# Copyright (C) 2024 Jakub Więckowski
import numpy as np
from ...validator import Validator
from ...utils import memory_guard
[docs]
@memory_guard
def chisquare_distribution(size: int, df: float = 1.0) -> np.ndarray:
"""
Generate a set of normalized weights sampled from a normal distribution.
Parameters:
------------
size : int
Number of weights to generate.
df : float, optional, default=1.0
Number of degrees of freedom. Must be > 0.
Returns:
---------
ndarray
Array of normalized weights sampled from a normal distribution.
Examples:
----------
Example 1: Generate normalized weights from a chi-square distribution with default parameters
>>> weights = chisquare_distribution(3)
>>> print(weights)
Example 2: Generate normalized weights from a chi-square distribution with explicit parameters
>>> weights = chisquare_distribution(3, 5)
>>> print(weights)
"""
Validator.is_type_valid(size, (int, np.integer), 'size')
Validator.is_positive_value(size, var_name='size')
Validator.is_type_valid(df, (int, np.integer, float, np.floating), 'df')
Validator.is_positive_value(df, var_name='df')
weights = np.abs(np.random.chisquare(df, size=size))
return np.array(weights) / np.sum(weights)