function [ args ] = geadientDescent( data )
where the lines of the matrix are datapoints and columns are dimensions, last dimension is one to be infered. It is implemented according to lectures of Andrew Ng.
You can set learning rate, normalization and stop criterion, edit the file.
default values are:
learning rate: 0.2
stop criterion: 0.0000001
Also small dataset and script to load the dataset is present, also at the end of the file, there is commented out code to visualize 2D version of the algorithm.
If you wish to see the algorithm perform, do the following:
It is free for any use. If you use it, please cite: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6310525
Lukas Tencer (2022). Linear Gradient Descent from lectures of Andrew Ng (https://www.mathworks.com/matlabcentral/fileexchange/40225-linear-gradient-descent-from-lectures-of-andrew-ng), MATLAB Central File Exchange. Retrieved .
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