WTF is Bubble Sort ?

Bubble sort is a type of sorting algorithm. Its not the best for everything but it does have its uses. It gets its name since it makes the largest values “bubble” to the top. Bubble sort does its job by using pair comparisons. So it takes the current value and compares it to it’s neighbor directly to the right.

Below you can see what I mean. We first take 5 and compare it 3, then ask the question “is 5 greater then 3 ? ” Then if it is, 5 switches places with 3 and if it isn’t then they stay in the same place, and move on to the next value.

BubbleSortEx1

Here you can see a non optimized implementation of it:

 

Understanding Unity – Part 1

Introduction:

Unity is one of the most popular and easily accessible game engines to date. Which has lead it to become of the best documented and beginner friendly games engines. It can be used to create both 2D and 3D games, and uses C# as it’s main scripting language. And the best part is that every part of it is completely free. Combined with its robust asset store and plethora of free online tutorials it is the game engine to learn these days.

How Unity Works:

To understand Unity, you need to first understand the differences between a library and a engine. A library is something to that you use to build your application, whereas a engine is something that only calls your code.

Unity is an engine, it is something that you have to understand and use in your pursuit to make a game. You do not get to decide on how the physics engine works or how the lighting effects work. You are just given a number of ways to use the sub systems that were created for you.

When you create anything in unity your making a object. Every object in unity has certain properties attached to it, like its position and scale. You can add other components to it like a particle emitter or sprite render. The way you start coding your game is by way of using scripts that you attach to the object like any other component. Once the script is attached you can then start manipulating the different attributes your game object has.

In the second part of this series I am going to talk about the life cycle of a unity object.

Django – Part 2

Intro

Django’s slogan is “The web framework for perfectionists with deadlines.” It accomplishes this in a few different ways, forced compartmentalization, built in development tools, and every feature you could ask for. Below we will discuss how to get started, and how see how Django handles MVC.

Getting started:

You first want to setup a virtual env. I am using Anaconda, so I would type in:

  1. conda create –name <your env name> django
  2. source activate <your env name>

Then you would use Django’s great command line tools to create your first project:

  • django-admin startproject <your app name>

Once you the command above you will have your very first Django app. However it won’t do very much. You can execute the following command to run the application:

  • python manage.py runserver

As you can see its pretty boring and does not due much. Django is built around the idea of applications, being different modules in your over all application. These modules are separate from the main project and encapsulate the different features of your application. You create a Django app by then running:

  • python manage.py startapp <app name>

By running the command you get a new directory generated in your folder structure.

Getting started with MVC (MTV):

Dajango does not use the MVC design pattern per say, rather it uses the MTV design pattern. MTV stands for:

M – model : This is a simple python classes.

As you can see it is very easy to start building the inter model relationships. All models need to inherit from the Model class. This is all possible due to Djangos built in ORM. The ORM abstracts away the whole concept of having to work with a SQL DB, since it also provides a easy method revise your tables to reflect changes to your python models. This is simply done by using the:

  • python manage.py migrate
  • python manage.py make migrations < Dajango app name>

T – template : This is your html that displays model data through server side rendering.

Note that to serve these files, they need to be outside your main Django app, and they need to be reregistered in the Django settings file.

V – view : This is interestingly your controller.

As you can see above, you tie the data from your controller to your view by way of dictionaries. And tie your templates to your controllers to your views by specifying the url path name, and the file name.

A point of interest here, is that unlike some other frameworks your mapping a url to a method, rather then a whole class that contains different methods. This method mapping to is done using url.py files that exist both in the directory of the main project and the django app.

The main project directory:

The Django app:

As you can see your using regex, to find a specific url, then mapping it to a method. The main urls.py file contains the main urls for the whole project and imports the urls specifically for the Django app. Whereas the Django app just contains the sub urls for that particular app.

The “http://127.0.0.1:8000/first_app&#8221; would hit the “first_app” app, then it would then hit any of the sub urls stored in the Django app urls file.

