Practica1-Medida_empirica_de_la_eficiencia_de_los_algoritmos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Práctica 1: Medida empírica de la eficiencia de algoritmos\n",
    "## Diseño de algoritmos 2017/18 - Francisco Martínez Esteso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indice de contenido\n",
    "\n",
    "* [Introducción](#Introducción)\n",
    "    * [Paquetes y librerías](#Paquetes-y-librerías)\n",
    "* [Inserción](#Inserción)\n",
    "    * [Orden Directo](#Orden-Directo)\n",
    "    * [Orden Inverso](#Orden-Inverso)\n",
    "    * [Orden Aleatorio](#Orden-Aleatorio)\n",
    "* [Selección](#Selección)\n",
    "    * [Orden Directo](#Orden-Directo)\n",
    "    * [Orden Inverso](#Orden-Inverso)\n",
    "    * [Orden Aleatorio](#Orden-Aleatorio)\n",
    "* [Burbuja](#Burbuja)\n",
    "    * [Orden Directo](#Orden-Directo)\n",
    "    * [Orden Inverso](#Orden-Inverso)\n",
    "    * [Orden Aleatorio](#Orden-Aleatorio)\n",
    "* [Ajuste función analítica](#Ajuste-función-analítica)\n",
    "* [Conclusiones](#Conclusiones)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El objetivo de esta primera práctica es doble. En primer lugar, comenzar a familiarizarse con el lenguaje\n",
    "Python, para quien no lo domine, y por otro lado, realizar un enfoque práctico a la comparativa de\n",
    "algoritmos mediante técnicas experimentales. Sin duda, la forma correcta de realizar una comparativa ha\n",
    "de ser mediante tácnicas analíticas, sin embargo, eso no siempre será posible, por lo que, de necesitar\n",
    "recurrir al estudio empírico experimental estudiaremos unas nociones elementales.\n",
    "\n",
    "Para ello, se describe cómo se evalúa el tiempo de ejecución de una función, el cual usaremos como medida analítica para comparar dichos algoritmos de ordenación. Para usamos el paquete `time`, con el siguiente esquema:\n",
    "```javascript\n",
    "    t = time()\n",
    "    Proc\n",
    "    t = time()-t\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paquetes y librerías"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos previamente todos los paquetes y librerías en python que nos serán de utilidad en esta práctica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy.polynomial.polynomial as poly\n",
    "import numpy as np\n",
    "from time import time\n",
    "import random\n",
    "import copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inserción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esquema algorítmico:\n",
    "```python\n",
    "def Insercion(a,n):\n",
    "    for i in range(1,n):\n",
    "    v = a[i]\n",
    "    j = i-1\n",
    "    while j >= 0 and a[j] > v:\n",
    "        a[j+1] = a[j]\n",
    "        j = j-1\n",
    "    a[j+1] = v\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Citando expresamente [[Fuente]](https://es.wikipedia.org/wiki/Ordenamiento_por_inserci%C3%B3n), el algoritmo de ordenación mediante inserción:\n",
    "> Inicialmente se tiene un solo elemento, que obviamente es un conjunto ordenado. Después, cuando hay k elementos ordenados de menor a mayor, se toma el elemento k+1 y se compara con todos los elementos ya ordenados, deteniéndose cuando se encuentra un elemento menor (todos los elementos mayores han sido desplazados una posición a la derecha) o cuando ya no se encuentran elementos (todos los elementos fueron desplazados y este es el más pequeño). En este punto se inserta el elemento k+1 debiendo desplazarse los demás elementos.\n",
    "\n",
    "![Inserción](https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/Insertion-sort-example-300px.gif/250px-Insertion-sort-example-300px.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos ahora la función dada y creamos un diccionario auxiliar que nos ayudará a almacenar los valores obtenidos para más tarde poder graficar los resultados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Insercion(a,n):\n",
    "    for i in range(1,n):\n",
    "        v=a[i]\n",
    "        j=i-1\n",
    "        while j >= 0 and a[j] > v:\n",
    "            a[j+1] = a[j]\n",
    "            j=j-1\n",
    "        a[j+1]=v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "saveIns = {'Directo': [], 'Inverso': [], 'Aleatorio': []}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Directo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero creamos un bucle externo el cual se ejecutará 20 veces para el incremento de items en el rango (100,2000). Dentro de este bucle, creamos un array de números en orden directo para el número de items que corresponda, y ejecutamos `Inserción` sobre dicho array. Por último actualizamos índices y guardamos los resultados en el diccionario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// INSERCIÓN\n",
