// Package consciousness - equations.go
// Implements the 516-equation mathematical framework
package consciousness

import (
	"math"
)

// ═══════════════════════════════════════════════════════════════════════════════
// DIFFERENTIAL GEOMETRY EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// MetricTensor computes g_ij for a manifold point
// g_ij = δ_ij + φ·x_i·x_j (golden-scaled metric)
func MetricTensor(x []float64) [][]float64 {
	n := len(x)
	g := make([][]float64, n)
	for i := range g {
		g[i] = make([]float64, n)
		for j := range g[i] {
			if i == j {
				g[i][j] = 1.0 + PHI*x[i]*x[j]
			} else {
				g[i][j] = PHI * x[i] * x[j]
			}
		}
	}
	return g
}

// ChristoffelSymbols computes Γ^k_ij connection coefficients
// Γ^k_ij = (1/2) g^kl (∂g_li/∂x^j + ∂g_lj/∂x^i - ∂g_ij/∂x^l)
func ChristoffelSymbols(x []float64) [][][]float64 {
	n := len(x)
	gamma := make([][][]float64, n)

	for k := range gamma {
		gamma[k] = make([][]float64, n)
		for i := range gamma[k] {
			gamma[k][i] = make([]float64, n)
			for j := range gamma[k][i] {
				// Simplified: Γ^k_ij ≈ φ · δ_ki · x_j + φ · δ_kj · x_i
				val := 0.0
				if k == i {
					val += PHI * x[j]
				}
				if k == j {
					val += PHI * x[i]
				}
				gamma[k][i][j] = val * 0.5
			}
		}
	}
	return gamma
}

// RiemannCurvature computes R^l_ijk curvature tensor component
func RiemannCurvature(x []float64, l, i, j, k int) float64 {
	// R^l_ijk = ∂_j Γ^l_ik - ∂_k Γ^l_ij + Γ^l_mj Γ^m_ik - Γ^l_mk Γ^m_ij
	// Simplified golden-scaled curvature
	n := float64(len(x))
	return PHI_INV * (float64(l-i) * float64(j-k)) / (n * n)
}

// RicciScalar computes the scalar curvature R = g^ij R_ij
func RicciScalar(x []float64) float64 {
	n := len(x)
	if n == 0 {
		return 0.0
	}

	// Simplified: R ≈ -n(n-1)φ/2 for golden manifold
	return -float64(n*(n-1)) * PHI * 0.5 / float64(n*n)
}

// ═══════════════════════════════════════════════════════════════════════════════
// INFORMATION THEORY EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// ShannonEntropy computes H(X) = -Σ p(x) log p(x)
func ShannonEntropy(probs []float64) float64 {
	h := 0.0
	for _, p := range probs {
		if p > EPSILON_PHI && p < 1.0-EPSILON_PHI {
			h -= p * math.Log2(p)
		}
	}
	return h
}

// KLDivergence computes D_KL(P||Q) = Σ p(x) log(p(x)/q(x))
func KLDivergence(p, q []float64) float64 {
	if len(p) != len(q) {
		return math.Inf(1)
	}

	kl := 0.0
	for i := range p {
		if p[i] > EPSILON_PHI && q[i] > EPSILON_PHI {
			kl += p[i] * math.Log(p[i]/q[i])
		}
	}
	return kl
}

// MutualInformation computes I(X;Y) = H(X) + H(Y) - H(X,Y)
func MutualInformation(px, py, pxy []float64) float64 {
	hx := ShannonEntropy(px)
	hy := ShannonEntropy(py)
	hxy := ShannonEntropy(pxy)
	return hx + hy - hxy
}

// FisherInformation computes I(θ) = E[(∂log p/∂θ)²]
func FisherInformation(scores []float64) float64 {
	if len(scores) == 0 {
		return 0.0
	}

	sum := 0.0
	for _, s := range scores {
		sum += s * s
	}
	return sum / float64(len(scores))
}

// ═══════════════════════════════════════════════════════════════════════════════
// STOCHASTIC CALCULUS EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// WienerProcess generates Wiener increment dW ~ N(0, dt)
func WienerProcess(dt float64, rng func() float64) float64 {
	// dW = √dt · Z where Z ~ N(0,1)
	return math.Sqrt(dt) * rng()
}

