body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
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def scan_status_command(client: Client, args: Dict[(str, Any)]) -> CommandResults:
"helloworld-scan-status command: Returns status for HelloWorld scans\n\n :type client: ``Client``\n :param Client: HelloWorld client to use\n\n :type args: ``Dict[str, Any]``\n :param args:\n all command arguments,... | 1,625,731,828,764,689,200 | helloworld-scan-status command: Returns status for HelloWorld scans
:type client: ``Client``
:param Client: HelloWorld client to use
:type args: ``Dict[str, Any]``
:param args:
all command arguments, usually passed from ``demisto.args()``.
``args['scan_id']`` list of scan IDs or single scan ID
:return:
A... | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | scan_status_command | DeanArbel/content | python | def scan_status_command(client: Client, args: Dict[(str, Any)]) -> CommandResults:
"helloworld-scan-status command: Returns status for HelloWorld scans\n\n :type client: ``Client``\n :param Client: HelloWorld client to use\n\n :type args: ``Dict[str, Any]``\n :param args:\n all command arguments,... |
def scan_results_command(client: Client, args: Dict[(str, Any)]) -> Union[(Dict[(str, Any)], CommandResults, List[CommandResults])]:
"helloworld-scan-results command: Returns results for a HelloWorld scan\n\n :type client: ``Client``\n :param Client: HelloWorld client to use\n\n :type args: ``Dict[str, Any... | -1,730,858,595,813,137,200 | helloworld-scan-results command: Returns results for a HelloWorld scan
:type client: ``Client``
:param Client: HelloWorld client to use
:type args: ``Dict[str, Any]``
:param args:
all command arguments, usually passed from ``demisto.args()``.
``args['scan_id']`` scan ID to retrieve results
``args['format'... | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | scan_results_command | DeanArbel/content | python | def scan_results_command(client: Client, args: Dict[(str, Any)]) -> Union[(Dict[(str, Any)], CommandResults, List[CommandResults])]:
"helloworld-scan-results command: Returns results for a HelloWorld scan\n\n :type client: ``Client``\n :param Client: HelloWorld client to use\n\n :type args: ``Dict[str, Any... |
def main() -> None:
'main function, parses params and runs command functions\n\n :return:\n :rtype:\n '
api_key = demisto.params().get('apikey')
base_url = urljoin(demisto.params()['url'], '/api/v1')
verify_certificate = (not demisto.params().get('insecure', False))
first_fetch_time = arg_t... | -8,174,009,125,033,763,000 | main function, parses params and runs command functions
:return:
:rtype: | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | main | DeanArbel/content | python | def main() -> None:
'main function, parses params and runs command functions\n\n :return:\n :rtype:\n '
api_key = demisto.params().get('apikey')
base_url = urljoin(demisto.params()['url'], '/api/v1')
verify_certificate = (not demisto.params().get('insecure', False))
first_fetch_time = arg_t... |
def get_ip_reputation(self, ip: str) -> Dict[(str, Any)]:
"Gets the IP reputation using the '/ip' API endpoint\n\n :type ip: ``str``\n :param ip: IP address to get the reputation for\n\n :return: dict containing the IP reputation as returned from the API\n :rtype: ``Dict[str, Any]``\n ... | -5,505,118,003,103,052,000 | Gets the IP reputation using the '/ip' API endpoint
:type ip: ``str``
:param ip: IP address to get the reputation for
:return: dict containing the IP reputation as returned from the API
:rtype: ``Dict[str, Any]`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | get_ip_reputation | DeanArbel/content | python | def get_ip_reputation(self, ip: str) -> Dict[(str, Any)]:
"Gets the IP reputation using the '/ip' API endpoint\n\n :type ip: ``str``\n :param ip: IP address to get the reputation for\n\n :return: dict containing the IP reputation as returned from the API\n :rtype: ``Dict[str, Any]``\n ... |
def get_domain_reputation(self, domain: str) -> Dict[(str, Any)]:
"Gets the Domain reputation using the '/domain' API endpoint\n\n :type domain: ``str``\n :param domain: domain name to get the reputation for\n\n :return: dict containing the domain reputation as returned from the API\n :r... | 4,621,716,766,601,556,000 | Gets the Domain reputation using the '/domain' API endpoint
:type domain: ``str``
:param domain: domain name to get the reputation for
:return: dict containing the domain reputation as returned from the API
:rtype: ``Dict[str, Any]`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | get_domain_reputation | DeanArbel/content | python | def get_domain_reputation(self, domain: str) -> Dict[(str, Any)]:
"Gets the Domain reputation using the '/domain' API endpoint\n\n :type domain: ``str``\n :param domain: domain name to get the reputation for\n\n :return: dict containing the domain reputation as returned from the API\n :r... |
def search_alerts(self, alert_status: Optional[str], severity: Optional[str], alert_type: Optional[str], max_results: Optional[int], start_time: Optional[int]) -> List[Dict[(str, Any)]]:
'Searches for HelloWorld alerts using the \'/get_alerts\' API endpoint\n\n All the parameters are passed directly to the A... | 2,007,290,296,748,268,500 | Searches for HelloWorld alerts using the '/get_alerts' API endpoint
All the parameters are passed directly to the API as HTTP POST parameters in the request
:type alert_status: ``Optional[str]``
:param alert_status: status of the alert to search for. Options are: 'ACTIVE' or 'CLOSED'
:type severity: ``Optional[str]`... | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | search_alerts | DeanArbel/content | python | def search_alerts(self, alert_status: Optional[str], severity: Optional[str], alert_type: Optional[str], max_results: Optional[int], start_time: Optional[int]) -> List[Dict[(str, Any)]]:
'Searches for HelloWorld alerts using the \'/get_alerts\' API endpoint\n\n All the parameters are passed directly to the A... |
def get_alert(self, alert_id: str) -> Dict[(str, Any)]:
'Gets a specific HelloWorld alert by id\n\n :type alert_id: ``str``\n :param alert_id: id of the alert to return\n\n :return: dict containing the alert as returned from the API\n :rtype: ``Dict[str, Any]``\n '
return self... | -3,893,194,839,806,734,300 | Gets a specific HelloWorld alert by id
:type alert_id: ``str``
:param alert_id: id of the alert to return
:return: dict containing the alert as returned from the API
:rtype: ``Dict[str, Any]`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | get_alert | DeanArbel/content | python | def get_alert(self, alert_id: str) -> Dict[(str, Any)]:
'Gets a specific HelloWorld alert by id\n\n :type alert_id: ``str``\n :param alert_id: id of the alert to return\n\n :return: dict containing the alert as returned from the API\n :rtype: ``Dict[str, Any]``\n '
return self... |
def update_alert_status(self, alert_id: str, alert_status: str) -> Dict[(str, Any)]:
"Changes the status of a specific HelloWorld alert\n\n :type alert_id: ``str``\n :param alert_id: id of the alert to return\n\n :type alert_status: ``str``\n :param alert_status: new alert status. Option... | 4,261,590,240,170,449,000 | Changes the status of a specific HelloWorld alert
:type alert_id: ``str``
:param alert_id: id of the alert to return
:type alert_status: ``str``
:param alert_status: new alert status. Options are: 'ACTIVE' or 'CLOSED'
:return: dict containing the alert as returned from the API
:rtype: ``Dict[str, Any]`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | update_alert_status | DeanArbel/content | python | def update_alert_status(self, alert_id: str, alert_status: str) -> Dict[(str, Any)]:
"Changes the status of a specific HelloWorld alert\n\n :type alert_id: ``str``\n :param alert_id: id of the alert to return\n\n :type alert_status: ``str``\n :param alert_status: new alert status. Option... |
def scan_start(self, hostname: str) -> Dict[(str, Any)]:
'Starts a HelloWorld scan on a specific hostname\n\n :type hostname: ``str``\n :param hostname: hostname of the machine to scan\n\n :return: dict containing the scan status as returned from the API\n :rtype: ``Dict[str, Any]``\n ... | -6,631,833,082,852,968,000 | Starts a HelloWorld scan on a specific hostname
:type hostname: ``str``
:param hostname: hostname of the machine to scan
:return: dict containing the scan status as returned from the API
:rtype: ``Dict[str, Any]`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | scan_start | DeanArbel/content | python | def scan_start(self, hostname: str) -> Dict[(str, Any)]:
'Starts a HelloWorld scan on a specific hostname\n\n :type hostname: ``str``\n :param hostname: hostname of the machine to scan\n\n :return: dict containing the scan status as returned from the API\n :rtype: ``Dict[str, Any]``\n ... |
def scan_status(self, scan_id: str) -> Dict[(str, Any)]:
'Gets the status of a HelloWorld scan\n\n :type scan_id: ``str``\n :param scan_id: ID of the scan to retrieve status for\n\n :return: dict containing the scan status as returned from the API\n :rtype: ``Dict[str, Any]``\n '
... | 4,874,480,294,823,485,000 | Gets the status of a HelloWorld scan
:type scan_id: ``str``
:param scan_id: ID of the scan to retrieve status for
:return: dict containing the scan status as returned from the API
:rtype: ``Dict[str, Any]`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | scan_status | DeanArbel/content | python | def scan_status(self, scan_id: str) -> Dict[(str, Any)]:
'Gets the status of a HelloWorld scan\n\n :type scan_id: ``str``\n :param scan_id: ID of the scan to retrieve status for\n\n :return: dict containing the scan status as returned from the API\n :rtype: ``Dict[str, Any]``\n '
... |
def scan_results(self, scan_id: str) -> Dict[(str, Any)]:
'Gets the results of a HelloWorld scan\n\n :type scan_id: ``str``\n :param scan_id: ID of the scan to retrieve results for\n\n :return: dict containing the scan results as returned from the API\n :rtype: ``Dict[str, Any]``\n ... | -3,338,946,734,264,717,300 | Gets the results of a HelloWorld scan
:type scan_id: ``str``
:param scan_id: ID of the scan to retrieve results for
:return: dict containing the scan results as returned from the API
:rtype: ``Dict[str, Any]`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | scan_results | DeanArbel/content | python | def scan_results(self, scan_id: str) -> Dict[(str, Any)]:
'Gets the results of a HelloWorld scan\n\n :type scan_id: ``str``\n :param scan_id: ID of the scan to retrieve results for\n\n :return: dict containing the scan results as returned from the API\n :rtype: ``Dict[str, Any]``\n ... |
def say_hello(self, name: str) -> str:
"Returns 'Hello {name}'\n\n :type name: ``str``\n :param name: name to append to the 'Hello' string\n\n :return: string containing 'Hello {name}'\n :rtype: ``str``\n "
return f'Hello {name}' | -5,721,078,814,974,353,000 | Returns 'Hello {name}'
:type name: ``str``
:param name: name to append to the 'Hello' string
:return: string containing 'Hello {name}'
:rtype: ``str`` | Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.py | say_hello | DeanArbel/content | python | def say_hello(self, name: str) -> str:
"Returns 'Hello {name}'\n\n :type name: ``str``\n :param name: name to append to the 'Hello' string\n\n :return: string containing 'Hello {name}'\n :rtype: ``str``\n "
return f'Hello {name}' |
def calculate_psnr(img1, img2):
'\n data range [0, 1]\n '
img1 = img1.clamp(0, 1)
img2 = img2.clamp(0, 1)
mse = torch.mean(((img1 - img2) ** 2), [1, 2, 3])
PIXEL_MAX = 1
return (20 * torch.mean(torch.log10((PIXEL_MAX / torch.sqrt(mse))))) | 847,792,582,663,107,200 | data range [0, 1] | utils/metrics.py | calculate_psnr | Wang-jiahao/SimDeblur | python | def calculate_psnr(img1, img2):
'\n \n '
img1 = img1.clamp(0, 1)
img2 = img2.clamp(0, 1)
mse = torch.mean(((img1 - img2) ** 2), [1, 2, 3])
PIXEL_MAX = 1
return (20 * torch.mean(torch.log10((PIXEL_MAX / torch.sqrt(mse))))) |
def printParaNum(model):
'\n function: print the number of total parameters and trainable parameters\n '
total_params = sum((p.numel() for p in model.parameters()))
total_trainable_params = sum((p.numel() for p in model.parameters() if p.requires_grad))
print(('Total parameters: %d' % total_params... | 2,902,576,970,362,084,000 | function: print the number of total parameters and trainable parameters | src/train_amp.py | printParaNum | suiyizhao/Pytorch-speedup | python | def printParaNum(model):
'\n \n '
total_params = sum((p.numel() for p in model.parameters()))
total_trainable_params = sum((p.numel() for p in model.parameters() if p.requires_grad))
print(('Total parameters: %d' % total_params))
print(('Trainable parameters: %d' % total_trainable_params)) |
def set_random_seed(seed, deterministic=False):
'\n function: Set random seed.\n\n Args:\n seed (int): Seed to be used.\n deterministic (bool): Whether to set the deterministic option for\n CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`\n to True and `torch.b... | -1,521,102,580,318,788,400 | function: Set random seed.
