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use crate::point::*;
use crate::simplex::*;
use crate::search_space::*;
use priority_queue::PriorityQueue;
use ordered_float::OrderedFloat;
use num_traits::Float;
use std::rc::Rc;
/// Stores the parameters and current state of a search.
///
/// - `ValueFloat` is the float type used to represent the evaluations (such as f64)
/// - `CoordFloat` is the float type used to represent the coordinates (such as f32)
pub struct Optimizer<'f_lifetime, CoordFloat: Float, ValueFloat: Float>
{
exploration_depth: ValueFloat,
search_space: SearchSpace<'f_lifetime, CoordFloat, ValueFloat>,
best_point: Rc<Point<CoordFloat, ValueFloat>>,
min_value: ValueFloat,
queue: PriorityQueue<Simplex<CoordFloat, ValueFloat>, OrderedFloat<ValueFloat>>
}
impl<'f_lifetime, CoordFloat: Float, ValueFloat: Float> Optimizer<'f_lifetime, CoordFloat, ValueFloat>
{
/// Creates a new optimizer to explore the given search space with the iterator interface.
///
/// Takes a function, a vector of intervals describing the input and a boolean describing wether it is a minimization problem (as oppozed to a miximization problem).
/// Each cal to the `.next()` function (cf iterator trait) will run an iteration of search and output the best result so far.
///
/// **Warning:** In d dimenssions, this function will perform d+1 evaluation (call to f) for the initialisation of the search (those should be taken into account when counting iterations).
///
/// ```rust
/// # use simplers_optimization::Optimizer;
/// # fn main() {
/// let f = |v:&[f64]| v[0] * v[1];
/// let input_interval = vec![(-10., 10.), (-20., 20.)];
/// let should_minimize = true;
///
/// // runs the search for 30 iterations
/// // then waits until we find a point good enough
/// // finally stores the best value so far
/// let (min_value, coordinates) = Optimizer::new(&f, &input_interval, should_minimize)
/// .skip(30)
/// .skip_while(|(value,coordinates)| *value > 1. )
/// .next().unwrap();
///
/// println!("min value: {} found in [{}, {}]", min_value, coordinates[0], coordinates[1]);
/// # }
/// ```
pub fn new(f: &'f_lifetime impl Fn(&[CoordFloat]) -> ValueFloat,
input_interval: &[(CoordFloat, CoordFloat)],
should_minimize: bool)
-> Self
{
// builds initial conditions
let search_space = SearchSpace::new(f, input_interval, should_minimize);
let initial_simplex = Simplex::initial_simplex(&search_space);
// various values track through the iterations
let best_point = initial_simplex.corners
.iter()
.max_by_key(|c| OrderedFloat(c.value))
.expect("You need at least one dimension!")
.clone();
let min_value = initial_simplex.corners
.iter()
.map(|c| c.value)
.min_by_key(|&v| OrderedFloat(v))
.expect("You need at least one dimension!");
// initialize priority queue
// no need to evaluate the initial simplex as it will be poped immediatly
let mut queue: PriorityQueue<Simplex<CoordFloat, ValueFloat>, OrderedFloat<ValueFloat>> =
PriorityQueue::new();
queue.push(initial_simplex, OrderedFloat(ValueFloat::zero()));
let exploration_depth = ValueFloat::from(6.).unwrap();
Optimizer { exploration_depth, search_space, best_point, min_value, queue }
}
/// Sets the exploration depth for the algorithm, useful when using the iterator interface.
///
/// `exploration_depth` represents the number of splits we can exploit before requiring higher-level exploration.
/// As long as one stays in a reasonable range (5-10), the algorithm should not be very sensible to the parameter :
///
/// - 0 represents full exploration (similar to grid search)
/// - high numbers focus on exploitation (no need to go very high)
/// - 5 appears to be a good default value
///
/// **WARNING**: this function should not be used before after an iteration
/// (as it will not update the score of already computed points for the next iterations
/// which will degrade the quality of the algorithm)
///
/// ```rust
/// # use simplers_optimization::Optimizer;
/// # fn main() {
/// let f = |v:&[f64]| v[0] * v[1];
/// let input_interval = vec![(-10., 10.), (-20., 20.)];
/// let should_minimize = true;
///
/// // sets exploration_depth to be very greedy
/// let (min_value_greedy, _) = Optimizer::new(&f, &input_interval, should_minimize)
/// .set_exploration_depth(20)
/// .skip(100)
/// .next().unwrap();
///
/// // sets exploration_depth to focus on exploration
/// let (min_value_explore, _) = Optimizer::new(&f, &input_interval, should_minimize)
/// .set_exploration_depth(0)
/// .skip(100)
/// .next().unwrap();
///
/// println!("greedy result : {} vs exploration result : {}", min_value_greedy, min_value_explore);
/// # }
/// ```
pub fn set_exploration_depth(mut self, exploration_depth: usize) -> Self
{
self.exploration_depth = ValueFloat::from(exploration_depth + 1).unwrap();
self
}
/// Self contained optimization algorithm.
