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extern crate rand;
use rand::Rand;
use rand::Rng;
use std::cmp::PartialEq;
use genetic::*;
pub trait State
where Self: Sized + Clone + Send + Sync + 'static
{
fn get_initial_state() -> Self;
fn get_random_action() -> Action<Self>;
fn is_goal(&self) -> bool;
fn get_heuristic(&self) -> i32;
}
pub struct Plan<T>
where T: State + Clone + Send + Sync + 'static
{
pub state: T,
pub actions: Vec<Action<T>>,
}
impl<T> Plan<T>
where T: State + Clone
{
pub fn new(state: T) -> Plan<T> {
Plan {
state: state,
actions: Vec::new(),
}
}
}
#[derive(Clone)]
pub struct Action<T>
where T: State + Clone + Send + Sync + 'static
{
pub action: fn(state: T) -> Option<T>,
pub name: String,
}
impl<T> Rand for Action<T>
where T: State + Clone
{
fn rand<R: Rng>(_: &mut R) -> Action<T> {
T::get_random_action()
}
}
impl<T> PartialEq for Action<T>
where T: State + Clone + Send + Sync + 'static
{
fn eq(&self, other: &Action<T>) -> bool {
self.name != other.name
}
}
pub struct PlannerConfiguration {
pub max_actions: usize,
pub population_size: usize,
pub elitism_size: usize,
pub tournmant_size: usize,
pub uniform_rate: f32,
pub mutation_rate: f32,
pub threadpool_size: usize,
}
fn apply_actions<T>(i: Individual<Action<T>>) -> Plan<T>
where T: State + Clone + Send + Sync + 'static
{
let mut state = Some(T::get_initial_state());
let mut old_state = state.clone();
let mut used_actions: Vec<Action<T>> = Vec::new();
let mut actions = i.genes.iter();
let mut action = actions.next();
while action.is_some() && state.clone().is_some() && !state.clone().unwrap().is_goal() {
state = (action.clone().unwrap().action)(state.unwrap());
if state.clone().is_some() {
old_state = state.clone();
let tmp = action.clone().unwrap();
let last_action = Action {
action: tmp.action,
name: tmp.name.to_string(),
};
used_actions.push(last_action);
action = actions.next();
}
}
match state {
None => {
Plan {
state: old_state.unwrap(),
actions: used_actions,
}
}
Some(sstate) => {
Plan {
state: sstate,
actions: used_actions,
}
}
}
}
fn fitness_planner<T>(i: Individual<Action<T>>) -> i32
where T: State + Clone + Send + Sync + 'static
{
let node = apply_actions(i);
-node.state.get_heuristic()
}
fn get_population_configuration<T>(c: PlannerConfiguration) -> PopulationConfiguration<Action<T>>
where T: State + Clone + Send + Sync + 'static
{
PopulationConfiguration {
genenumber: c.max_actions,
population_size: c.population_size,
elitism_size: c.elitism_size,
tournmant_size: c.tournmant_size,
uniform_rate: c.uniform_rate,
mutation_rate: c.mutation_rate,
fitness: fitness_planner,
threadpool_size: c.threadpool_size,
}
}
pub fn find_solution_and_population_from_population<T>(pop: Population<Action<T>>)
-> (Plan<T>, Population<Action<T>>)
where T: State + Clone + Send + Sync + 'static
{
let mut pop = pop.clone();
let mut best_actions = pop.get_fittest();
let mut node: Plan<T> = apply_actions(best_actions.unwrap().0);
while !node.state.is_goal() {
pop = pop.evolve();
best_actions = pop.get_fittest();
node = apply_actions(best_actions.unwrap().0);
}
(Plan {
state: node.state,
actions: node.actions,
},
pop)
}
pub fn find_solution_and_population<T>(c: PlannerConfiguration) -> (Plan<T>, Population<Action<T>>)
where T: State + Clone + Send + Sync + 'static
{
let pc = get_population_configuration(c);
let pop = Population::new(pc);
find_solution_and_population_from_population(pop)
}
pub fn find_solution<T>(c: PlannerConfiguration) -> Plan<T>
where T: State + Clone + Send + Sync + 'static
{
find_solution_and_population(c).0
}
pub fn find_solution_from_population<T>(pop: Population<Action<T>>) -> Plan<T>
where T: State + Clone + Send + Sync + 'static
{
find_solution_and_population_from_population(pop).0
}
pub fn find_best_and_population_after_iterations_from_population<T>
(pop: Population<Action<T>>,
iterations: usize)
-> (Plan<T>, Population<Action<T>>)
where T: State + Clone + Send + Sync + 'static
{
let mut pop = pop.clone();
for _ in 0..iterations {
pop = pop.evolve();
}
let best_actions = pop.get_fittest();
let node = apply_actions(best_actions.unwrap().0);
(Plan {
state: node.state,
actions: node.actions,
},
pop)
}
pub fn find_best_and_population_after_iterations<T>(c: PlannerConfiguration,
iterations: usize)
-> (Plan<T>, Population<Action<T>>)
where T: State + Clone + Send + Sync + 'static
{
let pc = get_population_configuration(c);
let pop = Population::new(pc);
find_best_and_population_after_iterations_from_population(pop, iterations)
}
pub fn find_best_after_iterations<T>(c: PlannerConfiguration, iterations: usize) -> Plan<T>
where T: State + Clone + Send + Sync + 'static
{
find_best_and_population_after_iterations(c, iterations).0
}
pub fn find_best_after_iterations_from_population<T>(pop: Population<Action<T>>,
iterations: usize)
-> Plan<T>
where T: State + Clone + Send + Sync + 'static
{
find_best_and_population_after_iterations_from_population(pop, iterations).0
}