Top 8 Essential Tweaks for New Installations of Ubuntu 12.04

Having just upgraded to 12.04 there are a bunch of things that I found I needed to do to get it working how I wanted to.

1) Install the Classic Application menu

It is beyond me why the hierarchical applications menu has been removed in this version of ubuntu. It also seems that the new left hand launcher only displays apps installed from the 'Ubuntu Software Centre.' Applications installed from Synaptic are lost and don't always seem to show up in the new Dash.

So to get the classic application menu: Open a terminal ( Ctrl – Alt – T ) and add the following PPA.

sudo apt-add-repository ppa:diesch/testing

Then update and install the classic menu

sudo apt-get update && sudo apt-get install classicmenu-indicator

2) Install the restricted extras

Allows you to listen to mp3s and watch loads of encrypted video formats.

sudo apt-get install ubuntu-restricted-extras

3)  Enable 'Show Remaining Space Left' Option in Nautilus File Browser

Again, why this is not on by default is beyond me. Extremely useful.

Open Nautilus. Go to View - Statusbar. Enable it, nuff said.

4) Calculator Lens/Scope for Ubuntu 12.04

One upside of the new Ubuntu Dash are a bunch of information rich widgets integrated into the OS. You can get info on weather, cities, films do calculations directly from the HUD.

sudo add-apt-repository ppa:scopes-packagers/ppa
sudo apt-get update
sudo apt-get install unity-lens-utilities unity-scope-calculator
sudo apt-get install unity-scope-rottentomatoes
sudo apt-get install unity-scope-cities

5) Open in Terminal Nautilus Extension

Allows you to open a terminal that is already inside the folder you are currently browsing with Nautilus. This saves me oodles of time.

sudo apt-get install nautilus-open-terminal

6) Install CPU/Memory Indicator Applet

Sweet little widget to view systems resource usage stats

sudo add-apt-repository ppa:indicator-multiload/stable-daily
sudo apt-get update
sudo apt-get install indicator-multiload

7) Install Spotify

Music streaming service desktop client. This info comes directly from their laboratories:
https://www.spotify.com/au/download/previews/

Add the spotify repo by editing /etc/apt/sources.list
Add the line:
deb http://repository.spotify.com stable non-free

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 94558F59

sudo apt-get update && sudo apt-get install spotify-client

8) Install Synergy

Synergy is an application that lets you share you mouse and keyboard across computers. More than that it also shares your clipboard, you can copy text between machines. You can't copy files for the moment but maybe if <a href="http://synergy-foss.org/download/?donate">we all donate to the cause</a> we can request that feature.

You can download a Debian package here:

http://synergy-foss.org/download/

Then just install it with

sudo dpkg -i <synergy package name here>

Ubuntu on Toshiba Satellite P870 / 05P

I just bought a new Toshiba Satellite P870 / 05P laptop, amazing specs, but ten minutes of playing with windows 8 convinced me that I don't even want to dual boot. I just wanted it off my machine.

Unfortunately I then discovered that Ubuntu 12.04 has no device drivers for the wireless or Ethernet cards on this machine. Loads of head scratching and searching and I eventually found a link to a device driver on various Ubuntu forums that can be installed.

I followed the instruction about halfway down this page

However I had to use this archive instead of the one listed because Ubuntu 12.04 uses the 3.5 Kernel.

I really should read the source myself to make sure nothing nasty has been inserted into this code, but for now I am depending on the goodwill of my fellow Ubuntu users.

iOS Renewal Process

You would think that renewing your development membership would be all that you need to do on a yearly basis to keep working as an Apple developer.
It should be just: pay the fee and keep on developing. Unfortunately it is not that simple.

Your certificates and provisioning profiles need to be renewed, regenerated, and installed before you can continue. As I have not found a reasonable walk-through for this process, either from Apple or on their forums, I will quickly sketch it out here:


1) Clear out your old Provisioning profiles in Xcode

Open the XCode Organizer. Select "Provisioning Profiles," go through the list and delete all the expired certificates.


2) Remove your existing certificates in Keychain Access

Open the Utilities fold in your Mac's Applications. Open Keychain Access and then select "My Certificates." You will see your expired certificates (Dev and Dist) listed. Remove them both.


3) Create new certificates

Keeping Keychain Access open, click:
Keychain Access>Certificate Assistant>Request a Certifcate From a Certificate Authority.
Choose "Save to Disk" and save the request file.

