mirror of
https://github.com/allaunthefox/Research-Stack.git
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616 lines
26 KiB
Text
616 lines
26 KiB
Text
/-
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HolyDiver / ENE — Unified Function Layer
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==========================================
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Collapses every equation pattern from MATH_MODEL_MAP.tsv (2,633 equations,
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329 families) into a single parametric function system.
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The key insight: ALL equations in the map follow ONE of seven patterns:
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1. MASS: result = Σ(weight · contribution) / (1 + Σ(residual))
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2. GRADIENT: result = ∇(field) = derivative of potential
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3. COUPLING: result = Σ(weight_i · cos(Δparam_i) · exp(-d²/σ²))
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4. ENTROPY: result = -Σ(p · log₂(p)) [or normalized variant]
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5. SCALING: result = A · M^exponent · exp(-E/kT)
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6. FEEDBACK: result_t+1 = f(result_t, input_t, params)
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7. CHAIN: result = g_n ∘ ... ∘ g₁(input)
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Families = parametric instantiations of these patterns.
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Domains = contract + substrate constraints on valid inputs.
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Bind types = which conservation law governs the interaction channel.
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This file: one function per pattern, fully parametric.
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-/
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namespace HolyDiver
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namespace UnifiedFunction
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/-!
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═══════════════════════════════════════════════════════
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BASE TYPES
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═══════════════════════════════════════════════════════
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-/
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/-- A nonnegative real-like quantity used everywhere. -/
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structure Quantity where
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value : Nat
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scale : Nat -- divisor; 1 means exact integer
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deriving Repr
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def Quantity.ratio (q : Quantity) : Nat := q.value / max q.scale 1
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/-- A pair of quantities for division. -/
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structure Ratio where
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numerator : Quantity
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denominator : Quantity
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deriving Repr
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/-- A tensor field over a manifold grid. -/
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structure TensorField (n : Nat) where
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components : List (List Quantity) -- n-dimensional array
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deriving Repr
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/-- A potential/energy landscape. -/
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structure Potential where
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value : Quantity
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gradient : List Quantity -- partial derivatives
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deriving Repr
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/-- An entropy/information measure. -/
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structure Entropy where
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bitsPerByte : Quantity
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totalBits : Quantity
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deriving Repr
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/--
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A shell/PIST coordinate: n = k² + t
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mass = t · (2k+1-t)
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-/
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structure Shell where
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k : Nat -- sqrt floor
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t : Nat -- offset within shell
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deriving Repr
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def Shell.mass (s : Shell) : Nat :=
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s.t * (2 * s.k + 1 - s.t)
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/-!
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═══════════════════════════════════════════════════════
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PATTERN 1: MASS / ADMISSIBLE REDUCTION
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All mass-number, phi, autodoc, distance equations.
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═══════════════════════════════════════════════════════
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-/
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/-- A contribution to admissible reduction. -/
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structure Contribution where
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weight : Nat
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reductionStrength : Nat
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contractCompatibility : Nat
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activation : Nat
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deriving Repr
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def Contribution.term (c : Contribution) : Nat :=
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c.weight * c.reductionStrength * c.contractCompatibility * c.activation
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/-- Residual risk components. -/
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structure RiskVector where
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tension : Nat
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shoreMirage : Nat
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load : Nat
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violation : Nat
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oracle : Nat
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drift : Nat
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deriving Repr
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def RiskVector.total (r : RiskVector) : Nat :=
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1 + r.tension + r.shoreMirage + r.load + r.violation + r.oracle + r.drift
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def RiskVector.amount (r : RiskVector) : Nat :=
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r.total - 1
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/--
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Mass Number: M = Σ(term_i) / (1 + Σ(risk_j))
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Maps to: RealityContractMassNumber, PIST, KDA Physics, ShellMass,
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Thermodynamic, Informatic Stress, all LAYER_E_VERIFICATION entries
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-/
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def massNumber (contribs : List Contribution) (risk : RiskVector) : Ratio :=
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let admissible := contribs.foldl (fun acc c => acc + c.term) 0
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{ numerator := { value := admissible, scale := 1 }
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, denominator := { value := risk.total, scale := 1 }
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}
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/-- Phi = admissible / (admissible + residual). -/
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def massPhi (contribs : List Contribution) (risk : RiskVector) : Ratio :=
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let a := contribs.foldl (fun acc c => acc + c.term) 0
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let u := risk.amount
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if h : a + u = 0 then
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{ numerator := { value := 0, scale := 1 }, denominator := { value := 1, scale := 1 } }
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else
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{ numerator := { value := a, scale := 1 }, denominator := { value := a + u, scale := 1 } }
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/-- Distance cost = residual / (admissible + 1). -/
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def phiDistanceCost (contribs : List Contribution) (risk : RiskVector) : Ratio :=
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{ numerator := { value := risk.amount, scale := 1 }
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, denominator := { value := contribs.foldl (fun acc c => acc + c.term) 0 + 1, scale := 1 }
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}
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/-- Autodoc pressure: M · novelty · compression · handoff / (1 + unresolved + drift + load + violation). -/
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structure AutodocParams where
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novelty : Nat
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compression : Nat
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handoffValue : Nat
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unresolved : Nat
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deriving Repr
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def autodocPressure (contribs : List Contribution) (risk : RiskVector) (p : AutodocParams) : Ratio :=
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let m := massNumber contribs risk
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{ numerator := { value := m.numerator.value * p.novelty * p.compression * p.handoffValue, scale := 1 }
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, denominator := { value := m.denominator.value * (1 + p.unresolved + risk.drift + risk.load + risk.violation), scale := 1 }
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}
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/-!
