pyk

char buff[6] = {'h', 'e', 'l', 'l', 'o', '\0'};

/* or */
char *buff = "hello"; /* \0 implicitly appended */

/* or */
char buff[] = "hello";

pyk

# http://stackoverflow.com/questions/23281050/makefile-warning-warning-file-main-cpp-has-modification-time-2-1e04-s-in-th
# http://stackoverflow.com/questions/4210042/exclude-directory-from-find-command
find . -path ./.git -prune -o -type f -exec touch {} +

pyk

NEW_VERSION=206179d
OLD_VERSION=cd5726e

if [ ! -f "${NEW_VERSION}.tar.gz" ]; then
    wget https://api.github.com/repos/facebookresearch/fasttext/tarball/${NEW_VERSION} -O ${NEW_VERSION}.tar.gz
else
    echo "fastText: ${NEW_VERSION}.tar.gz exists"
fi

if [ ! -d "facebookresearch-fastText-${OLD_VERSION}" ]; then

pyk

/* The ziggurat method for RNOR and REXP
Combine the code below with the main program in which you want
normal or exponential variates.   Then use of RNOR in any expression
will provide a standard normal variate with mean zero, variance 1,
while use of REXP in any expression will provide an exponential variate
with density exp(-x),x>0.
Before using RNOR or REXP in your main, insert a command such as
zigset(86947731 );
with your own choice of seed value>0, rather than 86947731.
(If you do not invoke zigset(...) you will get all zeros for RNOR and REXP.)

pyk

// file: whitespace_tokenizer.rs
use std::env;
use std::process;
use std::fs::File;
use std::io::BufReader;
use std::io::Read;

fn main() {
    let args: Vec<String> = env::args().collect();
    if args.len() != 2 {

pyk

import tensorflow as tf
import sys
import os

def create_graph(pattern):
    print 'pattern:', pattern
    graph = tf.Graph()
    with graph.as_default():
        # Initializer
        tf.variables_initializer(tf.global_variables(), name='init')

pyk

(function() {

  var width = 320; 
  var height = 0;
  var streaming = false;

  var video = null;
  var canvas = null;
  var photo = null;
  var startbutton = null;

pyk

    # Convolutional layer 1
    with tf.name_scope('conv1'):
        W = tf.Variable(
            tf.truncated_normal(
                shape=(
                    CONV1_FILTER_SIZE, 
                    CONV1_FILTER_SIZE,
                    NUM_CHANNELS,
                    CONV1_FILTER_COUNT),
                dtype=tf.float32,

pyk

def read_images(data_dir):
    pattern = os.path.join(data_dir, '*.png')
    filenames = tf.train.match_filenames_once(pattern, name='list_files')
    
    queue = tf.train.string_input_producer(
        filenames, 
        num_epochs=NUM_EPOCHS, 
        shuffle=True, 
        name='queue')
    

pyk

CLI

Install aspell first. For example on debian-based system:

apt-get install aspell

Check all markdown files inside src directory: