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refactor(batch): replace HashMap<Index, Skill> with dense SkillStore

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SkillStore is a Vec<Skill>-backed dense store with a parallel present
mask, indexed directly by Index.0. Eliminates per-iteration hashing
in the within-slice convergence loop; O(1) array lookup replaces O(1)
amortised hash lookup with better cache behaviour.

Iteration order is now ascending-by-Index (was arbitrary for HashMap);
EP fixed point is order-independent so posteriors are unchanged.

Part of T0 engine redesign.
This commit is contained in:
Anders Olsson
2026-04-24 07:08:20 +02:00
parent 709ece335f
commit 8f60258dba
5 changed files with 239 additions and 104 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
src/batch.rs
View File

@@ -2,7 +2,7 @@ use std::collections::HashMap;
use crate::{ use crate::{
Index, N_INF, agent::Agent, drift::Drift, game::Game, gaussian::Gaussian, player::Player, Index, N_INF, agent::Agent, drift::Drift, game::Game, gaussian::Gaussian, player::Player,
tuple_gt, tuple_max, storage::SkillStore, tuple_gt, tuple_max,
}; };
#[derive(Debug)] #[derive(Debug)]
@@ -43,11 +43,11 @@ impl Item {
&self, &self,
online: bool, online: bool,
forward: bool, forward: bool,
skills: &HashMap<Index, Skill>, skills: &SkillStore,
agents: &HashMap<Index, Agent<D>>, agents: &HashMap<Index, Agent<D>>,
) -> Player<D> { ) -> Player<D> {
let r = &agents[&self.agent].player; let r = &agents[&self.agent].player;
let skill = &skills[&self.agent]; let skill = skills.get(self.agent).unwrap();
if online { if online {
Player::new(skill.online, r.beta, r.drift) Player::new(skill.online, r.beta, r.drift)
@@ -84,7 +84,7 @@ impl Event {
&self, &self,
online: bool, online: bool,
forward: bool, forward: bool,
skills: &HashMap<Index, Skill>, skills: &SkillStore,
agents: &HashMap<Index, Agent<D>>, agents: &HashMap<Index, Agent<D>>,
) -> Vec<Vec<Player<D>>> { ) -> Vec<Vec<Player<D>>> {
self.teams self.teams
@@ -102,7 +102,7 @@ impl Event {
#[derive(Debug)] #[derive(Debug)]
pub struct Batch { pub struct Batch {
pub(crate) events: Vec<Event>, pub(crate) events: Vec<Event>,
pub(crate) skills: HashMap<Index, Skill>, pub(crate) skills: SkillStore,
pub(crate) time: i64, pub(crate) time: i64,
p_draw: f64, p_draw: f64,
} }
@@ -111,7 +111,7 @@ impl Batch {
pub fn new(time: i64, p_draw: f64) -> Self { pub fn new(time: i64, p_draw: f64) -> Self {
Self { Self {
events: Vec::new(), events: Vec::new(),
skills: HashMap::new(), skills: SkillStore::new(),
time, time,
p_draw, p_draw,
} }
@@ -137,16 +137,16 @@ impl Batch {
}); });
for idx in this_agent { for idx in this_agent {
let elapsed = compute_elapsed(agents[&idx].last_time, self.time); let elapsed = compute_elapsed(agents[idx].last_time, self.time);
if let Some(skill) = self.skills.get_mut(idx) { if let Some(skill) = self.skills.get_mut(*idx) {
skill.elapsed = elapsed; skill.elapsed = elapsed;
