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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.min | def min(cls, x: 'TensorFluent', y: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the minimum function.
Args:
x: The first operand.
y: The second operand.
Returns:
A TensorFluent wrapping the minimum function.
'''
return cls... | python | def min(cls, x: 'TensorFluent', y: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the minimum function.
Args:
x: The first operand.
y: The second operand.
Returns:
A TensorFluent wrapping the minimum function.
'''
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.if_then_else | def if_then_else(cls,
condition: 'TensorFluent',
true_case: 'TensorFluent',
false_case: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the control op if-then-else.
Args:
condition: Boolean fluent for the if condition.
true_ca... | python | def if_then_else(cls,
condition: 'TensorFluent',
true_case: 'TensorFluent',
false_case: 'TensorFluent') -> 'TensorFluent':
'''Returns a TensorFluent for the control op if-then-else.
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condition: Boolean fluent for the if condition.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent._binary_op | def _binary_op(cls,
x: 'TensorFluent',
y: 'TensorFluent',
op: Callable[[tf.Tensor, tf.Tensor], tf.Tensor],
dtype: tf.DType) -> 'TensorFluent':
'''Returns a TensorFluent for the binary `op` applied to fluents `x` and `y`.
Args:
x: The first ope... | python | def _binary_op(cls,
x: 'TensorFluent',
y: 'TensorFluent',
op: Callable[[tf.Tensor, tf.Tensor], tf.Tensor],
dtype: tf.DType) -> 'TensorFluent':
'''Returns a TensorFluent for the binary `op` applied to fluents `x` and `y`.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent._unary_op | def _unary_op(cls,
x: 'TensorFluent',
op: Callable[[tf.Tensor], tf.Tensor],
dtype: tf.DType) -> 'TensorFluent':
'''Returns a TensorFluent for the unary `op` applied to fluent `x`.
Args:
x: The input fluent.
op: The unary operation.
... | python | def _unary_op(cls,
x: 'TensorFluent',
op: Callable[[tf.Tensor], tf.Tensor],
dtype: tf.DType) -> 'TensorFluent':
'''Returns a TensorFluent for the unary `op` applied to fluent `x`.
Args:
x: The input fluent.
op: The unary operation.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent._aggregation_op | def _aggregation_op(cls,
op: Callable[[tf.Tensor, Optional[Sequence[int]]], tf.Tensor],
x: 'TensorFluent',
vars_list: List[str]) -> 'TensorFluent':
'''Returns a TensorFluent for the aggregation `op` applied to fluent `x`.
Args:
op: The aggregation operati... | python | def _aggregation_op(cls,
op: Callable[[tf.Tensor, Optional[Sequence[int]]], tf.Tensor],
x: 'TensorFluent',
vars_list: List[str]) -> 'TensorFluent':
'''Returns a TensorFluent for the aggregation `op` applied to fluent `x`.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent._varslist2axis | def _varslist2axis(cls, fluent: 'TensorFluent', vars_list: List[str]) -> List[int]:
'''Maps the `vars_list` into a list of axis indices
corresponding to the `fluent` scope.
Args:
x: The fluent.
vars_list: The list of variables to be aggregated over.
Returns:
... | python | def _varslist2axis(cls, fluent: 'TensorFluent', vars_list: List[str]) -> List[int]:
'''Maps the `vars_list` into a list of axis indices
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x: The fluent.
vars_list: The list of variables to be aggregated over.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.cast | def cast(self, dtype: tf.DType) -> 'TensorFluent':
'''Returns a TensorFluent for the cast operation with given `dtype`.
Args:
dtype: The output's data type.
Returns:
A TensorFluent wrapping the cast operation.
'''
if self.dtype == dtype:
retu... | python | def cast(self, dtype: tf.DType) -> 'TensorFluent':
'''Returns a TensorFluent for the cast operation with given `dtype`.
Args:
dtype: The output's data type.
Returns:
A TensorFluent wrapping the cast operation.
'''
if self.dtype == dtype:
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.reshape | def reshape(self, shape: tf.TensorShape) -> 'TensorFluent':
'''Returns a TensorFluent for the reshape operation with given `shape`.
