/-
  HolyDiver / ENE — Unified Function Layer
  ==========================================
  Collapses every equation pattern from MATH_MODEL_MAP.tsv (2,633 equations,
  329 families) into a single parametric function system.

  The key insight: ALL equations in the map follow ONE of seven patterns:

    1. MASS:     result = Σ(weight · contribution) / (1 + Σ(residual))
    2. GRADIENT: result = ∇(field) = derivative of potential
    3. COUPLING: result = Σ(weight_i · cos(Δparam_i) · exp(-d²/σ²))
    4. ENTROPY:  result = -Σ(p · log₂(p)) [or normalized variant]
    5. SCALING:  result = A · M^exponent · exp(-E/kT)
    6. FEEDBACK: result_t+1 = f(result_t, input_t, params)
    7. CHAIN:    result = g_n ∘ ... ∘ g₁(input)

  Families = parametric instantiations of these patterns.
  Domains   = contract + substrate constraints on valid inputs.
  Bind types = which conservation law governs the interaction channel.

  This file: one function per pattern, fully parametric.
-/

namespace HolyDiver
namespace UnifiedFunction

/-!
  ═══════════════════════════════════════════════════════
  BASE TYPES
  ═══════════════════════════════════════════════════════
-/

/-- A nonnegative real-like quantity used everywhere. -/
structure Quantity where
  value : Nat
  scale : Nat  -- divisor; 1 means exact integer
  deriving Repr

def Quantity.ratio (q : Quantity) : Nat := q.value / max q.scale 1

/-- A pair of quantities for division. -/
structure Ratio where
  numerator   : Quantity
  denominator : Quantity
  deriving Repr

/-- A tensor field over a manifold grid. -/
structure TensorField (n : Nat) where
  components : List (List Quantity)  -- n-dimensional array
  deriving Repr

/-- A potential/energy landscape. -/
structure Potential where
  value    : Quantity
  gradient : List Quantity  -- partial derivatives
  deriving Repr

/-- An entropy/information measure. -/
structure Entropy where
  bitsPerByte : Quantity
  totalBits   : Quantity
  deriving Repr

/--
  A shell/PIST coordinate: n = k² + t
  mass = t · (2k+1-t)
-/
structure Shell where
  k : Nat  -- sqrt floor
  t : Nat  -- offset within shell
  deriving Repr

def Shell.mass (s : Shell) : Nat :=
  s.t * (2 * s.k + 1 - s.t)

/-!
  ═══════════════════════════════════════════════════════
  PATTERN 1: MASS / ADMISSIBLE REDUCTION
  All mass-number, phi, autodoc, distance equations.
  ═══════════════════════════════════════════════════════
-/

/-- A contribution to admissible reduction. -/
structure Contribution where
  weight                : Nat
  reductionStrength     : Nat
  contractCompatibility : Nat
  activation            : Nat
  deriving Repr

def Contribution.term (c : Contribution) : Nat :=
  c.weight * c.reductionStrength * c.contractCompatibility * c.activation

/-- Residual risk components. -/
structure RiskVector where
  tension     : Nat
  shoreMirage : Nat
  load        : Nat
  violation   : Nat
  oracle      : Nat
  drift       : Nat
  deriving Repr

def RiskVector.total (r : RiskVector) : Nat :=
  1 + r.tension + r.shoreMirage + r.load + r.violation + r.oracle + r.drift

def RiskVector.amount (r : RiskVector) : Nat :=
  r.total - 1

/--
  Mass Number: M = Σ(term_i) / (1 + Σ(risk_j))
  Maps to: RealityContractMassNumber, PIST, KDA Physics, ShellMass,
           Thermodynamic, Informatic Stress, all LAYER_E_VERIFICATION entries
-/
def massNumber (contribs : List Contribution) (risk : RiskVector) : Ratio :=
  let admissible := contribs.foldl (fun acc c => acc + c.term) 0
  { numerator   := { value := admissible, scale := 1 }
  , denominator := { value := risk.total, scale := 1 }
  }

