Search is not available for this dataset
text stringlengths 75 104k |
|---|
def xmlrpc_run(self, port=23333, bind='127.0.0.1', logRequests=False):
'''Start xmlrpc interface'''
from pyspider.libs.wsgi_xmlrpc import WSGIXMLRPCApplication
application = WSGIXMLRPCApplication()
application.register_function(self.quit, '_quit')
application.register_function(... |
def on_new_request(self, task):
'''Called when a new request is arrived'''
task['status'] = self.taskdb.ACTIVE
self.insert_task(task)
self.put_task(task)
project = task['project']
self._cnt['5m'].event((project, 'pending'), +1)
self._cnt['1h'].event((project, 'pe... |
def on_old_request(self, task, old_task):
'''Called when a crawled task is arrived'''
now = time.time()
_schedule = task.get('schedule', self.default_schedule)
old_schedule = old_task.get('schedule', {})
if _schedule.get('force_update') and self.projects[task['project']].task_q... |
def on_task_status(self, task):
'''Called when a status pack is arrived'''
try:
procesok = task['track']['process']['ok']
if not self.projects[task['project']].task_queue.done(task['taskid']):
logging.error('not processing pack: %(project)s:%(taskid)s %(url)s', ta... |
def on_task_done(self, task):
'''Called when a task is done and success, called by `on_task_status`'''
task['status'] = self.taskdb.SUCCESS
task['lastcrawltime'] = time.time()
if 'schedule' in task:
if task['schedule'].get('auto_recrawl') and 'age' in task['schedule']:
... |
def on_task_failed(self, task):
'''Called when a task is failed, called by `on_task_status`'''
if 'schedule' not in task:
old_task = self.taskdb.get_task(task['project'], task['taskid'], fields=['schedule'])
if old_task is None:
logging.error('unknown status pack... |
def on_select_task(self, task):
'''Called when a task is selected to fetch & process'''
# inject informations about project
logger.info('select %(project)s:%(taskid)s %(url)s', task)
project_info = self.projects.get(task['project'])
assert project_info, 'no such project'
... |
def _check_select(self):
"""
interactive mode of select tasks
"""
if not self.interactive:
return super(OneScheduler, self)._check_select()
# waiting for running tasks
if self.running_task > 0:
return
is_crawled = []
def run(proj... |
def on_task_status(self, task):
"""Ignore not processing error in interactive mode"""
if not self.interactive:
super(OneScheduler, self).on_task_status(task)
try:
procesok = task['track']['process']['ok']
except KeyError as e:
logger.error("Bad status... |
def build_module(project, env=None):
'''Build project script as module'''
from pyspider.libs import base_handler
assert 'name' in project, 'need name of project'
assert 'script' in project, 'need script of project'
if env is None:
env = {}
# fix for old non-p... |
def _need_update(self, project_name, updatetime=None, md5sum=None):
'''Check if project_name need update'''
if project_name not in self.projects:
return True
elif md5sum and md5sum != self.projects[project_name]['info'].get('md5sum'):
return True
elif updatetime a... |
def _check_projects(self):
'''Check projects by last update time'''
for project in self.projectdb.check_update(self.last_check_projects,
['name', 'updatetime']):
if project['name'] not in self.projects:
continue
i... |
def _update_project(self, project_name):
'''Update one project from database'''
project = self.projectdb.get(project_name)
if not project:
return None
return self._load_project(project) |
def _load_project(self, project):
'''Load project into self.projects from project info dict'''
try:
project['md5sum'] = utils.md5string(project['script'])
ret = self.build_module(project, self.env)
self.projects[project['name']] = ret
except Exception as e:
... |
def get(self, project_name, updatetime=None, md5sum=None):
'''get project data object, return None if not exists'''
if time.time() - self.last_check_projects > self.CHECK_PROJECTS_INTERVAL:
self._check_projects()
if self._need_update(project_name, updatetime, md5sum):
sel... |
def get_all(self, name, default=None):
"""make cookie python 3 version use this instead of getheaders"""
if default is None:
default = []
return self._headers.get_list(name) or default |
def status_count(self, project):
'''
return a dict
'''
pipe = self.redis.pipeline(transaction=False)
for status in range(1, 5):
pipe.scard(self._gen_status_key(project, status))
ret = pipe.execute()
result = {}
for status, count in enumerate(r... |
def increment(self, n=1):
""" Increment the counter by n (default = 1) """
with self.count.get_lock():
self.count.value += n |
def refresh(self):
"""
Explicitly refresh one or more index, making all operations
performed since the last refresh available for search.