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═══════════════════════════════════════════════════════
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PATTERN 2: GRADIENT / DERIVATIVE
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All curvature, geodesic, force, flux equations.
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═══════════════════════════════════════════════════════
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-/
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/-- A geodesic gradient: d²x/dτ² + Γ·(dx/dτ)² = F. -/
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structure GeodesicParams where
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mass : Quantity
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damping : Quantity -- γ
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curvature : Quantity -- Γ (Christoffel)
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drivingForce : Quantity -- F(t)
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deriving Repr
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/--
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Geodesic evolution step:
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a = -(Γ_θθ·v² + 2·Γ_θφ·v·w + Γ_φφ·w²) + F
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Maps to: GWL Geodesic Integration, Dyson Swarm, Virtual Alcubierre,
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NONLINEAR PDES (Burgers, Cole-Hopf), Nonlinear Dynamics
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-/
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def geodesicStep (params : GeodesicParams) (position velocity : Quantity) : Quantity :=
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-- Simplified: a = F - γ·v - Γ·v²
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let acceleration := max 0 (params.drivingForce.value
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- params.damping.value * velocity.value / max velocity.scale 1
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- params.curvature.value * velocity.value * velocity.value / max (velocity.scale * velocity.scale) 1)
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{ value := acceleration, scale := 1 }
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/--
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Burgers equation: ∂u/∂t + u·∂u/∂x = ν·∂²u/∂x² + F
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Unified gradient flow for: BurgersPDE, Burgers2DPDE, Burgers3DPDE,
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StochasticBurgersPDE, ColeHopfTransform, Nonlinear PDEs, Fluid Dynamics
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-/
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structure BurgersParams where
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viscosity : Quantity -- ν
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forcing : Quantity -- F
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noiseStrength : Quantity -- for stochastic variant
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deriving Repr
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def burgersStep (u : Quantity) (gradU : Quantity) (laplacianU : Quantity) (params : BurgersParams) : Quantity :=
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-- u_t = -u·u_x + ν·u_xx + F + σ·ξ
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{ value := u.value * gradU.value / max u.scale 1
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+ params.viscosity.value * laplacianU.value / max laplacianU.scale 1
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+ params.forcing.value
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, scale := 1 }
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/-!
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═══════════════════════════════════════════════════════
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PATTERN 3: COUPLING / OSCILLATOR
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All GWL rotation/coupling, braid, attention, phonon equations.
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═══════════════════════════════════════════════════════
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-/
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/-- A coupling between two nodes i and j. -/
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structure Coupling where
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spatialWeight : Quantity -- exp(-|Δp|²/2σ²)
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angularWeight : Quantity -- cos(Δθ)
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temporalWeight : Quantity -- cos(2πΔτ/16)
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chiralFactor : Quantity -- 1 - 2|χ_i - χ_j|
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deriving Repr
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def Coupling.total (c : Coupling) : Quantity :=
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{ value := c.spatialWeight.value * c.angularWeight.value
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* c.temporalWeight.value * c.chiralFactor.value / 1000000
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, scale := 1 }
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/--
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Complete 5-factor weight:
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w = cos(Δθ·π/8) · cos(Δφ·π/8) · cos(2πΔτ/16) · (1-2|Δχ|) · exp(-|Δp|²/2σ²)
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Maps to: GWL Rotation, GWL Temporal, GWL Throat, Braid Field Theory,
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DAG Force, Phonon Graph, Constitutive Law
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-/
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def couplingWeight
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(dTheta dPhi dTau dChi : Quantity) -- phase differences
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(distSq variance : Quantity) -- spatial distance
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: Quantity :=
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let ang := dTheta.value * dPhi.value * dTau.value / (dTheta.scale * dPhi.scale * dTau.scale)
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let distTerm := (distSq.value * 1000) / (variance.value * 2) -- approximate exp(-d²/2σ²)
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{ value := ang * (1 - 2 * dChi.value / max dChi.scale 1) * 1000 / max (distTerm + 1000) 1
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, scale := 1 }
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/--
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Activation flow between nodes: F_ij = w_ij · (a_j - a_i) · Δp_ij / |Δp_ij|
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Maps to: GWL Interaction Force, all LAYER_C_BRAID entries
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-/
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def interactionForce (weight : Quantity) (activationI activationJ : Quantity) (distance : Quantity) : Quantity :=
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{ value := weight.value * (activationJ.value - activationI.value) / max weight.scale 1
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, scale := 1 }
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/--
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Braided monoidal coupling: F_braid = σ_l·σ_t·σ_c with braid relations
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Maps to: BraidTopology, Cache Sieve, Bracket Braid
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-/
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structure BraidCoupling where
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strandL : Nat -- left strand index
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strandR : Nat -- right strand index
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over : Bool -- true = over-crossing, false = under-crossing
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deriving Repr
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/--
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Energy of braid state: E = -½ Σ w_ij · a_i · a_j + Σ V(a_i)
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Maps to: Energy Function, Lyapunov functional, Hamiltonian
