gam 0.3.121

//! Measure-jet frame data interface: per-cell frozen-weight polynomial
//! moment tables with a binomial-shift merge monoid
//! (`docs/measure_jet_frame.md`, §2 "Data interface: moments or
//! nothing").
//!
//! This module aggregates caller-computed weights into order-0..2 coordinate
//! moments. Those tables exactly determine polynomial couplings under the
//! same frozen weights, including the local affine sufficient statistics used
//! by `measure_jet_smooth.rs`. They do NOT exactly determine Gaussian
//! transforms at moved kernel centers: support curves, Gaussian Gram entries,
//! and Gaussian `XᵀWX` products need their own kernel pass or a separately
//! controlled approximation. Truncation does NOT live here either: the caller
//! computes the Gaussian weights `w_i` (mass × kernel profile, with whatever
//! cutoff its explicit `e^{−ρ²/2}` tolerance budget licenses) and this module
//! only aggregates what it is handed.
//!
//! # The monoid
//!
//! A table holds, per response channel `g` and per coordinate multi-index
//! `α` with `|α| ≤ 2`, the centered moment `μ_α = Σ_i w_i g_i (x_i − c)^α`
//! about the cell reference point `c`. The binomial shift
//!
//! ```text
//!   μ′_α = Σ_{β ≤ α}  C(α, β) (c − c′)^{α−β} μ_β
//! ```
//!
//! re-expresses the same frozen-weight polynomial table about any other
//! center `c′` exactly as a finite polynomial identity. It does not move the
//! Gaussian kernel center or recompute weights. Merging two tables with
//! already-compatible frozen weights is therefore "recenter to a common
//! reference, add componentwise":
//! an associative, commutative monoid whose identity is the empty (all-zero)
//! table at any center. Exact distributed fitting, exact online updates, and
//! bit-reproducibility under sorted reduction are corollaries of that one
//! algebraic fact ([`merge_moment_tables`] is a monoid homomorphism from
//! disjoint row sets under union to tables under ⊕).
//!
//! # Determinism / bit-exactness convention (sorted reduction)
//!
//! Floating-point addition is commutative but not associative, so the monoid
//! laws hold algebraically while bit-patterns depend on reduction ORDER.
//! This module pins one order everywhere:
//!
//! - [`accumulate_moment_table`] splits rows into fixed-size chunks
//!   ([`MEASURE_JET_MOMENT_CHUNK_ROWS`], never derived from thread count),
//!   accumulates each chunk sequentially in row order, and folds the chunk
//!   partials sequentially in chunk-index order — the sorted reduction. The
//!   result is bit-identical across runs, machines, and rayon pool sizes.
//! - [`recenter_moment_table`] evaluates the shift in ONE fixed expression
//!   order (documented at the site).
//! - [`merge_moment_tables`] canonically orients its operands by the
//!   lexicographic total order on centers (`f64::total_cmp` per coordinate),
//!   so `a ⊕ b` and `b ⊕ a` execute the SAME instruction stream and are
//!   bit-identical for arbitrary inputs.
//!
//! Cross-GROUPING bit-identity — `(A⊕B)⊕C` vs `A⊕(B⊕C)` — additionally
//! requires the moment arithmetic itself to be exact; the in-module tests
//! pin it on dyadic lattices (integer coordinates/channels, dyadic weights),
//! where every product and sum is exactly representable, and callers
//! reducing many chunks get run-to-run determinism by folding in chunk-index
//! order exactly as the accumulator does.
//!
//! # 1:1 contract with `assemble_weighted_forms`
//!
//! [`jet_sufficient_stats`] reproduces, in closed form from a stored table
//! whose weights were computed for the same center and scale, exactly the
//! local-fit quantities the current workhorse
//! (`measure_jet_smooth.rs::assemble_weighted_forms`) computes from raw
//! points per (center, scale) block: the kernel mass `q`, the dimensionless
//! weighted feature mean `a_mean`, the dimensionless slope Gram
//! `G = Φ̃ᵀWΦ̃/q`, the weighted channel mean `uᵀv`, and the exact-projection
//! right-hand side `Bᵀv/q` — so the substrate can later replace that
//! same-center point loop without changing a single number.

use std::cmp::Ordering;
use std::ops::Range;

use ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use rayon::prelude::*;

use super::BasisError;

/// Rows per chunk in the streaming accumulation fan-out. Fixed (never
/// derived from the thread count) so the chunk partition — and therefore the
/// sorted-reduction bit pattern — is invariant across machines and rayon
/// pool sizes. Sized like the design evaluators' streaming blocks: large
/// enough to amortize per-chunk setup, small enough that per-chunk partial
/// tables stay cache-resident for the d ≤ 8 regimes the jet order targets.
pub(crate) const MEASURE_JET_MOMENT_CHUNK_ROWS: usize = 8192;

/// Per-cell moment table: Gaussian-weighted coordinate moments of orders
/// 0..=2 crossed with response channels, all centered at the cell's
/// reference point `c`.
///
/// Channel convention: channel 0 is the UNIT channel (`g ≡ 1`); further
/// channels carry responses (`y`, and later `y²`, PIRLS working `z`, `w` per
/// the frame notes). The table itself never enforces the convention — it
/// aggregates whatever the caller hands it — but [`jet_sufficient_stats`]
/// reads `q`, `a_mean`, and the Gram off channel 0.
///
/// `m2` is stored as the full (symmetric-by-construction) `d×d` second
/// moment per channel.
#[derive(Debug, Clone, PartialEq)]
pub struct MeasureJetMomentTable {
    /// Reference point `c` (length `d`).
    pub center: Array1<f64>,
    /// Per channel: `Σ_i w_i g_i`.
    pub m0: Array1<f64>,
    /// Per channel × d: `Σ_i w_i g_i (x_i − c)`.
    pub m1: Array2<f64>,
    /// Per channel: `d×d` matrix `Σ_i w_i g_i (x_i − c)(x_i − c)ᵀ`.
    pub m2: Vec<Array2<f64>>,
}