Args:
shape: The output's shape.
Returns:
A TensorFluent wrapping the reshape operation.
'''
t = tf.reshape(self.tensor, s... | python | def reshape(self, shape: tf.TensorShape) -> 'TensorFluent':
'''Returns a TensorFluent for the reshape operation with given `shape`.
Args:
shape: The output's shape.
Returns:
A TensorFluent wrapping the reshape operation.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.transpose | def transpose(self, permutation: Optional[List[int]] = None) -> 'TensorFluent':
'''Returns a TensorFluent for the transpose operation with given `permutation`.
Args:
permutation: The output's shape permutation.
Returns:
A TensorFluent wrapping the transpose operation.
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Args:
permutation: The output's shape permutation.
Returns:
A TensorFluent wrapping the transpose operation.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.sum | def sum(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the sum aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the sum aggregation function.
'''
operand ... | python | def sum(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the sum aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the sum aggregation function.
'''
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.avg | def avg(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the avg aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the avg aggregation function.
'''
operand ... | python | def avg(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the avg aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the avg aggregation function.
'''
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.prod | def prod(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the prod aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the prod aggregation function.
'''
opera... | python | def prod(self, vars_list: List[str]) -> 'TensorFluent':
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Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the prod aggregation function.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.maximum | def maximum(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the maximum aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the maximum aggregation function.
'''
... | python | def maximum(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the maximum aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the maximum aggregation function.
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.minimum | def minimum(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the minimum aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the minimum aggregation function.
'''
... | python | def minimum(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the minimum aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the minimum aggregation function.
'''
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thiagopbueno/rddl2tf | rddl2tf/fluent.py | TensorFluent.forall | def forall(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the forall aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the forall aggregation function.
'''
... | python | def forall(self, vars_list: List[str]) -> 'TensorFluent':
'''Returns the TensorFluent for the forall aggregation function.
Args:
vars_list: The list of variables to be aggregated over.
Returns:
A TensorFluent wrapping the forall aggregation function.
'''
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nion-software/nionswift-instrumentation-kit | nionswift_plugin/nion_instrumentation_ui/VideoControlPanel.py | VideoSourceStateController.initialize_state | def initialize_state(self):
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RowleyGroup/pyqueue | pyqueue/systems/slurm.py | SlurmPrinter.generate | def generate(self, job):
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:param job: An instance of JobInterface
:type job: pyqueue.job.JobInterface
"""
options = job.get_options().copy()
job_name = options.pop('name', None)
job_account = options... | python | def generate(self, job):
"""
Generates a job submission script from a job object
:param job: An instance of JobInterface
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"""
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RowleyGroup/pyqueue | pyqueue/systems/slurm.py | SlurmRemoteSubmitter.submit | def submit(self, job):
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ocaballeror/LyricFetch | lyricfetch/song.py | get_info_mpris2 | def get_info_mpris2(name):
"""
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"""
# qdbus org.mpris.MediaPlayer2.<name> /org/mpris/MediaPlayer2\
# org.freedesktop.DBus.Properties.Get org.mpris.MediaPlayer2.Player Metadat
bus_name = 'org.mpris.MediaPlayer2.' + name
path = '/org... | python | def get_info_mpris2(name):
"""
Get the current playing song from an mpris2 compliant player.
"""
# qdbus org.mpris.MediaPlayer2.<name> /org/mpris/MediaPlayer2\
# org.freedesktop.DBus.Properties.Get org.mpris.MediaPlayer2.Player Metadat
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ocaballeror/LyricFetch | lyricfetch/song.py | get_current_clementine | def get_current_clementine():
"""
Get the current song from clementine.
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# mpris_version 2
try:
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bus_name = 'org.mpris.clementine'
path = '/Player'
interface = 'org.freedesktop.MediaPlayer'
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"""
Get the current song from clementine.
"""
# mpris_version 2
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return get_info_mpris2('clementine')
except DBusErrorResponse:
bus_name = 'org.mpris.clementine'
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ocaballeror/LyricFetch | lyricfetch/song.py | get_current_cmus | def get_current_cmus():
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info = {}
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line = line.split(' ')... | python | def get_current_cmus():
"""
Get the current song from cmus.
