Python typing.Sized() Examples
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Example #1
Source File: itertools.py From deep_pipe with MIT License | 5 votes |
def zip_equal(*args: Union[Sized, Iterable]) -> Iterable[Tuple]: """ zip over the given iterables, but enforce that all of them exhaust simultaneously. Examples -------- >>> zip_equal([1, 2, 3], [4, 5, 6]) # ok >>> zip_equal([1, 2, 3], [4, 5, 6, 7]) # raises ValueError # ValueError is raised even if the lengths are not known >>> zip_equal([1, 2, 3], map(np.sqrt, [4, 5, 6])) # ok >>> zip_equal([1, 2, 3], map(np.sqrt, [4, 5, 6, 7])) # raises ValueError """ if not args: return lengths = [] all_lengths = [] for arg in args: try: lengths.append(len(arg)) all_lengths.append(len(arg)) except TypeError: all_lengths.append('?') if lengths and not all(x == lengths[0] for x in lengths): from .checks import join raise ValueError(f'The arguments have different lengths: {join(all_lengths)}.') iterables = [iter(arg) for arg in args] while True: result = [] for it in iterables: with suppress(StopIteration): result.append(next(it)) if len(result) != len(args): break yield tuple(result) if len(result) != 0: raise ValueError(f'The iterables did not exhaust simultaneously.')
Example #2
Source File: test_typing.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
def test_sized(self): assert isinstance([], typing.Sized) assert not isinstance(42, typing.Sized)
Example #3
Source File: model.py From FractalAI with GNU Affero General Public License v3.0 | 5 votes |
def predict_batch(self, observations: [Sized, Iterable]) -> np.ndarray: """ Returns a vector of actions chosen at random. :param observations: Represents a vector of observations. Only used in determining the size of the returned array. :return: Numpy array containing the action chosen for each observation. """ if not self.use_block: return np.random.randint(0, high=int(self.n_actions), size=(len(observations),)) self._i += 1 return self.noise[: len(observations), self._i % self.samples]
Example #4
Source File: model.py From FractalAI with GNU Affero General Public License v3.0 | 5 votes |
def predict_batch(self, observations: [Sized, Iterable]) -> np.ndarray: """ Returns a vector of actions chosen at random. :param observations: Represents a vector of observations. Only used in determining the size of the returned array. :return: Numpy array containing the action chosen for each observation. """ return np.random.randint(1, high=13, size=(len(observations),))
Example #5
Source File: model.py From FractalAI with GNU Affero General Public License v3.0 | 5 votes |
def predict_batch(self, observations: [Sized, Iterable]) -> np.ndarray: """ Returns a vector of actions chosen at random. :param observations: Represents a vector of observations. Only used in determining the size of the returned array. :return: Numpy array containing the action chosen for each observation. """ perturbations = [] for i in range(len(observations)): x = [np.random.randn(*shape) * self.sigma for shape in self.weigths_shapes] perturbations.append(x) return np.array(perturbations)
Example #6
Source File: dataset_type.py From ebonite with Apache License 2.0 | 5 votes |
def serialize(self, instance: Sized): _check_type_and_size(instance, self.actual_type, len(self.items), SerializationError) return self.actual_type(serialize(o, t) for t, o in zip(self.items, instance))
Example #7
Source File: spaces.py From habitat-api with MIT License | 5 votes |
def contains(self, x): if not isinstance(x, Sized): return False if not (self.min_seq_length <= len(x) <= self.max_seq_length): return False return all([self.space.contains(el) for el in x])
Example #8
Source File: data_io.py From sockeye with Apache License 2.0 | 5 votes |
def are_none(sequences: Sequence[Sized]) -> bool: """ Returns True if all sequences are None. """ if not sequences: return True return all(s is None for s in sequences)
Example #9
Source File: data_io.py From sockeye with Apache License 2.0 | 5 votes |
def are_token_parallel(sequences: Sequence[Sized]) -> bool: """ Returns True if all sequences in the list have the same length. """ if not sequences or len(sequences) == 1: return True return all(len(s) == len(sequences[0]) for s in sequences)
Example #10