impl MeasureJetMomentTable {
    /// The monoid identity at `center`: an all-zero table over `n_channels`
    /// channels. Merging it (at ANY center) into another table leaves that
    /// table's moments unchanged up to the exact zero shift.
    pub fn zero(center: Array1<f64>, n_channels: usize) -> Self {
        let d = center.len();
        Self {
            center,
            m0: Array1::zeros(n_channels),
            m1: Array2::zeros((n_channels, d)),
            m2: (0..n_channels).map(|_| Array2::zeros((d, d))).collect(),
        }
    }

    /// Ambient dimension `d` of the cell.
    pub fn dim(&self) -> usize {
        self.center.len()
    }

    /// Number of response channels stored (channel 0 = unit by convention).
    pub fn n_channels(&self) -> usize {
        self.m0.len()
    }
}

/// Shape/finiteness self-consistency of a (publicly constructible) table:
/// returns `(n_channels, d)`. Single validation source for the fallible
/// consumers ([`merge_moment_tables`], [`jet_sufficient_stats`]).
pub(crate) fn validate_table_shape(
    t: &MeasureJetMomentTable,
    label: &str,
) -> Result<(usize, usize), BasisError> {
    let d = t.center.len();
    let n_channels = t.m0.len();
    if t.center.iter().any(|v| !v.is_finite()) {
        crate::bail_invalid_basis!("measure-jet moment table `{label}` has a non-finite center");
    }
    if t.m1.dim() != (n_channels, d) {
        crate::bail_dim_basis!(
            "measure-jet moment table `{label}` m1 shape {:?} does not match (channels, d) = ({n_channels}, {d})",
            t.m1.dim()
        );
    }
    if t.m2.len() != n_channels {
        crate::bail_dim_basis!(
            "measure-jet moment table `{label}` has {} m2 blocks for {n_channels} channels",
            t.m2.len()
        );
    }
    for (ch, block) in t.m2.iter().enumerate() {
        if block.dim() != (d, d) {
            crate::bail_dim_basis!(
                "measure-jet moment table `{label}` m2[{ch}] shape {:?} is not ({d}, {d})",
                block.dim()
            );
        }
    }
    Ok((n_channels, d))
}

/// Sequential moment accumulation over one row chunk, in row order. The
/// per-entry update order is fixed — `wg = w·g`, then `m1 += wg·dx_k`, then
/// `m2 += (wg·dx_k)·dx_l` with that exact association — as part of the
/// module's bit-determinism contract.
pub(crate) fn accumulate_chunk(
    coords: ArrayView2<'_, f64>,
    weights: ArrayView1<'_, f64>,
    channels: &[ArrayView1<'_, f64>],
    center: ArrayView1<'_, f64>,
    rows: Range<usize>,
) -> Result<(Array1<f64>, Array2<f64>, Vec<Array2<f64>>), BasisError> {
    let d = center.len();
    let n_channels = channels.len();
    let mut m0 = Array1::<f64>::zeros(n_channels);
    let mut m1 = Array2::<f64>::zeros((n_channels, d));
    let mut m2: Vec<Array2<f64>> = (0..n_channels).map(|_| Array2::zeros((d, d))).collect();
    let mut dx = vec![0.0_f64; d];
    for r in rows {
        let w = weights[r];
        if !(w.is_finite() && w >= 0.0) {
            crate::bail_invalid_basis!(
                "measure-jet moment accumulation needs finite nonnegative weights; got {w} at row {r}"
            );
        }
        for k in 0..d {
            let x = coords[(r, k)];
            if !x.is_finite() {
                crate::bail_invalid_basis!(
                    "measure-jet moment accumulation hit a non-finite coordinate at row {r}, axis {k}"
                );
            }
            dx[k] = x - center[k];
        }
        for (ch, g) in channels.iter().enumerate() {
            let gv = g[r];
            if !gv.is_finite() {
                crate::bail_invalid_basis!(
                    "measure-jet moment accumulation hit a non-finite channel value at row {r}, channel {ch}"
                );
            }
            let wg = w * gv;
            m0[ch] += wg;
            let m2_ch = &mut m2[ch];
            for k in 0..d {
                let wg_dk = wg * dx[k];
                m1[(ch, k)] += wg_dk;
                for l in 0..d {
                    m2_ch[(k, l)] += wg_dk * dx[l];
                }
            }
        }
    }
    Ok((m0, m1, m2))
}

