T0 + T1 + T2: engine redesign through new API surface (#1)
Implements tiers T0, T1, T2 of `docs/superpowers/specs/2026-04-23-trueskill-engine-redesign-design.md`. All three tiers have landed together on this branch because they build on one another; this PR rolls them up for a single review pass. Per-tier plans: - T0: `docs/superpowers/plans/2026-04-23-t0-numerical-parity.md` - T1: `docs/superpowers/plans/2026-04-24-t1-factor-graph.md` - T2: `docs/superpowers/plans/2026-04-24-t2-new-api-surface.md` ## Summary ### T0 — Numerical parity (internal) - `Gaussian` switched to natural-parameter storage `(pi, tau)`; mul/div now ~7× faster (218 ps vs 1.57 ns). - `HashMap<Index, _>` → dense `Vec<_>` keyed by `Index.0` (via `AgentStore<D>`, `SkillStore`). - `ScratchArena` eliminates per-event allocations in `Game::likelihoods`. - `InferenceError` seed type added (1 variant). - 38 → 53 tests passing through T1. - Benchmark: `Batch::iteration` 29.84 → 21.25 µs. ### T1 — Factor graph machinery (internal) - `Factor` trait + `BuiltinFactor` enum (TeamSum / RankDiff / Trunc) driving within-game inference. - `VarStore` flat storage for variable marginals. - `Schedule` trait + `EpsilonOrMax` impl replacing the hand-rolled EP loop. - `Game::likelihoods` rebuilt on the factor-graph machinery; iteration counts and goldens preserved to within 1e-6. - 53 tests passing. - Benchmark: `Batch::iteration` 23.01 µs (slight regression absorbed in T2). ### T2 — New API surface (breaking) **Renames:** - `IndexMap → KeyTable`, `Player → Rating`, `Agent → Competitor`, `Batch → TimeSlice` **New types:** - `Time` trait with `Untimed` ZST and `i64` impls; `Drift<T>`, `Rating<T, D>`, `Competitor<T, D>`, `TimeSlice<T>`, `History<T, D, O, K>` all generic. - `Event<T, K>`, `Team<K>`, `Member<K>`, `Outcome` (`Ranked` variant; `#[non_exhaustive]`). - `Observer<T>` trait + `NullObserver`. - `ConvergenceOptions`, `ConvergenceReport`. - `GameOptions`, `OwnedGame<T, D>`. **Three-tier ingestion:** - `history.record_winner(&K, &K, T)` / `record_draw(&K, &K, T)` — 1v1 convenience. - `history.add_events(iter)` — typed bulk. - `history.event(T).team([...]).weights([...]).ranking([...]).commit()` — fluent. **Query API:** `current_skill`, `learning_curve`, `learning_curves` (keyed on `K`), `log_evidence`, `log_evidence_for`, `predict_quality`, `predict_outcome`. **Game constructors:** `ranked`, `one_v_one`, `free_for_all`, `custom` — all returning `Result<_, InferenceError>`. **`factors` module:** `Factor`, `Schedule`, `VarStore`, `VarId`, `BuiltinFactor`, `EpsilonOrMax`, `ScheduleReport`, `TeamSumFactor`, `RankDiffFactor`, `TruncFactor` now public. **Errors:** `InferenceError` gains `MismatchedShape`, `InvalidProbability`, `ConvergenceFailed`; boundary panics converted to `Result`. **Removed (breaking):** `History::convergence(iters, eps, verbose)`, `HistoryBuilder::gamma(f64)`, `HistoryBuilder::time(bool)`, `History.time: bool`, `learning_curves_by_index`, nested-Vec public `add_events`. ## Behavior change (documented in CHANGELOG) `Time = Untimed` has `elapsed_to → 0`, so no drift accumulates between slices. The old `time=false` mode implicitly forced `elapsed=1` on reappearance via an `i64::MAX` sentinel — that quirk is not reproducible under a typed time axis. Tests that depended on it now use `History::<i64, _>` with explicit `1..=n` timestamps. One test (`test_env_ttt`) had 3 Gaussian goldens updated to reflect the corrected semantics; documented in commit `33a7d90`. ## Final numbers | Metric | Before T0 | After T2 | Delta | |---|---|---|---| | `Batch::iteration` | 29.84 µs | 21.36 µs | **-28%** | | `Gaussian::mul` | 1.57 ns | 219 ps | **-86%** | | `Gaussian::div` | 1.57 ns | 219 ps | **-86%** | | Tests passing | 38 | 90 | +52 | All other Gaussian ops unchanged (~219 ps add/sub, ~264 ps pi/tau reads). ## Test plan - [x] `cargo test --features approx` — 90/90 pass (68 lib + 10 api_shape + 6 game + 4 record_winner + 2 equivalence) - [x] `cargo clippy --all-targets --features approx -- -D warnings` — clean - [x] `cargo +nightly fmt --check` — clean - [x] `cargo bench --bench batch` — 21.36 µs - [x] `cargo bench --bench gaussian` — unchanged from T1 - [x] `cargo run --example atp --features approx` — rewritten in new API, runs clean - [x] Historical Game-level goldens preserved in `tests/equivalence.rs` - [x] Public API matches spec Section 4 (verified by integration tests in `tests/api_shape.rs`) ## Commit history ~45 commits total across T0 + T1 + T2. Each task is self-contained and individually tested; the branch is bisectable. See `git log main..t2-new-api-surface` for the full list. ## Deferred to later tiers - `Outcome::Scored` + `MarginFactor` — T4 - `Damped` / `Residual` schedules — T4 - `Send + Sync` bounds + Rayon parallelism — T3 - N-team `predict_outcome` — T4 - `Game::custom` full ergonomics — T4 🤖 Generated with [Claude Code](https://claude.com/claude-code) Reviewed-on: #1 Co-authored-by: Anders Olsson <anders.e.olsson@gmail.com> Co-committed-by: Anders Olsson <anders.e.olsson@gmail.com>
This commit was merged in pull request #1.
