haru_cmaes 0.1.0

use crate::{fitness::square_and_sum, params::CmaesParams, state::CmaesState, strategy::Cmaes};
use anyhow::Result;

#[allow(unused_imports)]
use blas_src;

/// Example usage of the CMA-ES algorithm.
///
/// This function demonstrates a basic workflow of the CMA-ES optimization algorithm using
/// predefined parameters and fitness function. It initializes the CMA-ES algorithm, iterates
/// through a fixed number of generations, and prints the average fitness of the best solutions.
///
/// # Returns
/// - `Result<()>`: Returns `Ok(())` if the function completes successfully, or an error if any
///   operation fails.
pub fn example() -> Result<()> {
    // Initialize CMA-ES parameters
    let params = CmaesParams {
        popsize: 50,
        xstart: vec![0.0; 50],
        sigma: 0.75,
    };

    // Create a new CMA-ES instance
    let cmaes = Cmaes::new(&params)?;
    
    // Initialize the CMA-ES state
    let mut state = CmaesState::init_state(&params)?;
    
    // Run the CMA-ES algorithm for 150 iterations
    for _i in 0..150 {
        // Generate a new population
        let mut pop = cmaes.ask(&mut state)?;
        
        // Evaluate the fitness of the population
        let mut fitness = square_and_sum(&pop)?;
        
        // Update the state with the new population and fitness values
        state = cmaes.tell(state, &mut pop, &mut fitness)?;
    }

    // Print the average fitness of the best solutions
    println!("Fitness (mean): {:+.4?}", &state.best_y_fit.mean());

    Ok(())
}