Problema_Sencillo_AG.ipynb

{
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    "tags": []
   },
   "source": [
    "## Algoritmos Genéticos ----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61524831-6b85-4ce3-9118-90c12c8e909b",
   "metadata": {},
   "source": [
    "* Técnicas de **optimización metaherísticas**, **estocásticas** o **probabilísticas** (Holland-1962-1965-1992 y Goldberg-2006)\n",
    "* **Computacionalmente intensivos** de ahí el tiempo que pasó desde su propuesta en 1962 hasta su aplicación en problemas de ingeniería reales (1992)\n",
    "* Realiza una **búsqueda guiada** a partir de un conjunto inicial de posibles soluciones (**Población Inicial**) y va **evolucionando** a mejor en cada iteración del algoritmo (**generaciones**)\n",
    "* Normalmente, las últimas generaciones contienen la mejor o las mejores soluciones al problema de optimización\n",
    "* Cada **posible solución** se conoce como **individuo** y cada individuo **codifica** las variables independientes del problema de optimización. Estas variables representan los **genes** de la cadena **cromosomática** que representa a cada individuo\n",
    "* Las **modificaciones genéticas** que hacen que los individuos se **adapten** mejor al medio, tienen **mayor probabilidad de perdurar en el tiempo**\n",
    "* La **adaptación al medio** se realiza mediante la **función que queremos optimizar** (**función objetivo**, **función de evaluación** o **fitness function**). Llamaremos **fitness** a la **calidad** o **desempeño** de un individuo\n",
    "* Pasar de un problema de **maximización** a uno de **minimización** es tan sencillo como **multiplicar** por **-1** el **resultado de la función objetivo**\n",
    "* En cada iteración, **nuevos individuos** (**descendientes** u **offsprings**) son creados mediante **operaciones genéticas** dando lugar a nuevas poblaciones\n",
    "* Operaciones genéticas: **Selección**, **Cruce** y **Mutación**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b00b50-5120-44a9-baf7-95e5e36a7b7a",
   "metadata": {},
   "source": [
    "### ¿Porqué decidimos usar una **optimización metaheurística** (**no** garantizan la obtención de una solución óptima) en lugar de un **método exacto** (garantizan la obtención de una solución óptima)?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a886c8c2-1e8d-473b-aa59-e5d6131927ec",
   "metadata": {},
   "source": [
    "No es una pregunta fácil de responder. Se usa para problemas de **gran complejidad**:\n",
    "* Gran **cantidad** de **variables**\n",
    "* Gran **complejidad** de las **ecuaciones**\n",
    "* Nos llegamos a preguntar si el problema tendrá solución\n",
    "\n",
    "No siempre podemos encontrar un método exacto que permita resolver nuestro problema, y si lo hay, es posible que su **aplicación** **no** sea **viable** para un problema de cierta complejidad por el **tiempo** de **resolución** o **consumo** de **recursos**. (Una búsqueda extensiva para un problema combinatorio de algunos cientos de variables, puede tardar meses o años)\n",
    "\n",
    "Además, las estratégias de **resolución analíticas**, como los **métodos basados en el gradiente** , pueden **converger a óptimos locales** y no alcanzar el óptimo global del problema"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5badb8c5-939d-4ecb-acca-84766a0509aa",
   "metadata": {},
   "source": [
    "### Cómo decidir la estrategia para abordar un problema\n",
    "1. **Cómo de grande es mi problema**: Estimar tiempo de una búsqueda **extensiva** a partir del tiempo necesario para evaluar una única solución\n",
    "2. **Que rapidez necesito para obtener una solución**: A veces horas es aceptable, pero no meses...\n",
    "3. **Cuantas restricciones hay**: A más restricciones, menos probable es que nos interese un método analítico exacto\n",
    "4. **Precisión que necesito en los resultados**: A veces es preferible rapidez y aproximación antes que lentitud y total precisión."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55604cc6-e060-49d9-805e-3cf30c371e11",
   "metadata": {},
   "source": [
    "### Qué necesitamos para resolver Algoritmos Genéticos con Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffea0fe5-9042-4088-8367-ce9f62bdd68c",
   "metadata": {},
   "source": [
    "Utilizaremos la librería de Python **`deap`** [Documentación de deap](https://deap.readthedocs.io/en/master/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b372009-8c31-4e2a-9a4b-839c77a9a18a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: deap in /opt/anaconda3/lib/python3.9/site-packages (1.3.3)\n",
      "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.9/site-packages (from deap) (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install deap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd641dfe-a15b-4d0e-8bcc-be9861fe3334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.9/site-packages (3.4.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: numpy>=1.16 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib) (1.20.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib) (8.4.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib) (3.0.4)\n",
      "Requirement already satisfied: six in /opt/anaconda3/lib/python3.9/site-packages (from cycler>=0.10->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e718fa60-b2e8-432e-afb8-3cb3b259fb5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.9/site-packages (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1636feb-f488-4df1-b81f-0863b811fd57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /opt/anaconda3/lib/python3.9/site-packages (1.7.1)\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /opt/anaconda3/lib/python3.9/site-packages (from scipy) (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28845db-dd62-404c-9025-0066adefc3f1",
   "metadata": {},
   "source": [
    "Necesitamos también la librería de Python de **multiprocesamiento** (mecanismo de **paralelización**) **`SCOOP`** [Enlace al GitHub de SCOOP](https://github.com/soravux/scoop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1baccdcd-950d-4bb4-90d3-fac358d8278b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scoop in /opt/anaconda3/lib/python3.9/site-packages (0.7.2.0)\n",
      "Requirement already satisfied: pyzmq>=13.1.0 in /opt/anaconda3/lib/python3.9/site-packages (from scoop) (22.2.1)\n",
      "Requirement already satisfied: greenlet>=0.3.4 in /opt/anaconda3/lib/python3.9/site-packages (from scoop) (1.1.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install scoop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28a1414-ac27-4930-bb1b-1871c559bcc4",
   "metadata": {},
   "source": [
    "### RESOLUCIÓN DE UN PROBLEMA SENCILLO ---------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d8c81f-5427-40b1-a765-dc5c7cbc4f30",
   "metadata": {},
   "source": [
    "Se desea encontrar el **máximo** de la función:\n",
    "$$f(x,y) = \\sqrt{x^2 + y^2}$$\n",
    "En el dominio $$\\{x,y\\} \\in [-100,100]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7334aa93-ac24-4da9-8a78-d51745ce5273",
   "metadata": {},
   "source": [
    "En este problema, la cadena cromosomática estaría formada por dos genes (cromosomas), que, al confinarse en forma de lista, quedaría como $$[x_i, y_i]$$ con $$i = 1, 2, ... , n$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d38c5de-d457-4930-bd05-ddb55c31933c",
   "metadata": {},
   "source": [
    "La **población** del algoritmo **no cambia de tamaño** a lo largo de las generaciones, con lo que **n** será **constante**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2e5ece-293e-48e2-8659-31c62637b3ee",
   "metadata": {},
   "source": [
    "La población inicial se **elige de forma aleatoria** porque queremos **diversidad genética**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d42f87a4-ab4c-4ad3-9b1c-6b326da09c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "from deap import base\n",
    "from deap import creator\n",
    "from deap import tools\n",
    "from deap import algorithms\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54949f15-4532-41b5-830d-0590fb3f40d9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### NOTA SOBRE NÚMEROS ALEATORIOS --------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd08d4b-2bc2-4392-b62c-f1ac9a8cd315",
   "metadata": {},
   "source": [
    "Queremos generar números aleatorios entre -100 y 100 (sin incluir a estos). Podemos usar el módulo nativo random de generación de números pseudoaleatorios [Documentación librería random](https://docs.python.org/3/library/random.html) y `numpy.random`(para generación de vectores psudoaleatorios) [Documentación numpy.random](https://docs.scipy.org/doc/numpy-1.16.1/reference/routines.random.html). Recordad que los números totalmente aleatorios no existen en programación.\n",
    "\n",
    "Utilizamos, además, el método `uniform`: [Documentación método uniform](https://docs.python.org/3.6/library/random.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ce06dee-d95a-47f0-a211-4cca260eace0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=  [68.88437030500961, 51.590880588060486, -15.885683833830996, -48.21664994140733, 2.254944273721705, -19.01317250991714, 56.75971780695451, -39.33745478421451, -4.680609169528836, 16.676407891006235]\n",
      "y=  [81.62257703906704, 0.9373711634780477, -43.63243112005924, 51.16084083144477, 23.67379933506632, -49.898731727511894, 81.94925119364802, 96.55709520753061, 62.04344719931791, 80.43319008791653]\n"
     ]
    }
   ],
   "source": [
    "# Importamos la librería\n",
    "import random\n",
    "# Fijamos la semilla. Si no cambiamos la semilla, \n",
    "# la función generará los mismos número aleatorios\n",
    "random.seed(0)\n",
    "# Generamos los números pseudo-aleatorios en 2 list comprenhension\n",
    "# para encapsular todos los datos en las listas x e y\n",
    "x = [random.uniform(-100, 100) for i in range(10)]\n",
    "y = [random.uniform(-100, 100) for i in range(10)]\n",
    "print('x= ', x)\n",
    "print('y= ', y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18eb1cd6-52ed-4e71-bd00-d2062c2b4a73",
   "metadata": {},
   "source": [
    "Otra posibilidad es generar dos vectores de diez valores comprendidos entre -100 y 100, con una forma (1,10) (1 fila y 10 columnas). Serían dos vectores de tipo fila con diez valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44c25d1e-ba6e-43cc-bfd6-a732e5df2a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z=  [[  9.76270079  43.03787327  20.55267521   8.9766366  -15.26904013\n",
      "   29.17882261 -12.48255775  78.35460016  92.7325521  -23.31169623]]\n",
      "t=  [[ 58.34500762   5.77898395  13.60891222  85.11932766 -85.79278836\n",
      "  -82.57414006 -95.95632051  66.52396911  55.63135019  74.00242965]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Fijamos la semilla\n",
    "np.random.seed(0)\n",
    "# Generamos los números pseudo-aleatorios\n",
    "# Son los mismos mientras no cambiemos la semilla\n",
    "# Numpy genera directamente los vectores\n",
    "z = np.random.uniform(-100, 100, 10).reshape(1, 10) \n",
    "t = np.random.uniform(-100, 100, 10).reshape(1, 10) \n",
    "print('z= ', z)\n",
    "print('t= ', t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57459d16-b5d1-404a-a09f-471333c37891",
   "metadata": {},
   "source": [
    "Hay que tener en cuenta que las soluciones deben ser válidas y una solución sería **no válida** si alguna de las variables independientes se saliera de los **rangos establecidos**. Es común en los problemas de optimización tener restricciones en las variables, por lo que siempre tendremos que **comprobar la validez de nuestras soluciones**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a935fd89-89df-425c-99ea-1a5bd668b878",
   "metadata": {},
   "source": [
    "#### ------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f967799-f819-4d3d-9288-47c62d81704d",
   "metadata": {},
   "source": [
    "#### NOTA 2: ESPACIO DE BÚSQUEDA O DOMINIO DE LA SOLUCIÓN -"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00b93b5-1058-421e-8086-3bf23adf44db",
   "metadata": {},
   "source": [
    "Sabemos que las 4 soluciones óptimas para nuestro problema de ejemplo son:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0307957-bd11-4526-8450-d7db65c5f416",
   "metadata": {},
   "source": [
    "|Individuo|x|y|\n",
    "|-|-|-|\n",
    "|1|100|100|\n",
    "|2|-100|100|\n",
    "|3|100|-100|\n",
    "|4|-100|-100|"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd15e61-99d4-4fa7-82cc-ab5386ce2a14",
   "metadata": {},
   "source": [
    "Vamos a resolverlo con Algoritmo Genético como ejercício, aunque cualquier algoritmo de optimización basado en gradiente de los que vienen incluidos en el módulo `optimize` de `scipy`([módulo optimize en scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize)) nos valdría para obtener una solución a nuestro problema de manera sencilla, ya que nuestra función es **convexa**\n",
    "\n",
    "Recordar que: Los AGs se deben emplear en **problemas de optimización** en los que la solución óptima **no se conozca** y ni siquiera tengamos una **aproximación** a la misma o incluso **ni siquiera se tenga la certeza** de que podamos obtenerla.\n",
    "\n",
    "Aunque el **uso de AGs no nos da certeza de obtener la solución óptima**, si obtendremos una solución **bastante buena** y en un **tiempo razonable**. Siempre vamos a terminar con una solución al problema que **será mejor que realizar una búsqueda totalmente aleatoria** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735db5ba-fb4f-4da2-8a33-6876950a8b97",
   "metadata": {},
   "source": [
    "##### Visualicemos nuestra función"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef168180-ea01-46a2-b51a-1419f5fa326f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos las librerías necesarias\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0c2efe5-e609-493e-8575-75b67d37000b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos la función objetivo\n",
    "def funcion_prueba(x):\n",
    "    # x es x[0] e y es x[1]\n",
    "    res = np.sqrt(x[0]**2 + x[1]**2)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d62592-99ea-41ae-a85a-ac7f76696737",
   "metadata": {},
   "source": [
    "[numpy.linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8429f3c-5960-4b35-88fe-0d57c590fa71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=  [-100.          -99.7997998   -99.5995996   -99.3993994   -99.1991992\n",
      "  -98.998999    -98.7987988   -98.5985986   -98.3983984   -98.1981982\n",
      "  -97.997998    -97.7977978   -97.5975976   -97.3973974   -97.1971972\n",
      "  -96.996997    -96.7967968   -96.5965966   -96.3963964   -96.1961962\n",
      "  -95.995996    -95.7957958   -95.5955956   -95.3953954   -95.1951952\n",
      "  -94.99499499  -94.79479479  -94.59459459  -94.39439439  -94.19419419\n",
      "  -93.99399399  -93.79379379  -93.59359359  -93.39339339  -93.19319319\n",
      "  -92.99299299  -92.79279279  -92.59259259  -92.39239239  -92.19219219\n",
      "  -91.99199199  -91.79179179  -91.59159159  -91.39139139  -91.19119119\n",
      "  -90.99099099  -90.79079079  -90.59059059  -90.39039039  -90.19019019\n",
      "  -89.98998999  -89.78978979  -89.58958959  -89.38938939  -89.18918919\n",
      "  -88.98898899  -88.78878879  -88.58858859  -88.38838839  -88.18818819\n",
      "  -87.98798799  -87.78778779  -87.58758759  -87.38738739  -87.18718719\n",
      "  -86.98698699  -86.78678679  -86.58658659  -86.38638639  -86.18618619\n",
      "  -85.98598599  -85.78578579  -85.58558559  -85.38538539  -85.18518519\n",
      "  -84.98498498  -84.78478478  -84.58458458  -84.38438438  -84.18418418\n",
      "  -83.98398398  -83.78378378  -83.58358358  -83.38338338  -83.18318318\n",
      "  -82.98298298  -82.78278278  -82.58258258  -82.38238238  -82.18218218\n",
      "  -81.98198198  -81.78178178  -81.58158158  -81.38138138  -81.18118118\n",
      "  -80.98098098  -80.78078078  -80.58058058  -80.38038038  -80.18018018\n",
      "  -79.97997998  -79.77977978  -79.57957958  -79.37937938  -79.17917918\n",
      "  -78.97897898  -78.77877878  -78.57857858  -78.37837838  -78.17817818\n",
      "  -77.97797798  -77.77777778  -77.57757758  -77.37737738  -77.17717718\n",
      "  -76.97697698  -76.77677678  -76.57657658  -76.37637638  -76.17617618\n",
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      "  -28.92892893  -28.72872873  -28.52852853  -28.32832833  -28.12812813\n",
