antecedent-expr 0.6.0

//! Distribution providers for compiled expression evaluation.
//!
//! SPDX-License-Identifier: MIT OR Apache-2.0

use std::borrow::Borrow;
use std::collections::HashMap;
use std::fmt;
use std::hash::{Hash, Hasher};
use std::sync::{Arc, PoisonError, RwLock};

use antecedent_core::{Value, VariableId};

use crate::{DomainRef, InterventionAssignment};

/// Weighted quadrature nodes: assignment rows paired with integration weights.
pub type QuadratureNodes = Arc<[(Arc<[Value]>, f64)]>;

/// Shared cartesian support rows, as returned by [`DistributionProvider::support`].
type SupportRows = Arc<[Arc<[Value]>]>;

/// Evaluation context (optional posterior draw index).
#[derive(Clone, Copy, Debug, Default, Eq, PartialEq)]
pub struct EvalContext {
    /// Posterior draw index when evaluating against a draw-indexed provider.
    pub draw: Option<usize>,
}

/// Variable → value binding for density / outcome lookup.
#[derive(Clone, Debug, Default)]
pub struct Assignment {
    /// Sorted by variable id.
    entries: Vec<(VariableId, Value)>,
}

impl Assignment {
    /// Empty assignment.
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }

    /// Build from unsorted pairs (sorted + last-wins on duplicate vars).
    #[must_use]
    pub fn from_pairs(pairs: impl IntoIterator<Item = (VariableId, Value)>) -> Self {
        let mut entries: Vec<(VariableId, Value)> = pairs.into_iter().collect();
        entries.sort_by_key(|(v, _)| v.raw());
        entries.dedup_by_key(|(v, _)| *v);
        Self { entries }
    }

    /// Insert or replace a binding.
    pub fn set(&mut self, var: VariableId, value: Value) {
        match self.entries.binary_search_by_key(&var.raw(), |(v, _)| v.raw()) {
            Ok(i) => self.entries[i].1 = value,
            Err(i) => self.entries.insert(i, (var, value)),
        }
    }

    /// Borrow value for `var`, if present.
    #[must_use]
    pub fn get(&self, var: VariableId) -> Option<&Value> {
        self.entries
            .binary_search_by_key(&var.raw(), |(v, _)| v.raw())
            .ok()
            .map(|i| &self.entries[i].1)
    }

    /// All bindings, sorted.
    #[must_use]
    pub fn entries(&self) -> &[(VariableId, Value)] {
        &self.entries
    }

    /// Extend with another assignment (other wins on conflict).
    pub fn extend_from(&mut self, other: &Assignment) {
        for (v, val) in &other.entries {
            self.set(*v, val.clone());
        }
    }

    /// Remove the binding for `var`, returning the previous value (if any).
    ///
    /// Lets evaluators treat one shared assignment as a binding stack: scoped
    /// bindings are `set` on entry and removed (or restored) on exit instead
    /// of cloning the whole assignment per scope.
    pub fn remove(&mut self, var: VariableId) -> Option<Value> {
        match self.entries.binary_search_by_key(&var.raw(), |(v, _)| v.raw()) {
            Ok(i) => Some(self.entries.remove(i).1),
            Err(_) => None,
        }
    }

    /// Restrict to the given variables (order of `vars` preserved in returned values).
    pub fn values_for(&self, vars: &[VariableId]) -> Result<Vec<Value>, EvalError> {
        let mut out = Vec::with_capacity(vars.len());
        for &v in vars {
            let Some(val) = self.get(v) else {
                return Err(EvalError::MissingBinding(v));
            };
            out.push(val.clone());
        }
        Ok(out)
    }
}

/// Resolved distribution factor identity (no string keys).
#[derive(Clone, Debug)]
pub struct FactorSpec<'a> {
    /// Factor variables.
    pub variables: &'a [VariableId],
    /// Conditioning variables.
    pub conditioned_on: &'a [VariableId],
    /// Hard intervention assignments.
    pub intervention: &'a [InterventionAssignment],
    /// Observational vs interventional domain.
    pub domain: DomainRef,
}

