{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXECUTION TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INSERTION SORT  \n",
    "\n",
    "Algoritmo in place per l'_ordinamento_ di un'array, si suddivide la sequenza in due sottosequenze di cui una ordinata e una non ordinata e si inseriscono ad ogni iterazione gli elementi della sequenza non ordinata in quella ordinata secondo l'ordine stabilito. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def InsertionSort(array_to_sort):\n",
    "    array_sorted = [i for i in array_to_sort]\n",
    "    len_array = len(array_sorted)\n",
    "    \n",
    "    for j in range(1, len_array):\n",
    "        el = array_sorted[j]\n",
    "        i = j -1\n",
    "        while i >=0 and array_sorted[i] > el:\n",
    "            array_sorted[i+1] = array_sorted[i]\n",
    "            i = i-1\n",
    "        array_sorted[i+1] = el\n",
    "        \n",
    "    return array_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definiamo una funzione che genera un un numero fissato di liste randomiche con lungheza random, entro una certa soglia sempre sccelta da noi, da inserire come input della funzione InsertionSort.\n",
    "La funzione deve avere come input:\n",
    "- num : numero di liste che vogliamo generare\n",
    "- lenn : lunghezza della lista \n",
    "- max_el : upper-bound per i numeri che possono essere inseriti in quella lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random_list(num, _len, max_el):\n",
    "    out = []\n",
    "    for i in range(num):\n",
    "        lis = [random.randint(1, max_el) for j in range(_len)]\n",
    "        out.append(lis)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[4, 1, 3, 5, 8, 15, 9, 19, 4, 7, 4, 14, 4, 18, 15, 2, 15, 19, 16, 17],\n",
       " [9, 14, 14, 9, 3, 10, 1, 1, 17, 3, 11, 13, 8, 7, 4, 9, 19, 6, 4, 19],\n",
       " [15, 19, 11, 11, 7, 11, 20, 8, 10, 20, 18, 13, 13, 19, 7, 7, 4, 4, 17, 18],\n",
       " [3, 8, 12, 5, 3, 16, 15, 2, 8, 20, 11, 4, 20, 10, 13, 15, 14, 19, 8, 14],\n",
       " [17, 17, 10, 11, 20, 2, 15, 11, 13, 16, 19, 4, 18, 6, 11, 3, 9, 10, 3, 16]]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_random_list(5, 20, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analizziamo quanto è il tempo medio di ordinamento di esecuzione del porgramma che abbiamo implementato. \n",
    "Per farlo, è necessario generare randomicamente $n_1$ liste della stessa lunghezza $l$ e applicare il nostro algoritmo ad ogni singola lista $n_2$ volte, con $n_2 > 1$.\n",
    "\n",
    "Per avere una media, dunque, del tempo di esecuzione dell'algoritmo quando riceve in input liste di lunghezza $l$, dovremmo prima fare la media del tempo di esecuzione della stessa lista sulle $n_2$ prove e poi fare la media delle medie sulle $n_1$ liste diverse generate ranodmicamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_time(l, n1, n2):\n",
    "    \"\"\"\n",
    "    input : lunghezza della lista \n",
    "    output : media delle medie dei tempi di esecuzione di liste con lungheezza fissata l \n",
    "    \"\"\"\n",
    "    random_list_ = generate_random_list(n1, l, 100)\n",
    "    time_ = []\n",
    "    for j in range(len(random_list_)):\n",
    "        list_considered = random_list_[j]\n",
    "        time_fixed_list = []\n",
    "        for i in range(n2):\n",
    "            begin_time = datetime.datetime.now()\n",
    "            InsertionSort(list_considered)\n",
    "            execution_time = (datetime.datetime.now() - begin_time).microseconds\n",
    "            time_fixed_list.append(execution_time)\n",
    "        mean_fixed_list = mean(time_fixed_list)\n",
    "        time_.append(mean_fixed_list)\n",
    "    time = mean(time_)\n",
    "    return time "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Misuriamo ora il tempo medio di esecuzione per liste da lunghezza $1$ a lunghezza $100$ e plottiamo il risultato in un grafico che ha come ascisse la lunghezza e come ordinate il tempo di esecuzione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def execution_time(l_min, l_max, n1, n2):\n",
    "    y = []\n",
    "    for i in range(l_max-l_min):\n",
    "        y_ = measure_time(l_min + i, n1, n2)\n",
    "        y.append(y_)\n",
    "    return y "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plottiamo i risultati utilizzando la libreria _matplotlib_ per confermare i tempi di esecuzione che abbiamo calcolato teoricamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_(l_min, l_max, n1, n2):\n",
    "    x = [i for i in range(l_min, l_max)]\n",
    "    y = execution_time(l_min, l_max, n1, n2)\n",
    "    plt.plot(x, y, color = 'm')\n",
    "    plt.title('InsertionSort() execution time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Che forma ci aspettiamo che abbia il grafico? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_(1, 50, 50, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BUCKETSORT\n",
    "Proviamo a vedere che succede se utilizziamo un algoritmo con costo lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bucket_sort(A):\n",
    "    B = []\n",
    "    n = len(A)\n",
    "    for i in range(0, n):\n",
    "        B.append([])\n",
    "    for i in range(0, n):\n",
    "        bucket = int(A[i] * n) \n",
    "        B[bucket].append(A[i])\n",
    "    for i in range(0, n):\n",
    "        B[i] = InsertionSort(B[i])\n",
    "    C = []\n",
    "    for i in range(0, n):\n",
    "        C += B[i]\n",
    "    return C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random_list(num, _len):\n",
    "    out = []\n",
    "    for i in range(num):\n",
    "        lis = [random.random() for j in range(_len)]\n",
    "        out.append(lis)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate_random_list(5, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_time(l, n1, n2):\n",
    "    \"\"\"\n",
    "    input\n",
    "    l : lunghezza della lista\n",
    "    n1 : numero di liste generate \n",
    "    n2: numero di misurazioni \n",
    "    \n",
    "    output : media delle medie dei tempi di esecuzione di liste con lungheezza fissata l \n",
    "    \"\"\"\n",
    "    random_list_ = generate_random_list(n1, l)\n",
    "    time_ = []\n",
    "    for j in range(len(random_list_)):\n",
    "        list_considered = random_list_[j]\n",
    "        time_fixed_list = []\n",
    "        for i in range(n2):\n",
    "            begin_time = datetime.datetime.now()\n",
    "            bucket_sort(list_considered)\n",
    "            execution_time = (datetime.datetime.now() - begin_time).microseconds\n",
    "            time_fixed_list.append(execution_time)\n",
    "        mean_fixed_list = mean(time_fixed_list)\n",
    "        time_.append(mean_fixed_list)\n",
    "    time = mean(time_)\n",
    "    return time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def execution_time(l_min, l_max, n1, n2):\n",
    "    y = []\n",
    "    for i in range(l_max-l_min):\n",
    "        y_ = measure_time(l_min + i, n1, n2)\n",
    "        y.append(y_)\n",
    "    return y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_(l_min, l_max, n1, n2):\n",
    "    x = [i for i in range(l_min, l_max)]\n",
    "    y = execution_time(l_min, l_max, n1, n2)\n",
    "    plt.plot(x, y, color = 'm')\n",
    "    plt.title('BucketSort() execution time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_(1, 50, 50, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BUCKETSORT - WORST CASE SCENARIO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}