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#[macro_use]
extern crate colour;
extern crate rand;
mod optimizer;
pub use optimizer::{job_config, swarm_config, PSO};
#[cfg(test)]
mod tests {
use super::*;
use job_config::JobConfig;
use std::cell::RefCell;
use std::f64::consts::PI;
use swarm_config::{SwarmConfig, TransientBehavior};
#[test]
fn exp_sin_cost_fn() {
let num_variables = 3;
let mut jc = JobConfig::new(num_variables);
jc.max_iterations(1000);
jc.update_console(250);
jc.variable_bound([-2.0 * PI, 2.0 * PI]);
let mut tb = TransientBehavior::new();
tb.stochastic_behavior(3.0, 5);
tb.momentum_mode(0);
tb.motion_mode(3);
let mut sc = SwarmConfig::new();
sc.synergic_behavior(0.4, 128);
sc.motion_coefficients(0.5, 0.6, 1.0);
sc.num_particles(128);
sc.set_transient_behavior(tb);
let pso = PSO::from_swarm_config(8, true, &sc);
let min = pso.run_job_fn(jc, move |pt: &[f64]| -> f64 {
let mut sum = 0.0;
let mut sin_sum = 0.0;
for i in 0..num_variables {
sum += pt[i].abs();
sin_sum += pt[i].powi(2).sin();
}
sum * (-1.0 * sin_sum).exp()
});
println!("minimum of: {}, located at: {:?}", min.0, min.1);
assert!(min.0 < 10e-10);
}
#[test]
fn mut_cost_fn() {
let num_variables = 5;
let num_swarms = 4;
let mut jc = JobConfig::new(num_variables);
jc.max_iter_and_exit_cost(100000, 10e-10);
jc.update_console(100);
jc.variable_bound([-10.0, 10.0]);
let mut sc = SwarmConfig::new();
sc.motion_coefficients(0.7, 0.9, 0.8);
sc.num_particles(256);
let pso = PSO::from_swarm_config(num_swarms, true, &sc);
#[derive(Clone)]
struct CostFuncDataThing {
data: Vec<f64>,
previous_cost: RefCell<f64>,
}
impl CostFuncDataThing {
pub fn compute_cost(&mut self, point: &[f64]) -> f64 {
let cost = self
.data
.iter()
.zip(point.iter())
.map(|(d, p)| (d - p).powi(2))
.sum::<f64>();
self.previous_cost.replace(cost);
cost
}
}
let mut cfd = CostFuncDataThing {
data: vec![1.0; num_variables],
previous_cost: RefCell::new(0.0),
};
let min = pso.run_job_fn_mut(jc, move |point: &[f64]| -> f64 { cfd.compute_cost(point) });
println!("minimum of: {}, located at: {:?}", min.0, min.1);
assert!(min.0 < 10e-9);
}
#[test]
fn super_simple() {
let pso = PSO::default(8, false);
let mut jc = JobConfig::new(5);
jc.exit_cost(10e-10);
jc.variable_bound([-5.0, 5.0]);
let obj = |x_vec: &[f64]| -> f64 { x_vec.iter().map(|x| x.powi(2)).sum::<f64>() };
let min = pso.run_job_fn(jc, obj);
assert!(min.0 < 10e-9);
for x in min.1 {
assert!(x.abs() < 0.001);
}
}
#[test]
fn advanced() {
let mut sc = SwarmConfig::new();
sc.synergic_behavior(0.4, 100);
sc.motion_coefficients(0.5, 0.6, 1.0);
sc.num_particles(256);
let pso = PSO::from_swarm_config(8, false, &sc);
let mut jc = JobConfig::new(5);
jc.max_iter_and_exit_cost(10000, 10e-10);
jc.variable_bounds(vec![
[-10.0, 10.0],
[-8.0, 8.0],
[-6.0, 6.0],
[-4.0, 4.0],
[-2.0, 2.0],
]);
jc.update_console(100);
let mins = [1.0; 5];
let obj = move |x_vec: &[f64]| -> f64 {
x_vec
.iter()
.enumerate()
.map(|(i, x)| (x - mins[i]).powi(2))
.sum::<f64>()
};
let min = pso.run_job_fn(jc, obj);
assert!(min.0 < 10e-9);
for (i, x) in min.1.iter().enumerate() {
assert!((x - mins[i]).abs() < 0.001);
}
}
}