entropyx/base/distance.js

A simple data mining library, written in TypeScript

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.Distance = void 0;
class Distance {
    static squaredEuclidean(a, b) {
        if (a.length !== b.length) {
            throw new Error('Points must have the same dimension');
        }
        let sum = 0;
        for (let i = 0; i < a.length; i++) {
            const diff = a[i] - b[i];
            sum += diff * diff;
        }
        return sum;
    }
    static euclidean(a, b) {
        return Math.sqrt(Distance.squaredEuclidean(a, b));
    }
    static manhattan(a, b) {
        if (a.length !== b.length) {
            throw new Error('Points must have the same dimension');
        }
        let total = 0;
        for (let i = 0; i < a.length; i++) {
            total += Math.abs(a[i] - b[i]);
        }
        return total;
    }
    static levenshtein(first, second, options = {}) {
        const deletionCost = options.deletionCost ?? 1;
        const insertionCost = options.insertionCost ?? 1;
        const substitutionCost = options.substitutionCost ?? 1;
        const cache = new Map();
        function lev(i, j) {
            if (i <= 0)
                return j * insertionCost;
            if (j <= 0)
                return i * deletionCost;
            const key = `${i}:${j}`;
            if (cache.has(key)) {
                return cache.get(key) || 0;
            }
            const costDel = lev(i - 1, j) + deletionCost;
            const costIns = lev(i, j - 1) + insertionCost;
            const costSub = lev(i - 1, j - 1) + (first[i - 1] === second[j - 1] ? 0 : substitutionCost);
            const minCost = Math.min(costDel, costIns, costSub);
            cache.set(key, minCost);
            return minCost;
        }
        return lev(first.length, second.length);
    }
}
exports.Distance = Distance;
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