/// Errors from compiling or evaluating causal expressions.
#[derive(Clone, Debug, Eq, PartialEq)]
#[non_exhaustive]
pub enum EvalError {
    /// Continuous `IntegralOut` without quadrature nodes and without discrete support.
    UnsupportedIntegralOut,
    /// Provider has no entry for the requested factor / assignment.
    MissingTableEntry,
    /// Required variable binding absent from the assignment.
    MissingBinding(VariableId),
    /// Provider reported empty support for a summed variable.
    EmptySupport(VariableId),
    /// Division by zero while evaluating a ratio.
    DivisionByZero,
    /// Posterior draw index out of range.
    DrawOutOfRange {
        /// Requested draw.
        draw: usize,
        /// Number of draws available.
        n_draws: usize,
    },
    /// Support row length does not match requested variable count.
    SupportShape {
        /// Expected arity.
        expected: usize,
        /// Actual arity.
        actual: usize,
    },
    /// Empirical provider used where posterior draws are required (or vice versa).
    ProviderKind(&'static str),
    /// Provider cannot answer a conditional query (non-empty `conditioned_on`) —
    /// e.g. an independent-factor provider that only models unconditional marginals.
    UnsupportedConditioning(&'static str),
}

impl fmt::Display for EvalError {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            Self::UnsupportedIntegralOut => {
                write!(f, "IntegralOut requires provider quadrature nodes or discrete support")
            }
            Self::MissingTableEntry => write!(f, "missing probability table entry"),
            Self::MissingBinding(v) => write!(f, "missing binding for V{}", v.raw()),
            Self::EmptySupport(v) => write!(f, "empty support for V{}", v.raw()),
            Self::DivisionByZero => write!(f, "division by zero in ratio"),
            Self::DrawOutOfRange { draw, n_draws } => {
                write!(f, "draw {draw} out of range (n_draws={n_draws})")
            }
            Self::SupportShape { expected, actual } => {
                write!(f, "support row arity {actual} != expected {expected}")
            }
            Self::ProviderKind(msg) | Self::UnsupportedConditioning(msg) => write!(f, "{msg}"),
        }
    }
}

impl std::error::Error for EvalError {}

/// Provides densities, discrete supports, and outcome values for evaluation.
pub trait DistributionProvider {
    /// Probability / density mass for a factor under an assignment.
    ///
    /// # Errors
    ///
    /// Missing table entries, bad draw index, or shape errors.
    fn probability(
        &self,
        spec: &FactorSpec<'_>,
        assignment: &Assignment,
        ctx: &EvalContext,
    ) -> Result<f64, EvalError>;

    /// Discrete support for variables (cartesian rows of values aligned to `vars`).
    ///
    /// # Errors
    ///
    /// Empty domains or unsupported queries.
    fn support(
        &self,
        vars: &[VariableId],
        ctx: &EvalContext,
    ) -> Result<Arc<[Arc<[Value]>]>, EvalError>;

    /// Optional continuous quadrature nodes `(assignment_row, Lebesgue weight)` for
    /// [`crate::ExprNode::IntegralOut`].
    ///
    /// Returning `Ok(None)` asks the evaluator to fall back to discrete [`Self::support`].
    ///
    /// # Errors
    ///
    /// Provider-specific continuous-integration failures.
    fn quadrature(
        &self,
        _vars: &[VariableId],
        _ctx: &EvalContext,
    ) -> Result<Option<QuadratureNodes>, EvalError> {
        Ok(None)
    }

    /// Outcome function value (identity: the bound value of `var`).
    ///
    /// # Errors
    ///
    /// Missing binding or non-numeric value.
    fn outcome(
        &self,
        var: VariableId,
        assignment: &Assignment,
        ctx: &EvalContext,
    ) -> Result<f64, EvalError>;

    /// Number of posterior draws, or `None` for a single empirical world.
    fn n_draws(&self) -> Option<usize>;
}

/// Canonical key for a factor table row.
///
/// `Hash`/`PartialEq` are defined via [`FactorKeyView`] (not derived) so the
/// owned key and the borrowed lookup view hash and compare identically — the
/// `Borrow` contract the allocation-free `probability` lookup relies on.
#[derive(Clone, Debug, Eq)]
struct FactorKey {
    variables: Arc<[VariableId]>,
    conditioned_on: Arc<[VariableId]>,
    intervention: Arc<[InterventionAssignment]>,
    domain: DomainRef,
    /// Concatenation of values for `variables` then `conditioned_on`.
    values: Arc<[Value]>,
}

/// Borrowed view of a [`FactorKey`], usable as a `HashMap` lookup key.
///
/// `probability` is the innermost evaluator call; building an owned
/// `FactorKey` there costs several `Arc` allocations per lookup. The view
/// borrows the spec slices directly and only needs the caller to assemble the
/// value row, so a lookup allocates that single row and nothing else.
#[derive(Clone, Copy)]
struct FactorKeyView<'a> {
    variables: &'a [VariableId],
    conditioned_on: &'a [VariableId],
    intervention: &'a [InterventionAssignment],
    domain: DomainRef,
    /// Concatenation of values for `variables` then `conditioned_on`.
    values: &'a [Value],
}

/// Unifies owned and borrowed factor keys behind one hash/equality identity,
/// so `HashMap<FactorKey, f64>::get` accepts a [`FactorKeyView`] through
/// `Borrow<dyn FactorKeyLookup>` without constructing a `FactorKey`.
trait FactorKeyLookup {
    fn view(&self) -> FactorKeyView<'_>;
}

impl FactorKeyLookup for FactorKey {
    fn view(&self) -> FactorKeyView<'_> {
        FactorKeyView {
            variables: &self.variables,
            conditioned_on: &self.conditioned_on,
            intervention: &self.intervention,
            domain: self.domain,
            values: &self.values,
        }
    }
}

impl FactorKeyLookup for FactorKeyView<'_> {
    fn view(&self) -> FactorKeyView<'_> {
        *self
    }
}

impl<'a> Borrow<dyn FactorKeyLookup + 'a> for FactorKey {
    fn borrow(&self) -> &(dyn FactorKeyLookup + 'a) {
        self
    }
}

/// Single hashing routine for both key forms; hashing slices (rather than the
/// `Arc` wrappers) keeps the streams identical for owned and borrowed keys.
fn hash_factor_view<H: Hasher>(v: &FactorKeyView<'_>, state: &mut H) {
    v.variables.hash(state);
    v.conditioned_on.hash(state);
    v.intervention.hash(state);
    v.domain.hash(state);
    v.values.hash(state);
}

impl Hash for FactorKey {
    fn hash<H: Hasher>(&self, state: &mut H) {
        hash_factor_view(&self.view(), state);
    }
}

impl PartialEq for FactorKey {
    fn eq(&self, other: &Self) -> bool {
        factor_views_eq(&self.view(), &other.view())
    }
}

impl Hash for dyn FactorKeyLookup + '_ {
    fn hash<H: Hasher>(&self, state: &mut H) {
        hash_factor_view(&self.view(), state);
    }
}

impl PartialEq for dyn FactorKeyLookup + '_ {
    fn eq(&self, other: &Self) -> bool {
        factor_views_eq(&self.view(), &other.view())
    }
}

impl Eq for dyn FactorKeyLookup + '_ {}

fn factor_views_eq(a: &FactorKeyView<'_>, b: &FactorKeyView<'_>) -> bool {
    a.variables == b.variables
        && a.conditioned_on == b.conditioned_on
        && a.intervention == b.intervention
        && a.domain == b.domain
        && a.values == b.values
}

/// Value row for a factor key: `variables` then `conditioned_on`, cloned out
/// of the assignment. This is the only per-lookup allocation on the hot path.
fn factor_values(spec: &FactorSpec<'_>, assignment: &Assignment) -> Result<Vec<Value>, EvalError> {
    let mut values = Vec::with_capacity(spec.variables.len() + spec.conditioned_on.len());
    for &v in spec.variables.iter().chain(spec.conditioned_on.iter()) {
        let Some(val) = assignment.get(v) else {
            return Err(EvalError::MissingBinding(v));
        };
        values.push(val.clone());
    }
    Ok(values)
}

/// Owned key for table inserts (cold path; lookups use [`FactorKeyView`]).
fn factor_key(spec: &FactorSpec<'_>, assignment: &Assignment) -> Result<FactorKey, EvalError> {
    let values = factor_values(spec, assignment)?;
    Ok(FactorKey {
        variables: Arc::from(spec.variables),
        conditioned_on: Arc::from(spec.conditioned_on),
        intervention: Arc::from(spec.intervention.to_vec()),
        domain: spec.domain,
        values: Arc::from(values),
    })
}

