Getting started with GNU Octave (or MATLAB) for machine learning

82% of Fortune 100 companies use MATLAB; Octave allows you to prototype your Machine Learning experiments faster, saving you time and money. Since it's a high level programming language you can do things really fast. Some people prefer to get their algorithms working in MATLAB and then migrate to another language such as Python, R, Java or C++. So learning to use MATLAB is a good investment.

MathWorks provides a free online self-paced MATLAB Course. You also get a certificate which you can share to show others you have completed the course. The course is really well made.

Installation

Since MATLAB has a price tag, we will use an open source alternative called Octave. Octave is largely compatible with MATLAB - so you can still follow along if your're a MATLAB user.

On Ubuntu 20.04

sudo apt update
sudo apt install octave

For other distributions, please consult GNU Octave download website.

Basics

Instead of replicating a tutorial, below are commands you will find helpful when running your machine learning experiments.

Command(s)	Usage
`+, -, \\, *`	Elementary math operations
`==`	Logical equal to
`~=`	Logical not equal to
`&`	Logical AND
`\|`	Logical OR
`xor(a, b)`	Logical XOR
`eye(n)`	n x n identity matrix
`ones(n, m)`	n x m matrix of ones
`rand(n, m)`	n x m matrix of randoms
`randi(n, m)`	n x m matrix of random integers
`size(A)`	Dimensions of matrix
`clear A`	Remove variable
`clc`	Clear command window
`A'`	Transpose of matrix
`A(1,2)`	Get element
`A(2,:)`	Get the 2nd row of
`A(end, :)`	Get the last row using the end keyword
`pinv(A)`	Pseudo inverse of matrix
`a = 3`	Variable assignment
`a = 3;`	Variable assignment - semicolon suppresses output
`pi`	Constant PI
`A = [1 2; 3 4; 5 6]`	A 3x2 matrix with denoted elements
`V = [1; 2; 3]`	A 3x1 column vector i.e
`1:4`	Creates a matrix
`0:2:4`	Creates a matrix values from 0 to 4 with spacing of 2
`linspace(first, last, num_elements)`	Useful when creating a range of values (no need to calculate spacing yourself)
`help eye`	Shows man page for eye
`doc eye`	Open doc page for eye
`pwd`	Print working directory
`cd`	Change directory
`ls`	List files
`load someData.dat`	Load data
`save filename variable`	Save variables
`save workspace.mat`	Save variables in workspace to a MAT-file
`load workspace.mat`	Load saved workspace variables
`format long`	Increase precision (default is short)
`who`	Show variables in the current scope
`whos`	Show variables in the current scope with more info

Watch out: in Octave/MATLAB vectors are not zero-indexed i.e. they start at one

Note: All MATLAB variables are arrays. Understand the difference between matrix, column vector, row vectror and scalar. Fun fact: MATLAB is an abbreviation for MATrix LABoratory - makes sense.

Tip: If you only use one index with a matrix, it will traverse down each column in order. Using one index, try extracting the eighth element of data.

Manipulating data (matrices/vectors)

Command	Usage
`A * B`	Matrix multiplication
`A .* B`	Element-wise multiplication
`abs(V)`	Element-wise absolute
`exp(V)`	Element-wise exponentiation
`log(V)`	Element-wise log
`max(A)`	Maximum element in
`round(A)`	Round each element to the nearest integer
`a < 2`	Element-wise comparison
`find(A < 3)`	Elements that are less than 3
`sum(A)`	Sum of
`magic(n)`	Generate a n x n magic matrix
`A .* eye(n)`	Assuming is an n x n matrix then this yields the diagonal and all other elements are zero
`flipud(A)`	Flip matrix up-down
`A(A > 2)`	Logical indexing. You can use a logical array as an array index, in which case MATLAB extracts the array elements where the index is true.

Plotting data

Plot the function.

x = [0:0.01:0.98];
y = sin(2 * pi * 4 * x);

plot(x, y);

Plot the and function on the same graph.

% Create an array from 0 to 0.98 in steps of 0.01
x = [0:0.01:0.98];

% Sine function
y_sin = sin(2 * pi * 4 * x); % notice the use of x

% Cosine function
y_cos = cos(2 * pi * 4 * x);

% Multple plots using 'hold on'
plot(x, y_sin);
hold on;
plot(x, y_cos, 'r'); % cos function in red

% Tip: use xlim([xmin xmax]) to zoom in on an area of interest

% Add title, axis labels and legend
title('Plot title')
xlabel('x-value')
ylabel('y-value')
legend('sin', 'cos')

% Save/export the plot as png
print -dpng 'plot.png'

% See plot gallery at https://uk.mathworks.com/products/matlab/plot-gallery.html

Use figure to create multiple figures. Tip: use help plot for more information on how to use the plot command. Use subplot to plot side by side.

Visualise a matrix.

% Plot an 8x8 matrix with a color map (using command chaining)
imagesc(magic(8)), colorbar

% Close the plot
close

Control statements

% for loop
for i = 1:10
    % double each element of matrix A
    A(i) = 2 * A(i);
end

% while loop
i = 0;
while i <= 10
    i = i + 1;
end

% if, elseif, else
if i == 5
    disp('Found a 5');
elseif i == 4
    disp('Found a 4');
else
    disp('Keep searching');
end

Functions

Functions are defined in files such as addTwoNumbers.m. Make sure your function is in your octave search path or current working directory.

% File: addTwoNumbers.m

function y = addTwoNumbers(a, b)
y = a + b;

Pro tip: Octave can natively return multiple values.

Vectorisation (advance topic)

By using the vectorised implementation we can take advantage of matrix multiplication provided by the linear algebra library which will be faster than implementing a summation yourself (usually with a for loop). Below is an example.

where

It is easy to see how vectorisation works.