/** * SIMD Operations for vector computations * * Uses native SIMD instructions (AVX2/AVX512/SSE4.1/NEON) when available, * falls back to JavaScript implementations otherwise. */ /** * SIMD Operations class * * Provides hardware-accelerated vector operations when native module is available. * * @example * ```typescript * import { SimdOps } from '@ruvector/ruvllm'; * * const simd = new SimdOps(); * * // Compute dot product * const result = simd.dotProduct([1, 2, 3], [4, 5, 6]); * console.log(result); // 32 * * // Check capabilities * console.log(simd.capabilities()); // ['AVX2', 'FMA'] * ``` */ export declare class SimdOps { private native; constructor(); /** * Compute dot product of two vectors */ dotProduct(a: number[], b: number[]): number; /** * Compute cosine similarity between two vectors */ cosineSimilarity(a: number[], b: number[]): number; /** * Compute L2 (Euclidean) distance between two vectors */ l2Distance(a: number[], b: number[]): number; /** * Matrix-vector multiplication */ matvec(matrix: number[][], vector: number[]): number[]; /** * Softmax activation function */ softmax(input: number[]): number[]; /** * Element-wise addition */ add(a: number[], b: number[]): number[]; /** * Element-wise multiplication */ mul(a: number[], b: number[]): number[]; /** * Scale vector by scalar */ scale(a: number[], scalar: number): number[]; /** * Normalize vector to unit length */ normalize(a: number[]): number[]; /** * ReLU activation */ relu(input: number[]): number[]; /** * GELU activation (approximate) */ gelu(input: number[]): number[]; /** * Sigmoid activation */ sigmoid(input: number[]): number[]; /** * Layer normalization */ layerNorm(input: number[], eps?: number): number[]; /** * Check if native SIMD is available */ isNative(): boolean; /** * Get available SIMD capabilities */ capabilities(): string[]; } //# sourceMappingURL=simd.d.ts.map