// derived from https://git.dervland.net/biglyderv/new-bigly-chat/src/branch/master/docs/stats.php function rankCalc(result, iterations = 10, main = [], domain_mode = false) { let matrixe = {} let fng = {}; let fnc = {}; let frs = {}; let msum_old = 0.001; let pr = {}; let rl = Object.keys(result).length; for (let unn in result) { let v = false; if (domain_mode) { v = false; try { let test = new URL(unn); v = true; } catch (err) { v = false; } } if (!v) { delete result[unn]; continue; } matrixe[unn] = {}; matrixe[unn][unn] = 1; frs[unn] = result[unn].followers; fng[unn] = result[unn].following; let lf = Object.keys(fng[unn]).length; if (domain_mode) { let domains = []; for (let x of fng[unn]) { try { let a = new URL(x); domains.push(a.host); } catch (err) { } } domains = [...new Set(domains)]; fnc[unn] = lf / (1 + domains.length); } else { fnc[unn] = lf; } pr[unn] = 0.1 / rl; } for (let unn in result) { let fnu = frs[unn]; for (let follow of fnu) { if (follow == unn) continue; let dst = fnc[fnu] || 0; matrixe[unn][follow] = 1 + 1 / (dst + 3); msum_old += matrixe[unn][follow]; } for (let unn2 in result) { if (!matrixe[unn][unn2]) { matrixe[unn][unn2] = 0; } } } let mm = (process.env.matrixIterations || iterations); for (let i = 0; i < mm; i++) { let prold = pr; let matrixf = matrixe; pr = []; matrixe = []; let msum = 1; let intv = Math.pow(0.001 / rl, Math.pow(0.09, i / mm)); console.log(`Completed ${i} iterations with ${intv} threshold`) let th = -1; for (let una in result) { th++; pr[una] = 0.1 / rl; matrixe[una] = []; if (frs[una].length == 0) { pr[una] = prold[una]; matrixe[una] = matrixf[una]; continue; } let ar = Object.keys(result); let rf = pr[una]; let muna = matrixe[una]; let muna2 = matrixe[una]; for (let unb of ar) { let prb = prold[unb]; muna[unb] = 0.03; let munb = muna2[unb]; if (prb * munb < intv || fnc[unb] == 0) { if (isNaN(mfb) || !munb) continue; pr[una] += prb * munb; continue; } for (let unc in result) { let mfc = muna2[unc]; let mfb = matrixf[unc][unb]; if (isNaN(mfc) || isNaN(mfb) || !mfc || !mfb) continue; matrixe[una][unb] += mfc * mfb; } msum += munb; pr[una] += prb * munb; } } for (let una in result) { if ((frs[una]).length == 0) continue; for (let unb in result) { matrixe[una][unb] *= msum_old / msum; } } let ov = Object.keys(pr); let new_sum = ov.filter(i => !isNaN(pr[i]) && main.indexOf(i) != -1).map(n => pr[n]).reduce((a, b) => a + b, 1e-9); 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); 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); for (let unn of ov) { if (!result[unn]) { pr[unn] = 0; } else if ((domain_mode && new URL(unn).host == new URL(main[0]).host) && main.indexOf(unn) == -1) { pr[unn] /= new_sum2 * 3; } else if (main.indexOf(unn) == -1) { pr[unn] /= new_sum3 * (domain_mode ? 3 : 2); } else { pr[unn] /= new_sum * (domain_mode ? 3 : 2); } } } return pr; } export { rankCalc }