/// Accumulate one cell's moment table from raw rows. The single point where
/// data rows are read; everything downstream is closed-form algebra on the
/// result.
///
/// `weights` are the caller-computed Gaussian kernel weights
/// `w_i = mass_i · exp(−‖x_i − c‖²/(2ε²))` (or their truncated variant — the
/// cutoff and its `e^{−ρ²/2}` budget are the caller's responsibility);
/// `channels` are the per-row response channels `g_i`, typically
/// `[ones, y]`, with channel 0 = unit by convention.
///
/// Streaming/parallel layout: rows are split into fixed
/// [`MEASURE_JET_MOMENT_CHUNK_ROWS`]-sized chunks (the bms-style chunked row
/// reduction), each chunk is accumulated sequentially in row order, and the
/// chunk partials are folded sequentially in chunk-index order — the sorted
/// reduction that makes the output bit-deterministic regardless of thread
/// scheduling. `rows == 0` is allowed and yields the monoid identity at
/// `center`.
pub fn accumulate_moment_table(
    coords: ArrayView2<'_, f64>,
    weights: ArrayView1<'_, f64>,
    channels: &[ArrayView1<'_, f64>],
    center: ArrayView1<'_, f64>,
) -> Result<MeasureJetMomentTable, BasisError> {
    let n = coords.nrows();
    let d = coords.ncols();
    if d == 0 {
        crate::bail_invalid_basis!(
            "measure-jet moment accumulation needs at least one coordinate axis"
        );
    }
    if center.len() != d {
        crate::bail_dim_basis!(
            "measure-jet moment center length {} does not match coordinate dimension {d}",
            center.len()
        );
    }
    if center.iter().any(|v| !v.is_finite()) {
        crate::bail_invalid_basis!("measure-jet moment accumulation needs a finite center");
    }
    if weights.len() != n {
        crate::bail_dim_basis!(
            "measure-jet moment weights length {} does not match {n} rows",
            weights.len()
        );
    }
    if channels.is_empty() {
        crate::bail_invalid_basis!(
            "measure-jet moment accumulation needs at least one response channel (channel 0 = unit)"
        );
    }
    for (ch, g) in channels.iter().enumerate() {
        if g.len() != n {
            crate::bail_dim_basis!(
                "measure-jet moment channel {ch} length {} does not match {n} rows",
                g.len()
            );
        }
    }
    let n_chunks = n.div_ceil(MEASURE_JET_MOMENT_CHUNK_ROWS).max(1);
    let partials: Vec<(Array1<f64>, Array2<f64>, Vec<Array2<f64>>)> = if n_chunks == 1 {
        vec![accumulate_chunk(coords, weights, channels, center, 0..n)?]
    } else {
        (0..n_chunks)
            .into_par_iter()
            .map(|chunk| {
                let start = chunk * MEASURE_JET_MOMENT_CHUNK_ROWS;
                let end = (start + MEASURE_JET_MOMENT_CHUNK_ROWS).min(n);
                accumulate_chunk(coords, weights, channels, center, start..end)
            })
            .collect::<Result<Vec<_>, BasisError>>()?
    };
    // Sorted reduction: fold chunk partials in chunk-index order. All
    // partials share `center`, so the fold is plain componentwise addition.
    let mut iter = partials.into_iter();
    let (mut m0, mut m1, mut m2) = iter
        .next()
        .expect("chunk count is clamped to at least one partial");
    for (p0, p1, p2) in iter {
        m0 += &p0;
        m1 += &p1;
        for (total, part) in m2.iter_mut().zip(&p2) {
            *total += part;
        }
    }
    Ok(MeasureJetMomentTable {
        center: center.to_owned(),
        m0,
        m1,
        m2,
    })
}

/// Exact recentering via the binomial shift: the same frozen-weight
/// polynomial table re-expressed about `new_center`, with no kernel
/// re-evaluation. This is not a moving-kernel identity; if the Gaussian
/// center changes, the caller must recompute or approximate the weights.
///
/// Derivation (per channel; write `Δ = c − c′` so `x − c′ = (x − c) + Δ`):
///
/// - order 0: `μ′_0 = Σ w g = μ_0` — unchanged;
/// - order 1: `μ′_1 = Σ w g ((x−c) + Δ) = μ_1 + Δ·μ_0`;
/// - order 2: `μ′_2 = Σ w g ((x−c)+Δ)((x−c)+Δ)ᵀ
///                 = μ_2 + Δ·μ_1ᵀ + μ_1·Δᵀ + ΔΔᵀ·μ_0`,
///
/// which is the multi-index binomial identity
/// `μ′_α = Σ_{β≤α} C(α,β)(c−c′)^{α−β} μ_β` specialized to `|α| ≤ 2`. Every
/// term is a finite product of stored moments and `Δ`, so the shift is an
/// algebraic identity of the frozen-weight table — exact up to floating-point
/// rounding, and exactly exact whenever the arithmetic is (dyadic lattices;
/// pinned in the tests).
///
/// Bit-determinism: the order-2 entry is evaluated in the ONE fixed order
/// `((μ_2 + Δ_k·μ_{1,l}) + μ_{1,k}·Δ_l) + (Δ_k·Δ_l)·μ_0`; same inputs always
/// produce the same bits.
pub fn recenter_moment_table(
    t: &MeasureJetMomentTable,
    new_center: ArrayView1<'_, f64>,
) -> MeasureJetMomentTable {
    let d = t.center.len();
    assert_eq!(
        new_center.len(),
        d,
        "measure-jet recenter: new center length {} does not match table dimension {d}",
        new_center.len()
    );
    let n_channels = t.m0.len();
    let mut delta = Array1::<f64>::zeros(d);
    for k in 0..d {
        delta[k] = t.center[k] - new_center[k];
    }
    let m0 = t.m0.clone();
    let mut m1 = Array2::<f64>::zeros((n_channels, d));
    for ch in 0..n_channels {
        for k in 0..d {
            m1[(ch, k)] = t.m1[(ch, k)] + delta[k] * t.m0[ch];
        }
    }
    let mut m2 = Vec::with_capacity(n_channels);
    for ch in 0..n_channels {
        let src = &t.m2[ch];
        let mut out = Array2::<f64>::zeros((d, d));
        for k in 0..d {
            for l in 0..d {
                out[(k, l)] = ((src[(k, l)] + delta[k] * t.m1[(ch, l)]) + t.m1[(ch, k)] * delta[l])
                    + (delta[k] * delta[l]) * t.m0[ch];
            }
        }
        m2.push(out);
    }
    MeasureJetMomentTable {
        center: new_center.to_owned(),
        m0,
        m1,
        m2,
    }
}