///
/// Takes a function to maximize, a vector of intervals describing the input and a number of iterations.
///
/// ```rust
/// # use simplers_optimization::Optimizer;
/// # fn main() {
/// let f = |v:&[f64]| v[0] + v[1];
/// let input_interval = vec![(-10., 10.), (-20., 20.)];
/// let nb_iterations = 100;
///
/// let (max_value, coordinates) = Optimizer::maximize(&f, &input_interval, nb_iterations);
/// println!("max value: {} found in [{}, {}]", max_value, coordinates[0], coordinates[1]);
/// # }
/// ```
pub fn maximize(f: &'f_lifetime impl Fn(&[CoordFloat]) -> ValueFloat,
input_interval: &[(CoordFloat, CoordFloat)],
nb_iterations: usize)
-> (ValueFloat, Coordinates<CoordFloat>)
{
let initial_iteration_number = input_interval.len() + 1;
let should_minimize = false;
Optimizer::new(f, input_interval, should_minimize).skip(nb_iterations - initial_iteration_number)
.next()
.unwrap()
}
/// Self contained optimization algorithm.
///
/// Takes a function to minimize, a vector of intervals describing the input and a number of iterations.
///
/// ```rust
/// # use simplers_optimization::Optimizer;
/// # fn main() {
/// let f = |v:&[f64]| v[0] * v[1];
/// let input_interval = vec![(-10., 10.), (-20., 20.)];
/// let nb_iterations = 100;
///
/// let (min_value, coordinates) = Optimizer::minimize(&f, &input_interval, nb_iterations);
/// println!("min value: {} found in [{}, {}]", min_value, coordinates[0], coordinates[1]);
/// # }
/// ```
pub fn minimize(f: &'f_lifetime impl Fn(&[CoordFloat]) -> ValueFloat,
input_interval: &[(CoordFloat, CoordFloat)],
nb_iterations: usize)
-> (ValueFloat, Coordinates<CoordFloat>)
{
let initial_iteration_number = input_interval.len() + 1;
let should_minimize = true;
Optimizer::new(f, input_interval, should_minimize).skip(nb_iterations - initial_iteration_number)
.next()
.unwrap()
}
}
/// implements iterator for the Optimizer to give full control on the stopping condition to the user
impl<'f_lifetime, CoordFloat: Float, ValueFloat: Float> Iterator
for Optimizer<'f_lifetime, CoordFloat, ValueFloat>
{
type Item = (ValueFloat, Coordinates<CoordFloat>);
/// runs an iteration of the optimization algorithm and returns the best result so far
fn next(&mut self) -> Option<Self::Item>
{
// gets the exploration depth for later use
let exploration_depth = self.exploration_depth;
// gets an up to date simplex
let mut simplex = self.queue.pop().expect("Impossible: The queue cannot be empty!").0;
let current_difference = self.best_point.value - self.min_value;
while simplex.difference != current_difference
{
// updates the simplex and pushes it back into the queue
simplex.difference = current_difference;
let new_evaluation = simplex.evaluate(exploration_depth);
self.queue.push(simplex, OrderedFloat(new_evaluation));
// pops a new simplex
simplex = self.queue.pop().expect("Impossible: The queue cannot be empty!").0;
}
// evaluate the center of the simplex
let coordinates = simplex.center.clone();
let value = self.search_space.evaluate(&coordinates);
let new_point = Rc::new(Point { coordinates, value });
// splits the simplex around its center and push the subsimplex into the queue
simplex.split(new_point.clone(), current_difference)
.into_iter()
.map(|s| (OrderedFloat(s.evaluate(exploration_depth)), s))
.for_each(|(e, s)| {
self.queue.push(s, e);
});
// updates the difference
if value > self.best_point.value
{
self.best_point = new_point;
}
else if value < self.min_value
{
self.min_value = value;
}
// gets the best value so far
let best_value =
if self.search_space.minimize { -self.best_point.value } else { self.best_point.value };
let best_coordinate = self.search_space.to_hypercube(self.best_point.coordinates.clone());
Some((best_value, best_coordinate))
}
}