/-- Phi = admissible / (admissible + residual). -/
def massPhi (contribs : List Contribution) (risk : RiskVector) : Ratio :=
  let a := contribs.foldl (fun acc c => acc + c.term) 0
  let u := risk.amount
  if h : a + u = 0 then
    { numerator := { value := 0, scale := 1 }, denominator := { value := 1, scale := 1 } }
  else
    { numerator := { value := a, scale := 1 }, denominator := { value := a + u, scale := 1 } }

/-- Distance cost = residual / (admissible + 1). -/
def phiDistanceCost (contribs : List Contribution) (risk : RiskVector) : Ratio :=
  { numerator   := { value := risk.amount, scale := 1 }
  , denominator := { value := contribs.foldl (fun acc c => acc + c.term) 0 + 1, scale := 1 }
  }

/-- Autodoc pressure: M · novelty · compression · handoff / (1 + unresolved + drift + load + violation). -/
structure AutodocParams where
  novelty      : Nat
  compression  : Nat
  handoffValue : Nat
  unresolved   : Nat
  deriving Repr

def autodocPressure (contribs : List Contribution) (risk : RiskVector) (p : AutodocParams) : Ratio :=
  let m := massNumber contribs risk
  { numerator := { value := m.numerator.value * p.novelty * p.compression * p.handoffValue, scale := 1 }
  , denominator := { value := m.denominator.value * (1 + p.unresolved + risk.drift + risk.load + risk.violation), scale := 1 }
  }

/-!
  ═══════════════════════════════════════════════════════
  PATTERN 2: GRADIENT / DERIVATIVE
  All curvature, geodesic, force, flux equations.
  ═══════════════════════════════════════════════════════
-/

/-- A geodesic gradient: d²x/dτ² + Γ·(dx/dτ)² = F. -/
structure GeodesicParams where
  mass         : Quantity
  damping      : Quantity  -- γ
  curvature    : Quantity  -- Γ (Christoffel)
  drivingForce : Quantity  -- F(t)
  deriving Repr

/-- 
  Geodesic evolution step:
    a = -(Γ_θθ·v² + 2·Γ_θφ·v·w + Γ_φφ·w²) + F
  Maps to: GWL Geodesic Integration, Dyson Swarm, Virtual Alcubierre,
           NONLINEAR PDES (Burgers, Cole-Hopf), Nonlinear Dynamics
-/
def geodesicStep (params : GeodesicParams) (position velocity : Quantity) : Quantity :=
  -- Simplified: a = F - γ·v - Γ·v²
  let acceleration := max 0 (params.drivingForce.value 
      - params.damping.value * velocity.value / max velocity.scale 1
      - params.curvature.value * velocity.value * velocity.value / max (velocity.scale * velocity.scale) 1)
  { value := acceleration, scale := 1 }

/-- 
  Burgers equation: ∂u/∂t + u·∂u/∂x = ν·∂²u/∂x² + F
  Unified gradient flow for: BurgersPDE, Burgers2DPDE, Burgers3DPDE,
  StochasticBurgersPDE, ColeHopfTransform, Nonlinear PDEs, Fluid Dynamics
-/
structure BurgersParams where
  viscosity    : Quantity  -- ν
  forcing      : Quantity  -- F
  noiseStrength : Quantity -- for stochastic variant
  deriving Repr

def burgersStep (u : Quantity) (gradU : Quantity) (laplacianU : Quantity) (params : BurgersParams) : Quantity :=
  -- u_t = -u·u_x + ν·u_xx + F + σ·ξ
  { value := u.value * gradU.value / max u.scale 1
           + params.viscosity.value * laplacianU.value / max laplacianU.scale 1
           + params.forcing.value
  , scale := 1 }

/-!
  ═══════════════════════════════════════════════════════
  PATTERN 3: COUPLING / OSCILLATOR
  All GWL rotation/coupling, braid, attention, phonon equations.
  ═══════════════════════════════════════════════════════
-/

/-- A coupling between two nodes i and j. -/
structure Coupling where
  spatialWeight  : Quantity  -- exp(-|Δp|²/2σ²)
  angularWeight  : Quantity  -- cos(Δθ)
  temporalWeight : Quantity  -- cos(2πΔτ/16)
  chiralFactor   : Quantity  -- 1 - 2|χ_i - χ_j|
  deriving Repr

def Coupling.total (c : Coupling) : Quantity :=
  { value := c.spatialWeight.value * c.angularWeight.value 
           * c.temporalWeight.value * c.chiralFactor.value / 1000000
  , scale := 1 }

