tsterker

1) tabs to spaces
:set tabstop=4 shiftwidth=4 expandtab
:retab

2) Whitespace found at end of line
:%s/^M\+$//g
:%s/\s\+$//g

NOTE: ^M must be inserted using CTRL-v RETURN

tsterker

\usepackage{fancyhdr}
\setlength{\headheight}{15.2pt}
\pagestyle{fancy}
\fancyhf{}
\rhead{\bfseries\nouppercase\leftmark}
\cfoot{\bfseries\thepage}

tsterker

var l = [1,2,3,4,5,6,7,8,9,10];

while(l.length){
    var r = (Math.random()*1000)%l.length;
    var removed = l.splice(r, 1);
    console.log('Removed: ' + removed);
}

tsterker

// Taken from:
// http://readystate4.com/2008/08/17/javascript-argument-unpacking-converting-an-array-into-a-list-of-arguments/

var item1 = ['Jack', '39', 'Panda'];
 
function hi(name, age, type){
     console.log('Hi ' + name + '! You are ' + age + ' and a ' + type + '!');
}
 

tsterker

// Remove all children DOM elements

while (node.hasChildNodes()) {
    node.removeChild(node.lastChild);
}

tsterker

#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from numpy import newaxis, r_, c_, mat
from numpy.linalg import *

def plotData(X, y):
    plt.plot(X, y, 'rx', markersize=7)
    plt.ylabel('Profit in $10,000s')
    plt.xlabel('Population of City in 10,000s')

tsterker

def feature_normalize(X):
    '''
    Returns a normalized version of X where
    the mean value of each feature is 0 and the standard deviation
    is 1. This is often a good preprocessing step to do when
    working with learning algorithms.
    '''
    mean_r = []
    std_r = []

tsterker

#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples
    m = y.size

    predictions = X.dot(theta).flatten()

tsterker

#Newbie programmer
def factorial(x):
    if x == 0:
        return 1
    else:
        return x * factorial(x - 1)
print factorial(6)


#First year programmer, studied Pascal

tsterker

p = ''.join(random.sample('ABCDEFGHJKLMNPQRSTUVWXYZ23456789', 6))