      "// ORDEN DIRECTO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.0\n",
      "Items: 300\tTiempo: 0.0\n",
      "Items: 400\tTiempo: 0.0\n",
      "Items: 500\tTiempo: 0.0\n",
      "Items: 600\tTiempo: 0.0\n",
      "Items: 700\tTiempo: 0.0\n",
      "Items: 800\tTiempo: 0.0\n",
      "Items: 900\tTiempo: 0.0\n",
      "Items: 1000\tTiempo: 0.015119552612304688\n",
      "Items: 1100\tTiempo: 0.0\n",
      "Items: 1200\tTiempo: 0.0\n",
      "Items: 1300\tTiempo: 0.0\n",
      "Items: 1400\tTiempo: 0.0\n",
      "Items: 1500\tTiempo: 0.0\n",
      "Items: 1600\tTiempo: 0.0\n",
      "Items: 1700\tTiempo: 0.0\n",
      "Items: 1800\tTiempo: 0.0\n",
      "Items: 1900\tTiempo: 0.0\n",
      "Items: 2000\tTiempo: 0.0\n",
      "\n",
      "Media Tiempo: 0.000755977630615\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// INSERCIÓN\\n// ORDEN DIRECTO\\n\")\n",
    "items = 100;\n",
    "for x in range(0,20):\n",
    "    array = list(range(0, items));\n",
    "    t = time()\n",
    "    Insercion(array,items)\n",
    "    t = time() - t\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(t))\n",
    "    saveIns['Directo'].append(t)\n",
    "    items = items + 100\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveIns['Directo'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Inverso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La estructura es igual que para el orden directo, con la salvedad de que al generar el array se utiliza la función `reverse()` para darle la vuelta al array y conseguir así el orden inverso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// INSERCIÓN\n",
      "// ORDEN INVERSO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.0\n",
      "Items: 300\tTiempo: 0.015244483947753906\n",
      "Items: 400\tTiempo: 0.015588760375976562\n",
      "Items: 500\tTiempo: 0.03124523162841797\n",
      "Items: 600\tTiempo: 0.031250953674316406\n",
      "Items: 700\tTiempo: 0.06250238418579102\n",
      "Items: 800\tTiempo: 0.07812213897705078\n",
      "Items: 900\tTiempo: 0.09375715255737305\n",
      "Items: 1000\tTiempo: 0.10937142372131348\n",
      "Items: 1100\tTiempo: 0.12499785423278809\n",
      "Items: 1200\tTiempo: 0.1874985694885254\n",
      "Items: 1300\tTiempo: 0.17192292213439941\n",
      "Items: 1400\tTiempo: 0.20312714576721191\n",
      "Items: 1500\tTiempo: 0.28197550773620605\n",
      "Items: 1600\tTiempo: 0.2968630790710449\n",
      "Items: 1700\tTiempo: 0.3750026226043701\n",
      "Items: 1800\tTiempo: 0.35938239097595215\n",
      "Items: 1900\tTiempo: 0.4687516689300537\n",
      "Items: 2000\tTiempo: 0.5001707077026367\n",
      "\n",
      "Media Tiempo: 0.170338749886\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// INSERCIÓN\\n// ORDEN INVERSO\\n\")\n",
    "items = 100;\n",
    "for x in range(0,20):\n",
    "    array = list(range(0, items));\n",
    "    array.reverse()\n",
    "    t = time()\n",
    "    Insercion(array,items)\n",
    "    t = time() - t\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(t))\n",
    "    saveIns['Inverso'].append(t)\n",
    "    items = items + 100\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveIns['Inverso'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Aleatorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// INSERCIÓN\n",
      "// ORDEN ALEATORIO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.00468685626984\n",
      "Items: 300\tTiempo: 0.00625009536743\n",
      "Items: 400\tTiempo: 0.00781276226044\n",
      "Items: 500\tTiempo: 0.0140624284744\n",
      "Items: 600\tTiempo: 0.0171917915344\n",
      "Items: 700\tTiempo: 0.0281203985214\n",
      "Items: 800\tTiempo: 0.0359372854233\n",
      "Items: 900\tTiempo: 0.045312833786\n",
      "Items: 1000\tTiempo: 0.0547092199326\n",
      "Items: 1100\tTiempo: 0.0687503576279\n",
      "Items: 1200\tTiempo: 0.0843748092651\n",
      "Items: 1300\tTiempo: 0.110949921608\n",
      "Items: 1400\tTiempo: 0.114080381393\n",
      "Items: 1500\tTiempo: 0.175372934341\n",
      "Items: 1600\tTiempo: 0.225924706459\n",
      "Items: 1700\tTiempo: 0.193768382072\n",
      "Items: 1800\tTiempo: 0.179706120491\n",
      "Items: 1900\tTiempo: 0.215641832352\n",
      "Items: 2000\tTiempo: 0.270334005356\n",
      "\n",
      "Media Tiempo: 0.0926493561268\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// INSERCIÓN\\n// ORDEN ALEATORIO\\n\")\n",
    "items = 100;\n",
    "array = []\n",
    "tiempos = []\n",
    "for x in range(0,20):\n",
    "    # Generación del array\n",
    "    for y in range(0,items):\n",
    "        array.append(random.randrange(0,items))\n",
    "    t = time()\n",
    "    # Bucle minimización errores\n",
    "    for num in range(0,10):\n",
    "        t = time()\n",
    "        Insercion(copy.copy(array),items)\n",
    "        t = time() - t\n",
    "        tiempos.append(t)\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(np.mean(tiempos)))\n",
    "    # Actualizacion índices y estructuras\n",
    "    saveIns['Aleatorio'].append(np.mean(tiempos))\n",
    "    items = items + 100\n",
    "    tiempos = []\n",
    "    array = []\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveIns['Aleatorio'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selección"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esquema algorítmico:\n",
    "\n",
    "```python\n",
    "def Seleccion(a,n):\n",
    "    for i in range(0,n-1):\n",
    "        min = i\n",
    "        for j in range(i+1,n):\n",
    "            if a[min] > a[j]:\n",
    "                min = j\n",
    "        aux = a[min]\n",
    "        a[min] = a[i]\n",
    "        a[i] = aux\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtenemos igual que antes una introducción acerca del funcionamiento del algoritmo [Fuente](https://es.wikipedia.org/wiki/Ordenamiento_por_selecci%C3%B3n):\n",
    "\n",
    "Su funcionamiento es el siguiente:\n",
    "    1. Buscar el mínimo elemento de la lista\n",
    "    2. Intercambiarlo con el primero\n",
    "    3. Buscar el siguiente mínimo en el resto de la lista\n",
    "    4. Intercambiarlo con el segundo\n",
    "Y en general:\n",
    "    - Buscar el mínimo elemento entre una posición i y el final de la lista\n",
    "    - Intercambiar el mínimo con el elemento de la posición i\n",
    "    \n",
    "![Selección](https://upload.wikimedia.org/wikipedia/commons/9/94/Selection-Sort-Animation.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volvemos a cargar la función dada y creamos el diccionario que nos ayuda a almacenar los valores obtenidos para más tarde poder graficar los resultados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Seleccion(a,n):\n",
    "    for i in range(0,n-1):\n",
    "        min=i\n",
    "        for j in range(i+1,n):\n",
    "            if a[min] > a[j]:\n",
    "                min=j\n",
    "        aux=a[min]\n",
    "        a[min]=a[i]\n",
    "        a[i]=aux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "saveSel = {'Directo': [], 'Inverso': [], 'Aleatorio': []}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Directo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// SELECCIÓN\n",
      "// ORDEN DIRECTO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.015625\n",
      "Items: 300\tTiempo: 0.0\n",
      "Items: 400\tTiempo: 0.0\n",
      "Items: 500\tTiempo: 0.015625476837158203\n",
      "Items: 600\tTiempo: 0.015623092651367188\n",
      "Items: 700\tTiempo: 0.0312502384185791\n",
      "Items: 800\tTiempo: 0.046875715255737305\n",
      "Items: 900\tTiempo: 0.03125166893005371\n",
      "Items: 1000\tTiempo: 0.06249880790710449\n",
      "Items: 1100\tTiempo: 0.0781254768371582\n",
      "Items: 1200\tTiempo: 0.0781259536743164\n",
      "Items: 1300\tTiempo: 0.09375286102294922\n",
      "Items: 1400\tTiempo: 0.0937509536743164\n",
      "Items: 1500\tTiempo: 0.12499427795410156\n",
      "Items: 1600\tTiempo: 0.15629792213439941\n",
      "Items: 1700\tTiempo: 0.15633916854858398\n",
      "Items: 1800\tTiempo: 0.17188096046447754\n",
      "Items: 1900\tTiempo: 0.21875333786010742\n",
      "Items: 2000\tTiempo: 0.21874523162841797\n",
      "\n",
      "Media Tiempo: 0.0804758071899\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// SELECCIÓN\\n// ORDEN DIRECTO\\n\")\n",
    "items = 100;\n",
    "for x in range(0,20):\n",
    "    array = list(range(0, items));\n",
    "    t = time()\n",
    "    Seleccion(array,items)\n",
    "    t = time() - t\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(t))\n",
    "    saveSel['Directo'].append(t)\n",
    "    items = items + 100\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveSel['Directo'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Inverso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// SELECCIÓN\n",
      "// ORDEN INVERSO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.0\n",
      "Items: 300\tTiempo: 0.015623807907104492\n",
      "Items: 400\tTiempo: 0.0\n",
      "Items: 500\tTiempo: 0.01565861701965332\n",
      "Items: 600\tTiempo: 0.031215667724609375\n",
      "Items: 700\tTiempo: 0.03125119209289551\n",
      "Items: 800\tTiempo: 0.04687190055847168\n",
      "Items: 900\tTiempo: 0.046875\n",
      "Items: 1000\tTiempo: 0.0625002384185791\n",
      "Items: 1100\tTiempo: 0.07812356948852539\n",
      "Items: 1200\tTiempo: 0.07812857627868652\n",
      "Items: 1300\tTiempo: 0.10941576957702637\n",
      "Items: 1400\tTiempo: 0.12495636940002441\n",
      "Items: 1500\tTiempo: 0.14063811302185059\n",
      "Items: 1600\tTiempo: 0.15624022483825684\n",
      "Items: 1700\tTiempo: 0.18750405311584473\n",
      "Items: 1800\tTiempo: 0.15625286102294922\n",
      "Items: 1900\tTiempo: 0.21891498565673828\n",
      "Items: 2000\tTiempo: 0.2500004768371582\n",
      "\n",
      "Media Tiempo: 0.0875085711479\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// SELECCIÓN\\n// ORDEN INVERSO\\n\")\n",
    "items = 100;\n",
    "for x in range(0,20):\n",
    "    array = list(range(0, items));\n",
    "    array.reverse()\n",
    "    t = time()\n",
    "    Seleccion(array,items)\n",
    "    t = time() - t\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(t))\n",
    "    saveSel['Inverso'].append(t)\n",
    "    items = items + 100\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveSel['Inverso'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Aleatorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// SELECCIÓN\n",
      "// ORDEN ALEATORIO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.00156264305115\n",
      "Items: 300\tTiempo: 0.00624995231628\n",
      "Items: 400\tTiempo: 0.00781292915344\n",
      "Items: 500\tTiempo: 0.0156245708466\n",
      "Items: 600\tTiempo: 0.0265626192093\n",
      "Items: 700\tTiempo: 0.0312499523163\n",
      "Items: 800\tTiempo: 0.034375166893\n",
      "Items: 900\tTiempo: 0.0909913539886\n",
      "Items: 1000\tTiempo: 0.111600089073\n",
      "Items: 1100\tTiempo: 0.118300032616\n",
      "Items: 1200\tTiempo: 0.101399827003\n",
      "Items: 1300\tTiempo: 0.12529938221\n",
      "Items: 1400\tTiempo: 0.142604541779\n",
      "Items: 1500\tTiempo: 0.15306289196\n",
      "Items: 1600\tTiempo: 0.165625238419\n",
      "Items: 1700\tTiempo: 0.192254447937\n",
      "Items: 1800\tTiempo: 0.278127884865\n",
      "Items: 1900\tTiempo: 0.246918416023\n",
      "Items: 2000\tTiempo: 0.206260895729\n",
      "\n",
      "Media Tiempo: 0.102794141769\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// SELECCIÓN\\n// ORDEN ALEATORIO\\n\")\n",
    "items = 100;\n",
    "array = []\n",
    "tiempos = []\n",
    "for x in range(0,20):\n",
    "    # Generación del array\n",
    "    for y in range(0,items):\n",
    "        array.append(random.randrange(0,items))\n",
    "    t = time()\n",
    "    # Bucle minimización errores\n",
    "    for num in range(0,10):\n",
    "        t = time()\n",
    "        Seleccion(copy.copy(array),items)\n",
    "        t = time() - t\n",
    "        tiempos.append(t)\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(np.mean(tiempos)))\n",
    "    # Actualizacion índices y estructuras\n",
    "    saveSel['Aleatorio'].append(np.mean(tiempos))\n",
    "    items = items + 100\n",
    "    tiempos = []\n",
    "    array = []\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveSel['Aleatorio'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Burbuja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esquema algorítmico:\n",
    "\n",
    "```python\n",
    "def Burbuja(a,n):\n",
    "    for i in range(1,n):\n",
    "        for j in range(0,n-i):\n",
    "            if (a[j] > a[j+1]):\n",
    "                k = a[j+1]\n",
    "                a[j+1] = a[j]\n",
    "                a[j] = k\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volvemos a citar expresamente [Fuente](https://es.wikipedia.org/wiki/Ordenamiento_de_burbuja):\n",
    "\n",
    "> El algoritmo de ordenamiento de la Burbuja funciona revisando cada elemento de la lista que va a ser ordenada con el siguiente, intercambiándolos de posición si están en el orden equivocado. Es necesario revisar varias veces toda la lista hasta que no se necesiten más intercambios, lo cual significa que la lista está ordenada. Este algoritmo obtiene su nombre de la forma con la que suben por la lista los elementos durante los intercambios, como si fueran pequeñas \"burbujas\". También es conocido como el método del intercambio directo. Dado que solo usa comparaciones para operar elementos, se lo considera un algoritmo de comparación, siendo uno de los más sencillo de implementar.\n",
    "\n",
    "\n",
    "![Burbuja](https://upload.wikimedia.org/wikipedia/commons/thumb/c/c8/Bubble-sort-example-300px.gif/250px-Bubble-sort-example-300px.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volvemos a cargar la función dada y creamos el diccionario que nos ayuda a almacenar los valores obtenidos para más tarde poder graficar los resultados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Burbuja(a,n):\n",
    "    for i in range(1,n):\n",
    "        for j in range(0,n-i):\n",
    "            if(a[j] > a[j+1]):\n",
    "                k = a[j+1]\n",
    "                a[j+1] = a[j]\n",
    "                a[j] = k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "saveBur = {'Directo': [], 'Inverso': [], 'Aleatorio': []}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Directo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// BURBUJA\n",
      "// ORDEN DIRECTO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.0\n",
      "Items: 300\tTiempo: 0.0\n",
      "Items: 400\tTiempo: 0.015183687210083008\n",
      "Items: 500\tTiempo: 0.01566767692565918\n",
      "Items: 600\tTiempo: 0.015625\n",
      "Items: 700\tTiempo: 0.031206130981445312\n",
      "Items: 800\tTiempo: 0.04687643051147461\n",
      "Items: 900\tTiempo: 0.0468754768371582\n",
      "Items: 1000\tTiempo: 0.04687809944152832\n",
      "Items: 1100\tTiempo: 0.07812261581420898\n",
      "Items: 1200\tTiempo: 0.0937492847442627\n",
      "Items: 1300\tTiempo: 0.09374809265136719\n",
      "Items: 1400\tTiempo: 0.09375286102294922\n",
      "Items: 1500\tTiempo: 0.12499856948852539\n",
      "Items: 1600\tTiempo: 0.14140772819519043\n",
      "Items: 1700\tTiempo: 0.2187485694885254\n",
      "Items: 1800\tTiempo: 0.2187507152557373\n",
      "Items: 1900\tTiempo: 0.2187511920928955\n",
      "Items: 2000\tTiempo: 0.2656228542327881\n",
      "\n",
      "Media Tiempo: 0.0882982492447\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// BURBUJA\\n// ORDEN DIRECTO\\n\")\n",
    "items = 100;\n",
    "for x in range(0,20):\n",
    "    array = list(range(0, items));\n",
    "    t = time()\n",
    "    Burbuja(array,items)\n",
    "    t = time() - t\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(t))\n",
    "    saveBur['Directo'].append(t)\n",
    "    items = items + 100\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveBur['Directo'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Inverso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// BURBUJA\n",
      "// ORDEN INVERSO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.015679121017456055\n",
      "Items: 300\tTiempo: 0.015568733215332031\n",
      "Items: 400\tTiempo: 0.015625476837158203\n",
      "Items: 500\tTiempo: 0.04687762260437012\n",
      "Items: 600\tTiempo: 0.06249666213989258\n",
      "Items: 700\tTiempo: 0.0937950611114502\n",
      "Items: 800\tTiempo: 0.0937051773071289\n",
      "Items: 900\tTiempo: 0.1250450611114502\n",
      "Items: 1000\tTiempo: 0.1562061309814453\n",
      "Items: 1100\tTiempo: 0.17191076278686523\n",
      "Items: 1200\tTiempo: 0.2503995895385742\n",
      "Items: 1300\tTiempo: 0.45313072204589844\n",
      "Items: 1400\tTiempo: 0.4531211853027344\n",
      "Items: 1500\tTiempo: 0.4531261920928955\n",
      "Items: 1600\tTiempo: 0.7499992847442627\n",
      "Items: 1700\tTiempo: 0.9687626361846924\n",
      "Items: 1800\tTiempo: 0.5624868869781494\n",
      "Items: 1900\tTiempo: 0.5530600547790527\n",
      "Items: 2000\tTiempo: 0.6093747615814209\n",
      "\n",
      "Media Tiempo: 0.292518556118\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// BURBUJA\\n// ORDEN INVERSO\\n\")\n",
    "items = 100;\n",
    "for x in range(0,20):\n",
    "    array = list(range(0, items));\n",
    "    array.reverse()\n",
    "    t = time()\n",
    "    Burbuja(array,items)\n",
    "    t = time() - t\n",
    "    saveBur['Inverso'].append(t)\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(t))\n",
    "    items = items + 100\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveBur['Inverso'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Aleatorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "// BURBUJA\n",
      "// ORDEN ALEATORIO\n",
      "\n",
      "Items: 100\tTiempo: 0.0\n",
      "Items: 200\tTiempo: 0.00312490463257\n",
      "Items: 300\tTiempo: 0.009375\n",
      "Items: 400\tTiempo: 0.0156294584274\n",
      "Items: 500\tTiempo: 0.0234331607819\n",
      "Items: 600\tTiempo: 0.0343749284744\n",
      "Items: 700\tTiempo: 0.0531626701355\n",
      "Items: 800\tTiempo: 0.0656303882599\n",
      "Items: 900\tTiempo: 0.0829496860504\n",
      "Items: 1000\tTiempo: 0.106254506111\n",
      "Items: 1100\tTiempo: 0.13286345005\n",
      "Items: 1200\tTiempo: 0.156266570091\n",
      "Items: 1300\tTiempo: 0.181314539909\n",
      "Items: 1400\tTiempo: 0.193750143051\n",
      "Items: 1500\tTiempo: 0.225019478798\n",
      "Items: 1600\tTiempo: 0.259409427643\n",
      "Items: 1700\tTiempo: 0.300018501282\n",
      "Items: 1800\tTiempo: 0.315663123131\n",
      "Items: 1900\tTiempo: 0.359403920174\n",
      "Items: 2000\tTiempo: 0.385957455635\n",
      "\n",
      "Media Tiempo: 0.145180065632\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n// BURBUJA\\n// ORDEN ALEATORIO\\n\")\n",
    "items = 100;\n",
    "array = []\n",
    "tiempos = []\n",
    "for x in range(0,20):\n",
    "    # Generación del array\n",
    "    for y in range(0,items):\n",
    "        array.append(random.randrange(0,items))\n",
    "    t = time()\n",
    "    # Bucle minimización errores\n",
    "    for num in range(0,10):\n",
    "        t = time()\n",
    "        Burbuja(copy.copy(array),items)\n",
    "        t = time() - t\n",
    "        tiempos.append(t)\n",
    "    print(\"Items: \" + str(len(array)) + \"\\tTiempo: \" + str(np.mean(tiempos)))\n",
    "    # Actualizacion índices y estructuras\n",
    "    saveBur['Aleatorio'].append(np.mean(tiempos))\n",
    "    items = items + 100\n",
    "    tiempos = []\n",
    "    array = []\n",
    "print(\"\\nMedia Tiempo: \" + str(np.mean(saveBur['Aleatorio'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ajuste función analítica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso, puede hacerse debido a que sabemos que las respectivas funciones de tiempo de ejecución respecto al tamaño del problema son de tipo cuadrático, es decir\n",
    "\n",
    "```\n",
    "T(n) = A * n2 + B * n + C\n",
    "```\n",
    "\n",
    "Por ello, es posible ajustar los valores de A, B y C mediante métodos numéricos. En particular, `Python` incorpora en su librería matemática `numpy` la función `polyfit` que nos proporciona un ajuste a una función polinómica, como ocurre en este caso, de grado 2. Por ello la tercera salida debe ser un ajuste a la función cuadrática de cada uno de los resultados experimentales.\n",
    "\n",
    "Para ver este ajuste a la función cuadrática, generamos diferentes gráficas para los diferentes experimentos:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Esquivar los warnings que produce numpy debido a la pobre parametrización de la función polyfit\n",
    "np.warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Directo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ALGORITMO: INSERCIÓN\n",
      "- ORDEN: DIRECTO\n"
     ]
    },
    {
     "data": {
      "image/png": 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PYNBOsOMRRUdT1SzG3Q4bjshGwh65AF6bVnQ0UkUzAZNK4YFfwzvzYMwvnHos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\nJKnETMAkSZJK7P8D3TJpvPCvmKoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bb7c8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ORDEN: INVERSO\n"
     ]
    },
    {
     "data": {
      "image/png": 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ObXbOfQMsALqbWVOgjnMu1/mFx54ETi5vaBGJsW8+hEf7Q1EhnP+mP+VTghphS9xIS4Pj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\nl/k6K2ZUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bccab860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ORDEN: ALEATORIO\n"
     ]
    },
    {
     "data": {
      "image/png": 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FhVhFJM7et2HBz2DclTDs3Ha/TVPj7MP1jXTNy0n/leuT6fyvw9I/wv/872hIMq9b6EQi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\nMBEREZEkUwEmIiIikmT/PwWeySCvXdbhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bb58d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n- ALGORITMO: INSERCIÓN\\n- ORDEN: DIRECTO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveIns['Directo'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 0.2)\n",
    "_ = plt.plot(saveIns['Directo'], range(0,2000,100), '.', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n- ORDEN: INVERSO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveIns['Inverso'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 1)\n",
    "_ = plt.plot(saveIns['Inverso'], range(0,2000,100), '.', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n- ORDEN: ALEATORIO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveIns['Aleatorio'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 1)\n",
