# Copyright (C) 2024 Jakub Więckowski
import numpy as np
from ...validator import Validator
from ...utils import memory_guard
[docs]
@memory_guard
def normal_distribution(size: int, loc: float = 0.0, scale: float = 1.0) -> np.ndarray:
"""
Generate a set of normalized weights sampled from a normal distribution.
Parameters:
------------
size : int
Number of weights to generate.
loc : float, optional, default=0.0
Mean of the normal distribution.
scale : float, optional, default=1.0
Standard deviation of the normal distribution.
Returns:
---------
ndarray
Array of normalized weights sampled from a normal distribution.
Examples:
----------
Example 1: Generate normalized weights from a normal distribution with default parameters
>>> weights = normal_distribution(3)
>>> print(weights)
Example 2: Generate normalized weights from a normal distribution with explicit parameters
>>> weights = normal_distribution(3, 5, 2)
>>> print(weights)
"""
Validator.is_type_valid(size, (int, np.integer), 'size')
Validator.is_positive_value(size, var_name='size')
Validator.is_type_valid(loc, (int, np.integer, float, np.floating), 'loc')
Validator.is_type_valid(scale, (int, np.integer, float, np.floating), 'scale')
Validator.is_positive_value(scale, var_name='scale')
weights = np.abs(np.random.normal(loc, scale, size=size))
return np.array(weights) / np.sum(weights)