Args:
seed (int): Seed to be used.
deterministic (bool): Whether to set the deterministic option for
CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
to True and `torch.backends.cudnn.benchmark` to False.
Default: False. | src/train_amp.py | set_random_seed | suiyizhao/Pytorch-speedup | python | def set_random_seed(seed, deterministic=False):
'\n function: Set random seed.\n\n Args:\n seed (int): Seed to be used.\n deterministic (bool): Whether to set the deterministic option for\n CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`\n to True and `torch.b... |
@ops.RegisterGradient('Batch')
def _BatchGrad(op, *out_grads):
'Gradient for batch op.'
gradients = []
for i in range(len(op.inputs)):
gradients.append(gen_batch_ops.unbatch(out_grads[i], op.outputs[(- 2)], op.outputs[(- 1)], timeout_micros=op.get_attr('grad_timeout_micros'), shared_name='batch_grad... | 1,933,694,469,260,478,700 | Gradient for batch op. | tensorflow/contrib/batching/python/ops/batch_ops.py | _BatchGrad | ekyuho/tensorflow | python | @ops.RegisterGradient('Batch')
def _BatchGrad(op, *out_grads):
gradients = []
for i in range(len(op.inputs)):
gradients.append(gen_batch_ops.unbatch(out_grads[i], op.outputs[(- 2)], op.outputs[(- 1)], timeout_micros=op.get_attr('grad_timeout_micros'), shared_name='batch_gradient_{}_{}'.format(op.na... |
def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, allowed_batch_sizes=None, grad_timeout_micros=((60 * 1000) * 1000), unbatch_timeout_micros=((60 * 1000) * 1000)):
'Batches the computation done by the decorated function.\n\n So, for example, in the following code\n\n ```python\n @batch_... | -9,184,000,206,858,053,000 | Batches the computation done by the decorated function.
So, for example, in the following code
```python
@batch_function(1, 2, 3)
def layer(a):
return tf.matmul(a, a)
b = layer(w)
```
if more than one session.run call is simultaneously trying to compute `b`
the values of `w` will be gathered, non-deterministicall... | tensorflow/contrib/batching/python/ops/batch_ops.py | batch_function | ekyuho/tensorflow | python | def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, allowed_batch_sizes=None, grad_timeout_micros=((60 * 1000) * 1000), unbatch_timeout_micros=((60 * 1000) * 1000)):
'Batches the computation done by the decorated function.\n\n So, for example, in the following code\n\n ```python\n @batch_... |
async def execute_wkhtmltopdf(uri: str) -> bytes:
'Run wkhtmltopdf on the command-line and return the output.'
cmd = ['wkhtmltopdf', '--log-level', 'none', uri, '-']
return check_output(cmd) | -8,748,739,963,590,872,000 | Run wkhtmltopdf on the command-line and return the output. | src/app.py | execute_wkhtmltopdf | mtik00/wkhtmltopdf-service | python | async def execute_wkhtmltopdf(uri: str) -> bytes:
cmd = ['wkhtmltopdf', '--log-level', 'none', uri, '-']
return check_output(cmd) |
async def convert_body(request: Request):
"\n It's just _way_ easier to deal with files rather than STDIN.\n\n Take the body of the request, write it to a temporary file, then use\n wkhtmltopdf to convert it.\n "
data = (await request.body())
if (not data):
return Response('ERROR: No bod... | 7,125,513,509,955,753,000 | It's just _way_ easier to deal with files rather than STDIN.
Take the body of the request, write it to a temporary file, then use
wkhtmltopdf to convert it. | src/app.py | convert_body | mtik00/wkhtmltopdf-service | python | async def convert_body(request: Request):
"\n It's just _way_ easier to deal with files rather than STDIN.\n\n Take the body of the request, write it to a temporary file, then use\n wkhtmltopdf to convert it.\n "
data = (await request.body())
if (not data):
return Response('ERROR: No bod... |
def get_mbreplacer_dir():
'\n Get the mbreplacer dir\n :return str: mbreplacer root dir\n '
return os.getcwd() | 1,429,617,218,071,645,200 | Get the mbreplacer dir
:return str: mbreplacer root dir | mbreplacer.py | get_mbreplacer_dir | ackhoury/mbreplacer | python | def get_mbreplacer_dir():
'\n Get the mbreplacer dir\n :return str: mbreplacer root dir\n '
return os.getcwd() |
@app.route('/')
def home():
'Step 1: User Authorization.\n\n Redirect the user/resource owner to the OAuth provider (i.e. Github)\n using an URL with a few key OAuth parameters.\n '
return render_template('index.html') | -7,705,316,012,026,364,000 | Step 1: User Authorization.
Redirect the user/resource owner to the OAuth provider (i.e. Github)
using an URL with a few key OAuth parameters. | td/oauth.py | home | Aspire1Inspire2/td-ameritrade-python-api | python | @app.route('/')
def home():
'Step 1: User Authorization.\n\n Redirect the user/resource owner to the OAuth provider (i.e. Github)\n using an URL with a few key OAuth parameters.\n '
return render_template('index.html') |
@app.route('/login')
def demo():
'Step 1: User Authorization.\n\n Redirect the user/resource owner to the OAuth provider (i.e. Github)\n using an URL with a few key OAuth parameters.\n '
auth_tuple = app.config['auth_client'].authorization_url()
session['oauth_state'] = auth_tuple[1]
return red... | -5,519,529,732,331,011,000 | Step 1: User Authorization.
Redirect the user/resource owner to the OAuth provider (i.e. Github)
using an URL with a few key OAuth parameters. | td/oauth.py | demo | Aspire1Inspire2/td-ameritrade-python-api | python | @app.route('/login')
def demo():
'Step 1: User Authorization.\n\n Redirect the user/resource owner to the OAuth provider (i.e. Github)\n using an URL with a few key OAuth parameters.\n '
auth_tuple = app.config['auth_client'].authorization_url()
session['oauth_state'] = auth_tuple[1]
return red... |
@app.route('/login/callback', methods=['GET'])
def callback():
' Step 3: Retrieving an access token.\n\n The user has been redirected back from the provider to your registered\n callback URL. With this redirection comes an authorization code included\n in the redirect URL. We will use that to obtain an acc... | -1,592,033,070,512,526,000 | Step 3: Retrieving an access token.
The user has been redirected back from the provider to your registered
callback URL. With this redirection comes an authorization code included
in the redirect URL. We will use that to obtain an access token. | td/oauth.py | callback | Aspire1Inspire2/td-ameritrade-python-api | python | @app.route('/login/callback', methods=['GET'])
def callback():
' Step 3: Retrieving an access token.\n\n The user has been redirected back from the provider to your registered\n callback URL. With this redirection comes an authorization code included\n in the redirect URL. We will use that to obtain an acc... |
def _test_repr_or_str(self, fn, expect_id):
"Test Queue's repr or str.\n\n fn is repr or str. expect_id is True if we expect the Queue's id to\n appear in fn(Queue()).\n "
def gen():
when = (yield)
self.assertAlmostEqual(0.1, when)
when = (yield 0.1)
self.as... | -2,233,485,092,732,088,300 | Test Queue's repr or str.
