wifi-densepose/vendor/ruvector/crates/ruvector-graph-transformer-node/index.d.ts

/* tslint:disable */
/* eslint-disable */

/* auto-generated by NAPI-RS */

/** Get the library version. */
export declare function version(): string
/** Module initialization message. */
export declare function init(): string
/**
 * Graph Transformer with proof-gated operations for Node.js.
 *
 * Provides sublinear attention over graph structures, physics-informed
 * layers (Hamiltonian dynamics), biologically-inspired learning (spiking
 * networks, Hebbian plasticity), and verified training with proof receipts.
 *
 * # Example
 * ```javascript
 * const { GraphTransformer } = require('ruvector-graph-transformer-node');
 * const gt = new GraphTransformer();
 * console.log(gt.version());
 * ```
 */
export declare class GraphTransformer {
  /**
   * Create a new Graph Transformer instance.
   *
   * # Arguments
   * * `config` - Optional JSON configuration (reserved for future use)
   *
   * # Example
   * ```javascript
   * const gt = new GraphTransformer();
   * const gt2 = new GraphTransformer({ maxFuel: 10000 });
   * ```
   */
  constructor(config?: any | undefined | null)
  /**
   * Get the library version string.
   *
   * # Example
   * ```javascript
   * console.log(gt.version()); // "2.0.4"
   * ```
   */
  version(): string
  /**
   * Create a proof gate for a given dimension.
   *
   * Returns a JSON object describing the gate (id, dimension, verified).
   *
   * # Arguments
   * * `dim` - The dimension to gate on
   *
   * # Example
   * ```javascript
   * const gate = gt.createProofGate(128);
   * console.log(gate.dimension); // 128
   * ```
   */
  createProofGate(dim: number): any
  /**
   * Prove that two dimensions are equal.
   *
   * Returns a proof result with proof_id, expected, actual, and verified fields.
   *
   * # Arguments
   * * `expected` - The expected dimension
   * * `actual` - The actual dimension
   *
   * # Example
   * ```javascript
   * const proof = gt.proveDimension(128, 128);
   * console.log(proof.verified); // true
   * ```
   */
  proveDimension(expected: number, actual: number): any
  /**
   * Create a proof attestation (serializable receipt) for a given proof ID.
   *
   * Returns the attestation as a byte buffer (82 bytes) that can be
   * embedded in RVF WITNESS_SEG entries.
   *
   * # Arguments
   * * `proof_id` - The proof term ID to create an attestation for
   *
   * # Example
   * ```javascript
   * const proof = gt.proveDimension(64, 64);
   * const attestation = gt.createAttestation(proof.proof_id);
   * console.log(attestation.length); // 82
   * ```
   */
  createAttestation(proofId: number): Array<number>
  /**
   * Compose a chain of pipeline stages, verifying type compatibility.
   *
   * Each stage must have `name`, `input_type_id`, and `output_type_id`.
   * Returns a composed proof with the overall input/output types and
   * the number of stages verified.
   *
   * # Arguments
   * * `stages` - Array of stage descriptors as JSON objects
   *
   * # Example
   * ```javascript
   * const composed = gt.composeProofs([
   *   { name: 'embed', input_type_id: 1, output_type_id: 2 },
   *   { name: 'align', input_type_id: 2, output_type_id: 3 },
   * ]);
   * console.log(composed.chain_name); // "embed >> align"
   * ```
   */
  composeProofs(stages: Array<any>): any
  /**
   * Verify an attestation from its byte representation.
   *
   * Returns `true` if the attestation is structurally valid.
   *
   * # Arguments
   * * `bytes` - The attestation bytes (82 bytes minimum)
   *
   * # Example
   * ```javascript
   * const valid = gt.verifyAttestation(attestationBytes);
   * ```
   */
  verifyAttestation(bytes: Array<number>): boolean
  /**
   * Sublinear graph attention using personalized PageRank sparsification.
   *
   * Instead of attending to all N nodes (O(N*d)), uses PPR to select
   * the top-k most relevant nodes, achieving O(k*d) complexity.
   *
   * # Arguments
   * * `query` - Query vector (length must equal `dim`)
   * * `edges` - Adjacency list: edges[i] is the list of neighbor indices for node i
   * * `dim` - Dimension of the query vector
   * * `k` - Number of top nodes to attend to
   *
   * # Returns
   * JSON object with `scores`, `top_k_indices`, and `sparsity_ratio`
   *
   * # Example
   * ```javascript
   * const result = gt.sublinearAttention([1.0, 0.5], [[1, 2], [0, 2], [0, 1]], 2, 2);
   * console.log(result.top_k_indices);
   * ```
   */
  sublinearAttention(query: Array<number>, edges: Array<Array<number>>, dim: number, k: number): any
  /**
   * Compute personalized PageRank scores from a source node.
   *
   * # Arguments
   * * `source` - Source node index
   * * `adjacency` - Adjacency list for the graph
   * * `alpha` - Teleport probability (typically 0.15)
   *
   * # Returns
   * Array of PPR scores, one per node
   *
   * # Example
   * ```javascript
   * const scores = gt.pprScores(0, [[1], [0, 2], [1]], 0.15);
   * ```
   */
  pprScores(source: number, adjacency: Array<Array<number>>, alpha: number): Array<number>
  /**
   * Symplectic integrator step (leapfrog / Stormer-Verlet).