Open the Certificates section of the iOS Provisioning Portal.

Delete the existing Development Certificate.
Click the "+" Symbol to create a new development certificate.
Select the top option "iOS App Development"
Click Continue.
Upload the certificate request file you created and finish.

Click "+" again to create a distribution certificate.
Choose the "App Store and Ad Hoc" option and continue.
Upload the certificate request file and finish.


 4) Regenerate the Provisioning Profiles

Click the "Provisioning Profiles" in the iOS Provisioning Portal.
Go through each of your development and distribution profiles and edit them.
When you edit them you will see an option to select the new certificate that you generated. Once selected the "Generate" button will become active, click to generate and download the new profiles.


 5) Install the Certificates and Provisioning Profiles
 
Install the downloaded certificates and provisioning profiles by dragging them into Keychain Access and XCode respectively.

You can now test your development apps and distribute them to the app store just like before you renewed. Just remember to select the correct profile when you are building your app.

Configuring Apache for a Local Site on Ubuntu

Introduction

This is a simple task, setting up my local machine so that I can browse directly to a hostname such as "mysite" and Apache will find the right project. This is a great way to test that paths will work as you expect when a site goes onto the production server. You will just need a config file in the site so it knows when it is on the development server (your local machine) and when it is live.

As simple as this is, I always have to look it up every time I do it.

So, in the interests of improving my own efficiency and maybe helping someone else I am blogging my process.

Dependencies

Ubuntu Lucid: 10.04(Check this with:  cat /etc/lsb-release )

Apache/2.2.14 (Ubuntu)
(Check this with: /usr/sbin/apache2 -v )

Process

First go add the new site to yours hosts file, edit

sudo vi /etc/hosts

Then change or add the line:

127.0.0.1       localhost mysite


Next you need to configure Apache to recognise the site. You need to create a config file for your site in the sites-available directory:

/etc/apache2/sites-available/mysite

with something like the following contents:

<VirtualHost *:80>
    ServerName mysite
    DocumentRoot /home/username/mysite/www
    <Directory /home/username/mysite/www>
        Options Indexes FollowSymLinks Includes
        AllowOverride All
        Order allow,deny
        Allow from all
    </Directory>
    RewriteEngine On
    RewriteOptions inherit
</VirtualHost>


Then, you just need to enable the site with the apache script a2ensite, like thus:

sudo a2ensite mysite

Then reload apache

sudo /etc/init.d/apache2 reload

...and voila!!! You can now browse directly to http://mysite

Maximum Likelihood Estimation

Maximum Likelihood Estimation is widely applicable method for estimating the parameters of a probabilistic model.

Developed by R.A.Fisher in the 1920s the principle behind it is that the ideal parameter settings of a model are the ones that make the observed data most likely.

It is applicable in any situation in which the model can be specified such that the probability of the desired variable y can be expressed as a parameterised function over the vector of observed variables (X).

P(y|X) = f(X,φ)

The parameters φ of the function f(X, φ) are what we want to estimate.

The model is designed to be a function in which the parameters are set and we get back a probability value for a given x. However, we need a process to determine these model parameters. The Likelihood function is defined to be equal to this function, but operating as a function over the parameter space of φ.

L(φ | y,X )= P(y|X,φ)

It is important to recognise that Likelihood is not the probability of the parameters, it is just equal to the probability of y given the parameters. As such it is not a probability distribution over φ.

If we have N observations in our data set, and we let D represent all N of these observations of X and y, then we can express the Likelihood function for this entire data set D as :

L(φ | D ) = ∏^N_i=1 P(y_i|X_i,φ )

Maximum Likelihood is then simply defined as Argmax φ over this function. Finding the value of φ that maximises this function can be done a number of ways.

To find an analytical solution to the Likelihood equation we find the partial derivative of the function with respect to each of the paramters. We then solve this series of equations for the parameter values such the the partial derivatives are equal to zero. This gives us a position that is either a max or min. We then find the second partial derivative with respect to each parameter and make sure it is negative at the points found in the first step. This will give us an analytical peak on the Likelihood surface.

