 76 | 76 | 256 | 7 | 69 | mohamad-amin/falkon | notebooks/uci_datasets_download.py | Python | download_boston | download_boston | 31 | 48 | 31 | 31 | 191a1ffaf5b2854a3f99b8877af967dbd6c76647 | bigcode/the-stack | train |
cd368feed5b4645b6efa35c9 | train | function | def download_kin40k(out_dir):
"""
Data is impossible to find from reputable sources. Delve repository does not have 40k points (only 8192).
Github repository with full data: https://github.com/trungngv/fgp
"""
url_test_y = "https://github.com/trungngv/fgp/raw/master/data/kin40k/kin40k_test_labels.as... | def download_kin40k(out_dir):
| """
Data is impossible to find from reputable sources. Delve repository does not have 40k points (only 8192).
Github repository with full data: https://github.com/trungngv/fgp
"""
url_test_y = "https://github.com/trungngv/fgp/raw/master/data/kin40k/kin40k_test_labels.asc"
url_train_y = "https://... | _float=False)
df = df.drop(["Unnamed: 10", "Unnamed: 11"], axis=1)
df = df.dropna(axis=0, how='all')
df.head()
df = df.astype(float)
Y = df["Y1"].values.reshape(-1, 1) # heating load
X = df.drop(["Y1", "Y2"], axis=1).values
assert X.shape == (768, 8)
asse... | 195 | 195 | 650 | 9 | 186 | mohamad-amin/falkon | notebooks/uci_datasets_download.py | Python | download_kin40k | download_kin40k | 73 | 109 | 73 | 73 | 5a43473eaff9de9043ae161cfe1950c2ed30c370 | bigcode/the-stack | train |
b8d4edff1eb6ec40c5593ac8 | train | function | def download_protein(out_dir):
protein_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00265/CASP.csv"
with tempfile.TemporaryDirectory() as tmp_dir:
protein_file = os.path.join(tmp_dir, 'protein.csv')
r = requests.get(protein_url, allow_redirects=True)
with open(protein... | def download_protein(out_dir):
| protein_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00265/CASP.csv"
with tempfile.TemporaryDirectory() as tmp_dir:
protein_file = os.path.join(tmp_dir, 'protein.csv')
r = requests.get(protein_url, allow_redirects=True)
with open(protein_file, 'wb') as fh:
... | import argparse
import os
import tempfile
import h5py
import numpy as np
import pandas as pd
import requests
def download_protein(out_dir):
| 34 | 83 | 278 | 7 | 26 | mohamad-amin/falkon | notebooks/uci_datasets_download.py | Python | download_protein | download_protein | 11 | 28 | 11 | 11 | accc4f113b554fb13ded4da9df06df8a84f7f71d | bigcode/the-stack | train |
7d9309d6cc921e8e96b64a3d | train | class | class Transaction(BaseTransaction):
def __init__(self, connection: sqlite3.Connection, bus: EventBus) -> None:
self._bus = bus
self._connection = connection
self._cursor = self._connection.cursor()
self._cursor.execute("begin")
self._season = SeasonRepository(self._connecti... | class Transaction(BaseTransaction):
| def __init__(self, connection: sqlite3.Connection, bus: EventBus) -> None:
self._bus = bus
self._connection = connection
self._cursor = self._connection.cursor()
self._cursor.execute("begin")
self._season = SeasonRepository(self._connection, self._cursor, self._bus)
... | import sqlite3
from fbsrankings.common import EventBus
from fbsrankings.infrastructure import Transaction as BaseTransaction
from fbsrankings.infrastructure.sqlite.write.affiliation import AffiliationRepository
from fbsrankings.infrastructure.sqlite.write.game import GameRepository
from fbsrankings.infrastructure.sqli... | 130 | 131 | 437 | 5 | 124 | mikee385/fbsrankings | src/fbsrankings/infrastructure/sqlite/write/transaction.py | Python | Transaction | Transaction | 14 | 79 | 14 | 14 | 3965e8c435aea395de4632c6d68a4e68a58eeeb9 | bigcode/the-stack | train |
4e0545c63b39d479c5d4bcdd | train | function | @app.route('/logout')
def logout():
logout_user()
return redirect(url_for('index'))
| @app.route('/logout')
def logout():
| logout_user()
return redirect(url_for('index'))
| next_page = request.args.get('next')
if not next_page or url_parse(next_page).netloc != '':
next_page = url_for('index')
return redirect(next_page)
return render_template('login.html', title='Sign In', form=form)
@app.route('/logout')
def logout():
| 64 | 64 | 20 | 8 | 56 | evaristofm/microblog | app/routes.py | Python | logout | logout | 70 | 73 | 70 | 71 | 24af72d1eb73694ee7f6bdff88b5741fa957d6ce | bigcode/the-stack | train |
f2dfab0a82f27b6dff178dd1 | train | function | @app.route('/register', methods=['GET', 'POST'])
def register():
if current_user.is_authenticated:
return redirect(url_for('index'))
form = RegistrationForm()
if form.validate_on_submit():
user = User(username=form.username.data, email=form.email.data)
user.set_password(form.password... | @app.route('/register', methods=['GET', 'POST'])
def register():
| if current_user.is_authenticated:
return redirect(url_for('index'))
form = RegistrationForm()
if form.validate_on_submit():
user = User(username=form.username.data, email=form.email.data)
user.set_password(form.password.data)
db.session.add(user)
db.session.commit()
... | = url_for('index')
return redirect(next_page)
return render_template('login.html', title='Sign In', form=form)
@app.route('/logout')
def logout():
logout_user()
return redirect(url_for('index'))
@app.route('/register', methods=['GET', 'POST'])
def register():
| 63 | 64 | 111 | 15 | 48 | evaristofm/microblog | app/routes.py | Python | register | register | 76 | 88 | 76 | 77 | 7d21e62818ec0f49beba17dbe22a5904149395fd | bigcode/the-stack | train |
e71af928878ca93b5a97f1b8 | train | function | @app.route('/unfollow/<username>', methods=['POST'])
@login_required
def unfollow(username):
form = EmptyForm()
if form.validate_on_submit():
user = User.query.filter_by(username=username).first()
if user is None:
flash('User {} not found.'.format(username))
return redire... | @app.route('/unfollow/<username>', methods=['POST'])
@login_required
def unfollow(username):
| form = EmptyForm()
if form.validate_on_submit():
user = User.query.filter_by(username=username).first()
if user is None:
flash('User {} not found.'.format(username))
return redirect(url_for('index'))
if user == current_user:
flash('You cannot unfollow ... | _user.follow(user)
db.session.commit()
flash('You are following {}!'.format(username))
return redirect(url_for('user', username=username))
else:
return redirect(url_for('index'))
@app.route('/unfollow/<username>', methods=['POST'])
@login_required
def unfollow(username):
| 63 | 64 | 143 | 20 | 43 | evaristofm/microblog | app/routes.py | Python | unfollow | unfollow | 150 | 167 | 150 | 152 | e4fd70c33ee1cbdc11a9b0148d75ecc795ab709c | bigcode/the-stack | train |
c4c3a63c40f2df5396ef34d7 | train | function | @app.route('/user/<username>')
@login_required
def user(username):
user = User.query.filter_by(username=username).first_or_404()
page = request.args.get('page', 1, type=int)
posts = user.posts.order_by(Post.timestamp.desc()).paginate(
page, app.config['POSTS_PER_PAGE'], False)
next_url = url_for... | @app.route('/user/<username>')
@login_required
def user(username):
| user = User.query.filter_by(username=username).first_or_404()
page = request.args.get('page', 1, type=int)
posts = user.posts.order_by(Post.timestamp.desc()).paginate(
page, app.config['POSTS_PER_PAGE'], False)
next_url = url_for('user', username=user.username, page=posts.next_num) \
if ... | (form.password.data)
db.session.add(user)
db.session.commit()
flash('Congratulations, you are now a registered user!')
