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use anyhow::Result;
use nalgebra::DVector;
/// Parameters for CMA-ES (Covariance Matrix Adaptation Evolution Strategy).
#[derive(Debug, Clone)]
pub struct CmaesParams {
pub popsize: i32, // Population size
pub xstart: Vec<f32>, // Initial guess (mean vector)
pub sigma: f32, // Step-size (standard deviation)
pub tol: f32, // Tolerance for convergence, optional
pub only_diag: bool, // Whether to use only diag and no covariances or not
pub n: f32, // Dimension of the problem space (xstart size)
pub mu: i32, // Number of parents (best individuals)
pub weights: DVector<f32>, // Weights for recombination
pub mueff: f32, // Effective number of parents
pub cc: f32, // Cumulation constant for the rank-one update
pub cs: f32, // Cumulation constant for the rank-mu update
pub c1: f32, // Learning rate for the rank-one update
pub cmu: f32, // Learning rate for the rank-mu update
pub damps: f32, // Damping for step-size adaptation
// pub lazy_gap_evals: f32, // Gap to postpone eigendecomposition
}
/// Trait for CMA-ES parameters.
pub trait CmaesParamsValidator {
type Validated;
fn new() -> Result<Self::Validated>;
// Fundamental
fn set_popsize(self, popsize: i32) -> Result<Self::Validated>;
fn set_xstart(self, capacity: usize, origin: f32) -> Result<Self::Validated>;
fn set_sigma(self, sigma: f32) -> Result<Self::Validated>;
// Helper
fn update_dependent_params(&mut self);
// Other worth specifying
fn set_tol(self, tol: f32) -> Result<Self::Validated>;
fn set_only_diag(self, only_diag: bool) -> Result<Self::Validated>;
}
/// Implmenting Trait for CMA-ES parameters.
impl CmaesParamsValidator for CmaesParams {
type Validated = CmaesParams;
/// Creates default parameters for the CMA-ES algorithm based on the provided parameters.
///
/// ```rust
/// use haru_cmaes::params::{CmaesParams, CmaesParamsValidator};
///
/// let params = CmaesParams::new();
///
/// assert!(params.is_ok());
/// ```
fn new() -> Result<Self::Validated> {
// Must update all parameters if this one's setter is used
let popsize: i32 = 10;
// Must update all parameters if this one's setter is used
let xstart = vec![0.0; 6];
let sigma = 0.75;
let tol = 0.001;
let only_diag = false;
let n = xstart.len() as f32;
let mu = popsize / 2;
let k = popsize as f32;
let iterable: Vec<f32> = (0..popsize)
.map(|x| {
if x < mu {
(k / 2.0 + 0.5).ln() - ((x + 1) as f32).ln()
} else {
0.0
}
})
.collect();
let weights: DVector<f32> = DVector::from_vec(iterable);
let w_sum: f32 = weights.rows(0, mu as usize).iter().sum();
let weights: DVector<f32> = weights.map(|x| x / w_sum);
let weights_mu = weights.rows(0, mu as usize).into_owned();
let mueff: f32 = (weights_mu.iter().sum::<f32>().powi(2)) / weights_mu.map(|x| x * x).sum();
let cc = (4. + mueff / n) / (n + 4. + 2. * mueff / n);
let cs = (mueff + 2.) / (n + mueff + 5.);
let c1 = 2. / ((n + 1.3) * (n + 1.3) + mueff);
let cmu = (1. - c1).min(2. * (mueff - 2. + 1. / mueff) / ((n + 2.) * (n + 2.) + mueff));
let damps = 2. * mueff / k + 0.3 + cs;
let params = CmaesParams {
// Fundamental
popsize,
xstart,
sigma,
// Objective's
tol,
// Computational's
only_diag,
// Others
n,
mu,
weights,
mueff,
cc,
cs,
c1,
cmu,
damps,
};
Ok(params)
}
/// Updates all parameters that depend on other fields.
fn update_dependent_params(&mut self) {
self.n = self.xstart.len() as f32;
self.mu = self.popsize / 2;
let k = self.popsize as f32;
let iterable: Vec<f32> = (0..self.popsize)
.map(|x| {
if x < self.mu {
(k / 2.0 + 0.5).ln() - ((x + 1) as f32).ln()
} else {
0.0
}
})
.collect();
let weights: DVector<f32> = DVector::from_vec(iterable);
let w_sum: f32 = weights.rows(0, self.mu as usize).iter().sum();
self.weights = weights.map(|x| x / w_sum);
let weights_mu = &self.weights.rows(0, self.mu as usize).into_owned();
self.mueff = (weights_mu.iter().sum::<f32>().powi(2)) / weights_mu.map(|x| x * x).sum();
self.cc = (4. + self.mueff / self.n) / (self.n + 4. + 2. * self.mueff / self.n);
self.cs = (self.mueff + 2.) / (self.n + self.mueff + 5.);
self.c1 = 2. / ((self.n + 1.3).powi(2) + self.mueff);
self.cmu = (1. - self.c1)
.min(2. * (self.mueff - 2. + 1. / self.mueff) / ((self.n + 2.).powi(2) + self.mueff));
self.damps = 2. * self.mueff / k + 0.3 + self.cs;
}
/// Sets population size.
///
/// ```rust
/// use haru_cmaes::params::{CmaesParams, CmaesParamsValidator};
///
/// let params = CmaesParams::new()
/// .and_then(|p| p.set_popsize(15));
///
/// assert!(params.is_ok());
/// ```
fn set_popsize(mut self, popsize: i32) -> Result<Self> {
self.popsize = popsize;
self.update_dependent_params();
Ok(self)
}
/// Sets origin of search.
///
/// ```rust
/// use haru_cmaes::params::{CmaesParams, CmaesParamsValidator};
///
/// let params = CmaesParams::new()
/// .and_then(|p| p.set_xstart(15, 0.75) );
///
/// assert!(params.is_ok());
/// ```
fn set_xstart(mut self, capacity: usize, origin: f32) -> Result<Self::Validated> {
self.xstart = vec![origin; capacity];
self.update_dependent_params();
Ok(self)
}
/// Sets step size (sigma).
///
/// ```rust
/// use haru_cmaes::params::{CmaesParams, CmaesParamsValidator};
///
/// let params = CmaesParams::new()
/// .and_then(|p| p.set_sigma(0.85));
///
/// assert!(params.is_ok());
/// ```
fn set_sigma(mut self, sigma: f32) -> Result<Self::Validated> {
self.sigma = sigma;
Ok(self)
}
/// Sets tolerance.
///
/// ```rust
/// use haru_cmaes::params::{CmaesParams, CmaesParamsValidator};
///
/// let params = CmaesParams::new()
/// .and_then(|p| p.set_tol(0.1));
///
/// assert!(params.is_ok());
/// ```
fn set_tol(mut self, tol: f32) -> Result<Self::Validated> {
self.tol = tol;
Ok(self)
}
/// Sets enforce covariance sparsity.
///
/// ```rust
/// use haru_cmaes::params::{CmaesParams, CmaesParamsValidator};
///
/// let params = CmaesParams::new()
/// .and_then(|p| p.set_only_diag(true));
///
/// assert!(params.is_ok());
/// ```
fn set_only_diag(mut self, only_diag: bool) -> Result<Self::Validated> {
self.only_diag = only_diag;
Ok(self)
}
}