#!/usr/bin/env python
# coding: utf-8

# # Matrix Decomposition Example for Machine Learning for Computational Linguistics

# **(C) 2017-2024 by [Damir Cavar](http://damir.cavar.me/)**

# **Download:** This and various other Jupyter notebooks are available from my [GitHub repo](https://github.com/dcavar/python-tutorial-notebooks).

# **Version:** 1.3, January 2024

# **License:** [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/) ([CA BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/))

# **Prerequisites:**

# In[ ]:


get_ipython().system('pip install -U numpy')


# This is a tutorial related to the discussion of matrix decomposition of feature sets in classification tasksin the textbook [Machine Learning: The Art and Science of Algorithms that Make Sense of Data](https://www.cs.bris.ac.uk/~flach/mlbook/) by [Peter Flach](https://www.cs.bris.ac.uk/~flach/).

# This tutorial was developed as part of my course material for the course Machine Learning for Computational Linguistics in the [Computational Linguistics Program](http://cl.indiana.edu/) of the [Department of Linguistics](http://www.indiana.edu/~lingdept/) at [Indiana University](https://www.indiana.edu/).

# ## Matrix operations

# In[1]:


from numpy import array

ratings = array([
        [1, 0, 1, 0],
        [0, 2, 2, 2],
        [0, 0, 0, 1],
        [1, 2, 3, 2],
        [1, 0, 1, 1],
        [0, 2, 2, 3]])

print(ratings)


# Decomposition of the matrix into sub-matrices:

# In[2]:


from numpy import dot

filmsGenres = array([
        [1, 0, 1, 0],
        [0, 1, 1, 1],
        [0, 0, 0, 1]
    ])

preferencesGenres = array([
        [1, 0, 0],
        [0, 1, 0],
        [0, 0, 1],
        [1, 1, 0],
        [1, 0, 1],
        [0, 1, 1]
    ])

importanceGenres = array([
        [1, 0, 0],
        [0, 2, 0],
        [0, 0, 1]
    ])

print(dot(preferencesGenres, dot(importanceGenres, filmsGenres)))


#