Week 1 (May 27 - June 2) Progress

Week 1 (May 27 - June 2) Progress

Implemented the constructor of the ClassificationSVM and added the predict method to the classdef.

This week, I started my work by creating the necessary files (ClassificationSVM, ClassificationNeuralNetwork, fitcsvm, fitcnet) and placing them in the appropriate folders.

Note: I have added ClassificationNeuralNetwork and fitcnet to my fork, but not much work has been done on them yet. According to the timeline, I will be focusing on these files at the end of July and August.

I wrote the help documentation for ClassificationSVM and fitcsvm in Texinfo, within their respective files.

  • The fitcsvm Texinfo contains help related to the expected inputs from the user.
  • The ClassificationSVM Texinfo contains help related to the returned object properties.

I studied the svmtrain and svmpredict functions from LIBSVM for Octave. I will be using them for the background algorithm.

I was implementing the following tasks in the classdef:

  1. Taking the inputs and performing validation tests on them.
  2. Converting the inputs to a form that can be fed into the svmtrain function.

I encountered a problem with the second step, as svmtrain was returning a scalar instead of a structure, resulting in an indexing error. I spent a day trying to debug this but did not find a solution. I asked for help on the forum, and Dasergatskov helped me.

This Tuesday, I had a meeting with Andreas, and we discussed the project's progress. Some key points from that meeting:

  • The current SVM-related functions are from LIBSVM 3.25, but the latest available version is 3.32. We will look into upgrading if we have enough time after finishing the main tasks. Since version 3.32 is very similar to 3.25, it won't affect the classdef code and should not be our main focus for now.
  • Emphasis on using the forum and providing regular updates on the blog.

I have added demos and tests for both the constructor and the predict function in the classdef. However, I am facing an issue with the svmtrain function: whenever it is called, it prints the cross-validation accuracy. I want to suppress this output. I tried using evalc(), but it didn't solve the problem. You can see the problem by running the command test ClassificationSVM, which prints the accuracy repeatedly for each test.

Comments

Post a Comment

Popular posts from this blog

My Plans for Google Summer of Code 2024: A Brief Overview of My Project and Timeline.

  My Plans for Google Summer of Code 2024: A Brief Overview of My Project and Timeline. Hello everyone! I am Pallav Purbia from Rajasthan, India currently pursuing my Btech in Electrical and Electronics Engineering with a minor degree in Artificial Intelligence and Machine learning from National Institute of Technology Delhi. I am excited to share that I will be contributing to the  Statistics package  of  GNU Octave  this summer.  Andreas Bertsatos  will be mentoring me throughout this project. I feel incredibly fortunate to be working with him. My Fork of the repository can be found  here  where you can check out the progress of the project. Weekly updates and the project’s overall progress will be documented in  my blog . Overview of the Project: Octave is a pioneer in open-source mathematical-oriented programming language for scientific computing and numerical computation and...
Read more

Week 7 (July 8 –July 14) Progress

Started studying algorithms and mathematics for implementing the ClassificationNeuralNetwork class This week, I started work on the ClassificationNeuralNetwork.m and fitcnet.m files. I also began studying the mathematics behind neural network classifiers. After reviewing the  MATLAB documentation , I identified the following components for the ClassificationNeuralNetwork class: Weights initializer: Layer weights are initialized using either 'glorot' or 'he'. Biases initializer: Biases are initialized using either 'zeros' or 'ones'. Loss function: Cross entropy. Solver used: L-BFGS (Limited-memory quasi-Newton code for bound-constrained optimization). Initially, I am focusing on getting the forward propagation to work properly. You can see my work  here . Resources I found helpful include: " Neural Network from scratch in Python " by Sentdex (unfortunately, the playlist was never completed). Andrew Ng's  lecture , where he mentions t...
Read more