import numpy as np
import time

rng = np.random.default_rng(int(time.monotonic()))

def uniform(shape: tuple, mu: float = 0.5, std: float = 1.0) -> np.ndarray:
	return std * (rng.uniform(size=shape) + mu - 0.5)

def gaussian(shape: tuple, mu: float = 0.5, std: float = 1.0) -> np.ndarray:
	return rng.normal(size=shape, loc=mu, scale=std)

def sample(src: np.ndarray) -> np.ndarray:
	return (src > uniform(src.shape)).astype(float)

def prob(src: np.ndarray) -> np.ndarray:
	return 1.0 / (1 + np.exp(-src))

def rms_error(d_err: np.ndarray):
	d_err_squared = d_err * d_err
	return np.sum(d_err_squared, 1) / d_err_squared[1]

def rms_error_accu(d_err: np.ndarray):
	d_err_squared = d_err * d_err
	s =  np.sum(d_err_squared) / d_err.size
	return s