"""
self._changed = False
self.es.indices.refresh(index=self.index) |
def send_result(self, type, task, result):
'''Send fetch result to processor'''
if self.outqueue:
try:
self.outqueue.put((task, result))
except Exception as e:
logger.exception(e) |
def async_fetch(self, task, callback=None):
'''Do one fetch'''
url = task.get('url', 'data:,')
if callback is None:
callback = self.send_result
type = 'None'
start_time = time.time()
try:
if url.startswith('data:'):
type = 'data'
... |
def sync_fetch(self, task):
'''Synchronization fetch, usually used in xmlrpc thread'''
if not self._running:
return self.ioloop.run_sync(functools.partial(self.async_fetch, task, lambda t, _, r: True))
wait_result = threading.Condition()
_result = {}
def callback(ty... |
def data_fetch(self, url, task):
'''A fake fetcher for dataurl'''
self.on_fetch('data', task)
result = {}
result['orig_url'] = url
result['content'] = dataurl.decode(url)
result['headers'] = {}
result['status_code'] = 200
result['url'] = url
result... |
def http_fetch(self, url, task):
'''HTTP fetcher'''
start_time = time.time()
self.on_fetch('http', task)
handle_error = lambda x: self.handle_error('http', url, task, start_time, x)
# setup request parameters
fetch = self.pack_tornado_request_parameters(url, task)
... |
def phantomjs_fetch(self, url, task):
'''Fetch with phantomjs proxy'''
start_time = time.time()
self.on_fetch('phantomjs', task)
handle_error = lambda x: self.handle_error('phantomjs', url, task, start_time, x)
# check phantomjs proxy is enabled
if not self.phantomjs_pro... |
def run(self):
'''Run loop'''
logger.info("fetcher starting...")
def queue_loop():
if not self.outqueue or not self.inqueue:
return
while not self._quit:
try:
if self.outqueue.full():
break
... |
def quit(self):
'''Quit fetcher'''
self._running = False
self._quit = True
self.ioloop.add_callback(self.ioloop.stop)
if hasattr(self, 'xmlrpc_server'):
self.xmlrpc_ioloop.add_callback(self.xmlrpc_server.stop)
self.xmlrpc_ioloop.add_callback(self.xmlrpc_io... |
def xmlrpc_run(self, port=24444, bind='127.0.0.1', logRequests=False):
'''Run xmlrpc server'''
import umsgpack
from pyspider.libs.wsgi_xmlrpc import WSGIXMLRPCApplication
try:
from xmlrpc.client import Binary
except ImportError:
from xmlrpclib import Binar... |
def on_result(self, type, task, result):
'''Called after task fetched'''
status_code = result.get('status_code', 599)
if status_code != 599:
status_code = (int(status_code) / 100 * 100)
self._cnt['5m'].event((task.get('project'), status_code), +1)
self._cnt['1h'].even... |
def to_dict(self, get_value=None):
"""Dump counters as a dict"""
result = {}
for key, value in iteritems(self):
if isinstance(value, BaseCounter):
if get_value is not None:
value = getattr(value, get_value)
result[key] = value
... |
def value(self, key, value=1):
"""Set value of a counter by counter key"""
if isinstance(key, six.string_types):
key = (key, )
# assert all(isinstance(k, six.string_types) for k in key)
assert isinstance(key, tuple), "event key type error"
if key not in self.counters:... |
def trim(self):
"""Clear not used counters"""
for key, value in list(iteritems(self.counters)):
if value.empty():
del self.counters[key] |
def to_dict(self, get_value=None):
"""Dump counters as a dict"""
self.trim()
result = {}
for key, value in iteritems(self.counters):
if get_value is not None:
value = getattr(value, get_value)
r = result
for _key in key[:-1]:
... |
def dump(self, filename):
"""Dump counters to file"""
try:
with open(filename, 'wb') as fp:
cPickle.dump(self.counters, fp)
except Exception as e:
logging.warning("can't dump counter to file %s: %s", filename, e)
return False
return Tru... |
def load(self, filename):
"""Load counters to file"""
try:
with open(filename, 'rb') as fp:
self.counters = cPickle.load(fp)
except:
logging.debug("can't load counter from file: %s", filename)
return False
return True |
def cli(ctx, **kwargs):
"""
A powerful spider system in python.