/// Lexicographic total order on cell centers (`f64::total_cmp` per
/// coordinate). The canonical-orientation key that makes the merge bitwise
/// argument-order-independent.
pub(crate) fn lex_cmp_centers(a: &Array1<f64>, b: &Array1<f64>) -> Ordering {
    for (x, y) in a.iter().zip(b.iter()) {
        let ord = x.total_cmp(y);
        if ord != Ordering::Equal {
            return ord;
        }
    }
    Ordering::Equal
}

/// Monoid merge: recenter compatible frozen-weight tables onto a common
/// reference, then add componentwise. Exact for those polynomial moments
/// (pure binomial shift, no kernel re-evaluation) and deterministic.
///
/// Canonical orientation: the merged table lives at the lexicographically
/// SMALLER of the two operand centers ([`lex_cmp_centers`]), and the other
/// operand is the one recentered. Because the (host, guest) roles depend
/// only on the centers — never on argument position — `merge(a, b)` and
/// `merge(b, a)` execute identical arithmetic and agree BITWISE for
/// arbitrary inputs (IEEE addition is commutative; only grouping is not).
/// This is a deliberate strengthening of the naive "recenter `b` onto
/// `a.center`" rule, which is only commutative up to a recentering.
pub fn merge_moment_tables(
    a: &MeasureJetMomentTable,
    b: &MeasureJetMomentTable,
) -> Result<MeasureJetMomentTable, BasisError> {
    let (a_channels, a_dim) = validate_table_shape(a, "a")?;
    let (b_channels, b_dim) = validate_table_shape(b, "b")?;
    if a_dim != b_dim || a_channels != b_channels {
        crate::bail_dim_basis!(
            "measure-jet merge needs matching tables; got (channels, d) = ({a_channels}, {a_dim}) vs ({b_channels}, {b_dim})"
        );
    }
    let (host, guest) = if lex_cmp_centers(&a.center, &b.center) != Ordering::Greater {
        (a, b)
    } else {
        (b, a)
    };
    let moved = recenter_moment_table(guest, host.center.view());
    let mut m2 = Vec::with_capacity(a_channels);
    for (h, g) in host.m2.iter().zip(&moved.m2) {
        m2.push(h + g);
    }
    Ok(MeasureJetMomentTable {
        center: host.center.clone(),
        m0: &host.m0 + &moved.m0,
        m1: &host.m1 + &moved.m1,
        m2,
    })
}

/// The local jet-fit sufficient statistics read off one table — exactly the
/// per-block quantities `assemble_weighted_forms` (measure_jet_smooth.rs)
/// computes from raw points when the table weights are frozen at the same
/// center and scale, reproduced in closed form from stored moments.
#[derive(Debug, Clone, PartialEq)]
pub struct MeasureJetJetStats {
    /// Kernel mass `q = Σ w_i` (unit-channel zeroth moment).
    pub q: f64,
    /// Weighted mean of the requested value channel: `uᵀv = m0[ch]/q`.
    pub mean: f64,
    /// Dimensionless slope Gram `G = Φ̃ᵀWΦ̃/q = m2[0]/(qε²) − ā·āᵀ` with
    /// `ā = m1[0]/(qε)` (`Φ` rows are `(x_i − c)/ε`).
    pub gram: Array2<f64>,
    /// Local-fit right-hand side `Bᵀv/q = m1[ch]/(qε) − ā·(m0[ch]/q)` — the
    /// vector the exact weighted affine projection consumes.
    pub cross: Array1<f64>,
}

/// Read the local jet-fit sufficient statistics off a moment table at scale
/// `eps`, for value channel `channel`.
///
/// 1:1 with `assemble_weighted_forms`' per-block math (its symbols on the
/// right), under the energy convention that local features are the
/// ε-SCALED offsets `Φ_{jk} = (x_{jk} − c_k)/ε`:
///
/// - `q     = m0[0]`                          ↔ `q = Σ_j w_j`,
/// - `ā_k   = m1[0,k]/(q·ε)`                  ↔ `a_mean = Φᵀw/q`,
/// - `G_kl  = m2[0][k,l]/(q·ε²) − ā_k·ā_l`    ↔ `G = (ΦᵀWΦ)/q − a·aᵀ`,
/// - `mean  = m0[ch]/q`                       ↔ `uᵀv` (the weighted-centering
///   projection `Cv = v − (uᵀv)·1` of the constant-annihilation contract),
/// - `cross_k = m1[ch,k]/(q·ε) − ā_k·mean`    ↔ `Bᵀv/q` with
///   `B = WΦ − w·aᵀ` (column-centering makes `Φ̃ᵀW·1 = 0`, so
///   `Φ̃ᵀWCv/q = Bᵀv/q` — the exact RHS of the local affine projection).
///
/// For `channel == 0` (the unit channel) `mean` is exactly `1.0` and `cross`
/// is identically `+0.0` (the same division is subtracted from itself) —
/// the moment-level restatement of exact constant annihilation.
pub fn jet_sufficient_stats(
    t: &MeasureJetMomentTable,
    eps: f64,
    channel: usize,
) -> Result<MeasureJetJetStats, BasisError> {
    let (n_channels, d) = validate_table_shape(t, "t")?;
    if !(eps.is_finite() && eps > 0.0) {
        crate::bail_invalid_basis!(
            "measure-jet jet stats need a finite positive scale eps; got {eps}"
        );
    }
    if channel >= n_channels {
        crate::bail_invalid_basis!(
            "measure-jet jet stats channel {channel} out of range for {n_channels} channels"
        );
    }
    let q = t.m0[0];
    if !(q.is_finite() && q > 0.0) {
        crate::bail_invalid_basis!(
            "measure-jet jet stats need positive unit-channel kernel mass q; got {q}"
        );
    }
    let q_eps = q * eps;
    let mut a_mean = Array1::<f64>::zeros(d);
    for k in 0..d {
        a_mean[k] = t.m1[(0, k)] / q_eps;
    }
    let q_eps2 = q * eps * eps;
    let m2_unit = &t.m2[0];
    let mut gram = Array2::<f64>::zeros((d, d));
    for k in 0..d {
        for l in 0..d {
            gram[(k, l)] = m2_unit[(k, l)] / q_eps2 - a_mean[k] * a_mean[l];
        }
    }
    let mean = t.m0[channel] / q;
    let mut cross = Array1::<f64>::zeros(d);
    for k in 0..d {
        cross[k] = t.m1[(channel, k)] / q_eps - a_mean[k] * mean;
    }
    Ok(MeasureJetJetStats {
        q,
        mean,
        gram,
        cross,
    })
}

#[cfg(test)]
mod tests {
    use super::*;
    use ndarray::s;

    /// Closeness metric for the recenter-exactness gate: relative at scale,
    /// absolute `tol` below unit scale (`|x−y| ≤ tol·(1 + max(|x|,|y|))`).
    pub(crate) fn assert_tables_close(
        a: &MeasureJetMomentTable,
        b: &MeasureJetMomentTable,
        tol: f64,
    ) {
        let pairs = |xs: &[f64], ys: &[f64], label: &str| {
            assert_eq!(xs.len(), ys.len(), "{label}: length mismatch");
            for (i, (x, y)) in xs.iter().zip(ys.iter()).enumerate() {
                let scale = 1.0 + x.abs().max(y.abs());
                assert!(
                    (x - y).abs() <= tol * scale,
                    "{label}[{i}]: {x} vs {y} differ beyond {tol} rel"
                );
            }
        };
        pairs(
            a.center.as_slice().expect("contiguous center"),
            b.center.as_slice().expect("contiguous center"),
            "center",
        );
        pairs(
            a.m0.as_slice().expect("contiguous m0"),
            b.m0.as_slice().expect("contiguous m0"),
            "m0",
        );
        pairs(
            a.m1.as_slice().expect("contiguous m1"),
            b.m1.as_slice().expect("contiguous m1"),
            "m1",
        );
        assert_eq!(a.m2.len(), b.m2.len(), "m2: channel count mismatch");
        for (ch, (x, y)) in a.m2.iter().zip(b.m2.iter()).enumerate() {
            pairs(
                x.as_slice().expect("contiguous m2"),
                y.as_slice().expect("contiguous m2"),
                &format!("m2[{ch}]"),
            );
        }
    }

    /// Bit-identity gate: every stored f64 must agree by `to_bits`.
    pub(crate) fn assert_tables_bit_identical(
        a: &MeasureJetMomentTable,
        b: &MeasureJetMomentTable,
    ) {
        let bits = |xs: &[f64], ys: &[f64], label: &str| {
            assert_eq!(xs.len(), ys.len(), "{label}: length mismatch");
            for (i, (x, y)) in xs.iter().zip(ys.iter()).enumerate() {
                assert_eq!(
                    x.to_bits(),
                    y.to_bits(),
                    "{label}[{i}]: {x} vs {y} differ bitwise"
                );
            }
        };
        bits(
            a.center.as_slice().expect("contiguous center"),
            b.center.as_slice().expect("contiguous center"),
            "center",
        );
        bits(
            a.m0.as_slice().expect("contiguous m0"),
            b.m0.as_slice().expect("contiguous m0"),
            "m0",
        );
        bits(
            a.m1.as_slice().expect("contiguous m1"),
            b.m1.as_slice().expect("contiguous m1"),
            "m1",
        );
        assert_eq!(a.m2.len(), b.m2.len(), "m2: channel count mismatch");
        for (ch, (x, y)) in a.m2.iter().zip(b.m2.iter()).enumerate() {
            bits(
                x.as_slice().expect("contiguous m2"),
                y.as_slice().expect("contiguous m2"),
                &format!("m2[{ch}]"),
            );
        }
    }

    /// Deterministic generic-float dataset (no RNG): low-discrepancy
    /// fractional parts, d = 3, with a unit channel and one value channel.
    pub(crate) fn float_dataset(n: usize) -> (Array2<f64>, Array1<f64>, Array1<f64>, Array1<f64>) {
        let mut coords = Array2::<f64>::zeros((n, 3));
        let mut weights = Array1::<f64>::zeros(n);
        let mut ones = Array1::<f64>::zeros(n);
        let mut y = Array1::<f64>::zeros(n);
        for i in 0..n {
            let t = (i + 1) as f64;
            coords[(i, 0)] = (t * 0.618034).fract() * 4.0 - 2.0;
            coords[(i, 1)] = (t * 0.414214).fract() * 3.0 - 1.0;
            coords[(i, 2)] = (t * 0.732051).fract() * 2.0 - 1.5;
            weights[i] = 0.05 + (t * 0.292893).fract();
            ones[i] = 1.0;
            y[i] = (t * 0.539345).fract() * 6.0 - 3.0;
        }
        (coords, weights, ones, y)
    }