   *
   * Integrates Hamiltonian dynamics with a harmonic potential V(q) = 0.5*|q|^2,
   * preserving the symplectic structure (energy-conserving).
   *
   * # Arguments
   * * `positions` - Position coordinates
   * * `momenta` - Momentum coordinates (same length as positions)
   * * `dt` - Time step
   *
   * # Returns
   * JSON object with `positions`, `momenta`, and `energy`
   *
   * # Example
   * ```javascript
   * const state = gt.hamiltonianStep([1.0, 0.0], [0.0, 1.0], 0.01);
   * console.log(state.energy);
   * ```
   */
  hamiltonianStep(positions: Array<number>, momenta: Array<number>, dt: number): any
  /**
   * Hamiltonian step with graph edge interactions.
   *
   * `positions` and `momenta` are arrays of coordinates. `edges` is an
   * array of `{ src, tgt }` objects defining graph interactions.
   *
   * # Returns
   * JSON object with `positions`, `momenta`, `energy`, and `energy_conserved`
   *
   * # Example
   * ```javascript
   * const state = gt.hamiltonianStepGraph(
   *   [1.0, 0.0], [0.0, 1.0],
   *   [{ src: 0, tgt: 1 }], 0.01
   * );
   * ```
   */
  hamiltonianStepGraph(positions: Array<number>, momenta: Array<number>, edges: Array<any>, dt: number): any
  /**
   * Spiking neural attention: event-driven sparse attention.
   *
   * Nodes emit attention only when their membrane potential exceeds
   * a threshold, producing sparse activation patterns.
   *
   * # Arguments
   * * `spikes` - Membrane potentials for each node
   * * `edges` - Adjacency list for the graph
   * * `threshold` - Firing threshold
   *
   * # Returns
   * Output activation vector (one value per node)
   *
   * # Example
   * ```javascript
   * const output = gt.spikingAttention([0.5, 1.5, 0.3], [[1], [0, 2], [1]], 1.0);
   * ```
   */
  spikingAttention(spikes: Array<number>, edges: Array<Array<number>>, threshold: number): Array<number>
  /**
   * Hebbian learning rule update.
   *
   * Applies the outer-product Hebbian rule: w_ij += lr * pre_i * post_j.
   * The weight vector is a flattened (pre.len * post.len) matrix.
   *
   * # Arguments
   * * `pre` - Pre-synaptic activations
   * * `post` - Post-synaptic activations
   * * `weights` - Current weight vector (flattened matrix)
   * * `lr` - Learning rate
   *
   * # Returns
   * Updated weight vector
   *
   * # Example
   * ```javascript
   * const updated = gt.hebbianUpdate([1.0, 0.0], [0.0, 1.0], [0, 0, 0, 0], 0.1);
   * ```
   */
  hebbianUpdate(pre: Array<number>, post: Array<number>, weights: Array<number>, lr: number): Array<number>
  /**
   * Spiking step over 2D node features with adjacency matrix.
   *
   * `features` is an array of arrays (n x dim). `adjacency` is a flat
   * row-major array (n x n). Returns `{ features, spikes, weights }`.
   *
   * # Example
   * ```javascript
   * const result = gt.spikingStep(
   *   [[0.8, 0.6], [0.1, 0.2]],
   *   [0, 0.5, 0.3, 0]
   * );
   * ```
   */
  spikingStep(features: Array<Array<number>>, adjacency: Array<number>): any
  /**
   * A single verified SGD step with proof of gradient application.
   *
   * Applies w' = w - lr * grad and returns the new weights along with
   * a proof receipt, loss before/after, and gradient norm.
   *
   * # Arguments
   * * `weights` - Current weight vector
   * * `gradients` - Gradient vector (same length as weights)
   * * `lr` - Learning rate
   *
   * # Returns
   * JSON object with `weights`, `proof_id`, `loss_before`, `loss_after`, `gradient_norm`
   *
   * # Example
   * ```javascript
   * const result = gt.verifiedStep([1.0, 2.0], [0.1, 0.2], 0.01);
   * console.log(result.loss_after < result.loss_before); // true
   * ```
   */
  verifiedStep(weights: Array<number>, gradients: Array<number>, lr: number): any
  /**
   * Verified training step with features, targets, and weights.
   *
   * Computes MSE loss, applies SGD, and produces a training certificate.
   *
   * # Arguments
   * * `features` - Input feature vector
   * * `targets` - Target values
   * * `weights` - Current weight vector
   *
   * # Returns
   * JSON object with `weights`, `certificate_id`, `loss`,
   * `loss_monotonic`, `lipschitz_satisfied`
   *
   * # Example
   * ```javascript
   * const result = gt.verifiedTrainingStep([1.0, 2.0], [0.5, 1.0], [0.5, 0.5]);
   * ```
   */
  verifiedTrainingStep(features: Array<number>, targets: Array<number>, weights: Array<number>): any
  /**
   * Product manifold distance (mixed curvature spaces).
