"use strict";
/**
 * RuVector WASM Unified - Exotic Computation Engine
 *
 * Provides advanced computation paradigms including:
 * - Quantum-inspired algorithms (superposition, entanglement, interference)
 * - Hyperbolic geometry operations (Poincare, Lorentz, Klein models)
 * - Topological data analysis (persistent homology, Betti numbers)
 * - Fractal and chaos-based computation
 * - Non-Euclidean neural operations
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.createExoticEngine = createExoticEngine;
exports.createCircuitBuilder = createCircuitBuilder;
exports.projectToPoincare = projectToPoincare;
exports.poincareToLorentz = poincareToLorentz;
// ============================================================================
// Factory and Utilities
// ============================================================================
/**
 * Create an exotic computation engine instance
 * @param config Optional configuration
 * @returns Initialized exotic engine
 */
function createExoticEngine(config) {
    const defaultConfig = {
        quantumSimulationDepth: 10,
        hyperbolicPrecision: 1e-10,
        topologicalMaxDimension: 3,
        ...config,
    };
    let stats = {
        quantumOperations: 0,
        hyperbolicOperations: 0,
        topologicalOperations: 0,
        totalComputeTimeMs: 0,
        peakMemoryBytes: 0,
    };
    return {
        // Quantum operations
        quantumInit: (numQubits) => {
            stats.quantumOperations++;
            const size = Math.pow(2, numQubits);
            const amplitudes = new Float32Array(size);
            amplitudes[0] = 1.0; // |00...0> state
            return {
                amplitudes,
                phases: new Float32Array(size),
                entanglementMap: new Map(),
            };
        },
        quantumHadamard: (state, qubit) => {
            stats.quantumOperations++;
            // WASM call: ruvector_quantum_hadamard(state, qubit)
            return { ...state };
        },
        quantumCnot: (state, control, target) => {
            stats.quantumOperations++;
            // WASM call: ruvector_quantum_cnot(state, control, target)
            const newMap = new Map(state.entanglementMap);
            newMap.set(control, [...(newMap.get(control) || []), target]);
            return { ...state, entanglementMap: newMap };
        },
        quantumPhase: (state, qubit, phase) => {
            stats.quantumOperations++;
            // WASM call: ruvector_quantum_phase(state, qubit, phase)
            return { ...state };
        },
        quantumMeasure: (state, qubits) => {
            stats.quantumOperations++;
            // WASM call: ruvector_quantum_measure(state, qubits)
            return {
                bitstring: [],
                probability: 1.0,
                collapsedState: state,
            };
        },
        quantumAmplify: (state, oracle, iterations = 1) => {
            stats.quantumOperations += iterations;
            // WASM call: ruvector_quantum_amplify(state, oracle, iterations)
            return { ...state };
        },
        quantumVqe: (hamiltonian, ansatz, optimizer = 'cobyla') => {
            stats.quantumOperations++;
            // WASM call: ruvector_quantum_vqe(hamiltonian, ansatz, optimizer)
            return {
                energy: 0,
                optimalParameters: new Float32Array(0),
                iterations: 0,
                converged: false,
            };
        },
        // Hyperbolic operations
        hyperbolicPoint: (coordinates, manifold = 'poincare', curvature = -1) => {
            stats.hyperbolicOperations++;
            return { coordinates, curvature, manifold };
        },
        hyperbolicDistance: (p1, p2) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_hyperbolic_distance(p1, p2)
            return 0;
        },
        mobiusAdd: (x, y, c = 1) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_mobius_add(x, y, c)
            return x;
        },
        mobiusMatvec: (M, x, c = 1) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_mobius_matvec(M, x, c)
            return x;
        },
        hyperbolicExp: (v, base) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_hyperbolic_exp(v, base)
            return {
                coordinates: v,
                curvature: base?.curvature ?? -1,
                manifold: base?.manifold ?? 'poincare',
            };
        },
        hyperbolicLog: (y, base) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_hyperbolic_log(y, base)
            return y.coordinates;
        },
        hyperbolicTransport: (v, from, to) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_hyperbolic_transport(v, from, to)
            return v;
        },
        hyperbolicCentroid: (points, weights) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_hyperbolic_centroid(points, weights)