    /// Dyadic-lattice dataset: integer coordinates and channel values,
    /// dyadic weights — every moment product and sum is exactly
    /// representable in f64, so the algebraic monoid laws become BIT
    /// identities and the tests below can pin them with `to_bits`.
    pub(crate) fn dyadic_dataset() -> (Array2<f64>, Array1<f64>, Array1<f64>, Array1<f64>) {
        let coords = ndarray::array![
            [3.0, -2.0],
            [1.0, 4.0],
            [-5.0, 2.0],
            [2.0, 2.0],
            [4.0, -1.0],
            [0.0, 5.0],
            [-3.0, -4.0],
            [6.0, 1.0],
            [-1.0, 3.0],
            [5.0, -3.0],
            [2.0, 7.0],
            [-6.0, -2.0],
            [3.0, 3.0],
            [1.0, -5.0],
            [4.0, 6.0],
            [-2.0, -3.0],
        ];
        let weights = ndarray::array![
            0.5, 1.0, 2.0, 0.25, 1.5, 0.75, 1.0, 0.5, 2.5, 1.25, 0.5, 3.0, 0.75, 1.0, 1.75, 2.0
        ];
        let ones = Array1::<f64>::ones(16);
        let y = ndarray::array![
            2.0, -3.0, 5.0, 1.0, -4.0, 7.0, 2.0, -6.0, 3.0, 4.0, -2.0, 8.0, 1.0, -7.0, 5.0, -1.0
        ];
        (coords, weights, ones, y)
    }

    #[test]
    pub(crate) fn recenter_is_exact() {
        let (coords, weights, ones, y) = float_dataset(40);
        let channels = [ones.view(), y.view()];
        let c = ndarray::array![0.4, -0.3, 0.9];
        let c_prime = ndarray::array![-1.1, 0.25, 0.5];
        let at_c = accumulate_moment_table(coords.view(), weights.view(), &channels, c.view())
            .expect("accumulation about c");
        let shifted = recenter_moment_table(&at_c, c_prime.view());
        let direct =
            accumulate_moment_table(coords.view(), weights.view(), &channels, c_prime.view())
                .expect("accumulation about c'");
        assert_tables_close(&shifted, &direct, 1e-14);
        // Round trip back to c reproduces the original to the same gate.
        let back = recenter_moment_table(&shifted, c.view());
        assert_tables_close(&back, &at_c, 1e-14);
    }

    #[test]
    pub(crate) fn merge_is_associative_and_commutative_bitwise() {
        // Dyadic lattice ⇒ all moment/shift arithmetic is exact, so the
        // monoid laws hold BITWISE across groupings (the sorted-reduction
        // convention covers generic-float grouping determinism; see the
        // module docs).
        let (coords, weights, ones, y) = dyadic_dataset();
        let chunk = |rows: Range<usize>, center: &Array1<f64>| {
            let ones_c = ones.slice(s![rows.clone()]);
            let y_c = y.slice(s![rows.clone()]);
            accumulate_moment_table(
                coords.slice(s![rows.clone(), ..]),
                weights.slice(s![rows]),
                &[ones_c, y_c],
                center.view(),
            )
            .expect("chunk accumulation")
        };
        let c_a = ndarray::array![2.0, -1.0];
        let c_b = ndarray::array![0.0, 3.0];
        let c_c = ndarray::array![-4.0, 1.0];
        let a = chunk(0..5, &c_a);
        let b = chunk(5..9, &c_b);
        let c = chunk(9..14, &c_c);

        // Commutativity is bitwise for ARBITRARY inputs: the canonical
        // center orientation makes merge(a, b) and merge(b, a) execute
        // identical arithmetic. No recentering needed before comparing.
        let ab = merge_moment_tables(&a, &b).expect("a+b");
        let ba = merge_moment_tables(&b, &a).expect("b+a");
        assert_tables_bit_identical(&ab, &ba);
        // ... including on generic (non-dyadic) float data.
        let (fc, fw, fo, fy) = float_dataset(24);
        let fa = accumulate_moment_table(
            fc.slice(s![0..12, ..]),
            fw.slice(s![0..12]),
            &[fo.slice(s![0..12]), fy.slice(s![0..12])],
            ndarray::array![0.3, -0.7, 0.1].view(),
        )
        .expect("float chunk a");
        let fb = accumulate_moment_table(
            fc.slice(s![12..24, ..]),
            fw.slice(s![12..24]),
            &[fo.slice(s![12..24]), fy.slice(s![12..24])],
            ndarray::array![-0.9, 0.4, 0.6].view(),
        )
        .expect("float chunk b");
        assert_tables_bit_identical(
            &merge_moment_tables(&fa, &fb).expect("fa+fb"),
            &merge_moment_tables(&fb, &fa).expect("fb+fa"),
        );

        // Associativity, bitwise on the exact lattice.
        let ab_c = merge_moment_tables(&ab, &c).expect("(a+b)+c");
        let bc = merge_moment_tables(&b, &c).expect("b+c");
        let a_bc = merge_moment_tables(&a, &bc).expect("a+(b+c)");
        assert_tables_bit_identical(&ab_c, &a_bc);
        // And after recentering both to a common reference.
        let c_ref = ndarray::array![1.0, 2.0];
        assert_tables_bit_identical(
            &recenter_moment_table(&ab_c, c_ref.view()),
            &recenter_moment_table(&a_bc, c_ref.view()),
        );
    }

    #[test]
    pub(crate) fn jet_stats_match_assemble_weighted_forms_math() {
        // Small 2-D point set, replicating assemble_weighted_forms' local
        // loop verbatim from raw points: w_j = mass_j·exp(−d²/(2ε²)),
        // q = Σ w, Φ_{jk} = (x_{jk} − c_k)/ε, a = Φᵀw/q,
        // G = (ΦᵀWΦ)/q − a·aᵀ, uᵀv = wᵀv/q, Bᵀv/q with B = WΦ − w·aᵀ.
        let pts = ndarray::array![
            [0.0, 0.0],
            [0.45, -0.2],
            [-0.35, 0.4],
            [0.25, 0.55],
            [-0.5, -0.45],
            [0.6, 0.3]
        ];
        let masses = ndarray::array![0.22, 0.13, 0.19, 0.11, 0.2, 0.15];
        let y = ndarray::array![0.7, -1.3, 2.1, 0.4, -0.6, 1.9];
        let center = ndarray::array![0.0, 0.0];
        let eps = 0.75;
        let m = pts.nrows();
        let d = pts.ncols();