"""
if kwargs['add_sys_path']:
sys.path.append(os.getcwd())
logging.config.fileConfig(kwargs['logging_config'])
# get db from env
for db in ('taskdb', 'projectdb', 'resultdb'):
if kwargs[db] is not None:
... |
def scheduler(ctx, xmlrpc, xmlrpc_host, xmlrpc_port,
inqueue_limit, delete_time, active_tasks, loop_limit, fail_pause_num,
scheduler_cls, threads, get_object=False):
"""
Run Scheduler, only one scheduler is allowed.
"""
g = ctx.obj
Scheduler = load_cls(None, None, schedul... |
def fetcher(ctx, xmlrpc, xmlrpc_host, xmlrpc_port, poolsize, proxy, user_agent,
timeout, phantomjs_endpoint, puppeteer_endpoint, splash_endpoint, fetcher_cls,
async_mode=True, get_object=False, no_input=False):
"""
Run Fetcher.
"""
g = ctx.obj
Fetcher = load_cls(None, None, f... |
def processor(ctx, processor_cls, process_time_limit, enable_stdout_capture=True, get_object=False):
"""
Run Processor.
"""
g = ctx.obj
Processor = load_cls(None, None, processor_cls)
processor = Processor(projectdb=g.projectdb,
inqueue=g.fetcher2processor, status_queu... |
def result_worker(ctx, result_cls, get_object=False):
"""
Run result worker.
"""
g = ctx.obj
ResultWorker = load_cls(None, None, result_cls)
result_worker = ResultWorker(resultdb=g.resultdb, inqueue=g.processor2result)
g.instances.append(result_worker)
if g.get('testing_mode') or get_o... |
def webui(ctx, host, port, cdn, scheduler_rpc, fetcher_rpc, max_rate, max_burst,
username, password, need_auth, webui_instance, process_time_limit, get_object=False):
"""
Run WebUI
"""
app = load_cls(None, None, webui_instance)
g = ctx.obj
app.config['taskdb'] = g.taskdb
app.confi... |
def phantomjs(ctx, phantomjs_path, port, auto_restart, args):
"""
Run phantomjs fetcher if phantomjs is installed.
"""
args = args or ctx.default_map and ctx.default_map.get('args', [])
import subprocess
g = ctx.obj
_quit = []
phantomjs_fetcher = os.path.join(
os.path.dirname(py... |
def puppeteer(ctx, port, auto_restart, args):
"""
Run puppeteer fetcher if puppeteer is installed.
"""
import subprocess
g = ctx.obj
_quit = []
puppeteer_fetcher = os.path.join(
os.path.dirname(pyspider.__file__), 'fetcher/puppeteer_fetcher.js')
cmd = ['node', puppeteer_fetcher,... |
def all(ctx, fetcher_num, processor_num, result_worker_num, run_in):
"""
Run all the components in subprocess or thread
"""
ctx.obj['debug'] = False
g = ctx.obj
# FIXME: py34 cannot run components with threads
if run_in == 'subprocess' and os.name != 'nt':
run_in = utils.run_in_sub... |
def bench(ctx, fetcher_num, processor_num, result_worker_num, run_in, total, show,
taskdb_bench, message_queue_bench, all_bench):
"""
Run Benchmark test.
In bench mode, in-memory sqlite database is used instead of on-disk sqlite database.
"""
from pyspider.libs import bench
from pyspid... |
def one(ctx, interactive, enable_phantomjs, enable_puppeteer, scripts):
"""
One mode not only means all-in-one, it runs every thing in one process over
tornado.ioloop, for debug purpose
"""
ctx.obj['debug'] = False
g = ctx.obj
g['testing_mode'] = True
if scripts:
from pyspider.... |
def send_message(ctx, scheduler_rpc, project, message):
"""
Send Message to project from command line
"""
if isinstance(scheduler_rpc, six.string_types):
scheduler_rpc = connect_rpc(ctx, None, scheduler_rpc)
if scheduler_rpc is None and os.environ.get('SCHEDULER_NAME'):
scheduler_rpc... |
def pprint(object, stream=None, indent=1, width=80, depth=None):
"""Pretty-print a Python object to a stream [default is sys.stdout]."""
printer = PrettyPrinter(
stream=stream, indent=indent, width=width, depth=depth)
printer.pprint(object) |
def pformat(object, indent=1, width=80, depth=None):
"""Format a Python object into a pretty-printed representation."""
return PrettyPrinter(indent=indent, width=width, depth=depth).pformat(object) |
def format(self, object, context, maxlevels, level):
"""Format object for a specific context, returning a string
and flags indicating whether the representation is 'readable'
and whether the object represents a recursive construct.
