@nexys/math-ts/dist/stat/index.js

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"use strict";
var __importStar = (this && this.__importStar) || function (mod) {
    if (mod && mod.__esModule) return mod;
    var result = {};
    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
    result["default"] = mod;
    return result;
};
Object.defineProperty(exports, "__esModule", { value: true });
const V = __importStar(require("../vector"));
const E = __importStar(require("./estimate"));
const M = __importStar(require("../matrix"));
const Regresion = __importStar(require("./regression"));
exports.Regresion = Regresion;
exports.covarianceWMeans = (x, y, mux, muy, n) => {
    const r = x.map((xi, i) => {
        const yi = y[i];
        return (xi - mux) * (yi - muy);
    })
        .reduce((a, b) => a + b);
    return r / (n - 1);
};
exports.covariance = (x, y) => exports.covarianceWMeans(x, y, V.mean(x), V.mean(y), x.length);
exports.covarianceMatrix = (x) => {
    const t = M.transpose(x);
    const nCol = t.length;
    return new Array(nCol).fill(null).map((_, i) => {
        return new Array(nCol).fill(null).map((_, j) => {
            return exports.covariance(t[i], t[j]);
        });
    });
};
exports.correlation = (x, y) => exports.covarianceWMeans(x, y, V.mean(x), V.mean(y), x.length) / (E.stddev(x) * E.stddev(y));
exports.autocovarianceWithN = (x, k, n) => {
    return exports.covariance(x.slice(k), x.slice(0, n - k));
};
exports.autocovariance = (x, k) => exports.autocovarianceWithN(x, k, x.length);
const autocorrelationWithC0 = (x, k, c0) => exports.autocovariance(x, k) / c0;
exports.autocorrelation = (x, k) => autocorrelationWithC0(x, k, exports.autocovariance(x, 0));
exports.markovProcess = (T, x, n = 1) => {
    return [0];
};
exports.test = (mu, avg, s, n) => (avg - mu) / (s / Math.sqrt(n));