return redirect(url_for('login'))
return render_template('register.html', title='Register', form=form)
@app.route('/user/<username>')
@login_required
def user(userna... | 64 | 64 | 164 | 14 | 50 | evaristofm/microblog | app/routes.py | Python | user | user | 91 | 104 | 91 | 93 | 1a8b3d5cb1c32a662915bce2ce746ec24d0acb38 | bigcode/the-stack | train |
1fefbf2c179413674200eb55 | train | function | @app.route('/', methods=['GET', 'POST'])
@app.route('/index', methods=['GET', 'POST'])
@login_required
def index():
form = PostForm()
if form.validate_on_submit():
post = Post(body=form.post.data, author=current_user)
db.session.add(post)
db.session.commit()
flash('Your post is n... | @app.route('/', methods=['GET', 'POST'])
@app.route('/index', methods=['GET', 'POST'])
@login_required
def index():
| form = PostForm()
if form.validate_on_submit():
post = Post(body=form.post.data, author=current_user)
db.session.add(post)
db.session.commit()
flash('Your post is now live!')
return redirect(url_for('index'))
page = request.args.get('page', 1, type=int)
posts = cu... | , Post
from app.forms import LoginForm, RegistrationForm, EditProfileForm, PostForm, EmptyForm
from werkzeug.urls import url_parse
from datetime import datetime
@app.route('/', methods=['GET', 'POST'])
@app.route('/index', methods=['GET', 'POST'])
@login_required
def index():
| 64 | 64 | 203 | 28 | 35 | evaristofm/microblog | app/routes.py | Python | index | index | 13 | 33 | 13 | 16 | b7934b0f21ef813e5c9d54e5a86ea47d0d05b172 | bigcode/the-stack | train |
6030ff09d9efa7ba5437254e | train | function | @app.route('/edit_profile', methods=['GET', 'POST'])
@login_required
def edit_profile():
form = EditProfileForm(current_user.username)
if form.validate_on_submit():
current_user.username = form.username.data
current_user.about_me = form.about_me.data
db.session.commit()
flash('Yo... | @app.route('/edit_profile', methods=['GET', 'POST'])
@login_required
def edit_profile():
| form = EditProfileForm(current_user.username)
if form.validate_on_submit():
current_user.username = form.username.data
current_user.about_me = form.about_me.data
db.session.commit()
flash('You changes have been saved.')
return redirect(url_for('edit_profile'))
elif re... | next_url=next_url, prev_url=prev_url, form=form)
@app.before_request
def before_request():
if current_user.is_authenticated:
current_user.last_seen = datetime.utcnow()
db.session.commit()
@app.route('/edit_profile', methods=['GET', 'POST'])
@login_required
def edit_profile():
| 64 | 64 | 125 | 20 | 44 | evaristofm/microblog | app/routes.py | Python | edit_profile | edit_profile | 113 | 127 | 113 | 115 | 98c9c7cc8a028ab1155b16f6af5aafcf774fe49b | bigcode/the-stack | train |
b00905f6c6ea278d71e036dd | train | function | @app.before_request
def before_request():
if current_user.is_authenticated:
current_user.last_seen = datetime.utcnow()
db.session.commit()
| @app.before_request
def before_request():
| if current_user.is_authenticated:
current_user.last_seen = datetime.utcnow()
db.session.commit()
| =user.username, page=posts.prev_num) \
if posts.has_prev else None
form = EmptyForm()
return render_template('user.html', user=user, posts=posts.items,
next_url=next_url, prev_url=prev_url, form=form)
@app.before_request
def before_request():
| 64 | 64 | 29 | 8 | 56 | evaristofm/microblog | app/routes.py | Python | before_request | before_request | 106 | 110 | 106 | 107 | 5cb6daede5004badd7f6e1f0759e9c44c399ecd0 | bigcode/the-stack | train |
e07655a26b3f699e3e33ef14 | train | function | @app.route('/explore')
@login_required
def explore():
page = request.args.get('page', 1, type=int)
posts = Post.query.order_by(Post.timestamp.desc()).paginate(
page, app.config['POSTS_PER_PAGE'], False)
next_url = url_for('explore', page=posts.next_num) \
if posts.has_next else None
prev... | @app.route('/explore')
@login_required
def explore():
| page = request.args.get('page', 1, type=int)
posts = Post.query.order_by(Post.timestamp.desc()).paginate(
page, app.config['POSTS_PER_PAGE'], False)
next_url = url_for('explore', page=posts.next_num) \
if posts.has_next else None
prev_url = url_for('explore', page=posts.prev_num) \
... | ('index', page=posts.prev_num) \
if posts.has_prev else None
return render_template('index.html', title='Home', form=form,
posts=posts.items, next_url=next_url,
prev_url=prev_url)
@app.route('/explore')
@login_required
def explore():
| 64 | 64 | 133 | 12 | 52 | evaristofm/microblog | app/routes.py | Python | explore | explore | 37 | 48 | 37 | 39 | 65053378e1fabe0466e73027f4a78a57d5f9e9c4 | bigcode/the-stack | train |
005b9d2ae6529b3ce642f8cc | train | function | @app.route('/follow/<username>', methods=['POST'])
@login_required
def follow(username):
form = EmptyForm()
if form.validate_on_submit():
user = User.query.filter_by(username=username).first()
if user is None:
flash('User {} not found.'.format(username))
return redirect(u... | @app.route('/follow/<username>', methods=['POST'])
@login_required
def follow(username):
| form = EmptyForm()
if form.validate_on_submit():
user = User.query.filter_by(username=username).first()
if user is None:
flash('User {} not found.'.format(username))
return redirect(url_for('index'))
if user == current_user:
flash('You cannot follow yo... | '))
elif request.method == 'GET':
form.username.data = current_user.username
form.about_me.data = current_user.about_me
return render_template('edit_profile.html', title='Edit Profile', form=form)
@app.route('/follow/<username>', methods=['POST'])
@login_required
def follow(username):
| 64 | 64 | 139 | 18 | 46 | evaristofm/microblog | app/routes.py | Python | follow | follow | 130 | 147 | 130 | 132 | a0cebc5cf9023bd06f8089f97c08b7cb42f8e8a4 | bigcode/the-stack | train |
a199e93b30d8eccd62a8004f | train | function | @app.route('/login', methods=['GET', 'POST'])
def login():
if current_user.is_authenticated:
return redirect(url_for('index'))
form = LoginForm()
if form.validate_on_submit():
user = User.query.filter_by(username=form.username.data).first()
if user is None or not user.check_password(... | @app.route('/login', methods=['GET', 'POST'])
def login():
| if current_user.is_authenticated:
return redirect(url_for('index'))
form = LoginForm()
if form.validate_on_submit():
user = User.query.filter_by(username=form.username.data).first()
if user is None or not user.check_password(form.password.data):
flash('Invalid username or... | ('explore', page=posts.prev_num) \
if posts.has_prev else None
return render_template("index.html", title='Explore', posts=posts.items,
next_url=next_url, prev_url=prev_url)