"""
return _safe_repr(object, context, maxlevels... |
def on_result(self, task, result):
'''Called every result'''
if not result:
return
if 'taskid' in task and 'project' in task and 'url' in task:
logger.info('result %s:%s %s -> %.30r' % (
task['project'], task['taskid'], task['url'], result))
re... |
def run(self):
'''Run loop'''
logger.info("result_worker starting...")
while not self._quit:
try:
task, result = self.inqueue.get(timeout=1)
self.on_result(task, result)
except Queue.Empty as e:
continue
except ... |
def on_result(self, task, result):
'''Called every result'''
if not result:
return
if 'taskid' in task and 'project' in task and 'url' in task:
logger.info('result %s:%s %s -> %.30r' % (
task['project'], task['taskid'], task['url'], result))
pr... |
def get(self):
'''Get the number of tokens in bucket'''
now = time.time()
if self.bucket >= self.burst:
self.last_update = now
return self.bucket
bucket = self.rate * (now - self.last_update)
self.mutex.acquire()
if bucket > 1:
self.buc... |
def migrate(pool, from_connection, to_connection):
"""
Migrate tool for pyspider
"""
f = connect_database(from_connection)
t = connect_database(to_connection)
if isinstance(f, ProjectDB):
for each in f.get_all():
each = unicode_obj(each)
logging.info("projectdb: ... |
def encode(data, mime_type='', charset='utf-8', base64=True):
"""
Encode data to DataURL
"""
if isinstance(data, six.text_type):
data = data.encode(charset)
else:
charset = None
if base64:
data = utils.text(b64encode(data))
else:
data = utils.text(quote(data))... |
def decode(data_url):
"""
Decode DataURL data
"""
metadata, data = data_url.rsplit(',', 1)
_, metadata = metadata.split('data:', 1)
parts = metadata.split(';')
if parts[-1] == 'base64':
data = b64decode(data)
else:
data = unquote(data)
for part in parts:
if p... |
def _build_url(url, _params):
"""Build the actual URL to use."""
# Support for unicode domain names and paths.
scheme, netloc, path, params, query, fragment = urlparse(url)
netloc = netloc.encode('idna').decode('utf-8')
if not path:
path = '/'
if six.PY2:
if isinstance(scheme, ... |
def quote_chinese(url, encodeing="utf-8"):
"""Quote non-ascii characters"""
if isinstance(url, six.text_type):
return quote_chinese(url.encode(encodeing))
if six.PY3:
res = [six.int2byte(b).decode('latin-1') if b < 128 else '%%%02X' % b for b in url]
else:
res = [b if ord(b) < 12... |
def DownloadResource(url, path):
'''Downloads resources from s3 by url and unzips them to the provided path'''
import requests
from six import BytesIO
import zipfile
print("Downloading... {} to {}".format(url, path))
r = requests.get(url, stream=True)
z = zipfile.ZipFile(BytesIO(r.content))
... |
def AddLeNetModel(model, data):
'''
This part is the standard LeNet model: from data to the softmax prediction.
For each convolutional layer we specify dim_in - number of input channels
and dim_out - number or output channels. Also each Conv and MaxPool layer changes the
image size. For example, ke... |
def AddAccuracy(model, softmax, label):
"""Adds an accuracy op to the model"""
accuracy = brew.accuracy(model, [softmax, label], "accuracy")
return accuracy |
def AddTrainingOperators(model, softmax, label):
"""Adds training operators to the model."""
xent = model.LabelCrossEntropy([softmax, label], 'xent')
# compute the expected loss
loss = model.AveragedLoss(xent, "loss")
# track the accuracy of the model
AddAccuracy(model, softmax, label)
# use... |
def AddBookkeepingOperators(model):
"""This adds a few bookkeeping operators that we can inspect later.
These operators do not affect the training procedure: they only collect
statistics and prints them to file or to logs.
"""
# Print basically prints out the content of the blob. to_file=1 routes t... |
def get_loss_func(self, C=1.0, k=1):
"""Get loss function of VAE.
