MatrixClass.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Matrix(object):\n",
    "    def __init__(self, size, data=[]):\n",
    "        self.shape = size\n",
    "        self._rows, self._cols = self.shape\n",
    "        self._data = data\n",
    "        \n",
    "        \n",
    "    def identity(self):\n",
    "        if not self._rows == self._cols:\n",
    "            raise ValueError('Identity matrix must be square')\n",
    "        self._data = []\n",
    "        for idx in range(self._rows):\n",
    "            row = [0] * self._cols\n",
    "            row[idx] = 1\n",
    "            self._data.extend(row)\n",
    "            \n",
    "    def create_identity(self):\n",
    "        i = Matrix((self._cols, self._cols))\n",
    "        i.identity()\n",
    "        return i\n",
    "            \n",
    "    def raw(self):\n",
    "        return [self._data[x:x+self._cols] for x in range(0, len(self._data), self._cols)]\n",
    "    \n",
    "    def column(self, idx):\n",
    "        return [row[idx] for row in self.raw()]\n",
    "    \n",
    "    def zero(self):\n",
    "        row, col = self.shape\n",
    "        self._data = [0] * (row * col)\n",
    "        \n",
    "    def one(self):\n",
    "        row, col = self.shape\n",
    "        self._data = [1] * (row * col)\n",
    "        \n",
    "    def dot(self, rhs):\n",
    "        rows_rhs, cols_rhs = rhs.shape\n",
    "\n",
    "        if not self._cols == rows_rhs:\n",
    "            message = 'got ({0}, {1})'.format(self._cols, rows_rhs)\n",
    "            raise ValueError('columns on left must equal rows on right, ' + message)\n",
    "\n",
    "        p = Matrix((self._rows, cols_rhs))\n",
    "        p.zero()\n",
    "\n",
    "        for row in range(self._rows):\n",
    "            for col in range(cols_rhs):\n",
    "                p[row,col] = self._dot_vector(self[row], rhs.column(col))\n",
    "\n",
    "        return p\n",
    "    \n",
    "    def transpose(self):\n",
    "        row, col = self.shape\n",
    "        self.shape = (col, row)\n",
    "        self._rows, self._cols = self.shape\n",
    "            \n",
    "    def _longest_str(self, str_seq):\n",
    "        longest = 0\n",
    "        for s in str_seq:\n",
    "            longest = max(len(s), longest)\n",
    "        return longest    \n",
    "    \n",
    "    def _pad(self, s, total):\n",
    "        return s + ' ' * abs(len(s) - total)\n",
    "    \n",
    "    def _pad_row(self, row, total):\n",
    "        return ' '.join([self._pad(value, total) for value in row])\n",
    "    \n",
    "    def _header(self, w, total):\n",
    "        l = (total * w) + (w - 1)\n",
    "        h = ' /' + ' ' * (l - 2) + '\\\\' + '\\n'\n",
    "        h += '/' + ' ' * l + '\\\\' + '\\n'\n",
    "        return h\n",
    "\n",
    "    def _footer(self, w, total):\n",
    "        l = (total * w) + (w - 1)\n",
    "        f = '\\n' + '\\\\' + ' ' * l + '/'\n",
    "        f += '\\n' + ' \\\\' + ' ' * (l - 2) + '/'\n",
    "        return f\n",
    "    \n",
    "    def _dot_vector(self, l, r):\n",
    "        return sum([t[0] * t[1] for t in list(zip(l, r))])\n",
    "    \n",
    "    def __str__(self):\n",
    "        data = [str(item) for item in self._data]\n",
    "\n",
    "        longest = self._longest_str(data)\n",
    "\n",
    "        rows = [data[x:x+self._cols] for x in range(0, len(data), self._cols)]\n",
    "\n",
    "        repr = header(self._cols, longest)\n",
    "        repr += '\\n'.join(['|' + pad_row(row, longest) + '|' for row in rows])\n",
    "        repr += footer(self._cols, longest)\n",
    "\n",
    "        return repr\n",
    "    \n",
    "    def __getitem__(self, key):\n",
    "        if isinstance(key, int):\n",
    "            lower = key * self._cols\n",
    "            return self._data[lower:lower + self._cols]\n",
    "        else:\n",
    "            row, col = key\n",
    "            return self._data[row * self._cols + col]\n",
    "    \n",
    "    def __setitem__(self, key, value):\n",
    "        if isinstance(key, int):\n",
