Brummel
ff4a1b3d4a
Cycle-1 infrastructure for #165 (World/C21). Behaviour-preserving + workspace green; the CLI arm + tests (Tasks 4-5) follow. Task 1 — restructured stage1_r_graph into stage1_signal() (the serializable signal leg: SMA-cross -> Bias, a `price` input-role + a `bias` output) + wrap_stage1r(signal, ...) (broker/sinks/exec/cost around a nested signal), with stage1_r_graph() = wrap_stage1r(stage1_signal(...)). DEVIATION from the plan's "callers untouched" claim, verified behaviour-preserving: nesting the signal prefixes its param-space names (stage1_signal.fast.length), which broke the sweep's bare .axis("fast.length"); fixed exactly as the #137 stop-knob machinery does — FAST_LENGTH_SUFFIX/SLOW_LENGTH_SUFFIX suffix-resolution + stage1_r_friendly_name arms mapping the prefixed names back to the bare manifest names. All observable behaviour (manifest names, member keys, metrics, traces) byte-identical; the flat graph and every metric unchanged (full suite green, incl. sweep/walkforward/MC). Task 2 — RunManifest.topology_hash: Option<String> (Tier-1 optional, serde default/skip — old manifests byte-identical, #156), threaded None into all 24 literal sites across 7 crates. Also fixed the aura-registry RunManifestRead read-mirror to carry topology_hash (it was hardcoding None on load) — else a stored hash would silently drop on the registry round-trip once Task 3 stores a real one. Task 3 — the shared run_signal_stage1r seam (hash the signal, wrap, compile with params, bootstrap, run, manifest with params from param_space + topology_hash) + the sha256_hex topology_hash helper (sha2 in aura-cli, off the frozen engine, invariant 8). run_stage1_r now carries its topology_hash too (every run becomes self-identifying, #158). The seam is unwired until Task 4 (transient #[allow(dead_code)], retired there); RED-first seam tests added. Verified: cargo test --workspace green (51 suites, 0 failures); cargo clippy --workspace --all-targets -D warnings clean. refs #165
519 lines
22 KiB
Rust
519 lines
22 KiB
Rust
//! Monte-Carlo orchestration family (C12 axis 4): Monte-Carlo as a sweep over
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//! seeds. `monte_carlo(base_point, seeds, run_one)` runs a fixed base point over
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//! a seed set — each seed a disjoint C1 realization — and collects an
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//! [`McFamily`]: the per-seed [`McDraw`]s in seed-input order plus a stored
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//! [`McAggregate`] (mean + quantiles of all three run metrics across the
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//! realizations). It reuses the disjoint-parallel [`run_indexed`](crate::sweep)
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//! core `sweep` drives — the varying dimension is the *seed*, not a tuning param.
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//! Eager-agnostic (C12/#71): the API takes seeds + a per-draw closure, never a
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//! materialized stream `Vec`; the seed -> `Source` construction is a closure-body
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//! concern.
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use crate::sweep::run_indexed;
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use crate::{RunMetrics, RunReport, Scalar};
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use crate::harness::SplitMix64;
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/// One Monte-Carlo realization: the seed that drove it and the full `RunReport`.
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/// Self-describing, analog to [`SweepPoint`](crate::SweepPoint).
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#[derive(Clone, Debug, PartialEq)]
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pub struct McDraw {
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pub seed: u64,
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pub report: RunReport,
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}
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/// The result family of a Monte-Carlo run — one [`McDraw`] per seed, in
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/// seed-**input** order (independent of thread completion), plus the stored
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/// [`McAggregate`]. Analog to [`SweepFamily`](crate::SweepFamily).
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#[derive(Clone, Debug, PartialEq)]
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pub struct McFamily {
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pub draws: Vec<McDraw>,
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pub aggregate: McAggregate,
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}
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/// Distribution summary of all three run metrics across the realizations: covers
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/// every metric (not a single "chosen" one). A pure post-run reduction over the
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/// draws — stored for the common robustness case; custom statistics read the raw
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/// draws directly.
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#[derive(Clone, Debug, PartialEq)]
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pub struct McAggregate {
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pub total_pips: MetricStats,
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pub max_drawdown: MetricStats,
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pub bias_sign_flips: MetricStats,
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}
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/// Mean + a fixed quantile set of one metric across the realizations. `p50` is
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/// the median (the two coincide by definition), so "mean/median/quantiles" is
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/// `mean` + `p50` + the surrounding quantiles, with no redundant field.