@app.route('/login', methods=['GET', 'POST'])
def login():
| 64 | 64 | 155 | 15 | 49 | evaristofm/microblog | app/routes.py | Python | login | login | 52 | 67 | 52 | 53 | e037400cebdf220624521ca34a7a2b146a8c3ab4 | bigcode/the-stack | train |
f44a8c3aa1cc639c3d6d21ab | train | class | class TestAuthorisation(TestCase):
"""
Test that events can only be created and edited by users who should be allowed to do so
"""
def setUp(self):
"""
Creates users but doesn't log any of them in. The individual tests should do that
"""
self.client = Client()
#Cr... | class TestAuthorisation(TestCase):
| """
Test that events can only be created and edited by users who should be allowed to do so
"""
def setUp(self):
"""
Creates users but doesn't log any of them in. The individual tests should do that
"""
self.client = Client()
#Create the 'Contributors' group
... | 'event-location': u'',
'event-start': VALID_DATE_STRING,
'event-topics': [],
'event-group-description': u'',
'event-department_organiser': u'',
'event-group-event_group_select': u'',
'event-speakers': [],
'event-group-group_... | 256 | 256 | 2,017 | 7 | 249 | alan-turing-institute/talks.ox | talks/contributors/tests.py | Python | TestAuthorisation | TestAuthorisation | 543 | 757 | 543 | 543 | 37cc184aad24528d041a122b39c91f4cf66832a7 | bigcode/the-stack | train |
41a94f7ced3c7a10d6c22230 | train | class | class TestEventGroupForm(TestCase):
def test_empty(self):
form = forms.EventGroupForm({})
self.assertEquals(form.is_valid(), False, "empty form should not validate")
errors = form.errors.as_data()
self.assertIn('title', errors)
self.assertEquals(len(errors), 1)
def test... | class TestEventGroupForm(TestCase):
| def test_empty(self):
form = forms.EventGroupForm({})
self.assertEquals(form.is_valid(), False, "empty form should not validate")
errors = form.errors.as_data()
self.assertIn('title', errors)
self.assertEquals(len(errors), 1)
def test_all_fields_blanked(self):
da... | errors: %s", errors)
self.assertNotIn("Either provide the Title or mark it as TBA", form.errors.get('__all__', []))
self.assertNotIn('title', form.errors)
self.assertNotIn('title_not_announced', form.errors)
class TestEventGroupForm(TestCase):
| 66 | 66 | 221 | 8 | 58 | alan-turing-institute/talks.ox | talks/contributors/tests.py | Python | TestEventGroupForm | TestEventGroupForm | 158 | 186 | 158 | 159 | c7b53eb3883b7aaa81f9a868533c5b12b38cde01 | bigcode/the-stack | train |
1ac8a81cfdfc856d2bc225d3 | train | class | class TestEventForm(TestCase):
def test_empty(self):
form = forms.EventForm({})
self.assertEquals(form.is_valid(), False, "empty form should not validate")
errors = form.errors.as_data()
logging.info("form errors: %s", errors)
self.assertEquals(len(errors), 5)
self.a... | class TestEventForm(TestCase):
| def test_empty(self):
form = forms.EventForm({})
self.assertEquals(form.is_valid(), False, "empty form should not validate")
errors = form.errors.as_data()
logging.info("form errors: %s", errors)
self.assertEquals(len(errors), 5)
self.assertIn('booking_type', errors)
... | from __future__ import absolute_import
import logging
import mock
from django.test import TestCase
from django.contrib.contenttypes.models import ContentType
from django.contrib.auth.models import User, Group, Permission
from django.test.client import Client
from talks.events import models, factories
from talks.contr... | 140 | 256 | 1,182 | 7 | 133 | alan-turing-institute/talks.ox | talks/contributors/tests.py | Python | TestEventForm | TestEventForm | 19 | 155 | 19 | 20 | d75d086737caed3dada5cc8d819d4c4c5a82ff9e | bigcode/the-stack | train |
e189ad62081f40e9c54c0bbd | train | class | class TestCreateEventView(AuthTestCase):
def test_get_happy_no_group_id(self):
response = self.client.get('/talks/new')
logging.info("Form errors: %s", response.context['event_form'].errors)
self.assertEquals(response.status_code, 200)
self.assertTemplateUsed(response, 'contributors... | class TestCreateEventView(AuthTestCase):
| def test_get_happy_no_group_id(self):
response = self.client.get('/talks/new')
logging.info("Form errors: %s", response.context['event_form'].errors)
self.assertEquals(response.status_code, 200)
self.assertTemplateUsed(response, 'contributors/event_form.html')
self.assertCont... | self.assertTemplateUsed(response, "contributors/event_group_form.html")
def test_create_event_group_post_happy(self):
data = {
'title': 'lkfjlfkds',
'description': 'dflksfoingf',
'group_type': '',
}
response = self.client.post("/talks/series/new"... | 256 | 256 | 2,230 | 9 | 247 | alan-turing-institute/talks.ox | talks/contributors/tests.py | Python | TestCreateEventView | TestCreateEventView | 320 | 540 | 320 | 321 | 4d735cd59ab3bbffd9a07b02112de3b2343ea0ca | bigcode/the-stack | train |
f0b4df79eaf3c0903d2ff6c6 | train | class | class AuthTestCase(TestCase):
"""Subclass AuthTestCase if you need to create/edit
Event or EventGroup (requiring auth)
"""
def setUp(self):
self.client = Client()
#Create the 'Contributors' group
self.group = Group(name=GROUP_EDIT_EVENTS)
self.group.save()
#Add ... | class AuthTestCase(TestCase):
| """Subclass AuthTestCase if you need to create/edit
Event or EventGroup (requiring auth)
"""
def setUp(self):
self.client = Client()
#Create the 'Contributors' group
self.group = Group(name=GROUP_EDIT_EVENTS)
self.group.save()
#Add edit permissions for Event, Ev... | ):
data = {
'description': u'something',
'title': u'some title',
'group_type': u'SE',
}
form = forms.EventGroupForm(data)
self.assertEquals(form.is_valid(), True, "form should validate")
class AuthTestCase(TestCase):
| 64 | 64 | 178 | 7 | 57 | alan-turing-institute/talks.ox | talks/contributors/tests.py | Python | AuthTestCase | AuthTestCase | 189 | 211 | 189 | 189 | cb6d4c4e84c11bdbda0d9807ff871414dae121fb | bigcode/the-stack | train |
cc43da1f61108c1b4c70b688 | train | class | class TestEventGroupViews(AuthTestCase):
def test_show_event_group_404(self):
response = self.client.get("/talks/series/1")
self.assertEquals(response.status_code, 404)
def test_list_event_groups_empty(self):
response = self.client.get("/talks/series/")
self.assertEquals(respon... | class TestEventGroupViews(AuthTestCase):
| def test_show_event_group_404(self):
response = self.client.get("/talks/series/1")
self.assertEquals(response.status_code, 404)
def test_list_event_groups_empty(self):
response = self.client.get("/talks/series/")
self.assertEquals(response.status_code, 200)
def test_list_ev... | (errors), 1)
def test_all_fields_valid(self):
data = {
'description': u'something',
'title': u'some title',
'group_type': u'SE',
}
form = forms.EventGroupForm(data)