    "            raise NotImplementedError()\n",
    "        row, col = key\n",
    "        self._data[row * self._cols + col] = value\n",
    "                       \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /   \\\n",
      "/     \\\n",
      "|0 1 2|\n",
      "|3 4 5|\n",
      "\\     /\n",
      " \\   /\n"
     ]
    }
   ],
   "source": [
    "M = Matrix((2,3), data=list(range(6)))\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /         \\\n",
      "/           \\\n",
      "|0  1  2  3 |\n",
      "|4  5  6  7 |\n",
      "|8  9  10 11|\n",
      "\\           /\n",
      " \\         /\n"
     ]
    }
   ],
   "source": [
    "N = Matrix((3, 4), data=list(range(12)))\n",
    "print(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /         \\\n",
      "/           \\\n",
      "|20 23 26 29|\n",
      "|56 68 80 92|\n",
      "\\           /\n",
      " \\         /\n"
     ]
    }
   ],
   "source": [
    "MN = M.dot(N)\n",
    "print(MN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "columns on left must equal rows on right, got (3, 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-205-2e0147828f4b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-192-28be6e69d3dc>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(self, rhs)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cols\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mrows_rhs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'got ({0}, {1})'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows_rhs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'columns on left must equal rows on right, '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols_rhs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: columns on left must equal rows on right, got (3, 2)"
     ]
    }
   ],
   "source": [
    "MM = M.dot(M)\n",
    "print(MM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /   \\\n",
      "/     \\\n",
      "|0 1 2|\n",
      "|3 4 5|\n",
      "\\     /\n",
      " \\   /\n"
     ]
    }
   ],
   "source": [
    "MI = Matrix((M.shape[1], M.shape[1]))\n",
    "MI.identity()\n",
    "MMI = M.dot(MI)\n",
    "print(MMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /         \\\n",
      "/           \\\n",
      "|0  1  2  3 |\n",
      "|4  5  6  7 |\n",
      "|8  9  10 11|\n",
      "\\           /\n",
      " \\         /\n"
     ]
    }
   ],
   "source": [
    "NI = N.create_identity()\n",
    "NNI = N.dot(NI)\n",
    "print(NNI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " / \\\n",
      "/   \\\n",
      "|0 0|\n",
      "|0 0|\n",
      "|0 0|\n",
      "\\   /\n",
      " \\ /\n"
     ]
    }
   ],
   "source": [
    "P = Matrix((3, 2))\n",
    "P.zero()\n",
    "print(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /   \\\n",
      "/     \\\n",
      "|0 0 0|\n",
      "|0 0 0|\n",
      "\\     /\n",
      " \\   /\n"
     ]
    }
   ],
   "source": [
    "P.transpose()\n",
    "print(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Identity matrix must be square",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-211-e773846bb0f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-192-28be6e69d3dc>\u001b[0m in \u001b[0;36midentity\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rows\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cols\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Identity matrix must be square'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Identity matrix must be square"
     ]
    }
   ],
   "source": [
    "P.identity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N.raw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6, 7]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N[1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /         \\\n",
      "/           \\\n",
      "|0  1  2  3 |\n",
      "|4  5  10 7 |\n",
      "|8  9  10 11|\n",
      "\\           /\n",
      " \\         /\n"
     ]
    }
   ],
   "source": [
    "N[1, 2] = 10\n",
    "print(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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