This commit is contained in:
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benches/baseline.txt
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benches/baseline.txt
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# Baseline numbers captured before T0 changes
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# Hardware: lrrr.local / Apple M5 Pro
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# Date: 2026-04-24
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Batch::iteration 29.840 µs
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Gaussian::add 219.58 ps
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Gaussian::sub 219.41 ps
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Gaussian::mul 1.568 ns ← hot path; target ≥1.5× improvement
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Gaussian::div 1.572 ns ← hot path; target ≥1.5× improvement
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Gaussian::pi 262.89 ps
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Gaussian::tau 262.47 ps
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Gaussian::pi_tau_combined 219.40 ps
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# After T0 (2026-04-24, same hardware)
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Batch::iteration 21.253 µs (1.40× — below 3× target; see post-mortem)
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Gaussian::add 218.62 ps (1.00× — unchanged, Add/Sub use moment form)
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Gaussian::sub 220.15 ps (1.00×)
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Gaussian::mul 218.69 ps (7.17× — nat-param: now two f64 adds, no sqrt)
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Gaussian::div 218.64 ps (7.19× — nat-param: now two f64 subs, no sqrt)
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Gaussian::pi 263.19 ps (1.00× — now a field read, same cost)
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Gaussian::tau 263.51 ps (1.00× — now a field read, same cost)
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Gaussian::pi_tau_combined 219.13 ps (1.00×)
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# Post-mortem: Batch::iteration 1.40× vs. 3× target
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#
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# Root cause: the bench has 100 tiny 2-team events. Each event still allocates
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# ~10 Vecs per iteration (down from ~18). The arena covers teams/diffs/ties/margins
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# (was 4 Vecs, now 0 new allocs) but the following remain:
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# - within_priors() returns Vec<Vec<Player<D>>>: 3 Vecs per event (300 total)
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# - event.outputs() returns Vec<f64>: 1 Vec per event (100 total)
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# - sort_perm() allocates 2 scratch Vecs: 200 total
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# - Game::likelihoods = collect() allocates Vec<Vec<Gaussian>>: 4 Vecs (400 total)
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# Total remaining: ~1000 allocs per iteration call vs. ~1800 before (44% reduction).
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#
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# The HashMap → dense Vec win (target 2–4×) benefits the History-level forward/backward
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# sweep, NOT Batch::iteration in isolation — so this bench doesn't show it.
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#
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# To hit ≥3× on Batch::iteration:
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# - Arena-ify sort_perm (use a stack-fixed array for small n_teams)
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# - Pass a within_priors output buffer through the arena
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# - Make Game::likelihoods write into an arena slice rather than allocating
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# These land in T1 (factor graph) when we redesign Game's internals.
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# After T1 (2026-04-24, same hardware)
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Batch::iteration 23.010 µs (1.08× vs T0 21.253 µs — slight regression)
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Gaussian::add 231.23 ps (unchanged)
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Gaussian::sub 235.38 ps (unchanged)
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Gaussian::mul 234.55 ps (unchanged — nat-param storage)
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Gaussian::div 233.27 ps (unchanged)
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Gaussian::pi 272.68 ps (unchanged)
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Gaussian::tau 272.73 ps (unchanged)
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Gaussian::pi_tau_combined 234.xx ps (unchanged)
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# Notes:
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# - Batch::iteration 23.0 µs vs target ≤ 21.5 µs (8% above target).
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# Root cause: TruncFactor::propagate adds one extra Gaussian mul + div per
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# diff vs the old inline EP computation. trunc Vec is still a fresh
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# per-game allocation (borrow checker prevents putting it in the arena
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# alongside vars). These are addressable in T2.
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# - arena.team_prior, lhood_lose, lhood_win, inv_buf, sort_buf all reuse
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# capacity across games (pooled in ScratchArena). sort_perm() allocation
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# eliminated. message.rs deleted.
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# - Gaussian operations unchanged vs T0.
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# - All 53 tests pass. factor graph infrastructure (VarStore, Factor trait,
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# BuiltinFactor, TruncFactor, EpsilonOrMax schedule) in place for T2.
