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use std::collections::{HashMap, HashSet};
use rand::{self, seq::IndexedRandom};
use serde::{Deserialize, Serialize};
use crate::{config::Config, trajectory::Action};
fn key(a: &str, b: &str) -> (String, String) {
let a = a.to_string();
let b = b.to_string();
if a < b { (a, b) } else { (b, a) }
}
fn compute_sum(
cand: &str,
known_keys: &HashSet<&String>,
map: &HashMap<(String, String), f32>,
) -> f32 {
known_keys
.iter()
.filter_map(|k| map.get(&key(k, cand)))
.sum()
}
fn softmax_sample(items: &[(&String, f32)], temperature: f32) -> Option<(String, f32)> {
if items.is_empty() {
return None;
}
let mut rng = rand::rng();
let max_val = items
.iter()
.map(|(_, v)| *v)
.fold(f32::NEG_INFINITY, f32::max);
let exp_values: Vec<_> = items
.iter()
.map(|&(k, val)| (k, ((val - max_val) / temperature).exp()))
.collect();
let sum: f32 = exp_values.iter().map(|(_, v)| *v).sum();
let norm_values: Vec<_> = exp_values.iter().map(|&(k, v)| (k, v / sum)).collect();
norm_values
.choose_weighted(&mut rng, |item| item.1)
.map(|p| (p.0.clone(), p.1))
.ok()
}
#[derive(Serialize, Deserialize, Default)]
pub struct Learner {
similar: HashMap<(String, String), f32>,
different: HashMap<(String, String), f32>,
}
#[derive(Debug)]
pub enum Learning {
SkipExtend,
MoreExtend(Vec<String>, String),
SkipToMore(Vec<String>, String),
MoreToSkip(Vec<String>, String),
}
impl Learner {
pub fn prune(&mut self, valid: &HashSet<String>) {
self.similar
.retain(|(a, b), _v| valid.contains(a) && valid.contains(b));
self.different
.retain(|(a, b), _v| valid.contains(a) && valid.contains(b));
}
pub fn learn(&mut self, config: &Config, learning: &Learning) {
// global decay
for (_k, v) in self.similar.iter_mut().chain(self.different.iter_mut()) {
*v *= 1.0 - config.decay_rate;
}
// update
match learning {
Learning::SkipExtend => {
// This is the least information carrying case, it does not
// follow that the trajectory contains similar or different
// items, we may simply be seeking something in particular.
}
Learning::MoreExtend(trajectory, new) => {
// We're continuing a good run so `new` is similar to everything
// in `trajectory`, but to account for the possibilty that our
// mood has changed over the course of this streak we damp
// sub-linearly that update by a proxy of temporal distance.
for (distance, e) in trajectory.iter().rev().enumerate() {
let damp = ((distance + 1) as f32).powf(-0.5);
let v = self.similar.entry(key(e, new)).or_default();
*v = (1.0 - config.learning_rate) * *v + config.learning_rate * damp;
}
}
Learning::SkipToMore(trajectory, new) => {
// We have learnt that `new` is different to everything in
// `trajectory`, the strongest signal we have.
for e in trajectory {
let v = self.different.entry(key(e, new)).or_default();
*v = (1.0 - config.learning_rate) * *v + config.learning_rate;
}
}
Learning::MoreToSkip(trajectory, new) => {
// `new` could be different to everything in `trajectory`, or we
// simply changed our minds, so we have only weak evidence of
// difference. The positive coherence of trajectory was taken
// care of during MoreExtend above.
let damp = (trajectory.len() + 1) as f32;
for e in trajectory {
let v = self.different.entry(key(e, new)).or_default();
*v = (1.0 - config.learning_rate) * *v + config.learning_rate / damp;
}
}
};
// global threshold drop
self.similar = self
.similar
.extract_if(|_k, v| (*v).abs() > config.min_score_thresh)
.collect();
self.different = self
.different
.extract_if(|_k, v| (*v).abs() > config.min_score_thresh)
.collect();
}
pub fn sample(
&self,
trajectory: &[String],
action: &Action,
candidates: &HashSet<String>,
temperature: f32,
) -> Option<(String, f32)> {
let trajectory: HashSet<_> = trajectory.iter().collect();
let normalisation: f32 = f32::max(trajectory.len() as f32, 1.0);
let candidates: Vec<_> = candidates
.iter()
.filter(|c| !trajectory.contains(c))
.collect();
if candidates.is_empty() {
return None;
}
let items = candidates
.iter()
.map(|&c| {
let sim = compute_sum(c, &trajectory, &self.similar);
let dif = compute_sum(c, &trajectory, &self.different);
let score = match action {
Action::Skip => dif - sim,
Action::More => sim - dif,
};
(c, score / normalisation)
})
.collect::<Vec<_>>();
softmax_sample(&items, temperature)
}
}
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