        // Kernel weights exactly as the workhorse forms them.
        let inv_two_eps2 = 1.0 / (2.0 * eps * eps);
        let mut w = Array1::<f64>::zeros(m);
        let mut q = 0.0_f64;
        for j in 0..m {
            let mut dist2 = 0.0_f64;
            for k in 0..d {
                let dlt = pts[(j, k)] - center[k];
                dist2 += dlt * dlt;
            }
            w[j] = masses[j] * (-dist2 * inv_two_eps2).exp();
            q += w[j];
        }
        let mut phi = Array2::<f64>::zeros((m, d));
        for j in 0..m {
            for k in 0..d {
                phi[(j, k)] = (pts[(j, k)] - center[k]) / eps;
            }
        }
        let a_mean = phi.t().dot(&w) / q;
        let mut wphi = phi.clone();
        for (j, mut row) in wphi.outer_iter_mut().enumerate() {
            row.mapv_inplace(|v| v * w[j]);
        }
        let mut g_ref = phi.t().dot(&wphi);
        g_ref.mapv_inplace(|v| v / q);
        for r in 0..d {
            for c in 0..d {
                g_ref[(r, c)] -= a_mean[r] * a_mean[c];
            }
        }
        let mean_ref = w.dot(&y) / q;
        let mut cross_ref = Array1::<f64>::zeros(d);
        for k in 0..d {
            let mut acc = 0.0_f64;
            for j in 0..m {
                acc += (wphi[(j, k)] - w[j] * a_mean[k]) * y[j];
            }
            cross_ref[k] = acc / q;
        }

        // Substrate path: same caller-computed weights into a moment table.
        let ones = Array1::<f64>::ones(m);
        let table = accumulate_moment_table(
            pts.view(),
            w.view(),
            &[ones.view(), y.view()],
            center.view(),
        )
        .expect("moment table");
        let stats = jet_sufficient_stats(&table, eps, 1).expect("jet stats");

        let tol = 1e-14;
        let close = |x: f64, y_: f64, label: &str| {
            let scale = 1.0 + x.abs().max(y_.abs());
            assert!(
                (x - y_).abs() <= tol * scale,
                "{label}: {x} vs {y_} beyond {tol} rel"
            );
        };
        close(stats.q, q, "q");
        close(stats.mean, mean_ref, "mean");
        for k in 0..d {
            close(stats.cross[k], cross_ref[k], &format!("cross[{k}]"));
            for l in 0..d {
                close(stats.gram[(k, l)], g_ref[(k, l)], &format!("gram[{k},{l}]"));
            }
        }

        // Unit channel: exact constant annihilation at the moment level —
        // mean is exactly 1, cross is identically +0.0.
        let unit_stats = jet_sufficient_stats(&table, eps, 0).expect("unit-channel stats");
        assert_eq!(unit_stats.mean, 1.0, "unit-channel mean must be exactly 1");
        for k in 0..d {
            assert_eq!(
                unit_stats.cross[k], 0.0,
                "unit-channel cross[{k}] must be exactly zero"
            );
        }
    }

    /// LEVEL/TILT truth-recovery gate (#1041). The deficit pattern flagged in
    /// the 8-dataset benchmark — worst on pooled/pointwise risk (RMSE/Brier/R²)
    /// but only mid-pack on calibration SLOPE — is the fingerprint of a biased
    /// affine projection: a systematic shift in the recovered LEVEL `c₀` or a
    /// TILT in the recovered gradient `g`. The local affine sufficient statistic
    /// this module computes (`mean`, `G`, `cross`) is the exact object that
    /// projection consumes, so a bias there would surface here.
    ///
    /// Construct a channel value that is EXACTLY affine in the coordinates,
    /// `v(x) = c₀ + gᵀ(x − center)`, under ARBITRARY (non-symmetric) weights.
    /// The weighted affine projection must then recover `(c₀, g)` with ZERO
    /// residual — the curved/higher-order energy is empty, so any nonzero level
    /// or tilt error is pure projection bias, not a smoothing artifact. We
    /// assert this across SHRINKING kernel widths ε (concentrating the weights),
    /// the regime where a level/tilt bias in the centered second moment `G` or
    /// the centered cross `Bᵀv/q` would be amplified.
    #[test]
    pub(crate) fn affine_projection_recovers_level_and_tilt_without_bias() {
        // Asymmetric, off-center point cloud so the weighted barycenter does
        // NOT coincide with the reference center: this is exactly where a
        // mis-centered (biased) projection would leak the level into the tilt
        // and vice versa.
        let pts = ndarray::array![
            [0.10, -0.30],
            [0.62, 0.05],
            [-0.18, 0.44],
            [0.37, 0.51],
            [-0.46, -0.22],
            [0.71, 0.33],
            [0.05, 0.62],
            [-0.33, 0.14],
        ];
        // Strictly positive, deliberately uneven masses (no symmetry to lean on).
        let masses = ndarray::array![0.31, 0.07, 0.22, 0.05, 0.19, 0.11, 0.27, 0.13];
        let center = ndarray::array![0.05, 0.10];
        let m = pts.nrows();
        let d = pts.ncols();

