spezold

"""
Calculate sample mean and std. over all desired axes of given samples, using Chan et al.'s version of Welford's online
algorithm; cf. https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm (20240515) and
equations (2.1a), (2.1b) in the referenced paper [1]_; or likewise, equations (1.5a), (1.5b) in [2]_.
Mind the typo in [1]_, (2.1a): T_{1, m+n} on the right side of the equation should be T_{m+1, m+n} (cf. [2]_, (1.5a)).
References
----------
.. [1] T. F. Chan, G. H. Golub, and R. J. LeVeque, “Updating Formulae and a Pairwise Algorithm for Computing Sample
       Variances,” in COMPSTAT 1982 5th Symposium held at Toulouse 1982, Heidelberg, 1982, pp. 30–41,
       doi: 10.1007/978-3-642-51461-6_3.

spezold

"""
Calculate sample mean and std. over all desired axes of given samples, using Chan et al.'s version of Welford's online
algorithm; cf. https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm (20240515) and
equations (2.1a), (2.1b) in the referenced paper [1]_; or likewise, equations (1.5a), (1.5b) in [2]_.

Mind the typo in [1]_, (2.1a): T_{1, m+n} on the right side of the equation should be T_{m+1, m+n} (cf. [2]_, (1.5a)).

References
----------
.. [1] T. F. Chan, G. H. Golub, and R. J. LeVeque, “Updating Formulae and a Pairwise Algorithm for Computing Sample

spezold

"""
CAUTION: Make sure that
    1. this file is placed in the root directory of the project of interest
       (or otherwise, adjust `BASE_DIR` accordingly);
    2. the file is run in the same Python environment (conda environment, poetry environment, ...)
       as the project of interest (so activate the corresponding environment first, if necessary).
"""

from collections import defaultdict
from importlib.util import find_spec

spezold

from typing import Tuple

import torch
from torch import Tensor

# The straightforward solution
def rolling_window_inside_and_outside(t: Tensor, size: int, stride: int=1) -> Tuple[Tensor, Tensor]:
    """
    Given a 1D tensor, provide both the values inside the rolling window and outside the rolling window for each window
    position with the given window size and stride.

spezold

import importlib.util
import inspect
import json
from pathlib import Path
from typing import Optional, Union
import zipfile

import torch
from torch import nn

spezold

from typing import Union

import torch
import numpy as np


def percentile(t: torch.tensor, q: float) -> Union[int, float]:
    """
    Return the ``q``-th percentile of the flattened input tensor's data.