self.assertEquals(form.is_valid(), True, "form should validate")
class AuthTestC... | 256 | 256 | 955 | 9 | 247 | alan-turing-institute/talks.ox | talks/contributors/tests.py | Python | TestEventGroupViews | TestEventGroupViews | 214 | 317 | 214 | 215 | fea5c512a822210d3b0daae72cb02d66266613b1 | bigcode/the-stack | train |
c327d483b1c0cda893a5c5a3 | train | class | class TestEditEventView(AuthTestCase):
def test_edit_event_404(self):
response = self.client.get("/talks/id/1/edit")
self.assertEquals(response.status_code, 404)
self.assertTemplateNotUsed(response, "contributors/event_form.html")
def test_edit_event_200(self):
event = factorie... | class TestEditEventView(AuthTestCase):
| def test_edit_event_404(self):
response = self.client.get("/talks/id/1/edit")
self.assertEquals(response.status_code, 404)
self.assertTemplateNotUsed(response, "contributors/event_form.html")
def test_edit_event_200(self):
event = factories.EventFactory.create()
event.ed... | (
title='series title',
)
group.editor_set.add(self.contrib_user1)
group.save()
#create event and set user1 as an editor
event = factories.EventFactory.create(title='event title')
event.editor_set.add(self.contrib_user2)
event.group = group
eve... | 256 | 256 | 923 | 9 | 247 | alan-turing-institute/talks.ox | talks/contributors/tests.py | Python | TestEditEventView | TestEditEventView | 760 | 854 | 760 | 761 | a4444f770608b976034d209b9e21df7660203f87 | bigcode/the-stack | train |
6057f7779c4cadd18d6a3514 | train | class | class PeerExpressRouteCircuitConnectionsOperations(object):
"""PeerExpressRouteCircuitConnectionsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to mode... | class PeerExpressRouteCircuitConnectionsOperations(object):
| """PeerExpressRouteCircuitConnectionsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~a... | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may ... | 237 | 256 | 1,771 | 9 | 228 | xolve/azure-sdk-for-python | sdk/network/azure-mgmt-network/azure/mgmt/network/v2021_05_01/operations/_peer_express_route_circuit_connections_operations.py | Python | PeerExpressRouteCircuitConnectionsOperations | PeerExpressRouteCircuitConnectionsOperations | 26 | 194 | 26 | 26 | 40bf1afd17dc77b717396f7fe68d92c326e27cc6 | bigcode/the-stack | train |
824adad2bf6343667755b880 | train | class | class NLR2(nn.Module):
def __init__(self,netin,netout,nethidden1,nethidden2):
super().__init__()
self.netmodel= torch.nn.Sequential(torch.nn.Linear(netin, nethidden1),torch.nn.Sigmoid(),torch.nn.Linear(nethidden1, nethidden2),torch.nn.Linear(nethidden2, netout))
def myforward (self,inv):
outv=self.netmo... | class NLR2(nn.Module):
| def __init__(self,netin,netout,nethidden1,nethidden2):
super().__init__()
self.netmodel= torch.nn.Sequential(torch.nn.Linear(netin, nethidden1),torch.nn.Sigmoid(),torch.nn.Linear(nethidden1, nethidden2),torch.nn.Linear(nethidden2, netout))
def myforward (self,inv):
outv=self.netmodel(inv)
return out... | += 1
r /= len(nn_result)
#out += [('recall_top' + str(k) + '_correct_composition', r)]
out.append(str(k) + ' ---> '+ str(r*100))
print(out)
return out
class NLR2(nn.Module):
| 64 | 64 | 104 | 7 | 56 | mohamedaboalimaa/tirg | BK/mainM.py | Python | NLR2 | NLR2 | 729 | 735 | 729 | 729 | 2cc50a1c663496dcc86e033922d7313dc8528672 | bigcode/the-stack | train |
3ef086970b9728792b70bcc2 | train | function | def test_and_save(opt, model, testset):
"""Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
all_captions=[]
if test_queries:
# compute test query features
imgs = []
mod... | def test_and_save(opt, model, testset):
| """Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
all_captions=[]
if test_queries:
# compute test query features
imgs = []
mods = []
for t in tqdm(test_queries):
... | _eucld)
test_train=0
set_size_divider=1
normal_beta=1
create_load=0
filename='REGTS33NE.BTA'
# 5 E
print(' 5 E', file=sourceFile)
out =test_retrieval.test_on_saved(test_train,normal_beta,create_load,filename,normal_normalize, set_size_divider, dot_eucld)
print_results(sourceFile,out,test_train,normal... | 256 | 256 | 1,486 | 11 | 245 | mohamedaboalimaa/tirg | BK/mainM.py | Python | test_and_save | test_and_save | 367 | 511 | 367 | 367 | 8be4711b22ad87cd9eacd220ae4602acf4c9c06b | bigcode/the-stack | train |
5f5daabaecb36747b06cd5c5 | train | function | def ab_Ogetvaluesfilesaved():
trainset = datasets.Fashion200k(
path=Path1,
split='train',
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(224),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
t... | def ab_Ogetvaluesfilesaved():
| trainset = datasets.Fashion200k(
path=Path1,
split='train',
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(224),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize... | /= len(nn_result)
out += [('recall_top' + str(k) + '_correct_adj', r)]
r = 0.0
for i, nns in enumerate(nn_result):
if all_target_captions[i].split()[1] in [c.split()[1] for c in nns[:k]]:
r += 1
r /= len(nn_result)
out += [('recall_top' + str(k) + '_correct_noun', r)]
... | 116 | 116 | 389 | 8 | 107 | mohamedaboalimaa/tirg | BK/mainM.py | Python | ab_Ogetvaluesfilesaved | ab_Ogetvaluesfilesaved | 1,056 | 1,096 | 1,056 | 1,057 | ae447fcea568f5193555f214af502ffb8fdc80ac | bigcode/the-stack | train |
3909dcb3a34ab73135cc0548 | train | function | def ab_Otest(opt, model, testset):
"""Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if test_queries:
# compute test query features
imgs = []
mods = []
for t in tqd... | def ab_Otest(opt, model, testset):
| """Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if test_queries:
# compute test query features
imgs = []
mods = []
for t in tqdm(test_queries):
imgs += [tes... | (),
torchvision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]))
trig= img_text_composition_models.TIRG([t.encode().decode('utf-8') for t in trainset.get_all_texts()],512)
trig.load_state_dict(torch.load(Path1+r'\fashion200k.ti... | 256 | 256 | 1,151 | 11 | 245 | mohamedaboalimaa/tirg | BK/mainM.py | Python | ab_Otest | ab_Otest | 1,098 | 1,211 | 1,098 | 1,098 | 9c49762c7b7514f51e7ee0e59e2942accdc40881 | bigcode/the-stack | train |
c24c04d85c7205ccd76d7f39 | train | function | def train_network_on_saved(test_train,create_load,normal_normalize,filename,sz,dot_eucld):
if test_train==0:
with open(Path1+r"/"+'test_test_queries.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_queriesG.pkl', 'rb') as fp:
all_queries=pickle.load( fp)
with open(Path... | def train_network_on_saved(test_train,create_load,normal_normalize,filename,sz,dot_eucld):
| if test_train==0:
with open(Path1+r"/"+'test_test_queries.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_queriesG.pkl', 'rb') as fp:
all_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_imgsG.pkl', 'rb') as fp:
all_imgs=pickle.load( fp)
with open(Path1+... | nn_result = [[all_captions[nn] for nn in nns] for nns in nn_result]
for k in [1, 5, 10, 50, 100]:
r = 0.0
for i, nns in enumerate(nn_result):
for c in range(k):
if (all_target_captions[i] == nns[c]):
r += 1
r /= len(nn_result)
out2.append(str(k) + ' ---> '+ str(r*100))
fo... | 256 | 256 | 858 | 24 | 231 | mohamedaboalimaa/tirg | BK/mainM.py | Python | train_network_on_saved | train_network_on_saved | 649 | 727 | 649 | 649 | 74fccee5baba26b1af6192857a8837a018c033e0 | bigcode/the-stack | train |
0aba91fb4ce181030345172c | train | function | def neural_model(all_queries,all_imgs,model_option,test_queries):
if model_option==0:
hidden1=800
hidden2=700
batch_size=100
itr=10000
if not test_queries:
build_and_train_netMSE(hidden1,hidden2,itr, 0.02, all_queries,all_imgs,batch_size)
model=NLR2(all_queries.shape[1],all_imgs.shape[1]... | def neural_model(all_queries,all_imgs,model_option,test_queries):
| if model_option==0:
hidden1=800
hidden2=700
batch_size=100
itr=10000
if not test_queries:
build_and_train_netMSE(hidden1,hidden2,itr, 0.02, all_queries,all_imgs,batch_size)
model=NLR2(all_queries.shape[1],all_imgs.shape[1],800,700)
model.load_state_dict(torch.load(Path1+r"/"+r'\NLPM... | 1):
new_all_queries=regression(all_queries,all_imgs,0,test_queries)
if (option==2):
new_all_queries=neural_model(all_queries,all_imgs,0,test_queries)
return new_all_queries
def neural_model(all_queries,all_imgs,model_option,test_queries):
| 64 | 64 | 214 | 14 | 49 | mohamedaboalimaa/tirg | BK/mainM.py | Python | neural_model | neural_model | 1,415 | 1,438 | 1,415 | 1,415 | df3c8cb7df84188f0346c244fdbcecc382db5973 | bigcode/the-stack | train |
d8af14796bc466587ce2c22b | train | function | def mymodels(all_queries,all_imgs,all_target_captions,option,test_queries):
if (option==0):
return all_queries
if (option==1):
new_all_queries=regression(all_queries,all_imgs,0,test_queries)
if (option==2):
new_all_queries=neural_model(all_queries,all_imgs,0,test_queries)
return new_all_queri... | def mymodels(all_queries,all_imgs,all_target_captions,option,test_queries):
| if (option==0):
return all_queries
if (option==1):
new_all_queries=regression(all_queries,all_imgs,0,test_queries)
if (option==2):
new_all_queries=neural_model(all_queries,all_imgs,0,test_queries)
return new_all_queries
| += [('recall_top' + str(k) + '_correct_composition', r)]
out3.append(str(k) + ' ---> '+ str(r*100))
return out, out2, out3
def mymodels(all_queries,all_imgs,all_target_captions,option,test_queries):
| 64 | 64 | 86 | 19 | 44 | mohamedaboalimaa/tirg | BK/mainM.py | Python | mymodels | mymodels | 1,404 | 1,413 | 1,404 | 1,404 | 99ed1a8af97ac57ce27cd0a89400da41491074b0 | bigcode/the-stack | train |
52b5dfc39d590b1aca209343 | train | function | def print_results(sourceFile,out,test_train,normal_beta,create_load,filename,normal_normalize, set_size_divider, dot_eucld):
print(' Experiment setup : ', file = sourceFile)
if (test_train==1):
print('Dataset:Training Data set', file = sourceFile)
else:
print('Dataset:Testing Data set', file = sourceFile)... | def print_results(sourceFile,out,test_train,normal_beta,create_load,filename,normal_normalize, set_size_divider, dot_eucld):
| print(' Experiment setup : ', file = sourceFile)
if (test_train==1):
print('Dataset:Training Data set', file = sourceFile)
else:
print('Dataset:Testing Data set', file = sourceFile)
if (normal_beta==0):
print(' Trig', file = sourceFile)
else:
print(' Trig followed by Regression network', file... | tmp1=tmp1/len(l)
for j in range(len(l)):
new_all_imgs[l[j],:]=tmp1
with open(Path1+r"/"+'new_all_imgs2006172k.pkl', 'wb') as fp:
pickle.dump(new_all_imgs, fp)
def print_results(sourceFile,out,test_train,normal_beta,create_load,filename,normal_normalize, set_size_divider, dot_eucld):
| 93 | 94 | 314 | 31 | 62 | mohamedaboalimaa/tirg | BK/mainM.py | Python | print_results | print_results | 146 | 174 | 146 | 146 | d0966fd03ee00944a44aa37b04ff166a9cdf6f75 | bigcode/the-stack | train |
54c0088c4b47fe5970123326 | train | function | def adapt_dataset(size_limit):
with open(Path1+r"/"+'test_queries1806172k.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'all_queries1806172k.pkl', 'rb') as fp:
all_queries=pickle.load( fp)
with open(Path1+r"/"+'all_imgs1806172k.pkl', 'rb') as fp:
all_imgs=pickle.load( fp)
#... | def adapt_dataset(size_limit):
| with open(Path1+r"/"+'test_queries1806172k.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'all_queries1806172k.pkl', 'rb') as fp:
all_queries=pickle.load( fp)
with open(Path1+r"/"+'all_imgs1806172k.pkl', 'rb') as fp:
all_imgs=pickle.load( fp)
#with open(Path1+r"/"+'all_capti... | with open(Path1+r"/"+'all_imgs1806172k.pkl', 'wb') as fp:
pickle.dump(all_imgs, fp)
with open(Path1+r"/"+'all_captions1806172k.pkl', 'wb') as fp:
pickle.dump(all_captions, fp)
with open(Path1+r"/"+'all_target_captions1806172k.pkl', 'wb') as fp:
pickle.dump(all_target_captions, fp)
def adapt_dataset(si... | 109 | 110 | 367 | 6 | 103 | mohamedaboalimaa/tirg | BK/mainM.py | Python | adapt_dataset | adapt_dataset | 111 | 144 | 111 | 111 | 1c3c25ebdedb8aa320e1815e3b1653f92189d030 | bigcode/the-stack | train |
3f0c41e3efc8bc87f3319121 | train | function | def results_temp():
stime=time.strftime("%Y%m%d-%H%M%S")
sourceFile = open(Path1+r"/"+'results'+stime+'.txt', 'w')
test_train=1
normal_beta=0
set_size_divider=100
normal_normalize=0
create_load=0
filename='beta1806.pkl'
dot_eucld=0
# 1
print(' 1 ', file=sourceFile)
print ('1')
#out =test_on_sa... | def results_temp():
| stime=time.strftime("%Y%m%d-%H%M%S")
sourceFile = open(Path1+r"/"+'results'+stime+'.txt', 'w')
test_train=1
normal_beta=0
set_size_divider=100
normal_normalize=0
create_load=0
filename='beta1806.pkl'
dot_eucld=0
# 1
print(' 1 ', file=sourceFile)
print ('1')
#out =test_on_saved(test_train,norma... | ucld==0):
nn_result.append(np.argsort(-sims[0, :])[:105])
else:
nn_result.append(np.argsort(sims[0, :])[:105])
all_imgs=[]
all_queries=[]
# compute recalls
out = []
nn_result = [[all_captions[nn] for nn in nns] for nns in nn_result]
for k in [1, 5, 10, 50, 100]:
r = 0.0
for i, nn... | 196 | 197 | 658 | 4 | 192 | mohamedaboalimaa/tirg | BK/mainM.py | Python | results_temp | results_temp | 920 | 984 | 920 | 920 | 9532c0577f3cd3bfdcf8059850942df94e001325 | bigcode/the-stack | train |
9f9fb2a22525a2a62bd726eb | train | function | def test_on_saved(test_train,normal_beta,create_load,filename,normal_normalize,sz,dot_eucld):