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ocaballeror/LyricFetch | lyricfetch/song.py | Song.from_filename | def from_filename(cls, filename):
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Class constructor using the path to the corresponding mp3 file. The
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ocaballeror/LyricFetch | lyricfetch/song.py | Song.fetch_album_name | def fetch_album_name(self):
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response = get_lastfm('track.getInfo', artist=self.artist,
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response = get_lastfm('track.getInfo', artist=self.artist,
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numan/py-analytics | analytics/backends/redis.py | Redis._get_closest_week | def _get_closest_week(self, metric_date):
"""
Gets the closest monday to the date provided.
"""
#find the offset to the closest monday
days_after_monday = metric_date.isoweekday() - 1
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numan/py-analytics | analytics/backends/redis.py | Redis._get_daily_date_range | def _get_daily_date_range(self, metric_date, delta):
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numan/py-analytics | analytics/backends/redis.py | Redis._get_weekly_date_range | def _get_weekly_date_range(self, metric_date, delta):
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"""
dates = [metric_date]
end_date = metric_date + delta
#Figure out how many years our metric range spans
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"""
Gets the range of years that we need to use as keys to get metrics from redis.
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numan/py-analytics | analytics/backends/redis.py | Redis.clear_all | def clear_all(self):
"""
Deletes all ``sandsnake`` related data from redis.
.. warning::
Very expensive and destructive operation. Use with causion
"""
keys = self._analytics_backend.keys()
for key in itertools.chain(*keys):
with self._analytics... | python | def clear_all(self):
"""
Deletes all ``sandsnake`` related data from redis.
.. warning::
Very expensive and destructive operation. Use with causion
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numan/py-analytics | analytics/backends/redis.py | Redis.track_count | def track_count(self, unique_identifier, metric, inc_amt=1, **kwargs):
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numan/py-analytics | analytics/backends/redis.py | Redis.track_metric | def track_metric(self, unique_identifier, metric, date=None, inc_amt=1, **kwargs):
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numan/py-analytics | analytics/backends/redis.py | Redis.get_metrics | def get_metrics(self, metric_identifiers, from_date, limit=10, group_by="week", **kwargs):
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numan/py-analytics | analytics/backends/redis.py | Redis.get_count | def get_count(self, unique_identifier, metric, start_date=None, end_date=None, **kwargs):
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numan/py-analytics | analytics/backends/redis.py | Redis.get_counts | def get_counts(self, metric_identifiers, **kwargs):
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numan/py-analytics | analytics/backends/redis.py | Redis.set_metric_by_day | def set_metric_by_day(self, unique_identifier, metric, date, count, sync_agg=True, update_counter=True):
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numan/py-analytics | analytics/backends/redis.py | Redis.sync_agg_metric | def sync_agg_metric(self, unique_identifier, metric, start_date, end_date):
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numan/py-analytics | analytics/backends/redis.py | Redis.sync_week_metric | def sync_week_metric(self, unique_identifier, metric, start_date, end_date):
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non-Jedi/gyr | gyr/utils.py | intent | def intent(method):
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tipsi/tipsi_tools | tipsi_tools/doc_utils/tipsi_sphinx/dyn_serializer.py | parse_doc | def parse_doc(doc):
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key: value
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return {}
out = {}
for s in doc.split('\n'):
s = s.strip().split(':', maxsplit=1)
if len(s) == 2:
out[s[0]] = s[1]
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"""Merge a list of dictionaries into a single dictionary.
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as this function is using ChainMap to combine the dicts.
"""
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malramsay64/experi | src/experi/run.py | iterator_zip | def iterator_zip(variables: VarType, parent: str = None) -> Iterable[VarMatrix]:
"""Apply the zip operator to a set of variables.
This uses the python zip iterator to combine multiple lists of variables such that
the nth variable in each list is aligned.