/// Tabular empirical distribution provider (discrete factors + domains).
#[derive(Debug, Default)]
pub struct EmpiricalTableProvider {
    domains: HashMap<VariableId, Arc<[Value]>>,
    tables: HashMap<FactorKey, f64>,
    /// Memoized cartesian supports keyed by the queried variable list.
    ///
    /// `support` takes `&self` (trait signature), so interior mutability is
    /// required; `RwLock` rather than `RefCell` keeps the provider `Sync` for
    /// callers that share it across threads. A cache hit is a cheap `Arc`
    /// clone. Only `set_domain` changes what `support` would return, so it is
    /// the one invalidation point.
    support_cache: RwLock<HashMap<Vec<VariableId>, SupportRows>>,
}

impl Clone for EmpiricalTableProvider {
    fn clone(&self) -> Self {
        Self {
            domains: self.domains.clone(),
            tables: self.tables.clone(),
            // Carrying the memoized supports over is safe: they are a pure
            // function of `domains`, which is cloned alongside.
            support_cache: RwLock::new(
                self.support_cache.read().unwrap_or_else(PoisonError::into_inner).clone(),
            ),
        }
    }
}

impl EmpiricalTableProvider {
    /// Empty provider.
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }

    /// Declare discrete domain for a variable.
    pub fn set_domain(&mut self, var: VariableId, values: impl IntoIterator<Item = Value>) {
        let mut v: Vec<Value> = values.into_iter().collect();
        // Stable unique by hash equality.
        let mut seen = std::collections::HashSet::new();
        v.retain(|x| seen.insert(x.clone()));
        self.domains.insert(var, Arc::from(v));
        // Memoized supports are cartesian products of the domains; any domain
        // change invalidates every cached row set.
        self.support_cache.write().unwrap_or_else(PoisonError::into_inner).clear();
    }

    /// Insert a factor probability for the given spec + assignment.
    ///
    /// # Errors
    ///
    /// Missing bindings for factor variables / conditions.
    pub fn insert_probability(
        &mut self,
        spec: &FactorSpec<'_>,
        assignment: &Assignment,
        probability: f64,
    ) -> Result<(), EvalError> {
        let key = factor_key(spec, assignment)?;
        self.tables.insert(key, probability);
        Ok(())
    }
}

impl DistributionProvider for EmpiricalTableProvider {
    fn probability(
        &self,
        spec: &FactorSpec<'_>,
        assignment: &Assignment,
        _ctx: &EvalContext,
    ) -> Result<f64, EvalError> {
        // Borrowed-key lookup: no owned `FactorKey` (and its per-field `Arc`
        // allocations) on the hot path — only the value row is assembled.
        let values = factor_values(spec, assignment)?;
        let key = FactorKeyView {
            variables: spec.variables,
            conditioned_on: spec.conditioned_on,
            intervention: spec.intervention,
            domain: spec.domain,
            values: &values,
        };
        self.tables.get(&key as &dyn FactorKeyLookup).copied().ok_or(EvalError::MissingTableEntry)
    }

    fn support(
        &self,
        vars: &[VariableId],
        _ctx: &EvalContext,
    ) -> Result<Arc<[Arc<[Value]>]>, EvalError> {
        // Callers (SumOut / IntegralOut / Expectation) request the same
        // variable sets on every draw, replicate, and nesting level; the
        // cartesian product is a pure function of `domains`, so memoize it and
        // hand back a shared `Arc` on hits. Errors (missing / empty domain)
        // are not cached — that path aborts evaluation anyway.
        if let Some(hit) =
            self.support_cache.read().unwrap_or_else(PoisonError::into_inner).get(vars)
        {
            return Ok(Arc::clone(hit));
        }
        // `vars.is_empty()` needs no special case: the fold below yields the
        // single empty row, matching the pre-memoization behavior.
        let mut rows: Vec<Vec<Value>> = vec![Vec::new()];
        for &v in vars {
            let domain = self.domains.get(&v).ok_or(EvalError::EmptySupport(v))?;
            if domain.is_empty() {
                return Err(EvalError::EmptySupport(v));
            }
            let mut next = Vec::with_capacity(rows.len() * domain.len());
            for prefix in &rows {
                for val in domain.iter() {
                    let mut row = prefix.clone();
                    row.push(val.clone());
                    next.push(row);
                }
            }
            rows = next;
        }
        let out: Arc<[Arc<[Value]>]> = rows.into_iter().map(Arc::from).collect();
        self.support_cache
            .write()
            .unwrap_or_else(PoisonError::into_inner)
            .insert(vars.to_vec(), Arc::clone(&out));
        Ok(out)
    }