# test_queries:
if test_train==0:
with open(Path1+r"/"+'test_test_queries.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_queriesG.pkl', 'rb') as fp:
all_queries=pickle.load( ... | def test_on_saved(test_train,normal_beta,create_load,filename,normal_normalize,sz,dot_eucld):
# test_queries:
| if test_train==0:
with open(Path1+r"/"+'test_test_queries.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_queriesG.pkl', 'rb') as fp:
all_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_imgsG.pkl', 'rb') as fp:
all_imgs=pickle.load( fp)
with open(Path1+... | ns[:k]:
r += 1
r /= len(nn_result)
#out += [('recall_top' + str(k) + '_correct_composition', r)]
out.append(str(k) + ' ---> '+ str(r*100))
if opt.dataset == 'mitstates':
r = 0.0
for i, nns in enumerate(nn_result):
if all_target_captions[i].split()[0] in [c.split()[0] for c i... | 256 | 256 | 1,456 | 31 | 224 | mohamedaboalimaa/tirg | BK/mainM.py | Python | test_on_saved | test_on_saved | 513 | 647 | 513 | 514 | 97462d4d851edeecb4819082fd83f0d32f2d1b09 | bigcode/the-stack | train |
e1ff7a4e37ce29d6a52def9f | train | function | def build_and_train_netMSE(hidden1,hidden2,max_iterations, min_error, all_queries,all_imgs,batch_size):
all_queries=Variable(torch.Tensor(all_queries))
all_imgs=variable(torch.tensor(all_imgs))
model=NLR2(all_queries.shape[1],all_imgs.shape[1],hidden1,hidden2)
#model=model.cuda()
torch.manual_seed(3)
loss_f... | def build_and_train_netMSE(hidden1,hidden2,max_iterations, min_error, all_queries,all_imgs,batch_size):
| all_queries=Variable(torch.Tensor(all_queries))
all_imgs=variable(torch.tensor(all_imgs))
model=NLR2(all_queries.shape[1],all_imgs.shape[1],hidden1,hidden2)
#model=model.cuda()
torch.manual_seed(3)
loss_fn = torch.nn.MSELoss()
torch.manual_seed(3)
#criterion = nn.CosineSimilarity()
criterion=nn.MSELo... | __(self,netin,netout,nethidden1,nethidden2):
super().__init__()
self.netmodel= torch.nn.Sequential(torch.nn.Linear(netin, nethidden1),torch.nn.Sigmoid(),torch.nn.Linear(nethidden1, nethidden2),torch.nn.Linear(nethidden2, netout))
def myforward (self,inv):
outv=self.netmodel(inv)
return outv
def build_... | 120 | 120 | 402 | 27 | 92 | mohamedaboalimaa/tirg | BK/mainM.py | Python | build_and_train_netMSE | build_and_train_netMSE | 737 | 777 | 737 | 737 | ff3c2aeec2cd71330c5e986f78c2a8f96f699de6 | bigcode/the-stack | train |
89496cb0d1ffdad8e8e45231 | train | function | def regression(all_queries,all_imgs,option, test_queries):
if (option==0 ):
if (test_queries):
with open(Path1+r"/"+'beta0.pkl', 'rb') as fp:
beta=pickle.load( fp)
new_all_queries=np.zeros(all_queries.shape)
for i in range(new_all_queries.shape[0]):
new_all_queries[i,:]=np.ma... | def regression(all_queries,all_imgs,option, test_queries):
| if (option==0 ):
if (test_queries):
with open(Path1+r"/"+'beta0.pkl', 'rb') as fp:
beta=pickle.load( fp)
new_all_queries=np.zeros(all_queries.shape)
for i in range(new_all_queries.shape[0]):
new_all_queries[i,:]=np.matmul(all_queries[i,:],beta)
else:
new_all_queries=al... | forward(all_queries)
new_all_queries = torch.tensor(new_all_queries,requires_grad=False)
#all_queries.detach().numpy()
new_all_queries=np.array(new_all_queries)
return new_all_queries
else:
return all_queries
def regression(all_queries,all_imgs,option, test_queries):
| 66 | 67 | 225 | 14 | 51 | mohamedaboalimaa/tirg | BK/mainM.py | Python | regression | regression | 1,441 | 1,463 | 1,441 | 1,442 | d4ef2561c51a51f880d0f3af30e1243fb737b252 | bigcode/the-stack | train |
b30a70c4edd9bb8578230570 | train | function | def ab_Mgetvaluesfilesaved(option):
trainset = datasets.Fashion200k(
path=Path1,
split='train',
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(224),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
... | def ab_Mgetvaluesfilesaved(option):
| trainset = datasets.Fashion200k(
path=Path1,
split='train',
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(224),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize... | ns[:k]]:
r += 1
r /= len(nn_result)
out += [('recall_top' + str(k) + '_correct_adj', r)]
r = 0.0
for i, nns in enumerate(nn_result):
if all_target_captions[i].split()[1] in [c.split()[1] for c in nns[:k]]:
r += 1
r /= len(nn_result)
out += [('recall_top' ... | 129 | 129 | 433 | 9 | 119 | mohamedaboalimaa/tirg | BK/mainM.py | Python | ab_Mgetvaluesfilesaved | ab_Mgetvaluesfilesaved | 1,213 | 1,254 | 1,213 | 1,214 | 65c2cf23d5da755915822963442bcda057dccfd8 | bigcode/the-stack | train |
ec64c0f51412002314ea1684 | train | function | def abt_MtestLoaded(opt, model, testset,option):
"""Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if test_queries:
# compute test query features
all_imgs = datasets... | def abt_MtestLoaded(opt, model, testset,option):
| """Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if test_queries:
# compute test query features
all_imgs = datasets.Features33K().Get_all_images()
all_captions ... | r += 1
r /= len(new_nn_result)
#out += [('recall_top' + str(k) + '_correct_composition', r)]
out2.append(str(k) + ' ---> '+ str(r*100))
r = 0.0
for i, nns in enumerate(nn_result):
if all_target_captions[i] in nns[:k]:
r += 1
r /= len(nn_result)
#out += [('recall_top' +... | 160 | 160 | 536 | 15 | 144 | mohamedaboalimaa/tirg | BK/mainM.py | Python | abt_MtestLoaded | abt_MtestLoaded | 1,350 | 1,402 | 1,350 | 1,350 | cbcbf04043239025b584c4cc6790404aa6bec3ac | bigcode/the-stack | train |
7ea3bd734dc2416cc2f94e6a | train | function | def results():
sourceFile = open(Path1+r"/"+'results'+time.strftime("%Y%m%d-%H%M%S")+'.txt', 'w')
test_train=0
normal_beta=0
set_size_divider=1
normal_normalize=0
create_load=0
filename='na'
dot_eucld=0
# 1
print(' 1', file=sourceFile)
out =test_retrieval.test_on_saved(test_train,normal_beta,creat... | def results():
| sourceFile = open(Path1+r"/"+'results'+time.strftime("%Y%m%d-%H%M%S")+'.txt', 'w')
test_train=0
normal_beta=0
set_size_divider=1
normal_normalize=0
create_load=0
filename='na'
dot_eucld=0
# 1
print(' 1', file=sourceFile)
out =test_retrieval.test_on_saved(test_train,normal_beta,create_load,filename... | = sourceFile)
else:
print('Dataset:Testing Data set', file = sourceFile)
if (normal_beta==0):
print(' Trig', file = sourceFile)
else:
print(' Trig followed by Regression network', file = sourceFile)
if (normal_beta==1):
if (create_load==0):
print(' Regression Network Created, save to fil... | 256 | 256 | 2,191 | 3 | 253 | mohamedaboalimaa/tirg | BK/mainM.py | Python | results | results | 176 | 365 | 176 | 176 | 6d76d0ee90d6a3dde9cbd13cd2acdce289bc4de0 | bigcode/the-stack | train |
b0ab4bdddb1260e34757ead8 | train | function | def ab_OtestLoaded(opt, model, testset):