      "  -27.92792793  -27.72772773  -27.52752753  -27.32732733  -27.12712713\n",
      "  -26.92692693  -26.72672673  -26.52652653  -26.32632633  -26.12612613\n",
      "  -25.92592593  -25.72572573  -25.52552553  -25.32532533  -25.12512513\n",
      "  -24.92492492  -24.72472472  -24.52452452  -24.32432432  -24.12412412\n",
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      "  -21.92192192  -21.72172172  -21.52152152  -21.32132132  -21.12112112\n",
      "  -20.92092092  -20.72072072  -20.52052052  -20.32032032  -20.12012012\n",
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      "  -18.91891892  -18.71871872  -18.51851852  -18.31831832  -18.11811812\n",
      "  -17.91791792  -17.71771772  -17.51751752  -17.31731732  -17.11711712\n",
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      "  -15.91591592  -15.71571572  -15.51551552  -15.31531532  -15.11511512\n",
      "  -14.91491491  -14.71471471  -14.51451451  -14.31431431  -14.11411411\n",
      "  -13.91391391  -13.71371371  -13.51351351  -13.31331331  -13.11311311\n",
      "  -12.91291291  -12.71271271  -12.51251251  -12.31231231  -12.11211211\n",
      "  -11.91191191  -11.71171171  -11.51151151  -11.31131131  -11.11111111\n",
      "  -10.91091091  -10.71071071  -10.51051051  -10.31031031  -10.11011011\n",
      "   -9.90990991   -9.70970971   -9.50950951   -9.30930931   -9.10910911\n",
      "   -8.90890891   -8.70870871   -8.50850851   -8.30830831   -8.10810811\n",
      "   -7.90790791   -7.70770771   -7.50750751   -7.30730731   -7.10710711\n",
      "   -6.90690691   -6.70670671   -6.50650651   -6.30630631   -6.10610611\n",
      "   -5.90590591   -5.70570571   -5.50550551   -5.30530531   -5.10510511\n",
      "   -4.9049049    -4.7047047    -4.5045045    -4.3043043    -4.1041041\n",
      "   -3.9039039    -3.7037037    -3.5035035    -3.3033033    -3.1031031\n",
      "   -2.9029029    -2.7027027    -2.5025025    -2.3023023    -2.1021021\n",
      "   -1.9019019    -1.7017017    -1.5015015    -1.3013013    -1.1011011\n",
      "   -0.9009009    -0.7007007    -0.5005005    -0.3003003    -0.1001001\n",
      "    0.1001001     0.3003003     0.5005005     0.7007007     0.9009009\n",
      "    1.1011011     1.3013013     1.5015015     1.7017017     1.9019019\n",
      "    2.1021021     2.3023023     2.5025025     2.7027027     2.9029029\n",
      "    3.1031031     3.3033033     3.5035035     3.7037037     3.9039039\n",
      "    4.1041041     4.3043043     4.5045045     4.7047047     4.9049049\n",
      "    5.10510511    5.30530531    5.50550551    5.70570571    5.90590591\n",
      "    6.10610611    6.30630631    6.50650651    6.70670671    6.90690691\n",
      "    7.10710711    7.30730731    7.50750751    7.70770771    7.90790791\n",
      "    8.10810811    8.30830831    8.50850851    8.70870871    8.90890891\n",
      "    9.10910911    9.30930931    9.50950951    9.70970971    9.90990991\n",
      "   10.11011011   10.31031031   10.51051051   10.71071071   10.91091091\n",
      "   11.11111111   11.31131131   11.51151151   11.71171171   11.91191191\n",
      "   12.11211211   12.31231231   12.51251251   12.71271271   12.91291291\n",
      "   13.11311311   13.31331331   13.51351351   13.71371371   13.91391391\n",
      "   14.11411411   14.31431431   14.51451451   14.71471471   14.91491491\n",
      "   15.11511512   15.31531532   15.51551552   15.71571572   15.91591592\n",
      "   16.11611612   16.31631632   16.51651652   16.71671672   16.91691692\n",
      "   17.11711712   17.31731732   17.51751752   17.71771772   17.91791792\n",
      "   18.11811812   18.31831832   18.51851852   18.71871872   18.91891892\n",
      "   19.11911912   19.31931932   19.51951952   19.71971972   19.91991992\n",
      "   20.12012012   20.32032032   20.52052052   20.72072072   20.92092092\n",
      "   21.12112112   21.32132132   21.52152152   21.72172172   21.92192192\n",
      "   22.12212212   22.32232232   22.52252252   22.72272272   22.92292292\n",
      "   23.12312312   23.32332332   23.52352352   23.72372372   23.92392392\n",
      "   24.12412412   24.32432432   24.52452452   24.72472472   24.92492492\n",
      "   25.12512513   25.32532533   25.52552553   25.72572573   25.92592593\n",
      "   26.12612613   26.32632633   26.52652653   26.72672673   26.92692693\n",
      "   27.12712713   27.32732733   27.52752753   27.72772773   27.92792793\n",
      "   28.12812813   28.32832833   28.52852853   28.72872873   28.92892893\n",
      "   29.12912913   29.32932933   29.52952953   29.72972973   29.92992993\n",
      "   30.13013013   30.33033033   30.53053053   30.73073073   30.93093093\n",
      "   31.13113113   31.33133133   31.53153153   31.73173173   31.93193193\n",
      "   32.13213213   32.33233233   32.53253253   32.73273273   32.93293293\n",
      "   33.13313313   33.33333333   33.53353353   33.73373373   33.93393393\n",
      "   34.13413413   34.33433433   34.53453453   34.73473473   34.93493493\n",
      "   35.13513514   35.33533534   35.53553554   35.73573574   35.93593594\n",
      "   36.13613614   36.33633634   36.53653654   36.73673674   36.93693694\n",
      "   37.13713714   37.33733734   37.53753754   37.73773774   37.93793794\n",
      "   38.13813814   38.33833834   38.53853854   38.73873874   38.93893894\n",
      "   39.13913914   39.33933934   39.53953954   39.73973974   39.93993994\n",
      "   40.14014014   40.34034034   40.54054054   40.74074074   40.94094094\n",
      "   41.14114114   41.34134134   41.54154154   41.74174174   41.94194194\n",
      "   42.14214214   42.34234234   42.54254254   42.74274274   42.94294294\n",
      "   43.14314314   43.34334334   43.54354354   43.74374374   43.94394394\n",
      "   44.14414414   44.34434434   44.54454454   44.74474474   44.94494494\n",
      "   45.14514515   45.34534535   45.54554555   45.74574575   45.94594595\n",
      "   46.14614615   46.34634635   46.54654655   46.74674675   46.94694695\n",
      "   47.14714715   47.34734735   47.54754755   47.74774775   47.94794795\n",
      "   48.14814815   48.34834835   48.54854855   48.74874875   48.94894895\n",
      "   49.14914915   49.34934935   49.54954955   49.74974975   49.94994995\n",
      "   50.15015015   50.35035035   50.55055055   50.75075075   50.95095095\n",
      "   51.15115115   51.35135135   51.55155155   51.75175175   51.95195195\n",
      "   52.15215215   52.35235235   52.55255255   52.75275275   52.95295295\n",
      "   53.15315315   53.35335335   53.55355355   53.75375375   53.95395395\n",
      "   54.15415415   54.35435435   54.55455455   54.75475475   54.95495495\n",
      "   55.15515516   55.35535536   55.55555556   55.75575576   55.95595596\n",
      "   56.15615616   56.35635636   56.55655656   56.75675676   56.95695696\n",
      "   57.15715716   57.35735736   57.55755756   57.75775776   57.95795796\n",
      "   58.15815816   58.35835836   58.55855856   58.75875876   58.95895896\n",
      "   59.15915916   59.35935936   59.55955956   59.75975976   59.95995996\n",
      "   60.16016016   60.36036036   60.56056056   60.76076076   60.96096096\n",
      "   61.16116116   61.36136136   61.56156156   61.76176176   61.96196196\n",
      "   62.16216216   62.36236236   62.56256256   62.76276276   62.96296296\n",
      "   63.16316316   63.36336336   63.56356356   63.76376376   63.96396396\n",
      "   64.16416416   64.36436436   64.56456456   64.76476476   64.96496496\n",
      "   65.16516517   65.36536537   65.56556557   65.76576577   65.96596597\n",
      "   66.16616617   66.36636637   66.56656657   66.76676677   66.96696697\n",
      "   67.16716717   67.36736737   67.56756757   67.76776777   67.96796797\n",
      "   68.16816817   68.36836837   68.56856857   68.76876877   68.96896897\n",
      "   69.16916917   69.36936937   69.56956957   69.76976977   69.96996997\n",
      "   70.17017017   70.37037037   70.57057057   70.77077077   70.97097097\n",
      "   71.17117117   71.37137137   71.57157157   71.77177177   71.97197197\n",
      "   72.17217217   72.37237237   72.57257257   72.77277277   72.97297297\n",
      "   73.17317317   73.37337337   73.57357357   73.77377377   73.97397397\n",
      "   74.17417417   74.37437437   74.57457457   74.77477477   74.97497497\n",
      "   75.17517518   75.37537538   75.57557558   75.77577578   75.97597598\n",
      "   76.17617618   76.37637638   76.57657658   76.77677678   76.97697698\n",
      "   77.17717718   77.37737738   77.57757758   77.77777778   77.97797798\n",
      "   78.17817818   78.37837838   78.57857858   78.77877878   78.97897898\n",
      "   79.17917918   79.37937938   79.57957958   79.77977978   79.97997998\n",
      "   80.18018018   80.38038038   80.58058058   80.78078078   80.98098098\n",
      "   81.18118118   81.38138138   81.58158158   81.78178178   81.98198198\n",
      "   82.18218218   82.38238238   82.58258258   82.78278278   82.98298298\n",
      "   83.18318318   83.38338338   83.58358358   83.78378378   83.98398398\n",
      "   84.18418418   84.38438438   84.58458458   84.78478478   84.98498498\n",
      "   85.18518519   85.38538539   85.58558559   85.78578579   85.98598599\n",
      "   86.18618619   86.38638639   86.58658659   86.78678679   86.98698699\n",
      "   87.18718719   87.38738739   87.58758759   87.78778779   87.98798799\n",
      "   88.18818819   88.38838839   88.58858859   88.78878879   88.98898899\n",
      "   89.18918919   89.38938939   89.58958959   89.78978979   89.98998999\n",
      "   90.19019019   90.39039039   90.59059059   90.79079079   90.99099099\n",
      "   91.19119119   91.39139139   91.59159159   91.79179179   91.99199199\n",
      "   92.19219219   92.39239239   92.59259259   92.79279279   92.99299299\n",
      "   93.19319319   93.39339339   93.59359359   93.79379379   93.99399399\n",
      "   94.19419419   94.39439439   94.59459459   94.79479479   94.99499499\n",
      "   95.1951952    95.3953954    95.5955956    95.7957958    95.995996\n",
      "   96.1961962    96.3963964    96.5965966    96.7967968    96.996997\n",
      "   97.1971972    97.3973974    97.5975976    97.7977978    97.997998\n",
      "   98.1981982    98.3983984    98.5985986    98.7987988    98.998999\n",
      "   99.1991992    99.3993994    99.5995996    99.7997998   100.        ]\n",
      "y=  [-100.          -99.7997998   -99.5995996   -99.3993994   -99.1991992\n",
      "  -98.998999    -98.7987988   -98.5985986   -98.3983984   -98.1981982\n",
      "  -97.997998    -97.7977978   -97.5975976   -97.3973974   -97.1971972\n",
      "  -96.996997    -96.7967968   -96.5965966   -96.3963964   -96.1961962\n",
      "  -95.995996    -95.7957958   -95.5955956   -95.3953954   -95.1951952\n",
      "  -94.99499499  -94.79479479  -94.59459459  -94.39439439  -94.19419419\n",
      "  -93.99399399  -93.79379379  -93.59359359  -93.39339339  -93.19319319\n",
      "  -92.99299299  -92.79279279  -92.59259259  -92.39239239  -92.19219219\n",
      "  -91.99199199  -91.79179179  -91.59159159  -91.39139139  -91.19119119\n",
      "  -90.99099099  -90.79079079  -90.59059059  -90.39039039  -90.19019019\n",
      "  -89.98998999  -89.78978979  -89.58958959  -89.38938939  -89.18918919\n",
      "  -88.98898899  -88.78878879  -88.58858859  -88.38838839  -88.18818819\n",
      "  -87.98798799  -87.78778779  -87.58758759  -87.38738739  -87.18718719\n",
      "  -86.98698699  -86.78678679  -86.58658659  -86.38638639  -86.18618619\n",
      "  -85.98598599  -85.78578579  -85.58558559  -85.38538539  -85.18518519\n",
      "  -84.98498498  -84.78478478  -84.58458458  -84.38438438  -84.18418418\n",
      "  -83.98398398  -83.78378378  -83.58358358  -83.38338338  -83.18318318\n",
      "  -82.98298298  -82.78278278  -82.58258258  -82.38238238  -82.18218218\n",
      "  -81.98198198  -81.78178178  -81.58158158  -81.38138138  -81.18118118\n",
      "  -80.98098098  -80.78078078  -80.58058058  -80.38038038  -80.18018018\n",
      "  -79.97997998  -79.77977978  -79.57957958  -79.37937938  -79.17917918\n",
      "  -78.97897898  -78.77877878  -78.57857858  -78.37837838  -78.17817818\n",
      "  -77.97797798  -77.77777778  -77.57757758  -77.37737738  -77.17717718\n",
      "  -76.97697698  -76.77677678  -76.57657658  -76.37637638  -76.17617618\n",
      "  -75.97597598  -75.77577578  -75.57557558  -75.37537538  -75.17517518\n",
      "  -74.97497497  -74.77477477  -74.57457457  -74.37437437  -74.17417417\n",
      "  -73.97397397  -73.77377377  -73.57357357  -73.37337337  -73.17317317\n",
      "  -72.97297297  -72.77277277  -72.57257257  -72.37237237  -72.17217217\n",
      "  -71.97197197  -71.77177177  -71.57157157  -71.37137137  -71.17117117\n",
      "  -70.97097097  -70.77077077  -70.57057057  -70.37037037  -70.17017017\n",
      "  -69.96996997  -69.76976977  -69.56956957  -69.36936937  -69.16916917\n",
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      "  -66.96696697  -66.76676677  -66.56656657  -66.36636637  -66.16616617\n",
      "  -65.96596597  -65.76576577  -65.56556557  -65.36536537  -65.16516517\n",
      "  -64.96496496  -64.76476476  -64.56456456  -64.36436436  -64.16416416\n",
      "  -63.96396396  -63.76376376  -63.56356356  -63.36336336  -63.16316316\n",
      "  -62.96296296  -62.76276276  -62.56256256  -62.36236236  -62.16216216\n",
      "  -61.96196196  -61.76176176  -61.56156156  -61.36136136  -61.16116116\n",
      "  -60.96096096  -60.76076076  -60.56056056  -60.36036036  -60.16016016\n",
      "  -59.95995996  -59.75975976  -59.55955956  -59.35935936  -59.15915916\n",
      "  -58.95895896  -58.75875876  -58.55855856  -58.35835836  -58.15815816\n",
      "  -57.95795796  -57.75775776  -57.55755756  -57.35735736  -57.15715716\n",
      "  -56.95695696  -56.75675676  -56.55655656  -56.35635636  -56.15615616\n",
      "  -55.95595596  -55.75575576  -55.55555556  -55.35535536  -55.15515516\n",
      "  -54.95495495  -54.75475475  -54.55455455  -54.35435435  -54.15415415\n",
      "  -53.95395395  -53.75375375  -53.55355355  -53.35335335  -53.15315315\n",
      "  -52.95295295  -52.75275275  -52.55255255  -52.35235235  -52.15215215\n",
      "  -51.95195195  -51.75175175  -51.55155155  -51.35135135  -51.15115115\n",
      "  -50.95095095  -50.75075075  -50.55055055  -50.35035035  -50.15015015\n",
      "  -49.94994995  -49.74974975  -49.54954955  -49.34934935  -49.14914915\n",
      "  -48.94894895  -48.74874875  -48.54854855  -48.34834835  -48.14814815\n",
      "  -47.94794795  -47.74774775  -47.54754755  -47.34734735  -47.14714715\n",
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      "  -45.94594595  -45.74574575  -45.54554555  -45.34534535  -45.14514515\n",
      "  -44.94494494  -44.74474474  -44.54454454  -44.34434434  -44.14414414\n",
      "  -43.94394394  -43.74374374  -43.54354354  -43.34334334  -43.14314314\n",
      "  -42.94294294  -42.74274274  -42.54254254  -42.34234234  -42.14214214\n",
      "  -41.94194194  -41.74174174  -41.54154154  -41.34134134  -41.14114114\n",
      "  -40.94094094  -40.74074074  -40.54054054  -40.34034034  -40.14014014\n",
      "  -39.93993994  -39.73973974  -39.53953954  -39.33933934  -39.13913914\n",
      "  -38.93893894  -38.73873874  -38.53853854  -38.33833834  -38.13813814\n",
      "  -37.93793794  -37.73773774  -37.53753754  -37.33733734  -37.13713714\n",
      "  -36.93693694  -36.73673674  -36.53653654  -36.33633634  -36.13613614\n",
      "  -35.93593594  -35.73573574  -35.53553554  -35.33533534  -35.13513514\n",
      "  -34.93493493  -34.73473473  -34.53453453  -34.33433433  -34.13413413\n",
      "  -33.93393393  -33.73373373  -33.53353353  -33.33333333  -33.13313313\n",
      "  -32.93293293  -32.73273273  -32.53253253  -32.33233233  -32.13213213\n",
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      "  -21.92192192  -21.72172172  -21.52152152  -21.32132132  -21.12112112\n",
      "  -20.92092092  -20.72072072  -20.52052052  -20.32032032  -20.12012012\n",
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      "  -16.91691692  -16.71671672  -16.51651652  -16.31631632  -16.11611612\n",
      "  -15.91591592  -15.71571572  -15.51551552  -15.31531532  -15.11511512\n",
      "  -14.91491491  -14.71471471  -14.51451451  -14.31431431  -14.11411411\n",
      "  -13.91391391  -13.71371371  -13.51351351  -13.31331331  -13.11311311\n",
      "  -12.91291291  -12.71271271  -12.51251251  -12.31231231  -12.11211211\n",
      "  -11.91191191  -11.71171171  -11.51151151  -11.31131131  -11.11111111\n",
      "  -10.91091091  -10.71071071  -10.51051051  -10.31031031  -10.11011011\n",
