Малка разлика е, че Стефан използва хипергеометрично разпределение, а в оригиналния анализ (по модела на Виталик) аз използвам биномиално: https://github.com/ethereum/annotated-spec/blob/master/phase0/beacon-chain.md#misc
Функцията probge() изчислява вероятността в синк комитет от n=512
участника поне k=342 на брой от тях да бъдат "корумпирани".
Разликата между биномиалното и хипергеометричното разпределение е, че във второто веднъж изтеглен корумпиран валидатор не участва в
| (defun describe-treesit-node (node &optional indent) | |
| (setq indent (or indent 0)) | |
| (message "%s%s" (make-string (* 2 indent) ? ) node) | |
| (mapcar | |
| (lambda (n) (describe n (1+ indent))) | |
| (treesit-node-children node))) | |
| ;; Example | |
| ;; (describe (treesit-parse-string "fn f() { | |
| ;; let b = '\"'; |
| // ethers.js v5 | |
| import { ethers, network } from "hardhat"; | |
| import { SignerWithAddress } from "@nomiclabs/hardhat-ethers/signers"; | |
| async function impersonate( | |
| who: string, | |
| f: (signer: SignerWithAddress) => Promise<void> | |
| ): Promise<void> { | |
| await network.provider.request({ |
| #!/usr/bin/env python3 | |
| from http.server import HTTPServer, SimpleHTTPRequestHandler, test | |
| import sys | |
| class CORSRequestHandler (SimpleHTTPRequestHandler): | |
| def end_headers(self): | |
| self.send_header('Access-Control-Allow-Origin', '*') | |
| SimpleHTTPRequestHandler.end_headers(self) |
| async function waitFor(id: string): Promise<void> { | |
| return new Promise(( | |
| resolve: () => void, | |
| _reject: (reason?: any) => void | |
| ): void => { | |
| const timer: NodeJS.Timer = setInterval(() => { | |
| if (document.getElementById(id) != null) { | |
| clearInterval(timer); | |
| resolve(); | |
| } |
| #!/bin/bash | |
| # Update conda | |
| conda update -n base -c defaults conda | |
| # TODO | |
| # conda create -n ten python=3.10 pandas numpy=1.20.3 lightgbm ta-lib graphviz -c conda-forge | |
| # create -n ten python=3.10 graphviz lightgbm numpy pandas scikit-learn ta-lib -c conda-forge | |
| # conda install jupyterlab matplotlib |
| async function implementation(proxy: string): Promise<string> { | |
| return ethers.provider.getStorageAt( | |
| proxy, | |
| "0x360894a13ba1a3210667c828492db98dca3e2076cc3735a920a3ca505d382bbc" | |
| ).then(s => s.substring(26)).then(ethers.utils.getAddress); | |
| } |
| 14914437, ValidatorRegistered(0x8fb75dcc8e27ee05d79c2b1ae5bb1d2af1f062e571d084e283a97490d5b22c226fcf4f2a7c343227578217883dd1a4e3, 0xA11C358C20b2baA682956EA2C7a659f6e9583f96) | |
| 15576350, ValidatorRegistered(0x9683f167736891c9069f66a42c1213f52160ea945282f8f7420107c9673565342b356134d6401429295dd2592b688aba, 0xA11C358C20b2baA682956EA2C7a659f6e9583f96) | |
| 15581571, ValidatorRegistered(0xa82408050e1d94e62cdb44afa738b2321339624f9117afe495a47d566b41e3c2bd7a9320e11bab6bc8c1fb9b5eed1211, 0xA11C358C20b2baA682956EA2C7a659f6e9583f96) | |
| 15581571, ValidatorRegistered(0xb50c7df59e644ec767a8b6965744f572c163a1163db81fa0248a648a43f7c592a2a26af5a390cce28354b0dc8fd837d0, 0xA11C358C20b2baA682956EA2C7a659f6e9583f96) | |
| 15588857, ValidatorRegistered(0x997da29c3d83dad9e06f721982bccc3f6aaa9310cf16b680df48182427522a893bff5ec09148c4161e72eea777ca781b, 0xA11C358C20b2baA682956EA2C7a659f6e9583f96) | |
| 15588857, ValidatorRegistered(0xa8023ca5b88e651ae642fc20094b73051de3524a03d4ee2a20d1fd8ce422878e3bfa76e46573039b0a95f140c2739545, 0xA11C358C20b2baA682956E |
| { | |
| inputs = { | |
| nixpkgs.url = github:NixOS/nixpkgs/nixos-22.05; | |
| flake-utils.url = github:numtide/flake-utils; | |
| rust-overlay.url = github:oxalica/rust-overlay; | |
| }; | |
| outputs = { self, nixpkgs, flake-utils, rust-overlay }: | |
| flake-utils.lib.simpleFlake { | |
| inherit self nixpkgs; |