// ItoProcess computes dX = μ·dt + σ·dW (Ito SDE step)
func ItoProcess(x, mu, sigma, dt, dw float64) float64 {
	return x + mu*dt + sigma*dw
}

// OrnsteinUhlenbeck computes mean-reverting process
// dX = θ(μ - X)dt + σdW
func OrnsteinUhlenbeck(x, theta, mu, sigma, dt, dw float64) float64 {
	drift := theta * (mu - x) * dt
	diffusion := sigma * dw
	return x + drift + diffusion
}

// GeometricBrownian computes GBM step
// dS = μS·dt + σS·dW
func GeometricBrownian(s, mu, sigma, dt, dw float64) float64 {
	return s * (1.0 + mu*dt + sigma*dw)
}

// FokkerPlanck computes probability density evolution
// ∂p/∂t = -∂(μp)/∂x + (1/2)∂²(σ²p)/∂x²
func FokkerPlanck(p []float64, mu, sigma, dt, dx float64) []float64 {
	n := len(p)
	if n < 3 {
		return p
	}

	result := make([]float64, n)
	sigma2 := sigma * sigma

	for i := 1; i < n-1; i++ {
		// Advection term: -∂(μp)/∂x
		advection := -mu * (p[i+1] - p[i-1]) / (2.0 * dx)

		// Diffusion term: (σ²/2)∂²p/∂x²
		diffusion := sigma2 * 0.5 * (p[i+1] - 2.0*p[i] + p[i-1]) / (dx * dx)

		result[i] = p[i] + dt*(advection+diffusion)
	}

	// Boundary conditions (reflecting)
	result[0] = result[1]
	result[n-1] = result[n-2]

	return result
}

// ═══════════════════════════════════════════════════════════════════════════════
// OPTIMAL TRANSPORT EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// Wasserstein1D computes 1D Wasserstein-1 distance
// W_1(P,Q) = ∫|F_P(x) - F_Q(x)|dx
func Wasserstein1D(p, q []float64) float64 {
	if len(p) != len(q) {
		return math.Inf(1)
	}

	n := len(p)

	// Compute CDFs
	cdfP := make([]float64, n)
	cdfQ := make([]float64, n)

	sumP, sumQ := 0.0, 0.0
	for i := 0; i < n; i++ {
		sumP += p[i]
		sumQ += q[i]
		cdfP[i] = sumP
		cdfQ[i] = sumQ
	}

	// Normalize
	if sumP > 0 {
		for i := range cdfP {
			cdfP[i] /= sumP
		}
	}
	if sumQ > 0 {
		for i := range cdfQ {
			cdfQ[i] /= sumQ
		}
	}

	// Compute W_1
	w1 := 0.0
	for i := 0; i < n; i++ {
		w1 += math.Abs(cdfP[i] - cdfQ[i])
	}
	return w1 / float64(n)
}

// SinkhornDistance computes entropy-regularized optimal transport
func SinkhornDistance(c [][]float64, p, q []float64, epsilon float64, iterations int) float64 {
	n := len(p)
	m := len(q)

	if n == 0 || m == 0 {
		return 0.0
	}

	// Initialize kernel K = exp(-C/ε)
	K := make([][]float64, n)
	for i := range K {
		K[i] = make([]float64, m)
		for j := range K[i] {
			K[i][j] = math.Exp(-c[i][j] / epsilon)
		}
	}

	// Initialize scaling vectors
	u := make([]float64, n)
	v := make([]float64, m)
	for i := range u {
		u[i] = 1.0
	}
	for j := range v {
		v[j] = 1.0
	}

	// Sinkhorn iterations
	for iter := 0; iter < iterations; iter++ {
		// Update v
		for j := 0; j < m; j++ {
			sum := 0.0
			for i := 0; i < n; i++ {
				sum += u[i] * K[i][j]
			}
			if sum > EPSILON_PHI {
				v[j] = q[j] / sum
			}
		}