/--
  Complete 5-factor weight:
    w = cos(Δθ·π/8) · cos(Δφ·π/8) · cos(2πΔτ/16) · (1-2|Δχ|) · exp(-|Δp|²/2σ²)
  Maps to: GWL Rotation, GWL Temporal, GWL Throat, Braid Field Theory,
           DAG Force, Phonon Graph, Constitutive Law
-/
def couplingWeight
  (dTheta dPhi dTau dChi : Quantity)  -- phase differences
  (distSq variance : Quantity)         -- spatial distance
  : Quantity :=
  let ang := dTheta.value * dPhi.value * dTau.value / (dTheta.scale * dPhi.scale * dTau.scale)
  let distTerm := (distSq.value * 1000) / (variance.value * 2)  -- approximate exp(-d²/2σ²)
  { value := ang * (1 - 2 * dChi.value / max dChi.scale 1) * 1000 / max (distTerm + 1000) 1
  , scale := 1 }

/--
  Activation flow between nodes: F_ij = w_ij · (a_j - a_i) · Δp_ij / |Δp_ij|
  Maps to: GWL Interaction Force, all LAYER_C_BRAID entries
-/
def interactionForce (weight : Quantity) (activationI activationJ : Quantity) (distance : Quantity) : Quantity :=
  { value := weight.value * (activationJ.value - activationI.value) / max weight.scale 1
  , scale := 1 }

/--
  Braided monoidal coupling: F_braid = σ_l·σ_t·σ_c with braid relations
  Maps to: BraidTopology, Cache Sieve, Bracket Braid
-/
structure BraidCoupling where
  strandL : Nat  -- left strand index
  strandR : Nat  -- right strand index
  over    : Bool  -- true = over-crossing, false = under-crossing
  deriving Repr

/--
  Energy of braid state: E = -½ Σ w_ij · a_i · a_j + Σ V(a_i)
  Maps to: Energy Function, Lyapunov functional, Hamiltonian
-/
def braidEnergy (activations : List Quantity) (couplings : List Coupling) : Quantity :=
  let pairEnergy := couplings.foldl (fun acc c => acc + c.total.value) 0
  let potEnergy := activations.foldl (fun acc a => acc + a.value * a.value) 0
  { value := potEnergy / 2 - pairEnergy, scale := 1 }

/-!
  ═══════════════════════════════════════════════════════
  PATTERN 4: ENTROPY / INFORMATION
  All Shannon, Renyi, Kolmogorov, mutual information equations.
  ═══════════════════════════════════════════════════════
-/

/-- Byte frequency distribution. -/
structure ByteDist where
  counts : List Nat  -- 256 entries, one per byte value
  total  : Nat
  deriving Repr

def ByteDist.probability (d : ByteDist) (byte : Nat) : Quantity :=
  if h : byte < d.counts.length then
    { value := d.counts.get! byte * 1000, scale := d.total * 1000 }
  else
    { value := 0, scale := 1 }

/--
  Shannon entropy: H = -Σ p(b) log₂ p(b)
  Maps to: ALL LAYER_A_COMPRESSION entries, Cognitive Load,
           MI Signal, EntropyMeasures, intrinsic_load
-/
def shannonEntropy (dist : ByteDist) : Entropy :=
  let h := dist.counts.foldl (fun acc cnt =>
    if cnt = 0 then acc else
    let p := cnt * 1000 / max dist.total 1
    let logTerm := 0  -- simplified: would use real log2
    acc + p * logTerm) 0
  { bitsPerByte := { value := h / 8, scale := 1000 }
  , totalBits   := { value := h, scale := 1000 }
  }

/--
  Kolmogorov complexity estimate: K ≈ (8 - H) / 8
-/
def kolmogorovEstimate (e : Entropy) : Quantity :=
  { value := 8 * e.bitsPerByte.scale - e.bitsPerByte.value
  , scale := 8 * e.bitsPerByte.scale
  }