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# After T2 (2026-04-24, same hardware)
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Batch::iteration 21.36 µs (1.07× vs T1 22.88 µs — 7% improvement)
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Gaussian::add 218.97 ps (unchanged)
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Gaussian::sub 218.58 ps (unchanged)
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Gaussian::mul 218.59 ps (unchanged)
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Gaussian::div 218.57 ps (unchanged)
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Gaussian::pi 264.20 ps (unchanged)
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Gaussian::tau 260.80 ps (unchanged)
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# Notes:
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# - API-only tier; hot inference path unchanged. The 7% improvement on
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# Batch::iteration likely comes from the typed add_events(iter) path
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# being slightly more direct than the nested-Vec path it replaced
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# (one less layer of composition construction per event).
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# - Public surface now matches spec Section 4:
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# record_winner / record_draw / add_events(iter) / event(t).team().commit()
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# converge() -> Result<ConvergenceReport, InferenceError>
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# learning_curve(&K) / learning_curves() / current_skill(&K)
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# log_evidence() / log_evidence_for(&[&K])
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# predict_quality / predict_outcome
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# Game::ranked / one_v_one / free_for_all / custom
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# factors module (pub Factor/Schedule/VarStore/EpsilonOrMax/BuiltinFactor)
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# - Breaking type renames: Batch→TimeSlice, Player→Rating, Agent→Competitor,
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# IndexMap→KeyTable.
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# - Generic over T: Time (default i64), D: Drift<T>, O: Observer<T>,
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# K: Eq + Hash + Clone (default &'static str).
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# - Legacy removed: History::convergence(iters, eps, verbose),
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# HistoryBuilder::gamma(), HistoryBuilder::time(bool), History::time field,
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# learning_curves_by_index(), nested-Vec public add_events().
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# - 90 tests green: 68 lib + 10 api_shape + 6 game + 4 record_winner +
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# 2 equivalence.
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@@ -1,45 +1,27 @@
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use std::collections::HashMap;
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use criterion::{Criterion, criterion_group, criterion_main};
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use trueskill_tt::{
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BETA, GAMMA, IndexMap, MU, P_DRAW, SIGMA, agent::Agent, batch::Batch, drift::ConstantDrift,
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gaussian::Gaussian, player::Player,
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BETA, Competitor, GAMMA, KeyTable, MU, P_DRAW, Rating, SIGMA, TimeSlice, drift::ConstantDrift,
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gaussian::Gaussian, storage::CompetitorStore,
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};
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fn criterion_benchmark(criterion: &mut Criterion) {
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let mut index = IndexMap::new();
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let mut index_map = KeyTable::new();
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let a = index.get_or_create("a");
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let b = index.get_or_create("b");
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let c = index.get_or_create("c");
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let a = index_map.get_or_create("a");
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let b = index_map.get_or_create("b");
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let c = index_map.get_or_create("c");
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let agents = {
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let mut map = HashMap::new();
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let mut agents: CompetitorStore<i64, ConstantDrift> = CompetitorStore::new();
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map.insert(
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a,
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Agent {
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player: Player::new(Gaussian::from_ms(MU, SIGMA), BETA, ConstantDrift(GAMMA)),
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for agent in [a, b, c] {
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agents.insert(
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agent,
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Competitor {
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rating: Rating::new(Gaussian::from_ms(MU, SIGMA), BETA, ConstantDrift(GAMMA)),
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..Default::default()
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},
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);
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map.insert(
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b,
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Agent {
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player: Player::new(Gaussian::from_ms(MU, SIGMA), BETA, ConstantDrift(GAMMA)),
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..Default::default()
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},
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);
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map.insert(
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c,
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Agent {
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player: Player::new(Gaussian::from_ms(MU, SIGMA), BETA, ConstantDrift(GAMMA)),
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..Default::default()
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},
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);
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map
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};
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}
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let mut composition = Vec::new();
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let mut results = Vec::new();
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@@ -51,11 +33,11 @@ fn criterion_benchmark(criterion: &mut Criterion) {
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weights.push(vec![vec![1.0], vec![1.0]]);
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}
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let mut batch = Batch::new(1, P_DRAW);
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batch.add_events(composition, results, weights, &agents);
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let mut time_slice = TimeSlice::new(1, P_DRAW);
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time_slice.add_events(composition, results, weights, &agents);
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criterion.bench_function("Batch::iteration", |b| {
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b.iter(|| batch.iteration(0, &agents))
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b.iter(|| time_slice.iteration(0, &agents))
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});
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}
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@@ -23,8 +23,11 @@ fn benchmark_gaussian_arithmetic(criterion: &mut Criterion) {
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});
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// Benchmark division
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// NOTE: numerator must have higher precision (smaller sigma) than the
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// denominator in this representation; g2 (sigma=1) / g1 (sigma=8.33) is
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// well-defined, whereas g1 / g2 underflows and panics in mu_sigma.
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criterion.bench_function("Gaussian::div", |bencher| {
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bencher.iter(|| g1 / g2);
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bencher.iter(|| g2 / g1);
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});
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// Benchmark natural parameter conversions
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