        // Exact affine truth in ambient coordinates: level c0, gradient g.
        let c0 = 1.37_f64;
        let g = ndarray::array![-0.85_f64, 0.42_f64];
        let mut v = Array1::<f64>::zeros(m);
        for j in 0..m {
            let mut acc = c0;
            for k in 0..d {
                acc += g[k] * (pts[(j, k)] - center[k]);
            }
            v[j] = acc;
        }

        let ones = Array1::<f64>::ones(m);
        // Tighten the kernel across several scales: shrinking eps concentrates
        // the Gaussian weights and amplifies any centering/projection bias.
        for &eps in &[1.0_f64, 0.5, 0.25, 0.12, 0.06] {
            let inv_two_eps2 = 1.0 / (2.0 * eps * eps);
            let mut w = Array1::<f64>::zeros(m);
            for j in 0..m {
                let mut dist2 = 0.0_f64;
                for k in 0..d {
                    let dlt = pts[(j, k)] - center[k];
                    dist2 += dlt * dlt;
                }
                w[j] = masses[j] * (-dist2 * inv_two_eps2).exp();
            }

            let table = accumulate_moment_table(
                pts.view(),
                w.view(),
                &[ones.view(), v.view()],
                center.view(),
            )
            .expect("moment table");
            let stats = jet_sufficient_stats(&table, eps, 1).expect("affine jet stats");

            // The weighted affine projection solves `G b̂ = cross` for the
            // ε-scaled slope; the ambient gradient is b̂/ε and the recovered
            // LEVEL is `mean − āᵀ b̂` (the weighted mean minus the slope's
            // contribution at the weighted barycenter). For an exactly affine
            // truth both must equal the truth with zero residual.
            //
            // Solve the 2×2 SPD system directly (no external solver) so the
            // test pins the projection math, not a library inverse.
            let g00 = stats.gram[(0, 0)];
            let g01 = stats.gram[(0, 1)];
            let g11 = stats.gram[(1, 1)];
            let det = g00 * g11 - g01 * g01;
            assert!(
                det > 1e-10,
                "centered slope Gram must stay nondegenerate at eps={eps}; det={det}"
            );
            let b0 = (g11 * stats.cross[0] - g01 * stats.cross[1]) / det;
            let b1 = (-g01 * stats.cross[0] + g00 * stats.cross[1]) / det;
            // Ambient gradient = scaled slope / eps (Φ rows are (x−c)/ε).
            let grad = [b0 / eps, b1 / eps];

            // Recovered weighted barycenter offset ā (ambient) = a_mean·ε.
            // Level at the reference center = mean − gradᵀ·(barycenter − center)
            //                               = mean − (b̂ᵀ ā).
            let a_mean0 = table.m1[(0, 0)] / (stats.q * eps);
            let a_mean1 = table.m1[(0, 1)] / (stats.q * eps);
            let level = stats.mean - (b0 * a_mean0 + b1 * a_mean1);

            // TILT: the recovered gradient must match the truth — no systematic
            // rotation/scaling of the slope channel.
            assert!(
                (grad[0] - g[0]).abs() <= 1e-9 && (grad[1] - g[1]).abs() <= 1e-9,
                "TILT bias at eps={eps}: recovered gradient {grad:?} vs truth {g:?}"
            );
            // LEVEL: the recovered intercept at the reference center must match
            // the truth — no systematic offset of the reconstructed surface.
            assert!(
                (level - c0).abs() <= 1e-9,
                "LEVEL bias at eps={eps}: recovered {level} vs truth {c0}"
            );
        }
    }

    #[test]
    pub(crate) fn streaming_chunked_accumulation_matches_single_pass() {
        // Four chunks, each accumulated about its OWN center, merged in
        // chunk-index order (the sorted reduction) — versus one pass about
        // the lexicographically smallest chunk center. Dyadic lattice ⇒ the
        // agreement is exact, pinned bitwise.
        let (coords, weights, ones, y) = dyadic_dataset();
        let centers = [
            ndarray::array![-3.0, 2.0], // lexicographic minimum: merge target
            ndarray::array![0.0, 0.0],
            ndarray::array![1.0, -5.0],
            ndarray::array![4.0, 1.0],
        ];
        let chunk = |rows: Range<usize>, center: &Array1<f64>| {
            let ones_c = ones.slice(s![rows.clone()]);
            let y_c = y.slice(s![rows.clone()]);
            accumulate_moment_table(
                coords.slice(s![rows.clone(), ..]),
                weights.slice(s![rows]),
                &[ones_c, y_c],
                center.view(),
            )
            .expect("chunk accumulation")
        };
        let t0 = chunk(0..4, &centers[0]);
        let t1 = chunk(4..8, &centers[1]);
        let t2 = chunk(8..12, &centers[2]);
        let t3 = chunk(12..16, &centers[3]);
        let merged = merge_moment_tables(
            &merge_moment_tables(&merge_moment_tables(&t0, &t1).expect("t0+t1"), &t2)
                .expect("(t0+t1)+t2"),
            &t3,
        )
        .expect("((t0+t1)+t2)+t3");
        let single = accumulate_moment_table(
            coords.view(),
            weights.view(),
            &[ones.view(), y.view()],
            centers[0].view(),
        )
        .expect("single pass");
        // The fold target is the lex-min center, so no final recentering is
        // even needed; pin that and the bitwise agreement.
        assert_tables_bit_identical(&merged, &single);
        // Merging the identity is a no-op.
        let with_zero =
            merge_moment_tables(&merged, &MeasureJetMomentTable::zero(centers[0].clone(), 2))
                .expect("merge with identity");
        assert_tables_bit_identical(&with_zero, &merged);
    }
}