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variables: The variables object
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malramsay64/experi | src/experi/run.py | iterator_product | def iterator_product(variables: VarType, parent: str = None) -> Iterable[VarMatrix]:
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malramsay64/experi | src/experi/run.py | iterator_arange | def iterator_arange(variables: VarType, parent: str) -> Iterable[VarMatrix]:
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variables: The input variables for the creation of the range
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malramsay64/experi | src/experi/run.py | iterator_cycle | def iterator_cycle(variables: VarType, parent: str) -> Iterable[VarMatrix]:
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malramsay64/experi | src/experi/run.py | variable_matrix | def variable_matrix(
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) -> Iterable[Dict[str, YamlValue]]:
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malramsay64/experi | src/experi/run.py | uniqueify | def uniqueify(my_list: Any) -> List[Any]:
"""Remove duplicate entries in a list retaining order."""
if sys.version_info >= (3, 6):
# An implementation specific detail of py3.6 is the retention of order
# within a dictionary. In py3.7 this becomes the documented behaviour.
return list(dic... | python | def uniqueify(my_list: Any) -> List[Any]:
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malramsay64/experi | src/experi/run.py | process_command | def process_command(command: CommandInput, matrix: VarMatrix) -> List[Command]:
"""Generate all combinations of commands given a variable matrix.
Processes the commands to be sequences of strings.
"""
assert command is not None
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"""
assert command is not None
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malramsay64/experi | src/experi/run.py | read_file | def read_file(filename: PathLike = "experiment.yml") -> Dict[str, Any]:
"""Read and parse yaml file."""
logger.debug("Input file: %s", filename)
with open(filename, "r") as stream:
structure = yaml.safe_load(stream)
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logger.debug("Input file: %s", filename)
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malramsay64/experi | src/experi/run.py | run_bash_jobs | def run_bash_jobs(
jobs: Iterator[Job], directory: PathLike = Path.cwd(), dry_run: bool = False
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malramsay64/experi | src/experi/run.py | run_scheduler_jobs | def run_scheduler_jobs(
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malramsay64/experi | src/experi/run.py | determine_scheduler | def determine_scheduler(
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# Scheduler value from command line has first priority
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alfredodeza/notario | notario/validators/iterables.py | BasicIterableValidator.safe_type | def safe_type(self, data, tree):
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aptivate/ckanext-datasetversions | ckanext/datasetversions/helpers.py | get_context | def get_context(context):
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tipsi/tipsi_tools | tipsi_tools/django/__init__.py | request_uniq | def request_uniq(func):
"""
return unique dict for each uwsgi request.
note: won't work on non-uwsgi cases
"""
def _wrapped(*args, **kwargs):
data = _get_request_unique_cache()
return func(data, *args, **kwargs)
return _wrapped | python | def request_uniq(func):
"""
return unique dict for each uwsgi request.
note: won't work on non-uwsgi cases
"""
def _wrapped(*args, **kwargs):
data = _get_request_unique_cache()
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alfredodeza/notario | notario/utils.py | safe_repr | def safe_repr(obj):
"""
Try to get ``__name__`` first, ``__class__.__name__`` second
and finally, if we can't get anything acceptable, fallback
to user a ``repr()`` call.
"""
name = getattr(obj, '__name__', getattr(obj.__class__, '__name__'))
if name == 'ndict':
name = 'dict'
ret... | python | def safe_repr(obj):
"""
Try to get ``__name__`` first, ``__class__.__name__`` second
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alfredodeza/notario | notario/utils.py | re_sort | def re_sort(data):
"""
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>>> data = {16: ('1', 'b'), 3: ('1', 'a')}
>>> re_sort(data)
>>> {0: ('1', 'a'), 1: ('1', 'b')}
"""
keys = sorted(data.keys())
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"""
A data with keys that are not enumerated sequentially will be
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>>> data = {16: ('1', 'b'), 3: ('1', 'a')}
>>> re_sort(data)
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alfredodeza/notario | notario/utils.py | ensure | def ensure(assertion, message=None):
"""
Checks an assertion argument for truth-ness. Will return ``True`` or
explicitly raise ``AssertionError``. This is to deal with environments
using ``python -O` or ``PYTHONOPTIMIZE=``.
:param assertion: some value to evaluate for truth-ness
:param message:... | python | def ensure(assertion, message=None):
"""
Checks an assertion argument for truth-ness. Will return ``True`` or
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using ``python -O` or ``PYTHONOPTIMIZE=``.
