Gerjo

cat("\014")

library(base)


if(require(tm) == FALSE) {
    print("Attempting to install TM support...");
    install.packages("tm")
    
    library(tm);

Gerjo

<?php

$input  = "Warped text!";
$width  = 200;
$height = 200;

// Allocate initial image with a solid white background.
$original = imagecreatetruecolor($width, $height);
imagefill($original, 0, 0, imagecolorallocate($original, 255, 255, 255));

Gerjo

define(function(require) {
    
    /// Here's a scene I baked earlier. Bunch of cubes and a plane.
    ///
    ///// 6 float triplets
    ///
    /// struct Face {
    ///    Vector3 a;
    ///    Vector3 b;
    ///    Vector3 c;

Gerjo

Vector2 primo(int num) {

    std::deque<int> primes;

    const int root = (int) std::sqrt(num);
    
    std::function<void(int)> recurse;

    // Mostly from: http://www.coderenaissance.com/2011/06/finding-prime-factors-in-javascript.html
    recurse = [&primes, &recurse, root] (int num) -> void {

Gerjo

// Initial seed.
var x = 42;

function rand() {
  
  // Old random number becomes next seed.
  x = (1103515245 * x + 12345) % 2147483648;
  
  // Generate a floating point, then modulo to only keep

Gerjo

node::insert(int value) {

   // Value falls in range
   if(value >= this.min && value <= this.max) {
       
   } 
   
   // Extend range
   if(value == this.min - 1) {
       this.min = value;

Gerjo

struct Textures {
    static GLuint LoadPNG(const std::string& filename) {
    
        if( ! FileExists(filename)) {
            Exit("Cannot open png: %s.", filename.c_str());
        }
    
        FILE* fp = ::fopen(filename.c_str(), "r");

        png_structp png_ptr = png_create_read_struct(PNG_LIBPNG_VER_STRING, NULL, NULL, NULL);

Gerjo

        LeastSquaresLinearRegression: function(data, getter) {
            
            // Assign a default trivial case getter:
            if( ! getter) {
                getter = function(row) {
                    return row;
                };
            }
        
            var xy = 0;

Gerjo

// Quick test to see who converges quicker

// squared error
function e(w, t, x, y) {
  return Math.pow(t - w * x - w*w*y, 2);
}

// e's derivative
function d(w, t, x, y) {
  return 2 * (t - w * x - w*w * y) * (-x - 2*w*y);

Gerjo

cat("\014")
library(class)

# I like boolean support.
no  <- 0
yes <- 1

# Entry to clasify
test <- c(
    "cat", # The correct class