I just finished writing my first machine learning algorithm in Matlab. The algorithm is based on gradient descent search for estimating parameters of linear regression (but can be easily extended to quadratic or even higher-dimensional polynomials). It’s fairly easy if you know the theory behind the model. So, first a very brief theory portion. This isn’t a tutorial on statistics. Go read a book if you don’t know about regression.

Just to recall: Regression comes in when you want to estimate a response variable (y) given explanatory variables (x_i). The goal for linear regression is to come up with values for parameters (\theta_i) such that you get a ‘best possible fit’ for each jth instance in the dataset of m instances:

y^j = \sum_{i=1}^m{\theta_i  \times x_i^j}

In matrix notation, that comes simply to:

Y = \theta^T X

(For ease, we assume that x_1 is a vector of all 1s thus making \theta_1 the y-intercept).

The idea of gradient descent is to come up with the best (locally) values of \theta. It does this by repeatedly updating the value of \theta using the formula:

\theta_i = \theta_i - \alpha * \sum_{j=1}^m{(\theta^T X^j - Y^j) X_i^j}

If you have no idea what this is or if you want to know this in-depth, read till the end.

Here’s the Matlab code for this whole procedure I wrote at first. Note the use of W for \theta:

% Gradient descent algo for linear regression
% author: Nauman (recluze@gmail.com)

%set the data
X=[1 1 1 1 1 1 1; 22 49 80 26 40 54 91];
Y=[20 24 42 22 23 26 55];
hold on;
plot(X(2,:),Y, 'x');

% set the actual values of W
W = [5.775 0.474]';
YAct = (W' * X);

W = zeros(2,1);     % initialize W to all zeros
m = size(X,2);      % number of instances
n = size(W,1);      % number of parameters
alpha = 0.000025;    % gradient descent step size

disp('Starting Weights:');

% 1) do the loop for W estimation using gradient descent ------------
for iter = 1 : 20 % let's just do 5 iterations for now ... not til convergence
% for each iteration
for i = 1 : n       % looping for each parameter W(i)
    % find the derivative of J(W) for all instances
    sum_j = 0;
    for j = 1 : m
        hW_j = 0;
        for inner_i = 1 : n
            hW_j = hW_j + W(inner_i) * X(inner_i,j);
        sum_j = sum_j + ( (hW_j - Y(j)) * X(i,j) ) ;
    % calculate new value of W(i)
    W(i) = W(i) - alpha * sum_j;
% plot the thingy
newY = (W' * X);
gColor = 0.05 * iter;
plot(X(2,:),newY, 'Color', [0 gColor 0]);

% end 1) ------------
% output the final weights
disp ('Final calculated weights');
% output actual weights from formula in Red W = [5.775 0.474]'
plot(X(2,:),YAct, 'Color',[1 0 0]);
% finish off
hold off;

Then I figured that I was just doing loops and didn’t really need the matrices. So, I went back and thought about the whole nested loop thing and replaced the loop portion with this:

for i = 1 : n       % looping for each parameter W(i)
    % calculate new value of W(i)
    W(i) = W(i) - alpha * sum( ((W'*X) - Y) .* X(i,:) );

Seems suspiciously like the original formula, doesn’t it? Here’s what the plot look like. The red line shows the linear regression line calculated from a closed-form formula and the other lines show estimations of \theta with the darkest colored ones being the initial guess and lighter ones are successive guesses.

And finally, as promised, here’s where you can learn all about this stuff: machine learning video lectures from Stanford.