skill.forward = agents[&idx].receive(elapsed); skill.forward = agents[idx].receive(elapsed);
} else { } else {
self.skills.insert( self.skills.insert(
*idx, *idx,
Skill { Skill {
forward: agents[&idx].receive(elapsed), forward: agents[idx].receive(elapsed),
elapsed, elapsed,
..Default::default() ..Default::default()
}, },
@@ -204,7 +204,7 @@ impl Batch {
pub(crate) fn posteriors(&self) -> HashMap<Index, Gaussian> { pub(crate) fn posteriors(&self) -> HashMap<Index, Gaussian> {
self.skills self.skills
.iter() .iter()
.map(|(&idx, skill)| (idx, skill.posterior())) .map(|(idx, skill)| (idx, skill.posterior()))
.collect::<HashMap<_, _>>() .collect::<HashMap<_, _>>()
} }
@@ -217,10 +217,9 @@ impl Batch {
for (t, team) in event.teams.iter_mut().enumerate() { for (t, team) in event.teams.iter_mut().enumerate() {
for (i, item) in team.items.iter_mut().enumerate() { for (i, item) in team.items.iter_mut().enumerate() {
self.skills.get_mut(&item.agent).unwrap().likelihood = let old_likelihood = self.skills.get(item.agent).unwrap().likelihood;
(self.skills[&item.agent].likelihood / item.likelihood) let new_likelihood = (old_likelihood / item.likelihood) * g.likelihoods[t][i];
* g.likelihoods[t][i]; self.skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
item.likelihood = g.likelihoods[t][i]; item.likelihood = g.likelihoods[t][i];
} }
} }
@@ -255,8 +254,7 @@ impl Batch {
} }
pub(crate) fn forward_prior_out(&self, agent: &Index) -> Gaussian { pub(crate) fn forward_prior_out(&self, agent: &Index) -> Gaussian {
let skill = &self.skills[agent]; let skill = self.skills.get(*agent).unwrap();
skill.forward * skill.likelihood skill.forward * skill.likelihood
} }
@@ -265,25 +263,22 @@ impl Batch {
agent: &Index, agent: &Index,
agents: &HashMap<Index, Agent<D>>, agents: &HashMap<Index, Agent<D>>,
) -> Gaussian { ) -> Gaussian {
let skill = &self.skills[agent]; let skill = self.skills.get(*agent).unwrap();
let n = skill.likelihood * skill.backward; let n = skill.likelihood * skill.backward;
n.forget(agents[agent].player.drift.variance_delta(skill.elapsed)) n.forget(agents[agent].player.drift.variance_delta(skill.elapsed))
} }
pub(crate) fn new_backward_info<D: Drift>(&mut self, agents: &HashMap<Index, Agent<D>>) { pub(crate) fn new_backward_info<D: Drift>(&mut self, agents: &HashMap<Index, Agent<D>>) {
for (agent, skill) in self.skills.iter_mut() { for (agent, skill) in self.skills.iter_mut() {
skill.backward = agents[agent].message; skill.backward = agents[&agent].message;
} }
self.iteration(0, agents); self.iteration(0, agents);
} }
pub(crate) fn new_forward_info<D: Drift>(&mut self, agents: &HashMap<Index, Agent<D>>) { pub(crate) fn new_forward_info<D: Drift>(&mut self, agents: &HashMap<Index, Agent<D>>) {
for (agent, skill) in self.skills.iter_mut() { for (agent, skill) in self.skills.iter_mut() {
skill.forward = agents[agent].receive(skill.elapsed); skill.forward = agents[&agent].receive(skill.elapsed);
} }
self.iteration(0, agents); self.iteration(0, agents);
} }

