code snippets to practice applications of data structures used in ml: refer to https://www.kdnuggets.com/2018/01/data-structures-related-machine-learning-algorithms.html/2 Problems:

If you want to practice and realize data structures for ML algorithm yourself, try to solve some of problems below:

1. Encapsulate the matrix-vector multiplication code snippet into a subroutine called matrix_times_vector. Design the calling syntax for the subroutine.
2. Using struct, typedef or class, encapsulate both vectors and matrices into a pair of abstract types called vect and matrix, respectively. Design an API for the types.
3. Find at least three libraries online that do the above.
4. Download and install the LIBSVM library. Consider the method Kernel::k_function on line 316 of “svm.cpp”. What are the advantages and disadvantages of the data structure used to hold vectors?
5. How would you re-factor calculation of kernel functions in the LIBSVM library?
6. Which data structures described in the text are abstract types?
7. What internal representation or data structure could you use to implement the abstract data types? Are there any that are not included in the list above?
8. Using a binary tree, design an associative array.
9. Consider the vector type in LIBSVM. How can this be used to represent a sparse matrix? Contrast this with the sparse matrix class described above. Look at the complete type. What are the advantages and disadvantages of each representation?
10. Implement a treesort and a heapsort. Now use the same data structures to find the top k elements. What common machine learning algorithm is this good for?
11. Implement your favorite data structure in your favourite language.