      "   -9.90990991   -9.70970971   -9.50950951   -9.30930931   -9.10910911\n",
      "   -8.90890891   -8.70870871   -8.50850851   -8.30830831   -8.10810811\n",
      "   -7.90790791   -7.70770771   -7.50750751   -7.30730731   -7.10710711\n",
      "   -6.90690691   -6.70670671   -6.50650651   -6.30630631   -6.10610611\n",
      "   -5.90590591   -5.70570571   -5.50550551   -5.30530531   -5.10510511\n",
      "   -4.9049049    -4.7047047    -4.5045045    -4.3043043    -4.1041041\n",
      "   -3.9039039    -3.7037037    -3.5035035    -3.3033033    -3.1031031\n",
      "   -2.9029029    -2.7027027    -2.5025025    -2.3023023    -2.1021021\n",
      "   -1.9019019    -1.7017017    -1.5015015    -1.3013013    -1.1011011\n",
      "   -0.9009009    -0.7007007    -0.5005005    -0.3003003    -0.1001001\n",
      "    0.1001001     0.3003003     0.5005005     0.7007007     0.9009009\n",
      "    1.1011011     1.3013013     1.5015015     1.7017017     1.9019019\n",
      "    2.1021021     2.3023023     2.5025025     2.7027027     2.9029029\n",
      "    3.1031031     3.3033033     3.5035035     3.7037037     3.9039039\n",
      "    4.1041041     4.3043043     4.5045045     4.7047047     4.9049049\n",
      "    5.10510511    5.30530531    5.50550551    5.70570571    5.90590591\n",
      "    6.10610611    6.30630631    6.50650651    6.70670671    6.90690691\n",
      "    7.10710711    7.30730731    7.50750751    7.70770771    7.90790791\n",
      "    8.10810811    8.30830831    8.50850851    8.70870871    8.90890891\n",
      "    9.10910911    9.30930931    9.50950951    9.70970971    9.90990991\n",
      "   10.11011011   10.31031031   10.51051051   10.71071071   10.91091091\n",
      "   11.11111111   11.31131131   11.51151151   11.71171171   11.91191191\n",
      "   12.11211211   12.31231231   12.51251251   12.71271271   12.91291291\n",
      "   13.11311311   13.31331331   13.51351351   13.71371371   13.91391391\n",
      "   14.11411411   14.31431431   14.51451451   14.71471471   14.91491491\n",
      "   15.11511512   15.31531532   15.51551552   15.71571572   15.91591592\n",
      "   16.11611612   16.31631632   16.51651652   16.71671672   16.91691692\n",
      "   17.11711712   17.31731732   17.51751752   17.71771772   17.91791792\n",
      "   18.11811812   18.31831832   18.51851852   18.71871872   18.91891892\n",
      "   19.11911912   19.31931932   19.51951952   19.71971972   19.91991992\n",
      "   20.12012012   20.32032032   20.52052052   20.72072072   20.92092092\n",
      "   21.12112112   21.32132132   21.52152152   21.72172172   21.92192192\n",
      "   22.12212212   22.32232232   22.52252252   22.72272272   22.92292292\n",
      "   23.12312312   23.32332332   23.52352352   23.72372372   23.92392392\n",
      "   24.12412412   24.32432432   24.52452452   24.72472472   24.92492492\n",
      "   25.12512513   25.32532533   25.52552553   25.72572573   25.92592593\n",
      "   26.12612613   26.32632633   26.52652653   26.72672673   26.92692693\n",
      "   27.12712713   27.32732733   27.52752753   27.72772773   27.92792793\n",
      "   28.12812813   28.32832833   28.52852853   28.72872873   28.92892893\n",
      "   29.12912913   29.32932933   29.52952953   29.72972973   29.92992993\n",
      "   30.13013013   30.33033033   30.53053053   30.73073073   30.93093093\n",
      "   31.13113113   31.33133133   31.53153153   31.73173173   31.93193193\n",
      "   32.13213213   32.33233233   32.53253253   32.73273273   32.93293293\n",
      "   33.13313313   33.33333333   33.53353353   33.73373373   33.93393393\n",
      "   34.13413413   34.33433433   34.53453453   34.73473473   34.93493493\n",
      "   35.13513514   35.33533534   35.53553554   35.73573574   35.93593594\n",
      "   36.13613614   36.33633634   36.53653654   36.73673674   36.93693694\n",
      "   37.13713714   37.33733734   37.53753754   37.73773774   37.93793794\n",
      "   38.13813814   38.33833834   38.53853854   38.73873874   38.93893894\n",
      "   39.13913914   39.33933934   39.53953954   39.73973974   39.93993994\n",
      "   40.14014014   40.34034034   40.54054054   40.74074074   40.94094094\n",
      "   41.14114114   41.34134134   41.54154154   41.74174174   41.94194194\n",
      "   42.14214214   42.34234234   42.54254254   42.74274274   42.94294294\n",
      "   43.14314314   43.34334334   43.54354354   43.74374374   43.94394394\n",
      "   44.14414414   44.34434434   44.54454454   44.74474474   44.94494494\n",
      "   45.14514515   45.34534535   45.54554555   45.74574575   45.94594595\n",
      "   46.14614615   46.34634635   46.54654655   46.74674675   46.94694695\n",
      "   47.14714715   47.34734735   47.54754755   47.74774775   47.94794795\n",
      "   48.14814815   48.34834835   48.54854855   48.74874875   48.94894895\n",
      "   49.14914915   49.34934935   49.54954955   49.74974975   49.94994995\n",
      "   50.15015015   50.35035035   50.55055055   50.75075075   50.95095095\n",
      "   51.15115115   51.35135135   51.55155155   51.75175175   51.95195195\n",
      "   52.15215215   52.35235235   52.55255255   52.75275275   52.95295295\n",
      "   53.15315315   53.35335335   53.55355355   53.75375375   53.95395395\n",
      "   54.15415415   54.35435435   54.55455455   54.75475475   54.95495495\n",
      "   55.15515516   55.35535536   55.55555556   55.75575576   55.95595596\n",
      "   56.15615616   56.35635636   56.55655656   56.75675676   56.95695696\n",
      "   57.15715716   57.35735736   57.55755756   57.75775776   57.95795796\n",
      "   58.15815816   58.35835836   58.55855856   58.75875876   58.95895896\n",
      "   59.15915916   59.35935936   59.55955956   59.75975976   59.95995996\n",
      "   60.16016016   60.36036036   60.56056056   60.76076076   60.96096096\n",
      "   61.16116116   61.36136136   61.56156156   61.76176176   61.96196196\n",
      "   62.16216216   62.36236236   62.56256256   62.76276276   62.96296296\n",
      "   63.16316316   63.36336336   63.56356356   63.76376376   63.96396396\n",
      "   64.16416416   64.36436436   64.56456456   64.76476476   64.96496496\n",
      "   65.16516517   65.36536537   65.56556557   65.76576577   65.96596597\n",
      "   66.16616617   66.36636637   66.56656657   66.76676677   66.96696697\n",
      "   67.16716717   67.36736737   67.56756757   67.76776777   67.96796797\n",
      "   68.16816817   68.36836837   68.56856857   68.76876877   68.96896897\n",
      "   69.16916917   69.36936937   69.56956957   69.76976977   69.96996997\n",
      "   70.17017017   70.37037037   70.57057057   70.77077077   70.97097097\n",
      "   71.17117117   71.37137137   71.57157157   71.77177177   71.97197197\n",
      "   72.17217217   72.37237237   72.57257257   72.77277277   72.97297297\n",
      "   73.17317317   73.37337337   73.57357357   73.77377377   73.97397397\n",
      "   74.17417417   74.37437437   74.57457457   74.77477477   74.97497497\n",
      "   75.17517518   75.37537538   75.57557558   75.77577578   75.97597598\n",
      "   76.17617618   76.37637638   76.57657658   76.77677678   76.97697698\n",
      "   77.17717718   77.37737738   77.57757758   77.77777778   77.97797798\n",
      "   78.17817818   78.37837838   78.57857858   78.77877878   78.97897898\n",
      "   79.17917918   79.37937938   79.57957958   79.77977978   79.97997998\n",
      "   80.18018018   80.38038038   80.58058058   80.78078078   80.98098098\n",
      "   81.18118118   81.38138138   81.58158158   81.78178178   81.98198198\n",
      "   82.18218218   82.38238238   82.58258258   82.78278278   82.98298298\n",
      "   83.18318318   83.38338338   83.58358358   83.78378378   83.98398398\n",
      "   84.18418418   84.38438438   84.58458458   84.78478478   84.98498498\n",
      "   85.18518519   85.38538539   85.58558559   85.78578579   85.98598599\n",
      "   86.18618619   86.38638639   86.58658659   86.78678679   86.98698699\n",
      "   87.18718719   87.38738739   87.58758759   87.78778779   87.98798799\n",
      "   88.18818819   88.38838839   88.58858859   88.78878879   88.98898899\n",
      "   89.18918919   89.38938939   89.58958959   89.78978979   89.98998999\n",
      "   90.19019019   90.39039039   90.59059059   90.79079079   90.99099099\n",
      "   91.19119119   91.39139139   91.59159159   91.79179179   91.99199199\n",
      "   92.19219219   92.39239239   92.59259259   92.79279279   92.99299299\n",
      "   93.19319319   93.39339339   93.59359359   93.79379379   93.99399399\n",
      "   94.19419419   94.39439439   94.59459459   94.79479479   94.99499499\n",
      "   95.1951952    95.3953954    95.5955956    95.7957958    95.995996\n",
      "   96.1961962    96.3963964    96.5965966    96.7967968    96.996997\n",
      "   97.1971972    97.3973974    97.5975976    97.7977978    97.997998\n",
      "   98.1981982    98.3983984    98.5985986    98.7987988    98.998999\n",
      "   99.1991992    99.3993994    99.5995996    99.7997998   100.        ]\n"
     ]
    }
   ],
   "source": [
    "# Generamos vectores que barran todo el dominio\n",
    "x = np.linspace(-100, 100, 1000)\n",
    "y = np.linspace(-100, 100, 1000)\n",
    "print('x= ', x)\n",
    "print('y= ', y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c6b59e-7c03-4a8c-8c75-aecaa2b3fe02",
   "metadata": {},
   "source": [
    "[numpy.meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html)\n",
    "\n",
    "[numpy.ravel](https://numpy.org/doc/stable/reference/generated/numpy.ravel.html)\n",
    "\n",
    "[numpy.c_](https://numpy.org/doc/stable/reference/generated/numpy.c_.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1d60d267-bce7-40d4-b689-4826bd8a69b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluamos todos los valores de las variables\n",
    "x_ax, y_ax = np.meshgrid(x, y)\n",
    "vals = np.c_[x_ax.ravel(), y_ax.ravel()]\n",
    "fx = np.reshape([funcion_prueba(val) for val in vals],(1000, 1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21c36a7-9eb8-4965-b9a7-418888faec93",
   "metadata": {},
   "source": [
    "[matplotlib.pyplot.gca](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.gca.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1367e8c7-e4af-47f7-93a6-55bfe0cdae0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b_/3xd9dj1x65l095882xsl1nlr0000gn/T/ipykernel_20386/3236261982.py:3: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
      "  ax = figure_3d.gca(projection=\"3d\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'f(x,y)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representamos los resultados obtenidos\n",
    "figure_3d = plt.figure(figsize=(8,6))\n",
    "ax = figure_3d.gca(projection=\"3d\")\n",
    "ax.plot_surface(x_ax, y_ax, fx, cmap=cm.coolwarm)\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\")\n",
    "ax.set_zlabel(\"f(x,y)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa806895-1d2f-4bda-86b4-d9ba8597f974",
   "metadata": {},
   "source": [
    "#### ------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e912ba-6bd6-4a1c-9bc2-442628178da2",
   "metadata": {},
   "source": [
    "A partir de ahora veremos los siguientes pasos:\n",
    "1. Definición del problema y generación de la población inicial\n",
    "- 1.1 Creación del problema\n",
    "- 1.2 Creación de la plantilla del individuo\n",
    "- 1.3 Crear individuos aleatorios y población inicial\n",
    "2. Función objetivo y operadores genéticos\n",
    "- 2.1 Función objetivo\n",
    "3. Incorporación de los operadores genéticos\n",
    "4. Últimos pasos: Algortimo Genético como caja negra\n",
    "- 4.1 Configuración algoritmo genético\n",
    "- 4.2 Resultados del algoritmo genético\n",
    "5. ¿Cómo conseguir resultados consistentes?\n",
    "6. Convergencia del algoritmo\n",
    "7. Para seguir aprendiendo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c56cea9-ef48-4084-a79c-e25d45222fc6",
   "metadata": {},
   "source": [
    "#### 1. Definición del problema y generación de la población inicial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987a1efc-5216-411b-a826-242fd96be59e",
   "metadata": {},
   "source": [
    "Se importan las librerías necesarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd25ac80-6bbd-401b-95dc-de908108df29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from deap import base\n",
    "from deap import creator\n",
    "from deap import tools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65cf7f7d-95bd-4bfe-bdd0-8a21c8034295",
   "metadata": {},
   "source": [
    "##### 1.1 Creación del problema ---------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c02cb79-d0a6-4a00-8193-5b36a536ca86",
   "metadata": {},
   "source": [
    "Creamos los objetos para **definir el tipo de problema de optimización** (maximizar o minimizar), con el método `create` de la clase `creator`. Crea una nueva clase llamada `name=FitnessMax` (podría ser otro), que hereda de `base=base.Fitness` y que tiene un atributo llamado `attribute=weights` que se añadirá a la clase cuando esta se cree (opcional) y que determina el tipo de problema de optimización que estamos definiendo (contiene una tupla con tantas componentes como objetivos tenga el problema y con un valor que indica si es maximización o minimización). En nuestro caso, un solo objetivo y maximización (porque 1.0 es positivo). El objeto `base.Fitness` contiene los atributos encargados de almacenar el **fitness** o desempeño de un individuo y otros [atributos de base.Fitness](https://deap.readthedocs.io/en/master/api/base.html#deap.base.Fitness), que los heredará nuestra nueva clase `FitnessMax`:\n",
    "* `values`: Una tupla con los valores de **fitness** de cada uno de los objetivos de nuestro problema. Contiene, por tanto, la calidad de cada individuo en cada uno de los objetivos de nuestro problema\n",
    "* `dominates`: Devuelve `True` si una solución es estrictamente peor que otra (tiene sentido en AGs de varios objetivos)\n",
    "* `valid`: Indica si el **fitness** de un individuo es válido. Se usa para saber el número de individuos que se tienen que evaluar en cada iteración. Si un individuo tiene el atributo `values` vacío, el atributo `valid` será `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "38ef88f9-3c39-43fe-b4d1-9d5c2577eb54",
   "metadata": {},
   "outputs": [],
   "source": [
    "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc96165-7ded-4064-ad61-119bd84aade3",
   "metadata": {},
   "source": [
    "Lo que realmente hemos hecho es:\n",
    "```\n",
    "# Importamos las librerías necesarias\n",
    "from deap import base\n",
    "\n",
    "# Generamos la clase\n",
    "class FitnessMax(base.Fitness):\n",
    "  weights = (1.0,)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1909fa1a-2f5f-4b77-b0f3-a1b7961d0138",
   "metadata": {},
   "source": [
    "##### 1.2 Creación de la plantilla del individuo ---------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3793b0c2-8558-4fe8-9e28-8c7e99c45f72",
   "metadata": {},
   "source": [
    "Creamos el **tipo de individuo** (el tipo de objeto de Python o plantilla que va a contener el individuo (lista, vector, etc) \n",
    "y sus atributos. Creamos la clase `Individual` que hereda de la clase `list` [listas en Python](https://docs.python.org/3/tutorial/datastructures.html) y que contiene el atributo `fitness` inicializado con el objeto `FitnessMax`: Un individuo es una lista con un atributo que almacenará la calidad o desempeño de si mismo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e882ca7-5af8-4553-8c6a-0bf672951b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "creator.create(\"Individual\", list, fitness=creator.FitnessMax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b4225c-5f6b-4b4f-86b3-6f7c7d70bcbc",
   "metadata": {},
   "source": [
    "Lo que realmente hemos hecho hasta ahora:\n",
    "```\n",
    "# Importamos las librerías necesarias\n",
    "from deap import base\n",
    "\n",
    "# Generamos la clase\n",
    "class MaxFitness(base.Fitness):\n",
    "  weights = (1.0,)\n",
    "\n",
    "# Generamos la clase Individual\n",
    "class Individual(list)\n",
    "  fitness = MaxFitness\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289228de-76d9-430d-b482-55c7aa774f97",
   "metadata": {},
   "source": [
    "##### 1.3 Crear individuos aleatorios y población inicial ---------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874ae073-c5d9-4d32-8505-9434d086d41b",
   "metadata": {},
   "source": [