170
src/history.rs
View File

@@ -145,8 +145,8 @@ impl<D: Drift> History<D> {
for j in (0..self.batches.len() - 1).rev() { for j in (0..self.batches.len() - 1).rev() {
for agent in self.batches[j + 1].skills.keys() { for agent in self.batches[j + 1].skills.keys() {
self.agents.get_mut(agent).unwrap().message = self.agents.get_mut(&agent).unwrap().message =
self.batches[j + 1].backward_prior_out(agent, &self.agents); self.batches[j + 1].backward_prior_out(&agent, &self.agents);
} }
let old = self.batches[j].posteriors(); let old = self.batches[j].posteriors();
@@ -164,8 +164,8 @@ impl<D: Drift> History<D> {
for j in 1..self.batches.len() { for j in 1..self.batches.len() {
for agent in self.batches[j - 1].skills.keys() { for agent in self.batches[j - 1].skills.keys() {
self.agents.get_mut(agent).unwrap().message = self.agents.get_mut(&agent).unwrap().message =
self.batches[j - 1].forward_prior_out(agent); self.batches[j - 1].forward_prior_out(&agent);
} }
let old = self.batches[j].posteriors(); let old = self.batches[j].posteriors();
@@ -231,10 +231,10 @@ impl<D: Drift> History<D> {
for (agent, skill) in b.skills.iter() { for (agent, skill) in b.skills.iter() {
let point = (b.time, skill.posterior()); let point = (b.time, skill.posterior());
if let Some(entry) = data.get_mut(agent) { if let Some(entry) = data.get_mut(&agent) {
entry.push(point); entry.push(point);
} else { } else {
data.insert(*agent, vec![point]); data.insert(agent, vec![point]);
} }
} }
} }
@@ -343,7 +343,7 @@ impl<D: Drift> History<D> {
// TODO: Is it faster to iterate over agents in batch instead? // TODO: Is it faster to iterate over agents in batch instead?
for agent_idx in &this_agent { for agent_idx in &this_agent {
if let Some(skill) = batch.skills.get_mut(agent_idx) { if let Some(skill) = batch.skills.get_mut(*agent_idx) {
skill.elapsed = skill.elapsed =
batch::compute_elapsed(self.agents[agent_idx].last_time, batch.time); batch::compute_elapsed(self.agents[agent_idx].last_time, batch.time);
@@ -378,10 +378,10 @@ impl<D: Drift> History<D> {
batch.add_events(composition, results, weights, &self.agents); batch.add_events(composition, results, weights, &self.agents);
for agent_idx in batch.skills.keys() { for agent_idx in batch.skills.keys() {
let agent = self.agents.get_mut(agent_idx).unwrap(); let agent = self.agents.get_mut(&agent_idx).unwrap();
agent.last_time = if self.time { t } else { i64::MAX }; agent.last_time = if self.time { t } else { i64::MAX };
agent.message = batch.forward_prior_out(agent_idx); agent.message = batch.forward_prior_out(&agent_idx);
} }
} else { } else {
let mut batch: Batch = Batch::new(t, self.p_draw); let mut batch: Batch = Batch::new(t, self.p_draw);
@@ -392,10 +392,10 @@ impl<D: Drift> History<D> {
let batch = &self.batches[k]; let batch = &self.batches[k];
for agent_idx in batch.skills.keys() { for agent_idx in batch.skills.keys() {
let agent = self.agents.get_mut(agent_idx).unwrap(); let agent = self.agents.get_mut(&agent_idx).unwrap();
agent.last_time = if self.time { t } else { i64::MAX }; agent.last_time = if self.time { t } else { i64::MAX };
agent.message = batch.forward_prior_out(agent_idx); agent.message = batch.forward_prior_out(&agent_idx);
} }
k += 1; k += 1;
@@ -411,7 +411,7 @@ impl<D: Drift> History<D> {
// TODO: Is it faster to iterate over agents in batch instead? // TODO: Is it faster to iterate over agents in batch instead?
for agent_idx in &this_agent { for agent_idx in &this_agent {
if let Some(skill) = batch.skills.get_mut(agent_idx) { if let Some(skill) = batch.skills.get_mut(*agent_idx) {
skill.elapsed = skill.elapsed =
batch::compute_elapsed(self.agents[agent_idx].last_time, batch.time); batch::compute_elapsed(self.agents[agent_idx].last_time, batch.time);
@@ -476,13 +476,21 @@ mod tests {
epsilon = 1e-6 epsilon = 1e-6
); );
let observed = h.batches[1].skills[&a].forward.sigma(); let observed = h.batches[1].skills.get(a).unwrap().forward.sigma();
let gamma: f64 = 0.15 * 25.0 / 3.0; let gamma: f64 = 0.15 * 25.0 / 3.0;
let expected = (gamma.powi(2) + h.batches[0].skills[&a].posterior().sigma().powi(2)).sqrt(); let expected = (gamma.powi(2)
+ h.batches[0]
.skills
.get(a)
.unwrap()
.posterior()
.sigma()
.powi(2))
.sqrt();
assert_ulps_eq!(observed, expected, epsilon = 0.000001); assert_ulps_eq!(observed, expected, epsilon = 0.000001);
let observed = h.batches[1].skills[&a].posterior(); let observed = h.batches[1].skills.get(a).unwrap().posterior();
let w = [vec![1.0], vec![1.0]]; let w = [vec![1.0], vec![1.0]];
let p = Game::new( let p = Game::new(
@@ -531,12 +539,12 @@ mod tests {
h1.add_events_with_prior(composition, results, times, vec![], priors); h1.add_events_with_prior(composition, results, times, vec![], priors);
assert_ulps_eq!( assert_ulps_eq!(
h1.batches[0].skills[&a].posterior(), h1.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(22.904409, 6.010330), Gaussian::from_ms(22.904409, 6.010330),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h1.batches[0].skills[&c].posterior(), h1.batches[0].skills.get(c).unwrap().posterior(),