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#[derive(Clone, Debug, PartialEq, serde::Serialize, serde::Deserialize)]
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pub struct MetricStats {
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pub mean: f64,
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pub p5: f64,
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pub p25: f64,
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pub p50: f64, // == median
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pub p75: f64,
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pub p95: f64,
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}
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impl MetricStats {
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/// Mean + the fixed type-7 quantile set over a value set. Sorts a copy;
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/// `values` must be finite and non-empty. The shared reduction behind both the
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/// MC aggregate (a metric across draws) and the walk-forward param-stability
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/// summary (a param across windows, [`crate::param_stability`]).
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pub fn from_values(values: &[f64]) -> MetricStats {
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let mut xs = values.to_vec();
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xs.sort_by(|a, b| a.partial_cmp(b).expect("values are finite"));
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MetricStats {
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mean: xs.iter().sum::<f64>() / xs.len() as f64,
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p5: quantile(&xs, 0.05),
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p25: quantile(&xs, 0.25),
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p50: quantile(&xs, 0.50),
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p75: quantile(&xs, 0.75),
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p95: quantile(&xs, 0.95),
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}
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}
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}
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impl McAggregate {
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/// Pure reduction over the realizations — recomputable from `draws` alone (it
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/// is exactly what [`monte_carlo`] stored). `draws` must be non-empty (a
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/// Monte-Carlo over zero realizations has no defined mean/quantile); on a
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/// non-empty slice every field is finite.
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pub fn from_draws(draws: &[McDraw]) -> McAggregate {
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let pick = |f: fn(&RunMetrics) -> f64| -> MetricStats {
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let xs: Vec<f64> = draws.iter().map(|d| f(&d.report.metrics)).collect();
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MetricStats::from_values(&xs)
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};
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McAggregate {
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total_pips: pick(|m| m.total_pips),
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max_drawdown: pick(|m| m.max_drawdown),
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bias_sign_flips: pick(|m| m.bias_sign_flips as f64),
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}
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}
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}
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/// Linear-interpolation quantile (the numpy/Excel "type 7" default) over a
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/// pre-sorted, non-empty slice. `p` in `[0, 1]`. `rank = p * (n - 1)`;
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/// interpolate between the two bracketing order statistics. `n == 1` returns the
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/// sole value.
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fn quantile(sorted: &[f64], p: f64) -> f64 {
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let n = sorted.len();
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if n == 1 {
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return sorted[0];
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}
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let rank = p * (n - 1) as f64;
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let lo = rank.floor() as usize;
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let frac = rank - lo as f64;
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if lo + 1 < n {
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sorted[lo] + frac * (sorted[lo + 1] - sorted[lo])
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} else {
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sorted[n - 1]
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}
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}
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/// Run `run_one(seed, base_point)` over every seed, disjointly in parallel (C1),
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/// and collect the family in seed-input order plus the aggregate. The varying
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/// dimension is the *seed* (C12 axis 4: MC = sweep over seeds), not a tuning
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/// param; `base_point` is constant across draws. Eager-agnostic: the seed ->
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/// `Source` construction lives inside `run_one`, never a materialized stream
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/// `Vec` in this API (#71).
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///
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/// Precondition: `seeds` is non-empty (a Monte-Carlo over zero realizations has
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/// no defined aggregate). A `debug_assert!` guards it; the `-> McFamily`
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/// signature is preserved (no `Result`), matching [`sweep`](crate::sweep).
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pub fn monte_carlo<F>(base_point: &[Scalar], seeds: &[u64], run_one: F) -> McFamily
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where
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F: Fn(u64, &[Scalar]) -> RunReport + Sync,
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{
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let nthreads = std::thread::available_parallelism().map(|n| n.get()).unwrap_or(1);
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monte_carlo_with_threads(base_point, seeds, nthreads, run_one)
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}
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/// The thread-count-explicit core of [`monte_carlo`]. Module-private: the public
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/// `monte_carlo` derives the count, while the tests drive it at 1 and at N to pin
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/// determinism under concurrency (C1). A thin adapter over
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/// [`run_indexed`](crate::sweep): it runs each seed disjointly and zips the
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/// reports back onto their seeds in seed-input order.
