# Copyright (C) 2024 Jakub Więckowski
import numpy as np
from ...validator import Validator
from ...utils import memory_guard
[docs]
@memory_guard
def triangular_distribution(size: int, left: float = 0.0, mode: float = 0.5, right: float = 1.0) -> np.ndarray:
"""
Generate a set of normalized weights sampled from a triangular distribution.
Parameters:
------------
size : int
Number of weights to generate.
left : float, optional, default=0.0
The lower bound of the triangular distribution.
mode : float, optional, default=0.5
The mode of the triangular distribution.
right : float, optional, default=1.0
The upper bound of the triangular distribution.
Returns:
---------
ndarray
Array of normalized weights sampled from a triangular distribution.
Examples:
----------
Example 1: Generate normalized weights from a triangular distribution with default parameters
>>> weights = triangular_distribution(3)
>>> print(weights)
Example 2: Generate normalized weights from a triangular distribution with explicit parameters
>>> weights = triangular_distribution(3, 2, 5, 6)
>>> print(weights)
"""
Validator.is_type_valid(size, (int, np.integer), 'size')
Validator.is_positive_value(size, var_name='size')
if left > mode or mode > right or left > right:
raise ValueError('Parameters should follow the condition left <= mode <= right')
weights = np.abs(np.random.triangular(left, mode, right, size=size))
return np.array(weights) / np.sum(weights)