fn is repr or str. expect_id is True if we expect the Queue's id to
appear in fn(Queue()). | tests/python/test_queues.py | _test_repr_or_str | ProvoK/trio-asyncio | python | def _test_repr_or_str(self, fn, expect_id):
"Test Queue's repr or str.\n\n fn is repr or str. expect_id is True if we expect the Queue's id to\n appear in fn(Queue()).\n "
def gen():
when = (yield)
self.assertAlmostEqual(0.1, when)
when = (yield 0.1)
self.as... |
def _submit_filtering_jobs(self, uuid):
'\n Here we create the task and put it on the job queue.\n '
two_weeks_ago = (datetime.date.today() - datetime.timedelta(14))
params = {'from': int(two_weeks_ago.strftime('%s')), 'to': int(time.time()), 'unit': 'seconds'}
location_api_resp = requests... | -3,618,480,378,836,912,000 | Here we create the task and put it on the job queue. | users-api/routes.py | _submit_filtering_jobs | pwegrzyn/pandemic-monitor | python | def _submit_filtering_jobs(self, uuid):
'\n \n '
two_weeks_ago = (datetime.date.today() - datetime.timedelta(14))
params = {'from': int(two_weeks_ago.strftime('%s')), 'to': int(time.time()), 'unit': 'seconds'}
location_api_resp = requests.get(f'http://location-api:5000/geohashRegionsForUse... |
def __init__(self, path: str):
"\n Create an instance of GitRepoVersionInfo\n :param path: The path to search for git information. It searches for '.git' in this folder or any parent\n folder.\n "
self._is_repo = False
try:
self._repo = git.Repo(path, search_parent_direct... | -4,167,830,116,496,115,700 | Create an instance of GitRepoVersionInfo
:param path: The path to search for git information. It searches for '.git' in this folder or any parent
folder. | step_exec_lib/utils/git.py | __init__ | giantswarm/step-exec-lib | python | def __init__(self, path: str):
"\n Create an instance of GitRepoVersionInfo\n :param path: The path to search for git information. It searches for '.git' in this folder or any parent\n folder.\n "
self._is_repo = False
try:
self._repo = git.Repo(path, search_parent_direct... |
@property
def is_git_repo(self) -> bool:
'\n Checks if the path given in constructor is a sub-path of a valid git repo.\n :return: Boolean true, if repo was found.\n '
return self._is_repo | 5,407,171,041,855,514,000 | Checks if the path given in constructor is a sub-path of a valid git repo.
:return: Boolean true, if repo was found. | step_exec_lib/utils/git.py | is_git_repo | giantswarm/step-exec-lib | python | @property
def is_git_repo(self) -> bool:
'\n Checks if the path given in constructor is a sub-path of a valid git repo.\n :return: Boolean true, if repo was found.\n '
return self._is_repo |
def get_git_version(self, strip_v_in_version: bool=True) -> str:
'\n Gets application version in the format [last-tag]-[last-commit-sha].\n :param strip_v_in_version: If the version tag starts with \'v\' (like \'v1.2.3),\n this chooses if the \'v\' should be stripped, so the resulting tag is \'... | -2,077,656,113,087,697,700 | Gets application version in the format [last-tag]-[last-commit-sha].
:param strip_v_in_version: If the version tag starts with 'v' (like 'v1.2.3),
this chooses if the 'v' should be stripped, so the resulting tag is '1.2.3'.
If there's a "-", "." or "_" separator after "v", it is removed as well.
:return: The version st... | step_exec_lib/utils/git.py | get_git_version | giantswarm/step-exec-lib | python | def get_git_version(self, strip_v_in_version: bool=True) -> str:
'\n Gets application version in the format [last-tag]-[last-commit-sha].\n :param strip_v_in_version: If the version tag starts with \'v\' (like \'v1.2.3),\n this chooses if the \'v\' should be stripped, so the resulting tag is \'... |
def contract_by_lifespan(agent_stats, lifespans):
'Pull agents close to their mean according to how short-lived they were. For punishing abundance of premature death\n when rewarding diversity.'
weights = sigmoid_lifespan(lifespans)
n_agents = lifespans.shape[0]
mean_agent = agent_stats.mean(axis=0)
... | 8,577,192,660,219,871,000 | Pull agents close to their mean according to how short-lived they were. For punishing abundance of premature death
when rewarding diversity. | evolution/diversity.py | contract_by_lifespan | narendasan/neural-mmo | python | def contract_by_lifespan(agent_stats, lifespans):
'Pull agents close to their mean according to how short-lived they were. For punishing abundance of premature death\n when rewarding diversity.'
weights = sigmoid_lifespan(lifespans)
n_agents = lifespans.shape[0]
mean_agent = agent_stats.mean(axis=0)
... |
def expand_by_lifespan(agent_stats, lifespans):
'Push agents further from their mean according to how short-lived they were. For punishing abundance of premature\n death when rewarding homogeneity.'
weights = sigmoid_lifespan(lifespans)
n_agents = lifespans.shape[0]
mean_agent = agent_stats.mean(axis=... | 556,244,921,716,009,500 | Push agents further from their mean according to how short-lived they were. For punishing abundance of premature
death when rewarding homogeneity. | evolution/diversity.py | expand_by_lifespan | narendasan/neural-mmo | python | def expand_by_lifespan(agent_stats, lifespans):
'Push agents further from their mean according to how short-lived they were. For punishing abundance of premature\n death when rewarding homogeneity.'
weights = sigmoid_lifespan(lifespans)
n_agents = lifespans.shape[0]
mean_agent = agent_stats.mean(axis=... |
def sum_experience(agent_stats, skill_headers=None, verbose=False, pop=None):
'Simply take the sum of XP over skills and agents.'
agent_skills = get_pop_stats(agent_stats['skills'], pop)
lifespans = get_pop_stats(agent_stats['lifespans'], pop)
a_skills = np.vstack(agent_skills)
a_lifespans = np.hsta... | 1,694,325,565,475,479,300 | Simply take the sum of XP over skills and agents. | evolution/diversity.py | sum_experience | narendasan/neural-mmo | python | def sum_experience(agent_stats, skill_headers=None, verbose=False, pop=None):
agent_skills = get_pop_stats(agent_stats['skills'], pop)
lifespans = get_pop_stats(agent_stats['lifespans'], pop)
a_skills = np.vstack(agent_skills)
a_lifespans = np.hstack(lifespans)
(n_agents, n_skills) = a_skills.s... |
def calc_convex_hull(agent_stats, skill_headers=None, verbose=False, infos={}, pop=None, punish_youth=True):
'Calculate the diversity of a population of agents in skill-space by computing the volume inside the convex hull of\n the agents when treated as points in this space.'
agent_skills = get_pop_stats(agen... | 1,588,951,508,444,711,700 | Calculate the diversity of a population of agents in skill-space by computing the volume inside the convex hull of
the agents when treated as points in this space. | evolution/diversity.py | calc_convex_hull | narendasan/neural-mmo | python | def calc_convex_hull(agent_stats, skill_headers=None, verbose=False, infos={}, pop=None, punish_youth=True):
'Calculate the diversity of a population of agents in skill-space by computing the volume inside the convex hull of\n the agents when treated as points in this space.'
agent_skills = get_pop_stats(agen... |
def calc_homogeneity_l2(agent_stats, skill_headers=None, verbose=False, pop=None, punish_youth=True):
'Use L2 distance to punish agents for having high mean pairwise distance. Optimal state is all agents at the same\n point in skill-space, with maximal lifespans.'
if ('skills' not in agent_stats):
rai... | 366,864,616,967,479,600 | Use L2 distance to punish agents for having high mean pairwise distance. Optimal state is all agents at the same
point in skill-space, with maximal lifespans. | evolution/diversity.py | calc_homogeneity_l2 | narendasan/neural-mmo | python | def calc_homogeneity_l2(agent_stats, skill_headers=None, verbose=False, pop=None, punish_youth=True):
'Use L2 distance to punish agents for having high mean pairwise distance. Optimal state is all agents at the same\n point in skill-space, with maximal lifespans.'
if ('skills' not in agent_stats):
rai... |
def test(env, actor_model, is_discrete):
'\n\t\tTests the model.\n\t\tParameters:\n\t\t\tenv - the environment to test the policy on\n\t\t\tactor_model - the actor model to load in\n\t\tReturn:\n\t\t\tNone\n\t'
print(f'Testing {actor_model}', flush=True)
if (actor_model == ''):
print(f"Didn't specif... | 4,032,199,025,681,221,600 | Tests the model.
Parameters:
env - the environment to test the policy on
actor_model - the actor model to load in
Return:
None | ppoPolicyTraining.py | test | britig/S2RL-Policies | python | def test(env, actor_model, is_discrete):
'\n\t\tTests the model.\n\t\tParameters:\n\t\t\tenv - the environment to test the policy on\n\t\t\tactor_model - the actor model to load in\n\t\tReturn:\n\t\t\tNone\n\t'
print(f'Testing {actor_model}', flush=True)
if (actor_model == ):
print(f"Didn't specify ... |
def __init__(self, env, **hyperparameters):
'\n\t\t\tInitializes the PPO model, including hyperparameters.\n\n\t\t\tParameters:\n\t\t\t\tpolicy_class - the policy class to use for our actor/critic networks.\n\t\t\t\tenv - the environment to train on.\n\t\t\t\thyperparameters - all extra arguments passed into PPO th... | 1,361,639,296,199,345,000 | Initializes the PPO model, including hyperparameters.
Parameters:
policy_class - the policy class to use for our actor/critic networks.
env - the environment to train on.
hyperparameters - all extra arguments passed into PPO that should be hyperparameters.
Returns:
None | ppoPolicyTraining.py | __init__ | britig/S2RL-Policies | python | def __init__(self, env, **hyperparameters):
'\n\t\t\tInitializes the PPO model, including hyperparameters.\n\n\t\t\tParameters:\n\t\t\t\tpolicy_class - the policy class to use for our actor/critic networks.\n\t\t\t\tenv - the environment to train on.\n\t\t\t\thyperparameters - all extra arguments passed into PPO th... |
def learn(self, env_name, failure_observations, subpolicy):
'\n\t\t\tTrain the actor and critic networks. Here is where the main PPO algorithm resides.\n\n\t\t\tParameters:\n\t\t\t\ttotal_timesteps - the total number of timesteps to train for\n\n\t\t\tReturn:\n\t\t\t\tNone\n\t\t'
print(f'Learning... Running {se... | 270,654,134,278,599,580 | Train the actor and critic networks. Here is where the main PPO algorithm resides.
Parameters:
total_timesteps - the total number of timesteps to train for
Return:
None | ppoPolicyTraining.py | learn | britig/S2RL-Policies | python | def learn(self, env_name, failure_observations, subpolicy):
'\n\t\t\tTrain the actor and critic networks. Here is where the main PPO algorithm resides.\n\n\t\t\tParameters:\n\t\t\t\ttotal_timesteps - the total number of timesteps to train for\n\n\t\t\tReturn:\n\t\t\t\tNone\n\t\t'
print(f'Learning... Running {se... |
def rollout(self, subpolicy, failure_observations):
"\n\t\t\tThis is where we collect the batch of data\n\t\t\tfrom simulation. Since this is an on-policy algorithm, we'll need to collect a fresh batch\n\t\t\tof data each time we iterate the actor/critic networks.\n\n\t\t\tParameters:\n\t\t\t\tNone\n\n\t\t\tReturn:... | 1,873,087,376,621,526,000 | This is where we collect the batch of data
from simulation. Since this is an on-policy algorithm, we'll need to collect a fresh batch
of data each time we iterate the actor/critic networks.
