GSoC - I
15 Jun 2017
Hi guys,
Just spoke with one of my project mentors, Yann Debray yesterday. Since MATLAB’s syntax is almost similar to Scilab, it would be easy to search for existing code base in MATLAB scripts and port them to Scilab.
In last November-December, I have already created and studied most of the machine learning algorithms through MATLAB.
As discussed with Yann, first I will need to assess the existing functions in both Scilab and MATLAB. Once a list of all such functions and features has been gathered, then it would be decided how I should start the development work.
While searching for existing toolboxes for ML in Scilab, I ended up reading about and working on the below set(not exhuastive):
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Neural Network Module - This is a Scilab Neural Network Module which covers supervised and unsupervised training algorithms
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Regression tools - A toolbox for linear and non linear regression analysis
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NaN-toolbox - A statistics and machine learning toolbox for naive bayes classifier , k-means clustering
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libsvm and liblinear -Libraries for SVM and large-scale linear classification
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Stixbox- For logistic Regression
Since there was a lack of documentation for these toolboxes, I followed the working of the neural networks module through material provided by Tritytech. I studied the following 2 courses to get started with Scilab tooboxes:
Currently, I am going through the usage of all the above toolboxes and also trying out MATLAB’s Statistics and Machine learning toolbox
Once I was done, possible integration approach for using PIMS- python integration mechanism in Scilab was selected as the way to follow. I worked with PIMS for a week, getting to know its syntaxes and finally making it import the scikit-learn library in Scilab.
Major time was spent trying to work with PIMS and scikit-learn to implement basic ml models in Scilab. I was successfully able to port linear regression for Scilab. Link to the source code is here
After discussions with Philippe Saade and Yann Debray, it was decided to take up a more complex ml problem implementation, to test for any issues faced while using PIMS. Tried to port this example through PIMS in Scilab.
All the issues which I faced have been documented in this document.
Had a detailed and fruitful discussion with Simon Marchetto(developer of PIMS) regarding possible methods to resolve the above reported issues. More discussions regarding the final approach to be followed to be held tomorrow.
Really getting awesome exposure discussing the development work with actual long-time developers.
The work is just getting started now, anyways. More awesomeness to follow!