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   "source": [
    "# Matrix Decomposition Example for Machine Learning for Computational Linguistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(C) 2017-2024 by [Damir Cavar](http://damir.cavar.me/)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Download:** This and various other Jupyter notebooks are available from my [GitHub repo](https://github.com/dcavar/python-tutorial-notebooks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Version:** 1.3, January 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**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/))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prerequisites:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "!pip install -U numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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/)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix operations"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[1 0 1 0]\n",
      " [0 2 2 2]\n",
      " [0 0 0 1]\n",
      " [1 2 3 2]\n",
      " [1 0 1 1]\n",
      " [0 2 2 3]]\n"
     ]
    }
   ],
   "source": [
    "from numpy import array\n",
    "\n",
    "ratings = array([\n",
    "        [1, 0, 1, 0],\n",
    "        [0, 2, 2, 2],\n",
    "        [0, 0, 0, 1],\n",
    "        [1, 2, 3, 2],\n",
    "        [1, 0, 1, 1],\n",
    "        [0, 2, 2, 3]])\n",
    "\n",
    "print(ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decomposition of the matrix into sub-matrices:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1 0]\n",
      " [0 2 2 2]\n",
      " [0 0 0 1]\n",
      " [1 2 3 2]\n",
      " [1 0 1 1]\n",
      " [0 2 2 3]]\n"
     ]
    }
   ],
   "source": [
    "from numpy import dot\n",
    "\n",
    "filmsGenres = array([\n",
    "        [1, 0, 1, 0],\n",
    "        [0, 1, 1, 1],\n",
    "        [0, 0, 0, 1]\n",
    "    ])\n",
    "\n",
    "preferencesGenres = array([\n",
    "        [1, 0, 0],\n",
    "        [0, 1, 0],\n",
    "        [0, 0, 1],\n",
    "        [1, 1, 0],\n",
    "        [1, 0, 1],\n",
    "        [0, 1, 1]\n",
    "    ])\n",
    "\n",
    "importanceGenres = array([\n",
    "        [1, 0, 0],\n",
    "        [0, 2, 0],\n",
    "        [0, 0, 1]\n",
    "    ])\n",
    "\n",
    "print(dot(preferencesGenres, dot(importanceGenres, filmsGenres)))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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   "affiliation": "Indiana University, Department of Linguistics, Bloomington, IN, USA",
   "author": "Damir Cavar",
   "title": "Matrix Decomposition Example for Machine Learning for Computational Linguistics"
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