N8python

async function fmait(callbacks, array) {
    for (const callback of callbacks) {
        array = await Promise.all(array.map(item => Promise.resolve(callback(item))));
    }
    return array;
}

N8python

      window.keybindr = (() => {
        // Declare set of keys that are pressed
        const keys = new Set();
        // Declare "associative array" of bindings to callback
        const bindings = [];
        function tokenizeKeys(keys){
          // Get array of different keys from string
          return keys.split("+");
        }
        window.addEventListener("keydown", ({key}) => {

N8python

const degrees = radians => radians * 180 / Math.PI;
const radians = degrees => degrees * Math.PI / 180;
function vecTo(x1, y1, x2, y2) {
  const xDist = x2 - x1;
  const yDist = y2 - y1;
  let direction;
  if (xDist > 0 && yDist > 0) {
    direction = degrees(Math.atan(yDist / xDist));
  } else if (xDist > 0 && yDist < 0) {
    direction = 360 + degrees(Math.atan(yDist / xDist));

N8python

library(deSolve)
library(reshape2)
library(ggplot2)
initial_state_values <- c(S = 0.999, E = 0, I = 0.001, R = 0, M = 0) # S = Susceptible, E = Exposed, I = Infected, R = Recovered, M = Dead
parameters <- c(
  beta = 0.07142857142 * 3, # Contact Rate
  theta = 0.2, # 1 / (Length of Latent Period)
  gamma = 0.07142857142, # 1 / (Length of Infection)
  ro = 0.00130718954, # 1 / (Length of Immunity)
  nu = 0.00003281917, # Birth Rate

N8python

library(deSolve)
library(reshape2)
library(ggplot2)
initial_state_values <- c(I = 1000000, R = 0, M = 0)
parameters <- c(gamma = 0.1, mu = 0.2)
times <- seq(from = 0, to = 28, by = 1)
cohort_model <- function(time, state, parameters) {
  with (as.list(c(state, parameters)), {
    dI <- -gamma * I - mu * I
    dR <- gamma * I

N8python

runs <- 1
for(run in 1:runs) {
  beta <- 0.65
  gamma <- 0.15
  N <- 1000
  S <- c(999)
  I <- c(1)
  R <- c(0)
  for(i in 1:(7*8)){
    l <- rbinom(1, round(I[length(I)]), beta)

N8python

library(deSolve)
library(reshape2)
library(ggplot2)
N <- 1000
initial_state_values <- c(
  S = 0.999, 
  E = 0,
  IMI = 0.001,
  IM = 0,
  A = 0,

N8python

const SQUARE_SIZE = 5;
let storage = [];

function setup() {
    createCanvas(600, 600);
    for (let x = 0; x < width / SQUARE_SIZE; x++) {
        storage[x] = [];
        for (let y = 0; y < height / SQUARE_SIZE; y++) {
            storage[x].push((Math.random() > 0.5) ? "red" : "blue")
        }

N8python

const fs = require("fs");
const R = require("ramda");
const tf = require("@tensorflow/tfjs-node");
const fsExtra = require('fs-extra')
const text = fs.readFileSync("input.txt").toString();
const chars = Array.from(new Set(text.split("")));
const encoding = Object.fromEntries(chars.map((x, i) => [x, i]));
const decoding = Object.fromEntries(chars.map((x, i) => [i, x]));
const sampleLength = 20; // when I change this to 100, my lstm's loss goes to NaN
const epochSize = 5000;

N8python

async function main() {
    const fs = require("fs");
    const R = require("ramda");
    const tf = require("@tensorflow/tfjs-node");
    const fsExtra = require('fs-extra');
    const text = fs.readFileSync("input.txt").toString();
    const chars = Array.from(new Set(text.split("")));
    const encoding = Object.fromEntries(chars.map((x, i) => [x, i]));
    const decoding = Object.fromEntries(chars.map((x, i) => [i, x]));
    const sampleLength = 50;