Parameters:
None
Return:
batch_obs - the observations collected this batch. Shape: (number of timesteps, dimensi... | ppoPolicyTraining.py | rollout | britig/S2RL-Policies | python | def rollout(self, subpolicy, failure_observations):
"\n\t\t\tThis is where we collect the batch of data\n\t\t\tfrom simulation. Since this is an on-policy algorithm, we'll need to collect a fresh batch\n\t\t\tof data each time we iterate the actor/critic networks.\n\n\t\t\tParameters:\n\t\t\t\tNone\n\n\t\t\tReturn:... |
def compute_rtgs(self, batch_rews):
'\n\t\t\tCompute the Reward-To-Go of each timestep in a batch given the rewards.\n\n\t\t\tParameters:\n\t\t\t\tbatch_rews - the rewards in a batch, Shape: (number of episodes, number of timesteps per episode)\n\n\t\t\tReturn:\n\t\t\t\tbatch_rtgs - the rewards to go, Shape: (numbe... | 4,242,929,496,582,007,000 | Compute the Reward-To-Go of each timestep in a batch given the rewards.
Parameters:
batch_rews - the rewards in a batch, Shape: (number of episodes, number of timesteps per episode)
Return:
batch_rtgs - the rewards to go, Shape: (number of timesteps in batch) | ppoPolicyTraining.py | compute_rtgs | britig/S2RL-Policies | python | def compute_rtgs(self, batch_rews):
'\n\t\t\tCompute the Reward-To-Go of each timestep in a batch given the rewards.\n\n\t\t\tParameters:\n\t\t\t\tbatch_rews - the rewards in a batch, Shape: (number of episodes, number of timesteps per episode)\n\n\t\t\tReturn:\n\t\t\t\tbatch_rtgs - the rewards to go, Shape: (numbe... |
def get_action(self, obs):
'\n\t\t\tQueries an action from the actor network, should be called from rollout.\n\n\t\t\tParameters:\n\t\t\t\tobs - the observation at the current timestep\n\n\t\t\tReturn:\n\t\t\t\taction - the action to take, as a numpy array\n\t\t\t\tlog_prob - the log probability of the selected act... | 7,726,324,014,643,275,000 | Queries an action from the actor network, should be called from rollout.
Parameters:
obs - the observation at the current timestep
Return:
action - the action to take, as a numpy array
log_prob - the log probability of the selected action in the distribution | ppoPolicyTraining.py | get_action | britig/S2RL-Policies | python | def get_action(self, obs):
'\n\t\t\tQueries an action from the actor network, should be called from rollout.\n\n\t\t\tParameters:\n\t\t\t\tobs - the observation at the current timestep\n\n\t\t\tReturn:\n\t\t\t\taction - the action to take, as a numpy array\n\t\t\t\tlog_prob - the log probability of the selected act... |
def evaluate(self, batch_obs, batch_acts):
'\n\t\t\tEstimate the values of each observation, and the log probs of\n\t\t\teach action in the most recent batch with the most recent\n\t\t\titeration of the actor network. Should be called from learn.\n\n\t\t\tParameters:\n\t\t\t\tbatch_obs - the observations from the m... | -3,305,831,494,162,423,000 | Estimate the values of each observation, and the log probs of
each action in the most recent batch with the most recent
iteration of the actor network. Should be called from learn.
Parameters:
batch_obs - the observations from the most recently collected batch as a tensor.
Shape... | ppoPolicyTraining.py | evaluate | britig/S2RL-Policies | python | def evaluate(self, batch_obs, batch_acts):
'\n\t\t\tEstimate the values of each observation, and the log probs of\n\t\t\teach action in the most recent batch with the most recent\n\t\t\titeration of the actor network. Should be called from learn.\n\n\t\t\tParameters:\n\t\t\t\tbatch_obs - the observations from the m... |
def _init_hyperparameters(self, hyperparameters):
'\n\t\t\tInitialize default and custom values for hyperparameters\n\n\t\t\tParameters:\n\t\t\t\thyperparameters - the extra arguments included when creating the PPO model, should only include\n\t\t\t\t\t\t\t\t\thyperparameters defined below with custom values.\n\n\t... | 319,362,538,887,235,700 | Initialize default and custom values for hyperparameters
Parameters:
hyperparameters - the extra arguments included when creating the PPO model, should only include
hyperparameters defined below with custom values.
Return:
None | ppoPolicyTraining.py | _init_hyperparameters | britig/S2RL-Policies | python | def _init_hyperparameters(self, hyperparameters):
'\n\t\t\tInitialize default and custom values for hyperparameters\n\n\t\t\tParameters:\n\t\t\t\thyperparameters - the extra arguments included when creating the PPO model, should only include\n\t\t\t\t\t\t\t\t\thyperparameters defined below with custom values.\n\n\t... |
def _log_summary(self):
"\n\t\t\tPrint to stdout what we've logged so far in the most recent batch.\n\n\t\t\tParameters:\n\t\t\t\tNone\n\n\t\t\tReturn:\n\t\t\t\tNone\n\t\t"
t_so_far = self.logger['t_so_far']
i_so_far = self.logger['i_so_far']
avg_ep_lens = np.mean(self.logger['batch_lens'])
avg_ep_r... | 5,219,838,179,941,541,000 | Print to stdout what we've logged so far in the most recent batch.
Parameters:
None
Return:
None | ppoPolicyTraining.py | _log_summary | britig/S2RL-Policies | python | def _log_summary(self):
"\n\t\t\tPrint to stdout what we've logged so far in the most recent batch.\n\n\t\t\tParameters:\n\t\t\t\tNone\n\n\t\t\tReturn:\n\t\t\t\tNone\n\t\t"
t_so_far = self.logger['t_so_far']
i_so_far = self.logger['i_so_far']
avg_ep_lens = np.mean(self.logger['batch_lens'])
avg_ep_r... |
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(('train', 'test'))
def AmazonReviewPolarity(root: str, split: Union[(Tuple[str], str)]):
"AmazonReviewPolarity Dataset\n\n For additional details refer to https://arxiv.org/abs/1509.01626\n\n Number of lines per split:\n - trai... | 1,040,654,380,379,947,600 | AmazonReviewPolarity Dataset
For additional details refer to https://arxiv.org/abs/1509.01626
Number of lines per split:
- train: 3600000
- test: 400000
Args:
root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
split: split or splits to be returned. Can be ... | torchtext/datasets/amazonreviewpolarity.py | AmazonReviewPolarity | abhinavarora/text | python | @_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(('train', 'test'))
def AmazonReviewPolarity(root: str, split: Union[(Tuple[str], str)]):
"AmazonReviewPolarity Dataset\n\n For additional details refer to https://arxiv.org/abs/1509.01626\n\n Number of lines per split:\n - trai... |
def get_relative_errors(test_data_id):
'\n Compute and save the relative errors of every point found on every network in a testing set.\n Relative error is defined in (Katz and Reggia 2017).\n test_data_id should be as in fxpt_experiments.generate_test_data (without file extension).\n '
(network_siz... | -6,441,740,488,261,575,000 | Compute and save the relative errors of every point found on every network in a testing set.
Relative error is defined in (Katz and Reggia 2017).
test_data_id should be as in fxpt_experiments.generate_test_data (without file extension). | roundoff.py | get_relative_errors | garrettkatz/rnn-fxpts | python | def get_relative_errors(test_data_id):
'\n Compute and save the relative errors of every point found on every network in a testing set.\n Relative error is defined in (Katz and Reggia 2017).\n test_data_id should be as in fxpt_experiments.generate_test_data (without file extension).\n '
(network_siz... |
def show_traverse_re_fig(test_data_ids, Ns, samp_range):
'\n Plot relative errors from points found by fiber traversal.\n test_data_ids and Ns should be length-2 lists.\n Subplots in the first column will show errors networks of size Ns[0] from test_data_ids[0].\n Similarly the second column draws from ... | -1,005,911,344,185,639,000 | Plot relative errors from points found by fiber traversal.
test_data_ids and Ns should be length-2 lists.
Subplots in the first column will show errors networks of size Ns[0] from test_data_ids[0].
Similarly the second column draws from Ns[1], test_data_ids[1].
Each network sample within samp_range is shown on a separa... | roundoff.py | show_traverse_re_fig | garrettkatz/rnn-fxpts | python | def show_traverse_re_fig(test_data_ids, Ns, samp_range):
'\n Plot relative errors from points found by fiber traversal.\n test_data_ids and Ns should be length-2 lists.\n Subplots in the first column will show errors networks of size Ns[0] from test_data_ids[0].\n Similarly the second column draws from ... |
def baseline_re_single_analysis(test_data_id, N, samp, cap=10):
'\n Analyze edge cases of relative errors on a single network\n Uses the samp^{th} sample network of size N in test data test_data_id.\n Relative errors in the range (0, 2^{cap}) are considered edge cases.\n Returns the number of edge cases... | -598,339,714,970,083,200 | Analyze edge cases of relative errors on a single network
Uses the samp^{th} sample network of size N in test data test_data_id.
Relative errors in the range (0, 2^{cap}) are considered edge cases.
Returns the number of edge cases divided by the difference |T-B| - |B-T| as a percent.
T and B are as defined in (Katz and... | roundoff.py | baseline_re_single_analysis | garrettkatz/rnn-fxpts | python | def baseline_re_single_analysis(test_data_id, N, samp, cap=10):
'\n Analyze edge cases of relative errors on a single network\n Uses the samp^{th} sample network of size N in test data test_data_id.\n Relative errors in the range (0, 2^{cap}) are considered edge cases.\n Returns the number of edge cases... |
def baseline_re_batch_analysis(test_data_id, Ns, cap=10):
'\n Runs baseline_re_single_analysis on all networks in test_data_id of size N.\n cap is as in baseline_re_single_analysis.\n returns numpy.array percents, where\n percents[i] is as in baseline_re_single_analysis for the i^{th} sample network.\... | -2,609,222,182,299,046,000 | Runs baseline_re_single_analysis on all networks in test_data_id of size N.
cap is as in baseline_re_single_analysis.
returns numpy.array percents, where
percents[i] is as in baseline_re_single_analysis for the i^{th} sample network. | roundoff.py | baseline_re_batch_analysis | garrettkatz/rnn-fxpts | python | def baseline_re_batch_analysis(test_data_id, Ns, cap=10):
'\n Runs baseline_re_single_analysis on all networks in test_data_id of size N.\n cap is as in baseline_re_single_analysis.\n returns numpy.array percents, where\n percents[i] is as in baseline_re_single_analysis for the i^{th} sample network.\... |
def show_baseline_re_fig(test_data_ids, Ns, samp_range):
'\n Plot relative errors from points found by the baseline solver.\n test_data_ids and Ns should be length-2 lists.\n Subplots in the first column will show errors networks of size Ns[0] from test_data_ids[0].\n Similarly the second column draws f... | 4,852,017,577,796,613,000 | Plot relative errors from points found by the baseline solver.
test_data_ids and Ns should be length-2 lists.
Subplots in the first column will show errors networks of size Ns[0] from test_data_ids[0].
Similarly the second column draws from Ns[1], test_data_ids[1].
Each network sample within samp_range is shown on a se... | roundoff.py | show_baseline_re_fig | garrettkatz/rnn-fxpts | python | def show_baseline_re_fig(test_data_ids, Ns, samp_range):
'\n Plot relative errors from points found by the baseline solver.\n test_data_ids and Ns should be length-2 lists.\n Subplots in the first column will show errors networks of size Ns[0] from test_data_ids[0].\n Similarly the second column draws f... |
def get_baseline_rd(test_data_id, N, samp, cap, logfilename=os.devnull):
'\n Compute and save relative distances between pairs of points found by the baseline solver.\n Relative distance is defined in (Katz and Reggia 2017).\n Computes for the samp^{th} sample network of size N in test_data_id.\n test_d... | 2,217,433,724,058,219,500 | Compute and save relative distances between pairs of points found by the baseline solver.
Relative distance is defined in (Katz and Reggia 2017).
Computes for the samp^{th} sample network of size N in test_data_id.
test_data_id should be as in fxpt_experiments.generate_test_data (without file extension).
Only pairs wit... | roundoff.py | get_baseline_rd | garrettkatz/rnn-fxpts | python | def get_baseline_rd(test_data_id, N, samp, cap, logfilename=os.devnull):
'\n Compute and save relative distances between pairs of points found by the baseline solver.\n Relative distance is defined in (Katz and Reggia 2017).\n Computes for the samp^{th} sample network of size N in test_data_id.\n test_d... |
def pool_get_baseline_rd(args):
'\n Wrapper function passed to multiprocessing.Pool\n '
get_baseline_rd(*args) | -1,554,045,137,290,081,300 | Wrapper function passed to multiprocessing.Pool | roundoff.py | pool_get_baseline_rd | garrettkatz/rnn-fxpts | python | def pool_get_baseline_rd(args):
'\n \n '
get_baseline_rd(*args) |
def run_baseline_rd(test_data_id, Ns, num_procs):
'\n Run get_baseline_rd on all networks in test_data_id whose size is in the list Ns.\n Multiprocessing is used to run on multiple networks in parallel.\n num_procs is the number of processors to use.\n '
cpu_count = mp.cpu_count()
print(('%d cpu... | 8,696,587,791,661,715,000 | Run get_baseline_rd on all networks in test_data_id whose size is in the list Ns.
Multiprocessing is used to run on multiple networks in parallel.
num_procs is the number of processors to use. | roundoff.py | run_baseline_rd | garrettkatz/rnn-fxpts | python | def run_baseline_rd(test_data_id, Ns, num_procs):
'\n Run get_baseline_rd on all networks in test_data_id whose size is in the list Ns.\n Multiprocessing is used to run on multiple networks in parallel.\n num_procs is the number of processors to use.\n '
cpu_count = mp.cpu_count()
print(('%d cpu... |
def get_traverse_rd(test_data_id, N, samp, cap, logfilename=os.devnull):
'\n Compute and save relative distances between pairs of points found by the baseline solver.\n Relative distance is defined in (Katz and Reggia 2017).\n Computes for the samp^{th} sample network of size N in test_data_id.\n test_d... | -2,238,623,223,670,777,900 | Compute and save relative distances between pairs of points found by the baseline solver.
Relative distance is defined in (Katz and Reggia 2017).
Computes for the samp^{th} sample network of size N in test_data_id.
test_data_id should be as in fxpt_experiments.generate_test_data (without file extension).
Only pairs wit... | roundoff.py | get_traverse_rd | garrettkatz/rnn-fxpts | python | def get_traverse_rd(test_data_id, N, samp, cap, logfilename=os.devnull):
'\n Compute and save relative distances between pairs of points found by the baseline solver.\n Relative distance is defined in (Katz and Reggia 2017).\n Computes for the samp^{th} sample network of size N in test_data_id.\n test_d... |
def pool_get_traverse_rd(args):
'\n Wrapper function passed to multiprocessing.Pool\n '
get_traverse_rd(*args) | -951,652,383,376,324,400 | Wrapper function passed to multiprocessing.Pool | roundoff.py | pool_get_traverse_rd | garrettkatz/rnn-fxpts | python | def pool_get_traverse_rd(args):
'\n \n '
get_traverse_rd(*args) |
def run_traverse_rd(test_data_id, Ns, num_procs):
'\n Run get_traverse_rd on all networks in test_data_id whose size is in the list Ns.\n Multiprocessing is used to run on multiple networks in parallel.\n num_procs is the number of processors to use.\n '
cpu_count = mp.cpu_count()
print(('%d cpu... | -4,067,689,719,634,688,500 | Run get_traverse_rd on all networks in test_data_id whose size is in the list Ns.
Multiprocessing is used to run on multiple networks in parallel.
num_procs is the number of processors to use. | roundoff.py | run_traverse_rd | garrettkatz/rnn-fxpts | python | def run_traverse_rd(test_data_id, Ns, num_procs):
'\n Run get_traverse_rd on all networks in test_data_id whose size is in the list Ns.\n Multiprocessing is used to run on multiple networks in parallel.\n num_procs is the number of processors to use.\n '
cpu_count = mp.cpu_count()
print(('%d cpu... |
def get_simple_rd(test_data_id, N, samp, cap, logfilename=os.devnull):
'\n Use simple unique test: if max absolute coordinate-wise difference < 2**-32\n Compute and save distances between pairs of points found by both solvers.\n Computes for the samp^{th} sample network of size N in test_data_id.\n test... | -3,566,493,107,082,433,000 | Use simple unique test: if max absolute coordinate-wise difference < 2**-32
Compute and save distances between pairs of points found by both solvers.
Computes for the samp^{th} sample network of size N in test_data_id.
test_data_id should be as in fxpt_experiments.generate_test_data (without file extension).
Only pairs... | roundoff.py | get_simple_rd | garrettkatz/rnn-fxpts | python | def get_simple_rd(test_data_id, N, samp, cap, logfilename=os.devnull):
'\n Use simple unique test: if max absolute coordinate-wise difference < 2**-32\n Compute and save distances between pairs of points found by both solvers.\n Computes for the samp^{th} sample network of size N in test_data_id.\n test... |
def pool_get_simple_rd(args):
'\n Wrapper function passed to multiprocessing.Pool\n '
get_simple_rd(*args) | -4,100,977,010,512,307,000 | Wrapper function passed to multiprocessing.Pool | roundoff.py | pool_get_simple_rd | garrettkatz/rnn-fxpts | python | def pool_get_simple_rd(args):
'\n \n '
get_simple_rd(*args) |
def run_simple_rd(test_data_id, Ns, num_procs):
'\n Run get_simple_rd on all networks in test_data_id whose size is in the list Ns.\n Multiprocessing is used to run on multiple networks in parallel.\n num_procs is the number of processors to use.\n '
cpu_count = mp.cpu_count()
print(('%d cpus, u... | 7,719,977,757,564,353,000 | Run get_simple_rd on all networks in test_data_id whose size is in the list Ns.
Multiprocessing is used to run on multiple networks in parallel.
num_procs is the number of processors to use. | roundoff.py | run_simple_rd | garrettkatz/rnn-fxpts | python | def run_simple_rd(test_data_id, Ns, num_procs):
'\n Run get_simple_rd on all networks in test_data_id whose size is in the list Ns.\n Multiprocessing is used to run on multiple networks in parallel.\n num_procs is the number of processors to use.\n '
cpu_count = mp.cpu_count()
print(('%d cpus, u... |
def show_traverse_rd_fig(test_data_ids, Ns, samp_range):
'\n Plot relative distances from points found by fiber traversal.\n test_ids, Ns, and samp_range should be as in show_traverse_re_fig.\n '
log = True
mpl.rcParams['mathtext.default'] = 'regular'
sp = 1
for samp in samp_range:
... | 783,642,926,598,277,500 | Plot relative distances from points found by fiber traversal.
test_ids, Ns, and samp_range should be as in show_traverse_re_fig. | roundoff.py | show_traverse_rd_fig | garrettkatz/rnn-fxpts | python | def show_traverse_rd_fig(test_data_ids, Ns, samp_range):
'\n Plot relative distances from points found by fiber traversal.\n test_ids, Ns, and samp_range should be as in show_traverse_re_fig.\n '
log = True
mpl.rcParams['mathtext.default'] = 'regular'
sp = 1
for samp in samp_range:
... |
def show_baseline_rd_fig(test_data_ids, Ns, samp_range):
'\n Plot relative distances from points found by the baseline solver.\n test_ids, Ns, and samp_range should be as in show_baseline_re_fig.\n '
log = True
mpl.rcParams['mathtext.default'] = 'regular'
sp = 1
for samp in samp_range:
... | 8,544,105,541,260,878,000 | Plot relative distances from points found by the baseline solver.
test_ids, Ns, and samp_range should be as in show_baseline_re_fig. | roundoff.py | show_baseline_rd_fig | garrettkatz/rnn-fxpts | python | def show_baseline_rd_fig(test_data_ids, Ns, samp_range):
'\n Plot relative distances from points found by the baseline solver.\n test_ids, Ns, and samp_range should be as in show_baseline_re_fig.\n '
log = True
mpl.rcParams['mathtext.default'] = 'regular'
sp = 1
for samp in samp_range:
... |
def show_simple_rd_all_fig(test_data_ids, Ns, samp_range):
'\n Plot relative distances from points found by fiber traversal or baseline.\n test_ids, Ns, and samp_range should be as in show_traverse_re_fig.\n '
log = True
mpl.rcParams['mathtext.default'] = 'regular'
mpl.rcParams['pdf.fonttype'] ... | -3,905,793,942,477,665,300 | Plot relative distances from points found by fiber traversal or baseline.
test_ids, Ns, and samp_range should be as in show_traverse_re_fig. | roundoff.py | show_simple_rd_all_fig | garrettkatz/rnn-fxpts | python | def show_simple_rd_all_fig(test_data_ids, Ns, samp_range):
'\n Plot relative distances from points found by fiber traversal or baseline.\n test_ids, Ns, and samp_range should be as in show_traverse_re_fig.\n '
log = True
mpl.rcParams['mathtext.default'] = 'regular'
mpl.rcParams['pdf.fonttype'] ... |
def take_damage(self, dmg, source):
' after taking damage, if the priestess is not dead, it heals itself'
hp_before_attack = self.hp
super().take_damage(dmg, source)
if (self._is_alive and (hp_before_attack > self.hp) and (source != 'pit')):
heal_message = self.heal_itself()
self.model.a... | 1,302,554,236,194,353,700 | after taking damage, if the priestess is not dead, it heals itself | priestess.py | take_damage | nvanbaak/dungeon-adventure-2 | python | def take_damage(self, dmg, source):
' '
hp_before_attack = self.hp
super().take_damage(dmg, source)
if (self._is_alive and (hp_before_attack > self.hp) and (source != 'pit')):
heal_message = self.heal_itself()
self.model.announce(f'{self.name}: {heal_message}') |
def resolve_workout(self, info, **kwargs):
'query resolver for workout property'
all_exercises = Exercise.objects.all()
if kwargs.get('body_part'):
all_exercises = all_exercises.select_related('body_part').filter(body_part__name=kwargs.get('body_part').lower())
if kwargs.get('level'):
al... | -8,813,676,179,882,051,000 | query resolver for workout property | quarantineworkout/workout/schema.py | resolve_workout | adeoke/django-quarantine-workout-graphql | python | def resolve_workout(self, info, **kwargs):
all_exercises = Exercise.objects.all()
if kwargs.get('body_part'):
all_exercises = all_exercises.select_related('body_part').filter(body_part__name=kwargs.get('body_part').lower())
if kwargs.get('level'):
all_exercises = all_exercises.select_re... |
def __init__(self, zip_code, house_number, house_addition=''):
'\n To fetch the garbage calendar, you need to set a zip_code and house_number.\n '
self.zip_code = zip_code.replace(' ', '')
self.house_number = house_number.strip()
self.house_addition = house_addition.strip() | 3,134,568,172,365,344,000 | To fetch the garbage calendar, you need to set a zip_code and house_number. | rova/rova.py | __init__ | synoniem/rova | python | def __init__(self, zip_code, house_number, house_addition=):
'\n \n '
self.zip_code = zip_code.replace(' ', )
self.house_number = house_number.strip()
self.house_addition = house_addition.strip() |
def is_rova_area(self):
'\n Check if ROVA collects garbage at this address\n '
url = 'https://www.rova.nl/api/waste-calendar/upcoming'
response = requests.get(url, params={'postalcode': self.zip_code, 'houseNumber': self.house_number, 'addition': self.house_addition, 'take': '1'})
response... | -2,616,346,550,750,675,500 | Check if ROVA collects garbage at this address | rova/rova.py | is_rova_area | synoniem/rova | python | def is_rova_area(self):
'\n \n '
url = 'https://www.rova.nl/api/waste-calendar/upcoming'
response = requests.get(url, params={'postalcode': self.zip_code, 'houseNumber': self.house_number, 'addition': self.house_addition, 'take': '1'})
response.raise_for_status()
rova_response = respon... |
def get_calendar_items(self, take=5):
'\n Get next pickup date for each garbage types\n '
url = 'https://www.rova.nl/api/waste-calendar/upcoming'
response = requests.get(url, params={'postalcode': self.zip_code, 'houseNumber': self.house_number, 'addition': self.house_addition, 'take': take})
... | -7,547,873,869,175,699,000 | Get next pickup date for each garbage types | rova/rova.py | get_calendar_items | synoniem/rova | python | def get_calendar_items(self, take=5):
'\n \n '
url = 'https://www.rova.nl/api/waste-calendar/upcoming'
response = requests.get(url, params={'postalcode': self.zip_code, 'houseNumber': self.house_number, 'addition': self.house_addition, 'take': take})
response.raise_for_status()
rova_re... |
def __init__(self, state_size, action_size, seed):
'Initialize an Agent object.\n \n Params\n ======\n state_size (int): dimension of each state\n action_size (int): dimension of each action\n seed (int): random seed\n '
self.state_size = state_size
... | 2,056,519,366,746,090,000 | Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed | dqn/exercise/dqn_agent.py | __init__ | 0xtristan/deep-reinforcement-learning | python | def __init__(self, state_size, action_size, seed):
'Initialize an Agent object.\n \n Params\n ======\n state_size (int): dimension of each state\n action_size (int): dimension of each action\n seed (int): random seed\n '
self.state_size = state_size
... |
def act(self, state, eps=0.0):
'Returns actions for given state as per current policy.\n \n Params\n ======\n state (array_like): current state\n eps (float): epsilon, for epsilon-greedy action selection\n '
state = torch.from_numpy(state).float().unsqueeze(0).t... | 3,284,820,839,670,036,500 | Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection | dqn/exercise/dqn_agent.py | act | 0xtristan/deep-reinforcement-learning | python | def act(self, state, eps=0.0):
'Returns actions for given state as per current policy.\n \n Params\n ======\n state (array_like): current state\n eps (float): epsilon, for epsilon-greedy action selection\n '
state = torch.from_numpy(state).float().unsqueeze(0).t... |
def learn(self, experiences, gamma):
"Update value parameters using given batch of experience tuples.\n\n Params\n ======\n experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples \n gamma (float): discount factor\n "
(states, actions, rewards, next_stat... | 8,166,505,585,780,385,000 | Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor | dqn/exercise/dqn_agent.py | learn | 0xtristan/deep-reinforcement-learning | python | def learn(self, experiences, gamma):
"Update value parameters using given batch of experience tuples.\n\n Params\n ======\n experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples \n gamma (float): discount factor\n "
(states, actions, rewards, next_stat... |
def soft_update(self, local_model, target_model, tau):
'Soft update model parameters.\n θ_target = τ*θ_local + (1 - τ)*θ_target\n\n Params\n ======\n local_model (PyTorch model): weights will be copied from\n target_model (PyTorch model): weights will be copied to\n ... | 3,655,770,241,422,866,000 | Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter | dqn/exercise/dqn_agent.py | soft_update | 0xtristan/deep-reinforcement-learning | python | def soft_update(self, local_model, target_model, tau):
'Soft update model parameters.\n θ_target = τ*θ_local + (1 - τ)*θ_target\n\n Params\n ======\n local_model (PyTorch model): weights will be copied from\n target_model (PyTorch model): weights will be copied to\n ... |
def __init__(self, action_size, buffer_size, batch_size, seed):
'Initialize a ReplayBuffer object.\n\n Params\n ======\n action_size (int): dimension of each action\n buffer_size (int): maximum size of buffer\n batch_size (int): size of each training batch\n ... | -1,162,416,917,650,856,000 | Initialize a ReplayBuffer object.
Params
======
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
seed (int): random seed | dqn/exercise/dqn_agent.py | __init__ | 0xtristan/deep-reinforcement-learning | python | def __init__(self, action_size, buffer_size, batch_size, seed):
'Initialize a ReplayBuffer object.\n\n Params\n ======\n action_size (int): dimension of each action\n buffer_size (int): maximum size of buffer\n batch_size (int): size of each training batch\n ... |
def add(self, state, action, reward, next_state, done):
'Add a new experience to memory.'
e = self.experience(state, action, reward, next_state, done)
self.memory.append(e) | -8,881,662,531,665,694,000 | Add a new experience to memory. | dqn/exercise/dqn_agent.py | add | 0xtristan/deep-reinforcement-learning | python | def add(self, state, action, reward, next_state, done):
e = self.experience(state, action, reward, next_state, done)
self.memory.append(e) |
def sample(self):
'Randomly sample a batch of experiences from memory.'
experiences = random.sample(self.memory, k=self.batch_size)
states = torch.from_numpy(np.vstack([e.state for e in experiences if (e is not None)])).float().to(device)
actions = torch.from_numpy(np.vstack([e.action for e in experienc... | 7,523,822,767,090,451,000 | Randomly sample a batch of experiences from memory. | dqn/exercise/dqn_agent.py | sample | 0xtristan/deep-reinforcement-learning | python | def sample(self):
experiences = random.sample(self.memory, k=self.batch_size)
states = torch.from_numpy(np.vstack([e.state for e in experiences if (e is not None)])).float().to(device)
actions = torch.from_numpy(np.vstack([e.action for e in experiences if (e is not None)])).long().to(device)
reward... |
def __len__(self):
'Return the current size of internal memory.'
return len(self.memory) | -960,517,394,760,847,000 | Return the current size of internal memory. | dqn/exercise/dqn_agent.py | __len__ | 0xtristan/deep-reinforcement-learning | python | def __len__(self):
return len(self.memory) |
@classmethod
def _connect(cls):
'Connects to vertica.\n \n :return: a connection to vertica.\n '
return connect(**cls._conn_info) | 783,110,972,030,852,000 | Connects to vertica.
:return: a connection to vertica. | vertica_python/tests/base.py | _connect | etsy/vertica-python | python | @classmethod
def _connect(cls):
'Connects to vertica.\n \n :return: a connection to vertica.\n '
return connect(**cls._conn_info) |
def _query_and_fetchall(self, query):
'Creates a new connection, executes a query and fetches all the results.\n \n :param query: query to execute\n :return: all fetched results as returned by cursor.fetchall()\n '
with self._connect() as conn:
cur = conn.cursor()
cur... | 6,632,299,217,410,403,000 | Creates a new connection, executes a query and fetches all the results.
:param query: query to execute
:return: all fetched results as returned by cursor.fetchall() | vertica_python/tests/base.py | _query_and_fetchall | etsy/vertica-python | python | def _query_and_fetchall(self, query):
'Creates a new connection, executes a query and fetches all the results.\n \n :param query: query to execute\n :return: all fetched results as returned by cursor.fetchall()\n '
with self._connect() as conn:
cur = conn.cursor()
cur... |
def _query_and_fetchone(self, query):
'Creates a new connection, executes a query and fetches one result.\n \n :param query: query to execute\n :return: the first result fetched by cursor.fetchone()\n '
with self._connect() as conn:
cur = conn.cursor()
cur.execute(que... | -3,428,955,883,212,195,000 | Creates a new connection, executes a query and fetches one result.
:param query: query to execute
:return: the first result fetched by cursor.fetchone() | vertica_python/tests/base.py | _query_and_fetchone | etsy/vertica-python | python | def _query_and_fetchone(self, query):
'Creates a new connection, executes a query and fetches one result.\n \n :param query: query to execute\n :return: the first result fetched by cursor.fetchone()\n '
with self._connect() as conn:
cur = conn.cursor()
cur.execute(que... |
def get_interaction_table(self, user_id, item_id, y):
'Get interaction_table that is used for fetching user-item interaction label in LS regularization.\n\n Args:\n user_id(torch.Tensor): the user id in user-item interactions, shape: [n_interactions, 1]\n item_id(torch.Tensor): the item... | -8,299,757,703,930,673,000 | Get interaction_table that is used for fetching user-item interaction label in LS regularization.
Args:
user_id(torch.Tensor): the user id in user-item interactions, shape: [n_interactions, 1]
item_id(torch.Tensor): the item id in user-item interactions, shape: [n_interactions, 1]
y(torch.Tensor): the labe... | recbole/model/knowledge_aware_recommender/kgnnls.py | get_interaction_table | xingkongxiaxia/RecBole | python | def get_interaction_table(self, user_id, item_id, y):
'Get interaction_table that is used for fetching user-item interaction label in LS regularization.\n\n Args:\n user_id(torch.Tensor): the user id in user-item interactions, shape: [n_interactions, 1]\n item_id(torch.Tensor): the item... |
def sample_neg_interaction(self, pos_interaction_table, offset):
'Sample neg_interaction to construct train data.\n\n Args:\n pos_interaction_table(dict): the interaction_table that only contains pos_interaction.\n offset(int): The offset that is used for calculating the key(index) in i... | 3,427,626,011,596,649,500 | Sample neg_interaction to construct train data.
Args:
pos_interaction_table(dict): the interaction_table that only contains pos_interaction.
offset(int): The offset that is used for calculating the key(index) in interaction_table
Returns:
interaction_table(dict): key: user_id * 10^offset + item_id; value:... | recbole/model/knowledge_aware_recommender/kgnnls.py | sample_neg_interaction | xingkongxiaxia/RecBole | python | def sample_neg_interaction(self, pos_interaction_table, offset):
'Sample neg_interaction to construct train data.\n\n Args:\n pos_interaction_table(dict): the interaction_table that only contains pos_interaction.\n offset(int): The offset that is used for calculating the key(index) in i... |
def construct_adj(self, kg_graph):
'Get neighbors and corresponding relations for each entity in the KG.\n\n Args:\n kg_graph(scipy.sparse.coo_matrix): an undirected graph\n\n Returns:\n tuple:\n - adj_entity (torch.LongTensor): each line stores the sampled neighbo... | 2,217,805,210,382,188,500 | Get neighbors and corresponding relations for each entity in the KG.
Args:
kg_graph(scipy.sparse.coo_matrix): an undirected graph
Returns:
tuple:
- adj_entity (torch.LongTensor): each line stores the sampled neighbor entities for a given entity,
shape: [n_entities, neighbor_sample_size]
... | recbole/model/knowledge_aware_recommender/kgnnls.py | construct_adj | xingkongxiaxia/RecBole | python | def construct_adj(self, kg_graph):
'Get neighbors and corresponding relations for each entity in the KG.\n\n Args:\n kg_graph(scipy.sparse.coo_matrix): an undirected graph\n\n Returns:\n tuple:\n - adj_entity (torch.LongTensor): each line stores the sampled neighbo... |
def get_neighbors(self, items):
"Get neighbors and corresponding relations for each entity in items from adj_entity and adj_relation.\n\n Args:\n items(torch.LongTensor): The input tensor that contains item's id, shape: [batch_size, ]\n\n Returns:\n tuple:\n - enti... | 6,309,155,545,897,962,000 | Get neighbors and corresponding relations for each entity in items from adj_entity and adj_relation.
Args:
items(torch.LongTensor): The input tensor that contains item's id, shape: [batch_size, ]
Returns:
tuple:
- entities(list): Entities is a list of i-iter (i = 0, 1, ..., n_iter) neighbors for the b... | recbole/model/knowledge_aware_recommender/kgnnls.py | get_neighbors | xingkongxiaxia/RecBole | python | def get_neighbors(self, items):
"Get neighbors and corresponding relations for each entity in items from adj_entity and adj_relation.\n\n Args:\n items(torch.LongTensor): The input tensor that contains item's id, shape: [batch_size, ]\n\n Returns:\n tuple:\n - enti... |
def aggregate(self, user_embeddings, entities, relations):
'For each item, aggregate the entity representation and its neighborhood representation into a single vector.\n\n Args:\n user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size, embedding_size]\n entitie... | -6,236,746,642,292,884,000 | For each item, aggregate the entity representation and its neighborhood representation into a single vector.
Args:
user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size, embedding_size]
entities(list): entities is a list of i-iter (i = 0, 1, ..., n_iter) neighbors for the batch of ite... | recbole/model/knowledge_aware_recommender/kgnnls.py | aggregate | xingkongxiaxia/RecBole | python | def aggregate(self, user_embeddings, entities, relations):
'For each item, aggregate the entity representation and its neighborhood representation into a single vector.\n\n Args:\n user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size, embedding_size]\n entitie... |
def label_smoothness_predict(self, user_embeddings, user, entities, relations):
'Predict the label of items by label smoothness.\n\n Args:\n user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size*2, embedding_size],\n user(torch.FloatTensor): the index of users,... | 1,820,534,822,281,268,500 | Predict the label of items by label smoothness.
Args:
user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size*2, embedding_size],
user(torch.FloatTensor): the index of users, shape: [batch_size*2]
entities(list): entities is a list of i-iter (i = 0, 1, ..., n_iter) neighbors for the... | recbole/model/knowledge_aware_recommender/kgnnls.py | label_smoothness_predict | xingkongxiaxia/RecBole | python | def label_smoothness_predict(self, user_embeddings, user, entities, relations):
'Predict the label of items by label smoothness.\n\n Args:\n user_embeddings(torch.FloatTensor): The embeddings of users, shape: [batch_size*2, embedding_size],\n user(torch.FloatTensor): the index of users,... |
def calculate_ls_loss(self, user, item, target):
'Calculate label smoothness loss.\n\n Args:\n user(torch.FloatTensor): the index of users, shape: [batch_size*2],\n item(torch.FloatTensor): the index of items, shape: [batch_size*2],\n target(torch.FloatTensor): the label of u... | -1,483,023,215,681,424,100 | Calculate label smoothness loss.
Args:
user(torch.FloatTensor): the index of users, shape: [batch_size*2],
item(torch.FloatTensor): the index of items, shape: [batch_size*2],
target(torch.FloatTensor): the label of user-item, shape: [batch_size*2],
Returns:
ls_loss: label smoothness loss | recbole/model/knowledge_aware_recommender/kgnnls.py | calculate_ls_loss | xingkongxiaxia/RecBole | python | def calculate_ls_loss(self, user, item, target):
'Calculate label smoothness loss.\n\n Args:\n user(torch.FloatTensor): the index of users, shape: [batch_size*2],\n item(torch.FloatTensor): the index of items, shape: [batch_size*2],\n target(torch.FloatTensor): the label of u... |
def to_python(self, value):
'Convert our string value to JSON after we load it from the DB'
if ((value is None) or (value == '')):
return {}
elif isinstance(value, basestring):
res = loads(value)
if isinstance(res, dict):
return JSONDict(**res)
else:
r... | -834,000,970,839,273,900 | Convert our string value to JSON after we load it from the DB | vendor-local/src/django-extensions/build/lib/django_extensions/db/fields/json.py | to_python | Mozilla-GitHub-Standards/b6a5bb5c98b18d87c72c770f29c4270008fc6fc6b787d531a2afcd382dc4cbad | python | def to_python(self, value):
if ((value is None) or (value == )):
return {}
elif isinstance(value, basestring):
res = loads(value)
if isinstance(res, dict):
return JSONDict(**res)
else:
return JSONList(res)
else:
return value |
def get_db_prep_save(self, value, connection):
'Convert our JSON object to a string before we save'
if (not isinstance(value, (list, dict))):
return super(JSONField, self).get_db_prep_save('', connection=connection)
else:
return super(JSONField, self).get_db_prep_save(dumps(value), connectio... | -3,618,754,902,002,978,000 | Convert our JSON object to a string before we save | vendor-local/src/django-extensions/build/lib/django_extensions/db/fields/json.py | get_db_prep_save | Mozilla-GitHub-Standards/b6a5bb5c98b18d87c72c770f29c4270008fc6fc6b787d531a2afcd382dc4cbad | python | def get_db_prep_save(self, value, connection):
if (not isinstance(value, (list, dict))):
return super(JSONField, self).get_db_prep_save(, connection=connection)
else:
return super(JSONField, self).get_db_prep_save(dumps(value), connection=connection) |
def south_field_triple(self):
'Returns a suitable description of this field for South.'
from south.modelsinspector import introspector
field_class = 'django.db.models.fields.TextField'
(args, kwargs) = introspector(self)
return (field_class, args, kwargs) | -532,884,842,270,397,060 | Returns a suitable description of this field for South. | vendor-local/src/django-extensions/build/lib/django_extensions/db/fields/json.py | south_field_triple | Mozilla-GitHub-Standards/b6a5bb5c98b18d87c72c770f29c4270008fc6fc6b787d531a2afcd382dc4cbad | python | def south_field_triple(self):
from south.modelsinspector import introspector
field_class = 'django.db.models.fields.TextField'
(args, kwargs) = introspector(self)
return (field_class, args, kwargs) |
@cached_property
def additional_properties_type():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n '
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type) | 1,702,168,743,392,494,600 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded | code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20013.py | additional_properties_type | factset/enterprise-sdk | python | @cached_property
def additional_properties_type():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n '
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type) |
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n ... | 7,408,037,427,849,946,000 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type. | code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20013.py | openapi_types | factset/enterprise-sdk | python | @cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n ... |
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs):
'InlineResponse20013 - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError... | 9,188,032,339,138,415,000 | InlineResponse20013 - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_it... | code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20013.py | _from_openapi_data | factset/enterprise-sdk | python | @classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs):
'InlineResponse20013 - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError... |
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'InlineResponse20013 - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n ... | -3,725,525,108,762,265,600 | InlineResponse20013 - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_it... | code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20013.py | __init__ | factset/enterprise-sdk | python | @convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'InlineResponse20013 - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n ... |
def load_data(traindir, valdir, **kwargs):
'generate the train and val dataloader, you can change this for your specific task\n\n Args:\n traindir (str): train dataset dir\n valdir (str): validation dataset dir\n\n Returns:\n tuple: the train dataset and validation dataset\n '
trai... | 5,464,277,367,755,207,000 | generate the train and val dataloader, you can change this for your specific task
Args:
traindir (str): train dataset dir
valdir (str): validation dataset dir
Returns:
tuple: the train dataset and validation dataset | torchsat/scripts/train_cd.py | load_data | alina2204/contrastive_SSL_ship_detection | python | def load_data(traindir, valdir, **kwargs):
'generate the train and val dataloader, you can change this for your specific task\n\n Args:\n traindir (str): train dataset dir\n valdir (str): validation dataset dir\n\n Returns:\n tuple: the train dataset and validation dataset\n '
trai... |
def aggregate_gradients_using_nccl(replica_grads):
'Aggregate gradients using nccl allreduce.'
agg_all_g_and_v = []
for single_g_and_v in zip(*replica_grads):
single_grads = [g for (g, _) in single_g_and_v]
agg_grads = nccl_ops.all_sum(single_grads)
agg_all_g_and_v.append([(g, v) for... | -460,152,936,942,818,200 | Aggregate gradients using nccl allreduce. | tensorflow/python/distribute/cross_device_utils.py | aggregate_gradients_using_nccl | DeuroIO/Deuro-tensorflow | python | def aggregate_gradients_using_nccl(replica_grads):
agg_all_g_and_v = []
for single_g_and_v in zip(*replica_grads):
single_grads = [g for (g, _) in single_g_and_v]
agg_grads = nccl_ops.all_sum(single_grads)
agg_all_g_and_v.append([(g, v) for (g, (_, v)) in zip(agg_grads, single_g_and... |
def aggregate_gradients_using_hierarchical_copy(avail_devices, replica_grads):
'Aggregate gradients using hierarchical copies.\n\n Args:\n avail_devices: available GPU devices.\n replica_grads: List of lists of (gradient, variable) tuples. The outer list\n is over replicas. The inner list is over indivi... | -5,807,300,737,462,545,000 | Aggregate gradients using hierarchical copies.
Args:
avail_devices: available GPU devices.
replica_grads: List of lists of (gradient, variable) tuples. The outer list
is over replicas. The inner list is over individual gradients.
Returns:
The list of (aggregated_gradient, variable), where the gradient has b... | tensorflow/python/distribute/cross_device_utils.py | aggregate_gradients_using_hierarchical_copy | DeuroIO/Deuro-tensorflow | python | def aggregate_gradients_using_hierarchical_copy(avail_devices, replica_grads):
'Aggregate gradients using hierarchical copies.\n\n Args:\n avail_devices: available GPU devices.\n replica_grads: List of lists of (gradient, variable) tuples. The outer list\n is over replicas. The inner list is over indivi... |
def aggregate_single_gradient_using_copy(grad_and_vars, use_mean, check_inf_nan):
'Calculate the average gradient for a shared variable across all replicas.\n\n Note that this function provides a synchronization point across all replicas.\n\n Args:\n grad_and_vars: A list or tuple of (gradient, variable) tuple... | -739,028,824,022,532,700 | Calculate the average gradient for a shared variable across all replicas.
Note that this function provides a synchronization point across all replicas.
Args:
grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
(gradient, variable) pair within the outer list represents the gradient
of the var... | tensorflow/python/distribute/cross_device_utils.py | aggregate_single_gradient_using_copy | DeuroIO/Deuro-tensorflow | python | def aggregate_single_gradient_using_copy(grad_and_vars, use_mean, check_inf_nan):
'Calculate the average gradient for a shared variable across all replicas.\n\n Note that this function provides a synchronization point across all replicas.\n\n Args:\n grad_and_vars: A list or tuple of (gradient, variable) tuple... |
def group_device_names(devices, group_size):
'Group device names into groups of group_size.\n\n Args:\n devices: a list of canonical device strings.\n group_size: integer which is equal to or greater than 1.\n\n Returns:\n list of lists of devices, where each inner list is group_size long,\n and eac... | -402,665,421,571,026,240 | Group device names into groups of group_size.
Args:
devices: a list of canonical device strings.
group_size: integer which is equal to or greater than 1.
Returns:
list of lists of devices, where each inner list is group_size long,
and each device appears at least once in an inner list. If
len(devices) ... | tensorflow/python/distribute/cross_device_utils.py | group_device_names | DeuroIO/Deuro-tensorflow | python | def group_device_names(devices, group_size):
'Group device names into groups of group_size.\n\n Args:\n devices: a list of canonical device strings.\n group_size: integer which is equal to or greater than 1.\n\n Returns:\n list of lists of devices, where each inner list is group_size long,\n and eac... |
def split_grads_by_size(threshold_size, device_grads):
'Break gradients into two sets according to tensor size.\n\n Args:\n threshold_size: int size cutoff for small vs large tensor.\n device_grads: List of lists of (gradient, variable) tuples. The outer\n list is over devices. The inner list is over... | 7,115,999,087,250,416,000 | Break gradients into two sets according to tensor size.
Args:
threshold_size: int size cutoff for small vs large tensor.
device_grads: List of lists of (gradient, variable) tuples. The outer
list is over devices. The inner list is over individual gradients.
Returns:
small_grads: Subset of device_grads wh... | tensorflow/python/distribute/cross_device_utils.py | split_grads_by_size | DeuroIO/Deuro-tensorflow | python | def split_grads_by_size(threshold_size, device_grads):
'Break gradients into two sets according to tensor size.\n\n Args:\n threshold_size: int size cutoff for small vs large tensor.\n device_grads: List of lists of (gradient, variable) tuples. The outer\n list is over devices. The inner list is over... |
def build_collective_reduce(input_tensors, num_workers, collective_keys, reduction_op='Add', unary_op='Id'):
'Build a subgraph that does one full all-reduce, using the collective Op.\n\n Args:\n input_tensors: tensors within a single worker graph that are to be reduced\n together; must be one per device.\n... | -1,641,276,473,819,680,500 | Build a subgraph that does one full all-reduce, using the collective Op.
Args:
input_tensors: tensors within a single worker graph that are to be reduced
together; must be one per device.
num_workers: total number of workers with identical independent graphs that
will be doing this same reduction. The red... | tensorflow/python/distribute/cross_device_utils.py | build_collective_reduce | DeuroIO/Deuro-tensorflow | python | def build_collective_reduce(input_tensors, num_workers, collective_keys, reduction_op='Add', unary_op='Id'):
'Build a subgraph that does one full all-reduce, using the collective Op.\n\n Args:\n input_tensors: tensors within a single worker graph that are to be reduced\n together; must be one per device.\n... |
def sum_grad_and_var_all_reduce(grad_and_vars, num_workers, alg, gpu_indices, aux_devices=None, num_shards=1):
'Apply all-reduce algorithm over specified gradient tensors.'
with ops.name_scope('allreduce'):
scaled_grads = [g for (g, _) in grad_and_vars]
if (alg == 'nccl'):
summed_gra... | -2,988,830,372,582,981,600 | Apply all-reduce algorithm over specified gradient tensors. | tensorflow/python/distribute/cross_device_utils.py | sum_grad_and_var_all_reduce | DeuroIO/Deuro-tensorflow | python | def sum_grad_and_var_all_reduce(grad_and_vars, num_workers, alg, gpu_indices, aux_devices=None, num_shards=1):
with ops.name_scope('allreduce'):
scaled_grads = [g for (g, _) in grad_and_vars]
if (alg == 'nccl'):
summed_grads = nccl_ops.all_sum(scaled_grads)
elif (alg == 'xri... |
def sum_gradients_all_reduce(dev_prefixes, replica_grads, num_workers, alg, num_shards, gpu_indices):
'Apply all-reduce algorithm over specified gradient tensors.\n\n Args:\n dev_prefixes: list of prefix strings to use to generate PS device names.\n replica_grads: the gradients to reduce.\n num_workers: n... | 4,814,734,914,225,489,000 | Apply all-reduce algorithm over specified gradient tensors.
Args:
dev_prefixes: list of prefix strings to use to generate PS device names.
replica_grads: the gradients to reduce.
num_workers: number of worker processes across entire job.
alg: the all-reduce algorithm to apply.
num_shards: alg-specific shardi... | tensorflow/python/distribute/cross_device_utils.py | sum_gradients_all_reduce | DeuroIO/Deuro-tensorflow | python | def sum_gradients_all_reduce(dev_prefixes, replica_grads, num_workers, alg, num_shards, gpu_indices):
'Apply all-reduce algorithm over specified gradient tensors.\n\n Args:\n dev_prefixes: list of prefix strings to use to generate PS device names.\n replica_grads: the gradients to reduce.\n num_workers: n... |
def extract_ranges(index_list, range_size_limit=32):
'Extract consecutive ranges and singles from index_list.\n\n Args:\n index_list: List of monotone increasing non-negative integers.\n range_size_limit: Largest size range to return. If a larger\n consecutive range exists, it will be returned as multi... | 7,368,154,166,958,740,000 | Extract consecutive ranges and singles from index_list.
Args:
index_list: List of monotone increasing non-negative integers.
range_size_limit: Largest size range to return. If a larger
consecutive range exists, it will be returned as multiple
ranges.
Returns:
(ranges, singles) where ranges is a list of... | tensorflow/python/distribute/cross_device_utils.py | extract_ranges | DeuroIO/Deuro-tensorflow | python | def extract_ranges(index_list, range_size_limit=32):
'Extract consecutive ranges and singles from index_list.\n\n Args:\n index_list: List of monotone increasing non-negative integers.\n range_size_limit: Largest size range to return. If a larger\n consecutive range exists, it will be returned as multi... |
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