    fn outcome(
        &self,
        var: VariableId,
        assignment: &Assignment,
        _ctx: &EvalContext,
    ) -> Result<f64, EvalError> {
        let value = assignment.get(var).ok_or(EvalError::MissingBinding(var))?;
        value.as_f64().ok_or(EvalError::MissingBinding(var))
    }

    fn n_draws(&self) -> Option<usize> {
        None
    }
}

/// Draw-indexed posterior provider: one [`EmpiricalTableProvider`] per draw.
#[derive(Clone, Debug, Default)]
pub struct PosteriorDrawProvider {
    draws: Vec<EmpiricalTableProvider>,
}

impl PosteriorDrawProvider {
    /// Empty posterior provider.
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }

    /// Construct from per-draw empirical tables.
    #[must_use]
    pub fn from_draws(draws: Vec<EmpiricalTableProvider>) -> Self {
        Self { draws }
    }

    /// Number of draws.
    #[must_use]
    pub fn len(&self) -> usize {
        self.draws.len()
    }

    /// Whether there are no draws.
    #[must_use]
    pub fn is_empty(&self) -> bool {
        self.draws.is_empty()
    }

    fn table(&self, ctx: &EvalContext) -> Result<&EmpiricalTableProvider, EvalError> {
        let draw = ctx
            .draw
            .ok_or(EvalError::ProviderKind("PosteriorDrawProvider requires EvalContext.draw"))?;
        self.draws.get(draw).ok_or(EvalError::DrawOutOfRange { draw, n_draws: self.draws.len() })
    }
}

impl DistributionProvider for PosteriorDrawProvider {
    fn probability(
        &self,
        spec: &FactorSpec<'_>,
        assignment: &Assignment,
        ctx: &EvalContext,
    ) -> Result<f64, EvalError> {
        self.table(ctx)?.probability(spec, assignment, ctx)
    }

    fn support(
        &self,
        vars: &[VariableId],
        ctx: &EvalContext,
    ) -> Result<Arc<[Arc<[Value]>]>, EvalError> {
        self.table(ctx)?.support(vars, ctx)
    }

    fn outcome(
        &self,
        var: VariableId,
        assignment: &Assignment,
        ctx: &EvalContext,
    ) -> Result<f64, EvalError> {
        self.table(ctx)?.outcome(var, assignment, ctx)
    }

    fn n_draws(&self) -> Option<usize> {
        Some(self.draws.len())
    }
}

/// Independent Gaussian density provider with Gauss–Hermite quadrature.
///
/// `probability` returns the product of univariate N(μ, σ²) densities for factor
/// variables. [`Self::quadrature`] returns product Gauss–Hermite nodes in
/// Lebesgue measure (suitable for ∫ body(x) dx near each Gaussian mode).
#[derive(Clone, Debug, Default)]
pub struct GaussianDensityProvider {
    /// Per-variable (mean, variance).
    params: HashMap<VariableId, (f64, f64)>,
}

impl GaussianDensityProvider {
    /// Empty provider.
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }

    /// Declare an independent Gaussian for `var` with mean `mean` and variance `variance`.
    ///
    /// # Panics
    ///
    /// Never panics; non-positive variance is rejected by returning early no-op... actually
    /// we clamp: variance must be > 0 or the insert is skipped. Prefer validating at call sites.
    pub fn set_gaussian(&mut self, var: VariableId, mean: f64, variance: f64) {
        if variance > 0.0 && variance.is_finite() && mean.is_finite() {
            self.params.insert(var, (mean, variance));
        }
    }
}