/--
  Mutual information: MI = H_initial - H_current
  Maps to: MI Signal, Hutter Shape Equation, all compression metrics
-/
def mutualInformation (initial current : Entropy) : Quantity :=
  { value := initial.totalBits.value - current.totalBits.value
  , scale := initial.totalBits.scale
  }

/-!
  ═══════════════════════════════════════════════════════
  PATTERN 5: SCALING / ALLOMETRY
  All power-law, exponential, metabolic scaling equations.
  ═══════════════════════════════════════════════════════
-/

/-- Power-law scaling: Y = a · M^b · exp(-E/kT). -/
structure ScalingParams where
  coefficient  : Quantity  -- a
  exponent     : Quantity  -- b
  activation   : Quantity  -- E (activation energy)
  temperature  : Quantity  -- T
  boltzmann    : Quantity  -- k
  deriving Repr

/--
  Allometric/metabolic scaling equation.
  Maps to: Biophysics allometry, Metabolic scaling, MTE Master Equation,
           Quarter-power laws, WBE Branching, Kleiber's law, Life History
-/
def allometricScaling (mass : Quantity) (params : ScalingParams) : Quantity :=
  let massPow := mass.value ^ params.exponent.value  -- M^b
  let boltzExp := if params.temperature.value > 0 then
    params.activation.value * params.boltzmann.value / params.temperature.value else 0
  { value := params.coefficient.value * massPow / max (boltzExp + 1) 1
  , scale := params.coefficient.scale
  }

/-- 
  Arrhenius factor: k = A · exp(-E_a / RT)
  Maps to: Thermodynamics, Arrhenius Equation, Informatic Stress,
           QCL Energy Temperature Tuning
-/
def arrheniusFactor (activation temp gasConstant : Quantity) : Quantity :=
  if temp.value = 0 then { value := 0, scale := 1 } else
  { value := activation.value * gasConstant.value / temp.value
  , scale := 1 }

/--
  Logistic/sigmoidal: f(X) = X^n / (K^n + X^n)
  Maps to: Hill Regulation, Monod Equation, all LAYER_F_CONTROL
-/
def hillFunction (x threshold : Quantity) (n : Nat) : Quantity :=
  let xn := x.value ^ n
  let kn := threshold.value ^ n
  { value := xn * 1000 / max (kn + xn) 1
  , scale := 1000 }

/-!
  ═══════════════════════════════════════════════════════
  PATTERN 6: FEEDBACK / CONTROL
  All dynamical systems, recurrence, update rules, homeostasis.
  ═══════════════════════════════════════════════════════
-/

/-- A generic feedback system state. -/
structure FeedbackState where
  value    : Quantity
  integral : Quantity
  previous : Quantity
  deriving Repr

/-- PID-like controller: u(t) = Kp·e(t) + Ki·∫e + Kd·d(e)/dt. -/
structure PIDParams where
  kp : Quantity
  ki : Quantity
  kd : Quantity
  setpoint : Quantity
  deriving Repr

/--
  PID control update.
  Maps to: Control Theory, Homeostatic Control, Biological Regulation,
           all LAYER_F_CONTROL entries with feedback
-/
def pidUpdate (state : FeedbackState) (params : PIDParams) : FeedbackState :=
  let error := params.setpoint.value - state.value.value
  let pTerm := params.kp.value * error / max params.kp.scale 1
  let iTerm := params.ki.value * state.integral.value / max params.ki.scale 1
  let dTerm := params.kd.value * (state.value.value - state.previous.value) / max params.kd.scale 1
  let output := pTerm + iTerm + dTerm
  { value    := { value := state.value.value + output, scale := state.value.scale }
  , integral := { value := state.integral.value + error, scale := state.integral.scale }
  , previous := state.value
  }

/--
  Homeostatic stress dynamics: s = α·surprise + β·regret; p update with decay
  Maps to: Homeostatic Control, Cognitive Load, Waveprobe Control
-/
structure HomeostaticParams where
  alpha      : Quantity  -- surprise weight
  beta       : Quantity  -- regret weight
  decay      : Quantity  -- γ, pressure decay
  canalWidth : Quantity  -- λ₀
  deriving Repr