Source File: test_typing.py From Project-New-Reign---Nemesis-Main with GNU General Public License v3.0 | 5 votes |
def test_sized(self): self.assertIsInstance([], typing.Sized) self.assertNotIsInstance(42, typing.Sized)
Example #11
Source File: utils.py From DeepPavlov with Apache License 2.0 | 5 votes |
def _get_all_dimensions(batch: Sequence, level: int = 0, res: Optional[List[List[int]]] = None) -> List[List[int]]: """Return all presented element sizes of each dimension. Args: batch: Data array. level: Recursion level. res: List containing element sizes of each dimension. Return: List, i-th element of which is list containing all presented sized of batch's i-th dimension. Examples: >>> x = [[[1], [2, 3]], [[4], [5, 6, 7], [8, 9]]] >>> _get_all_dimensions(x) [[2], [2, 3], [1, 2, 1, 3, 2]] """ if not level: res = [[len(batch)]] if len(batch) and isinstance(batch[0], Sized) and not isinstance(batch[0], str): level += 1 if len(res) <= level: res.append([]) for item in batch: res[level].append(len(item)) _get_all_dimensions(item, level, res) return res
Example #12
Source File: robot.py From pybotics with MIT License | 5 votes |
def from_parameters(cls, parameters: Sequence[float]) -> Sized: """Construct Robot from Kinematic Chain parameters.""" # FIXME: assumes MDH revolute robot kc = MDHKinematicChain.from_parameters(parameters) return cls(kinematic_chain=kc)
Example #13
Source File: model.py From FractalAI with GNU Affero General Public License v3.0 | 5 votes |
def predict_batch(self, observations: [Sized, Iterable]) -> np.ndarray: """ Returns a vector of actions chosen at random. :param observations: Represents a vector of observations. Only used in determining the size of the returned array. :return: Numpy array containing the action chosen for each observation. """ if not self.use_block: return np.random.randint(0, high=int(self.n_actions), size=(len(observations),)) self._i += 1 return self.noise[:len(observations), self._i % self.samples]
Example #14
Source File: model.py From FractalAI with GNU Affero General Public License v3.0 | 5 votes |
def predict_batch(self, observations: [Sized, Iterable]) -> np.ndarray: """ Returns a vector of actions chosen at random. :param observations: Represents a vector of observations. Only used in determining the size of the returned array. :return: Numpy array containing the action chosen for each observation. """ perturbations = [] for i in range(len(observations)): x = [np.random.randn(*shape) * self.sigma for shape in self.weigths_shapes] perturbations.append(x) return np.array(perturbations)
Example #15
Source File: miscellaneous_routes.py From FACT_core with GNU General Public License v3.0 | 5 votes |
def _count_values(dictionary: Dict[str, Sized]) -> int: return sum(len(e) for e in dictionary.values())
Example #16
Source File: plotting.py From bindsnet with GNU Affero General Public License v3.0 | 4 votes |
def plot_assignments( assignments: torch.Tensor, im: Optional[AxesImage] = None, figsize: Tuple[int, int] = (5, 5), classes: Optional[Sized] = None, ) -> AxesImage: # language=rst """ Plot the two-dimensional neuron assignments. :param assignments: Vector of neuron label assignments. :param im: Used for re-drawing the assignments plot. :param figsize: Horizontal, vertical figure size in inches. :param classes: Iterable of labels for colorbar ticks corresponding to data labels. :return: Used for re-drawing the assigments plot. """ locals_assignments = assignments.detach().clone().cpu().numpy() if not im: fig, ax = plt.subplots(figsize=figsize) ax.set_title("Categorical assignments") if classes is None: color = plt.get_cmap("RdBu", 11) im = ax.matshow( locals_assignments, cmap=color, vmin=-1.5, vmax=9.5 ) else: color = plt.get_cmap("RdBu", len(classes) + 1) im = ax.matshow( locals_assignments, cmap=color, vmin=-1.5, vmax=len(classes) - 0.5, ) div = make_axes_locatable(ax) cax = div.append_axes("right", size="5%", pad=0.05) if classes is None: cbar = plt.colorbar(im, cax=cax, ticks=list(range(-1, 11))) cbar.ax.set_yticklabels(["none"] + list(range(10))) else: cbar = plt.colorbar(im, cax=cax, ticks=np.arange(-1, len(classes))) cbar.ax.set_yticklabels(["none"] + list(classes)) ax.set_xticks(()) ax.set_yticks(()) fig.tight_layout() else: im.set_data(locals_assignments) return im