1 Timing Algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "random.seed(1000)\n",
    "import matplotlib.pyplot as plt\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 100\n",
    "items = [int(random.random()*n) for _ in range(n)]\n",
    "i = int(random.random()*n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_mean(items):\n",
    "    n = 0\n",
    "    sum_of_items = 0\n",
    "    for item in items:\n",
    "        sum_of_items += item\n",
    "        n += 1\n",
    "    return sum_of_items / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.41"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_mean(items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Different Timing Possibilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.85 µs ± 1.52 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "calc_mean(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.3 µs ± 3.62 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "time = %timeit -oq calc_mean(items)\n",
    "print(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.732208516448736e-05\n"
     ]
    }
   ],
   "source": [
    "start = timer() \n",
    "calc_mean(items)\n",
    "end = timer() \n",
    "\n",
    "time = end - start\n",
    "print(time)\n",
    "# Time in seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algorithms with Different Time Complexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_first_item(items):\n",
    "    \"\"\"Get the first item in a list\n",
    "    \n",
    "    :param items: list of items\n",
    "    :returns: first element\n",
    "    \"\"\"\n",
    "    return(items[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_any_item(items, i):\n",
    "    if i == -1:\n",
    "        print(\"Not in the list\")\n",
    "    else:\n",
    "        return(items[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_max(items):\n",
    "    \"\"\"Get the maximum value in a list of items\n",
    "    \n",
    "    :param items: list of items\n",
    "    :returns: maximum value in the list\n",
    "    \"\"\"\n",
    "    max = -1000000000\n",
    "    for item in items:\n",
    "        if item > max:\n",
    "            max = item\n",
    "    return max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_minmax1(items):\n",
    "    \"\"\"Get the maximum value in a list of items\n",
    "    \n",
    "    :param items: list of items\n",
    "    :returns: tuple of mininum and maximum values \n",
    "    \"\"\"\n",
    "    max = -1000000000000\n",
    "    min = 10000000000000\n",
    "    for item in items:\n",
    "        if item > max:\n",
    "            max = item\n",
    "            \n",
    "    for item in items:\n",
    "        if item < min:\n",
    "            min = item\n",
    "    return (min, max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_minmax2(items):\n",
    "    \"\"\"Get the maximum value in a list of items\n",
    "    \n",
    "    :param items: list of items\n",
    "    :returns: tuple of mininum and maximum values \n",
    "    \"\"\"\n",
    "    max = -1000000000000\n",
    "    min = 10000000000000\n",
    "    for item in items:\n",
    "        if item > max:\n",
    "            max = item\n",
    "        if item < min:\n",
    "            min = item\n",
    "    return (min, max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_combinations(items):\n",
    "    combinations = []\n",
    "    for item1 in items:\n",
    "        for item2 in items:\n",
    "            combinations.append((item1, item2))\n",
    "    return combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparision of Times\n",
    "\n",
    "| Symbol  | Name        | Unit      |\n",
    "|---------|-------------|-----------|\n",
    "| ns      | nanosecond  | $10^{-9}$ |\n",
    "| μs (us) | microsecond | $10^{-6}$ |\n",
    "| ms      | millisecond | $10^{-3}$ |\n",
    "| s       | second      | $10^{0}$|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timing of the Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184 ns ± 35.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "get_first_item(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174 ns ± 14.8 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "get_any_item(items, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.47 µs ± 830 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "find_max(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.56 µs ± 250 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "find_minmax1(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.28 µs ± 1.29 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "find_minmax2(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.39 ms ± 202 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "all_combinations(items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Time as a Function of Input Size n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_n = [10, 50, 100, 200, 300, 400, 600, 800]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130 ns ± 7.84 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "136 ns ± 11.7 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "135 ns ± 19.8 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "166 ns ± 81.9 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "193 ns ± 78.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "151 ns ± 52.5 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "116 ns ± 3.91 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "122 ns ± 3.27 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "measures = []\n",
    "for n in list_of_n:\n",
    "    items = [int(random.random()*n) for _ in range(n)]\n",
    "    time = %timeit -o get_first_item(items)\n",
    "    measures.append((n, time.average))\n",
    "plt.plot(*zip(*measures))\n",
    "plt.ylim(ymin=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "569 ns ± 96 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "2.09 µs ± 169 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "3.47 µs ± 452 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "6.92 µs ± 951 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "9.66 µs ± 466 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "17.7 µs ± 9.83 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "17.6 µs ± 791 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "22.4 µs ± 223 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "measures = []\n",
    "for n in list_of_n:\n",
    "    items = [int(random.random()*n) for _ in range(n)]\n",
    "    time = %timeit -o find_max(items)\n",
    "    measures.append((n, time.average))\n",
    "plt.plot(*zip(*measures))\n",
    "plt.ylim(ymin=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "874 ns ± 40.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3.24 µs ± 165 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "6.31 µs ± 542 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "11.8 µs ± 671 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "20.2 µs ± 2.32 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "26.6 µs ± 3.78 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "40.2 µs ± 6.48 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "57.7 µs ± 9.01 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "measures = []\n",
    "for n in list_of_n:\n",
    "    items = [int(random.random()*n) for _ in range(n)]\n",
    "    time = %timeit -o find_minmax1(items)\n",
    "    measures.append((n, time.average))\n",
    "plt.plot(*zip(*measures))\n",
    "plt.ylim(ymin=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "877 ns ± 140 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3.24 µs ± 267 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "6.16 µs ± 1.51 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "11.1 µs ± 922 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "18.4 µs ± 3.58 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "27.9 µs ± 10.1 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "34 µs ± 5.06 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "45.4 µs ± 2.8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "measures = []\n",
    "for n in list_of_n:\n",
    "    items = [int(random.random()*n) for _ in range(n)]\n",
    "    time = %timeit -o find_minmax2(items)\n",
    "    measures.append((n, time.average))\n",
    "plt.plot(*zip(*measures))\n",
    "plt.ylim(ymin=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quadratic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.1 µs ± 1.38 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "305 µs ± 64.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "The slowest run took 4.01 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "2.9 ms ± 1.03 ms per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "5.74 ms ± 869 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "15.9 ms ± 3.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "34.8 ms ± 5.01 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "85.4 ms ± 3.96 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "175 ms ± 27.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "measures = []\n",
    "for n in list_of_n:\n",
    "    items = [int(random.random()*n) for _ in range(n)]\n",
    "    time = %timeit -o all_combinations(items)\n",
    "    measures.append((n, time.average))\n",
    "plt.plot(*zip(*measures))\n",
    "plt.ylim(ymin=0)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}