    "Definición del **objeto caja de herramientas** o `Toolbox` [Toolbox](https://deap.readthedocs.io/en/master/api/base.html#toolbox) que contendrá, mediante \n",
    "**registro**, un conjunto de funciones utilizadas por el algoritmo durante \n",
    "su ejecución, como suelen ser:\n",
    "* Función para **crear los individuos** de forma **aleatoria**\n",
    "* Función para **crear la población**\n",
    "* **Operadores genéticos** (selección, cruce y mutación)\n",
    "* Función **objetivo**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76782a39-c72a-4445-8336-411c1200fca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "toolbox = base.Toolbox()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766597f4-1a91-4fc5-9db2-4e0bc1730aaf",
   "metadata": {},
   "source": [
    "En el `Toolbox` registraremos funciones necesarias para ejecutar el AG, a través del método `register` de la clase `base.Toolbox` que tiene los atributos:\n",
    "* `alias`: El nombre con el que registramos la función en el `Toolbox`\n",
    "* `function`: La función que estamos registrando\n",
    "* `argument`: Los argumentos que se pasan a la función"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff0e013-fe58-4503-873c-7d49fbcf8196",
   "metadata": {},
   "source": [
    "Registramos la función `attr_uniform` (Que será un método de la clase `toolbox`) que permite **crear individuos aleatorios** desarrollando una función (usaremos `random.uniform`) que nos permita generar un valor aleatorio para cada variable independiente (cada gen del comosoma), esto es, cada una de las posiciones de la lista con valores sujetos a las restricciones de nuestro problema (-100, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "abd798bf-ebba-4e4e-91d7-dde072730f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generación de genes\n",
    "toolbox.register(\"attr_uniform\", random.uniform, -100, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "67bf9c28-bf5f-4e4f-b5e7-9758a3b1b359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-37.970486136133474"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probamos y vemos que generamos un número leatorio entre -100 y 100 distinto cada vez\n",
    "toolbox.attr_uniform()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8330bf67-3bfe-4be1-af0e-f3a48e17db43",
   "metadata": {},
   "source": [
    "Si se quiere detalle de como hace esto Python, investigar el método `partial` del módulo nativo `functools`: [functools](https://docs.python.org/3/library/functools.html) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef27d6f-c86e-40be-af18-734134431527",
   "metadata": {},
   "source": [
    "Lo que realmente hemos hecho hasta ahora:\n",
    "```\n",
    "# Importamos las librerías necesarias\n",
    "import random\n",
    "\n",
    "# Definimos la función correspondiente\n",
    "def funcion(a=-100, b=100):\n",
    "  return random.uniform(a,b)\n",
    "\n",
    "# Definimos la clase\n",
    "class Toolbox(object)\n",
    "  attr_uniform = function\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035176f8-5020-4c52-ad7e-665f22f442ba",
   "metadata": {},
   "source": [
    "Creamos el individuo completo llamando a la función que genera cada uno de los individuos tantas veces como variables independientes tengamos. Eso lo hacemos registrando la función `individual` que, a su vez, llama a la función `tools.initRepeat` que tiene los siguientes parámetros:\n",
    "* `container`: Tipo de dato donde se almacenará el resultado del argumento `func` -> En nuestro caso, la clase `creator.individual` creada anteriormente\n",
    "* `func`: Función a la que se llamará `n` veces -> En nuestro caso `toolbox.attr_uniform` creada anteriormente para crear cada gen\n",
    "* `n`: Número de veces que se llamará a la función `func`-> En nuestro caso 2 cromosomas a crear\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9962beb1-0764-4915-850d-835d7bf81de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generación de inviduos\n",
    "toolbox.register(\"individual\", tools.initRepeat, creator.Individual,\n",
    "                 toolbox.attr_uniform, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9676786f-6d1b-4c54-bd3c-3a67089b881b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[45.96634965202574, 79.7676575935987]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejemplo de la generación de un individuo aleatorio\n",
    "individuo = toolbox.individual()\n",
    "individuo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d7767615-a113-49ba-9875-f2033a2ba0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recordar que individuo es una lista que tiene un atributo fitness\n",
    "# donde se almacena la calidad del mismo, que al principio será\n",
    "# vacío, porque aun no ha sido evaluado\n",
    "individuo.fitness.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a8e93e0-c632-4e9e-a3ee-a9e6b466859d",
   "metadata": {},
   "source": [
    "El procedimiento para **crear una población** es análogo a la creación de un individuo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c4b95bf0-6821-4f2d-9bbe-e1f969d6adab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generación de población\n",
    "toolbox.register(\"population\", tools.initRepeat, list,\n",
    "                 toolbox.individual, 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de989e72-9240-49f8-9566-22de3f44c7bc",
   "metadata": {},
   "source": [
    " Como se puede ver, se registra la función `population` que utiliza `initRepeat` para llamar 32 veces a la función `Individual` (una vez por cad individuo que formará la población inicial). El resultado se guarda en una `list` que contiene la población inicial generada. Se recomienda elegir números **divisibles entre 4**, ya que algunas operaciones genéticas del módulo `deap` pueden dar problemas si no se cumple este requisito"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0de3dcdc-e574-465e-bbf9-12cc9048e555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[36.79678638308826, -5.571456909457325], [-79.85975838632683, -13.165632909243257], [22.177394688760316, 82.60221064757965], [93.32127355415176, -4.5980446894566], [73.061985554328, -47.90153792160812], [61.005565402604475, 9.739860767117861], [-97.19165996719622, 43.94093728079082], [-20.235291555146247, 64.96899542964661], [33.63064024637018, -99.77143613711435], [-1.284426706935065, 73.52055509855617], [-51.2178246225736, -34.95912745052199], [74.09424642173093, -61.78658169952189], [13.50214812413438, -52.27681427695596], [93.50805005802866, 60.63589385597402], [-10.406085712885925, -83.91083628949292], [-35.98907906549084, 1.5881285041147777], [86.56676484538136, -78.18843081377926], [10.253449218110248, 41.312281973377935], [9.488182265684756, 62.8933726582672], [8.056721394064795, 92.76770919476019], [20.63712559227659, 17.52341283508727], [-11.00219474489677, 19.257372316621257], [-23.019770805467914, 15.130202832977702], [-41.934099519448395, -62.121734289128774], [-62.654094348888975, 22.554635973721332], [31.331877797925756, -4.693801598123855], [-82.03512776088127, 51.52078439328736], [75.35407416455496, 84.67620318925611], [68.4920446280365, 79.6346242715758], [84.61648796403537, 8.119984989610884], [-21.740789953075023, 41.05667997088125], [-44.87317573757457, 62.32574170157571]]\n"
     ]
    }
   ],
   "source": [
    "# Ejemplo de creación de una población. Se ve que es\n",
    "# una lista de 32 listas (una por cada individuo)\n",
    "poblacion = toolbox.population()\n",
    "print(poblacion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fac45b51-d1ef-4c60-b4e7-926812999925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[36.79678638308826, -5.571456909457325]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cada individuo tiene 2 componentes\n",
    "poblacion[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8c89e0f0-ae16-4372-aa5e-4b69861a2362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poblacion[0].fitness.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6f0c8b-5e45-4026-848a-fac81d279f59",
   "metadata": {},
   "source": [
    "Respecto al **tamaño de la población**, en este caso, el criterio que seguiremos es que, como mínimo, sea de **un orden de magnitud** superior al **número de variables independientes** y como máximo 2 * ese orden de magnitud. En nuestro caso, hay dos variables independientes (población entre 20 y 40 individuos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7aa302f-a7f1-413e-9d7e-d56947300264",
   "metadata": {},
   "source": [
    "#### 2. Función objetivo y operadores genéticos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7678a949-a4d8-46c4-916b-3fc36876a626",
   "metadata": {},
   "source": [
    "Ahora crearemos nuestra **función objetivo** y la **registraremos** en nuestro `Toolbox`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca40492-84cb-4d6a-a231-c33da7eceff3",
   "metadata": {},
   "source": [
    "##### 2.1 Función Objetivo ---------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed9e187-fda1-4c12-bc2b-d6f6d45138df",
   "metadata": {},
   "source": [
    "Dividimos funciones objetivo en:\n",
    "* F.O. codificadas en Python: [math](https://docs.python.org/3.6/library/math.html) o [scipy](https://docs.scipy.org/doc/scipy) (en nuestro ejemplo lo haremos así)\n",
    "* F.O. que llaman a un programa externo para obtener el desempeño del individuo, para modelo muy complejos: [módulo nativo os](https://docs.python.org/3/library/os.html) o [módulo subproccess](https://docs.python.org/3.6/library/subprocess.html) (no lo usaremos en nuestro ejemplo)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "23c9f31f-3a4c-4831-aa36-ab80ee46a4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def funcion_objetivo(x):\n",
    "    \"\"\"\n",
    "    Función objetivo de nuestro problema\n",
    "    \"\"\"\n",
    "    for i in range(len(x)):\n",
    "        if x[i] > 100 or x[i] < -100:\n",
    "            return -1,\n",
    "    res = math.sqrt(x[0]**2 + x[1]**2)\n",
    "    return res,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8985c3a6-5566-4aa6-8a1a-fcb65311f7f3",
   "metadata": {},
   "source": [
    "Si una solución **no cumple las restricciones** (una de las dos variables independientestoma valores fuera del dominio de la función) debe ser **descartada** (devolvemos -1). A esto se le llama **aplicar la pena de muerte**.\n",
    "\n",
    "Al tratarse de una función de **maximización** las soluciones válidas solo aportarán valores de la función objetivo **positivas** y, por lo tanto, un -1 **penaliza totalmente el resultado**. La **pena de muerte** hace que **un individuo no participe en las operaciones de cruce y mutación** y, por lo tanto, **sus genes no se utilizarán para generar las siguientes generaciones**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd68d93e-9d0f-4b3c-9985-5d86794da64e",
   "metadata": {},
   "source": [
    "Ahora, **registramos la función de fitness**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "552daa96-2941-4961-93a0-ddce4402643d",
   "metadata": {},
   "outputs": [],
   "source": [
    "toolbox.register(\"evaluate\", funcion_objetivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c54ddcce-582d-4f08-8cf3-2d72ffb8091c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92.06402390891836,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejemplo de uso 1\n",
    "funcion_objetivo(individuo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "149a0416-d8cc-49ac-813c-a7bfa88ec283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92.06402390891836,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejemplo de uso 2\n",
    "toolbox.evaluate(individuo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d44e3a30-5fef-44a1-9708-390385454f5a",
   "metadata": {},
   "source": [
    "No olvidar que la **función fitness devuelve una tupla** ya que la solución es generalista para un problema genérico **multiobjetivo**, aunque en nuestro caso solo sea uno"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81794642-cb29-43c4-87de-ba16e8d6d5f7",
   "metadata": {},
   "source": [
    "**IMPORTANTE!!:** Sed conscientes de la relevancia de **codificar de manera eficiente la función objetivo**. Se ejecutará un gran número de veces y cualquier ahorro de tiempo cuenta. **Evitar bucles** o **condiciones que puedan dejar colgado el algoritmo** (siempre que se pueda)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06fe488b-41de-4fdf-ab26-e68cc0357e25",
   "metadata": {},
   "source": [
    "#### 3. Incorporar los operadores genéticos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a2610b-dfbc-48a5-8a27-f03e098aa059",
   "metadata": {},
   "source": [
    "* **Selección**:`selection`. Procedimiento por el cual se seleccionan los individuos que participarán en las operaciones de cruce y mutación. Procedimiento siempre **elitista**: Un individuo tendrá mayor probabilidad de dejar descendencia si su `fitness` es más adecuado al problema de optimización (razonamiento en línea con la teoría evolutiva de Darwin) -> Las probabilidades de dejar descendencia en las futuras generaciones crecen cuando crece la adaptación del individuo al medio. **OJO CON LOS SUPERINDIVIDUOS** \n",
    "* **Cruce**: `mate`. Operación probabilística que permite que dos individuos seleccionados **crucen** o **intercambien** su información genética para crear dos nuevos individuos\n",
    "* **Mutación**: `mutation`. Operación probabilística que permite que un individuo seleccionado **modifique** su información genética para crear un nuevo individuo.\n",
    "\n",
    "Ni para la probabilidad de cruce ni para la de mutación existen valores óptimos que sirva para todos los problemas. Tendrán que ajustarse a cada problema.\n",
    "\n",
    "Destacar que tanto el **cruce** como la **mutación** son el **motor para explorar y explotar zonas del espacio de búsqueda**."
   ]
  },
  {
   "attachments": {
    "45d31de3-d523-4ad0-9e71-d264fd22a4b7.png": {
     "image/png": 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hSpDawfdFaBC8+Wg9sgaPpuP/OT/WXtZQ/+tGD/oL8F2jf40yGSEB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\n3idvkVK4v+YAAAAASUVORK5CYII=\n"
    }
   },
   "cell_type": "markdown",
   "id": "36b814fd-dcc5-42bb-8646-5b0eb230e207",
   "metadata": {},
   "source": [
    "![AGs.png](attachment:45d31de3-d523-4ad0-9e71-d264fd22a4b7.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8918b37-1825-49b0-8292-520b000d218f",
   "metadata": {},
   "source": [
    "La librería `deap` tiene **gran variedad de algoritmos** de **selección**, **mutación** y **cruce**, aunque también nosotros nos podemos crear nuestros propios algoritmos (no lo haremos). La aplicación de cada una de ellas dependerá del problema en cuestión. Para ver cual es la más adecuada para cada caso, consultar [Algoritmos de selección, mutación y cruce de deap](https://deap.readthedocs.io/en/master/api/tools.html?highlight=tools) (algunos sirven cuando se usan variables continuas, otras cuando se usan variables discretas, etc)"
   ]
  },
  {
   "attachments": {
    "f85b133f-b0c1-4fff-bd9c-5c8f1b55ff6f.png": {
     "image/png": 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lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4xNzU2PC9leGlmOlBp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\nVYAZYvggtUEqudhzqbaDHpuk4MsNIdUcT6q0eJ0ESIAESCA9A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i4PNu4RtWqp1lphEPfD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2TAGo42skItzesG77LrUEpUg67UDy6Jg7UAY/2qO7nOK2hBDI9tgE6GIIt9WWa5kSbr4iHZa3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\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jH0qQimKM+2c7NncVbi9ed523Qsv20Wb7r8P9ELAyDRNck5mVL4mllPhSx29b4SZtzLY0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\nIRII8wxQ4VaINco8kUCOCITpHPyy4NrKblO/vsL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IcFccyTVIH/UuPgzkOLZoU5hxDMG8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\nIECAAAECBAjMROD/jL9OLkBdwLsAAAAASUVORK5CYII=\n"
    }
   },
   "cell_type": "markdown",
   "id": "d3162c7f-e3f7-4a29-b1e6-ebe00ad9602f",
   "metadata": {},
   "source": [
    "![Algoritmos Deap.png](attachment:f85b133f-b0c1-4fff-bd9c-5c8f1b55ff6f.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bf1725-9f72-4e3c-8c9c-58db1933493a",
   "metadata": {},
   "source": [
    "**CRUCE** ---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7f156d3f-8248-4842-ab9a-0aea82a9585e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Registro de operacion genética de cruce\n",
