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102 lines
2.6 KiB
Python
102 lines
2.6 KiB
Python
from __future__ import annotations
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from functools import lru_cache
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import hashlib
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import inspect
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import math
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import numpy as np
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from typing import Callable, TypeVar
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from manimlib.typing import FloatArray
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Scalable = TypeVar("Scalable", float, FloatArray)
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def sigmoid(x: float | FloatArray):
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return 1.0 / (1 + np.exp(-x))
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@lru_cache(maxsize=10)
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def choose(n: int, k: int) -> int:
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return math.comb(n, k)
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def gen_choose(n: int, r: int) -> int:
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return int(np.prod(range(n, n - r, -1)) / math.factorial(r))
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def get_num_args(function: Callable) -> int:
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return len(get_parameters(function))
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def get_parameters(function: Callable) -> list:
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return list(inspect.signature(function).parameters.keys())
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# Just to have a less heavyweight name for this extremely common operation
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#
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# We may wish to have more fine-grained control over division by zero behavior
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# in the future (separate specifiable values for 0/0 and x/0 with x != 0),
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# but for now, we just allow the option to handle indeterminate 0/0.
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def clip(a: float, min_a: float, max_a: float) -> float:
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if a < min_a:
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return min_a
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elif a > max_a:
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return max_a
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return a
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def arr_clip(arr: np.ndarray, min_a: float, max_a: float) -> np.ndarray:
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arr[arr < min_a] = min_a
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arr[arr > max_a] = max_a
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return arr
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def fdiv(a: Scalable, b: Scalable, zero_over_zero_value: Scalable | None = None) -> Scalable:
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if zero_over_zero_value is not None:
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out = np.full_like(a, zero_over_zero_value)
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where = np.logical_or(a != 0, b != 0)
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else:
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out = None
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where = True
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return np.true_divide(a, b, out=out, where=where)
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def binary_search(function: Callable[[float], float],
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target: float,
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lower_bound: float,
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upper_bound: float,
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tolerance:float = 1e-4) -> float | None:
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lh = lower_bound
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rh = upper_bound
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mh = (lh + rh) / 2
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while abs(rh - lh) > tolerance:
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lx, mx, rx = [function(h) for h in (lh, mh, rh)]
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if lx == target:
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return lx
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if rx == target:
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return rx
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if lx <= target and rx >= target:
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if mx > target:
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rh = mh
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else:
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lh = mh
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elif lx > target and rx < target:
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lh, rh = rh, lh
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else:
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return None
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mh = (lh + rh) / 2
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return mh
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def hash_string(string: str) -> str:
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# Truncating at 16 bytes for cleanliness
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hasher = hashlib.sha256(string.encode())
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return hasher.hexdigest()[:16]
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