Miscellaneous

This section contains various miscellaneous information about the Radiate library that doesn't fit into other sections.

Random

Random number generation is a crucial aspect of evolutionary algorithms. Radiate provides a random interface that governs all random number generation within the library through the random_provider. This allows for consistent and reproducible results across different runs of the algorithm.

Through the random_provider, you can access various random number generation methods, such as generating random floats, integers, bools, selecting random elements from a list, shuffling elements in a list, among others. This ensures that all stochastic processes within the library are controlled and can be easily managed.

Here's an example of how to use the random_provider:

Python Rust

import radiate as rd

# set a seed for reproducibility
rd.random.seed(42)  

# random float
rand_float = rd.random.float(min=0.0, max=1.0)

# random integer
rand_int = rd.random.int(min=0, max=10)

# random bool with 50% chance of being true
rand_bool = rd.random.bool(prob=0.5)

# randomly sample 2 elements from a list
rand_choice = rd.random.sample(data=[1, 2, 3, 4, 5], count=2)

# choose a random element from a list
rand_element = rd.random.choose(data=[1, 2, 3, 4, 5])

use radiate::*;

// set a seed for reproducibility
random_provider::set_seed(42);

// generate random values in ranges
let rand_float = random_provider::range(0.0..1.0);
let rand_int = random_provider::range(0..10);

// random bool with 50% chance of being true
let rand_bool = random_provider::bool(0.5);

// choose a random element from a slice and shuffle a vector
let rand_choice = random_provider::choose(&[1, 2, 3, 4, 5]);

// shuffle a vector in place
let mut vec = vec![1, 2, 3, 4, 5];
random_provider::shuffle(&mut vec);

// random gaussian float with mean 0 and stddev 1
let rand_gauss = random_provider::gaussian(0.0, 1.0);