agsamantha/node_modules/langchain/dist/util/ml-distance/distances.cjs
2024-10-02 15:15:21 -05:00

52 lines
1.4 KiB
JavaScript

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.manhattan = exports.chebyshev = exports.innerProduct = void 0;
/**
*Returns the Inner Product similarity between vectors a and b
* @link [Inner Product Similarity algorithm](https://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf)
* @param a - first vector
* @param b - second vector
*
*/
function innerProduct(a, b) {
let ans = 0;
for (let i = 0; i < a.length; i++) {
ans += a[i] * b[i];
}
return ans;
}
exports.innerProduct = innerProduct;
/**
*Returns the Chebyshev distance between vectors a and b
* @link [Chebyshev algorithm](https://en.wikipedia.org/wiki/Chebyshev_distance)
* @param a - first vector
* @param b - second vector
*
*/
function chebyshev(a, b) {
let max = 0;
let aux = 0;
for (let i = 0; i < a.length; i++) {
aux = Math.abs(a[i] - b[i]);
if (max < aux) {
max = aux;
}
}
return max;
}
exports.chebyshev = chebyshev;
/**
*Returns the Manhattan distance between vectors a and b
* @link [Manhattan algorithm](https://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf)
* @param a - first vector
* @param b - second vector
*
*/
function manhattan(a, b) {
let d = 0;
for (let i = 0; i < a.length; i++) {
d += Math.abs(a[i] - b[i]);
}
return d;
}
exports.manhattan = manhattan;