Gaussian::from_ms(25.110318, 5.866311), Gaussian::from_ms(25.110318, 5.866311),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -544,12 +552,12 @@ mod tests {
h1.convergence(ITERATIONS, EPSILON, false); h1.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!( assert_ulps_eq!(
h1.batches[0].skills[&a].posterior(), h1.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(25.000000, 5.419212), Gaussian::from_ms(25.000000, 5.419212),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h1.batches[0].skills[&c].posterior(), h1.batches[0].skills.get(c).unwrap().posterior(),
Gaussian::from_ms(25.000000, 5.419212), Gaussian::from_ms(25.000000, 5.419212),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -580,12 +588,12 @@ mod tests {
h2.add_events_with_prior(composition, results, times, vec![], priors); h2.add_events_with_prior(composition, results, times, vec![], priors);
assert_ulps_eq!( assert_ulps_eq!(
h2.batches[2].skills[&a].posterior(), h2.batches[2].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(22.903522, 6.011017), Gaussian::from_ms(22.903522, 6.011017),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h2.batches[2].skills[&c].posterior(), h2.batches[2].skills.get(c).unwrap().posterior(),
Gaussian::from_ms(25.110702, 5.866811), Gaussian::from_ms(25.110702, 5.866811),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -593,12 +601,12 @@ mod tests {
h2.convergence(ITERATIONS, EPSILON, false); h2.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!( assert_ulps_eq!(
h2.batches[2].skills[&a].posterior(), h2.batches[2].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(24.998668, 5.420053), Gaussian::from_ms(24.998668, 5.420053),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h2.batches[2].skills[&c].posterior(), h2.batches[2].skills.get(c).unwrap().posterior(),
Gaussian::from_ms(25.000532, 5.419827), Gaussian::from_ms(25.000532, 5.419827),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -685,21 +693,21 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false); h.convergence(ITERATIONS, EPSILON, false);
assert_eq!(h.batches[2].skills[&b].elapsed, 1); assert_eq!(h.batches[2].skills.get(b).unwrap().elapsed, 1);
assert_eq!(h.batches[2].skills[&c].elapsed, 1); assert_eq!(h.batches[2].skills.get(c).unwrap().elapsed, 1);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&a].posterior(), h.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(25.000267, 5.419381), Gaussian::from_ms(25.000267, 5.419381),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(24.999465, 5.419425), Gaussian::from_ms(24.999465, 5.419425),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[2].skills[&b].posterior(), h.batches[2].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(25.000532, 5.419696), Gaussian::from_ms(25.000532, 5.419696),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -743,8 +751,8 @@ mod tests {
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&b].posterior().mu(), h.batches[0].skills.get(b).unwrap().posterior().mu(),
-1.0 * h.batches[0].skills[&c].posterior().mu(), -1.0 * h.batches[0].skills.get(c).unwrap().posterior().mu(),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -763,33 +771,33 @@ mod tests {
assert_ulps_eq!(p_d_m_hat, 0.172432, epsilon = 1e-6); assert_ulps_eq!(p_d_m_hat, 0.172432, epsilon = 1e-6);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&a].posterior(), h.batches[0].skills.get(a).unwrap().posterior(),
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&c].posterior(), h.batches[0].skills.get(c).unwrap().posterior(),
h.batches[0].skills[&d].posterior(), h.batches[0].skills.get(d).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[1].skills[&e].posterior(), h.batches[1].skills.get(e).unwrap().posterior(),
h.batches[1].skills[&f].posterior(), h.batches[1].skills.get(f).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&a].posterior(), h.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(4.084902, 5.106919), Gaussian::from_ms(4.084902, 5.106919),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&c].posterior(), h.batches[0].skills.get(c).unwrap().posterior(),
Gaussian::from_ms(-0.533029, 5.106919), Gaussian::from_ms(-0.533029, 5.106919),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[2].skills[&e].posterior(), h.batches[2].skills.get(e).unwrap().posterior(),
Gaussian::from_ms(-3.551872, 5.154569), Gaussian::from_ms(-3.551872, 5.154569),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -822,21 +830,21 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false); h.convergence(ITERATIONS, EPSILON, false);
assert_eq!(h.batches[2].skills[&b].elapsed, 1); assert_eq!(h.batches[2].skills.get(b).unwrap().elapsed, 1);