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thiagopbueno/rddl2tf | rddl2tf/fluentshape.py | TensorFluentShape.fluent_shape | def fluent_shape(self) -> Sequence[int]:
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inodb/sufam | sufam/mpileup_parser.py | run | def run(bam, chrom, pos1, pos2, reffa, chr_reffa, parameters):
"""Run mpileup on given chrom and pos"""
# check for chr ref
is_chr_query = chrom.startswith('chr')
if is_chr_query and chr_reffa is None:
chr_reffa = reffa
# check bam ref type
bam_header = subprocess.check_output("samtool... | python | def run(bam, chrom, pos1, pos2, reffa, chr_reffa, parameters):
"""Run mpileup on given chrom and pos"""
# check for chr ref
is_chr_query = chrom.startswith('chr')
if is_chr_query and chr_reffa is None:
chr_reffa = reffa
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tipsi/tipsi_tools | tipsi_tools/python.py | execfile | def execfile(fname, _globals, _locals):
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"""
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return True
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tipsi/tipsi_tools | tipsi_tools/python.py | auto_directory | def auto_directory(rel_name):
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"""
dir_name = rel_path(rel_name, check=False)
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"""
if you're using py.path you make do that as:
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dir_name = rel_path(rel_name, check=False)
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craigahobbs/chisel | src/chisel/util.py | parse_iso8601_date | def parse_iso8601_date(string):
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Parse an ISO 8601 date string
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# Match ISO 8601?
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year = int(match.group('year'))
month = int(match.group('mo... | python | def parse_iso8601_date(string):
"""
Parse an ISO 8601 date string
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# Match ISO 8601?
match = _RE_ISO8601_DATE.search(string)
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craigahobbs/chisel | src/chisel/util.py | import_submodules | def import_submodules(package, parent_package=None, exclude_submodules=None):
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:param package: package name (e.g 'chisel.util'); may be relative if parent_package is provided
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Generator which imports all submodules of a module, recursively, including subpackages
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:param parent_package: parent package name (e.g 'chisel')
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_initial_state | def compile_initial_state(self, batch_size: Optional[int] = None) -> Sequence[tf.Tensor]:
'''Returns a tuple of tensors representing the initial state fluents.
Args:
batch_size (Optional[int]): The batch size.
Returns:
Sequence[tf.Tensor]: A tuple of tensors.
''... | python | def compile_initial_state(self, batch_size: Optional[int] = None) -> Sequence[tf.Tensor]:
'''Returns a tuple of tensors representing the initial state fluents.
Args:
batch_size (Optional[int]): The batch size.
Returns:
Sequence[tf.Tensor]: A tuple of tensors.
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_default_action | def compile_default_action(self, batch_size: Optional[int] = None) -> Sequence[tf.Tensor]:
'''Returns a tuple of tensors representing the default action fluents.
Args:
batch_size (int): The batch size.
Returns:
Sequence[tf.Tensor]: A tuple of tensors.
'''
... | python | def compile_default_action(self, batch_size: Optional[int] = None) -> Sequence[tf.Tensor]:
'''Returns a tuple of tensors representing the default action fluents.
Args:
batch_size (int): The batch size.
Returns:
Sequence[tf.Tensor]: A tuple of tensors.
'''
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.cpfs | def cpfs(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor],
noise: Optional[Noise] = None) -> Tuple[List[TensorFluent], List[TensorFluent]]:
'''Compiles the intermediate and next state fluent CPFs given
the current `state` and `action`.
Args:
... | python | def cpfs(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor],
noise: Optional[Noise] = None) -> Tuple[List[TensorFluent], List[TensorFluent]]:
'''Compiles the intermediate and next state fluent CPFs given
the current `state` and `action`.
Args:
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.reward | def reward(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor],
next_state: Sequence[tf.Tensor]) -> tf.Tensor:
'''Compiles the reward function given the current `state`, `action` and
`next_state`.
Args:
state (Sequence[tf.Tensor... | python | def reward(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor],
next_state: Sequence[tf.Tensor]) -> tf.Tensor:
'''Compiles the reward function given the current `state`, `action` and
`next_state`.
