weidagang

// Forked and modified from https://gist.github.com/DrBoolean/34c1d3651d1338b1b187

const daggy = require('daggy')

const compose = (f, g) => x => f(g(x))
const id = x => x
const kleisli_compose = (f, g) => x => f(x).flatMap(g)

//=============FREE=============
const Free = daggy.taggedSum({Suspend: ['x', 'f'], Pure: ['x']})

weidagang

// Utility function for detecting generators.
let isGenerator = x => {
  return Function.isGenerator &&
         Function.isGenerator.call(x)
}

// Data type represents channel into which values
// can be `put`, or `received` from. Channel is
// very much like queue where reads and writes are
// synchronized via continuation passing.

weidagang

import Data.IORef

main :: IO ()
main = do
  putStrLn "Create a mutable state 0."
  ref <- newIORef (0 :: Int)
  putStrLn "Increase by 1."
  modifyIORef ref (+1)
  result <- readIORef ref
  print result

weidagang

readInt :: IO Int
readInt = readLn

main = do
  print "Press any key to start:"
  line <- getLine
  print "Type the first integer x:"
  x <- readInt
  print "Type the second integer y:"
  y <- readInt

weidagang

// Simulating Haskell typeclass with Java generic interface.

public class Main {
  public static void main(String[] args) {
    MyInt i1 = new MyInt(1);
    MyInt i2 = new MyInt(2);
    MyInt i3 = i1.add(i2);
    System.out.println(i3); // "3"

    MyString s1 = new MyString("1");

weidagang

#!/usr/bin/python3.5

# Author: Dagang Wei (github.com/weidagang)
# Created: 2016-11-19
# Last modified: 2016-11-27
# License: MIT
# Self link: https://gist.github.com/weidagang/1b001d0e55c4eff15ad34cc469fafb84
#
# This code demonstrates the core algorithm for distributed MVCC based cross-row
# transactions. The algorithm is built on top of a distributed key-value database

weidagang

# In-memory MVCC transaction of read-committed isolation level.

next_xid = 1
active_xids = set()
records = []

def new_transaction():
  global next_xid
  print('Txn %d: start' % next_xid)
  xid = next_xid

weidagang

weidagang

% MATLAB code for finding the best fit line using least squares method.

% input in the form of matrix, each row is a (x, y).
input = [...
  1, 2;...
  2, 4.5;...
  3, 5.9;...
  4, 8.1;...
  5, 9.8;...
  6, 12.3];

weidagang

Prelude> let ones = 1 : ones
Prelude> take 3 ones
[1,1,1]

Prelude> let lessThan x y = y < x
Prelude> takeWhile (lessThan 5) [1..]
[1,2,3,4]