160 lines
No EOL
4.4 KiB
JavaScript
160 lines
No EOL
4.4 KiB
JavaScript
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// derived from https://git.dervland.net/biglyderv/new-bigly-chat/src/branch/master/docs/stats.php
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function rankCalc(result, iterations = 10, main = [], domain_mode = false) {
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let matrixe = {}
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let fng = {};
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let fnc = {};
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let frs = {};
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let msum_old = 0.001;
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let pr = {};
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let rl = Object.keys(result).length;
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for (let unn in result) {
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let v = false;
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if (domain_mode) {
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v = false;
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try {
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let test = new URL(unn);
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v = true;
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} catch (err) {
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v = false;
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}
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}
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if (!v) {
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delete result[unn];
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continue;
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}
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matrixe[unn] = {};
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matrixe[unn][unn] = 1;
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frs[unn] = result[unn].followers;
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fng[unn] = result[unn].following;
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let lf = Object.keys(fng[unn]).length;
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if (domain_mode) {
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let domains = [];
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for (let x of fng[unn]) {
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try {
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let a = new URL(x);
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domains.push(a.host);
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} catch (err) {
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}
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}
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domains = [...new Set(domains)];
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fnc[unn] = lf / (1 + domains.length);
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} else {
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fnc[unn] = lf;
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}
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pr[unn] = 0.1 / rl;
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}
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for (let unn in result) {
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let fnu = frs[unn];
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for (let follow of fnu) {
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if (follow == unn) continue;
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let dst = fnc[fnu] || 0;
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matrixe[unn][follow] = 1 + 1 / (dst + 3);
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msum_old += matrixe[unn][follow];
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}
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for (let unn2 in result) {
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if (!matrixe[unn][unn2]) {
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matrixe[unn][unn2] = 0;
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}
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}
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}
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let mm = (process.env.matrixIterations || iterations);
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for (let i = 0; i < mm; i++) {
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let prold = pr;
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let matrixf = matrixe;
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pr = [];
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matrixe = [];
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let msum = 1;
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let intv = Math.pow(0.001 / rl, Math.pow(0.09, i / mm));
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console.log(`Completed ${i} iterations with ${intv} threshold`)
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let th = -1;
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for (let una in result) {
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th++;
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pr[una] = 0.1 / rl;
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matrixe[una] = [];
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if (frs[una].length == 0) {
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pr[una] = prold[una];
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matrixe[una] = matrixf[una];
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continue;
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}
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let ar = Object.keys(result);
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let rf = pr[una];
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let muna = matrixe[una];
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let muna2 = matrixf[una];
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for (let unb of ar) {
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let prb = prold[unb];
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muna[unb] = 0.03;
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let munb = muna2[unb];
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if (prb * munb < intv || fnc[unb] == 0) {
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if (isNaN(mfb) || !munb) continue;
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pr[una] += prb * munb;
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continue;
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}
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for (let unc in result) {
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let mfc = muna2[unc];
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let mfb = matrixf[unc][unb];
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if (isNaN(mfc) || isNaN(mfb) || !mfc || !mfb) continue;
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matrixe[una][unb] += mfc * mfb;
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}
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msum += munb;
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pr[una] += prb * munb;
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}
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}
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for (let una in result) {
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if ((frs[una]).length == 0) continue;
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for (let unb in result) {
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matrixe[una][unb] *= msum_old / msum;
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}
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}
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let ov = Object.keys(pr);
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let new_sum = ov.filter(i => !isNaN(pr[i]) && main.indexOf(i) != -1).map(n => pr[n]).reduce((a, b) => a + b, 1e-9);
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let new_sum2 = ov.filter(i => !isNaN(pr[i]) && main.indexOf(i) == -1 && (domain_mode && new URL(i).host == new URL(main[0]).host)).map(n => pr[n]).reduce((a, b) => a + b, 1e-9);
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let new_sum3 = ov.filter(i => !isNaN(pr[i]) && main.indexOf(i) == -1 && !(domain_mode && new URL(i).host == new URL(main[0]).host)).map(n => pr[n]).reduce((a, b) => a + b, 1e-9);
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for (let unn of ov) {
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if (!result[unn]) {
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pr[unn] = 0;
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} else if ((domain_mode && new URL(unn).host == new URL(main[0]).host) && main.indexOf(unn) == -1) {
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pr[unn] /= new_sum2 * 3;
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} else if (main.indexOf(unn) == -1) {
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pr[unn] /= new_sum3 * (domain_mode ? 3 : 2);
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} else {
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pr[unn] /= new_sum * (domain_mode ? 3 : 2);
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}
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}
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}
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return pr;
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}
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export {
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rankCalc
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} |