"""Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
test_train=1
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if (test_train)==0:
# compute test query features
all_imgs ... | def ab_OtestLoaded(opt, model, testset):
| """Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
test_train=1
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if (test_train)==0:
# compute test query features
all_imgs = datasets.Features33K().Get_all_images()... | d)
print_results(sourceFile,out,test_train,normal_beta,create_load,filename,normal_normalize, set_size_divider, dot_eucld)
sourceFile.close()
sourceFile = open(Path1+r"/"+'results'+stime+'.txt', 'a')
test_train=1
normal_beta=1
set_size_divider=100
normal_normalize=0
create_load=0
filename='beta1806euc... | 210 | 210 | 700 | 12 | 198 | mohamedaboalimaa/tirg | BK/mainM.py | Python | ab_OtestLoaded | ab_OtestLoaded | 986 | 1,054 | 986 | 986 | dd010e7758077db7aee908b76b80c94c9dd40e4b | bigcode/the-stack | train |
3cc83e81886232e01458bd01 | train | function | def Reform_Training_Dataset():
train = datasets.Fashion200k(
path=Path1,
split='train',
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(224),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
... | def Reform_Training_Dataset():
| train = datasets.Fashion200k(
path=Path1,
split='train',
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(224),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0... |
import torch.nn as nn
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
from torch import optim
import torch.nn.functional as F
import math as m
import time
import os
#from google.colab import drive
import random
from PIL import Image
from torch.autograd import Variable, variable
f... | 196 | 196 | 655 | 8 | 188 | mohamedaboalimaa/tirg | BK/mainM.py | Python | Reform_Training_Dataset | Reform_Training_Dataset | 43 | 109 | 43 | 45 | 0839a2613cb7dbb4d550b63ec7b70ff7255430e6 | bigcode/the-stack | train |
256aad516a17a3f2953bb3e0 | train | function | def test_on_saved_NN_CMP(test_train,normal_beta_NN,create_load,filename,normal_normalize,sz,dot_eucld,hiddensize,model_fn):
# test_queries:
if test_train==0:
with open(Path1+r"/"+'test_test_queries.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_queriesG.pkl', 'rb') as fp:... | def test_on_saved_NN_CMP(test_train,normal_beta_NN,create_load,filename,normal_normalize,sz,dot_eucld,hiddensize,model_fn):
# test_queries:
| if test_train==0:
with open(Path1+r"/"+'test_test_queries.pkl', 'rb') as fp:
test_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_queriesG.pkl', 'rb') as fp:
all_queries=pickle.load( fp)
with open(Path1+r"/"+'test_all_imgsG.pkl', 'rb') as fp:
all_imgs=pickle.load( fp)
with open(Path1+... | _size:(l+1)*batch_size-1,:]
target_batch=all_imgs[l*batch_size:(l+1)*batch_size-1,:]
netoutbatch=model.myforward(item_batch)
loss = loss_fn(target_batch,netoutbatch)
losses.append(loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss+=loss
if (l%10... | 255 | 256 | 1,421 | 44 | 211 | mohamedaboalimaa/tirg | BK/mainM.py | Python | test_on_saved_NN_CMP | test_on_saved_NN_CMP | 779 | 918 | 779 | 780 | a8a9232c007d8c8f1992154bf730e95b66e523ea | bigcode/the-stack | train |
3d2fa5e506735ae2067aa873 | train | function | def ab_MtestLoaded(opt, model, testset,option):
"""Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if test_queries:
# compute test query features
all_imgs = datasets.... | def ab_MtestLoaded(opt, model, testset,option):
| """Tests a model over the given testset."""
model.eval()
test_queries = testset.get_test_queries()
all_imgs = []
all_captions = []
all_queries = []
all_target_captions = []
if test_queries:
# compute test query features
all_imgs = datasets.Features33K().Get_all_images()
all_captions ... | _models.TIRG([t.encode().decode('utf-8') for t in trainset.get_all_texts()],512)
trig.load_state_dict(torch.load(Path1+r'\fashion200k.tirg.iter160k.pth' , map_location=torch.device('cpu') )['model_state_dict'])
opt = argparse.ArgumentParser()
opt.add_argument('--batch_size', type=int, default=2)
opt.add_arg... | 256 | 256 | 1,010 | 14 | 242 | mohamedaboalimaa/tirg | BK/mainM.py | Python | ab_MtestLoaded | ab_MtestLoaded | 1,256 | 1,348 | 1,256 | 1,256 | 9de86cccc74bddd1bc02166bbd4252956090e20e | bigcode/the-stack | train |
c18852cf6d7a12638aa2dd6f | train | class | class ShapeBase(ResourceBase):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types... | class ShapeBase(ResourceBase):
| """
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'self_uri': 'Resourc... | of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnishe... | 256 | 256 | 3,333 | 7 | 248 | rizwanniazigroupdocs/aspose-slides-cloud-python | asposeslidescloud/models/shape_base.py | Python | ShapeBase | ShapeBase | 35 | 496 | 35 | 37 | 022d2865ac1b19ec58096755c687088f66c8aa74 | bigcode/the-stack | train |
dbc590113ea6baed7665e752 | train | class | class InitializersTest(absltest.TestCase):
def test_random_normal(self):
initializer = initializers.RandomNormalInitializer()
input_shape = (29, 5, 7, 20)
init_value = initializer(input_shape, random.get_prng(0))
self.assertEqual(tuple(init_value.shape), input_shape)
def test_lecun_uniform(self):
... | class InitializersTest(absltest.TestCase):
| def test_random_normal(self):
initializer = initializers.RandomNormalInitializer()
input_shape = (29, 5, 7, 20)
init_value = initializer(input_shape, random.get_prng(0))
self.assertEqual(tuple(init_value.shape), input_shape)
def test_lecun_uniform(self):
initializer = initializers.LeCunUniformI... | coding=utf-8
# Copyright 2020 The Trax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 207 | 207 | 692 | 10 | 196 | jackalhan/trax | trax/layers/initializers_test.py | Python | InitializersTest | InitializersTest | 27 | 91 | 27 | 28 | 22dfdd69e7f55c1eb364ff96dbc46b8079b66aaf | bigcode/the-stack | train |
b9d4672256f648c1b4b2e12c | train | function | @keras_export('keras.datasets.cifar100.load_data')
def load_data(label_mode='fine'):
"""Loads the CIFAR100 dataset.
This is a dataset of 50,000 32x32 color training images and
10,000 test images, labeled over 100 fine-grained classes that are
grouped into 20 coarse-grained classes. See more info at the
[CIFA... | @keras_export('keras.datasets.cifar100.load_data')
def load_data(label_mode='fine'):
| """Loads the CIFAR100 dataset.
This is a dataset of 50,000 32x32 color training images and
10,000 test images, labeled over 100 fine-grained classes that are
grouped into 20 coarse-grained classes. See more info at the
[CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html).
Args:
label_mode: on... | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 208 | 226 | 755 | 20 | 187 | Halo9Pan/dive-keras | keras/datasets/cifar100.py | Python | load_data | load_data | 27 | 93 | 27 | 28 | 3a8fda5019a231df23a71bee1f924d169e4be046 | bigcode/the-stack | train |
106161de3342062b17837bed | train | class | class FmColorEdit(QtGui.QLineEdit):
def __init__(self, parent):
super(FmColorEdit, self).__init__(parent)
self.setReadOnly(True)
def mousePressEvent(self, event):
self.color = QtGui.QColorDialog.getColor(Qt.blue)
palette = self.palette()
palette.setColor(QPalette.Base, ... | class FmColorEdit(QtGui.QLineEdit):
| def __init__(self, parent):
super(FmColorEdit, self).__init__(parent)
self.setReadOnly(True)
def mousePressEvent(self, event):
self.color = QtGui.QColorDialog.getColor(Qt.blue)
palette = self.palette()
palette.setColor(QPalette.Base, self.color)
self.setPalette(p... | from PyQt4.QtGui import QPalette, QColor
__author__ = 'pawel'
from PyQt4 import QtGui
from PyQt4.QtCore import Qt
class FmColorEdit(QtGui.QLineEdit):
| 48 | 64 | 137 | 11 | 36 | ComputerArchitectureGroupPWr/Floorplan-Maker | src/fmWidgets/FmColorEdit.py | Python | FmColorEdit | FmColorEdit | 9 | 28 | 9 | 10 | f089c3e312f92c83ab4056f45ecf3ba2a793436d | bigcode/the-stack | train |
7b01df86b1bf24d9d3deb949 | train | class | class SaharaOutputDataSourcesTestCase(test.ScenarioTestCase):
def setUp(self):
super(SaharaOutputDataSourcesTestCase, self).setUp()
fake_dict = objects.Credential("http://fake.example.org:5000/v2.0/",
"user", "passwd")
self.tenants_num = 2
self... | class SaharaOutputDataSourcesTestCase(test.ScenarioTestCase):
| def setUp(self):
super(SaharaOutputDataSourcesTestCase, self).setUp()
fake_dict = objects.Credential("http://fake.example.org:5000/v2.0/",
"user", "passwd")
self.tenants_num = 2
self.users_per_tenant = 2
self.users = self.tenants_num * s... | # All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
#... | 183 | 256 | 951 | 13 | 170 | varuntiwari27/rally | tests/unit/plugins/openstack/context/sahara/test_sahara_output_data_sources.py | Python | SaharaOutputDataSourcesTestCase | SaharaOutputDataSourcesTestCase | 24 | 145 | 24 | 25 | 99c5947d8a477b00f8867624afa9355ff84aba64 | bigcode/the-stack | train |
624e06e6c026cdf63fd3eb8d | train | function | @User.on_message(filters.private & filters.incoming & ~filters.bot & ~filters.service & ~filters.me & ~filters.edited)
async def nopm(client, message):
if REPLY_MESSAGE is not None:
try:
inline = await client.get_inline_bot_results(USERNAME, "UFSBotz")
await client.send_inline_bot_re... | @User.on_message(filters.private & filters.incoming & ~filters.bot & ~filters.service & ~filters.me & ~filters.edited)
async def nopm(client, message):
| if REPLY_MESSAGE is not None:
try:
inline = await client.get_inline_bot_results(USERNAME, "UFSBotz")
await client.send_inline_bot_result(
message.chat.id,
query_id=inline.query_id,
result_id=inline.results[0].id,
hide_vi... | = Config.REPLY_MESSAGE
User = ufs(
Config.SESSION_STRING,
Config.API_ID,
Config.API_HASH
)
@User.on_message(filters.private & filters.incoming & ~filters.bot & ~filters.service & ~filters.me & ~filters.edited)
async def nopm(client, message):
| 64 | 64 | 214 | 36 | 28 | jinspalakkattu/UFSStreamingBot | bot/ufsbotz/nopm.py | Python | nopm | nopm | 40 | 63 | 40 | 41 | 093a7675168a236ad26c52ab2770680ab243d343 | bigcode/the-stack | train |
9b2c64a3e1d89b358bc59e6b | train | function | def Main1Page(wd, url):
wd.get(url)
TypeXpath = 'XPath 값'
wd.find_element_by_xpath(TypeXpath).click()
time.sleep(2)
wd.find_element_by_name('searchWord').send_keys('입력값')
time.sleep(2)
SearchXpath = 'XPath 값'
wd.find_element_by_xpath(SearchXpath).click()
time.sleep(5)
#driver.save_screenshot(f'.\\Capture2\... | def Main1Page(wd, url):
| wd.get(url)
TypeXpath = 'XPath 값'
wd.find_element_by_xpath(TypeXpath).click()
time.sleep(2)
wd.find_element_by_name('searchWord').send_keys('입력값')
time.sleep(2)
SearchXpath = 'XPath 값'
wd.find_element_by_xpath(SearchXpath).click()
time.sleep(5)
#driver.save_screenshot(f'.\\Capture2\\Capture{i+1}.png')
c... | userid').send_keys('ID')
wd.find_element_by_name('password').send_keys('비밀번호')
time.sleep(1)
LoginXpath='//*[@id="big_login"]/fieldset/form/a'
wd.find_element_by_xpath(LoginXpath).click()
time.sleep(3)
def Main1Page(wd, url):
| 66 | 66 | 221 | 9 | 57 | liberte97/FileCpature | LoginA.py | Python | Main1Page | Main1Page | 19 | 42 | 19 | 19 | bdf911392b725a148895ebc22e517e3ebaeb60d2 | bigcode/the-stack | train |
ddb2631db8c6da7fc663efc1 | train | function | def Login(wd, url):
wd.get(url)
LoginXpath = '//*[@id="arGnb"]/div/div/div[3]/div/a[1]'
wd.find_element_by_xpath(LoginXpath).click()
time.sleep(2)
wd.find_element_by_name('userid').send_keys('ID')
wd.find_element_by_name('password').send_keys('비밀번호')
time.sleep(1)
LoginXpath='//*[@id="big_login"]/fieldset/form/... | def Login(wd, url):
| wd.get(url)
LoginXpath = '//*[@id="arGnb"]/div/div/div[3]/div/a[1]'
wd.find_element_by_xpath(LoginXpath).click()
time.sleep(2)
wd.find_element_by_name('userid').send_keys('ID')
wd.find_element_by_name('password').send_keys('비밀번호')
time.sleep(1)
LoginXpath='//*[@id="big_login"]/fieldset/form/a'
wd.find_element_... | from selenium import webdriver
import time, os, errno, shutil
import pandas as pd
import webbrowser, subprocess, pyautogui
from PIL import ImageGrab
def Login(wd, url):
| 43 | 64 | 115 | 7 | 35 | liberte97/FileCpature | LoginA.py | Python | Login | Login | 7 | 17 | 7 | 7 | eb76556ada2901fe378fd2edee701c225a7cd20e | bigcode/the-stack | train |
570e41d6f8944ad280979b7d | train | function | def WebCrawMain(url, title_site):
wd = webdriver.Chrome('chromedriver.exe')
wd.implicitly_wait(3)
wd.set_window_position(50,50)
wd.set_window_size(1200, 1350)
Login(wd, url)
Main1Page(wd, url)
for i in range(2):
wd.get(title_site[i+1])
time.sleep(3)
im = ImageGrab.grab(bbox=(60, 50, 1140, 1040))
#driver... | def WebCrawMain(url, title_site):
| wd = webdriver.Chrome('chromedriver.exe')
wd.implicitly_wait(3)
wd.set_window_position(50,50)
wd.set_window_size(1200, 1350)
Login(wd, url)
Main1Page(wd, url)
for i in range(2):
wd.get(title_site[i+1])
time.sleep(3)
im = ImageGrab.grab(bbox=(60, 50, 1140, 1040))
#driver.save_screenshot(f'.\\Capture2\\Ca... | time.sleep(2)
if (i+1)%20 == 0:
count = count + 1
NextXpath = f'XPath 값'
wd.find_element_by_xpath(NextXpath).click()
time.sleep(4)
def WebCrawMain(url, title_site):
| 64 | 64 | 191 | 10 | 54 | liberte97/FileCpature | LoginA.py | Python | WebCrawMain | WebCrawMain | 44 | 62 | 44 | 44 | 8af1b7fc6d68aa493b619e50c4755238e93c7580 | bigcode/the-stack | train |
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