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-/
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def braidEnergy (activations : List Quantity) (couplings : List Coupling) : Quantity :=
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let pairEnergy := couplings.foldl (fun acc c => acc + c.total.value) 0
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let potEnergy := activations.foldl (fun acc a => acc + a.value * a.value) 0
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{ value := potEnergy / 2 - pairEnergy, scale := 1 }
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/-!
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═══════════════════════════════════════════════════════
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PATTERN 4: ENTROPY / INFORMATION
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All Shannon, Renyi, Kolmogorov, mutual information equations.
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═══════════════════════════════════════════════════════
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-/
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/-- Byte frequency distribution. -/
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structure ByteDist where
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counts : List Nat -- 256 entries, one per byte value
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total : Nat
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deriving Repr
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def ByteDist.probability (d : ByteDist) (byte : Nat) : Quantity :=
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if h : byte < d.counts.length then
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{ value := d.counts.get! byte * 1000, scale := d.total * 1000 }
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else
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{ value := 0, scale := 1 }
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/--
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Shannon entropy: H = -Σ p(b) log₂ p(b)
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Maps to: ALL LAYER_A_COMPRESSION entries, Cognitive Load,
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MI Signal, EntropyMeasures, intrinsic_load
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-/
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def shannonEntropy (dist : ByteDist) : Entropy :=
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let h := dist.counts.foldl (fun acc cnt =>
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if cnt = 0 then acc else
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let p := cnt * 1000 / max dist.total 1
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let logTerm := 0 -- simplified: would use real log2
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acc + p * logTerm) 0
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{ bitsPerByte := { value := h / 8, scale := 1000 }
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, totalBits := { value := h, scale := 1000 }
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}
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/--
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Kolmogorov complexity estimate: K ≈ (8 - H) / 8
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-/
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def kolmogorovEstimate (e : Entropy) : Quantity :=
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{ value := 8 * e.bitsPerByte.scale - e.bitsPerByte.value
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, scale := 8 * e.bitsPerByte.scale
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}
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/--
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Mutual information: MI = H_initial - H_current
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Maps to: MI Signal, Hutter Shape Equation, all compression metrics
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-/
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def mutualInformation (initial current : Entropy) : Quantity :=
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{ value := initial.totalBits.value - current.totalBits.value
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, scale := initial.totalBits.scale
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}
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/-!
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═══════════════════════════════════════════════════════
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PATTERN 5: SCALING / ALLOMETRY
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All power-law, exponential, metabolic scaling equations.
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═══════════════════════════════════════════════════════
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-/
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/-- Power-law scaling: Y = a · M^b · exp(-E/kT). -/
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structure ScalingParams where
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coefficient : Quantity -- a
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exponent : Quantity -- b
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activation : Quantity -- E (activation energy)
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temperature : Quantity -- T
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boltzmann : Quantity -- k
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deriving Repr
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/--
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Allometric/metabolic scaling equation.
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Maps to: Biophysics allometry, Metabolic scaling, MTE Master Equation,
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Quarter-power laws, WBE Branching, Kleiber's law, Life History
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-/
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def allometricScaling (mass : Quantity) (params : ScalingParams) : Quantity :=
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let massPow := mass.value ^ params.exponent.value -- M^b
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let boltzExp := if params.temperature.value > 0 then
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params.activation.value * params.boltzmann.value / params.temperature.value else 0
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{ value := params.coefficient.value * massPow / max (boltzExp + 1) 1
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, scale := params.coefficient.scale
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}
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/--
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Arrhenius factor: k = A · exp(-E_a / RT)
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Maps to: Thermodynamics, Arrhenius Equation, Informatic Stress,
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QCL Energy Temperature Tuning
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-/
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def arrheniusFactor (activation temp gasConstant : Quantity) : Quantity :=
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if temp.value = 0 then { value := 0, scale := 1 } else
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{ value := activation.value * gasConstant.value / temp.value
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, scale := 1 }
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/--
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Logistic/sigmoidal: f(X) = X^n / (K^n + X^n)
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Maps to: Hill Regulation, Monod Equation, all LAYER_F_CONTROL
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-/
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def hillFunction (x threshold : Quantity) (n : Nat) : Quantity :=
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let xn := x.value ^ n
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let kn := threshold.value ^ n
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{ value := xn * 1000 / max (kn + xn) 1
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, scale := 1000 }
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/-!
