JavaScript · Dot Products

Dot Products

import * as numkong from 'numkong'; 8.0.0-rc.1 · b1f866ab

Dot products are separate from distances because dtype tagging and low-precision storage matter more here.

import { dot, inner } from "numkong";

const a = new Float32Array([1, 2, 3]);
const b = new Float32Array([4, 5, 6]);

console.log(dot(a, b));
console.log(inner(a, b)); // alias for ecosystem familiarity

For non-native numeric layouts, pass an explicit DType or wrap the storage in a typed NumKong view.

API

Functions

innerComputes the inner product of two vectors, the same as the dot product.

numkong.ts:278 ↗

function inner(a: NumericArray, b: NumericArray): number

function inner(
    a: DistanceArray,
    b: DistanceArray,
    dtype: DType,
): number

function inner(a: TensorBase, b: TensorBase): number

Parameters

`a`		The first vector, as TypedArray or TensorBase.
`b`		The second vector, matching the type and length of `a`.
`dtypeDType`		Optional dtype string for custom types, e.g. 'f16', 'bf16', 'e4m3'.

Returns

The inner product of a and b.

dotComputes the dot product of two vectors, the same as the inner product.

numkong.ts:293 ↗

function dot(a: NumericArray, b: NumericArray): number

function dot(
    a: DistanceArray,
    b: DistanceArray,
    dtype: DType,
): number

function dot(a: TensorBase, b: TensorBase): number

Parameters

`a`		The first vector, as TypedArray or TensorBase.
`b`		The second vector, matching the type and length of `a`.
`dtypeDType`		Optional dtype string for custom types, e.g. 'f16', 'bf16', 'e4m3'.

Returns

The dot product of a and b.