assert_eq!(h.batches[2].skills[&c].elapsed, 1); assert_eq!(h.batches[2].skills.get(c).unwrap().elapsed, 1);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&a].posterior(), h.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(0.000000, 1.300610), Gaussian::from_ms(0.000000, 1.300610),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 1.300610), Gaussian::from_ms(0.000000, 1.300610),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[2].skills[&b].posterior(), h.batches[2].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 1.300610), Gaussian::from_ms(0.000000, 1.300610),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -863,22 +871,22 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false); h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&a].posterior(), h.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[3].skills[&a].posterior(), h.batches[3].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[3].skills[&b].posterior(), h.batches[3].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[5].skills[&b].posterior(), h.batches[5].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -911,21 +919,21 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false); h.convergence(ITERATIONS, EPSILON, false);
assert_eq!(h.batches[2].skills[&b].elapsed, 1); assert_eq!(h.batches[2].skills.get(b).unwrap().elapsed, 1);
assert_eq!(h.batches[2].skills[&c].elapsed, 1); assert_eq!(h.batches[2].skills.get(c).unwrap().elapsed, 1);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&a].posterior(), h.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(0.000000, 1.300610), Gaussian::from_ms(0.000000, 1.300610),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 1.300610), Gaussian::from_ms(0.000000, 1.300610),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[2].skills[&b].posterior(), h.batches[2].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 1.300610), Gaussian::from_ms(0.000000, 1.300610),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -952,22 +960,22 @@ mod tests {
h.convergence(ITERATIONS, EPSILON, false); h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&a].posterior(), h.batches[0].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[3].skills[&a].posterior(), h.batches[3].skills.get(a).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[3].skills[&b].posterior(), h.batches[3].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[5].skills[&b].posterior(), h.batches[5].skills.get(b).unwrap().posterior(),
Gaussian::from_ms(0.000000, 0.931236), Gaussian::from_ms(0.000000, 0.931236),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -1103,32 +1111,32 @@ mod tests {
let end = h.batches.len() - 1; let end = h.batches.len() - 1;
assert_eq!(h.batches[0].skills[&c].elapsed, 0); assert_eq!(h.batches[0].skills.get(c).unwrap().elapsed, 0);
assert_eq!(h.batches[end].skills[&c].elapsed, 10); assert_eq!(h.batches[end].skills.get(c).unwrap().elapsed, 10);
assert_eq!(h.batches[0].skills[&a].elapsed, 0); assert_eq!(h.batches[0].skills.get(a).unwrap().elapsed, 0);
assert_eq!(h.batches[2].skills[&a].elapsed, 5); assert_eq!(h.batches[2].skills.get(a).unwrap().elapsed, 5);
assert_eq!(h.batches[0].skills[&b].elapsed, 0); assert_eq!(h.batches[0].skills.get(b).unwrap().elapsed, 0);
assert_eq!(h.batches[end].skills[&b].elapsed, 5); assert_eq!(h.batches[end].skills.get(b).unwrap().elapsed, 5);
h.convergence(ITERATIONS, EPSILON, false); h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
h.batches[end].skills[&b].posterior(), h.batches[end].skills.get(b).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&c].posterior(), h.batches[0].skills.get(c).unwrap().posterior(),
h.batches[end].skills[&c].posterior(), h.batches[end].skills.get(c).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&c].posterior(), h.batches[0].skills.get(c).unwrap().posterior(),
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
@@ -1191,32 +1199,32 @@ mod tests {
let end = h.batches.len() - 1; let end = h.batches.len() - 1;
assert_eq!(h.batches[0].skills[&c].elapsed, 0); assert_eq!(h.batches[0].skills.get(c).unwrap().elapsed, 0);
assert_eq!(h.batches[end].skills[&c].elapsed, 10); assert_eq!(h.batches[end].skills.get(c).unwrap().elapsed, 10);
assert_eq!(h.batches[0].skills[&a].elapsed, 0); assert_eq!(h.batches[0].skills.get(a).unwrap().elapsed, 0);
assert_eq!(h.batches[2].skills[&a].elapsed, 5); assert_eq!(h.batches[2].skills.get(a).unwrap().elapsed, 5);
assert_eq!(h.batches[0].skills[&b].elapsed, 0); assert_eq!(h.batches[0].skills.get(b).unwrap().elapsed, 0);
assert_eq!(h.batches[end].skills[&b].elapsed, 5); assert_eq!(h.batches[end].skills.get(b).unwrap().elapsed, 5);
h.convergence(ITERATIONS, EPSILON, false); h.convergence(ITERATIONS, EPSILON, false);
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
h.batches[end].skills[&b].posterior(), h.batches[end].skills.get(b).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&c].posterior(), h.batches[0].skills.get(c).unwrap().posterior(),
h.batches[end].skills[&c].posterior(), h.batches[end].skills.get(c).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
assert_ulps_eq!( assert_ulps_eq!(
h.batches[0].skills[&c].posterior(), h.batches[0].skills.get(c).unwrap().posterior(),
h.batches[0].skills[&b].posterior(), h.batches[0].skills.get(b).unwrap().posterior(),
epsilon = 1e-6 epsilon = 1e-6
); );
} }