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fn monte_carlo_with_threads<F>(
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base_point: &[Scalar],
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seeds: &[u64],
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nthreads: usize,
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run_one: F,
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) -> McFamily
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where
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F: Fn(u64, &[Scalar]) -> RunReport + Sync,
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{
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debug_assert!(!seeds.is_empty(), "monte_carlo requires a non-empty seed set");
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let reports = run_indexed(seeds.len(), nthreads, |i| run_one(seeds[i], base_point));
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let draws: Vec<McDraw> = seeds
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.iter()
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.zip(reports)
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.map(|(&seed, report)| McDraw { seed, report })
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.collect();
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let aggregate = McAggregate::from_draws(&draws);
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McFamily { draws, aggregate }
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}
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/// Distribution of `E[R]` under a moving-block bootstrap of an OOS per-trade R series
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/// (#139). `block_len == 1` is the i.i.d. trade-shuffle; `block_len > 1` resamples
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/// contiguous runs, preserving the serial correlation of sequential trades a pure
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/// shuffle would erase. Deterministic (C1) given `seed`.
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#[derive(Clone, Debug, PartialEq)]
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pub struct RBootstrap {
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pub e_r: MetricStats, // mean + p5/p25/p50/p75/p95 of the resampled E[R]
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pub prob_le_zero: f64, // fraction of resamples whose mean R <= 0
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pub n_trades: usize,
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pub block_len: usize,
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pub n_resamples: usize,
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}
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/// One moving-block resample of `rs` to length `rs.len()` (non-circular; the final
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/// block is truncated so the resample has exactly `n` values). `block_len` must be
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/// pre-clamped to `[1, rs.len()]` by the caller. Pure given the `rng` state — the
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/// shared kernel behind `r_bootstrap` and the trials-deflation reality-check (C1).
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pub fn resample_block(rs: &[f64], block_len: usize, rng: &mut SplitMix64) -> Vec<f64> {
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let n = rs.len();
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debug_assert!(
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(1..=n).contains(&block_len),
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"resample_block requires block_len in [1, rs.len()] (caller pre-clamps); \
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got block_len={block_len}, rs.len()={n}",
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);
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let mut sample: Vec<f64> = Vec::with_capacity(n);
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while sample.len() < n {
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let start = (rng.next_u64() % (n - block_len + 1) as u64) as usize;
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let take = block_len.min(n - sample.len());
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sample.extend_from_slice(&rs[start..start + take]);
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}
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sample
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}
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/// Moving-block bootstrap of `rs` (non-circular; the final block of each resample is
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/// truncated so the resample has exactly `n` values). `block_len` is clamped to
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/// `[1, n]`. Empty `rs` or zero resamples -> an all-zero `RBootstrap`. Pure given
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/// `seed` (drives the existing `SplitMix64`).
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pub fn r_bootstrap(rs: &[f64], n_resamples: usize, block_len: usize, seed: u64) -> RBootstrap {
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let n = rs.len();
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if n == 0 || n_resamples == 0 {
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return RBootstrap {
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e_r: MetricStats { mean: 0.0, p5: 0.0, p25: 0.0, p50: 0.0, p75: 0.0, p95: 0.0 },
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prob_le_zero: 0.0,
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n_trades: n,
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block_len: block_len.clamp(1, n.max(1)),
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n_resamples,
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};
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}
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let block_len = block_len.clamp(1, n);
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let mut rng = SplitMix64::new(seed);
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let mut means: Vec<f64> = Vec::with_capacity(n_resamples);
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for _ in 0..n_resamples {
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let sample = resample_block(rs, block_len, &mut rng);
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means.push(sample.iter().sum::<f64>() / n as f64);
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}
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let e_r = MetricStats::from_values(&means);
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let prob_le_zero = means.iter().filter(|&&m| m <= 0.0).count() as f64 / n_resamples as f64;
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RBootstrap { e_r, prob_le_zero, n_trades: n, block_len, n_resamples }
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::test_fixtures::composite_sma_cross_harness;
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use crate::{f64_field, summarize, RunManifest, SyntheticSpec, Timestamp};
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use aura_core::Scalar;
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/// Build + bootstrap + run + drain + summarize one (seed, point) into a
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/// `RunReport`. A free `fn` (Copy + Sync) so it serves as the `monte_carlo`
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/// closure AND a direct reference for the "draw == independent run"
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/// comparison. The stream is generated from `seed` (seed-as-input, #66), and
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/// `seed` is recorded into the manifest.