Args:
state (Sequence[tf.Tensor... | [
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_cpfs | def compile_cpfs(self,
scope: Dict[str, TensorFluent],
batch_size: Optional[int] = None,
noise: Optional[Noise] = None) -> Tuple[List[CPFPair], List[CPFPair]]:
'''Compiles the intermediate and next state fluent CPFs given the current `state` and `ac... | python | def compile_cpfs(self,
scope: Dict[str, TensorFluent],
batch_size: Optional[int] = None,
noise: Optional[Noise] = None) -> Tuple[List[CPFPair], List[CPFPair]]:
'''Compiles the intermediate and next state fluent CPFs given the current `state` and `ac... | [
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batch_size (Optional[int]): The batch size.
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_intermediate_cpfs | def compile_intermediate_cpfs(self,
scope: Dict[str, TensorFluent],
batch_size: Optional[int] = None,
noise: Optional[Noise] = None) -> List[CPFPair]:
'''Compiles the intermediate fluent CPFs given the current ... | python | def compile_intermediate_cpfs(self,
scope: Dict[str, TensorFluent],
batch_size: Optional[int] = None,
noise: Optional[Noise] = None) -> List[CPFPair]:
'''Compiles the intermediate fluent CPFs given the current ... | [
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batch_size (Optional[int]): The batch size.
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_state_cpfs | def compile_state_cpfs(self,
scope: Dict[str, TensorFluent],
batch_size: Optional[int] = None,
noise: Optional[Noise] = None) -> List[CPFPair]:
'''Compiles the next state fluent CPFs given the current `state` and `action` scope.
... | python | def compile_state_cpfs(self,
scope: Dict[str, TensorFluent],
batch_size: Optional[int] = None,
noise: Optional[Noise] = None) -> List[CPFPair]:
'''Compiles the next state fluent CPFs given the current `state` and `action` scope.
... | [
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_reward | def compile_reward(self, scope: Dict[str, TensorFluent]) -> TensorFluent:
'''Compiles the reward function given the fluent `scope`.
Args:
scope (Dict[str, :obj:`rddl2tf.fluent.TensorFluent`]): The fluent scope for reward evaluation.
Returns:
A :obj:`rddl2tf.fluent.Tenso... | python | def compile_reward(self, scope: Dict[str, TensorFluent]) -> TensorFluent:
'''Compiles the reward function given the fluent `scope`.
Args:
scope (Dict[str, :obj:`rddl2tf.fluent.TensorFluent`]): The fluent scope for reward evaluation.
Returns:
A :obj:`rddl2tf.fluent.Tenso... | [
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_state_action_constraints | def compile_state_action_constraints(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor]) -> List[TensorFluent]:
'''Compiles the state-action constraints given current `state` and `action` fluents.
Args:
state (Sequence[tf.Tensor]): The current state fluent... | python | def compile_state_action_constraints(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor]) -> List[TensorFluent]:
'''Compiles the state-action constraints given current `state` and `action` fluents.
Args:
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_action_preconditions | def compile_action_preconditions(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor]) -> List[TensorFluent]:
'''Compiles the action preconditions given current `state` and `action` fluents.
Args:
state (Sequence[tf.Tensor]): The current state fluents.
... | python | def compile_action_preconditions(self,
state: Sequence[tf.Tensor],
action: Sequence[tf.Tensor]) -> List[TensorFluent]:
'''Compiles the action preconditions given current `state` and `action` fluents.
Args:
state (Sequence[tf.Tensor]): The current state fluents.
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thiagopbueno/rddl2tf | rddl2tf/compiler.py | Compiler.compile_state_invariants | def compile_state_invariants(self,
state: Sequence[tf.Tensor]) -> List[TensorFluent]:
'''Compiles the state invarints given current `state` fluents.
Args:
state (Sequence[tf.Tensor]): The current state fluents.
Returns:
A list of :obj:`rddl2tf.fluent.TensorF... | python | def compile_state_invariants(self,
state: Sequence[tf.Tensor]) -> List[TensorFluent]:
'''Compiles the state invarints given current `state` fluents.
Args:
state (Sequence[tf.Tensor]): The current state fluents.
Returns:
A list of :obj:`rddl2tf.fluent.TensorF... | [
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