The loss value is equal to ELBO (Evidence Lower Bound)
multiplied by -1.
Args:
C (int): Usually this is 1.0. Can be changed to control the
second term of ELBO bound, which works as regularizat... |
def fit(self, env, nb_steps, action_repetition=1, callbacks=None, verbose=1,
visualize=False, nb_max_start_steps=0, start_step_policy=None, log_interval=10000,
nb_max_episode_steps=None):
"""Trains the agent on the given environment.
# Arguments
env: (`Env` instance)... |
def process_step(self, observation, reward, done, info):
"""Processes an entire step by applying the processor to the observation, reward, and info arguments.
# Arguments
observation (object): An observation as obtained by the environment.
reward (float): A reward as obtained by... |
def get_current_value(self):
"""Return current annealing value
# Returns
Value to use in annealing
"""
if self.agent.training:
# Linear annealed: f(x) = ax + b.
a = -float(self.value_max - self.value_min) / float(self.nb_steps)
b = float(s... |
def select_action(self, **kwargs):
"""Choose an action to perform
# Returns
Action to take (int)
"""
setattr(self.inner_policy, self.attr, self.get_current_value())
return self.inner_policy.select_action(**kwargs) |
def get_config(self):
"""Return configurations of LinearAnnealedPolicy
# Returns
Dict of config
"""
config = super(LinearAnnealedPolicy, self).get_config()
config['attr'] = self.attr
config['value_max'] = self.value_max
config['value_min'] = self.valu... |
def select_action(self, nb_actions, probs):
"""Return the selected action
# Arguments
probs (np.ndarray) : Probabilty for each action
# Returns
action
"""
action = np.random.choice(range(nb_actions), p=probs)
return action |
def select_action(self, q_values):
"""Return the selected action
# Arguments
q_values (np.ndarray): List of the estimations of Q for each action
# Returns
Selection action
"""
assert q_values.ndim == 1
nb_actions = q_values.shape[0]
if n... |
def get_config(self):
"""Return configurations of EpsGreedyQPolicy
# Returns
Dict of config
"""
config = super(EpsGreedyQPolicy, self).get_config()
config['eps'] = self.eps
return config |
def select_action(self, q_values):
"""Return the selected action
# Arguments
q_values (np.ndarray): List of the estimations of Q for each action
# Returns
Selection action
"""
assert q_values.ndim == 1
action = np.argmax(q_values)
return ... |
def get_config(self):
"""Return configurations of BoltzmannQPolicy
# Returns
Dict of config
"""
config = super(BoltzmannQPolicy, self).get_config()
config['tau'] = self.tau
config['clip'] = self.clip
return config |
def select_action(self, q_values):
"""Return the selected action
The selected action follows the BoltzmannQPolicy with probability epsilon
or return the Greedy Policy with probability (1 - epsilon)
# Arguments
q_values (np.ndarray): List of the estimations of Q for each acti... |
def get_config(self):
"""Return configurations of MaxBoltzmannQPolicy
# Returns
Dict of config
"""
config = super(MaxBoltzmannQPolicy, self).get_config()
config['eps'] = self.eps
config['tau'] = self.tau
config['clip'] = self.clip
return confi... |
def select_action(self, q_values):
"""Return the selected action
# Arguments
q_values (np.ndarray): List of the estimations of Q for each action
# Returns
Selection action
"""
# We can't use BGE during testing, since we don't have access to the
#... |
def get_config(self):
"""Return configurations of BoltzmannGumbelQPolicy
# Returns
Dict of config
"""
config = super(BoltzmannGumbelQPolicy, self).get_config()
config['C'] = self.C
return config |
def _set_env(self, env):
""" Set environment for each callback in callbackList """
for callback in self.callbacks:
if callable(getattr(callback, '_set_env', None)):
callback._set_env(env) |
def on_episode_begin(self, episode, logs={}):
""" Called at beginning of each episode for each callback in callbackList"""
for callback in self.callbacks:
# Check if callback supports the more appropriate `on_episode_begin` callback.
# If not, fall back to `on_epoch_begin` to be ... |
def on_episode_end(self, episode, logs={}):
""" Called at end of each episode for each callback in callbackList"""
for callback in self.callbacks:
# Check if callback supports the more appropriate `on_episode_end` callback.
# If not, fall back to `on_epoch_end` to be compatible w... |
def on_step_begin(self, step, logs={}):
""" Called at beginning of each step for each callback in callbackList"""
for callback in self.callbacks:
# Check if callback supports the more appropriate `on_step_begin` callback.
# If not, fall back to `on_batch_begin` to be compatible w... |
def on_step_end(self, step, logs={}):
""" Called at end of each step for each callback in callbackList"""
for callback in self.callbacks:
# Check if callback supports the more appropriate `on_step_end` callback.
# If not, fall back to `on_batch_end` to be compatible with built-in... |
def on_action_begin(self, action, logs={}):
""" Called at beginning of each action for each callback in callbackList"""
for callback in self.callbacks:
if callable(getattr(callback, 'on_action_begin', None)):
callback.on_action_begin(action, logs=logs) |
def on_action_end(self, action, logs={}):
""" Called at end of each action for each callback in callbackList"""
for callback in self.callbacks:
if callable(getattr(callback, 'on_action_end', None)):
callback.on_action_end(action, logs=logs) |
def on_train_begin(self, logs):
""" Print training values at beginning of training """
self.train_start = timeit.default_timer()
self.metrics_names = self.model.metrics_names
print('Training for {} steps ...'.format(self.params['nb_steps'])) |
def on_train_end(self, logs):
""" Print training time at end of training """
duration = timeit.default_timer() - self.train_start
print('done, took {:.3f} seconds'.format(duration)) |
def on_episode_begin(self, episode, logs):
""" Reset environment variables at beginning of each episode """
self.episode_start[episode] = timeit.default_timer()
self.observations[episode] = []
self.rewards[episode] = []
self.actions[episode] = []
self.metrics[episode] = [... |
def on_episode_end(self, episode, logs):
""" Compute and print training statistics of the episode when done """
duration = timeit.default_timer() - self.episode_start[episode]
episode_steps = len(self.observations[episode])
# Format all metrics.
metrics = np.array(self.metrics[e... |
def on_step_end(self, step, logs):
""" Update statistics of episode after each step """
episode = logs['episode']
self.observations[episode].append(logs['observation'])
self.rewards[episode].append(logs['reward'])
self.actions[episode].append(logs['action'])
self.metrics[... |
def reset(self):
""" Reset statistics """
self.interval_start = timeit.default_timer()
self.progbar = Progbar(target=self.interval)
self.metrics = []
self.infos = []
self.info_names = None
self.episode_rewards = [] |
def on_step_begin(self, step, logs):
""" Print metrics if interval is over """
if self.step % self.interval == 0:
if len(self.episode_rewards) > 0:
metrics = np.array(self.metrics)
assert metrics.shape == (self.interval, len(self.metrics_names))
... |
def on_step_end(self, step, logs):
""" Update progression bar at the end of each step """
if self.info_names is None:
self.info_names = logs['info'].keys()
values = [('reward', logs['reward'])]
if KERAS_VERSION > '2.1.3':
self.progbar.update((self.step % self.inte... |
def on_episode_begin(self, episode, logs):
""" Initialize metrics at the beginning of each episode """
assert episode not in self.metrics
assert episode not in self.starts
self.metrics[episode] = []
self.starts[episode] = timeit.default_timer() |
def on_episode_end(self, episode, logs):
""" Compute and print metrics at the end of each episode """
duration = timeit.default_timer() - self.starts[episode]
metrics = self.metrics[episode]
if np.isnan(metrics).all():
mean_metrics = np.array([np.nan for _ in self.metrics_n... |
def save_data(self):
""" Save metrics in a json file """
if len(self.data.keys()) == 0:
return
# Sort everything by episode.
assert 'episode' in self.data
sorted_indexes = np.argsort(self.data['episode'])
sorted_data = {}
for key, values in self.data.... |
def on_step_end(self, step, logs={}):
""" Save weights at interval steps during training """
self.total_steps += 1
if self.total_steps % self.interval != 0:
# Nothing to do.
return
filepath = self.filepath.format(step=self.total_steps, **logs)
if self.ver... |
def sample_batch_indexes(low, high, size):
"""Return a sample of (size) unique elements between low and high
# Argument
low (int): The minimum value for our samples
high (int): The maximum value for our samples
size (int): The number of samples to pick
# Returns... |
def zeroed_observation(observation):
"""Return an array of zeros with same shape as given observation
# Argument
observation (list): List of observation
# Return
A np.ndarray of zeros with observation.shape
"""
if hasattr(observation, 'shape'):
return np.zeros(observati... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.