"use strict"; Object.defineProperty(exports, "__esModule", { value: true }); exports.createKldivergence = void 0; var _factory = require("../../utils/factory.js"); const name = 'kldivergence'; const dependencies = ['typed', 'matrix', 'divide', 'sum', 'multiply', 'map', 'dotDivide', 'log', 'isNumeric']; const createKldivergence = exports.createKldivergence = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => { let { typed, matrix, divide, sum, multiply, map, dotDivide, log, isNumeric } = _ref; /** * Calculate the Kullback-Leibler (KL) divergence between two distributions * * Syntax: * * math.kldivergence(x, y) * * Examples: * * math.kldivergence([0.7,0.5,0.4], [0.2,0.9,0.5]) //returns 0.24376698773121153 * * * @param {Array | Matrix} q First vector * @param {Array | Matrix} p Second vector * @return {number} Returns distance between q and p */ return typed(name, { 'Array, Array': function (q, p) { return _kldiv(matrix(q), matrix(p)); }, 'Matrix, Array': function (q, p) { return _kldiv(q, matrix(p)); }, 'Array, Matrix': function (q, p) { return _kldiv(matrix(q), p); }, 'Matrix, Matrix': function (q, p) { return _kldiv(q, p); } }); function _kldiv(q, p) { const plength = p.size().length; const qlength = q.size().length; if (plength > 1) { throw new Error('first object must be one dimensional'); } if (qlength > 1) { throw new Error('second object must be one dimensional'); } if (plength !== qlength) { throw new Error('Length of two vectors must be equal'); } // Before calculation, apply normalization const sumq = sum(q); if (sumq === 0) { throw new Error('Sum of elements in first object must be non zero'); } const sump = sum(p); if (sump === 0) { throw new Error('Sum of elements in second object must be non zero'); } const qnorm = divide(q, sum(q)); const pnorm = divide(p, sum(p)); const result = sum(multiply(qnorm, map(dotDivide(qnorm, pnorm), x => log(x)))); if (isNumeric(result)) { return result; } else { return Number.NaN; } } });