searchlib 0.1.2

Satisficing and optimal search algorithms
Documentation 
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//! [Lazy Greedy Best First Search](https://en.wikipedia.org/wiki/Best-first_search)

use crate::{trace, FxIndexMap};
use indexmap::map::Entry::{Occupied, Vacant};
use std::{collections::BinaryHeap, hash::Hash};

#[derive(Debug, Clone, Eq, PartialEq)]
struct Element {
    index: usize,
    estimate: usize,
}

impl Ord for Element {
    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
        other.estimate.cmp(&self.estimate)
    }
}

impl PartialOrd for Element {
    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
        Some(self.cmp(other))
    }
}

pub struct LGBFS<STATE, SUCCESSORS, SUCCESS, HEURISTIC, ITER>
where
    STATE: Eq + Hash + Clone,
    SUCCESSORS: FnMut(&STATE) -> ITER,
    SUCCESS: FnMut(&STATE) -> bool,
    HEURISTIC: FnMut(&STATE) -> usize,
    ITER: IntoIterator<Item = STATE>,
{
    queue: BinaryHeap<Element>,
    states: FxIndexMap<STATE, usize>,
    successors: SUCCESSORS,
    success: SUCCESS,
    heuristic: HEURISTIC,
}

impl<STATE, SUCCESSORS, SUCCESS, HEURISTIC, ITER> LGBFS<STATE, SUCCESSORS, SUCCESS, HEURISTIC, ITER>
where
    STATE: Eq + Hash + Clone,
    SUCCESSORS: FnMut(&STATE) -> ITER,
    SUCCESS: FnMut(&STATE) -> bool,
    HEURISTIC: FnMut(&STATE) -> usize,
    ITER: IntoIterator<Item = STATE>,
{
    pub fn new(
        init: &STATE,
        successors: SUCCESSORS,
        success: SUCCESS,
        heuristic: HEURISTIC,
    ) -> Self {
        let mut states = FxIndexMap::default();
        states.insert(init.clone(), 0);
        let queue = BinaryHeap::from(vec![Element {
            index: 0,
            estimate: 0,
        }]);
        Self {
            queue,
            states,
            successors,
            success,
            heuristic,
        }
    }
}

impl<STATE, SUCCESSORS, SUCCESS, HEURISTIC, ITER> Iterator
    for LGBFS<STATE, SUCCESSORS, SUCCESS, HEURISTIC, ITER>
where
    STATE: Eq + Hash + Clone,
    SUCCESSORS: FnMut(&STATE) -> ITER,
    SUCCESS: FnMut(&STATE) -> bool,
    HEURISTIC: FnMut(&STATE) -> usize,
    ITER: IntoIterator<Item = STATE>,
{
    type Item = Option<Vec<STATE>>;

    fn next(&mut self) -> Option<Self::Item> {
        let Element { index, estimate: _ } = self.queue.pop()?;
        let (node, _) = self.states.get_index(index).unwrap();
        if (self.success)(node) {
            return Some(Some(trace(&self.states, index)));
        }
        let estimate = (self.heuristic)(node);
        for successor in (self.successors)(node) {
            let index = match self.states.entry(successor) {
                Vacant(e) => {
                    let i = e.index();
                    e.insert(index);
                    i
                }
                Occupied(_) => continue,
            };
            self.queue.push(Element { index, estimate });
        }
        Some(None)
    }
}

pub fn solve<STATE, SUCCESSORS, SUCCESS, HEURISTIC, ITER>(
    init: &STATE,
    successors: SUCCESSORS,
    success: SUCCESS,
    heuristic: HEURISTIC,
) -> Option<Vec<STATE>>
where
    STATE: Eq + Hash + Clone,
    SUCCESSORS: FnMut(&STATE) -> ITER,
    SUCCESS: FnMut(&STATE) -> bool,
    HEURISTIC: FnMut(&STATE) -> usize,
    ITER: IntoIterator<Item = STATE>,
{
    LGBFS::new(init, successors, success, heuristic)
        .into_iter()
        .find(|i| i.is_some())?
}

pub fn paths<STATE, SUCCESSORS, SUCCESS, HEURISTIC, ITER>(
    init: &STATE,
    successors: SUCCESSORS,
    success: SUCCESS,
    heuristic: HEURISTIC,
) -> impl Iterator<Item = Vec<STATE>>
where
    STATE: Eq + Hash + Clone,
    SUCCESSORS: FnMut(&STATE) -> ITER,
    SUCCESS: FnMut(&STATE) -> bool,
    HEURISTIC: FnMut(&STATE) -> usize,
    ITER: IntoIterator<Item = STATE>,
{
    LGBFS::new(init, successors, success, heuristic)
        .into_iter()
        .filter_map(|i| i)
}