phipsgabler

#!/usr/bin/env python3
# -*- truncate-lines: t; -*-

# Yes, it is slow. But sufficient for small sets.
# Usage:
#
#   > import mhs
#   > list(mhs.mhs({9,11}, {2,3,6,9,10}))
#   [frozenset({9}),
#    frozenset({2, 11}),

phipsgabler

data Tree a = Leaf | Branch a (Tree a) (Tree a) deriving Show

insertLeftBalanced :: a -> Tree a -> Tree a
insertLeftBalanced x Leaf = Branch x Leaf Leaf
insertLeftBalanced x (Branch m Leaf r) = Branch m (Branch x Leaf Leaf) r
insertLeftBalanced x (Branch m l Leaf) = Branch m l (Branch x Leaf Leaf)
insertLeftBalanced x (Branch m l r) = Branch m (insertLeftBalanced x l) r

listToTree :: [a] -> Tree a
listToTree xs = foldl (flip insertLeftBalanced) Leaf xs

phipsgabler

library(ggplot2)

qq.line <- function(data, qf, na.rm) {
    # from stackoverflow.com/a/4357932/1346276
    q.sample <- quantile(data, c(0.25, 0.75), na.rm = na.rm)
    q.theory <- qf(c(0.25, 0.75))
    slope <- diff(q.sample) / diff(q.theory)
    intercept <- q.sample[1] - slope * q.theory[1]

    list(slope = slope, intercept = intercept)

phipsgabler

'use strict';

// see: http://hackage.haskell.org/package/operational-0.2.3.2/docs/Control-Monad-Operational.html
// run in node, e.g.:
//   > let free = require('./free_monads.js')
//   undefined
//   > free.main()
//   Log: foo + bar: 52
//   test 1: [object Object]
//   k: 10, v: foo

phipsgabler

module Transducers

import Control.Category
import Control.Arrow

data Transducer : Type -> Type -> Type where
  T : ({r : Type} -> (b -> r -> r) -> (a -> r -> r)) -> Transducer a b

instance Category Transducer where
  id = T (\cons => cons)

phipsgabler

// TO SEE THE PRINTLN'S, RUN `showExamples` IN THE CONSOLE

package object example {
  def showExamples(): Unit = {
    // # Basics
    // > Goal of language: modular, functional, OO (+ Java interop)
    // > Sophisticated hierarchies, type system, and flexible syntax

    // # Classes
    // ## Normal & abstract ones

phipsgabler

phipsgabler

using Distributions
using Gadfly

function test()
    @show θ = 100 * rand()
    @show X = rand(Uniform(-θ, θ), 100)

    ls = 0.1:0.5:100.0
    plot(x = ls,
         y = broadcast(l -> loglikelihood(Uniform(-l, l), X), ls),

phipsgabler

using Primes
using BenchmarkTools

function σ{T}(k, n, ::Type{T} = Int)
    result = ones(T, n - 1)
    
    for p in primes(n)
        bound = round(Int, log(n) / log(p), RoundDown)
        pᵏ = convert(T, p)^k
        pᵏⁱ = one(T)

phipsgabler

Keybase proof

I hereby claim:

• I am phipsgabler on github.
• I am phipsgabler (https://keybase.io/phipsgabler) on keybase.
• I have a public key ASCsQ0akmVrVQzjf8wiXxDZf1qwTWGaGERmvjA8pxMEDmQo

To claim this, I am signing this object: