@astermind/astermind-pro/dist/retrieval/vectorization.d.ts

Astermind Pro - Premium ML Toolkit with Advanced RAG, Reranking, Summarization, and Information Flow Analysis

import type { Kernel } from '../types.js';
export type SparseVec = Map<number, number>;
/**
 * Compute TF-IDF vector from tokens
 */
export declare function toTfidf(tokens: string[], idf: number[], vmap: Map<string, number>, headingW?: number): SparseVec;
/**
 * Cosine similarity between two sparse vectors
 */
export declare function cosineSparse(a: SparseVec, b: SparseVec): number;
/**
 * Convert sparse vector to dense Float64Array
 */
export declare function sparseToDense(v: SparseVec, dim: number): Float64Array;
/**
 * Dot product of two dense vectors
 */
export declare function dotProd(a: Float64Array, b: Float64Array): number;
/**
 * Base kernel function (RBF, cosine, or poly2)
 */
export declare function baseKernel(a: Float64Array, b: Float64Array, k: Kernel, sigma: number): number;
/**
 * Kernel similarity between two dense vectors
 */
export declare function kernelSim(a: Float64Array, b: Float64Array, k: Kernel, sigma: number): number;
/**
 * Project sparse vector to dense using Nyström landmarks
 */
export declare function projectToDense(v: SparseVec, vocabSize: number, landmarkMat: Float64Array[], kernel: Kernel, sigma: number): Float64Array;
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