moors 0.2.10

Solving multi-objective optimization problems using genetic algorithms.
Documentation 
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use ndarray::Array1;

use crate::{operators::SamplingOperator, random::RandomGenerator};

#[derive(Debug, Clone)]
/// Sampling operator for floating-point variables using uniform random distribution.
pub struct RandomSamplingFloat {
    pub min: f64,
    pub max: f64,
}

impl RandomSamplingFloat {
    pub fn new(min: f64, max: f64) -> Self {
        Self { min, max }
    }
}

impl SamplingOperator for RandomSamplingFloat {
    fn sample_individual(&self, num_vars: usize, rng: &mut impl RandomGenerator) -> Array1<f64> {
        (0..num_vars)
            .map(|_| rng.gen_range_f64(self.min, self.max))
            .collect()
    }
}