Generating Permutation Matrices in Octave / Matlab

December 14, 2015December 14, 2015 Anirudh Technical function, Gilbert Strang, linear algebra, Math, MATLAB, Octave, permutation matrices

I have been doing Gilbert Strang’s linear algebra assignments, some of which require you to write short scripts in MatLab, though I use GNU Octave (which is kind of like a free MatLab). I was trying out this problem:

To solve this quickly, it would have been nice to have a function that would give a list of permutation matrices for every n-sized square matrix, but there was none in Octave, so I wrote a function permMatrices which creates a list of permutation matrices for a square matrix of size n.

	% function to generate permutation matrices given the size of the desired permutation matrices
	function x = permMatrices(n)
	x = zeros(n,n,factorial(n));
	permutations = perms(1:n);
	for i = 1:size(x,3)
	x(:,:,i) = eye(n)(permutations(i,:),:);
	end
	endfunction

view raw permMatrices.m hosted with ❤ by GitHub

For example:

The MatLab / Octave code to solve this problem is shown below:

	% Solution for part (a)
	p = permMatrices(3);
	n = size(p,3); % number of permutation matrices
	v = zeros(n,1); % vector of zeros with dimension equalling number of permutation matrices
	% check for permutation matrices other than identity matrix with 3rd power equalling identity matrix
	for i = 1:n
	if p(:,:,i)^3 == eye(3)
	v(i,1) = 1;
	end
	end
	v(1,1) = 0; % exclude identity matrix
	ans1 = p(:,:,v == 1)

	% Solution for part (b)
	P = permMatrices(4);
	m = size(P,3); % number of permutation matrices
	t = zeros(m,1); % vector of zeros with dimension equalling number of permutation matrices
	% check for permutation matrices with 4th power equalling identity matrix
	for i = 1:m
	if P(:,:,i)^4 == eye(4)
	t(i,1) = 1;
	end
	end
	% print the permutation matrices
	ans2 = P(:,:,t == 0)

view raw Section2_7_13.m hosted with ❤ by GitHub

Output:

Solutions to Machine Learning Programming Assignments

November 24, 2015July 25, 2016 Anirudh Technical Andrew Ng, Code Snippets, Coding, Machine Learning, Octave, Python, Solutions

This post contains links to a bunch of code that I have written to complete Andrew Ng’s famous machine learning course which includes several interesting machine learning problems that needed to be solved using the Octave / Matlab programming language. I’m not sure I’d ever be programming in Octave after this course, but learning Octave just so that I could complete this course seemed worth the time and effort. I would usually work on the programming assignments on Sundays and spend several hours coding in Octave, telling myself that I would later replicate the exercises in Python.

If you’ve taken this course and found some of the assignments hard to complete, I think it might not hurt to go check online on how a particular function was implemented. If you end up copying the entire code, it’s probably your loss in the long run. But then John Maynard Keynes once said, ‘In the long run we are all dead‘. Yeah, and we wonder why people call Economics the dismal science!

Most people disregard Coursera’s feeble attempt at reigning in plagiarism by creating an Honor Code, precisely because this so-called code-of-conduct can be easily circumvented. I don’t mind posting solutions to a course’s programming assignments because GitHub is full to the brim with such content. Plus, it’s always good to read others’ code even if you implemented a function correctly. It helps understand the different ways of tackling a given programming problem.

ex1
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ex8

Enjoy!

Getting Started

April 12, 2015April 12, 2015 Anirudh Technical Coursera, Data Science, Kaggle, Machine Learning, MATLAB, Octave, R

I have been searching for good MOOCs to get me started with R and Python programming languages. I’ve already begun the Johns Hopkins University Data Science Specialization on Coursera. It consists of 9 courses (including Data Scientist’s Toolbox, R programming, Getting and Cleaning Data, Exploratory Data Analysis, Reproducible Research, Statistical Inference, Regression Models, Practical Machine Learning and Developing Data Products), ending with a 7-week Capstone Project that I’m MOST excited about. I want to get there fast.

The Capstone would consist of :

Building a predictive data model for analyzing large textual data sets
Cleaning real-world data and perform complex regressions
Creating visualizations to communicate data analyses
Building a final data product in collaboration with SwiftKey, award-winning developer of leading keyboard apps for smartphones

I started with the R programming course where I found the programming assignments to be moderately difficult. They were good practice and also time-consuming for me since I haven’t yet gotten used to the R syntax, which is supposedly unintuitive. Anyway, I completed the course with distinction (90+ marks) scoring 95 on 100, losing 5 because I hadn’t familiarized myself with Git / GitHub. I did this course for a verified certificate, which cost me $29, and looks like this:

I won’t be paying for any of the remaining courses though, but still will get a certificate of accomplishment for each course I pass. I have alredy begun with Getting and Cleaning Data and Data Scientist’s Toolbox.

I checked today, and it seems Andrew Ng’s Machine Learning course has gone open to all and is self-paced. A lot of people have gone on to participate in Kaggle competitions with what they learnt in his course, so I’d like to experience it — even though it’s taught with Octave / MATLAB. My very short term goal is to start participating in these competitions ASAP.

I will be learning the basics of Git this week and along with that, about reading from MySQL, HDF5, the web and APIs. I intend to start reading Trevor Hastie’s highly recommended book, Introduction to Statistical Learning.

[DOWNLOAD LINK TO THE BOOK]

Meanwhile, I need to get started with Git and GitHub too, and I found a very useful blog by Kevin Markham and his short concise videos are great introductory material.

Incidentally, I was in a dilemma whether to start with Hastie’s material or Andrew Ng’s course first. This is what Kevin had to say

The only reason I have reservations against Andrew Ng’s course is that its instruction isn’t in R or Python. Also, CTO and co-founder of Kaggle, Ben Hamner mentions here how useful R and Python are vis-à-vis Matlab / Octave.