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fn run_draw(seed: u64, point: &[Scalar]) -> RunReport {
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let (bp, rx_eq, rx_ex) = composite_sma_cross_harness();
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let mut h = bp
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.bootstrap_with_params(point.to_vec())
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.expect("base point is kind-checked against param_space");
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let spec = SyntheticSpec { start: 1.0, len: 64, step: 1 };
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let window = (Timestamp(1), Timestamp((spec.len as i64 - 1) * spec.step + 1));
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h.run(vec![Box::new(spec.source(seed))]);
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let equity = f64_field(&rx_eq.try_iter().collect::<Vec<_>>(), 0);
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let exposure = f64_field(&rx_ex.try_iter().collect::<Vec<_>>(), 0);
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RunReport {
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manifest: RunManifest {
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commit: "test".to_string(),
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params: Vec::new(),
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window,
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seed,
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broker: "test".to_string(),
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selection: None,
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instrument: None,
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topology_hash: None,
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},
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metrics: summarize(&equity, &exposure),
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}
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}
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fn base_point() -> Vec<Scalar> {
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// matches the composite_sma_cross param_space order: fast, slow, scale
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vec![Scalar::i64(2), Scalar::i64(4), Scalar::f64(0.5)]
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}
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fn draws_with_metrics(values: &[f64]) -> Vec<McDraw> {
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values
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.iter()
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.enumerate()
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.map(|(i, &v)| McDraw {
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seed: i as u64,
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report: RunReport {
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manifest: RunManifest {
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commit: "t".to_string(),
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params: Vec::new(),
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window: (Timestamp(0), Timestamp(0)),
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seed: i as u64,
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broker: "t".to_string(),
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selection: None,
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instrument: None,
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topology_hash: None,
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},
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metrics: RunMetrics {
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total_pips: v,
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max_drawdown: v,
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bias_sign_flips: v as u64,
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r: None,
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},
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},
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})
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.collect()
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}
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#[test]
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fn monte_carlo_runs_one_draw_per_seed_in_input_order() {
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// N seeds -> N draws, seeds carried in INPUT order.
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let family = monte_carlo(&base_point(), &[1, 2, 3], run_draw);
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assert_eq!(family.draws.len(), 3);
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assert_eq!(
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family.draws.iter().map(|d| d.seed).collect::<Vec<_>>(),
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vec![1, 2, 3],
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);
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}
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#[test]
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fn family_is_deterministic_across_thread_counts() {
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// order = seed input, not completion (C1).
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let point = base_point();
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let seeds: Vec<u64> = (1..=8).collect();
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let one = monte_carlo_with_threads(&point, &seeds, 1, run_draw);
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let many = monte_carlo_with_threads(&point, &seeds, 8, run_draw);
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assert_eq!(one, many);
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assert_eq!(one, monte_carlo(&point, &seeds, run_draw));
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}
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#[test]
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fn draw_equals_independent_seeded_run() {
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// each draw == an independent run of that (seed, point)
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// — MC adds enumeration + execution, never a metrics change (C1).
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let point = base_point();
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let family = monte_carlo(&point, &[10, 11, 12], run_draw);
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for draw in &family.draws {
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assert_eq!(draw.report, run_draw(draw.seed, &point));
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assert!(draw.report.metrics.total_pips.is_finite());
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}
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}
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#[test]
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fn distinct_seeds_produce_distinct_draws() {
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// the seed actually perturbs each run — the family does
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// not collapse to one metric.
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let family = monte_carlo(&base_point(), &[1, 2, 3, 4, 5], run_draw);
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let first = family.draws[0].report.metrics.total_pips;
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assert!(
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family.draws.iter().any(|d| d.report.metrics.total_pips != first),
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"a multi-seed family must not collapse to one metric",
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);
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}
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#[test]
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fn aggregate_is_a_pure_reduction() {
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// the stored aggregate is recomputable from the draws.
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let family = monte_carlo(&base_point(), &[1, 2, 3, 4], run_draw);
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assert_eq!(McAggregate::from_draws(&family.draws), family.aggregate);
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}
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#[test]
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fn aggregate_stats_on_known_fixture() {
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// type-7 quantile + mean over known metric values
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// [0,1,2,3,4]: mean=2.0, p50=2.0, p5≈0.2, p95≈3.8.
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let agg = McAggregate::from_draws(&draws_with_metrics(&[0.0, 1.0, 2.0, 3.0, 4.0]));
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assert_eq!(agg.total_pips.mean, 2.0);
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assert_eq!(agg.total_pips.p50, 2.0);
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assert!((agg.total_pips.p5 - 0.2).abs() < 1e-9, "p5 = {}", agg.total_pips.p5);
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assert!((agg.total_pips.p95 - 3.8).abs() < 1e-9, "p95 = {}", agg.total_pips.p95);
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}
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#[test]
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fn quantile_endpoints_and_singleton() {
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// singleton returns the sole value; p=0 -> min, p=1 -> max.