1
src/lib.rs
View File

@@ -18,6 +18,7 @@ mod history;
mod matrix; mod matrix;
mod message; mod message;
pub mod player; pub mod player;
pub(crate) mod storage;
pub use drift::{ConstantDrift, Drift}; pub use drift::{ConstantDrift, Drift};
pub use error::InferenceError; pub use error::InferenceError;

3
src/storage/mod.rs Normal file
View File

@@ -0,0 +1,3 @@
mod skill_store;
pub(crate) use skill_store::SkillStore;

128
src/storage/skill_store.rs Normal file
View File

@@ -0,0 +1,128 @@
use crate::Index;
use crate::batch::Skill;
/// Dense Vec-backed store for per-agent skill state within a TimeSlice.
///
/// Indexed directly by Index.0, eliminating HashMap hashing in the inner
/// convergence loop. Uses a parallel `present` mask so iteration skips
/// absent slots without incurring per-slot Option overhead in the hot path.
#[derive(Debug, Default)]
pub struct SkillStore {
skills: Vec<Skill>,
present: Vec<bool>,
n_present: usize,
}
impl SkillStore {
pub fn new() -> Self {
Self::default()
}
fn ensure_capacity(&mut self, idx: usize) {
if idx >= self.skills.len() {
self.skills.resize_with(idx + 1, Skill::default);
self.present.resize(idx + 1, false);
}
}
pub fn insert(&mut self, idx: Index, skill: Skill) {
self.ensure_capacity(idx.0);
if !self.present[idx.0] {
self.n_present += 1;
}
self.skills[idx.0] = skill;
self.present[idx.0] = true;
}
pub fn get(&self, idx: Index) -> Option<&Skill> {
if idx.0 < self.present.len() && self.present[idx.0] {
Some(&self.skills[idx.0])
} else {
None
}
}
pub fn get_mut(&mut self, idx: Index) -> Option<&mut Skill> {
if idx.0 < self.present.len() && self.present[idx.0] {
Some(&mut self.skills[idx.0])
} else {
None
}
}
pub fn contains(&self, idx: Index) -> bool {
idx.0 < self.present.len() && self.present[idx.0]
}
pub fn len(&self) -> usize {
self.n_present
}
pub fn is_empty(&self) -> bool {
self.n_present == 0
}
pub fn iter(&self) -> impl Iterator<Item = (Index, &Skill)> {
self.present.iter().enumerate().filter_map(|(i, &p)| {
if p {
Some((Index(i), &self.skills[i]))
} else {
None
}
})
}
pub fn iter_mut(&mut self) -> impl Iterator<Item = (Index, &mut Skill)> {
self.skills
.iter_mut()
.zip(self.present.iter())
.enumerate()
.filter_map(|(i, (s, &p))| if p { Some((Index(i), s)) } else { None })
}
pub fn keys(&self) -> impl Iterator<Item = Index> + '_ {
self.present
.iter()
.enumerate()
.filter_map(|(i, &p)| if p { Some(Index(i)) } else { None })
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn insert_then_get() {
let mut store = SkillStore::new();
let idx = Index(3);
store.insert(idx, Skill::default());
assert!(store.contains(idx));
assert_eq!(store.len(), 1);
assert!(store.get(idx).is_some());
}
#[test]
fn missing_returns_none() {
let store = SkillStore::new();
assert!(store.get(Index(0)).is_none());
assert!(!store.contains(Index(42)));
}
#[test]
fn iter_skips_absent_slots() {
let mut store = SkillStore::new();
store.insert(Index(0), Skill::default());
store.insert(Index(5), Skill::default());
let keys: Vec<Index> = store.keys().collect();
assert_eq!(keys, vec![Index(0), Index(5)]);
}
#[test]
fn double_insert_does_not_double_count() {
let mut store = SkillStore::new();
store.insert(Index(2), Skill::default());
store.insert(Index(2), Skill::default());
assert_eq!(store.len(), 1);
}
}