    "_ = plt.plot(saveIns['Aleatorio'], range(0,2000,100), '.', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Inverso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ALGORITMO: SELECCIÓN\n",
      "- ORDEN: DIRECTO\n"
     ]
    },
    {
     "data": {
      "image/png": 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/REsUSctQASbSBlXV1PLIB5sYmZrAOcOTvY4jEvZunziQ+Bgff3x/o9dRJEyo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bb649da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ORDEN: INVERSO\n"
     ]
    },
    {
     "data": {
      "image/png": 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QDmtWe51IJKjumdSP8hPVLNl00OsoIu1ac68BS3POnV0W+SCQ5n+cAQSemy72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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bcf90198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ORDEN: ALEATORIO\n"
     ]
    },
    {
     "data": {
      "image/png": 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xX5vO+YiHnli0mcz0ZK47J8/rKCJhx9+eaCDbdh9m3tqdXscRiUqtOQL2R2Da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\nExEREQmx/w+cRFCi1QwVmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bd03fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n- ALGORITMO: SELECCIÓN\\n- ORDEN: DIRECTO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveSel['Directo'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 0.6)\n",
    "_ = plt.plot(saveSel['Directo'], range(0,2000,100), '.r', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n- ORDEN: INVERSO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveSel['Inverso'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 0.8)\n",
    "_ = plt.plot(saveSel['Inverso'], range(0,2000,100), '.r', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n- ORDEN: ALEATORIO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveSel['Aleatorio'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 1)\n",
    "_ = plt.plot(saveSel['Aleatorio'], range(0,2000,100), '.r', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orden Aleatorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ALGORITMO: BURBUJA\n",
      "- ORDEN: DIRECTO\n"
     ]
    },
    {
     "data": {
      "image/png": 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AW3ng9rHtIhGtsdHx6HtbGJPenTMye2qfR4kK1+UOJC4mhr++v8XrKCIhq60F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\nkSD7f+eojLVG1LAzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bcf9ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ORDEN: INVERSO\n"
     ]
    },
    {
     "data": {
      "image/png": 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RFWASNh7+cik/rtnOg+cNpleHRK/jiEgIMDP+fPZAeiW35Nfv/KiZ8qXR1FmA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bcf84b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- ORDEN: ALEATORIO\n"
     ]
    },
    {
     "data": {
      "image/png": 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oW8Qb147pxjOfbeL+D9fyZs/2uhJZwopOQUpYeeLTjezcX8pvLhioD3sRj8VE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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bb6b7940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n- ALGORITMO: BURBUJA\\n- ORDEN: DIRECTO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveBur['Directo'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 0.7)\n",
    "_ = plt.plot(saveBur['Directo'], range(0,2000,100), '.g', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n- ORDEN: INVERSO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveBur['Inverso'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 1.6)\n",
    "_ = plt.plot(saveBur['Inverso'], range(0,2000,100), '.g', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n- ORDEN: ALEATORIO\")\n",
    "plt.figure(figsize=(10,5))\n",
    "z = np.polyfit(saveBur['Aleatorio'], range(0,2000,100), 2)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "xp = np.linspace(-0.2, 1.2)\n",
    "_ = plt.plot(saveBur['Aleatorio'], range(0,2000,100), '.g', xp, p(xp), '-')\n",
    "plt.ylim(-100,2001)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusiones  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para resumir esta práctica 1 de acuerdo a los resultados obtenidos en las ejecuciones de los experimentos con cada algoritmo de ordenación, vamos a graficar estos datos para hacernos una mejor idea de las vetajas y desventajas de cada algoritmo.  \n",
    "Para ello usaremos la librería `matplotlib.pyplot`, la cual nos permite generar gráficos facilmente en `Python`.\n",
    "\n",