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assert_eq!(quantile(&[42.0], 0.0), 42.0);
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assert_eq!(quantile(&[42.0], 0.5), 42.0);
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assert_eq!(quantile(&[42.0], 1.0), 42.0);
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let xs = [1.0, 2.0, 3.0, 4.0, 5.0];
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assert_eq!(quantile(&xs, 0.0), 1.0);
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assert_eq!(quantile(&xs, 1.0), 5.0);
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}
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#[test]
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fn metric_stats_from_values_matches_known_fixture() {
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// type-7 quantile + mean over [0,1,2,3,4], directly on the
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// extracted reduction: mean=2.0, p50=2.0, p5≈0.2, p95≈3.8 (same numbers the
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// aggregate fixture pins, now on from_values).
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let s = MetricStats::from_values(&[0.0, 1.0, 2.0, 3.0, 4.0]);
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assert_eq!(s.mean, 2.0);
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assert_eq!(s.p50, 2.0);
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assert!((s.p5 - 0.2).abs() < 1e-9, "p5 = {}", s.p5);
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assert!((s.p95 - 3.8).abs() < 1e-9, "p95 = {}", s.p95);
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}
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#[test]
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fn metric_stats_serde_round_trips() {
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// MetricStats gains serde (consistent with the
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// report types) so the CLI summary renders it and #70 lineage can persist.
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let s = MetricStats::from_values(&[1.0, 2.0, 3.0]);
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let json = serde_json::to_string(&s).expect("serialize MetricStats");
|
|
let back: MetricStats = serde_json::from_str(&json).expect("deserialize MetricStats");
|
|
assert_eq!(back, s);
|
|
}
|
|
|
|
#[test]
|
|
fn resample_block_returns_n_values_from_contiguous_runs() {
|
|
// The extracted kernel's standalone contract (now a public entry point):
|
|
// given a pre-clamped block_len in [1, n], it yields exactly `n` values, each
|
|
// appearing in `rs`, arranged as contiguous runs of `rs` (the final run
|
|
// truncated to reach exactly n). Distinct powers of ten make membership a sum
|
|
// check. n=5, block_len=2 -> runs of length 2, 2, 1.
|
|
let rs = [1.0, 10.0, 100.0, 1000.0, 10000.0];
|
|
let n = rs.len();
|
|
let block_len = 2;
|
|
let mut rng = SplitMix64::new(7);
|
|
let sample = resample_block(&rs, block_len, &mut rng);
|
|
assert_eq!(sample.len(), n, "resample has exactly n values");
|
|
assert!(
|
|
sample.iter().all(|v| rs.contains(v)),
|
|
"every resampled value is drawn from rs",
|
|
);
|
|
// Reconstruct the contiguous-run structure: walk the sample in blocks of
|
|
// `block_len` (last truncated) and assert each block is a contiguous slice of rs.
|
|
let mut filled = 0usize;
|
|
while filled < n {
|
|
let take = block_len.min(n - filled);
|
|
let run = &sample[filled..filled + take];
|
|
let start = rs
|
|
.iter()
|
|
.position(|v| v == &run[0])
|
|
.expect("run head is in rs");
|
|
assert_eq!(
|
|
run,
|
|
&rs[start..start + take],
|
|
"each run is a contiguous slice of rs",
|
|
);
|
|
filled += take;
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn r_bootstrap_empty_series_is_all_zero() {
|
|
let b = r_bootstrap(&[], 100, 1, 7);
|
|
assert_eq!(b.n_trades, 0);
|
|
assert_eq!(b.e_r.mean, 0.0);
|
|
assert_eq!(b.prob_le_zero, 0.0);
|
|
}
|
|
|
|
#[test]
|
|
fn r_bootstrap_single_block_equals_full_series_mean() {
|
|
// block_len == n: the only valid start is 0, so every resample IS the full
|
|
// series -> every resample mean == the series mean -> zero spread.
