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// Copyright 2018-2022 argmin developers
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://apache.org/licenses/LICENSE-2.0> or the MIT license <LICENSE-MIT or
// http://opensource.org/licenses/MIT>, at your option. This file may not be
// copied, modified, or distributed except according to those terms.
use crate::core::{ArgminFloat, Problem, Solver, State};
use num_traits::{Float, FromPrimitive};
use std::cmp::Ordering;
use std::fmt;
/// Result of an optimization returned by after running an `Executor`.
///
/// Consists of the problem and the final state of the solver.
/// Both can be accessed via deconstructing or via the methods
/// [`problem`](`OptimizationResult::problem`) and [`state`](`OptimizationResult::state`).
#[derive(Clone)]
pub struct OptimizationResult<O, S, I> {
/// Problem
pub problem: Problem<O>,
/// Solver
pub solver: S,
/// Iteration state
pub state: I,
}
impl<O, S, I> OptimizationResult<O, S, I> {
/// Constructs a new instance of `OptimizationResult` from a `problem` and a `state`.
///
/// # Example
///
/// ```
/// # use argmin::core::{Problem, OptimizationResult, IterState, State};
/// # use argmin::core::test_utils::TestProblem;
/// #
/// # type Rosenbrock = TestProblem;
/// # #[derive(Eq, PartialEq, Debug)]
/// # struct SomeSolver {}
/// #
/// let rosenbrock = Rosenbrock::new();
/// let state: IterState<Vec<f64>, (), (), (), f64> = IterState::new();
/// let solver = SomeSolver {};
///
/// let result = OptimizationResult::new(Problem::new(rosenbrock), solver, state);
/// # let OptimizationResult { mut problem, solver, state } = result;
/// # assert_eq!(problem.take_problem().unwrap(), TestProblem::new());
/// # assert_eq!(solver, SomeSolver {});
/// ```
pub fn new(problem: Problem<O>, solver: S, state: I) -> Self {
OptimizationResult {
problem,
solver,
state,
}
}
/// Returns a reference to the stored problem.
///
/// # Example
///
/// ```
/// # use argmin::core::{Problem, OptimizationResult, IterState, State};
/// #
/// # struct Rosenbrock {}
/// # let solver = ();
/// #
/// # let state: IterState<Vec<f64>, (), (), (), f64> = IterState::new();
/// #
/// # let result = OptimizationResult::new(Problem::new(Rosenbrock {}), solver, state);
/// #
/// let problem: &Problem<Rosenbrock> = result.problem();
/// ```
pub fn problem(&self) -> &Problem<O> {
&self.problem
}
/// Returns a reference to the stored solver.
///
/// # Example
///
/// ```
/// # use argmin::core::{Problem, OptimizationResult, IterState, State};
/// #
/// # struct Rosenbrock {}
/// # let solver = ();
/// #
/// # let state: IterState<Vec<f64>, (), (), (), f64> = IterState::new();
/// #
/// # let result = OptimizationResult::new(Problem::new(Rosenbrock {}), solver, state);
/// #
/// let solver = result.solver();
/// ```
pub fn solver(&self) -> &S {
&self.solver
}
/// Returns a reference to the stored state.
///
/// # Example
///
/// ```
/// # use argmin::core::{Problem, OptimizationResult, IterState, State};
/// #
/// # struct Rosenbrock {}
/// # let solver = ();
/// #
/// # let state: IterState<Vec<f64>, (), (), (), f64> = IterState::new();
/// #
/// # let result = OptimizationResult::new(Problem::new(Rosenbrock {}), solver, state);
/// #
/// let state: &IterState<Vec<f64>, (), (), (), f64> = result.state();
/// ```
pub fn state(&self) -> &I {
&self.state
}
}
impl<O, S, I> std::fmt::Display for OptimizationResult<O, S, I>
where
I: State,
I::Param: fmt::Debug,
S: Solver<O, I>,
{
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
writeln!(f, "OptimizationResult:")?;
writeln!(f, " Solver: {}", S::NAME)?;
writeln!(
f,
" param (best): {}",
if let Some(best_param) = self.state.get_best_param() {
format!("{:?}", best_param)
} else {
String::from("None")
}
)?;
writeln!(f, " cost (best): {}", self.state.get_best_cost())?;
writeln!(f, " iters (best): {}", self.state.get_last_best_iter())?;
writeln!(f, " iters (total): {}", self.state.get_iter())?;
writeln!(
f,
" termination: {}",
self.state.get_termination_reason()
)?;
if let Some(time) = self.state.get_time() {
writeln!(f, " time: {:?}", time)?;
}
Ok(())
}
}
impl<O, S, I: State> PartialEq for OptimizationResult<O, S, I>
where
I::Float: ArgminFloat,
{
/// Two `OptimizationResult`s are equal if the absolute of the difference between their best
/// cost values is smaller than epsilon.
fn eq(&self, other: &OptimizationResult<O, S, I>) -> bool {
(self.state.get_best_cost() - other.state.get_best_cost()).abs() < I::Float::epsilon()
}
}
impl<O, S, I: State> Eq for OptimizationResult<O, S, I> {}
impl<O, S, I: State> Ord for OptimizationResult<O, S, I> {
/// Two `OptimizationResult`s are equal if the absolute of the difference between their best
/// cost values is smaller than epsilon.
/// Else, an `OptimizationResult` is better if the best cost function value is strictly better
/// than the other's.
fn cmp(&self, other: &OptimizationResult<O, S, I>) -> Ordering {
let t = self.state.get_best_cost() - other.state.get_best_cost();
if t.abs() < I::Float::epsilon() {
Ordering::Equal
} else if t > I::Float::from_f64(0.0).unwrap() {
Ordering::Greater
} else {
Ordering::Less
}
}
}
impl<O, S, I: State> PartialOrd for OptimizationResult<O, S, I> {
/// Two `OptimizationResult`s are equal if the absolute of the difference between their best
/// cost values is smaller than epsilon.
/// Else, an `OptimizationResult` is better if the best cost function value is strictly better
/// than the other's.
fn partial_cmp(&self, other: &OptimizationResult<O, S, I>) -> Option<Ordering> {
Some(self.cmp(other))
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::core::{
test_utils::{TestProblem, TestSolver},
IterState,
};
send_sync_test!(
optimizationresult,
OptimizationResult<TestProblem, TestSolver, IterState<(), (), (), (), f64>>
);
// TODO: More tests, in particular the checking that the output is as intended.
}