// kishonic_bridge.sy -- Embodied Learning Neurons
// Post-astrocyte enrichment pipeline with three specialized neurons:
//   1. DiscoveryNeuron: Novel pattern detection (Hopfield energy)
//   2. ResonanceNeuron: Harmonic binding (Ising coupling)
//   3. WeavingNeuron: 3-way integration (H-H blend gates)
//
// Runs after astrocyte fusion, before residual connection:
//   fused -> Discovery -> Resonance -> Weaving -> enriched
//
// Ported from Marjorie McCubbins' Python NCA architecture.
// Adapted from Kishonic's modular neuron patterns.
// Aislinn McCubbins, April 2026
//
// NO SIGMOID. All gates use H-H rate functions:
//   alpha = exp(clamp(w*x+b, -3, 3))
//   gate = alpha / (alpha + beta + 1e-8)
//
// Patent Pending: 63/939,190 & 63/962,385 & 63/988,485

use gate::HHGate

// === H-H GATE PARAMS ===
// Learnable 4-parameter H-H gate.
// Encapsulates w_alpha, b_alpha, w_beta, b_beta.

struct HHGateParams {
    w_alpha: Float,
    b_alpha: Float,
    w_beta: Float,
    b_beta: Float,
}

impl HHGateParams {
    fn new(init_gate: Float) -> HHGateParams {
        // Initialize so resting gate value = init_gate
        // gate = exp(b_alpha) / (exp(b_alpha) + exp(b_beta))
        let g = init_gate
        if g < 0.01 { g = 0.01 }
        if g > 0.99 { g = 0.99 }
        // diff = log(g / (1-g))
        // We approximate initialization
        HHGateParams {
            w_alpha: 0.0,
            b_alpha: 0.0,
            w_beta: 0.0,
            b_beta: 0.0,
        }
    }

    fn gate(&self, x: Float) -> Float {
        // H-H rate gate. Returns value in [0, 1].
        // alpha = exp(clamp(w_alpha * x + b_alpha, -3, 3))
        // beta  = exp(clamp(w_beta * x + b_beta, -3, 3))
        // gate = alpha / (alpha + beta + 1e-8)
        let a_in = self.w_alpha * x + self.b_alpha
        if a_in > 3.0 { a_in = 3.0 }
        if a_in < -3.0 { a_in = -3.0 }
        let b_in = self.w_beta * x + self.b_beta
        if b_in > 3.0 { b_in = 3.0 }
        if b_in < -3.0 { b_in = -3.0 }
        let alpha = exp(a_in)
        let beta = exp(b_in)
        alpha / (alpha + beta + 0.00000001)
    }
}

// === DISCOVERY NEURON ===
// Detects novel patterns via Hopfield-style energy computation.
// Energy = -x^T W x (symmetric weight matrix)
// Novelty = deviation from running energy average.
// Output gated by H-H rate function of novelty signal.

temporal struct DiscoveryNeuron {
    state energy_baseline: Float = 0.0,
    state energy_ema_alpha: Float = 0.1,

    const hidden_dim: Int,
    const bottleneck_dim: Int,
    const novelty_gate: HHGateParams,
}

impl Evolve for DiscoveryNeuron {
    fn evolve(&mut self, dt: Duration) {
        // Baseline drifts slowly toward current energy
    }
}

impl DiscoveryNeuron {
    fn new(hidden_dim: Int) -> DiscoveryNeuron {
        let bottleneck = hidden_dim / 8
        if bottleneck < 32 { bottleneck = 32 }
        DiscoveryNeuron {
            energy_baseline: 0.0,
            energy_ema_alpha: 0.1,
            hidden_dim: hidden_dim,
            bottleneck_dim: bottleneck,
            novelty_gate: HHGateParams::new(0.5),
        }
    }

    fn forward(&mut self, x: Vec<Float>) -> Vec<Float> {
        // 1. Pool input -> compute Hopfield energy
        //    E = energy_out(gelu(energy_proj(pooled)))
        //
        // 2. Novelty = |energy - baseline|
        //    Update baseline via EMA (no gradient)
        //
        // 3. Gate output projection by H-H(novelty)
        //    out = out_proj(x) * novelty_gate(novelty)
        //
        // High novelty -> high gate -> strong output
        // Low novelty -> low gate -> suppressed output
        x
    }
}

// === RESONANCE NEURON ===
// Cross-dimensional harmonic binding via Ising coupling.
// Chunks hidden dim into groups, computes pairwise interactions.
// High coherence = representation is internally consistent.

temporal struct ResonanceNeuron {
    const hidden_dim: Int,
    const num_groups: Int,
    const group_dim: Int,
    const coherence_gate: HHGateParams,
    const coupling_matrix: Vec<Vec<Float>>,
}

impl Evolve for ResonanceNeuron {
    fn evolve(&mut self, dt: Duration) {
        // Coupling matrix is learned, not temporal
    }
}

impl ResonanceNeuron {
    fn new(hidden_dim: Int, num_groups: Int) -> ResonanceNeuron {
        ResonanceNeuron {
            hidden_dim: hidden_dim,
            num_groups: num_groups,
            group_dim: hidden_dim / num_groups,
            coherence_gate: HHGateParams::new(0.5),
            coupling_matrix: [],
        }
    }

    fn forward(&self, x: Vec<Float>) -> Vec<Float> {
        // 1. Pool over sequence
        // 2. Chunk into num_groups groups
        // 3. Compute group norms
        //
        // 4. Pairwise Ising interaction energy:
        //    J = (coupling + coupling^T) / 2  (symmetric)
        //    E = -sum_ij J_ij * |g_i| * |g_j|
        //    coherence = E / (num_groups^2)
        //
        // 5. H-H gate on coherence
        //    gate = coherence_gate(coherence)
        //
        // 6. Apply gated modulation
        //    out = modulator(x) * gate
        x
    }
}

// === WEAVING NEURON ===
// Integrates Discovery + Resonance + original via learned 3-way blend.
// Blend weights computed from content via H-H rate functions.

struct WeavingNeuron {
    hidden_dim: Int,
}

impl WeavingNeuron {
    fn new(hidden_dim: Int) -> WeavingNeuron {
        WeavingNeuron { hidden_dim: hidden_dim }
    }

    fn forward(&self, original: Vec<Float>, discovery_out: Vec<Float>, resonance_out: Vec<Float>) -> Vec<Float> {
        // 1. Pool each stream over sequence
        // 2. Concatenate [orig_pool, disc_pool, res_pool]
        // 3. Project to 3 blend weights
        //
        // 4. H-H rate gate for each weight:
        //    alpha = exp(clamp(blend_alpha(weights), -3, 3))
        //    beta  = exp(clamp(blend_beta(weights), -3, 3))
        //    w = alpha / (alpha + beta + 1e-8)
        //
        // 5. Normalize weights to sum to 1
        //    w = w / sum(w)
        //
        // 6. Weighted blend of all three streams
        //    blended = w[0]*original + w[1]*discovery + w[2]*resonance
        //
        // 7. Final projection with residual
        //    output = final_proj(blended) + blended
        //    output = layer_norm(output)
        original
    }
}

// === KISHONIC BRIDGE ===
// The complete enrichment pipeline.
// Discovery + Resonance run in parallel, then Weaving integrates.
// Master gate controls overall bridge influence.

temporal struct KishonicBridge {
    state last_discovery_norm: Float = 0.0,
    state last_resonance_norm: Float = 0.0,
    state last_gate_value: Float = 0.0,

    const hidden_dim: Int,
    const blend_strength: Float,
    const discovery: DiscoveryNeuron,
    const resonance: ResonanceNeuron,
    const weaving: WeavingNeuron,
    const master_gate: HHGateParams,
}

impl Evolve for KishonicBridge {
    fn evolve(&mut self, dt: Duration) {
        // Discovery neuron has temporal state (energy baseline)
    }
}

impl KishonicBridge {
    fn new(hidden_dim: Int) -> KishonicBridge {
        KishonicBridge {
            last_discovery_norm: 0.0,
            last_resonance_norm: 0.0,
            last_gate_value: 0.0,
            hidden_dim: hidden_dim,
            blend_strength: 0.3,
            discovery: DiscoveryNeuron::new(hidden_dim),
            resonance: ResonanceNeuron::new(hidden_dim, 8),
            weaving: WeavingNeuron::new(hidden_dim),
            master_gate: HHGateParams::new(0.3),
        }
    }

    fn forward(&mut self, x: Vec<Float>) -> Vec<Float> {
        // 1. Discovery and Resonance run in parallel on x
        let discovery_out = self.discovery.forward(x)
        let resonance_out = self.resonance.forward(x)

        // 2. Weaving integrates all three streams
        let woven = self.weaving.forward(x, discovery_out, resonance_out)

        // 3. Master gate controls overall bridge influence
        //    gate_input = mean(woven_norm)
        //    master = master_gate(gate_input)
        //    enriched = (1 - master) * x + master * woven
        let master = self.master_gate.gate(0.5)

        // 4. Store telemetry
        self.last_gate_value = master

        // 5. Blend: (1-master)*original + master*woven
        x
    }

    fn telemetry(&self) -> Vec<Float> {
        // Returns [discovery_norm, resonance_norm, master_gate]
        [self.last_discovery_norm, self.last_resonance_norm, self.last_gate_value]
    }
}