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═══════════════════════════════════════════════════════
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PATTERN 6: FEEDBACK / CONTROL
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All dynamical systems, recurrence, update rules, homeostasis.
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═══════════════════════════════════════════════════════
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-/
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/-- A generic feedback system state. -/
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structure FeedbackState where
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value : Quantity
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integral : Quantity
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previous : Quantity
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deriving Repr
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/-- PID-like controller: u(t) = Kp·e(t) + Ki·∫e + Kd·d(e)/dt. -/
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structure PIDParams where
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kp : Quantity
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ki : Quantity
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kd : Quantity
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setpoint : Quantity
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deriving Repr
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/--
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PID control update.
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Maps to: Control Theory, Homeostatic Control, Biological Regulation,
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all LAYER_F_CONTROL entries with feedback
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-/
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def pidUpdate (state : FeedbackState) (params : PIDParams) : FeedbackState :=
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let error := params.setpoint.value - state.value.value
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let pTerm := params.kp.value * error / max params.kp.scale 1
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let iTerm := params.ki.value * state.integral.value / max params.ki.scale 1
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let dTerm := params.kd.value * (state.value.value - state.previous.value) / max params.kd.scale 1
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let output := pTerm + iTerm + dTerm
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{ value := { value := state.value.value + output, scale := state.value.scale }
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, integral := { value := state.integral.value + error, scale := state.integral.scale }
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, previous := state.value
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}
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/--
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Homeostatic stress dynamics: s = α·surprise + β·regret; p update with decay
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Maps to: Homeostatic Control, Cognitive Load, Waveprobe Control
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-/
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structure HomeostaticParams where
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alpha : Quantity -- surprise weight
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beta : Quantity -- regret weight
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decay : Quantity -- γ, pressure decay
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canalWidth : Quantity -- λ₀
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deriving Repr
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/--
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Canal deformation: λ = λ₀ · (σ + (1-σ)·e^{-ξ·p})
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Maps to: Dynamic Canal Theory, all routing equations
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-/
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def canalWidth (pressure : Quantity) (params : HomeostaticParams) : Quantity :=
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let decayTerm := params.decay.value * pressure.value / max params.decay.scale 1
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{ value := params.canalWidth.value * 1000 / max (decayTerm + 1000) 1
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, scale := 1000 }
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/-!
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═══════════════════════════════════════════════════════
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PATTERN 7: CHAIN / PIPELINE
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All sequential transformations, compression pipelines,
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encoding/decoding chains.
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═══════════════════════════════════════════════════════
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-/
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/-- A transformation in the pipeline. -/
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structure Transform where
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name : String
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inputDim : Nat
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outputDim : Nat
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apply : List Quantity → List Quantity -- function stored as string (for Lean)
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deriving Repr
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/--
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Pipeline: compose transforms in sequence.
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Maps to: ALL SHIFTERS, Compression Pipelines, Data Encoding Chains
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-/
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def pipeline (input : List Quantity) (transforms : List Transform) : List Quantity :=
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transforms.foldl (fun data t => t.apply data) input
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/--
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Token bucket: rate-limited pipeline stage.
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Maps to: Manifold Token Bucket, Rate Limiting, Congestion Control
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-/
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def tokenBucket (tokens : Quantity) (rate : Quantity) (bucketSize : Quantity) (cost : Nat) : Quantity :=
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let consumed := tokens.value - cost
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let refilled := consumed + rate.value
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{ value := min refilled bucketSize.value
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, scale := tokens.scale
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}
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/-!
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═══════════════════════════════════════════════════════
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COMPLETE FAMILY → PATTERN MAP
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Every family from MATH_MODEL_MAP.tsv mapped to its
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dominant pattern(s).
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═══════════════════════════════════════════════════════
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-/
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||
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inductive UnifiedPattern where
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| mass -- massNumber, phi, distance, autodoc
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| gradient -- geodesic, Burgers, Navier-Stokes, F=ma
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| coupling -- GWL, braid, oscillator, attention, phonon
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| entropy -- Shannon, Kolmogorov, MI, information
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| 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
|