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function[X_norm, mu, sigma] = zscore_normalize_data(X, mu, sigma)
X_norm=bsxfun(@minus,X,mu);
X_norm=bsxfun(@rdivide,X_norm,sigma);

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import urllib2
from BeautifulSoup import BeautifulSoup
import pdb
import os
import htmlentitydefs
from BeautifulSoup import BeautifulStoneSoup
import HTMLParser
import cgi

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function mix = gmm(dim, ncentres, covar_type, ppca_dim)
%GMM	Creates a Gaussian mixture model with specified architecture.
%
%	Description
%	 MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space
%	DIM, the number of centres in the mixture model and the type of the
%	mixture model, and returns a data structure MIX. The mixture model
%	type defines the covariance structure of each component  Gaussian:
%	  'spherical' = single variance parameter for each component: stored as a vector
%	  'diag' = diagonal matrix for each component: stored as rows of a matrix

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function [varargout] = rdir(rootdir,varargin)
% Lists the files in a directory and its sub directories. 
% 
% function [D] = rdir(ROOT,TEST)
%
% Recursive directory listing.
%
% ROOT is the directory starting point and includes the 
% wildcard specification.
% The function returns a structure D similar to the one 

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#!/usr/bin/env python


'''
Query on GoogleImageSearch and install resulted images by scraping.

To use this script install mechanize and BeautifulSoup packages as
easy_install mechanize
easy_install Beautiful

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function [] = sample_neg_examples(ROOT_PATH, num_sample, OUTPUT_PATH)
SEARCH_PATH = fullfile(ROOT_PATH,'**','*');
SAVE_PATH = 'neg_examples'
paths = rdir(SEARCH_PATH);


if exist('OUTPUT_PATH','var')
    SAVE_PATH = OUTPUT_PATH;
end

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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if ~exist('vlfeat', 'dir')
  from = 'http://www.vlfeat.org/download/vlfeat-0.9.13-bin.tar.gz' ;
  fprintf('Downloading vlfeat from %s\n', from) ;
  untar(from, 'data') ;
  movefile('data/vlfeat-0.9.13', 'vlfeat') ;
end

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function [ res ] = ie505_hw1( n,a,b )
%İE505_HW1 Summary of this function goes here
%   Detailed explanation goes here

[~,bins]= hist([a,b],1000);

r =unique( round( n*bins));

res = arrayfun(@(x)nchoosek(n,x)*double(1/(2^n)),r);

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'''
split a file into two randomly, line by line. 
Usage: split.py <input file> <output file 1> <output file 2> [<probability of writing to the first file>] [<random seed>]'
'''

import csv
import sys
import random

input_file = sys.argv[1]