As you can see Django is not that hard use at all, and in most ways is significantly easier then other frameworks, that don’t have all these built in features. In the next blog post I will be discussing Models, the Django admin console, and how the ORM handles model changes.

You can find part one here.

 

Machine Learning Explained – Part 1.2

Please note that I will not be covering the mathematical portions, but rather the big ideas.

Model Representation

Can come in different forms, however at the end of the day we are trying to learn a function in order to map or training. Such that it becomes a good predictor of an output given a inputs, this is a sort of regression problem. Where as functions who’s outputs are limited to a few discrete outputs, given various inputs are for classification problems.

Cost Functions

These functions are used to measure the level of accuracy of our hypothesis functions (the function we learned), by measuring the difference between our predicted value and the true output. And then computing the average error by way of the “Mean Squared Error” function. In a single variable regression problem the hypothesis function reduces to being the equation of a line.

The objective becomes to minimize the cost (or error) function. If you have ever taken Calculus before you can do that easily by taking the derivate of the function, setting it equal to zero, solving for the parameter, and using that value in your learned function.

However its not always easy when given a large set of parameters. Therefor you can also use contour plots, that act like maps to the values that reduce cost function to zero.

Gradient Descent

An easy way to think about gradient descent is by, imagining your a blind person trying to find a ball in a hilly area. You don’t know where the ball is, but you know the ball has rolled into the deepest valley in the area. However your blind so you can’t see the depth in your surroundings. Therefor you have to use your feet to feel for the steepness of the ground in front of you. By doing so you take little steps in the direction of the most steepness. This is exactly what the gradient descent algorithm does as well. It takes little steps, gauges the steepness, and then moves in that directions till it finds the global minimum of the cost function’s derivative.

You can find the first part of this series here.

Intro to Python ? – Part 2

You can find Part 1 here.

Intro

In this part of the series we are going to cover the very basics of Python. You may be asking: “Its Python, how hard can it get ? Its just sudo code” well that is both true and false to a degree.

Variables

Python is a dynamically typed language, however it is strongly typed. This just means that you can set a variable to any sort of data type you want without declaring it first. However it doesn’t implicitly convert types for you.

Collections

Python also contains different types array like structures. And they are all get dynamically sized of course. You have Lists and Dictionaries, they all work the way you intuitively think do, coming from another programming language. The only new sort of collection type, you will encounter is the Tuple. A Tuple is pretty much the same thing as a List however they are immutable and can not contain repeats of the same value.

Control Flow

It’s pretty basic.

Loops

Python does not find “for” loops pythonic that is way it favors “For In” loops instead. maybe asking: “What if I just want to print something 3 times ? do I have to make a list with three elements ?” The answer is no, you can use a “generator” shown in the gists below. Python also has something called “list comprehension” its very nifty to write compact and concise code.

 

You can go here to find all the relevant code, in order.

Machine Learning Explained – Part 1.1

You can see why I started this series here.

What is machine learning ?

  • My definition:
    • When you tell a machine to learn from experience rather then, explicitly giving it a bunch of instructions.
  • Course Definitions:
    • “the field of study that gives computers the ability to learn without being explicitly programmed.” – Arthur Samuel
    • “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” – Tom Mitchell

Supervised Learning:

You can think of this like teaching a small child how to do something you already know. Such as counting objects, or throwing a ball. Another way of thinking about it is that you give the machine the data, knowing that there is some relationship there. Then having the machine find it by its self.

This type of learning comes in two different flavours:

Regression

Given a bunch of data and asked to predict what will happen next. An example will could be: “given all the historical data about housing prices, what will be the price of a house in 2020 ?” We are mapping input data to a continuous function to.

Classification

Take the input data, and give me discrete outputs (classifications) . For example if you were to take data on students and predict which students would become engineers. Here we still know what factors really influence the result, which still makes it supervised learning.

Unsupervised Learning:

We have mountains of data that we think is random and has no structure. We have no idea what the relationships are between the variables. So we let our machine loose on the data to discover the relationships between the different variables. And it starts to cluster the data into different piles.