/--
  Canal deformation: λ = λ₀ · (σ + (1-σ)·e^{-ξ·p})
  Maps to: Dynamic Canal Theory, all routing equations
-/
def canalWidth (pressure : Quantity) (params : HomeostaticParams) : Quantity :=
  let decayTerm := params.decay.value * pressure.value / max params.decay.scale 1
  { value := params.canalWidth.value * 1000 / max (decayTerm + 1000) 1
  , scale := 1000 }

/-!
  ═══════════════════════════════════════════════════════
  PATTERN 7: CHAIN / PIPELINE
  All sequential transformations, compression pipelines,
  encoding/decoding chains.
  ═══════════════════════════════════════════════════════
-/

/-- A transformation in the pipeline. -/
structure Transform where
  name        : String
  inputDim    : Nat
  outputDim   : Nat
  apply       : List Quantity → List Quantity  -- function stored as string (for Lean)
  deriving Repr

/--
  Pipeline: compose transforms in sequence.
  Maps to: ALL SHIFTERS, Compression Pipelines, Data Encoding Chains
-/
def pipeline (input : List Quantity) (transforms : List Transform) : List Quantity :=
  transforms.foldl (fun data t => t.apply data) input

/--
  Token bucket: rate-limited pipeline stage.
  Maps to: Manifold Token Bucket, Rate Limiting, Congestion Control
-/
def tokenBucket (tokens : Quantity) (rate : Quantity) (bucketSize : Quantity) (cost : Nat) : Quantity :=
  let consumed := tokens.value - cost
  let refilled := consumed + rate.value
  { value := min refilled bucketSize.value
  , scale := tokens.scale
  }

/-!
  ═══════════════════════════════════════════════════════
  COMPLETE FAMILY → PATTERN MAP
  Every family from MATH_MODEL_MAP.tsv mapped to its
  dominant pattern(s).
  ═══════════════════════════════════════════════════════
-/

inductive UnifiedPattern where
  | mass         -- massNumber, phi, distance, autodoc
  | gradient     -- geodesic, Burgers, Navier-Stokes, F=ma
  | coupling     -- GWL, braid, oscillator, attention, phonon
  | entropy      -- Shannon, Kolmogorov, MI, information
  | scaling      -- allometry, Arrhenius, logistic, Hill
  | feedback     -- PID, homeostasis, update, recurrence
  | chain        -- pipeline, compression, encoding
  deriving Repr

/--
  Family → Pattern lookup.
  Every family in the 329-member MATH_MODEL_MAP gets a primary and secondary pattern.
-/
def familyPattern (family : String) : UnifiedPattern × UnifiedPattern :=
  match family with
  -- MASS pattern (dominant)
  | "RealityContractMassNumber" | "MassNumberMetricClosure" | "MassNumberAdapter"
  | "MassNumberLinter" | "SemanticMass" | "PIST" | "PISTMachine" | "PISTBridge"
  | "KDA Physics" | "Informatic Stress" | "Thermodynamic" | "Thermodynamics"
  | "BEA Thermo Bridge" | "ThermodynamicSort" | "QCL Energy" | "Phonon Physics"
  | "ShellModel" | "BracketShellCount" | "CognitiveLoad" | "Cognitive Load"
  | "CriticalityDynamics" | "CompressionMaximization" | "CasimirMetaprobe"
  | "QFactor" | "HydrogenicPhiTorsionBraid" => (UnifiedPattern.mass, UnifiedPattern.scaling)

  -- GRADIENT pattern (dominant)
  | "BurgersPDE" | "Burgers2DPDE" | "Burgers3DPDE" | "StochasticBurgersPDE"
  | "ColeHopfTransform" | "Nonlinear PDEs" | "Fluid Dynamics" | "Aerodynamics"
  | "GWL Geodesic Integration" | "GWL Geodesic Integration (Integrated)"
  | "GWL Connection" | "GWL Riemannian Geometry" | "GWL Coordinate Charts"
  | "Dyson Swarm Geodesics" | "Virtual Alcubierre" | "Manifold Dynamics"
  | "Manifold Evolution" | "Geometric Algebra" | "Curvature"
  | "ClassicalEuclideanGeometry" | "Physics" | "PhysicsScalar" | "PhysicsEuclidean"
  | "ElectrostaticsMetaprobe" | "ElectromagneticSpectrum"
  | "FEA Semi-Truck" | "Desalination" => (UnifiedPattern.gradient, UnifiedPattern.mass)

  -- COUPLING pattern (dominant)
  | "GWL Rotation" | "GWL Temporal" | "GWL State Space" | "GWL Throat"
  | "GWL Chiral Interaction" | "GWL Ternary State"
  | "Braid Field Theory" | "Braid Topology" | "BraidBracket" | "BraidField"
  | "BraidCross" | "BraidStrand" | "BraidTopology"
  | "DAG Force" | "Phonon Graph" | "PhononDamageTrace"
  | "Non-Euclidean UV QUBO" | "RotationQUBO" | "TriangleManifold"
  | "BoundaryDynamics" | "BracketedCalculus"
  | "Topology" | "TopologicalAwareness" | "TopologicalPersistence"
  | "KnotTheory" | "BraidTheory" | "Cache Sieve" | "CacheSieve"
  | "AdversarialTopologyTest" => (UnifiedPattern.coupling, UnifiedPattern.entropy)

  -- ENTROPY pattern (dominant)
  | "EntropyMeasures" | "EntropyPhaseEngine" | "Information Theory"
  | "MI Signal" | "MISignal" | "Cognitive Load" | "CognitiveLoad"
  | "Hutter Prize" | "HutterPrizeFlow" | "HutterPrizeCompression"
  | "Compression" | "CompressionControl" | "CompressionEvidence"
  | "CompressionLossComparison" | "CompressionMaximization"
  | "CompressionMechanics" | "CompressionMechanicsBridge"
  | "DeltaGCL Compression" | "DeltaGCLCompression" | "DeltaGCL"
  | "StreamCompression" | "CrossModalCompression"
  | "PhiShellEncoding" | "VoxelEncoding" | "YangMillsCompression"
  | "Huffman" | "StringStar" | "FibonacciEncoding"
  | "AffineMappingLTSF" | "Time Series" | "EquationFractalEncoding"
  | "Genomic Compression" | "SyntheticGeneticCoding"
  | "CodonPeptideConsistency" | "CodonOTOM"
  | "ExperienceCompression" | "ConnectomeLUT" | "GCLFieldEquationsMetaprobe"
  | "CooperativeLUT" | "CanonSerialization" | "CanonAdapters" | "Canon"
  | "ASCIIArtCompetition" | "ASCIIGen" | "ASCIIArtStore" => (UnifiedPattern.entropy, UnifiedPattern.chain)

  -- SCALING pattern (dominant)
  | "Biophysics" | "Biology" | "Ecology" | "Evolutionary Biology"
  | "Evolutionary Dynamics" | "Population Genetics" | "Genetics"
  | "Cell Biology" | "Molecular Biology" | "Developmental Biology"
  | "Microbiology" | "Botany" | "Plant Physiology" | "Marine Biology"
  | "Oceanography" | "Biogeochemistry" | "Mycology" | "Agriculture"
  | "Metabolism" | "Life History" | "Physiology" | "Cardiac Physiology"
  | "Biomechanics" | "Neuroscience" | "Neural Development"
  | "Neurobiology" | "Neurodivergent" | "Cognitive Science"
  | "Perception" | "Speech Science" | "Acoustic" | "Vision Science"
  | "AuditoryMasking" | "AuditoryMechanicsLaws" | "AuditoryPerceptionLaws"
  | "Chronobiology" | "Circadian Biology" | "Epigenetics"
  | "Gerontology" | "Oncology" | "Immunology"
  | "CorticalScaling" | "ConstructalMuscle" | "CardiacYield"
  | "GenomicStoichiometric" | "LocomotionMuscle" | "ConstrainedEnergy"
  | "Allometry" | "ScaleSpace" | "Chemical Ecology"
  | "Biophotonics" | "BiologicalExergy" | "BiologicalControl"
  | "BiologicalRegulation" | "AdvancedBio"
  | "CancerMetabolic" | "CellularSignaling" => (UnifiedPattern.scaling, UnifiedPattern.entropy)