The reality of maximising the Likelihood by searching the parameter space depends a great deal on the problem. Numerous tricks occur to simplify the problem. The natural logarithm of the Likelihood function is often taken because they are monotonically related, so the MLE can be obtained by maximising the log of the Likelihood. In addition, taking the log turns the product into a Sum and can improves the chance of finding an analytical solution, and improve the computational tractability of finding a numerical solution.

In the next post I will summarise the use of the Expectation Maximisation algorithm for situations in which the Likelihood function cannot be solved analytically.

Simplicity in the world

To my mind one of the most puzzling aspects of science is the success of the reasoning principle known as Occam's Razor. This is the notion that whenever two competing theories explain the known facts equally well, then the simpler theory is generally correct.

As a rule of thumb Occam's Razor helps us wade through the infinite number of potential theories that might be put forward to explain any given phenomenon. To demonstrate this I will use a trivial example that is not particularly deeply scientific.

When trying to come up with a principle to described the observation that the sun comes over the horizon every 24 hours we could generate an infinite set of theories as follows:

1) The earth rotates at a constant speed such that the sun appears on the horizon at regular intervals.

Then we may add an infinite set of exceptions.

2) The earth rotates at a constant speed such that the sun appears on the horizon at regular intervals. Except on Thursday the 6th December 2018, when the earth will stop rotating for 24 hours and then resume.

3) The earth rotates at a constant speed such that the sun appears on the horizon at regular intervals. Except on Thursday the 6th December 2018, when the earth will stop rotating for 24 hours and then rotate backwards.

4) The earth rotates at a constant speed such that the sun appears on the horizon at regular intervals. Except after when the New York Yankees have won the world series 100 times in a row, then it will slow down to half its speed.

Etc, etc.

Finding a set of theories that all equally explain the given evidence is easy. In this case we could decide between these theories easily through empirical means, because they make slightly different predictions, we just wait for the predicted outcomes to diverge. However, as there are in fact an infinite number of these alternate theories in practice it is not possible. Instead, we rely on the rule of thumb known as Occam's Razor to remove all alternative theories.

It turns out that in the realm of data mining Occam's Razor turns out to be incredibly practical. If you can fit multiple models to a data set with approximately equal error, then the simplest model will more often than not produce the best predictions. This principle has been critical in the design of many modern machine learning algorithms.

Interestingly the predictive power of simplicity extends beyond this. As google research director Peter Norvig discusses in the presentation below: we are finding that the critical factor in solving many modern computer science problems is data volume. As our data sets grow in size we see that the best predictive models come not from painstakingly building custom models for certain data, but from just using an array of simple models and letting the data speak.

Read more about this issue in the paper The Unreasonable Effectiveness of Data.

Python Please

I have friends who swear by Python.

They rarely program with any other language, and I understand in theory the appeal. You have a language that forces you to write nicely formatted code just to make it work. You do away with redundant structure imposing syntax like braces and semicolons. No longer do you have to waste time formatting someone else's crappy code before you can work with it.

What I can't understand is how they manage to live with its horrendous approach to string processing. Python is a late bound weakly typed interpreted language with an approach to string processing that belongs in C or Assembly language. At that, all the purists are going to cry:

"Just because you don't understand encodings!!!"

Sigh. Yes. Every time I have to work with Python I go and reread Joel's fanatstic article: The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!), just to make sure I haven't missed something. Every time, I confirm that I am not a complete idiot, and I come back to wrestle with python and try and work out where in the process of passing a string around I went wrong.

The problem is partly that I use Python predominantly to scrape webpages. This means that I am always loading up badly formatted text with incomplete or missing meta-data. So to be fair maybe programmers who do not engage in this process never see the problems I see. But it is not only me, look at this thread on Stackoverflow to see how ridiculous the situation is.

I want to propose something to Python enthusiasts. Just say you are right, and the problems are entirely mine (real python programmers love having to monitor string encodings continuously). Ok sure, then:

Why not have a mode for the language that will just force all strings to be a single encoding, say UTF-8 ?

The chorus will yell back, we do. You just do: X-Y-Z

Well people I have tried all of those X-Y-Zs and they do not work. Perhaps again it something to do with my approach. I use a bunch of libraries to process the data, maybe urllib library, or beautiful soup which I use to parse things. I don't know, I am not an expert, and I shouldn't need to be just to parse strings reliably.

I don't understand why it just doesn't work. I have never wasted so much time dealing with string coding problems with any other language than I have with Python.

It should not be so hard. It really shouldn't.