def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, Any]:
""" Take a step from the current state using the specified action. Action is assumed to be in [-1,1] unless
the environment was created with do_not_normalize = True.
:param action: The action to take in the current state for sample_time seconds
:return: A tuple with (next_state, reward, terminal, additional_info)"""
if self.do_not_normalize:
action = self.normalize_action(np.array(action))
assert -1 <= action.all(
) <= 1, 'Action was out of the allowed range'
else:
action = np.array(action)
assert -1 <= action.all(
) <= 1, 'Actions should be normalized between -1 and 1'
self._u = self.denormalize_action(action)
self._step_log_pre()
with warnings.catch_warnings():
self._state, self._reward, self._terminal, self._info = self._env.step(
action)
self.step_counter += 1
self._step_log_post(self._reward, self._terminal)
return self.state, self.reward, self._terminal, self._info
def check_bingo2(c: np.ndarray) -> np.ndarray | None:
u = c.all(axis=1) | c.all(axis=2)
u = (~u).all(axis=1)
res = np.argwhere(u)
if not res.size:
return None
return res[0]
def nanall(
values: np.ndarray,
axis: Optional[int] = None,
skipna: bool = True,
mask: Optional[np.ndarray] = None,
) -> bool:
"""
Check if all elements along an axis evaluate to True.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : bool
Examples
--------
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanall(s)
True
>>> import pandas.core.nanops as nanops
>>> s = pd.Series([1, 0])
>>> nanops.nanall(s)
False
"""
values, _, _, _, _ = _get_values(values, skipna, fill_value=True, mask=mask)
return values.all(axis)
def mean(values: np.ndarray, mask: np.ndarray, skipna: bool = True):
if not values.size or mask.all():
return libmissing.NA
_sum = _sumprod(np.sum, values=values, mask=mask, skipna=skipna)
count = np.count_nonzero(~mask)
mean_value = _sum / count
return mean_value
def uncertainty(preds: np.ndarray) -> float:
r"""Function used to calculate the fraction of uncertainty of the predicted confidence interval
Sometimes the nonconformist inductive conformal predictor returns an interval that contains all
the possile classes. In these cases no information is provided, thus adding uncertainty to the
prediction. This function returns the fraction of times the interval created has all the classes.
Parameters
----------
preds: numpy.ndarray
The confidence interval, represented by a numpy.ndarray of boolean
values with size (num_samples, num_classes), where num_classes is the total
number of classes for the data, and num_samples is the number of samples used
to create the matrix.
Returns
-------
uncertainty: float
Number of times the interval created contains all the possible classes.
Notes
-----
This fuction can only be used in confidence interval generated for classification
cases with the nonconformist package.
"""
return np.mean(preds.all(axis=1))
def srgb2lin(img_arr: np.ndarray) -> np.ndarray:
"""
Convert from sRGB to Linear RGB.
Method from reddit of all places
https://www.reddit.com/r/blender/comments/8moqk6/rgb_node_difference_between_rgb_values/
Another reference: https://entropymine.com/imageworsener/srgbformula/
Parameters
---
img_arr (ndarray)
Input numpy array
Result
---
np.ndarray
Converted array
"""
# convert to [0,1]
assert img_arr.all() >= 0
img_arr = img_arr / 255
cutoff = 0.0404482362771082
linear_img = np.where(img_arr >= cutoff, ((img_arr + 0.055) / 1.055)**2.4,
img_arr / 12.92)
return linear_img
def pre_edge_fit(
x: np.ndarray,
y: np.ndarray,
pre_feature_min: float,
pre_feature_max: float,
post_feature_min: float,
post_feature_max: float,
):
if (x.all() and y.all() and pre_feature_min and pre_feature_max
and post_feature_min and post_feature_max):
return calculate_pre_edge_fit(x, y, pre_feature_min, pre_feature_max,
post_feature_min, post_feature_max)
else:
return x, y, make_zero_array(x)
def verify_validity(similarity_matrix: ndarray):
"""Verifies validity of a tweet in similarity matrix.
Verifies validity of a tweet in similarity matrix, if it crosses
the configured threshold for similarity.
Args:
similarity_matrix: A list of lists containing similarity scores
Returns:
A Boolean representing validity of the tweet.
"""
if not isinstance(similarity_matrix, ndarray):
raise TypeError('Similarity matrix must type numpy.ndarray')
if not similarity_matrix.all():
raise ValueError('Similarity matrix must be a valid numpy array')
row = similarity_matrix[0]
for column_index in range(1, row.shape[0]):
if row[column_index] > app_config.SIMILARITY_THRESHOLD:
return (True, column_index, ResultStatus.ALL_OKAY)
return (False, None, ResultStatus.ALL_OKAY)
def check_game_over(board: np.ndarray,
last_action: Optional[PlayerAction] = None,
connected_function=CONNECTION) -> bool:
"""
Return if the board is a terminal state (either player has won, or a draw).
:param connected_function:
:param board:
:param last_action:
:return:
"""
if connected_function(board=board, player=PLAYER1,
last_action=last_action):
return True
elif connected_function(board=board,
player=PLAYER2,
last_action=last_action):
return True
elif board.all():
return True
else:
return False
def f(array: np.ndarray) -> bool:
result = tester(array)
return False if result is None else array.all()
def _is_all_freezing(mean_melting_alt: np.ndarray, t0_alt: np.ndarray,
height: np.ndarray) -> bool:
no_detected_melting = mean_melting_alt.all() is ma.masked
all_temperatures_below_freezing = (t0_alt <= height[0]).all()
return no_detected_melting and all_temperatures_below_freezing