/// Physicists' Gauss–Hermite nodes/weights for ∫ e^{-t²} g(t) dt (n = 5).
const GH5_NODES: [f64; 5] = [
    -2.020_182_870_456_085_6,
    -0.958_572_464_613_818_5,
    0.0,
    0.958_572_464_613_818_5,
    2.020_182_870_456_085_6,
];
const GH5_WEIGHTS: [f64; 5] = [
    0.019_953_242_059_045_913,
    0.393_619_323_152_241_35,
    0.945_308_720_482_941_9,
    0.393_619_323_152_241_35,
    0.019_953_242_059_045_913,
];

impl DistributionProvider for GaussianDensityProvider {
    fn probability(
        &self,
        spec: &FactorSpec<'_>,
        assignment: &Assignment,
        _ctx: &EvalContext,
    ) -> Result<f64, EvalError> {
        // Independent Gaussians only model unconditional marginals; a non-empty
        // `conditioned_on` would silently return P(variables) instead of the
        // requested P(variables | conditioned_on), so reject rather than guess.
        if !spec.conditioned_on.is_empty() {
            return Err(EvalError::UnsupportedConditioning(
                "GaussianDensityProvider models independent Gaussians and cannot answer \
                 conditional queries; conditioned_on must be empty",
            ));
        }
        let mut dens = 1.0;
        for &v in spec.variables {
            let (mean, var) = self.params.get(&v).copied().ok_or(EvalError::EmptySupport(v))?;
            let x =
                assignment.get(v).and_then(Value::as_f64).ok_or(EvalError::MissingBinding(v))?;
            let inv_sqrt = (2.0 * std::f64::consts::PI * var).sqrt().recip();
            let z = (x - mean) / var.sqrt();
            dens *= inv_sqrt * (-0.5 * z * z).exp();
        }
        Ok(dens)
    }

    fn support(
        &self,
        vars: &[VariableId],
        _ctx: &EvalContext,
    ) -> Result<Arc<[Arc<[Value]>]>, EvalError> {
        if vars.is_empty() {
            return Ok(Arc::from(vec![Arc::from(Vec::<Value>::new())]));
        }
        Err(EvalError::EmptySupport(vars[0]))
    }

    fn quadrature(
        &self,
        vars: &[VariableId],
        _ctx: &EvalContext,
    ) -> Result<Option<QuadratureNodes>, EvalError> {
        if vars.is_empty() {
            return Ok(Some(Arc::from([(Arc::from(Vec::<Value>::new()), 1.0)])));
        }
        // Product GH: start with empty prefix.
        let mut nodes: Vec<(Vec<Value>, f64)> = vec![(Vec::new(), 1.0)];
        for &v in vars {
            let (mean, variance) =
                self.params.get(&v).copied().ok_or(EvalError::EmptySupport(v))?;
            let sigma = variance.sqrt();
            let scale = sigma * std::f64::consts::SQRT_2;
            let mut next = Vec::with_capacity(nodes.len() * GH5_NODES.len());
            for (prefix, w0) in &nodes {
                for (i, &t) in GH5_NODES.iter().enumerate() {
                    let x = mean + scale * t;
                    // GH computes ∫ e^{-t²} g(t) dt. For Lebesgue ∫ h(x) dx with
                    // x = μ + σ√2 t we need g(t) = h(x) σ√2 e^{t²}, so the node
                    // weight applied to h(x) is w_i · σ√2 · e^{t²}.
                    let w = w0 * GH5_WEIGHTS[i] * scale * (t * t).exp();
                    let mut row = prefix.clone();
                    row.push(Value::f64(x));
                    next.push((row, w));
                }
            }
            nodes = next;
        }
        let out: Vec<(Arc<[Value]>, f64)> =
            nodes.into_iter().map(|(row, w)| (Arc::from(row), w)).collect();
        Ok(Some(Arc::from(out)))
    }

    fn outcome(
        &self,
        var: VariableId,
        assignment: &Assignment,
        _ctx: &EvalContext,
    ) -> Result<f64, EvalError> {
        let value = assignment.get(var).ok_or(EvalError::MissingBinding(var))?;
        value.as_f64().ok_or(EvalError::MissingBinding(var))
    }

    fn n_draws(&self) -> Option<usize> {
        None
    }
}

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

    fn v(id: u32) -> VariableId {
        VariableId::from_raw(id)
    }

    fn f(x: f64) -> Value {
        Value::f64(x)
    }

    #[test]
    fn empirical_table_missing_entry_errors() {
        // Domain declared but no `insert_probability` call for this cell: must
        // surface `MissingTableEntry` rather than silently yielding 0.0.
        let mut p = EmpiricalTableProvider::new();
        let y = v(0);
        p.set_domain(y, [f(0.0), f(1.0)]);
        let spec = FactorSpec {
            variables: &[y],
            conditioned_on: &[],
            intervention: &[],
            domain: DomainRef::Observational,
        };
        let assignment = Assignment::from_pairs([(y, f(0.0))]);
        let err = p.probability(&spec, &assignment, &EvalContext::default()).unwrap_err();
        assert_eq!(err, EvalError::MissingTableEntry);
    }

    #[test]
    fn support_memoizes_and_invalidates_on_set_domain() {
        // A repeated `support` query must be a cheap clone of the same shared
        // rows (memoization), and `set_domain` — the only mutation that can
        // change what `support` returns — must invalidate the cache.
        let mut p = EmpiricalTableProvider::new();
        let a = v(0);
        let b = v(1);
        p.set_domain(a, [f(0.0), f(1.0)]);
        p.set_domain(b, [f(0.0), f(1.0), f(2.0)]);
        let ctx = EvalContext::default();
        let first = p.support(&[a, b], &ctx).unwrap();
        assert_eq!(first.len(), 6);
        let second = p.support(&[a, b], &ctx).unwrap();
        assert!(Arc::ptr_eq(&first, &second), "cache hit must return the shared rows");
        p.set_domain(b, [f(0.0), f(1.0)]);
        let third = p.support(&[a, b], &ctx).unwrap();
        assert!(!Arc::ptr_eq(&first, &third), "set_domain must invalidate the cache");
        // Cartesian order (outer var major, domain order minor) is unchanged.
        let rows: Vec<Vec<f64>> =
            third.iter().map(|r| r.iter().map(|x| x.as_f64().unwrap()).collect()).collect();
        assert_eq!(rows, vec![vec![0.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0], vec![1.0, 1.0]]);
    }

    #[test]
    fn empty_support_query_yields_single_empty_row() {
        // The empty query has one row (the empty assignment); the memoized
        // path must preserve that vacuous-product convention.
        let p = EmpiricalTableProvider::new();
        let rows = p.support(&[], &EvalContext::default()).unwrap();
        assert_eq!(rows.len(), 1);
        assert!(rows[0].is_empty());
    }

    #[test]
    fn borrowed_key_lookup_matches_owned_insert() {
        // `probability` looks up via the borrowed `FactorKeyView`; it must hit
        // entries inserted via the owned `FactorKey` (same hash/equality
        // identity across every component) and still miss when any component
        // — value row or domain — differs.
        let mut p = EmpiricalTableProvider::new();
        let y = v(0);
        let z = v(1);
        let t = v(2);
        let interv = [InterventionAssignment { variable: t, value: f(1.0) }];
        let spec = FactorSpec {
            variables: &[y],
            conditioned_on: &[z],
            intervention: &interv,
            domain: DomainRef::Interventional,
        };
        let assign = Assignment::from_pairs([(y, f(1.0)), (z, f(0.0))]);
        p.insert_probability(&spec, &assign, 0.25).unwrap();
        let ctx = EvalContext::default();
        assert!((p.probability(&spec, &assign, &ctx).unwrap() - 0.25).abs() < 1e-15);
        let other = Assignment::from_pairs([(y, f(1.0)), (z, f(1.0))]);
        assert_eq!(p.probability(&spec, &other, &ctx).unwrap_err(), EvalError::MissingTableEntry);
        let obs = FactorSpec { domain: DomainRef::Observational, ..spec.clone() };
        assert_eq!(p.probability(&obs, &assign, &ctx).unwrap_err(), EvalError::MissingTableEntry);
    }

    #[test]
    fn gaussian_provider_rejects_conditional_query() {
        // `GaussianDensityProvider` models independent Gaussians; it must error on a
        // conditional query (non-empty `conditioned_on`) rather than silently
        // returning the unconditional marginal P(variables).
        let mut p = GaussianDensityProvider::new();
        let y = v(0);
        let z = v(1);
        p.set_gaussian(y, 0.0, 1.0);
        p.set_gaussian(z, 0.0, 1.0);
        let spec = FactorSpec {
            variables: &[y],
            conditioned_on: &[z],
            intervention: &[],
            domain: DomainRef::Observational,
        };
        let assignment = Assignment::from_pairs([(y, f(0.5)), (z, f(0.2))]);
        let err = p.probability(&spec, &assignment, &EvalContext::default()).unwrap_err();
        assert!(matches!(err, EvalError::UnsupportedConditioning(_)));
    }
}