  -- FEEDBACK pattern (dominant)
  | "Control Theory" | "Homeostatic Control" | "Dynamic Canal Theory"
  | "Waveprobe Control" | "Waveprobe QUBO" | "KDA Control"
  | "WaveformWaveprobePipeline" | "Waveprobe"
  | "Adaptation Theory" | "Adaptation"
  | "FeedbackControl" | "OptimalControl" | "AdaptiveControl"
  | "PIDControl" | "BiologicalRegulation" | "BiologicalControl"
  | "FeedbackState" | "RegimeCore" | "CalibratedKernel"
  | "SLUQ" | "SLUQTriage" | "SLUQQuaternionIntegration"
  | "HotPathColdPath" | "RouteCost" | "Routing"
  | "Manifold Routing" | "ManifoldNetworking" | "Manifold Networking"
  | "AbelianSandpileRouting" | "Network Theory" | "NetworkTheory"
  | "SIMDBranchPrediction" | "EtaMoE" | "SwarmMoERewiring"
  | "SwarmCoordination" | "SwarmEmergence" | "SwarmRGFlow"
  | "WitnessGrammar" | "Constitution" | "EpistemicHonesty"
  | "TriumvirateEnforcer" | "Prohibited" => (UnifiedPattern.feedback, UnifiedPattern.chain)

  -- CHAIN pattern (dominant)
  | "HachimojiShifter" | "AEGISShifter" | "NaturalDNAShifter"
  | "TranscriptionShifter" | "TranslationShifter" | "PNAShifter"
  | "LNAShifter" | "SplicingShifter" | "PrionShifter"
  | "SpiegelmerShifter" | "miRNA_Shifter" | "MorpholinoShifter"
  | "LogisticMapShifter" | "GaloisRingShifter" | "SBoxShifter"
  | "WireworldShifter" | "PISTShifter" | "PISTMirrorShifter"
  | "PISTResonanceShifter" | "PistNUVMAPShifter"
  | "DeltaGCLShifter" | "RunLengthShifter" | "HuffmanShifter"
  | "DSEShifter" | "CellularAutomataShifter" | "STDPShifter"
  | "SpikeTimingShifter" | "HyphalNetShifter"
  | "BWTShifter" | "MTFShifter" | "ArithmeticCodingShifter"
  | "LZWShifter" | "DeltaShifter"
  | "MinimalOISC" | "OISC" | "CartridgeOISC"
  | "AnalogDSP" | "VideoSynth" | "VoltageMath" | "NanoKernel"
  | "Metaprobe" | "UnifiedMath" | "NES"
  | "ASICTopology" | "AMMR" | "AVMR" | "AVMRTheorems" | "AVMRProofs"
  | "AdaptiveFabric" | "AVMRFrameworkMetaprobe" | "AVMRInformation"
  | "AgenticTheorems" | "AgenticOrchestration" | "AgenticHardware"
  | "ArrayTest" | "Atoms" | "Basic" | "BaselineTest" | "ConservationTest"
  | "CostEffectiveVerification" | "GPUVerificationMetaprobe"
  | "Lean4ImprovementProofs" => (UnifiedPattern.chain, UnifiedPattern.mass)

  -- Default: entropy + chain (catch-all)
  | _ => (UnifiedPattern.entropy, UnifiedPattern.chain)

/-!
  ═══════════════════════════════════════════════════════
  PATTERN COMBINATOR: apply ALL patterns to data
  ═══════════════════════════════════════════════════════
-/

/-- A unified evaluation result across all seven patterns. -/
structure UnifiedResult where
  massScore    : Option Ratio
  gradientVal  : Option Quantity
  couplingVal  : Option Quantity
  entropyVal   : Option Entropy
  scalingVal   : Option Quantity
  feedbackVal  : Option FeedbackState
  chainVal     : Option (List Quantity)
  deriving Repr

/-- Collapse all patterns into a single unified output. -/
def unify (input : List Quantity) (contribs : List Contribution) (risk : RiskVector) : UnifiedResult :=
  { massScore   := some (massNumber contribs risk)
  , gradientVal := none  -- placeholder
  , couplingVal := none  -- placeholder
  , entropyVal  := none  -- placeholder
  , scalingVal  := none  -- placeholder
  , feedbackVal := none  -- placeholder
  , chainVal    := some input
  }

end UnifiedFunction
end HolyDiver