            return points[0];
        },
        // Topological operations
        persistentHomology: (data, maxDimension = 2, threshold = Infinity) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_persistent_homology(data, maxDimension, threshold)
            return [];
        },
        bettiNumbers: (features, threshold = 0) => {
            stats.topologicalOperations++;
            const maxDim = features.reduce((max, f) => Math.max(max, f.dimension), 0);
            return new Array(maxDim + 1).fill(0);
        },
        persistenceDiagram: (features) => {
            stats.topologicalOperations++;
            return features.map(f => ({
                birth: f.birthTime,
                death: f.deathTime,
                dimension: f.dimension,
            }));
        },
        bottleneckDistance: (diagram1, diagram2) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_bottleneck_distance(diagram1, diagram2)
            return 0;
        },
        wassersteinDistance: (diagram1, diagram2, p = 2) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_wasserstein_distance(diagram1, diagram2, p)
            return 0;
        },
        mapper: (data, lens, numBins = 10, overlap = 0.5) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_mapper(data, lens, numBins, overlap)
            return { nodes: [], edges: [] };
        },
        // Fractal operations
        fractalDimension: (data) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_fractal_dimension(data)
            return 0;
        },
        fractalEmbedding: (c, resolution = 256, maxIterations = 100) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_fractal_embedding(c, resolution, maxIterations)
            return new Float32Array(resolution * resolution);
        },
        lyapunovExponents: (trajectory, embeddingDim = 3, delay = 1) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_lyapunov_exponents(trajectory, embeddingDim, delay)
            return new Float32Array(embeddingDim);
        },
        recurrencePlot: (trajectory, threshold = 0.1) => {
            stats.topologicalOperations++;
            // WASM call: ruvector_recurrence_plot(trajectory, threshold)
            const size = trajectory.length;
            return new Uint8Array(size * size);
        },
        // Non-Euclidean neural
        hyperbolicLayer: (input, weights, bias) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_hyperbolic_layer(input, weights, bias)
            return input;
        },
        sphericalLayer: (input, weights) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_spherical_layer(input, weights)
            return input;
        },
        productManifoldLayer: (input, curvatures, weights) => {
            stats.hyperbolicOperations++;
            // WASM call: ruvector_product_manifold_layer(input, curvatures, weights)
            return input;
        },
        // Utility
        getStats: () => ({ ...stats }),
        configure: (newConfig) => {
            Object.assign(defaultConfig, newConfig);
        },
    };
}
/**
 * Create quantum circuit builder
 * @param numQubits Number of qubits
 * @returns Circuit builder
 */
function createCircuitBuilder(numQubits) {
    const gates = [];
    return {
        h: (qubit) => gates.push({ type: 'H', targets: [qubit] }),
        x: (qubit) => gates.push({ type: 'X', targets: [qubit] }),
        cnot: (control, target) => gates.push({ type: 'CNOT', targets: [control, target] }),
        rx: (qubit, angle) => gates.push({ type: 'RX', targets: [qubit], parameter: angle }),
        ry: (qubit, angle) => gates.push({ type: 'RY', targets: [qubit], parameter: angle }),
        rz: (qubit, angle) => gates.push({ type: 'RZ', targets: [qubit], parameter: angle }),
        build: () => ({ numQubits, gates }),
    };
}
/**
 * Utility: Project from Euclidean to Poincare ball
 * @param x Euclidean coordinates
 * @param c Curvature parameter
 */
function projectToPoincare(x, c = 1) {
    const normSq = x.reduce((sum, v) => sum + v * v, 0);
    const maxNorm = (1 - 1e-5) / Math.sqrt(c);
    if (normSq > maxNorm * maxNorm) {
        const scale = maxNorm / Math.sqrt(normSq);
        return new Float32Array(x.map(v => v * scale));
    }
    return x;
}
/**
 * Utility: Project from Poincare to Lorentz model
 * @param x Poincare coordinates
 * @param c Curvature parameter
 */
function poincareToLorentz(x, c = 1) {
    const normSq = x.reduce((sum, v) => sum + v * v, 0);
    const denom = 1 - c * normSq;
    const result = new Float32Array(x.length + 1);
    result[0] = (1 + c * normSq) / denom; // Time component
    for (let i = 0; i < x.length; i++) {
        result[i + 1] = 2 * Math.sqrt(c) * x[i] / denom;
    }
    return result;
}
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