|
|
let rs = [1.0, -2.0, 3.0]; // mean = 2/3
|
|
let b = r_bootstrap(&rs, 500, rs.len(), 7);
|
|
let mean = 2.0 / 3.0;
|
|
assert!((b.e_r.mean - mean).abs() < 1e-12);
|
|
assert!((b.e_r.p5 - mean).abs() < 1e-12);
|
|
assert!((b.e_r.p95 - mean).abs() < 1e-12);
|
|
assert_eq!(b.prob_le_zero, 0.0); // mean > 0
|
|
assert_eq!(b.n_trades, 3);
|
|
assert_eq!(b.block_len, 3);
|
|
assert_eq!(b.n_resamples, 500);
|
|
}
|
|
|
|
#[test]
|
|
fn r_bootstrap_is_deterministic_given_seed() {
|
|
let rs = [0.5, -1.0, 2.0, -0.5, 1.5, -2.0, 0.25];
|
|
let a = r_bootstrap(&rs, 1000, 1, 42);
|
|
let b = r_bootstrap(&rs, 1000, 1, 42);
|
|
assert_eq!(a, b, "same rs+seed+params -> identical RBootstrap (C1)");
|
|
let c = r_bootstrap(&rs, 1000, 1, 43);
|
|
assert_ne!(a.e_r.p5, c.e_r.p5, "a different seed reshuffles differently");
|
|
}
|
|
|
|
#[test]
|
|
fn r_bootstrap_block_len_is_clamped_to_series_len() {
|
|
// block_len > n clamps to n -> single-block behaviour (no panic, no OOB).
|
|
let rs = [1.0, 2.0];
|
|
let b = r_bootstrap(&rs, 10, 99, 1);
|
|
assert_eq!(b.block_len, 2);
|
|
assert!((b.e_r.mean - 1.5).abs() < 1e-12);
|
|
}
|
|
|
|
#[test]
|
|
fn r_bootstrap_moving_block_resamples_are_contiguous_truncated_runs() {
|
|
// The serial-correlation-preserving branch: 1 < block_len < n with n NOT a
|
|
// multiple of block_len, so the final block of every resample is truncated
|
|
// (`take = block_len.min(n - sample.len())`). Every resample is a sequence of
|
|
// contiguous runs of `rs` (the last one cut short to reach exactly n values),
|
|
// so each resample mean must lie in the finite set of legal block-composition
|
|
// means. Distinct powers of ten make every distinct multiset of contributions
|
|
// a distinct sum -> a faithful membership check. n=5, block_len=2 -> blocks of
|
|
// length 2, 2, 1.
|
|
let rs = [1.0, 10.0, 100.0, 1000.0, 10000.0];
|
|
let n = rs.len();
|
|
let block_len = 2;
|
|
|
|
// Enumerate every legal resample sum: pick a start in [0, n-block_len] for each
|
|
// of the three blocks (lengths 2, 2, 1), summing min(block_len, n-filled) values
|
|
// from that start. Membership-by-sum, mirroring r_bootstrap's own construction.
|
|
let starts: Vec<usize> = (0..=n - block_len).collect();
|
|
let mut legal_means: Vec<f64> = Vec::new();
|
|
for &s0 in &starts {
|
|
for &s1 in &starts {
|
|
for &s2 in &starts {
|
|
let mut sum = 0.0;
|
|
let mut filled = 0usize;
|
|
for &start in &[s0, s1, s2] {
|
|
let take = block_len.min(n - filled);
|
|
if take == 0 {
|
|
break;
|
|
}
|
|
sum += rs[start..start + take].iter().sum::<f64>();
|
|
filled += take;
|
|
}
|
|
legal_means.push(sum / n as f64);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Drive enough resamples that the truncated final block is exercised many
|
|
// times; assert every produced mean is a legal block-composition mean.
|
|
let b = r_bootstrap(&rs, 2000, block_len, 7);
|
|
assert_eq!(b.block_len, 2);
|
|
assert_eq!(b.n_trades, 5);
|
|
// The mean of resample means is a weighted average of legal means, so it is
|
|
// bounded by their min and max (not itself a single legal mean).
|
|
let lo = legal_means.iter().cloned().fold(f64::INFINITY, f64::min);
|
|
let hi = legal_means.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
|
|
assert!(lo - 1e-6 <= b.e_r.mean && b.e_r.mean <= hi + 1e-6);
|
|
// Each quantile of the resampled E[R] is an order statistic of the resample
|
|
// means, hence itself a legal contiguous-block-composition mean.
|
|
for &m in &[b.e_r.p5, b.e_r.p25, b.e_r.p50, b.e_r.p75, b.e_r.p95] {
|
|
assert!(
|
|
legal_means.iter().any(|&lm| (lm - m).abs() < 1e-6),
|
|
"resampled quantile {m} is not a legal contiguous-block-composition mean",
|
|
);
|
|
}
|
|
}
|
|
}
|