   *
   * Splits vectors into sub-spaces according to the curvatures array:
   * - curvature > 0: spherical distance
   * - curvature < 0: hyperbolic distance
   * - curvature == 0: Euclidean distance
   *
   * # Arguments
   * * `a` - First point
   * * `b` - Second point (same length as `a`)
   * * `curvatures` - Curvature for each sub-space
   *
   * # Returns
   * The product manifold distance as a number
   *
   * # Example
   * ```javascript
   * const d = gt.productManifoldDistance([1, 0, 0, 1], [0, 1, 1, 0], [0.0, -1.0]);
   * ```
   */
  productManifoldDistance(a: Array<number>, b: Array<number>, curvatures: Array<number>): number
  /**
   * Product manifold attention with mixed curvatures.
   *
   * Computes attention in a product of spherical, hyperbolic, and
   * Euclidean subspaces, combining the results.
   *
   * # Arguments
   * * `features` - Input feature vector
   * * `edges` - Array of `{ src, tgt }` objects
   *
   * # Returns
   * JSON object with `output`, `curvatures`, `distances`
   *
   * # Example
   * ```javascript
   * const result = gt.productManifoldAttention(
   *   [1.0, 0.5, -0.3, 0.8],
   *   [{ src: 0, tgt: 1 }]
   * );
   * ```
   */
  productManifoldAttention(features: Array<number>, edges: Array<any>): any
  /**
   * Causal attention with temporal ordering.
   *
   * Attention scores are masked so that a key at time t_j can only
   * attend to queries at time t_i <= t_j (no information leakage
   * from the future).
   *
   * # Arguments
   * * `query` - Query vector
   * * `keys` - Array of key vectors
   * * `timestamps` - Timestamp for each key (same length as keys)
   *
   * # Returns
   * Softmax attention weights (one per key, sums to 1.0)
   *
   * # Example
   * ```javascript
   * const scores = gt.causalAttention(
   *   [1.0, 0.0],
   *   [[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]],
   *   [1.0, 2.0, 3.0]
   * );
   * ```
   */
  causalAttention(query: Array<number>, keys: Array<Array<number>>, timestamps: Array<number>): Array<number>
  /**
   * Causal attention over features, timestamps, and graph edges.
   *
   * Returns attention-weighted output features where each node can
   * only attend to neighbors with earlier or equal timestamps.
   *
   * # Arguments
   * * `features` - Feature value for each node
   * * `timestamps` - Timestamp for each node
   * * `edges` - Array of `{ src, tgt }` objects
   *
   * # Returns
   * Array of attention-weighted output values
   *
   * # Example
   * ```javascript
   * const output = gt.causalAttentionGraph(
   *   [1.0, 0.5, 0.8],
   *   [1.0, 2.0, 3.0],
   *   [{ src: 0, tgt: 1 }, { src: 1, tgt: 2 }]
   * );
   * ```
   */
  causalAttentionGraph(features: Array<number>, timestamps: Array<number>, edges: Array<any>): Array<number>
  /**
   * Extract Granger causality DAG from attention history.
   *
   * Tests pairwise Granger causality between all nodes and returns
   * edges where the F-statistic exceeds the significance threshold.
   *
   * # Arguments
   * * `attention_history` - Flat array (T x N, row-major)
   * * `num_nodes` - Number of nodes N
   * * `num_steps` - Number of time steps T
   *
   * # Returns
   * JSON object with `edges` and `num_nodes`
   *
   * # Example
   * ```javascript
   * const dag = gt.grangerExtract(flatHistory, 3, 20);
   * console.log(dag.edges); // [{ source, target, f_statistic, is_causal }]
   * ```
   */
  grangerExtract(attentionHistory: Array<number>, numNodes: number, numSteps: number): any
  /**
   * Game-theoretic attention: computes Nash equilibrium allocations.
   *
   * Each node is a player with features as utility parameters. Edges
   * define strategic interactions. Uses best-response iteration to
   * converge to Nash equilibrium.
   *
   * # Arguments
   * * `features` - Feature/utility value for each node
   * * `edges` - Array of `{ src, tgt }` objects
   *
   * # Returns
   * JSON object with `allocations`, `utilities`, `nash_gap`, `converged`
   *
   * # Example
   * ```javascript
   * const result = gt.gameTheoreticAttention(
   *   [1.0, 0.5, 0.8],
   *   [{ src: 0, tgt: 1 }, { src: 1, tgt: 2 }]
   * );
   * console.log(result.converged); // true
   * ```
   */
  gameTheoreticAttention(features: Array<number>, edges: Array<any>): any
  /**
   * Get aggregate statistics as a JSON object.
   *
   * # Example
   * ```javascript
   * const stats = gt.stats();
   * console.log(stats.proofs_verified);
   * ```
   */
  stats(): any
  /**
   * Reset all internal state (caches, counters, gates).
   *
   * # Example
   * ```javascript
   * gt.reset();
   * ```
   */
  reset(): void
}