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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 compute_weight(past: &HashSet<String>, now: &HashSet<&String>) -> f32 {
let inter = now.iter().filter(|&&p| past.contains(p)).count();
if inter == 0 {
return 0.0;
}
(inter as f32) / ((now.len() as f32).sqrt() * (past.len() as f32).sqrt())
}
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)]
enum Episode {
Continue {
group: HashSet<String>,
avoid: String,
committed: String,
},
Escape {
from: HashSet<String>,
to: String,
committed: String,
},
}
#[derive(Serialize, Deserialize, Default)]
pub struct Intuition {
history: Vec<Episode>,
}
pub enum Learning {
SkipToMore(Vec<String>, String),
MoreToSkip(Vec<String>, String),
}
impl Intuition {
pub fn learn(&mut self, learning: &Learning) {
let stamp = chrono::Utc::now().format("%+").to_string();
match learning {
Learning::SkipToMore(trajectory, new) => {
self.history.push(Episode::Escape {
from: trajectory.iter().map(String::clone).collect(),
to: new.clone(),
committed: stamp,
});
}
Learning::MoreToSkip(trajectory, new) => {
self.history.push(Episode::Continue {
group: trajectory.iter().map(String::clone).collect(),
avoid: new.clone(),
committed: stamp,
});
}
}
}
pub fn sample(
&self,
trajectory: &[String],
action: &Action,
candidates: &HashSet<String>,
config: &Config,
) -> Option<(String, f32)> {
let decay = f32::powf(
0.5,
1.0 / (config.lookback_window_halflife_in_entries as f32),
);
let trajectory: HashSet<_> = trajectory.iter().collect();
let candidates: Vec<_> = candidates
.iter()
.filter(|c| !trajectory.contains(c))
.collect();
if candidates.is_empty() {
return None;
}
let mut items: HashMap<String, f32> =
candidates.into_iter().map(|c| (c.clone(), 0.0)).collect();
for (dist, episode) in self.history.iter().rev().enumerate() {
let kernel = decay.powf(dist as f32);
match (episode, action) {
(Episode::Escape { from, to, .. }, Action::Skip) => {
let w = compute_weight(from, &trajectory);
if let Some(to_w) = items.get_mut(to) {
*to_w += config.max_weight * w * kernel;
}
for f in from {
if let Some(from_weight) = items.get_mut(f) {
*from_weight -= config.mid_weight * w * kernel;
}
}
}
(Episode::Escape { from, to, .. }, Action::More) => {
let w = compute_weight(from, &trajectory);
if trajectory.contains(to) {
for f in from {
if let Some(f_w) = items.get_mut(f) {
*f_w -= config.low_weight * w * kernel;
}
}
}
}
(Episode::Continue { group, avoid, .. }, Action::More) => {
let w = compute_weight(group, &trajectory);
for g in group {
if let Some(g_w) = items.get_mut(g) {
*g_w += config.max_weight * w * kernel;
}
}
if let Some(a_w) = items.get_mut(avoid) {
*a_w -= config.low_weight * w * kernel;
}
}
(Episode::Continue { group, .. }, Action::Skip) => {
let w = compute_weight(group, &trajectory);
for g in group {
if let Some(v) = items.get_mut(g) {
*v -= config.high_weight * w * kernel;
}
}
}
}
}
let pairs: Vec<_> = items.into_iter().collect();
softmax_sample(&pairs, config.temperature)
}
}
|
