vectorization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With Python Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.9 ms ± 2.52 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "a=list(range(1000000)) # 10^6 numbers\n",
    "\n",
    "\n",
    "%timeit [val + 5 for val in a] #Computing Element Wise Operation  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With Numpy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.52 ms ± 36.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a=np.array(a) #Converting into numpy array type\n",
    "\n",
    "\n",
    "%timeit a+5 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate random number for theta and x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=10000\n",
    "\n",
    "# Generate n random numbers between 0 - 1\n",
    "theta = np.random.rand(n)\n",
    "\n",
    "#Generate n random integers between 0 - 100 \n",
    "x = np.random.randint(0, 100, n)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unvectorized Implementation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unvectorized(n, theta, x):\n",
    "    \n",
    "    prediction= 0.0\n",
    "    \n",
    "    for j in range(n):\n",
    "        prediction= prediction + theta[j]*x[j]\n",
    "    \n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247159.0299402118\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(unvectorized(n, theta, x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorized Implementation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorized(theta, x):\n",
    "    \n",
    "    prediction= 0.0\n",
    "    \n",
    "    prediction = np.dot(theta.transpose() , x)\n",
    "   \n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247159.02994021226\n"
     ]
    }
   ],
   "source": [
    "print(vectorized( theta, x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.46 ms ± 32.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit unvectorized(n, theta, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.8 µs ± 374 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vectorized(theta, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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