    "Generemos tres gráficos que se corresponden con los tres algoritmos de ordenación, colocando en el eje x el `Tiempo` de ejecución de cada experimento y en el eje y, el número de `Items` que se ha usado en dicho experimento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- INSERCIÓN\n"
     ]
    },
    {
     "data": {
      "image/png": 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JiIhIyfEUwLznoWYbaHWp22n8hgqZiIiIlJx1UyBxI/QcpdGx06A/KRERESkZHg/MfQGq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bcc439b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inserción\n",
    "print(\"\\n- INSERCIÓN\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(range(100,2100,100), saveIns['Directo'], label='Directo')\n",
    "plt.plot(range(100,2100,100), saveIns['Inverso'], label='Inverso')\n",
    "plt.plot(range(100,2100,100), saveIns['Aleatorio'], label='Aleatorio')\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Tiempo')\n",
    "leg = plt.legend(ncol=1)\n",
    "# Contorno de la legenda\n",
    "leg.get_frame().set_alpha(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como podemos ver el algoritmo de `Inserción` ordenando arrays muestra sus mejores resultados cuando están en orden directo, y su peor resultado cuando se encuentran en orden inverso.  \n",
    "Esto es totalmente lógico, ya que va a tener que comparar n elementos, n-1 veces, ya que todos tendrán que posicionarse en la posición inversa a la actual.  \n",
    "\n",
    "No observamos grandes rasgos a destacar, su aumento en `Tiempo` en función de los `Items` es complemtamente regular."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- SELECCIÓN\n"
     ]
    },
    {
     "data": {
      "image/png": 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DoPtnYGMDWWnw9+vwx3PwzFywLbg/8tMyDYz9Yz/Vyjvxv851TB+mX4UdM6FO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\n4oLFLN29lLLaMgBSO6QyrNMwhicMZ3D8YCKCIqiqOMCq958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      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bcfae4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selección\n",
    "print(\"\\n- SELECCIÓN\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(range(100,2100,100), saveSel['Directo'], label='Directo')\n",
    "plt.plot(range(100,2100,100), saveSel['Inverso'], label='Inverso')\n",
    "plt.plot(range(100,2100,100), saveSel['Aleatorio'], label='Aleatorio')\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Tiempo')\n",
    "leg = plt.legend(ncol=1)\n",
    "# Contorno de la legenda\n",
    "leg.get_frame().set_alpha(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En la gráfica del algoritmo de `Selección` podemos ver como los valores obtenidos en `Tiempo` para el orden inverso y directo son muy similares.  \n",
    "Esto es debido a que en este algoritmo aunque encontremos en caso de orden directo el menor número al principio del array, debemos compararlo con todos los demás números del conjunto (n). Al igual que en el caso del orden inverso, el cual no tendrá  que recorrer toda la tabla comparando, sin embargo si que tendrá que hacer n comprobaciones buscando el menor elemento.\n",
    "\n",
    "También notamos algunos picos en el orden aleatorio que pueden deberse a determinadas configuraciones en la formación del array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- BURBUJA\n"
     ]
    },
    {
     "data": {
      "image/png": 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YGApc1Pl1Y3eeJyIi4jc2Pw9h0c4ImYgfOm2xMsZ8D7gf+I/Oh0KBv/gylIiI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207bd0b1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Burbuja\n",
    "print(\"\\n- BURBUJA\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(range(100,2100,100), saveBur['Directo'], label='Directo')\n",
    "plt.plot(range(100,2100,100), saveBur['Inverso'], label='Inverso')\n",
    "plt.plot(range(100,2100,100), saveBur['Aleatorio'], label='Aleatorio')\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Tiempo')\n",
    "leg = plt.legend(ncol=1)\n",
    "# Contorno de la legenda\n",
    "leg.get_frame().set_alpha(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último el algoritmo de ordenación de `Burbuja` obtiene los mejores resultados en función del `Tiempo` cuando el orden del array es directo y los peores cuando se encuentra en orden inverso.  \n",
    "Esto es debido a que aunque en el orden directo no realiza cambios de burbuja, en orden inverso si que tiene que realizar n cambios para cada elemento, y comprobar con n-1 elementos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}