jgoosey

--Haskell Quine
--Implementation 1
import Control.Monad
import Control.Monad.Instances
main = (putStr . ap (++) show)
  "--Haskell Quine\n--Implementation 1\nimport Control.Monad\nimport Control.Monad.Instances\nmain = (putStr . ap (++) show) "

--Implementation 2
--main = putStrLn (s ++ show s) where s =
--  "--Haskell Quine\n--Implementation 2\nmain = putStrLn (s ++ show s) where s ="

jfager

extern mod extra;

use std::{int,io,result};
use extra::{net,net_tcp,uv};

fn main() {
    let (accept_port, accept_chan) = stream();
    let (finish_port, finish_chan) = stream();

    let addr = extra::net_ip::v4::parse_addr("127.0.0.1");

AlexeyMK

def d(*args):
  d_name = 'd'
  import inspect

  cur = inspect.currentframe()
  calling_frame = inspect.getouterframes(cur)[1]
  frameinfo = inspect.getframeinfo(calling_frame[0])
  string = frameinfo.code_context[0].strip()
  import ast
  ast_module = ast.parse(string)

Airblader

#!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
# vim:ts=2:sw=2:expandtab

import os
import xcb
from xcb.xproto import *
from PIL import Image

XCB_MAP_STATE_VIEWABLE = 2

veggiemonk

#!/bin/sh

echo "installing fonts at $PWD to ~/.fonts/"
mkdir -p ~/.fonts/adobe-fonts/source-code-pro
git clone https://github.com/adobe-fonts/source-code-pro.git ~/.fonts/adobe-fonts/source-code-pro
# find ~/.fonts/ -iname '*.ttf' -exec echo \{\} \;
fc-cache -f -v ~/.fonts/adobe-fonts/source-code-pro
echo "finished installing"

b38tn1k

import input_data
import tensorflow as tf

# http://www.tensorflow.org/tutorials/mnist/beginners/index.html
print '\033[93m' + 'Tutorial1 from:\nhttp://www.tensorflow.org/tutorials/mnist/beginners/index.html' + '\033[0m'
print 'MNIST INPUT DATA'
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

'PLACE HOLDERS'
# Make a placeholder / value to input on run

vinhkhuc

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split