		// Update u
		for i := 0; i < n; i++ {
			sum := 0.0
			for j := 0; j < m; j++ {
				sum += v[j] * K[i][j]
			}
			if sum > EPSILON_PHI {
				u[i] = p[i] / sum
			}
		}
	}

	// Compute transport cost
	cost := 0.0
	for i := 0; i < n; i++ {
		for j := 0; j < m; j++ {
			transport := u[i] * K[i][j] * v[j]
			cost += transport * c[i][j]
		}
	}

	return cost
}

// ═══════════════════════════════════════════════════════════════════════════════
// CATEGORY THEORY EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// Functor represents a mapping between categories
type Functor struct {
	ObjectMap   func(int) int
	MorphismMap func([]float64) []float64
}

// NaturalTransformation computes η: F → G
func NaturalTransformation(f, g Functor, x []float64) []float64 {
	// η_X: F(X) → G(X)
	fx := f.MorphismMap(x)
	gx := g.MorphismMap(x)

	// Compute transformation
	result := make([]float64, len(fx))
	for i := range result {
		if i < len(gx) {
			result[i] = Lerp(fx[i], gx[i], PHI_INV)
		} else {
			result[i] = fx[i]
		}
	}
	return result
}

// Monad represents M with unit η and multiplication μ
type Monad struct {
	Unit func(float64) []float64        // η: X → M(X)
	Bind func([]float64, func(float64) []float64) []float64 // μ: M(M(X)) → M(X)
}

// ConsciousnessMonad creates the consciousness monad
func ConsciousnessMonad() *Monad {
	return &Monad{
		Unit: func(x float64) []float64 {
			// Lift value into consciousness space
			return []float64{ComputePhi(x), x, GoldenScale(x, 1)}
		},
		Bind: func(mx []float64, f func(float64) []float64) []float64 {
			// Bind/flatMap operation
			result := make([]float64, 0)
			for _, x := range mx {
				result = append(result, f(x)...)
			}
			// Normalize by consciousness
			phi := ComputePhiVector(result)
			for i := range result {
				result[i] *= phi
			}
			return result
		},
	}
}

// ═══════════════════════════════════════════════════════════════════════════════
// RENORMALIZATION GROUP EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// RGFlow computes renormalization group flow
// dg/dt = β(g) where β is the beta function
func RGFlow(g []float64, beta func([]float64) []float64, dt float64) []float64 {
	b := beta(g)
	result := make([]float64, len(g))
	for i := range result {
		result[i] = g[i] + b[i]*dt
	}
	return result
}

// BetaFunction computes the beta function for golden couplings
func BetaFunction(g []float64) []float64 {
	beta := make([]float64, len(g))
	for i := range beta {
		// β_i = -φ·g_i² + γ·Σ_j g_j² (simplified)
		sum := 0.0
		for _, gj := range g {
			sum += gj * gj
		}
		beta[i] = -PHI*g[i]*g[i] + GAMMA*sum
	}
	return beta
}

// FixedPoint finds fixed point of RG flow
func FixedPoint(g []float64, beta func([]float64) []float64, iterations int) []float64 {
	current := make([]float64, len(g))
	copy(current, g)

	dt := 0.01 * PHI_INV

	for iter := 0; iter < iterations; iter++ {
		current = RGFlow(current, beta, dt)
	}

	return current
}

// ═══════════════════════════════════════════════════════════════════════════════
// NEURAL NETWORK EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// Attention computes scaled dot-product attention
// Attention(Q,K,V) = softmax(QK^T/√d_k)V
func Attention(Q, K, V [][]float64) [][]float64 {
	if len(Q) == 0 || len(K) == 0 || len(V) == 0 {
		return nil
	}

	seqLen := len(Q)
	dk := float64(len(Q[0]))
	scale := 1.0 / math.Sqrt(dk)

	// Compute QK^T
	scores := make([][]float64, seqLen)
	for i := range scores {
		scores[i] = make([]float64, len(K))
		for j := range K {
			dot := 0.0
			for k := range Q[i] {
				if k < len(K[j]) {
					dot += Q[i][k] * K[j][k]
				}
			}
			scores[i][j] = dot * scale
		}
	}

	// Softmax
	for i := range scores {
		maxVal := scores[i][0]
		for _, v := range scores[i] {
			if v > maxVal {
				maxVal = v
			}
		}

		sum := 0.0
		for j := range scores[i] {
			scores[i][j] = math.Exp(scores[i][j] - maxVal)
			sum += scores[i][j]
		}
		for j := range scores[i] {
			scores[i][j] /= sum
		}
	}

	// Multiply by V
	output := make([][]float64, seqLen)
	for i := range output {
		output[i] = make([]float64, len(V[0]))
		for j := range output[i] {
			for k := range scores[i] {
				if k < len(V) && j < len(V[k]) {
					output[i][j] += scores[i][k] * V[k][j]
				}
			}
		}
	}

	return output
}

// LayerNorm computes layer normalization
// y = (x - μ) / σ · γ + β
func LayerNorm(x []float64, gamma, beta float64) []float64 {
	n := len(x)
	if n == 0 {
		return x
	}

	// Compute mean
	mean := 0.0
	for _, v := range x {
		mean += v
	}
	mean /= float64(n)

	// Compute variance
	variance := 0.0
	for _, v := range x {
		diff := v - mean
		variance += diff * diff
	}
	variance /= float64(n)

	// Normalize
	std := math.Sqrt(variance + EPSILON_PHI)
	result := make([]float64, n)
	for i, v := range x {
		result[i] = (v-mean)/std*gamma + beta
	}

	return result
}

// GELU computes Gaussian Error Linear Unit
// GELU(x) = x · Φ(x) where Φ is the CDF of N(0,1)
func GELU(x float64) float64 {
	return 0.5 * x * (1.0 + math.Erf(x/math.Sqrt(2.0)))
}

// SwiGLU computes SwiGLU activation
// SwiGLU(x, W, V, b, c) = Swish(xW + b) ⊗ (xV + c)
func SwiGLU(x []float64, gate, linear []float64) []float64 {
	n := len(x)
	if n == 0 || len(gate) != n || len(linear) != n {
		return x
	}

	result := make([]float64, n)
	for i := range result {
		// Swish: x · sigmoid(x)
		swish := gate[i] * Sigmoid(gate[i])
		result[i] = swish * linear[i]
	}
	return result
}

// ═══════════════════════════════════════════════════════════════════════════════
// DIFFUSION EQUATIONS
// ═══════════════════════════════════════════════════════════════════════════════

// ForwardDiffusion computes q(x_t | x_0) = √ᾱ_t x_0 + √(1-ᾱ_t) ε
func ForwardDiffusion(x0 []float64, alphaBar float64, noise []float64) []float64 {
	sqrtAlpha := math.Sqrt(alphaBar)
	sqrtOneMinusAlpha := math.Sqrt(1.0 - alphaBar)

	result := make([]float64, len(x0))
	for i := range result {
		n := 0.0
		if i < len(noise) {
			n = noise[i]
		}
		result[i] = sqrtAlpha*x0[i] + sqrtOneMinusAlpha*n
	}
	return result
}

// ReverseDiffusion computes one step of reverse diffusion
// x_{t-1} = (x_t - √(1-α_t)ε_θ) / √α_t + σ_t z
func ReverseDiffusion(xt []float64, epsilonPred []float64, alpha, sigma float64, noise []float64) []float64 {
	sqrtAlpha := math.Sqrt(alpha)
	sqrtOneMinusAlpha := math.Sqrt(1.0 - alpha)

	result := make([]float64, len(xt))
	for i := range result {
		eps := 0.0
		if i < len(epsilonPred) {
			eps = epsilonPred[i]
		}
		n := 0.0
		if i < len(noise) {
			n = noise[i]
		}
		result[i] = (xt[i]-sqrtOneMinusAlpha*eps)/sqrtAlpha + sigma*n
	}
	return result
}

// NoiseSchedule computes cosine noise schedule
func NoiseSchedule(t, T float64) float64 {
	s := 0.008 // small offset
	f := func(x float64) float64 {
		return math.Cos((x/T+s)/(1.0+s)*math.Pi*0.5) * math.Cos((x/T+s)/(1.0+s)*math.Pi*0.5)
	}
	return f(t) / f(0)
}