Arkoniak

using BenchmarkTools
using StableRNGs
using Random
using ThreadsX

function randomwalk!(tbl, i; rng = rng)
    len = size(tbl, 1)
    tbl[1, i] = rand(rng)
    @inbounds for j in 2:len
        tbl[j, i] = (tbl[j - 1, i]) + rand(rng)

Arkoniak

using Telegram
using Luxor

token = ENV["TELEGRAM_BOT"]

client = TelegramClient(token)
useglobally!(client)

function draw_turtle(angles)
    d = Drawing(600, 400, :png)

Arkoniak

using ParallelKMeans
using BenchmarkTools
using Distances
using Random
using UnsafeArrays

Random.seed!(2020)

X = rand(100, 1000)       # 1000 points
locations = rand(100, 10) # 10 cluster

Arkoniak

struct NeuralNet
    NetDimensions::Array
    LossFunction::String
    params::Dict
end

function NeuralNet(dims, loss="someloss")
    param = Dict()
    for l=2:length(dims)
        param["W_$(l-1)"] = rand(dims[l], dims[l-1]) * 0.1

Arkoniak

using HTTP, DataFrames, CSV
confirmed_url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"
deaths_url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv"
recovered_url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv"

confirmed_df = CSV.File(IOBuffer(HTTP.get(confirmed_url).body)) |> DataFrame
deaths_df = CSV.File(IOBuffer(HTTP.get(deaths_url).body)) |> DataFrame
recovered_df = CSV.File(IOBuffer(HTTP.get(recovered_url).body)) |> DataFrame

ncols = size(confirmed_df, 2)

Arkoniak

using Primes
using DataFrames
using VegaLite
from_polar(t::Tuple) = (t[1]*cos(t[2]), t[1]*sin(t[2]))
dots = primes(100000) |> z -> map(x -> (x, x), z) |> x -> from_polar.(x)
DataFrame(dots) |> df -> rename!(df, [:x, :y]) |>
           @vlplot(mark={
               type=:circle,
               size = 5, color = "gold"
               }, encoding={x = {:x, axis={grid=false}}, y = {:y, axis={grid=false}}},

Arkoniak

using BenchmarkTools
using Distributed
using Base.Threads

addprocs(4)
nworkers() # should be 4

function f1()
    nheads = @distributed (+) for i = 1:200000000
        Int(rand(Bool))

Arkoniak

using PyCall
using LinearAlgebra
using Statistics
using StatsBase
using BenchmarkTools
using Distances


# import the same data
data = pyimport("sklearn.datasets")

Arkoniak

using BenchmarkTools
using StatsBase: median
using DataFrames
using VegaLite

versioninfo()
# Julia Version 1.3.1
# Commit 2d5741174c (2019-12-30 21:36 UTC)
# Platform Info:
#   OS: Linux (x86_64-pc-linux-gnu)

Arkoniak

# Atom:
Version: 1.43.0
Dev Mode: false
Official Release: true
{
  "http_parser": "2.8.0",
  "node": "10.11.0",
  "v8": "6.9.427.31-electron.0",
  "uv": "1.23.0",
  "zlib": "1.2.11",