    "toolbox.register(\"mate\", tools.cxOnePoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87309448-24f8-4ba7-be55-64560df9b8ef",
   "metadata": {},
   "source": [
    "Utilizamos `cxOnePoint(ind1, ind2)` o cruce en un punto que ya conocéis [cxOnePoint](https://deap.readthedocs.io/en/master/api/tools.html#deap.tools.cxOnePoint), selecciona de manera aleatoria un punto (una posición) dentro de los individuos **ind1**, **ind2** para hacer un intercambio entre la información genética de dos individuos (padres) seleccionados"
   ]
  },
  {
   "attachments": {
    "5ef0b9e3-69c3-4286-b7d6-6b4d88ad6624.png": {
     "image/png": 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ZAhEP0vYMOTQUBEAABEAABEDAtQQgkFzbtWgYCIAACIAACIBAVQlAIFWVHO4D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\nFQRAAARAAARAwMYE/j9+5+hzIG4iKQAAAABJRU5ErkJggg==\n"
    }
   },
   "cell_type": "markdown",
   "id": "65b45e7e-55fa-49b4-825c-3f4165fe4551",
   "metadata": {},
   "source": [
    "![Cruce.png](attachment:5ef0b9e3-69c3-4286-b7d6-6b4d88ad6624.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e0045d-db6e-4ef0-ab29-955d2343b517",
   "metadata": {},
   "source": [
    "En este caso, al ser la longitud de los individuos 2, solo existe un posible punto de cruce (el cruce será simplemente intercambiar los valores de x e y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c00b9e3-9a8d-44c6-90f8-f365b4530002",
   "metadata": {},
   "source": [
    "**MUTACIÓN** ---------------------------------------------\n",
    "\n",
    "Utilizamos el operador `mutGaussian` (mutación Gaussiana) con una media de 0 y una deviación típica de 5 (son solo de ejemplo y no garantizan ser los más adecuados. Posiblemente habrá que probar valores distintos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "52f3f096-a0f6-4d21-bcfb-debbfea4e65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toolbox.register(\"mutate\", tools.mutGaussian, mu=0,\n",
    "                 sigma= 5, indpb=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1aca47-8f4c-4bfc-8716-beda31ad7668",
   "metadata": {},
   "source": [
    "`MutGaussian(individual, mu, sigma, indpb)` [MutGaussian](https://deap.readthedocs.io/en/master/api/tools.html#deap.tools.mutGaussian) consiste en mutar, con una probabilidad `indpb` cada uno de los genes del individuo `individual`, sumando a cada uno de ellos una cierta cantidad aleatoria. Esta cantidad se genera según una función de probabilidad Gaussiana (o normal), de media `mu` y desviación típica `sigma`"
   ]
  },
  {
   "attachments": {
    "3285c96e-9f38-49c1-a707-c8e8da282c0a.png": {
     "image/png": 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yG1/w+++/F10X6mXp1hui+0zGgu3oNibelF8TmjYV9aeffvICEQ0bNswThxaI\nx7y3urY0vsmEc5sm/MMPP8TydGsW0e1DYtcdOWmPgMqnsNR9MdN6evNpP8eyPVxcXY4QgECACARv\n2ScjggAEIAABCECgIwT8wXtUTHWkuVjd9gZZ8QfgsX01M0knnnhiQuAajdgaUaETq6rRYBPuX3zx\nxbF7dmIBdJqamhLy0l2MGTMmoU2ddpuuSsb328NPRXHCeDoSvMe2KtFX0NhHo9cm8Ez1IPmyn+uv\nPVxcXY4QgEBwCLCPZXBswUggAAEIQAACOSEQNGF54IEHxsSMCZuKioqITjFt81nV2xjRtY0J9Y45\n5piEOrfddlvCfQ3iE7HIpx1J/iiz1dXVEZ0O2pEmY3XbI6ByKSznzZuXwMtskUk04HzZz4FpDxdX\nlyMEIBAcAkyFDZD3mKFAAAIQgAAEckEgSFNh7Xks+I5FaFXvYezxBg4cKJMmTWq1J6MVsCmwO++8\nc0I0WFv7aO1Y4B2X7NqC+/iTRZK1gD5WJ/6zwgoreFNrR44cGZtO66+rQssro97O2K0NNthA7r77\nbrEptamSvtp5+2laAKCjjjpKVJC2KtqeKZ+5nAprA/LvC3rVVVfJ+PHjW401PiNf9nN9tIeLq8sR\nAhAIEIHgaFxGAgEIQAACEIBALggEzWNpz3Tssce28pb16tXLy9c1kZE33ngjcs8990ROOeWUiK79\na1X23HPPTYrGptXqa1VWH/OY7rPPPhGNDJu0zQkTJrRqTwVZ5IILLoioGPY8mPPnz4+o4Io88cQT\nEd3+JKJCNlbnnXfeSdpuezxzufRY2qB065nYOI2bjenvf/975Nlnn42oyIzoetLIHXfc0Wr8+bKf\nddQeLq0GSAYEIFB0AngsAyTyGQoEIAABCEAgFwSC5rG0Z7KAOKNHjxbzCGabbLuSRx55xPO2+evW\n19fLgAEDRIWe/1baa/Nmvvzyy60ixFp02LXXXlssem170muvvSYWfdaf2uOZy7XH8oorrhD7frSV\nzjzzTLnwwgsTiuTLftZJe7gkDI4LCEAgEAQQloEwA4OAAAQgAAEI5I5AEIWlPd0vv/ziRW29//77\nM3pYDS4jf/7zn+WMM84Q9TImraPr/0Q9bLF76v0Si/5qUWhtv0z7LFy4UD744AOxKLL+ZBFmdV2m\nFz02/p6uRxT1nspNN90Un5323KLU2tRR/x6ZVrE9AirXwtJ42FTh7777LuWz2Pfn8ssvb3U/H/Zr\nL5dWgyMDAhAoPoGi+0wZAAQgAAEIQAACOSWg3qaE6Y42NTYXafDgwbF2LUKrTQdtT7r11lsjG220\nUUQ9hrH29I0odm4Bc7bZZpuIev7abP7mm2+O1bH61t4rr7yStI4KqsgzzzwT2XfffRPqWD2bhpsq\n/fe//41sttlmEZu2Gz/G+HMb76abbhq55JJLIrNmzUrVVKQ9/IyT68v6sem3/pRtu7Nnz47svffe\nsXZd+3bs27dv5IUXXvB3kXCdK/u5RrMdv6vHEQIQCBYBPJb6/6IkCEAAAhCAQLkR+Pnnn2XBggWi\nkVWTes/a87yNjY2xPR7NI2ceuI4kfSWSadOmyccff+xNkVVRIxbUZ9111/X2p0zXtk1Xtbou3XDD\nDXLkkUe6y6RH25dx1KhRngfTFbjyyitFo8G6y5RHm8ar0Wy9KbLmGTUGFkzIAhPV1tamrOdutIef\nBTyyaag27n79+nnBiFx77tiedq3u3Llz5ZNPPvGep6amRlZccUUvmJKdZ5I6aj/XR3vH7+pzhAAE\ngkEAYRkMOzAKCEAAAhCAAASyIGDrIG2NpEsm7GydZSaiSD2QYusgXbrmmmvkuOOOc5ccIQABCECg\nHQQQlu2ARhUIQAACEIAABIpLYObMmaJTKBMG8eOPP3qBfBIyfRfff/+9rLPOOp63zt16++23vcBC\n7pojBCAAAQhkTwBhmT0zakAAAhCAAAQgEAACyy67rOh6wdhIxo4dK7plScqpv++9954cfvjhYkeX\nbF9HC0pjgYJIEIAABCDQfgIIy/azoyYEIAABCEAAAkUkoHtRyn333ZcwApsKq4F/PA+krUm07Ugs\nAupHH30kGpQmoaxdWP299tqrVT4ZEIAABCCQHQGEZXa8KA0BCEAAAhCAQEAIWICizTff3Auo054h\nnX322XLBBRe0pyp1IAABCEDARwBh6QPCJQQgAAEIQAACpUNg+vTpMmHCBLnrrrtk0aJFGQ18zJgx\ncuqpp8puu+2WUXkKQQACEIBAegIIy/SMKAEBCEAAAhCAQMAJWETYe++9V959912ZOnWqF5xH9670\ntltZeeWVxX023HBD2WCDDQL+NAwPAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwM\nBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAAB\nCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkR\nQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQg\nAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAA\nBCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgG\nyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAE\nIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCA\nQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0Y\nMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAAC\nEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUAR\nQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQ\ngAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAA\nAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiW\nns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEI\nQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAA\ngUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwM\nBgIQgAAEIAABCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAAB\nCEAAAhCAQOkRQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQgAAEIAABCEAAAhCAQOkR\nQFiWns0YMQQgAAEIQAACEIAABCAAgUARQFgGyhwMBgIQyITAh1/OkbufnJZJUcrkgcC+O6ws6wzr\nK3c99bV89MXcPPRAk5kQ+Mvx63vFsEMmtPJXBjvkj202LWOHbGjlryx2yB/bTFtee9U+st+vV8m0\neE7LISxzipPGIACBQhAwYXnuNe8Xoiv6SELg/OPXiwnLe56alqQEWYUg8PDlW3vdmLDEDoUgnrwP\n7JCcS6FznR3OvuY9+fhL/uBVaP6uP+zgSBTvuNawPuIEfqFHgbAsNHH6gwAEOkwAYdlhhB1qAGHZ\nIXw5q+xe4BCWOUParoawQ7uw5bySswPCMudos2oQO2SFKy+FEZZ5wUqjEIBAuRJAWBbXsgjL4vJ3\nvbsXOISlI1KcI3YoDnd/r84OCEs/mcJeY4fC8k7WG8IyGRXyIAABCKQggLBMAaZA2QjLAoFO0417\ngUNYpgGV59vYIc+AM2ze2QFhmSGwPBXDDnkCm0WzCMssYFEUAhCAAMKyuN8BhGVx+bve3QscwtIR\nKc4ROxSHu79XZweEpZ9MYa+xQ2F5J+sNYZmMCnkQgAAEUhBAWKYAU6BshGWBQKfpxr3AISzTgMrz\nbeyQZ8AZNu/sgLDMEFieimGHPIHNolmEZRawKAoBCEAAYVnc7wDCsrj8Xe/uBQ5h6YgU54gdisPd\n36uzA8LST6aw19ihsLyT9YawTEaFPAhAAAIpCCAsU4ApUDbCskCg03TjXuAQlmlA5fk2dsgz4Ayb\nd3ZAWGYILE/FsEOewGbRLMIyC1gUhQAEIICwLO53AGFZXP6ud/cCh7B0RIpzxA7F4e7v1dkBYekn\nU9hr7FBY3sl6Q1gmo0IeBCAAgRQEEJYpwBQoG2FZINBpunEvcAjLNKDyfBs75Blwhs07OyAsMwSW\np2LYIU9gs2gWYZkFLIpCAAIQQFgW9zuAsCwuf9e7e4FDWDoixTlih+Jw9/fq7ICw9JMp7DV2KCzv\nZL0hLJNRIQ8CEIBACgIIyxRgCpSNsCwQ6DTduBc4hGUaUHm+jR3yDDjD5p0dEJYZAstTMeyQJ7BZ\nNIuwzAIWRSEAAQggLIv7HUBYFpe/6929wCEsHZHiHLFDcbj7e3V2QFj6yRT2GjsUlney3hCWyaiQ\nBwEIQCAFAYRlCjAFykZYFgh0mm7cCxzCMg2oPN/GDnkGnGHzzg4IywyB5akYdsgT2CyaRVhmAYui\nEIAABBCWxf0OICyLy9/17l7gEJaOSHGO2KE43P29OjsgLP1kCnuNHQrLO1lvCMtkVMiDAAQgkIIA\nwjIFmAJlIywLBDpNN+4FDmGZBlSeb2OHPAPOsHlnB4RlhsDyVAw75AlsFs0iLLOARVEIQAACCMvi\nfgcQlsXl73p3L3AIS0ekOEfsUBzu/l6dHRCWfjKFvcYOheWdrDeEZTIq5EEAAhBIQQBhmQJMgbIR\nlgUCnaYb9wKHsEwDKs+3sUOeAWfYvLMDwjJDYHkqhh3yBDaLZhGWWcCiKAQgAAGEZXG/AwjL4vJ3\nvbsXOISlI1KcI3YoDnd/r84OCEs/mcJeY4fC8k7WG8IyGRXyIAABCKQggLBMAaZA2QjLAoFO0417\ngUNYpgGV59vYIc+AM2ze2QFhmSGwPBXDDnkCm0WzCMssYFEUAhCAAMKyuN8BhGVx+bve3QscwtIR\nKc4ROxSHu79XZweEpZ9MYa+xQ2F5J+sNYZmMCnkQgAAEUhBAWKYAU6BshGWBQKfpxr3AISzTgMrz\nbeyQZ8AZNu/sgLDMEFieimGHPIHNolmEZRawKAoBCECg1IVlrx7V0rtHjYTUlAvrGuWXeQ0lZdRS\nFpbL9K6RQf27yYBlukgkEpEZs+tk+o+LZVFdU0nZwAbrXuCCLCxXGNhNunetkvqGsEybsTAt4y61\nlVJdZf8y2k7NzRFZvKS57UIFuht0O2Rrg1TYenavkuUHdJeB+m8nHI7InAUN8s3MhbJgUTD+7Tg7\nBElYdu9aKSsP7inL9K6Vygpp+f+bRfr/N9l9d+13xorLdZdlemk7lSH58ec6man/3zVnfvB+dwTR\nDv7vdHt/B+fKnv7x5PoaYZlrorQHAQiUNYFSFpbjxgyWo/dcI8E+Nz38uTz24vcJeUG+KDVhWVER\nku03HST7br+y9OlZmxTtGx/9JLc99pVMn7U46f0gZroXuCAKy359arzv+UZr9Y+hO/nyt+Sr71OL\ny83WGyCnHDBCzF7pUrMKm6MveE1mz61PVzTv94Nqh/bYwA+rSkX+7lsNkV23GCK99I9h/mR/nHl3\nyi/yl5v+p2LTf7ew184OQRCWo4YvI7spt7WH9vWEoJ+E/Q677v6p3h+1/Pfirzddd1mP/+or9pJQ\nqPW/i8++mSc3Pvi5fPndgvhqRT0Pkh2SgWjP7+Bc2TPZePKRh7DMB1XahAAEypZAqQrLPj2r5Z9n\nbiLdulQl2ObfT34l9z79TUJekC9KSVjau9gFx68na+kLXrrU1ByW0698N1AvaW2N2b3ABUlYGu9f\nbzZYDtppWKvv+f/9/e022R6w41DZa9uV2nrkhHsnX/62CtXiv1AHzQ4dsUE84GFDesrpB6+l3v2u\n8dlJz48479Wii3xnh2ILyxP3W1PGbjgoKaf4TPv/mwtUkL8/ZU58duz8pP3XlK02SN+OifsJ138g\nH0xN3k6swQKdBMUOyR63Pb+Dc2XPZOPJVx7CMl9kaRcCEChLAqUqLH+vLwpbJ3lRQFjm72u6z/Yr\nyf47DI11UN/YLC+9+6O+BDfodOQqGbPugARPzIzZi+X4i97QabKxKoE9cS9wQRGWyw/oJuN/u4YM\nX6VPUma5FJY2dfko9VgGYQpzkOzQURs4w/XtVSOXnbyBN/XS5dn0V5vOPPXb+V7Wqio8h63Q0/Ok\nHTrhlaJPy3R2KLawfOiyrRK8i4uXNHmiu59Oh7Vp4fHppzlL5IRL3pQl9a2nxj5w6VYxb6ex//L7\n+fKjll9GZ10MX6V3Qh+z5y6R4/T/txoai+w21ocLih3iObvz9vwOzpU93RgKcURYFoIyfUAAAmVD\noBSF5Zqr9JKLTxid1AYIy6RYcpJ584TNYi/Hv8yvl1OveFt+VlHpkq3p+/Mx68oaK/V2WTL+r2/I\n97ruMujJvcAFQViutlIvufD49XV9pC4kS5GyEZb/efE7ufmRL1K0JIES/kGxQy5s4IBfeML6MiLu\nDwTvTvlZLrvt41ZrA80DtGzfLvL5t8HxHBdbWD7wNxWEOp37f1/8IvdP/kb+N3Wuh9U8ybtuOUQO\n2WVYgii8/I5P5EX9Y5c/3X/pllKhlZ5+fYY8+Oy3MuuXJbEi9vvkj0eumzAr4Iyr3pHPvo6K/ljB\nIpy4fw/FtoP/0dv7OzhX9vSPJ5/XCMt80qVtCECg7AiUmrC0Fwr76//Q5Xt6tvhI19cM0JcxN8UM\nYZmfr6gFaLjtvF/FGr/xoany+EvTY9fuZMSw3iqKRrlLuURfoF99f1bsOqgn7gUuCMJy+00Hy3F7\nR9cOL2lolvv+O02+0HVffz5mvRi+bITloy+0LSxjjQbgJCh2yIUNDKf/BfzV/82Sy27/RCxgUpCT\ns0OxBc2Ov1peZqln8e2Pf06K6/h91pDtNhkcu/fgs99467tjGS0nG6/TX2b+VCff/rDIf8u73nOb\nFeVAnXLu0rX3T5GnXp3hLot2DIod4gF05HdwruwZP558nyMs802Y9iEAgbIiUGrC0tacHbtX9KXb\ngo784W9vydlHrKPRFaNrlxCW+fl6rjiou1x56kaxxq+8+1N59s0fYtfuxNaSXfaHDdylXDTxQ3nj\nw9mx66CeuBe4IAjLX60/QP5PA++8oiJkonoazSs8YqgK9vFLBTvCMr/fpFzYwEZ48gHDZYtRy3mD\nbWwKy+G6fnL+wsb8Dj4Hrbt/D8UWlukeZb01+sqEo5f+weXFd3+Qy+/4NF21VvctoIx5LV2a+OgX\n8sjz37nLoh2DaId8/g7OlT1zaTCEZS5p0hYEIFD2BEpJWFqI/ms1YE+PbtWeXR7TKX43PfyFXH/O\nJgjLPH9Ta2sq5O6LtohNO0v1vTlgJw0as83SoDHHXvS65ynI8/A63Lx7gQuCsLSHqamuSFjjhbDs\nsImzbqCjNrAO/33h5rEpls+9PVP+8e/Psh5HMSq4fw9BF5ZrrNxL/nri0mURT7463YsQmy0zf3TT\nv976kbz2wU/ZNpPz8kGzQ75/B+fKnrk0BMIylzRpCwIQKHsCqQRCEB/8mL1Wlx02W94b2ryFDXLs\nha97++8hLAtjrYtPXF/WXHlpMBlbr3T9A1Nj0/rsL9lH7rGaVNkmc5ren/qLTLjug8IMroO9uBe4\noAhL/+MgLP1ECn+drQ2W699Vrjtrk9hA/3zD+/LeZ8GINhobVIoT9+8h6MIyfsqyPUp7Zqx061Ip\nV5yyYWw5hQX3OUZ/t8Svw0yBKe/ZQbNDvn8H58KeuTYKwjLXRGkPAhAoawKlIiyHasTEv500OrYv\nX/xUTIRlYb6iq6t34CJdP2mbirtkkS3v1C1ett5gOdls5ACXrdP9GuSMq9+TGSWyl6V7gStHYVmn\nUTLf/Wy2/ttRL6iu2fzh5yUy46fFGpF0gXqTgxVYKch2yFZY2r6Jpx+8duzfxIkasXTQsl1l5Kp9\nZaCKzh5dK6VeI49+O3ORfDV9obz83iyx6bJBSM4OQReWbrsmx+zMq9+VT7+a5y7THofrFPMjdl/N\ni8jrCgfJsxwkOxTid3BH7elsmMsjwjKXNGkLAhAoewKlIiwv/v0oWbMl2ujUb+fJaVe8G7MNwjKG\nIu8nG63dT049aO02I5bO+qVO95T7MGWgjLwPsh0duBe4chSWqXCYZ+b5d36Qf0/6uuj7JroxBtkO\n2QrLHcYMlmP2jK4Ht+czgd9VIyenStNV5F/x70/l82+IRpqKUXy+BUa6SNcdhyyajCbbJuSI816L\nL5JwbuvED911VS0v0l29lIP6d4stq3AFbZbFRTd/KPUNCHzHxB3z/Ts4W3u6ceX7iLDMN2HahwAE\nyopAKQjLrTdcTn6/33CPu21gfcoV7yRsDo+wLOxXcshy3eWq05YG8vH3/ofL3pKv1QNTSinIgsY4\nZitq/FEu27LFgkUNcuKlbxV970QbY5DtkK0NfjN2iBy086ptoW91r0E9lsde+FrCNj6tChUgw9kh\nqB5Ldb7L5f+3oaw8qEeMxj/vmyJPv5Y6kqt/mmWsYsuJ/Tv4660fy0dfRLc08d8vxnVQ7JDv38Ht\nsWeh7IGwLBRp+oEABMqCQNCFpa1/+eeZG0sf3cja0lOvTZdr75uawB5hmYAjrxfL9q2VYzXE/6g1\n+qXsp6k5LPc8PU23yfgmZZmg3XAvcOXisbQgGxuu1d8LALRwcaMsUQ9Md5162a93F9lkZP9W9gvK\netgg2yFbYbnfDqvIb7dfOfZVNw/xW5/Mllfe/0m+/H6BVGuAplUG95DfjVtF+vfpEiv3X127fM29\nU2LXxThxdgiqsDxgRw0Stu3SIGEffzVHzr76/TZR2WyLsw4b2WYZu/nEy9/LvzQacxC2hAmCHQrx\nO7g99kxryBwVQFjmCCTNQAACnYNA0IXlYbut6m2EbdawF2SLMrpgUVOCcRCWCTjydmFbiZyrW7s4\nkW8dfaUvyK99OEvGbjjIm1oW3/kLOs3y73dmH/o/vo1CnbsXuHIRlum42cvSn49ZNxZoycqP/+sb\n8v2PxV1zGWQ7ZCss99l+Jdl/h6ExU5x8+Vv676W1J9/+CHDdWZuq8K/yyi6qa5Lfnf1SrF4xTpwd\ngigstxw9UP7wuxExLDbF2NjaPpXpUr8+Nd7U2eqqCrE/ki2/bDcZN2YFWVFnYcSn+yd/I3c88VV8\nVlHOg2CHfP8O7og9C2EUhGUhKNMHBCBQNgSCLCwt0MXVp20cCxZz/QNTZNIrrac6ISzz/3U0b9c1\nZyz1HJv35Y4nvpQHn43u9VZREVIPworqoVlFKvXcpavv/Uwmvz7TXQb26F7gOouwNEP4hc9lt38s\nL2kAmWKmINshW2G521ZDvDV9judBf3w55R6Wh+46THbbakVXVIXli7Korjl2XegTZ4egCcs1V+kt\n5x+7XmyNt+1l/Jd//U/e/fSXDiH67a9Xlv1+vUqsDWv3yPNflV/mNcTyinFSbDvk+3dwvuyZS1sh\nLHNJk7YgAIGyJxBkYbnbVivoi9lqMRv8Mr9eTND4U/w0Mrtv5eyv2Lc+9qW8/fHP/uKBunZR8IIq\naBysg3YeKr8Zu3TqWar1TP7pZj9qIJ+jL3jdNRPYo3uBC6odshU1mYC2l7qLTxgVK3rDg1N1GuD0\n2HUxToJsh2xtEL+RvLE8Xj3C01N4hHfefHk5Yo/VPeQmag7/8ysyd0FjMUzg9ensECRhacF3Ljx+\n/YSAO1ffo3+4eiM3f7iaoB789VZfJsb8ookfyhsfzo5dF+Ok2HbI5+/gfNszV/ZCWOaKJO1AAAKd\ngkCQhaXfo5KtQSY++rk88vz32VYraPlSEZbxEQFti5GT/vZWSk4nHzBcthi1XOz+/me96O03GssI\n4Il7getMwtK2j7kkbnP5iY9+of9eoh7oYpkoyHbIVliOHrGMTh1fN4ZywvXvy/tTku9jucfWQ+Tg\nXZYG+jn8vFeKGsDH2SEowtI8ZxeNXz9hGv7dT02Tu5/6Osa3oyf+YEv/uOtTee6tHzrabIfqF9sO\n+fodXAh7dgh8XGWEZRwMTiEAAQikIxBkYbntxoNk/G/XTPcIKe/n8q/ZKTvp4I1SEZa3X/Ar6dmt\n2nvaV/83Sy655eOUT37E7qvKzlsM8e7bvnwHnvuyLFEPcpCTe4HrTMLSHyXzT9e9Lx9MTS58CmW7\nINshW2HZX9fw3XTuZjF0k175Xq5/4PPYdfzJcRoQa/tNBntZNttivzNfjL9d8HNnhyAIy/59auUi\n9awv23dpgCMLsHPDg8lZthfW3tutpIGUlq6J/ZtODbe9RYuZim2HfPwOLpQ9c2U3hGWuSNIOBCDQ\nKQgEWViaASwiXcs2ZSntcdXpG8syvaJRYx9+7lu5b/I0r2wx1yilHKzvRqkIy3/o9iIrtQS4mDF7\nsYy/+M2k05Lt8U49eISMWXeg96QWcOmAc172PXXwLt0LXGcRlhYo5opTNkx4WT/w3JdaBcYqtKWC\nbIdshaWxu/W8MdK7R42Hcf7CBjnsvFelqSlxOn/fXjVy/TmbSo0GlLH0ue7Te2rcPr1eZoF/ODsU\nW1j26lHtiUoLsuPSM2/OlKvu/sxdpj1awJ6B/brKJ1/OS1m2tqZC/q7/Hgbr3pYuHXfxGzJjVjCC\nWRXTDrn8HZwLezr7FOqIsCwUafqBAATKgkDQhWUmkAnekwmljpU5/rdryHYbRz0q1pJtiWAh+eM9\nkfYHgJ02X0GO2H3putigbGOR7undi3Q5CMt9NQjJDpsNkidfnSnPvjVTZv2yJOHxbQqseZVXX7F3\nLP/tT2fLBTd+GLsu1kmQ7dAeYXnQzsN0bfLSoDz27+Gimz+Uet3+xZIJnrM10vKKA5dGJb3kto/l\n1fc7t6fM2Fjk1ktOGu1tyWLXlt746Cf5222fSFj3M06V/NuEHLLLMNl96xXFpvA/pH94fP3Dn2L8\nrQ2zwTF7rS7rr7F0feXP85bIEee9Jm10k6r7nOa7fw/FFJaZPFAmv4NzZc9MxpPLMgjLXNKkLQhA\noOwJICyLa+JS8ViOGGbRGNdPiPhq3sj/fTFH5s5v8LZKWGPl3rKcvqS5ZC9451z7nnz6VWpPgStb\n7KN7gQuCsFx+QDeZcPS6YpuGu9RDpyHXVle6Sy841eIlS4O7THplutw/+Vvv/nj9I8C2cX8EMA/z\n7LlLvJfkATqdcFCcV8YqzNFgV7/XNbPzFy5tL9ZRgU+CYoeO2sBhM9Hyj1M3lC41S21n/26+VpHT\ntbZSVtZ9LKsqlxr6k6/nyllXveeqF+3o7FBMQTNmvQFy6kFrZc3g309+Jfc+vXQP3SP3WM37g5dr\nqKExLNN/Wizz1IPcp2eNDBnQPRZ53MpYALhzr31fPv5yrqtStGMQ7JDJw2ciLHNlz0zGk8syCMtc\n0qQtCECg7AkgLItr4lIRlkZp3JjBcvSea2QMLAhRRjMdrHuBC4Kw3Hid/nLmoetkOnSv3JOvTpfr\n7p/qnTsPTSYNzFTRecW/P5Up0+ZnUjzvZYJih47aIB6UtXXGIWt7+yfG5/vPP9Q/0vz9zk+KvsWF\njcvZoZjCcrtNBsnx+2S/xt68krf+58sY3q03XE5+v9/w2HVbJw26Jvw2jSb+2IvBCPoWBDu0xcvd\ny0RY5sqers9CHRGWhSJNPxCAQFkQKAdhGb/+rxQiwcZ/cUpJWNq4h67QQ/bQaWWbjRyQ8Fd+90z2\nYva6Bvd5WKOLJtsM3pUL2tG9wAVBWBrjv520gXosl+4Hmo7XI89/KxMfjb5MmydsJ92+4lfrD/TW\nxYZ8i5TNIzNrzhJ9ef5OJr06Q/xTB9P1lc/7QbFDR23gZ7SGTj+2LXvWGto34ZZtLfK9ruN7VtcN\nPvrCd0WfeukG5+xQTGE5fGhvOe+YpXtWurGlO9456Su5779LPZZW3tracczy3nRX8/770+IlTfKm\nTrO9Y9LXMntOvf920a6DYIdMHj6T38G5tGcmY8pVGYRlrkjSDgQg0CkIlIOwrKwM6d5mVd4L8sLF\nTSVlt1ITlg6uTdNcpnet2B6ixn6eTqOcrWJljk6LLcXkXuCCICxzyc8CbwzSwCc91Ub27+THn5fI\nDz/XtQogk8s+O9JWudrBMTF7LKdTkZfpXeN5Jr/9YWEgbeHsUExh6Zjl8mh/Y1muf1dvCqwFsFqw\nqFFmzq4LxDTwZM9ZKnYo5d/BybjH5yEs42lwDgEIQCANgXIQlmkeMdC3S1VYBhpqOwbnXuDKTVi2\nA0VRq2CHouKPde7sUG7CMvaAJXKCHYpvKIRl8W3ACCAAgRIigLAsrrEQlsXl73p3L3AIS0ekOEfs\nUBzu/l6dHRCWfjKFvcYOheWdrDeEZTIq5EEAAhBIQQBhmQJMgbIRlgUCnaYb9wKHsEwDKs+3sUOe\nAWfYvLMDwjJDYHkqhh3yBDaLZhGWWcCiKAQgAAGEZXG/AwjL4vJ3vbsXOISlI1KcI3YoDnd/r84O\nCEs/mcJeY4fC8k7WG8IyGRXyIAABCKQggLBMAaZA2QjLAoFO0417gUNYpgGV59vYIc+AM2ze2QFh\nmSGwPBXDDnkCm0WzCMssYFEUAhCAAMKyuN8BhGVx+bve3QscwtIRKc4ROxSHu79XZweEpZ9MYa+x\nQ2F5J+sNYZmMCnkQgAAEUhBAWKYAU6BshGWBQKfpxr3AISzTgMrzbeyQZ8AZNu/sgLDMEFieimGH\nPIHNolmEZRawKAoBCEAAYVnc7wDCsrj8Xe/uBQ5h6YgU54gdisPd36uzA8LST6aw19ihsLyT9Yaw\nTEaFPAhAAAIpCCAsU4ApUDbCskCg03TjXuAQlmlA5fk2dsgz4Aybd3ZAWGYILE/FsEOewGbRLMIy\nC1gUhQAEIICwLO53AGFZXP6ud/cCh7B0RIpzxA7F4e7v1dkBYeknU9hr7FBY3sl6Q1gmo0IeBCAA\ngRQEEJYpwBQoG2FZINBpunEvcAjLNKDyfBs75Blwhs07OyAsMwSWp2LYIU9gs2gWYZkFLIpCAAIQ\nQFgW9zuAsCwuf9e7e4FDWDoixTlih+Jw9/fq7ICw9JMp7DV2KCzvZL0hLJNRIQ8CEIBACgIIyxRg\nCpSNsCwQ6DTduBc4hGUaUHm+jR3yDDjD5p0dEJYZAstTMeyQJ7BZNIuwzAIWRSEAAQggLIv7HUBY\nFpe/6929wCEsHZHiHLFDcbj7e3V2QFj6yRT2GjsUlney3hCWyaiQBwEIQCAFAYRlCjAFykZYFgh0\nmm7cCxzCMg2oPN/GDnkGnGHzzg4IywyB5akYdsgT2CyaRVhmAYuiEIAABBCWxf0OICyLy9/17l7g\nEJaOSHGO2KE43P29OjsgLP1kCnuNHQrLO1lvCMtkVMiDAAQgkIIAwjIFmAJlIywLBDpNN+4FDmGZ\nBlSeb2OHPAPOsHlnB4RlhsDyVAw75AlsFs0iLLOARVEIQAACCMvifgcQlsXl73p3L3AIS0ekOEfs\nUBzu/l6dHRCWfjKFvcYOheWdrDeEZTIq5EEAAhBIQQBhmQJMgbIRlgUCnaYb9wKHsEwDKs+3sUOe\nAWfYvLMDwjJDYHkqhh3yBDaLZhGWWcCiKAQgAAEIQAACEIAABCAAAQgEi0AooilYQ2I0EIAABCAA\nAQhAAAIQgAAEIFBKBBCWpWQtxgoBCEAAAhCAAAQgAAEIQCCABBCWATQKQ4IABCAAAQhAAAIQgAAE\nIFBKBBCWpWQtxgoBCEAAAhCAAAQgAAEIQCCABBCWATQKQ4IABCAAAQhAAAIQgAAEIFBKBBCWpWQt\nxgoBCEAAAhCAAAQgAAEIQCCABBCWATQKQ4IABCAAAQhAAAIQgAAEIFBKBBCWpWQtxgoBCEAAAhCA\nAAQgAAEIQCCABBCWATQKQ4IABCAAAQhAAAIQgAAEIFBKBBCWpWQtxpqSQLPeCcV9rGCDfhZERCL6\nX89wSKrrIxKqDEmkRvOssCZ/nWguPyEAAQhAAAIQgAAEIACBbAggLLOhRdnAEmhS8WgiscL7aWJS\npFE/8/WkSf/rG6mQ2gYtUREVlk6IVmgZJy71lAQBCEAAAhCAAAQgAAEItIMAwrId0KgSPALNkbBE\nIhGpqqj0RKVeqccyJItUWH767RRZqXsfGdJ7oKciI1UmNk2ELv14lUxhkiAAAQhAAAIQgAAEIACB\nrAkgLLNGRoUgEjAPZVjFZWXI5KJ5K8MqLCMyJ9IsTzw/SUYPHiajV19b9FLCKiydx7JSy1ZYZUsI\nyygHfkIAAhCAAAQgAAEIQCBLAgjLLIFRPPgEwjpEE44mLBfrYsq7nrhXRq84TMasM1oi6qpsUmFp\nZUyCxoSlicuoJtUTEgQgAAEIQAACEIAABCCQDQGEZTa0KFsSBGwabLO6H5foaDVej9yuwnIDFZab\nq7AMq+JsUjVpwtJEJcKyJEzKICEAAQgEjsCECRNSjqmteykrcQMCEIBAiRNAWJa4ARl+CwHzOLZM\nZW2OE5Z1mn/HpHtlQxWWW6w9WppVUTaqZ9KKq+MSYdmCjwMEIAABCGRHIBRKvX7C1vyTIAABCHQ2\nAgjLzmbxcn1e9ztcf887YblY88xreceT6rEcMky2MmGpefUt7wLVei/msXQuzHLlw3NBAAIQgEBO\nCSAsc4qTxiAAgTIggLAsAyPyCErAJyyb1H1p3srFessTluqxHLtWVFguiROW5rWsMFFp9U1lkiAA\nAQhAAAIZEEBYZgCJIhCAQKcigLDsVOYu44eNE5a2p6UJS/NYmrC886n7ZPSQobKtCkvbZqS+BYN5\nLJ2w1ICyErILEgQgAAEIQCADAgjLDCBRBAIQ6FQEEJadytxl/LDmdTRPpH4a44RlnWbd8VR0KqwJ\ny0a9NmFpRWPCUgP62HIYhKVCIUEAAhCAQEYEEJYZYaIQBCDQiQggLDuRscv6UX3CsrHFY2nC8s6n\nox7L7UZEhaXlmbCs1Y/nsURYKgkSBCAAAQhkQwBhmQ0tykIAAp2BAMKyM1i5EzyjBeWxbShNMNp5\nWE8W6flC/dzWIix/rcIyur9ltFyVlrNllSE96v8kxD6WSoEEAQhAAAKZEEBYZkKJMhCAQGcigLDs\nTNYu42dt0Gcz72PIFlGqSgypYlyiQvEXPb/lv/fJ+isMlXEqLE1AWjIB6i4iemH56EqDQoIABCAA\ngUwIICwzoUQZCECgMxFAWHYma5fxsyYKy4gKS40Kq0pxjirGiSYsNXjPjsOjwjImIltUJsKyjL8Y\nPBoEIACBPBFAWOYJLM1CAAIlSwBhWbKmY+DxBNoSluaxHKUeyx1aPJYIy3hynEMAAhCAQHsIICzb\nQ406EIBAORNAWJazdTvRs7UlLPFYdqIvAo8KAQhAoEAEEJYFAk03EIBAyRBAWJaMqRhoWwTaEpZ4\nLNsixz0IQAACEGgPAYRle6hRBwIQKGcCCMtytm4nerY2heXk+2Q9nQrLGstO9IXgUSEAAQjkmQDC\nMs+AaR4CECg5AgjLkjMZA05GoE1h2dYaS11wGdEgP7F1l8kaJw8CEIAABCDgI4Cw9AHhEgIQ6PQE\nEJad/itQHgAShKU+ku1J6aLC3pLGY2l7Xlpiu5EoB35CAAIQgEB6AgjL9IwoAQEIdC4CCMvOZe+y\nfdo2hSUey7K1Ow8GAQhAoFgEEJbFIk+/EIBAUAkgLINqGcaVFYEEYRlJ3McSj2VWKCkMAQhAAAIZ\nEEBYZgCJIhCAQKcigLDsVOYu34dtU1jisSxfw/NkEIAABIpEAGFZJPB0CwEIBJYAwjKwpmFg2RBo\nU1imWWMZ0TWWBO/JhjZlIQABCEAAYcl3AAIQgEAiAYRlIg+uSpRAm8KyLY+lPi/CskSNzrAhAAEI\nFJEAwrKI8OkaAhAIJAGEZSDNwqCyJdCWsJyownL9IexjmS1TykMAAhCAQGoCCMvUbLgDAQh0TgII\ny85p97J76raE5S0mLFcYKuNGjPamvMamvdqJJjyWUQ78hAAEIACBzAkgLDNnRUkIQKBzEEBYdg47\nl/1TJgpL3ceyMm4fyxaP5bjhCMuy/yLwgBCAAAQKRABhWSDQdAMBCJQMAYRlyZiqfAfa4jjUB3Rn\nGk3HO2+UkISlublaIs2VUlkRzQ5VRiRS0agXJie7SSRSIU1apUqvQk36w45atk4/c7RJPJZRJvyE\nAAQgAIHcEUBY5o4lLUEAAuVBAGFZHnYs6adwclJUREaTKUg7r9NPs4Sbu0qkqUoqQ6oeTXPaoWqx\nlqjT094qLKsShaV/H0s8lgqNBAEIQAACuSSAsMwlTdqCAATKgQDCshysWOLPkFpYLtEnU4EZ6aL6\nskrqF0fk559VTjbMkSFDq6S2OqR+zV5aJiTmv4x5LJMJS9ZYlvi3hOFDAAIQCBYBhGWw7MFoIACB\n4hNAWBbfBp1+BEuFpZ3Zp2XOq9TruQnLWgnposnPP5ot/7z2KenWc56cce5vpUf3WgmHe3hVdKYs\nwrLTf5MAAAEIQKBwBBCWhWNNTxCAQGkQQFiWhp3KepRLhaWbCtsy39VbQ6l3IzUqLEMy6ZF3ZKfd\nj5URw/vJK2/8W3r0qFbd2UMiWi2sp216LHW7EYL3lPXXiIeDAAQgUFACCMuC4qYzCECgBAggLEvA\nSOU+xKXCsrnlUc1jaeLSJrhG9KxGwhqn587bXpeDjjxSRq49TF594y6p7RLRdZfdPKdmIx7LFnYc\nIAABCECgEAQQloWgTB8QgEApEUBYlpK1ynSsbQtLk5jVsmhuRK775/Nyytkny1ZjNpTnXr5BaTRr\n4J5Kz2PZ1IawnNgSvGdHthsp028QjwUBCECg8AQQloVnTo8QgECwCSAsg22fTjG6qLC0abD2MU+l\n81hGPZghqZRvpi6Rv132oFx9w5Wyx45by4OPX6TV2i+pAABAAElEQVTldNuRsG5IosV0R5I2p8KO\n0uA9O4xgH0uFRoIABCAAgRwQQFjmACJNQAACZUUAYVlW5izNh0ktLMOezAw3Vcj778yWK/72oDz3\n3Ity2JHj5M8X/c4L82NaNKx7VzbXxAlLxRC/jyUey9L8XjBqCEAAAkEmgLAMsnUYGwQgUAwCCMti\nUKfPBAIRz1NpWX6PZVRyhlRevjT5Yzn6qKt16mtETjn9IDnwsM2kRsWkeSutWlO8x1Kz4oXlLToV\nFo+l8SVBAAIQgECuCCAsc0WSdiAAgXIhgLAsF0uW8HO0EpaRlqmwoaXCctLDb8lee/9JBg0ZKLdP\nvFQ23bK/hDUcbGO9hvbR/SzbDN4z+T5ZT6fCssayhL8kDB0CEIBAwAggLANmEIYDAQgUnQDCsugm\nYACJwlKj8ERsnaWmuMM9tz0v+x58qqy/7kby7NNXSZ8BurYy0ixL6iqlaxcVlqpF29puJKXHUrWr\n6ViTsCZnSRCAAAQgAIFMCCAsM6FEGQhAoDMRQFh2JmsH9FnbEpamLZt0q5F/XjlZfn/q6bL1JmPl\n2VcvlbnzF0if3roNSaRWzLHZprBM47HU+D9eQlgG9AvCsCAAAQgEkADCMoBGYUgQgEBRCSAsi4qf\nzo1AdMKrra+0T6LH0jTfwnkRufzSh+RPf/mL7LLdjvLo0+fLL3PnyDJ9arVut2hU2Da2G2lzjSUe\nSyVMggAEIACBbAkgLLMlRnkIQKDcCSAsy93CJfB8UWFpA20J3hM3FdaE5Q8zFsuF590uV11/k+z/\nmz3lzgfO0JKNOlO2Tu92l+bmSgm3JSzxWBpcEgQgAAEI5JAAwjKHMGkKAhAoCwIIy7IwY2k/RDph\n+ebrU+SM/7tGPvn4Oznu+IPlj3/ZXYVlgzRH5kpVqK96LaukSWUmayxL+3vA6CEAAQiUEgGEZSlZ\ni7FCAAKFIICwLARl+miTQDphee/dj8vxR18s66z1K/n974+W3fZeWZpDS2Rx/Q/So8sgbbta/ZcV\nS4Wl5iRsN5LGY2kOUhsDayzbNBM3IQABCEAgjgDCMg4GpxCAAASUAMKSr0HxCbQoSzuEw2EVhU0t\nEWGr9LpC/nrRo3LOOWfLdtuOk6cmXSLhULOEKhdoYd3hMtzLOzbqnpYxj6V9sVUl1ulnjjba5hpL\nLYuwLP5XgBFAAAIQKDUCCMtSsxjjhQAE8k0AYZlvwrSfnkCLsLSCtsoyEmrQia0haQpXyzfT6uTv\nl94j1153lxx6yA5y88Q/6BTYZqkILdQS1RJp6uZVShCWkYgKT12B2SIsJ/73Pll/CPtYpjcEJSAA\nAQhAIFMCCMtMSVEOAhDoLAQQlp3F0kF+Ticsdd8Q23rE9qcMhSp1amqlTH7mIznrjOs1gM8COe20\nI+SIo8ZIl65Wrk6FpSrH5q4SUTXaWB3nsfQJS/NYrr/CUBk3YrQ35dW605ouHC0eyyB/NxgbBCAA\ngYASQFgG1DAMCwIQKBoBhGXR0NOxI2DCUEwMVoRV5OlHZV8korIyFJLrb3hCTjj+Etlg9CZy5ZUT\nZMTatdJVnZQRDd4TFYe1nseyoa2osC0ey3HDEZaOOUcIQAACEOgYAYRlx/hRGwIQKD8CCMvys2np\nPZEKy4iqy1BFoy6ODKlo1LWVKiwblkTkz3+6S/566SWy52/2lltvPUtqu6ifsiIizeEmqa6yzUgs\n8k5IGnRRZWyNJR7L0vsOMGIIQAACJUYAYVliBmO4EIBA3gkgLPOOmA7SEvCmwjarRqxvKVor9eqC\nnPLpEpnwp+vkoUcekNNPP0QuvvhwFZTRImE9qdbpr6KeS50Pqz+7ty0sdY0lHssoO35CAAIQgEDH\nCSAsO86QFiAAgfIigLAsL3uW5tOYsNRIryJL7ES9lbUarrhS7rnrfTnvvJulorJezjz3UNl/v01s\nxqz30eWYmm8VNTqsysoG6d+2sGSNpXIiQQACEIBArgggLHNFknYgAIFyIYCwLBdLlvJzxAnLiAbs\naaivkdraCrnwvAfk7D/dILvusrWcf+HxstpqPTRfp76aBjVhWWXuy3n6WaLCchDCspS/A4wdAhCA\nQIkRQFiWmMEYLgQgkHcCCMu8I6aDdATCTbp9iLdestEL3NOsIV5/nNkgp592rdx5z51y8EH7yQ03\n/0E9lC3RXL1gP3quwX4k9Is2X6fCcshSYak5/n0sbbsRpsKmswT3IQABCEAgUwIIy0xJUQ4CEOgs\nBBCWncXSAX7OpsYmCYebpbqm0ttmxKbDXnfVC7qm8lapq18k50w4SE4Yv5PnqPTC9ai3UoPHqrJ0\nwtI8lissFZZ2X0Wo28eS7UYCbHyGBgEIQKBECSAsS9RwDBsCEMgbAYRl3tDScMYEdOHkooWLpKam\nq1TXVsrM7xvl5JOukLsfuEv2228/ueSyk2XwoErd4bLFY6lHE5Yh78dcvapvcyrsxJbtRnZku5GM\nTUJBCEAAAhBomwDCsm0+3IUABDofAYRl57N54J64ob5Oamq7yJJFIl26h+Qflz4uf/nLv7zpsRdf\nerwccuhYadJR20YkIe8/PffWZdqP+fqx4D3LxnkstVxlKMFjOUqD9+wwgn0sA2d8BgQBCECgRAkg\nLEvUcAwbAhDIGwGEZd7Q0nCmBJbULZKqylpp0i1G5s2JyEEHni1Pv/C0HHv0UXLa2YfI4OVro+sp\ndTJsSIP76E6W+okKzWgkWdtupEdKYYnHMlNLUA4CEIAABDIlgLDMlBTlIACBzkIAYdlZLB3o5wzL\n7FlLpH//bnLN5c/JRRdPlIbGhXLFNSfK/gdsJYubIlJT1aRi0nkszW9ZoU9kHktLERWWVSmFpa2x\nxGMZJcVPCEAAAhDIDQGEZW440goEIFA+BBCW5WPLEn6SJlmkM1rDDRW6tci58vzrL8sxhx8uZ/xx\nH1lxxS46Ddb2F2lo8VKax9K8lfZRcRmp8p67QTPsLGRzZnXNZsJU2Mn3yXo6FZY1lh4qfkAAAhCA\nQA4IICxzAJEmIACBsiKAsCwrc5bmwyxaOEe69+gr1//jNfnj+TdKdaiL3HjjcTJu97WlMaJKMeRE\npXkqTVjalFgL5VOjarTac1zqLNrUwrItj6U6PSOmT7U184GSIAABCEAAApkQQFhmQokyEIBAZyKA\nsOxM1g7sszbLnNnNss22x8l7H8yQs37/Jzlq/Aay3EqVUlndoKKvXkdu6yrtY//ZlFibGmvCsra1\nsNTSCftYpvFYhs39qQlhGeXATwhAAAIQSE8AYZmeESUgAIHORQBh2bnsnYenVY9iRJVZRF2Gbsmj\nKTSdkxqRBfppVN9ipYq27prZRSLqaLRiJvzEi/VaL5WR7nLcwbfKw/+ZJD16huUfV58m43bdwApI\nWD2WkUizVFZo+wnJyUDzXNpE2bg1lnqdICzxWHqM+AEBCEAAArkjgLDMHUtaggAEyoMAwrI87FjE\np7BFjZo8Ydni+rNrE5ahOhWRdt/yu3ifxkYtqmsgdRNKqdRFkZW6b8hbzzfJKSdfJC+/95CcetIf\nZPz4fWQFXVtZofcjIVtfaVNfrY249mP+xSTCMss1lngsFS0JAhCAAASyIoCwzAoXhSEAgU5AAGHZ\nCYyc30c04Wg+SE8FRk+tQ9WAkZCJPvNcqprUFFEx2NSsgrKyi7cPZZNWrdUlkgfueaO8/vrb0tg8\nV267/SzZYrt1Jaz3QhU65bXChKW1Ey8szVvp91hWsMZSqZAgAAEIQKAwBBCWheFMLxCAQOkQQFiW\njq0COlInLE3oxU2HVa1pM2RNToY1+E5Y6vS8SeWgKknp5d1U3SjXX/uqXHLB9fLjjz/IpX+bIAcd\nson06KuheVRPmreyojKsktKEaweEZZo1lt5MXu3BSVU9JUEAAhCAAATaJICwbBMPNyEAgU5IAGHZ\nCY2e20c2j6IJPyf+VJ6F9aOXlmN6MKxex7CutwzJIhVvNZrfVxbMr5TP/tckvzvwNPlq2ivyuz33\nl1NOPUTWXre3VNVo3NdwWMs1qndTvZY6bTYq+9wxXgK6qbDt9FjayLVZG2t8q3pJggAEIAABCKQk\ngLBMiYYbEIBAJyWAsOykhs/dY5uws49JM/uo1zIc9Vw6YRmpCGuJhSrcFun9Gl0xuYzM+DYiE868\nR+69+yVZa22R667/Pxk5cqhUqqgMafXmcKM0RzQkjwrLipDtUKmZntfSHfXSS+mF5UQN3rP+EPax\ndMQ4QgACEIBAxwkgLDvOkBYgAIHyIoCwLC97FudpTEFqsJ6YsGzZGNJJTfMIhrz7Nh22RiO91spf\n/3K/3H3bG/L1F4vkmuuOlL32WVe692rxdJpjMtToeSxF12lGfZEmKM2naMf4FO2loaVUdBjq5awM\nSZ0Wn6O3bzFhucJQGTdidEz+et5Jq6oJj2WUAz8hAAEIQCCRwDbbbCMvvvhiYmbLVZMFCkiRqqrs\nD6Kt0xZbbCHPPPNM6xvkQAACECgDAgjLMjBiUR+hRZx50Xg82WajUdmm+XbLAsA266dS9WBE9xpZ\nvFhkymczZc/dj5Vvv6+Tw353ipx/wTay3PJV0tRkwXq0dpWKyUqLBNusbdhU20o9t1/SJgf9wlKz\n1B+aKCxVl2qxBGGpHstxwxGWRosEAQhAAAKZEXj22WfFxGWukonKsWPH5qo52oEABCAQKAIIy0CZ\no/QG06wBX72Vj6b77CQqJ72jykT1Tqo8tEiwpjVVI86dE5YDDzxT3n7rAxm8/Epy68RLZKSuq7Rt\nKptNQ5qwrGhq8XBGp9haNNmQF/RHC5l70aXYqU9Y6n3/PpZ4LB00jhCAAAQgkA2BLbfcMqXXMpt2\nNt9885y0k02flIUABCBQSAIIy0LSLsO+vG1B7LlU5JmH0jyXFd4WIeZttP906qsG86nS+w2Lm+Xy\nyybLXy78h/oY6+T2O/8mO+08Smp1XaW3TNMcklrfoseKt0WJnZt6VEHpeSxNWOqpS3bLyzBhWdnm\ndiO2xhKPpQPHEQIQgAAEMiWQK68l3spMiVMOAhAoVQIIy1K1XFDGbUJPP+ZtbA5HdMqreierosLQ\nQvaEpFaFZZXM/yUsH73/sxzwuwny3awpcsxR+8mlVxwmtbVaXpvwNKIJS2/qq9U396Uly2wJ3hPv\nrbRbXqVo8CAN89O2sGSNpREjQQACEIBAOwh01GuJt7Id0KkCAQiUHAGEZcmZLGADVlFpU1x1dxDP\nd2hTWm3vSfM6muYMR6pl0XyRjz5YIGefdb28/fr7Mnqz1eX228+V5YZU6BTZeqmI1GhJVYmeUHSi\n0tqwDCcs9WgNxievvAnQiHosEZbxaDiHAAQgAIHcEeio1xJvZe5sQUsQgEBwCSAsg2ub0hiZ6r+w\naTsVeSYqNT6Pikm91C1GKioqpFl14gvPT5eJ/3pI7rj7Llmh/xrywCN/lzXW6iU9eoekKTxHakK9\ntLpW9qbBWmMmKu1jotI+1rB2EC8sTVR6wtKEqAnL6rY9lkyFVU4kCEAAAhBoL4H2ei3xVraXOPUg\nAIFSI4CwLDWLBW28KvZsnWVMWOq1iUlLS+qa5fXXP5Krr35AHnlskqy20trym722kYsuOUCiO5JE\npL5prnSp7KXyMV5YOgVpylHzrXHTmfEpQVjaGsuatoUlU2Hj6XEOAQhAAAJZEmiv1xJvZZagKQ4B\nCJQsAYRlyZouOAMP6zYhzeqqtPWVFRaOVUXgrFnNMvnp9+Tqq26V1955QbbYdDv59biN5cyz9pF6\ndUpW6+zX6FYiWi9SpdJRlaLnsTQF6ROWdumy3GNnISwn2j6W6rHcke1GHD2OEIAABCDQDgLZei3x\nVrYDMlUgAIGSJYCwLFnTBWTgnuiL6PTXBp21qsJSPYezZzXIY4+8KTfd8LB88cX3styg5WT87/eU\nI47ZvGWSa1j3tNTNSHSzSdOHlU402oUXtMdltEyF9XsrrVhMWOp+J1qnQbq06bEcpR7LHUawj6Wh\nI0EAAhCAQPsIZOu1xFvZPs7UggAESpMAwrI07ZblqJ1Q81fzlFxLpqcQ4wqYqItPTgzG19E9KtVb\nGdIsW1MZ0XWQs2ctkXvveUpuvO4++X76Qllp2Mpy7ukny+57reJNf7Ulk826lUizzpetDHXRdZgm\nLFva9A6mIp2StDHoxz80NwTvaPNufVNhNSd+H0s8lgqEBAEIQAACOSGQqdcSb2VOcNMIBCBQQgQQ\nliVkrFZDzUQvtvgIW9U1tWYLHb01jCrkQibQzPtnjdq6xlo9VeVml6ocI7JExaDGXq2ye5qtdS1Q\nT2O9RXSt8ITcjJlhuWXif+T666+T2T9/I7/eYQu55JI/yogRy3uj8Ga6apNRXWgNW6AeSy3rKKMX\n7fqp/tKlHks9jxeWt+hUWDyW7cJKJQhAAAIQ8BHI1GuJt9IHjksIQKDsCSAsS9nEps2Spahy0zvO\n8+eO/sLmDTRxqQ2FrIw12BJ5J6oopUk9klWV1ZqvQtKmr+p0V5OG4UiTznzVqK6NlfoRWbQoIuef\nf5Ncf8Nt0rVLRLYau6mcdebJMnTYIOm7jJX3fI+eqowOz/pz47J9KjuWEoSljVPXe9bpo83Rfm+Z\nfJ+sp1NhWWPZMcbUhgAEIACBKIF0Xku8lXxTIACBzkgAYVnKVjcdmCzFhKXddOLNzq1CfCUnLOOy\nza0YqtOMxVqySRqawlJT1Vfj6nT1mlLN5k199dyOWr1OBeUPP4Zl/Pjz5YlJd0uPbn3kwIP3lfPO\nO1F69ghJTRcdjA0hqkf1JHrqncTGZuK2Y6lNYdmWx1Kfx7S1Uen4KDr2DNSGAAQgAIHSIJDOa4m3\nsjTsyCghAIHcEkBY5pZnYVszNeRPCaLSbrZHWNqU2EX6Me+leRNrda/KbjoVVq9UfVmLJjAbGiMy\nZUqdjD/hDHn1lckyoP9AOea4I+X3J+0nvXppnNeWsXiCrZjCMo3HMhw/Tn02EgQgAAEIQCAdgVRe\nS7yV6chxHwIQKFcCCMtStqxfWLYSle7h4sWl5VlF/YTNVacfT/lZnq15jFvv6C2KbFYhaQLTPrru\nUqqlvi6i011DMvmJRfKH006Qr75+TwYOHCAnnHicHHnErlJTG9Lps9q8NtfYrGW72hpN9VR6P/TE\njvEp5bjjC7V9jseybT7chQAEIACB3BJI5bXEW5lbzrQGAQiUDgGEZenYqvVI4wVam+IsjbD0pr+a\nqLRGVBHGtdugerLK4vN4gX2qZN48kZ9mheWyv94n993/gorSqTJs1d5yzDHHyd57byM9dPqreTO9\nKbPWjjZZYVo1eho9iWs/8Ub0dnt+tiks8Vi2Byl1IAABCEAgDQG/1xJvZRpg3IYABMqaAMKyrM2b\n6uGc0NRjpMVLGWkJoONEX4tQbbaiKgyXLI5I9+4huee+D+Tcc/4q8+c2yZK6kKw5vEaOH3+g7LjT\nWOnTtyrqldQ2vGA96gm16KwtTSUIVm9kri/PY5pqrJnltyksWWOZGURKQQACEIBAVgT8Xku8lVnh\nozAEIFBmBBCWZWbQzB/HVJ2tpVSXZERdkiYsLSumAqOX5nm0vSbffH2xXHDBpfLRx1Nk+g/TpVfv\nGjnssIPkoIPGytChA6W6qsrzTNoWJPbxPJUtgtELJKtZ8Z5Qu4xd51pYatMJ242k8Vi6XVVyMAzv\nsfgBAQhAAAKdh4DzWuKt7Dw250khAIHkBBCWyblIOKwrC5uaNABNSKqrq72tNhobG71zu1ehasvu\n2XmzRrWpUmFl196WHC5qTYq2i5XtTU+NE45O2YWbmqVZP5VV1SoObVsRFWb6adTgPM2qOy88/wl5\n6sl35etp38hP8z+TnXfZWv7vjH1l7bVXlr69untLNOM1qetH6eijug5bSjgVFw8hB4ouwWNpvWqb\nse1G2vJYalk3pBwMI/6pOIcABCAAgQwJNL7zhlSP3jjD0sEq5ryWeCuDZRdGAwEIFJ4AwjIJcxOH\n9jHRWFlZ6QlGK+YEpV88+q+TNJmnLBNulky8OQHnZWT0w/MsWk1VkfYMehJ91hYN+P7b9XLq//1F\nvv+2Tj6b9j9ZY+V1ZPxJ+8p2O64oQ1cboILS5JwKbC9yrBuBVY5+oiF74odiY1T55pScu5UDRZcg\nLPVZ4vexnKjCcv0h7GPpcHOEAAQgEDQCP22wqvS5/s6SFZfnnHOOzuq5IGhYGQ8EIACBghJAWPpw\nm3i0j3kgXbJr80baJz5ZvkvmwbSUqqwrl7ujibd4j2D82OLPoz2GddGjPoF30aIhNWJrWCVgRGqq\nK6NSUJtTh6Usmh+WM8+4VZ5/6X358Zv5Mm/BbDnimL3ldweMkdXWWEX69qvwepaQejk9YRnfn7KK\n3m39qDZkT1i2KEnvWrNagvu0rpB5TlvC8hYTlisMlXEjRrdIXm8UzmEb07k50LeZD5iSEIAABCDg\nEVh0w5WyWD/msTRxSYIABCAAgdIkgLD02c2EoYnEL7/8Um699VYZPny47Lfffr5SSy+tvKsTPxXW\nL0KX1sjVmakyF8nV2owXd60lkolJL7UcrXSTyizbw7FCBbPJ6MVLIvLAvW/KXbc/LB+895PM+Pkr\n2Wz0pnLQoTvJppuvLiNH9pdmryGtpO1YG57WtpOYZDNh6ZITvi05Xt86NtvixJIbUyGEpXosxw1H\nWEbB8xMCEIBAcAiYt9KlUvZaumfgCAEIQKCzEkBYprD85MmTZbvttpOxY8eKrZuwZGsu7dOlSxfv\n2glKbxqp5uRfTHrdtvwwVWbC0iVP5rVcxJ+7+3p0Qq4lK6xTYBts2qh6Ead+ukBuvO5hefLx9+Sr\nr6ZJv77d5Gj1Um6z3Wqy0WbDVXw2S3WN+TztP12HqZrR1mGaA1IzNNmFfUxMWnLXdt4yHi/LKkSF\nZWwq7lLnsBVuV8Jj2S5sVIIABCBQVALOW+kGgdfSkeAIAQhAoPQIICx9NnPrJR977DHZZZddZK21\n1pKPPvpI5s+fL7Nnz5bPP/9cZsyY4dUaOXKkrLbaatKrVy/v2oL4uKA+vmbzcGkCzok413y8oFx6\nbgLOTX91HsawalLbX/KnRUvk3jtfkvvvekHeeuM7aaoPybbbbSw77TZcdt19C1l+kE17XWJSUv/T\n9ZReBFlPSUY7jXXjhKT/6MamYtJumcj0PJZ2bBlXIYQlHktnCI4QgAAEAkPALyxtYHgtA2MeBgIB\nCEAgKwIISx8uv7Bcd9115f3335eXX35ZbrvtNvnss8/khx9+8NZg2jTZ/fffXyzUeP/+/WNRZC3g\nT/5TMmFpvTql544q3rSoLQe1Ydlaywrd/yOs6ytffPVrufv+d+SxR56W6d9+rOsnR8vuu28tu+62\nsWy66Qq6BlOkutI8mrYlianAqLfS88yGTK3qJ2SqMOqB1Aw917yogow76qknKC2rRViawi2ksGSN\npRmBBAEIQCBQBOKnwbqB4bV0JDhCAAIQKC0CCMsU9nryySdl3LhxsvHGG8t///tf2WeffcTyLPXs\n2VMWLlykWissG220kfzzn/+UUaNGeVNho9FVTUaZsMtnMhEXPxU2vi8TelHh5i2CtKItw6lbXOd5\nYN96+0WZePPP8vY7FdK1W5Nsv9OKsseea8nYbTeRIf1q1UfZoHLR/quKxtbRrqyJiO59GaqwfnXy\naUT3wQyptzZUrdeW/MIymhv7abedsNTWvOWamhUyHd5BXEyFjVHmBAIQgEBJEEjmrXQDx2vpSHCE\nAAQgUDoEEJY+WzmP5eOPPy4777yzN9V11113lSuuuMITmt26dZNlB/SXuro6+WHmj/LEE0/IMccc\nK+ed92fp27efthZRb2Z6j6X59SxFt+Qwr6CpLkumsFrWMHrXcT888WVeQk+hxd1wp9EJq9E2XIu2\nItLWh4q88cY0eeetj+T551+T//znCWkK95MNNhgn228/WvY7YCNZe3g3qdeyIRWP5nm0mpVSHevO\nm0YbMYVpHkz92DHUXcu6uazx43LPo7ddMlHpPadNi21RklYsPS7XQspjgrDUUv59LG27EYL3pMTH\nDQhAAAIFJ5DMWxk/iGXf/iL+knMIQAACEAg4AYSlz0BOWD7yyCM6LXR3b8prjx495KSTTpKDDz7Y\nm/Ja2yWqhExYHnvseDER+tprr8uo9TfwvJi1XdSDl0RXua7sVrOKQ5NW0a05lgrLqAyslLBqNmvD\nE3MtFW2/SamwGxbP1W7rOLRASNcs2nVIXX/RfI32qsJNZ7xKvWrETz/5Ul59eZrce/dLOqX3Ey3Z\nV1YbNlK2+7XIb387RKe9jpXq6h7aoNXWFN9pNCfwPxOEpT6GeUHrVL/O0XO2Gwm8+RggBCDQyQi0\n5a10KLoddaJ01w8JAhCAAARKgwDCMoWdXPCe5ZdfXk488UQ57bTTvDWU0f0tI7Jg4Vypqekqs378\nSafBbiAPPHC/bLHF5t6yQ2uybW1m23ws0VLmS7RPlYrDqAfPc0a2OPOi6rRlzaIn+pzwM39iRJp1\n4WSlbjxp6ycjkSqp0kWUVsJatLxPP5sj773zndz0r7vkhReekH7LrCKDl19B1hy+uhx08B7y6+2H\nSLU6G52Y9qbxal0X3dbla1bgU6KwVKFeGYoJy4m2j6V6LHdku5HA25EBQgACnYNAOm+lo8CUWEeC\nIwQgAIHgE0BYprDRQw89JL/5zW9kxIgR8vHHH3ulbPpr165dpbm5UQWdW1coMmTIihrY53bZeust\npbHR9sEM6X1Vhy06sHUXzToT9GfNrtVPV/3UxGLeeJrSfngfW8tok1Nb3JeextR+Q1pe53qGdQuQ\nqBjUaK0aRMcEZb3uRTn9+1/k269nyy3/ekduvedO6V7TV4as2F82HTNM9tn/17LN9qt7ZRs1Ok+X\n6qj31S8qtamSSm0JS/NYjtLgPTuMYB/LkjIqg4UABMqSQCbeSvfgBPJxJDhCAAIQCD4BhKXPRrZl\niEV1feqpp2SHHXaQDTfcUN58801PwLl7ixcvVG9lF50mq+sPVQCasLz99ttlq622VE+hCUuTeG0l\n8zcu1AIqEL31iSru1CMZWzppjdo8Vg3OE1FRGYoF6bE8a9umvtok2ujaRstpUmU1c8Zc+fjDOfKv\nm56QBx69S/p1X1WqujfLyBGryaGH7S+77jFMuvbQ1jQyrFTU60TaSm1pqUDWZrznNJFpz4DH0oiQ\nIAABCEAglwQy9Va6PvFaOhIcIQABCASbAMIyhX0effRR2W233WSbbbaRu+++W/r16yf19fVSW2te\nxpD8+OMszevvRYw97LBD5N//vlPGjh2r3kwTpib1TASayzKJ21KzIs3qZXTFLAiO55k0l6TV0ymt\n3n6RlZ5wjbXircuMrrEM27rKSK039faX2Qvls09mycUXPihPPTNZBvVfTmO2zpPBA3rK2eecInvt\nM1ID9XhOTh2bNq8pOrNWBaR256a+RvOXjjc+36sU4B94LANsHIYGAQhAoIVANt5KBw2vpSPBEQIQ\ngECwCSAsffZxHkfbYmT77beX3r17y+mnny5nnnmmJxqXLFkiTTrdtVv3LvLdd9Nl8803l7lzf5GX\nX3pR1h81WpbUL/KEZbV6M1MmE5aN6m1UYemJSy8gz2It3jLlVYVlWLqoxDSPZoV6GKNC0ByZVt6k\n3/x56vOc2yyzZv0oE/70b3l80kPSv+/ysqRprmw4aqScddYfZNvtV5B6VVw2FM8BqvViXlEbg7Vk\nY7CGNZmH0nkrvYwS+tGmsJx8n6ynU2FZY1lCBmWoEIBAWRJoj7A0EHgty/LrwENBAAJlRgBhmcKg\n7777rhxwwAHy6aefigXwufrqq72IsGuttZb8PHueBsN5Qf5y4fny9ddfyqRJk1SEbqst6RYdKtKc\nUEvRtCo43QqkUV2HLULRc1K6vSG9rT48yaeyz6a6dtGjejBVXHpBevS4cEGzvPf2QrntljfliUkP\nqHBcIvMXTJe1Rg6WU089SvbZe4z8Mm+h9OnVXQWlTae1XTXVwxm2CLLRUXkeS7vwhhEVlvHjNYGd\n0bPEVyrieZvCsq01liawW8S6HkgQgAAEIJAnAo3vvCFzj/5du1rHa9kubFSCAAQgUFACCEsfbvPY\nNTQ06PYb1fLSSy/pusmtpHv37rJo0SIZOHCgJy5nzpjtTYtdtHie7LLLbnLjjdd7XsHuusdlbRed\nKqsqMCouTbC1Fm2mZLypsO6WO3rKxoSgF/NV69q81SqdxqpCVOP4NDdF5J03Z6vIfVIef2yy1Glg\n2S41TTJk5d7yf6cdJIcctolGqZ0vg5bTrUN0MqwndLV+WNdUhpttPaV+1HXpOShNYFp/2rc9syW/\nILZ8f55XMIA/EoSlPYs+W2y7kTQeS8XrJQ9/AJ+NIUEAAhAoBwLt9Va6Z8dr6UhwhAAEIBBMAgjL\nFHYxj928efPk2WeflVNOOUWmTZsmffr08bYcqdE9H3+ZM1v22nMv+fN5f5I111xDBZuJQf14otL0\nmqmV6McEmi691DIm7Frki85vjajA9KbC2hii2s4bjZ2qY1Ka9cRbA6lVnnv2K7nyH/fIc8+8rgKz\ni/TsWSNDVukp48cfLr/Zc5Su/bT21S8ZdYR67bgRLG1bBxGylm3KrW5w6UWltTWjpZ/aFJZ4LEvf\nwDwBBCBQ8gSyDdqT7IGXffuLZNnkQQACEIBAAAggLNswggXimT17tkyZMkWee+45+fLLL2XGjBmy\n/OBVZMcdd5CddtpBuuj2IxYQJ6SCrbHJxJrGatWMUMi8jU7aaQRXc4vpFNiorlTFGGqRQmFdT2kC\nUkub47BJvZImQm3aa7fuIbnrrrfkwov+KV9+9a2WqJC6uoWy8sprynHjD5ADDt5Ellmmu/ald6xJ\n664lWXuxS2/+qwlKG599tANPXHbXo213UvopQViapzVuH8tb8FiWvoF5AghAoKQJdNRb6R6+21En\nSnf9kCAAAQhAIHgEEJZpbGKeS4sGO3fuXOmmU11tamhExWBIXY29eqkoM72oXkqVgy1HXRFpotJT\ndea3bBGYKvy8ZEcTerr2MZpMAupHdd+ChbqqUk8XzY/IxRc9Jo88/LwKzbDM/Gm61DfPla22HCVH\nHL2nbLnVutKjV6MKz0U60bW7Vu2i9au8dZjRDUiiDlDzXkb7MdllHzcIG5z1q/V8241oRkmmNoUl\nHsuStCmDhgAEyodALryVjgZTYh0JjhCAAASCRQBh6bNH/HrD+DWG5r20ZHtcekk1mnkVw7r4sVK9\nY+axtBRWkekd1Q1porKyokXqaXnzSJq2a9I5rpW1dmFbhogsXhSRnrq/5Fuvz5Gr/nGbvPTqB1LR\n1Fu+mvGVxoXtLgf8bm859MhNZKWh3aW2ezfpt4y1Wa+1f9GjBfcxr2OtznKN+iitTTvznKaeZ9Jk\nl4s4a4KyRex6otJJUc0u4dSmsEzjsVRHsie5jQwJAhCAAARySyBX3ko3KgL5OBIcIQABCASLAMIy\niT3ixaXddtd2tDWSph1t2qkJS/M+Vtg8VE+aWDRXOzelYjLFPJZW0NrQy5ZkwXQaqyLSpWW95WOP\nfiZXX/kv+WTqdxJq6iHfzvxG+vXqLyf8/kjZeZcRKiR7Sf8BXaVHz5C3H6VVi2gblTal1QbiTXV1\n01tNQJpwtI9teaJH17kW1Qv92MCtvInKTiAs2/JYKgGEpUIgQQACEMgTAfNWmhhMlixSbKpknkl/\nStWOvxzXEIAABCBQeAIIywyYx3surXizajfnuDTdGBVrUY+mXZmgtMA8pljcbdN29vG0pGq9WXMj\ncu3Vd8jTT74ks2Yu1jWUM1XyNcqYTcfKnnuMk03HLCurDFtBlh1Y49WzvSyrtJ7nJdVzdZKKt0OJ\n9VapGRUaIjakH88zab2aqLSprlrfpr06Yau3dMWn/tesRw0e5AlQLVbiqS2P5UQVlusPYR/LEjcx\nw4cABMqQQFtTZAnUU4YG55EgAIGyJoCwbId5W2a7es5CU47RSLDmBYyKx4gKSgvWU2H/mRhUIeqt\nd9T7zz8zVfeenCwvvDZP5v88Vz77/AXN7Sr77beXBgPaWEasPVAG676Z/8/eecBJVd17/DfbWGCX\nDiJFEJSqIB1RCSqCoiBRFEueSV6MsfdYok/BF3tU1MQSjWKJMRZExRYVjIXyQLGBgoCRtoDS65bZ\nef/fuXNmZ3Zn7s7ulDu7+z987txzT733d86w9zv/U9q1EcsoyzMAKB4DgVKZVMPFfhgpC9E6jrCY\nTeslA+Tw0WpJJxGSj7bNijPD6RzSJFrWB+cGltMJlp264YQ+g8xT88lpTw5KoBbL+tAB9BlUAVWg\nTiqgYFknm01vWhVQBVSBqAooWEaVxQl0RpAKhhDc5J8zzJWgKHY+komhE6YNAp34ymQuplkRNiDz\nK2lWFLdnZwCz358ne0++hUULl2Ldur0o2nwoOrU7ACeO74/RY3PQvmN3DBraFo1lZGqJ/MsSyyMt\ni07N3IOSQ1YJiFKm3JKBW0aHO94nowUwAwEpg7ApZXC2pwOWeXKWRX6Yx3kss50JL+u6iwRL0UGk\nCu1jGbRYntBbwbKut7PevyqgCtQvBRQs61d76tOoAqpAw1agjoElSYpYRFCyRzBITvG4ct+2YDJn\nmCjtihayTJGmfAEyM4GS5CiEwkl44uxURX9WmTPX0oQzzkE3znv0yyqueXkCgRJctLYcs16fL1uV\nfIxl367Ct998hz1l2zFi8Cj8fNLx6HxABxxxRC90OiAL+4oDyGvk1FOx0A7LZRg/eeb9BNNQBkOI\ncg53NloWFfKZuZ+MdDQzQ2JNGTYkVFp4CXXSHwGW8gTcHzQCLNViWSfbVW9aFVAF6rcCCpb1u331\n6VQBVaBhKVDHwJIWOAuWlSArznbz+zablD4z/7CR+HMcVJNiDZNxIRyaA8u5fQjrkngxf1moZGZ/\nzj5wziN8eTLXUYaaBjm0kUxrDMgo1EWLNuHjDxdi/oJv8NFHX8p2IcvQKKstxo8fg+GH90bPXh0w\nanQ7FDRtYcp3arbPJUFhXrlSF4cCEWApjRWxj6VaLONQUJOoAqqAKpB+Bbb97mzEWsBHtxVJf3to\njaqAKqAKJKJAHQPLcIslwTLMEcbicOWyQI4hN5q0ZJgoB7g6lkDZm9IsuONEEyqduZOEFAIt5y2y\nfvHJeNOy8iwZDttY8viQQyKVYj7/bBPeeHMB5s9dLkA5D9t3r8T+bXti1NHDMGjwwTh29GD069/e\nDFctKSlGbm6usXyaQvUjIQWqBUu1WCakr2ZWBVQBVSAVCihYpkJVLVMVUAVUAW8UqGNgSQi0IBhF\nMEa5OQFAWheNadJwKSGz2OTwyT6QCIjJUdjRWic5X9EsverbI569chBfZO3WwH4ChwKUsvIqUXPJ\nV+tkUZ7F+GDOt5j1xgco8e9B3179cPiIHhg+rC+OHDlUrJTca1JKKJMBqTJSNmgnNWGVV501gfpR\nIwUULGsklyZWBVQBVSAjFFCwzIhm0JtQBVQBVSApCtRBsDQDVuN4+OiUGZDVWo0zJ2Iht92QtMaC\nKYHGUskZjUKeAo9csMdnVlktFp+znYcP7ZkDixd/j3lzl8iw18/w/AsvIjerEP0O7Y/evWVRnhPH\n4eSJ/dC4wIdimT+ZK/Mn7R0FBEdzgnMdeS/lMp+T+2Nap6BplYj/XC1YynYjunhP/HpqSlVAFVAF\n0qGAgmU6VNY6VAFVQBVIjwJ1DCxjiWKRLTzeGbYaHuL4mVYgToayhkiPkGn27iiRoFI5uFFIIwE+\nWUFVkpP5WFq2z499JX4s/XwDli5dj5dffgMzZz2FvJzWOOigrhgx4jCMPm40Tho/Ak2bClAK7cho\nV8dCKid7l6Y6uaZz5nGSYQ3pOoH6WWMFqgVLHQpbY001gyqgCqgCqVZAwTLVCmv5qoAqoAqkT4F6\nAJbEtfCjGvG4MA+tk8GVXp3UAnXBPSAdsCRGNhLoyxG4dBbnKSkJ4PuV2/HpovV49qmZeP+jWWjb\nvC3atC9Av34dcYbsQzl+wlDZYgQoleG22WZeJkt3gJF7TwZkDif5kSHBnUiYQF0SFHADyyeDi/eM\n0+1GkqC0FqEKqAKZooDbiqqZco8N9T7aLlrRUB9dn1sVUAUasAJ1CyytyY8N5vCaeBhIELSRPIci\nxV/JlQv5ieURPs6v5IRLpucekTQtOns8yqYh4qddMxt7BShXLNuJ5d8U4YlHl+DN2W+jRf52FLTO\nxRFDD8Fvzz8Hx47pgFJZ24fwmJvL8lh2cJEgKaVc9rT0yxDcQHmusUxyD8wcOeh02KujQ6KfbmA5\nXcByoFgsj++j+1gmqrPmVwVUgcxRQMEyc9qi8p0oWFZWRK9VAVWgIShQd8GSrWP4kVDJg45Qx4Mu\nSG7Gb0FTzhwCS+skwZIr+XB7EZOWYOkAIMvlENh1a3fiqy834vVXF+Dxx2eiZUF35DUtRO8eW3HZ\nZRdjwsSuZv5kdq7MyCSbhu6HYOpAK0s38zWlDu5KSatllpgtxXZpoNLOr9ShsCJUAi4CLKUcGqXt\nPpZqsUxAWM2qCqgCGauAgmXGNg0ULDO3bfTOVAFVIHUK1COwJMLx4CfPRDeSngCdDHsNgRuHwkoY\nk8hWlQZAZJtKSSOL+Ihn2/YybN2yC+vXbMaf7noPr73xrmwnko+mLbMFKDvgsssvxumTO0oZkoc7\nkPDsnJxPqdJMl2TVBjQZa7cqsRDMe3NWiQ3Pqv7aK+AGlmqxrL2umlMVUAUyVwEFy8xtGwXLzG0b\nvTNVQBVInQJ1HCwJbYQ1nu1Bn0N1tAv6/bySPSot8DF50IW2FRGw3LG9HNt3lGLN6h/x2KOvYvqz\n/5R5kC3QtXN3tG1fiMuv/CUmT+6OjVvK0balA6a2nEiyDANLJoiAS1oxeQNEXgVLypMsFwGW0rA+\nmcRqLZbT33sRh8lQWJ1jmSy1vS8nULIB5bu+8v5GGugdZBUcCl9ee2mDLxAo2dRAVfD+sbeMucD7\nm9A7iKrAtF+/GDVcA1OnwK0XDTCF/+Od7/H1im2pq0hLdlVA28FVnrREHnJQC5w59sC01FW5knoE\nlpUfjZZKmd9Iq6TAXZYBPBmgKsZDeg1oGqAMyHDWciz+dAMeuO8VvPP+HEG/AJoWAIMH9sAFF03G\n+FMGYNfuErRonmcqySbDWhfuD4YZi6WNt+dg/RYsnbuwkXpOVAFXsHSbYyntJ93E/DYgJ3V1RAGC\npX/Le3XkbuvfbWa3Gh0CSwV879p360lTvKtca3ZV4LcjH3ON18jkKzDz3qNNoTf8ZTGWrFSwTL7C\n8ZWo7RCfTqlM1bd7C1jAT2U90cqup2ApQClzKclyHPlqoZICiAHThO/ZE0COgOWihZsw7Z4X8Ppb\ns5GX2wJNcpuid9+u+OMd52Ho4c2wU9K1bMEZk9zD0jF35gSasihxliol3Kwq64Q6Q3BZO1ElSJQ2\nKZPQrxRDJZLmXMGyGoul3dpUmyRpzZHyghQsUy6xawUKlq7ypC1SwTJtUte4IgXLGkuWcAYFmoQl\nTEoB2g5JkTGhQhQs45UvHM6Yxyy8Q9iriCiXS86phKzESqgkPNohr+TCvbJ0a5N8Hz5duAO33PwY\nZr+/SPahbCaWzTJ07toO199wGs44ayB27AqgsLlBUymfcySLJXuJ8KAMs/S3lmtxjOY9+BjPVWCd\noa52bicXA3JWm+VN8J7k0t4qo9QlTQFXsFSLZdJ0zpSCFCy9bQkFS2/1t7W7gWXLWVNsMj2nSAE3\n/RUsUyS6S7EKNC7ipDFK2yGNYseoSsEyhjBVgi2U2YgoYElTYIArv4orF97Lolfy7SIotvBh/qKN\nuO7aW7Bg/hIU5HfAT1t3oEvnbrjxhl9h0ukD0ESMkVzllRwYLEUKsHMjCZACluUyTtY6A5aM581V\nBkuWQPokRVYCS6dwCVeXDAVcwVItlsmQOKPKULD0tjkULL3V39buBjYKllal1J3d9FewTJ3usUpW\noImlTHrDtR3Sq3e02hQso6kSLaxasCStycF0wWPnTgco339rOf785wcFLHdKVBY2bvoeAwcfgquu\n/g2OProPmrXIQV4e52WWyp6TZcjNzhFjJC2Nsj2IFCljho0F1MyfzNrr3F2AW5SwPtZLcJQ0Um9o\njqWZyMko3oy1atLCSti0w2nFqy5hBVzBUi2WCeubaQUoWHrbIgqW3upva3cDGwVLq1Lqzm76K1im\nTvdYJSvQxFImveHaDunVO1ptCpbRVIkWRj6jI5cZxwCCmo0gGBLsCHpAyd4A/m/BMky770HMm7dY\nFu4pw49b+2HYsOE474KjcNTP9sN+HZqhkVmTZ4+UUipF8x/nZ+bJkWvgklxodikxpUr5OT+Jj3VI\nxkC+3EKOE8/b4MH7c27BbGciY2clbI8EFstBBGJkeznUJUuBCLCUQsP3sdRVYZOlcuaUo2DpbVso\nWHqrv63dDWwULK1KqTu76a9gmTrdY5WsQBNLmfSGazukV+9otTUMsBT+I5xxz0hnTiKtfhw6SgoT\nOLPDRQ2Zcb4iCS1HTmIVlHGpzEsDIpPLNEnjHINg8EJC/H4nXZ5keXXGMrFQ/g3/WbVB9qXcg807\nN2LcMWfiimtHokvXtmi/f2sUFkr54px9Ly2g8n548DNIh7yoqEYiaH206YLn8Himt84UxUhbvo1w\n6rZXek5MAVewdLNYSrV2+mtYayd2M5o75QooWKZcYtcKFCxd5UlbpBvYKFimvhnc9FewTL3+lWtQ\noKmsiDfX2g7e6B5ea8MCS769ZxEoab2zgMYhpYRIATBjbQyjtADDJVqcXyx/5WZYKYeoCvYJtJHb\nyoRDCZnZUvbs91ZjypSpWLt2O0pkG5GiTStx0tizcfkV42V11w7o2bcRGjcRi6RPhroGLZsB1ivO\nFxrDai71ow4pEAGW0p7h+1g+KWA5QObR6j6WdahBq7lVBctqBEpxtIJligWOs3g3sFGwjFPEBJK5\n6a9gmYCwtcyqQFNL4ZKcTdshyYLWoriGAZa02JnxpEGzY7gFz2zVQUR0gNFEyRUtSYb1fLvkap8E\nyzYiAqA+NJbrXOyToa75stBOeVkAb7y+EdOffhbLvluNNavWYHfpVowbewzOPGssBg7pgj6926PM\nGA2lFCk3y6zqI3UIhNiDYGnDpQJ1dUgBN7CcTrDs1A0n9BlkfqPgzwjGOhn8wUItlnWooYO3qmDp\nbZspWHqrv63dDWwULK1KqTu76a9gmTrdY5WsQBNLmfSGazukV+9otTUQsOSrPy2UnJdYYYV0RpTK\nG755yRfio5cAKM5AJTkUm+XYIVBZINEFKCvLQyPZR2SvgOVrr3yNV2a8i6++/hErl60XO+gunPHz\nUzB6TA8Bynboc2gXM4eyVOZPZkn92WbYLSHWceXcnyToCJZqtbRq1K1ztWApFssTeitY1q1WjX23\nCpaxtUlHjIJlOlSuvg43sFGwrF6/RFO46a9gmai6Nc+vQFNzzVKRQ9shFarWrMwGApaESkIcoVLs\nRQRJA5NyruA8E2ZGpgbDzJxMcD7mPpT5G8u+lPnYstWP11+dgw/mLMSSLzbg0y+WSskBTBhzMiZO\nPByDhh6Ig3u2QuMCn7FScsRrueTPFrDMQhMpy9irIiyVCpQiSx121YKlWizrcOtWvXUFy6qapDNE\nwTKdaseuyw1sFCxj65asGDf9FSyTpXL85SjQxK9VKlNqO6RS3fjKbiBgSVMkBQkSYxhYBqc4Boe9\nEgIFEwN+SZlthqZyDmWOGDo3FZXj7bfn4oMP5sk+lF9i6fLPkZtViNNOnYBRRw9Ev/69MGz4AYYb\nZQFYM10zOI1SSvSLJTSAnKxsKVeGz4qlkkNgdfhrfJ0001NVC5Zqscz0JqzR/SlY1kiupCdWsEy6\npLUq0A1sFCxrJWmNMrnpr2BZIymTkliBJikyJlyItkPCEiZcQMMAS8uVIhfRkjBptvGgfPQzkJZF\nX4nEF5t47vWYJWS4dpUfn3z0BT76aCVmz16Ab77/GC0L2uP4cUMwZPjBGHPcEeh7SEeWJFZNyS+T\nKAmMAQFTQiQd6/PLmkG5ZmhtwIAlwzmnUq2VVKJuu2rBUi2WdbuBK929gmUlQdJ8qWCZZsFjVOcG\nNgqWMURLYrCb/gqWSRQ6zqIUaOIUKsXJtB1SLHAcxTc4sAxpIrAXcsJ/5TIPEr7dgoMcMpsn24oU\nYNH85Xjp79/hxefn4T+bV6JLu07oP6ADRhx1EE45dSQO7tXcFFESkKGuvmLBSGcLk3KWEsgTVs13\n0FLq4tzNHBmJS+cnZYoLB0tdHdZIUic/FCzrZLPV+qYVLGstXVIyKlgmRcaEC3EDGwXLhOWttgA3\n/RUsq5Uv6QkUaJIuaa0K1HaolWxJzdQgwJIMSashz44NMUxD2UaEWOkP7BU4lJmQZVliqWyM12d+\niofun4H3xVrZvqAvjhzVDUOH98RJJw1Dr/6yMqwUxpVes7JlmKvMw/QRTINbmHD12IBsfJklVs/Q\nfpSSPrgYrMQ5q8GGrwJrF/IJDwu7S/VmsAIRYCn3ySHQe+XYKm1uVoXVobAZ3Ho1vzUFy5prlswc\nCpbJVLP2ZbmBjYJl7XWNN6eb/gqW8aqYvHQKNMnTMpGStB0SUS85eRsIWBIc5S1fFu6JHHpaLtfF\ngpVlAodZsolIUyxfthP/nvM1pt3zJL5d8QP69eqPCSePwW/PH4VOXQUYg9tfcvgst4owY2qNpZKW\nTh5Sj8FXRnKhHh6O47xNdfVPgQiwlObnok8RYKlDYetVoytYetucCpbe6m9rdwMbBUurUurObvor\nWKZO91glK9DEUia94doO6dU7Wm0NBCxpVeTarQJ5ZoNKWTjHACAh0C/Q6UOOrxGWflWEp6fPw7R7\nn0Cj3KayIM/BuO6GC3HcmP2R00hSlvtlZdjgtiCcR0lWlbw+mbBp5mmGyqTUFjQrzj4B13DQZCp1\ndV+BSLCUviB9xILlk9zHUiyW43S7kbrf0MEnULD0tikVLL3V39buBjYKllal1J3d9FewTJ3usUpW\noImlTHrDtR3Sq3e02hoEWBo7Iic5+soEKDn0tdSApk/mQPr9+SiRPSkXzv8Od976Bt7+4B0cuH8/\n9OvXBrfeeRkO6JaHxk0FPLN2SD7mpdWRQ11zZB6l+GkFlX+yZo9jqJSTXIqjaZMH8/DMu+CczAoL\nplyoqwcKuIElh8IOFIvl8X10H8t60NTmERQsvW1JBUtv9be1u4GNgqVVKXVnN/0VLFOne6ySFWhi\nKZPecG2H9OodrbYGAZZ+YTq/bPfhyyqReY5cZIeglyVwWICidX4sXrQSZ0y6BY1ymiO3MBunTTwO\nt905EU2bCzKa0av7JA/nUdLCmS2fzgGBSx8Px1xZoa8BS4IkoTL8KJBrBcsKoeqHzw0s1WJZP9o4\n/CkULMPVSL9fwTL9mker0Q1sFCyjKZbcMDf9FSyTq3U8pSnQxKNS6tNoO6Re4+pqaBBgWSIcWc59\nJHM5ZLVYNOHw1xysW7MXLz2/ANdcexda5HdHsxb5uOOOa3HmOZ1k6xFntmQAJQKSJYKSOYKEAqOS\nu2IFV1orCYrWXGmIMqg5wdJe009nKNXx6me9UcANLNViWW+aOfQgCpYhKTzxKFh6InuVSt3ARsGy\nilxJD3DTX8Ey6XJXW6ACTbUSpSWBtkNaZHatpEGApbEdygfnRHJl1tLSAJZ8vRo33fAA3nr7PRzQ\nfiBat2mJmTPvRvNWspYrLZXGsFgmCFpm0DFblvbxyXzKchlSS7D0SQJnBVcLj646a2Q9VsAVLN97\nEYfJUFidY1l/OoCCpbdtqWDprf62djewUbC0KqXu7Ka/gmXqdI9VsgJNLGXSG67tkF69o9XWIMDS\nTxgU/isr8WGn7AHx8Ucr8L9TnsCmTZuQm+fH2HFDcP+DFxkrZV6eI5OsIyv2Rg5+JThmIZtQKoez\nYI/4GezGlOFxWuQtFAAAED1JREFUPqItD1oswyPkUl2dV8AVLN3mWLI/yQ8YcjI/XtR5IRrIAyhY\netvQCpbe6m9rdwMbBUurUurObvorWKZO91glK9DEUia94doO6dU7Wm0NAiwDYnUs9QdQtL4cTz3+\nAe654xl5my9Aq9YFuOLqybj0qiEoFe7LyuJelCXGSpmNJuLPkUNmZEocfbLYJ7gGEA+7J6UEyzBb\nCRM6yLYjXS078hyCSqZUsKQK9c1FgKU8XMQ+llEslqZbkCbFccg1nTGQO179zHAFGgpY7t1bjHUb\ntsrevuXo1qUt8vJyM6JlGiJY7tlTjB/W/mhGyRx4QGa0hRvYpAIsOVKotLQMublR1jWopmdmal+u\n5rZdo930zySw7LRfEzRtnIPiknL8Z/0u12dyiyxsmoOO7Zpiv1b5ZmrT1p0l+KFoF3bu5poZ3rtM\nBJqmjbPRtUMhWjVvhGx5yVj/016s27gbu/dy7Y/4XbOCXBnZ1xStmkk58iK8cfNeFElZW3fw7Sez\nXCa2Q2WFqGfzgjxjZtq1txRbtsenY7Las/L9JPvaU7Ast5MVXZ6KUFgmC+cQ7XwyHDXLnOXlXV7M\nedCZk0++KAbiaBvkl4av6pwDmY2d8n/ZV1/58Zc/z8Dzz92P7CZlOLhXGxn6+gz2a1+IxrnZkk7o\n0LjKr/hOeI6UQwumOlWgsgI7pAPmSNeQHWlMr+OfuZ0Stl165tNzZqBfxwNxQs+BET3M/gZh/yTy\nZ4xgd65cfK2vq/TWeCqokqnm1TtDxSMLYlg5f4ERlx36BabmZWdCjkwBy0Wfr8LZ5/8F6zdsMT9s\ntW5ViOkP/g5HH9m31jL9uHkHHnnyPcx861N8uXR1qJwceSvp1rUdzjrlCPz+4hM9hUwvwPKFV+fh\n+v/9Z0iPeD1jj+6Hh+7+73iTR6Rbumwtpt49A/M/XYEiAXz79bVtceGvRuO8Xx5rQCsiY5ou3MAm\nmWDJ/zce+tu7mHL3y9i+cy+aNM7DqkXTwP7u5rzsy6n4blZ+Vjf9MwEsW7fIw+9O7YmhfduEbv3K\nexdi1dr44TJH/rBOHNUZE0Z2RjN5Ea/s+Hfls2VbcOvjX5of+CvHp/M6k4BmYO9WOFl0O6RbSwOC\nlXX4auVWPPLScoHMPZWjIq4P79/W6N/jgGZVF6mUlN/+sB2PzfgOK9fsjMjn5UUmtUM0HU44ooP5\nXoTHPT7zO8z6cG14UIQ/We0ZUWgKLzwFy4CQZXXPVk6w9HFV1mx5aSfhC+BJLguV/GvLUnzZ8tJK\nsJQLDn31cTlXs6Srs7zOnXe8huuufwAdO7fB+JNH4sabz0dBoQ+NG/EFmNuQ2Fth+nC45P6X8jIs\nW4soWFbXWg0zfrd0IfaYfDnYBf1yQbDcJj3nqdkz0F/AclyvgaYfsS8xbWWwbGS7n8TVxjE7h3tb\nF+a1QdJ/Y7jwumMmipG3UjD/0POgc+YgV0pQDy4zASz9fj+GH38zPv/6hwhFHxaIOfcXR0eExXvx\n3Muf4MLfPyG/Zrv/etqje3u888J16NShdbxFJzWdF2B57dTncO8jb9X4OQ47pAsWvvvHGue7fdqr\nmHLXyzKnP/zLWbWYngftj49n3YwWzblHcnqdG9gkCywJ1+dd+TgWfLYy4uF+WPwAOrRvGREWfuFl\nX07FdzP82azfTX8vwZKL5I8d0QHnnNgdTfI51qvCXXXforghpHvnQlz7y75o16pxRQExfOfeMhc/\nbePCjN65TAGaS8/shWOG7F+tEGWyXcIfBcg/X7Y1atrLz+qFUYOrL4d/76c8+gW+WB69nKiFpzAw\nU9oh2iO2KMzFQ9cPr/K9eO7tVXjhX5F/y23+ZLWnLS8dZ0/Bsry0mr+aooBP9p4MZJfIS7H8B2X2\njZStPmj4CP97K/+RcaVXn7zVOy+1AoHOPiHmDf6TOUW4deoMfDz3X5gwcTgeeew6FDRzbJQ+IVS/\nvxg52fY/QHntN2Xz9Z8V8eBJ6g1/c3dC9VMVgF+6jgFFmh+lu7Dr7ZNjm/SjJ/71EgZ25j6WA41S\n7FrGsG480tXYzeis6dK5qvmnfAf49UjE8WtkvkoJFGKtknYLHntOoMiMy5oJYPno9Pdx8fXTq2iT\nCFh2G3w51qzbHCqzpcBKF/khjmGbt0ZaGU4cfRhmPnNVKG06PV6A5TMvfoT/vvSvNX7Mwf0PxLy3\nb6lRvjkfL8Hxp98ZgsoWzZpg0oRh6LR/K2z6aTtefftTrCuqeIk7Q/6mPfPwRTWqIxmJ3cAmUbAs\nKSnF7dNew50Pvo7SsqrD9qoDSy/7ciq+m9Hay01/r8CyY7smuHhyT/Q+sEW0W0a8YNmyWR7uuXKw\nGXppC+LK/hxKu3w19xQHDhLw7N6pUN71fPj1lE88H5aZKUDzyj2jjCZWtz37ygx0t5bhsBySHO5+\n3LoPl9z1f9hXXPU79vLdo0LWTmq/cu0ObJT0rQobSfs2j6jjp237cOHtC1DCOWUeu0xph2gyXCaw\nfnQUWHcDy2S1Z7T7SVVYHQBL6fDZfOuWt2bZIoTQVxUsLVTytZhv13zLdk4+yXLX1DcwZcpfMWhQ\nb1x7/bkYe0J35OaLjVK+Axxqm5Vj3/IlT+hNPwiYhgKYUMFS1FEXRQH2MQJlwICl9CWZg1AqYdsl\n+Ml3HLAc3VfAMvh/bqj/Sjd1Vh+WvMG4KMXHFyRlyW8fNXbs+TzomN1yrglI0gd/7KlPgOk1WG7e\nshN9jvg9tmzbXaWFEgHLrgMvNcAyYexAM9x1+OAeofKfeO4DXHr9UzJPquIXkLlvTsGQAd1DadLl\n8QIs+Wyc32et8bGelUM1DxpyBfYErb5/uOxkTL1uUqzkUcOPmfhHfLRgmYmjRfLD125Cq5bcA9lx\n+/aVYPK5D+LN9z83AVnyYr11xWNo0oSD8dPn3MAmEbAkPB/781vx7Yqi0MO0lucP/3GjOrD0qi+n\n6rsZEiLM46a/F2B5cJdmuO2iAcjNif1XJF6wvO2SAegTBqefLduMe55eUmVuIC1AbVvm47vV3g/F\nzBSgeflPo2Q+pQ9frtiCl977AV8u32Z6DS3JE37WGb8a3z3i7/G9zy7Fh59tDOtZjvelu38mi1T6\n8K/56zFj9mps2rIvlKbXgc1w02/7R1jernvwU3z7vQP9oYQeeDKlHSo/OjW745JBlYPNtRtYJqs9\no1acokBPwTLgr8ZiGeQ9sxyrhcXwN2HzSixv5JxfGbK1ED7lGySuZF8Aa9fswdSbH8XTL/wNZ0+a\nhGdfnIpyGemVJW/RHGYUyJI9KnlBZ96wCZTh/zHaN36W6ZRr0uqHKmAVYHeRbhKQtZ9keLdYLGXB\nJ1nnhGD5RDhYBrupgUjbrSSvhUtbXI3P0m/Z5ctZlmQ23ThKIbbKKFEmKBVgyRdx+zJOuKwPgOk1\nWF4gw1Uff3aOabPJJw/DarEozlu0wlwnApbTn/83unRqE3OO5pQ7X8at02aGus/Dd/0a5/7XMaHr\ndHm8Ast4nu++R97ENVP/YZJyLuSKhfeho1ga43X8rrTpcR527HJe4p5/7GKcetKwKtlXfL8BvUf8\nPhTuBeS7gU0iYPnGu4sx8Zx7zbPRan7LtZMw6sg+OHTktaHnrQ4sverLqfpuhh48zOOmvxdgOebw\nDrjwtJ7mDveV+PHiu//BCpl7N/X8w0J3HQ9YVn4Bn/vlJtzzzFIZWRbrL1uoeE89mQI0447siE1i\nWVy0pGL0SbgwF53eE8cN7xAKmjH7Bzw9a1Xo2nqGHdoGRT/uxeoNVX/AZJpTjz0A/yXDna17+KVl\neGfuenvp2TlT2iFcAEI9LfDdOjrzwr+WOa7t5AcRO8zbDSyT1Z7h95Nqv7dgad84XZ6SL8M8+MLM\ng47bfjgXfFOvSBEQe0sgzLLIVbCmy5Cxu7gKrCwAcPkVk3HeBSeaSd6cOVlaViK/rjlzwbjMj+No\nfgp3LJ8u/A6cEP1UBagAe4h0NQOWMsHXrORDi6XsbIPpMhR2gAyFHcOhsPbvopwJl/zPxmS0XU8u\na+vCijbVRCvS9mTWwfjwa+Znz+dzJOI4FLYyQDLMhnPeZV2HSy/B8rMvv8fhMreSP4o1lUVMlnxy\nN84878GkgGV17c75bv1HXR9KdtUF43DHTWeGrtPlyVSwLCsrQ4/hV4eGExP6n33k4hrJskWGHO/X\n54JQntmv/AFHDe8durYerhLbvPu59hKfvDEFQwdWvOSFIlLocQObRMDyw3nfYOxpd+CcySNx6x9O\nR5vWhfhuVZFY6a8JPU11YBlKGMOTir6c7u+mm/5egOWRA9rhql/0wScCgk++ugKbt5WgT7fmuO1i\n+dsXdPGA5ZW/6I2RA9ubHKWyIvVvZP7kjl1csT+zXSYCTTTFDuvZElN+VwH7H362Afc++020pK5h\nXFCGVkvrnnxtBV79YI299Oycie3AOccXTHJ+dPGL8eGKPy3EDeceKiscNzY6uYFldUImqz2rq6cm\n8Z6CpUyxtO/DMe+ZL7/WHsmXXvvC7Cy2w0V3ODRLrCIykC8gq8aWEy4lpLQkgC1bArj+mofwj2ce\nwimnT8bdf7oRHTplBwuRPOWlYi3ySw7nH1+ruThQpHNev539LG3tkSn0qmErwD95hLJc6SrGGild\naI90Fel+mP6egGWnbhjXu2LxHvYi8+OInP1ywf6aSM9ifn43bM/ldbirfG3jKoeb+7KRtTwXFxeb\nnHmyIawFSAuWhMr6sKCPV2DJ/y6POmlqaCETvnRfc8l4jBw/NS1guXb9Zhw46PJQz7j56lNw41U/\nD12ny5OpYMmVY88+/6GQDB/PugnDBh0cuo7XQ4slh9TSXX3hONz+P1Xh/ZU3F+L03zxg0nC42o/L\nHkGzQq4tnT7nBjaJgCWfgMN98/MrVgFNNlgmuy978d10098LsGS75eVmRcyzqw1YPnfbUaEhlnMW\nFeH+575l0RnvMhFooonWs2sz3HlpxZDMt+euMyvERkvrFlZ5ddM7n/oa87740S1LWuIyrR24Tc7D\nsmBPQRNnu65ZH67B4zNX4NEbhycFLJPVnslsHE/BsrgGYGktKXz5dQ4CJZGTB1+Rc8SXJz4HOfkn\n6e8vfIE7bnsORUVf48Ybz8ZVl5yF3fLyzz0ouXQPoTTL5Od6r3wt52FrEq9xDljaWm2onlUBqwDX\n0GTPaSQHgZGwaFeF5XYjXBXWbjfC3mQtg+y17L10lXudExrfJ8th/lx64nCxkhkLahz5q0tCy004\nRNqvuQXN6vJnerxXYPnUPz/EuZc/ZuQ5uNt++HzO7Wbbj3SB5b/nLsXoU28PNY8OhQ1JYTwjTrgZ\nC2ULGLohh3XD3LemGn9NP07/zf145c1FJhuH0/790YtwyolDQ8XQ2jbuzLtCC/j8YtIRePLB80Px\n6fK4gU2iYFn5GZINlsnuy158N9309wosK7dbTcGyfZvGeOQPw0PFTP3r51j8bcVCVaGIDPRkGtDE\nkih8yDLT1MZa1iQ/G9OuHhIaysnFfc6/bX7EPMxY9ac6PNPa4fxJPXD8iI7msbfvKsEFotOeff6k\ngWUy2jPZbeIlWP4/AAAA//9+HPqyAABAAElEQVTsnQeAE1X+x39JttJFFERQQVCKBURF5bCcioBd\nVOxdz/I/y6nnWc/ey529g70g2MsB6umpWBFFxQZWRFE6bMlmk//v+yYvm12S2WQ3ySTZ79Nk2ps3\nM583y85nf2/e8wUjkYg0k5ABH19cPr9ZDum6kM7VS1i/IxLQT7nZYvc58fi75IH7X5HDj9pVzv3H\nONmwf3fdjoQc9foJSYlZ8mlZAZ3DJ/5IuhgtPSx+3avpNmxnausEahWALxKWSp9f/HozBn0RWa73\nzXK9ZyZOfVKG9u4ruw/aKnonOXcZ7jTct86966xrDUf8TARQmEn2J8ZO7fpmpri9o7e4/dH0+dK7\n5y+55BLp16+f7LHHHtK+fXspKSkRlGXLCYf1J8mPsy3cFAn+KvWLp+f0ApYtr5JBI86WhX8sN8d9\n4dGzZLedNjfz2+95icz48Fszf8d1x8hxh+2UlXM77bwH5PYJDdc9553rpF+fHlk5lluhga67iK+s\nh4RXfqKf2W5Zc7btnfe/kh32vjx2vAdvO0kO3m+72HI6M1/PXSBb7XqBVFUHzW74CTz5mF3ln2fv\nJ1Ne/FDOuOBBqa6pM9vW79VN3nz+IunZY410DpGRvEv2uDhpOWu8kHxb0p1cNnwzb4He/3+P5fjh\n45tbdc2ZvJe9+tl043/89vfEWHk5M6hvZ7ny/7aIncKZN30oc39aEVtuOrPt5mvJOUduElt96rXv\nyzprVcpm/daQ7t0qpUNlQGrrwvLjglUyb/5KeevjhVIXiv3ii+3nxcwzNzr/7p5/28fy+dylXpxC\nSse87JQhsumGDf9enHvrTJkzb1lK+yLTQK3T4/bpLxv26hjb5/UPF8i/H/0ytuzlTD7VQ19ldP3p\nw/SZx3mOuvnxOfLa+78aPHddsI1071pp5h99ZZ48OfWHFmFrbX226KDN7DR4wy5yxSlDm8mVnc2+\nevv0mqR8VAUejZ2v2DOvyW2k0ueIpZMHj+oqlhGfeVj/35sLVCZvlXffmy1XXHaSnHfeGKlTlywp\nRXm6h+4bidSLX2UA+4hKgHm8R2H42IOrJEBCIz4V1zQfsnVHpiImYG+RGr1G/NmhXO8j3C5hvZWW\nq2gu05to4tRJRizHDtrS3Fa4m3Cn4m5z7ixnam5LXdfShOOaWxUFmPsbMzjDaMLBNLn+KcePQpx8\n9tsKIZbr6+slEMDZJ0+33HKLPP3003LKKafIuHHj9HgR84FMxs8nLyH/t3ghlmde9LDcfM9/DJy9\ndttCJk88IwYqF2K5aPEKGbjd2bJk2Spz3C037yMzXrk0dg65nMlHsTzgmH/JMy9/ZDCs072LzP3g\nJiktxZ8tW5beevdL2euw62XFKvzZykkBfTipD0d/kHXVoI3WleceOVPW77WWzZLTqZvY5LNYZvpe\n9upn041/oYrl6BE95cRxG8fu4+raeqksT/47Z/7vVfKvR+fINz84f3CL7ejBTD4JTbLLH9Cnk1yl\nom9/r/+xtEaOu3RGsuyy3jrt5ei9+ml+kfYVAVmnWzvp0A5PKw1p1teL5ar7Z0ttkILfQMWZu/q0\nLWTA+p3Nwtc/LpO//2tmLEsmxDLd+owdPMsznopluM71Mbfh0ht+lzasQ8TRV6c3fPRhOKLiF9Yb\nXn8C6msjct4/JsjEiS9K/35bykUXHSy77bWBROqQV39CAoig1Gl+/UEwD9L6AKD7O4Kph4g/ng8/\nLPrx4x+3Jk/dcWfD2bZLoF5vH+Nk+neOSJ1G0gM+WVkSkWX+Epn4yhMybL0NZcxgFUu9r3BrxUsg\nJBTJhwB6KxJua9zCzr2L2LquwO2KA9oD64rVfuIa3esqgfqzYf7Y0uRcVq1aJT///LOUl6NVQOKE\n6ORXX30lxx13nJx88sly9tlnN5JJ+3ck+0stcSn5vzbXYvn5lz/JlrtcIKH6sFSUl8inb14jfdZb\nOwYqF2J5wt/ulQmPvRE75hQV2z1VcL1I+SaWc7//TQapdIejP1yX/H2cnHfGPq1G8/1PC2X8cbfI\nzE+/X62s9dZdU++Dq6V9u4rVtuVqhZvY5LNYZvJe9vJn041/oYrlfn/uLUfs0S+tWzioEcuTrpwh\ni5Y6Ef60ds5g5nwXSzQUuvHMrWSDdTrErvr2SV/J1Bm/xJabzozatqecfECD6DfdvmJVUK554HP5\n7Nv8idDmSz3stFUPOe3ggQYZnn3O+tdHjaL1rRXLltRn0/rL1rK3Ylm72mNu4uuMf/hFDuyGh2bR\n5kD6RB8JIXKJvw6XmYf6cE1E9tnnLHnh9TfknFOukHP+sat0WVsfqpENz9tor4gneY0qGdHEvkYs\nnQPZs8LDuhixhMSi/KgF6BwTCVgCkVK9B7V5jv6tQqf6x46yEqkpU7EMlMrD0ybLFtoUdueB2iwA\nf6PAPaVT3HrOveiUYu5NZ7ZF3xG9NUN6i5pbVkuIv1N90RvayCZucf2YVc7t7hwP87BjUwZKaZzm\nz58vZ511lmszVkQlly9fLlOnTpUbb7xRTjrpJCOWEEkrlZiyKWxjts0t7TruSvnvO3NMtgtUWP6p\n4hKfsi2W09+YLWMPutb5m4UeeP89t5bH7v5r/CnkdD7fxPL08x+U2+6fZhiU68/+dzP/LWut2alV\nTEL6O+3fd78iV9z4TKOoZXyhG23YQ+6+4TgZMTz5g198/kzPu4lNvoplpu9lL3823fgXqlgePLqP\njB+1QexWDWuE/oMv/pC3Z/0uc39eoa0A/NKnZwc5dEwf6dal4Y8q0979RW578qvYfl7M5IvQJLv2\nw8b2lf13WT+2+fN5S+T8W2fFlhPNbL3JmnLeMZsl2tRo3Utv/Sz3PfuttmqKf6holCVnC/lQD+00\nunv7ucOlS0fnD/H/mTFf7pj0dSMGrRXLltRnoxPI4oKnYhkJWYVr7gpxs8ZFW5xFXacP8xr+wcM8\nHqUj/lINLOpDsUZuxu33d22S944cfsix8o9z95eBm2h7cPNAr/nRBBZCqv/5tIkrpLLRg7Yt3zxf\n12seGCnC//GP67rIRAJKoFr/SOELaVPYMO5Rn/jL/FKjt9Ufeh898trTMnTdPjJqwBBzj5lbS7+s\nuqEVNtbhbx2tSSEtp0ZvT1uuc6eqxOEe1/8QzMQU202T2bjj2XsfPwrxt7iVQYjh999/L3369Gn2\nFK1E3nHHHXLiiSc2yo/3K5Eolo2wuC48+ewMOfTE200eRKk++981Ulnp/LKyO2ZTLL/85hcZuccl\nslTf8UTq1rWjfPLGVbJ2N6d5jz2HXE7zSSyXatPgPlucJiurnCarR47fXu791/GtwrFiZbWJVE5T\nobdp+202lvH7bCt3PvCqzJ7zk10tZaUBueem4+WQcSNi63I14yY2+SiWmb6Xvf7ZdONfqGJ54Kj1\n5ZDRfWO38N9u/EDm/bwytmxnOrYvkTvP21baV+IP/iKrqkNy6Pn/s5s9meaD0CS78B2GdZczDh0U\n24wmxmC74Pfq2LpkM2t20YCNPgOUlvhlrTXKZd212smYEb1kvR7tG+3y1PQf5OGX5jVa58VCPtTD\nMXv3k7126G0uf2VVnZx01bv6B0J4RENqjVi2pj4bziB7c56KpXolnqldk3kQNo/ezkNxosz6tiQU\n0+RCi0Io4M33vCS33/6U/PrTErnmqr/I/x0/Wqo0h1/z4aE7ok1pS/RxGyeA/fVHR+ec1DBnH9RD\nWr52QmIzcEoCcQTwT3NZ9IN7BJ+l+rVQ5x57fZJs2XtjGdt/c/xdw2yDv+GDhH9qsB6/Hlt6f+F+\nxZ9WqrQA5+dF5U1nnPvcKdsRTWcd5k0+HNB+dNastBmxKfrjiV8qP/30k+y99976F2P8dCVOaAr7\n8ccfy/Dhw+WKK66Q7bZzOi9BOSijWFKumsJWV9fKQO2wZ/6CJQbdpPtOk33GbrkaxmyJJd5F227s\nxTLvh4XmmO0qy2TaU+fJ1ltsuNo55HJFPonl9be9IOde/kTs8j+afrlsNnj92HJLZkbtf6W8/rYT\noYY43nzVUXLsoTuaovDHmTs0OnrOZY/rO03Og0pJwC/v/efSVh833XN1E5t8E8tM38te/2yirtz4\nF6pY7r1jb/NOn70Xj7joLVm+Er/dVk9H77Wh7L3jerENh57/pgomngC9SfkgNImufECfznLZSUOM\nGGI73tO+4r5PZeacxYmyp7xu/G4byMG79YnlR7nHX/aOLF7Wtpsko7OpW/8+XPujcJ557pr8lbz8\n9urNjVsqltmqz1hFZmDGU7H8cP73eKx1SagYjS6ax2+rhNFHcg312J1DAadn2BJ/heb0S124TJYv\ni8hddz8qUye/LEcdu4cce8p+UlurChCul1Jtslfvr5MSbTuIiGVIo5clYZTr3AiI8jhP3BqJwrE1\nYhmOQB1a8/ivuzMVJQENVpo7o0T/YcU9VlbRXpaV1ElN547y34/fkA3arSVb9dxILRJ/wlC50y8r\nlvg1qIFOTdjS8oS4JEpHNB5/MMFPC5rAhrWNbZk24y5X6Vu3+zpSgp5r9TD2g0ObCKbORPT88V/T\niCLEEE1cv/nmG/OOpRXOpmfbuXNn2X///WXbbbeVCy+8UNZaay3BgzDyo0wbzSx0ycyVWD7z0ody\nwLH/jmHeZ8yw2Hz8zKtvfhZrLjl4417Sv293QZNMNJnt33ed+Kwpz6+qqpHRB14j7370rdkHHcc8\nNeF02WOUN+9Vxp94vohlnb5P3X/432Lij6jiq09fEH+qac8/8cwMOewkJ0KNnZO9y4peaHcZd5X2\niOk8SI/58+bakc9ZaR+vNTu4iU0+iWU27mUvfzZtnbnxL1Sx3G27nnLS/hvbS5RTrnlP5v/mtJaI\nrYzO7DFyXTluX/29qglSc+wlb8vSFYklNLpLVif5KJbofOdK7Z0zvsOdW5/4Uqa/tyAjLC4+cXMZ\nslHXWFlXTZgt783+I7bsxYzX9bD3jr30jyP9Y5e+eHmtPget/nwX35Qb25EPkeQHXpgrH36+KLZ/\n/Ey26zP+WK2Z91QsL3/54dVpr3Y1EEv8BUQfxfEiWQQRE8wjo8qlPhBXdmxvHor9gTKN/pTpg/0a\nEqwLyJdzvpdf5y+R5Uu/lj79OsiWGklZsXyx1AWD2jusT2o1RF1aUi6duqwhSxehuUVAS0TRaP4a\nNB/ElExHPzGxxHGZSKCBQFAfMCv8GvOurZO62qC0U6GsqfRJuT7gf/rFJ1JR45M+HbrrO5joXVil\nTkOUkEvcbHgsxLpwK9vC4s7VfndMz63o2hoyWa/nFdEHz3ZlFdKxsr2M2X60VGhPlZBKiG0judRl\n01YXU02J5C+oPzdlZfgDS/KEXmGvvvpqufTSS+XYY481GSGW9gPRdIt6Ji85f7bkSizvffh1Oens\n+1t84XffeJwcffAOae9fq/fw3offKK/+7/PYvrddfZSccOTOsWUvZ/JFLB+d/LYc+X93xlAkiyjH\nMqQwc+Qpt8ujU2aYnLvtuJm88NjZSfc6+5+PyL/0PUykNdfoIL9+cUfSvNnY4CY2+SKW2bqXvfrZ\njK9HN/6FKpbDBnWVC4/bPHaZF981S2Z95bTYiK2Mzuy7U285cs9+sdXHXvq2px34eC00MRDRGUTO\nrvq/obH3/LD68f98r5/vmmZt8XLTzpb+/dgcef0DZziNFhfayh29roemzbnTvZwJz30jz/7359V2\ny0V9rnbQFq7wVCw/D1fj8dolofkrxqvEX6HMo7OZOo/FUEBIoPbAGV4m5b52JrYZVLXUx2rzwP7F\n17/I/Tfdpr3nvSRjjx0vfz39VBUAv27VsZC0YWxHX2ctvU5WRVZJe18X81yNJoRO0qd/c1wcG4//\nEFpGLBUCUxMCdRoV7KD3X6WqWr3+hwbXv0WWyyKdvjZrmgzs3ldG9MQvS+2JWL+t1GHeiKWZ4n5r\nedLYu97X2iOy+XnBPJpuI5KvYikVOsZmwDS5tTKJc8CtjmVM4z+6aEQQ00SCifWJEuQR+ffcc08Z\nNmyYXHzxxSYb1mOoEkwRuWxuyJJEZefTulyJ5UvTZ6ng3dDiS3/2oTNl7C5D0tofncagJ9Ln/jMz\ntt/FZ4+T8/+2T2zZ65l8EcutR10oH8/+3uDAeJJfv3fDatH+dFlh/MpZn/1gdjvzpLFytfZonizd\n/+h/5S9n3mc2V1aUyrJ596X185qs3FTXu4lNPohlNu9lL342m9aLG/9CFctu+g7fvRc6r1Dgel9+\n+2e5a/I3TS/dLJ984MYyapueZh6RnoPPfTNhvlyt9Fpo4q+zW5dyueqvW+g7kQ0dHKGDnbunJGYZ\nv2868wfsur52pNQ3tsv1D31uxhaNrfBgxut62GX4OvJ/4we0+MoTRZRzVZ8tPukmO3oqlvqg2YxY\nNjnb5hZRGp6QkdBRiD7EXn7JhXLZ5ffL8K32l3PPPU22376PtGvnNKPFaCflZWhmgXdVII3aKUYE\n06YJD/14BE8j2fMwKoH9cXKJyk6jTGbNSwL27jA9u2o1oxOcoN4uS3R+go5jiV5hRw1q0owRtwM+\nad5WuQBgfyzjpRLr8KCWLNoIccR2DEcyZswY2WGHHeQf//iHOd3a2lojlRUVFWYaX24urifTx8iV\nWOK8Mfi67fQo2XWMHn9NbEiK6y4+WNCBDAS+c6d2q+2Cevrt9+Xa+U5HwTux8QnHOfqvd8YiZth2\n9im7y5UXHBSfzfP5fBDLN2fMkZ33uzLG4tp/HixnnDg2ttzcTLJ6GKN1OV2bNiPtOWqoTHngb0mL\nuvKmZ+Sf10422wf272mGoUmaOQsb3MTGa7HM1L2crJ6AM9M/m+lWkRv/QhVLMHjg0hHSuYPTMmb5\nyqAcc+k7+rsFvywb0hqdyuSuC7aVMu1QBukbHSPw7LgxAhty5m7Oa6GxV9qpQ6mRSnSyY9Or7y+Q\nWx7/0i42O0WHPd3XrJQv5i5LmrdcOym86aytpKeObWnTyVe/J78sTNx02ebJ9jQf6gG9wjbXrcQt\n5wyXrp3UOTQ98/qPMmn692a+6XvCmahPU3AOv4pKLBM9EL///tty/rkT5P33F8upp42XCy4aL4Ho\n81REXy5Dj69+E+UEdURIo/EktE+MJfuPWvy62MbVZxplg3bgg9T4Qc5Zx+9CJ4CG2qhZ03mwChjG\nsayOiuXE6ZNkSK++MnbgMOORMZeM3lK4zWLr8gAEfobwUAb5s+9aYl1VVZUsWLDAiKX9ObPvTSI/\npBKi8thjj8kll1wi11xzjZx66qmC8S+xvWPHjlKnvTcjX2VlZR5cactPIZdimcpZptp5D6IsR2nT\nzSXak+kGvbvJV+82jrCddt4DcvuE6bFDnnLMrvKvK46ILefLTD6I5X5H3ijPT/3YIGmvnRr9MOuW\nhCKfiJlbPVx01SS56ubnzG74NfLYPf8n4/YYvloxGD9x+z0v1Y5Nasy2w/YfIRNuadwL82o7ZXiF\nm9h4LZaZuJfd6ilVlKn+bKZaXnw+N/6FLJZH7LGh7Pfn9WKXOuvrxXLV/bO1syrnOQrCc/5xm8p6\n3Rt6Jb32wc/lnVkLY/t4MZMPQoOeW689fZgZksUyeO+z3+X6B7+IjbNr18dPmw4TctSeG8o+O60n\n3/+yUp5W6Xl39u8x/tgPdXDi/hvJ0I0b3q9ctKxGjrt0hv7xOL7k3M/nQz2kctWpdN6TqfpM5Xwy\nmaeoxBIPsPZhGJCc5ZBceO6LcvnVj8muu2wmt911pqzfp1LQYRPuf3Rcgr8smE569F1OJ5apVhDR\nJgTxconf8qmkhPmsWOZheCqVa2IeVwKuYjlNI5YqlqM1Yon7DR9zF0QXzGvDdp3rUbzbCJH86quv\nZPz48bJs2TIjnTgbyCc+2I6/7OPnbdGiRVJdXW3k8oQTTjAyioglpLNdu3YyaNAg7y4kQ0cuVLE8\n8az75L5H/hujMOv1K2XwgN5mGc0v0QwzPm2i2/Bvo1u6/LwD025u61ZeKtu8Fsuv5y6QTf70d/Oz\njPM96ahdtOfWI1M5dZPHrR4gjOiJt6oa/6o4aZftN5FddhgsPbp3kaVLVskHs+bJE8+8K6F65/cK\nxPadly+RQRv3srvkZOomNq0Ry5WrquWEv90nwWivt7gYDOvyxoyGiMvOIwdLh/YNzfz+ou//7rrj\npua6M3Uvu9VTqoDbkliuu3Y7ufgvm6OhWCyh05hy7dnYJjRZraqps4va1HW+PDX9x9gyZiAt/z57\nK6koa9gPQzZ8p5JTWR6QDXQcS/SEbNMX3y2V825x/shj13kxzQehGTFkbTn7iMFpX/6jr8yTJ6f+\nENvv+H37y+4jG/49Ceo43fN/r5JlGkHu0rFMeq/dPtbrKXZC5zMX3jFLPp+7NFaGVzP5UA+pXHsq\nYpmp+kzlfDKZp6jEMh4MHnKRIJpPPTFXrrr0RZm/UMeEO2prueG6M6ROH+zxPiUGqsfDk9+H/BBL\n/KOHf7T0lxZ6ioUAIGFVMw9ZJl/SL5Tf8I9h0mzcUHAEXMWymYil0yNs/twZNhpphRFTpC+++EIG\nDx5sRDK+gvDzhX3sftiGefQIi3cply5dqg+I+uaz/jzut99+Mnmy03QvvoxCmy9UsTzu9HvkgSfe\njOF+f+plMnTTDczyi9M+ln2OuDG2LdUZL96/9Fos//qPiWY8STDCT8dsHVt04349U0UmbvWAQia/\n8J4cfPytsV89bgX79efz4TtPlgP22sYtW1a2ZUssP/h4rpHrdE76rJPHylUXOu+jZupebq6eUjm/\ntiSWwzftJuce7ch9KmyQ55V35sudT329WnaU9Y+jNlnt903TjLO/XSI3PfKF50Nc4LzyQWh23WYd\nOeXAAU0xNbuMqOQDz8+N5dtpqx5y2sEDY8tuM8FQWB7UnkxfePNnt2w525YP9ZDKxaYilpmqz1TO\nJ5N5ilosbURl0e9hueD8x+XOey6RrbcaI7ffepVsNKBCtENYfScMONFBEDrnQcc/EMCQmTfvXKpc\n6ggO2rwRJmotUzfHEh4tWmWcsZI4U5gEXMWyCCKWqBVEIk866aRYU1jII36+4sUSTV0x5AjWz507\nV+bMmSNLliwxTWAhlujU57nnnGZ+hVnTzlnnm1jGv5f30O0nyUH7bpcQ71PPvyeH/uU20yRqQL91\n5JM3ro618MDg8dvsdqGOA4e7OfV05/XHxsZYTH2v1uX0WiyH7Xy+9vb8o7mIA/YaLo/e9X9pXZBb\nPdiCUB833P6iPDblndh4lXYbphhTFPV8xoljZIC+X+lFypZY/jT/Dxmw7Vnas7v+4k0xXXPRQfK3\nk3Y3uTN1L6dST82dXqo/m82Vk2i7G38vmsL27dVBrj99S/03JfXnoWf/+6NMeK5BaOKvc+MNOskR\ne/SVwX3XiF9thhb5Wd/je03fG3zujZ/0D5mNNnu2kA9CM7BvZ7n0xIYxK1OF8cjL82TStB8aZUdZ\nY0esa5q7xg9XYjNV1YTkfW1m+/DL38kfS2rtas+n+VAPqUD499+3lvV7OM25k/UEm8n6TOWcMpWn\naMWyKaCHH3pNLr/0QVm2JKy/lEvltDMOlWOO30nK9b3jgEqjPiLrLlYssTd+qekL5BpSMlFNHSvT\nGfAP25KleMlkdDIZpWJb7yqWBRyxjK8niCHel8RwI5i3EUqIJUQSy/isWLFC0EkPEsa+/P3332Xx\n4sU6vmetdO/e3fQWG19uIc7nm1jivdVF2kSyTJucrdGlgyvSPxatkBodTqRXzzVd8+XzRq/FMhis\n0/dUq0xzvDW7dmwRqlTrAU3MF/y2VH6av0jreKX0WLuLrNdrTVlrzU7NRnNadGJp7OQmNq1pCpvG\nKWQ9a6r1lOxE0vnZTFZGsvVu/L0Qy2Tn2dr16Ailh3YQ07VzmYlM/vjrytU682ntMTKxf6EITbrX\nqr/elX+laQLbvrJEx0yukwV/VOv73Q1NmtMtM5v5C6UeAvo+Xod2OixcfURWViGYVTypKMXSRlNs\nNeEPWvi8NnW2nH3WnTJr9mcyqO8WsuXWfeWBx/6qY1tGpEMn5EBHCOiGBW37VQzj3rH0+SCaqf5p\nDFIZJ5Z2t9T/kKf7MxUKAVexLPCIJX6WIJI2+o86SSaWNl/8cCIQSrxzid5k8UFUs337hk4XCqWO\n488z38Qy/tzawrzXYtkWGKdyjW5iUyximQoHr/K48S8msfSKb7rHLRShSfe6Ci0/68H7GisKsbQP\ntHj4TZTQuBVb8JeB2bN+kVv//Yo89NCz+nJ4qfQf2E0ef+R26T8YMqlRF9MUNu7dSlMkIpqwQ5SU\nKNkj2ONjivI0WanEvN2MeaaiIdBILPWqfPo3hULtFdZWStM/ztj1mGIbPkjxwmn3sTKJ3l8x/AiW\nEXlB5z2YxounKaTAviiW3lYYxdJb/vbobmJDsbSUsjd140+xzB73ZCVTaJKRye161kNueSc6WlGI\nZaILi1+nAReNsmgsUoORYQ08zv1mmTz56Ntyz91PSE21SFftzeyYo8fKyX/dQ0oqfFKC1y01herr\n9H0BHdRdhRXDkDjNZa0pojmgflQ2sd1JmNp5RD412eyYt5swz1Q0BFzF0i1iqQQQFMctEhffLngu\nEEz7x574dzAxXwyJYultLVIsveVvj+4mNhRLSyl7Uzf+FMvscU9WMoUmGZncrmc95JZ3oqO1CbHE\nO5IaKDFiB7nE/IL5tfLOG9/IvXe/INPeelOGDdxMNt18fbnt7hNNa9hyFUzTS6z2FluvPcUGIpX6\n8B+VRd2iAyxoJxj1GrFRKTBiaQ3SPjwjYhm1Bp0zCUUyFR2BRmKpUhU/juUEFcuhvQtnHMuiq5ws\nXBDFMgtQ0yiSYpkGrCxmdRMbimUWwUeLduNPscw+/6ZHoNA0JeLNMuvBG+7xR20TYomQECKVpsGq\nyh0664lopzwrlkXkw/d/kmenvCc33/24rNWhp/Qb3EEm3n+5bDQooPl1QHepUl46+Huko3YKq535\nwBVRnmkKqGIZjWg6cad4c4Rg6sf6JqjHb8YyU1EQcBPLiRBLHcdyTKJxLPXqizFiWRSV6nIRFEsX\nODnYRLHMAeQUDuEmNhTLFAC2Mosbf4plK+G2YHcKTQugZWEX1kMWoKZZZNsRS1ilJgQXfX4s1Kog\nlkiwtkR+mLtKpkyaKRdd8oDKZJWMGL6xHHL0NnL8X0arC9ZpbHKVBMLttFNYFUskyGV06jPjX2Ip\n3iCxkWIJCm0hNSuWGrEcM3CYuUNwl5iYdvR2oVgW3h1CsfS2ziiW3vK3R3cTG4qlpZS9qRt/imX2\nuCcrmUKTjExu17Mecss70dHahFiiKSye6n0+/fKjTSye6rFSx6jU5q0R7el35YqIvPjcDLn5X6/K\nu7NekaGbDJddRm8iF156hHSo1IavmiegY12u9jqliULa8jC1iWJpSRT7tFmxZMSyqG4BiqW31Umx\n9Ja/Pbqb2FAsLaXsTd34Uyyzxz1ZyRSaZGRyu571kFveiY7WJsTSOKS5+nodXDdotNIfKNN3I/1m\nHpIJJfSrOr70/Bdy733T5bln39ReLEtktz36aac+h8lOIwaYSKXubnr9hGBiH/QAijm8cxmTTqxi\nxNJQaAtfzYolI5ZFdRtQLL2tToqlt/zt0d3EhmJpKWVv6safYpk97slKptAkI5Pb9ayH3PJOdLS2\nIZbmfUhcvuqfhh7xfmWgtERFEIOI6LI2d0Xvrj5R2dSo5Dtv/yrPPvuu3HbLk7Kq5jfZfptxMm7c\nBrLDjpvKZpv1Fp+2iK1XmwhoXz5OjNIZpsTp9NJGLRmxTHTDFeO6ZsWSEcuiqnaKpbfVSbH0lr89\nupvYUCwtpexN3fhTLLPHPVnJFJpkZHK7nvWQW96JjtZGxFLbsSJs6VMTDGuUUt3PRBq1aWxEEMHU\n9y1VMDGoiA4uIjU1iGZWyNWXPSNTHl0qn31XI2t3miEjd+gqO+40XAVzGxkwcAMpNT3HqlyiPDSz\n1b2dJrZAHRVLzGI1kmk268zyu3gIUCyLpy5TuRKKZSqUspeHYpk9tumU7CY2FMt0SLYsrxt/imXL\nmLZmLwpNa+hlbl/WQ+ZYtrSkNiSWsD8dAiRi2q4a14MMQig1/hid4r1LHZdSOkgwFJCyEp88eM98\neeut+TJz5hSZ9dlrUhYolVG77SRjdt9Bhm+ziWyyyTpSohHMsJFHNKl1xr2ERerAE450RmtHO5Bt\nnIxo2hdAcV5R84zLh85dYKaO+CKqWt64DGfz6usaSSwKjCvUSO/qu3BNywg0EkstAn+0qNbPEkVu\neoVlU9iWgc3TvSiW3lYMxdJb/vbobmJDsbSUsjd140+xzB73ZCVTaJKRye161kNueSc6WtsQy0RX\n3mSdE3V0VsbPY6iRsKyQRyZ8Lq+8OFdmf/65zP7yA30b0ydjxuwi++wzUkaM7C/r9u4uHTv4pLo2\nIqVljlCaHj9VLvxqlPA8fx1iotGkM+Y9TTMWpsqlH2Nk6sqo/5kJFs0OaK5brdt1PE3p6hQQzWcW\n4uedrTFH1a5sdU1UXk3hWI72bmvzctoqAo3EUvHi7xeNxJJNYVvFN992plh6WyMUS2/526O7iQ3F\n0lLK3tSNP8Uye9yTlUyhSUYmt+tZD7nlnehoFMsolVBIx6os0R5i1Srr6+t1rEtVx2hvPNXV1VJZ\nWSlLNQQ19T+z5ZlnnpdPZn0tc+f+IHWhehm37wGy515/lm22WVe6rdVZunT1S22duqIKBj6RSK2W\npWWGtVdZGy1UY8S7nsYcdeJ0AqQnA5FEJFXzwzIbOyMa6pZGM5nN+hU3dZacMhrNO5FYZxVKRBlM\nmSLQWCz1jwgB/QNDNGI5AeNYasRyLIcbyRRuz8uhWHpbBRRLb/nbo7uJDcXSUsre1I0/xTJ73JOV\nTKFJRia361kPueWd6GgUyyiVcFibwGrvO1YsMY+PTWGN+qGha4nGDEPqaa+88KVMmPCkNpH9URb/\nGpLqYFB2Hz1Kxo0fIcOHd5Fu3bvJGl190VjhKm1sW606px0GGbHEu56lEqmHyGoU00Ql9UgQTBVR\n8SFCuUpnIJdYUakfjTIiGmaEUxdNObpDY/PEhsbJlI1MKMsmREeZMkXATSzRFHYLjViOHsRxLDPF\n2+tyKJbe1gDF0lv+9uhuYkOxtJSyN3XjT7HMHvdkJVNokpHJ7XrWQ255JzoaxTIBFcilFU0TtVQv\nC/uqjFqKVOiYliVSDjdTV5v2yh9y862PyIcz58iqpdWyIjhfxuwySg45bKyM3LG7dO7aRTp3LNVG\nrFUqlkF1R5geJFHflcT7njEx1Bk0WzUvYuK9T6enWie6iKarTt4GsUQ5EN9m5DImljgQIpdIFEuH\nQ2a+3cSSEcvMMM6nUiiW3tYGxdJb/vbobmJDsbSUsjd140+xzB73ZCVTaJKRye161kNueSc6GsUy\nSsWKpIWEZQimk3zqkNqc1Y94Y5lKp9/oob4eKSVwO/W1aVMXyb/+fbN89eW3UltbI7/8/oOMHLGd\nHHzwftpMdoh07tRZ2nXWsS41eySikcuGoqNRSOgJhBIFQjy1uWpcHkQxHTHE9vjUjGA2Eku7X9My\n7HpOW0LATSwZsWwJ0fzeh2Lpbf1QLL3lb4/uJjYUS0spe1M3/hTL7HFPVjKFJhmZ3K5nPeSWd6Kj\nUSyjVILalBXvVeJjkxVLuFkIvejo/xGVSPgmWsmiCSv6ftWhMXWlT3RoTHn9tZ/kpptukf+99aF0\nat9Vfv11kWy7zUg55ZQTZOTO3aS8slzat9dxNE2TV+yPYU4w3EmtFgJxrdCyIJVaWLxY+hFttB+d\nNQKKKRLOELKIqVvC/kgUS4dDZr5dxXL6JBmiTWH5jmVmWOdDKRRLb2uBYuktf3t0N7GhWFpK2Zu6\n8adYZo97spIpNMnI5HY96yG3vBMdjWIZpYIIJT5o+op3K23HPRaabnKGFIFcqvBhqBK/32my6ry/\niPczyySkw5SUl/rlg/cWy2UX3yKv/3emdG63rsxf/KMMGTxcTjz5CNlr33WkQ2cdzqQspDIKoXTe\n3sRQIk7nPLrKSmXcFFLrK4XG2GQF0U4hls3Jpd2X00wRcBVLt3cstW5ta2hbg5k6J5aTPQIUy+yx\nTaVkimUqlLKfx01sKJbe8qdYZp9/0yNQaJoS8WaZ9eAN9/ijUizjaeg8eoS1chm/CWIJx8MnrINW\n+vw69IcfUgitwPuQeHsSY0y2M4JZWx2RilKfvDZtldx2y0Py7oyPpaS8vSxY+INsNKCT/O3sI2X0\n7tvIWt0qpLQhSGq0EFHQmFlGzQNSiYClT2U0cYqXyvj5xLm5NnMEGomlFttoHMtmIpboGBiJYulw\nKIRviqW3tUSx9Ja/PTrF0pLwZurGn2KZ+zqh0OSeeaIjsh4SUcntOopliryheg0JlodeVnVMEdM8\nFVuhBugUBx81xagIQkhXLKuXZctETjv7Mnn33ZlStTIiVavqZOON+8kJxx8mBx28tazdwy9Bdcby\nUkQvNXKq5eAdTjSz1dErnN5itWQJJBNLbIwXyvh5bGPKFgFXsWTEMlvYWS4JkICHBH7fsl/So6/1\n4bdJt3FDZgiQf2Y4shQSIIHiIeDTdxgb+1oeX1vERCYhk1bYMEVyprgSSGQgoMOI2PloGArLy5YF\npaydX2Z+9JtMvP9lefyxF8UXrpSAvpvZf0B3Ofa4fWTcASNk7e4Bqa7SaGeFDlWih0Pp2D8AX7WH\nxmE1OU13daOmpk13V8tscvErGwQaiaVWVvw4lhMZscwGcpZJAiTgMQE3sfH41Nr84Sn2bf4WIAAS\naJMECkosxYwriWihjUyi/SpsT5fV7WIyqauhevX12hmQbkZ/sghfRiIhHbKkk0qjs+27eXVy/90v\nyISJj0iNruux5nrSZY0KlctRctrfdpU1umkEszYipdq61ikD+6lg+nFMJ+GYaHaJaUTbyprOhKzv\nmixNTNTuyGlGCbiKJSOWGWXNwkiABPKDAMUyP+oh0VlQLBNR4ToSIIFiJ1CgYgmhxEd7bjVTlTeY\nZHzSVeEIenpVufTZZrMh7Vl2TakPo+dZUZnUrdU18tOPNfLQhBlyz32TZXnVMllnjbWlWw+fHDB+\nBzn2+H2kZ08d3sSUXa/TsJTUa0RUl+t1ZQlOQ48F4Yzof34dAxORSwhmw0lhwazASqYsEHAVy2Yi\nluhsGPUZDW5n4exYJAmQAAlkngDFMvNMM1UixTJTJFkOCZBAIREoMLFEtBIKEC9q0AErbbrNhBBR\nBVBBHYYkjHcwNfkwfmVEQvUdTI+zWAUZLFM3rdOo5LKlQZk3b7ncfddUuf+Bh6XCVy7rr7+u9Fq/\ni+w7bns5+PBR0rWLTzW1TkpDEFo0g8XhIhoV1eNHT8mciT0dc65Nz9fsyq8ME3AVS7eIpZ4HxTLD\nlcHiSIAEckKAYpkTzC06CMWyRdi4EwmQQIETKDCxdKcNyQuFtKdYDUdiuBLT/FXlzr5GCt8LaR6f\nbovU60djVIFomAqd86DjnoULVsonH8+Vhx9+Xx6fNEX3qJZNB28q3bqXyu57DpczTx9vTkJb2caa\nvcJlIZZ+RC+RKJYOhxx+u4nlBBXLob05jmUOq4OHIgESyAEBimUOILfwEBTLFoLjbiRAAgVNoMDE\n0mmQat+pbEoeghdG+1Q1O78/2hzVSB7MD0ljlr4QVBPBTDN1mqw628I6rEjAF5BaDXJ+++0S+ear\nb+XFFz6Xp56cJstWzJd+fYZL775d5bADBsnI7beX/gM7i762qZFPyKpt/qpHUcFsaAqLY+MkYraJ\ngzFlmICbWE6EWPbqK2MGDUO1m4/5ewIWsKxVg9no3xjMOn6RAAmQQL4ToFjmbw1RLPO3bnhmJEAC\n2SNQYGKJprBIqgCwAciaNQWzXhd12X4gd6tFEVUsnUhmWJvJBhuijrpW9VA/fn03s1RKfGWm6G++\nWiGfffKtTH5ysbyskrm8doFsPfRr6dZNm8juN1o/+8iaa2vTWJyHOi28FsdEhz7OSmyInitWMWWF\nQGOxVOJaB9VaB0sUvxFLjViOGUixzAp8FkoCJOAJAYqlJ9hTOijFMiVMzEQCJFBkBApMLCFpcaKG\n2SQJcgmdc77iptF3L1FO2PTianJpBsQxMY9CYYU6hqXOaiDSrP3my3p5+53P5Msv58ltt06WldV/\nSP++68t2fxoiO+64uRx40LZSrsOThNRbS+GZurPRVBsSDUePg+LRmZC/RjNp77KRMnNIHMscWqfm\n3PUUjJzqNOLDxqBurtUp3hmFHJfrWXbRKbY1TU7UNlag2Yx1+MQdSPTYpgMkk6GgvxqJpV4J2DUS\nS0YsC7p+efIkQALFR8BNjClmxVffvCISIIHiJ1BgYulFhTSIW0R7mf31t9/k6Sm/ybSp38gLzz8r\nofDPsvmQobLrqE11Olj22HNb6dJBpVRPtV4N0a9iCS/EB8KIDn98uuAvqdYVKpZhjYwicwiZHPmM\nhFVK8dIn/BbvbaqIOmN46j4ql5j3SYVuXluXIYtNEwrEBwlT5HGGXHHm7bp2ul7HUimC1EgsFWij\ncSyj71gyYlkEFc1LIAESKBoCFMuiqUpeCAmQAAkYAhTLZm6EWMc/Kn1htUJ8SkpK5M23F8s777wl\nb735vrz/3ify++8LZPCgTWXkn7aQESOGyW6jt5K10ERWU70GGE2TXHXFcNgRPL8OeYJhSSLapNen\nPdZijE2fDlUCEXTWIWSpguq0qdW1KocR7Is8mPpVWiGFWE6U7HqnTCeflVBM8cH+DWNyJiqlUNY1\nK5aMWBZKVfI8SYAE2ggBimUbqWheJgmQQJshQLFMoapNs9ZoNBHZ61Ts6kIBaadONnPWQpn+yn9l\n6tSP5f0ZX8uKml+lT89hstc+W8uWW/aWYVtuJgM3XcMcRfv4UYl03vH0BbUZarR1rM+vTVwD8c1c\nsQHSh6aq5SqaCF1qso6Iec0SdU4suaR4wbTZbEEoN1q23VSgU4plgVYcT5sESKDNEqBYttmq54WT\nAAkUKQGKZRoVG9Ymquhttk5qVMdK1e20B9maiFTqu5UfvrdEXnnpDfnog3nyySdz5btfZklF2Zpy\n+CF7yQ5/HiB9+q4pAwYPMGNhhhD1NFLpRCzxzqTPjygk3p/E1Ca0g0XUE+97qgCiwyINNKLprBlN\nBZuZDIFmxZKd9/BOIQESIIG8IkCxzKvq4MmQAAmQQKsJUCyTILRNYLEZTVaRbOSyPqId7xjhU7NT\nycP7k6G6iJSpYH73bUgeevB5eeN/b8vPP/8mP/34s2YJyfDhw2TM2JHyp5GDpf9GG8naa/mlTruQ\nDWAgTQQQNZnYIebNDCKbTnTTWYEegfR4EEu0YkXEMlkr1mh5mqv55Fxa8/nyPEezYsmmsHlegzw9\nEiCBtkaAYtnWapzXSwIkUOwEKJbN1LCVyfhs4VBYI4x+05IVjodk3o/UUGKwzi/l2oJ10aKwTJn8\nukx5+nmZO+93WbJohfyx6DcZNGArOfKog2SLLXvIuuv2lI02aidwyzp1SMcxERWFN6JkGKK1RNim\nfswq/YIQNtcW1u6qWWPFYN4mU4ZdKOypm1hOiHbeM5bDjRR2JfPsSYAEiooAxbKoqpMXQwIkQAIa\nbIsPzRFI8wRU1sLaYhWd8WiLVh2yRIWwtN7pgEebsWpXPOpwAY1GlkiZ9uy6sioizz7zkbz84hvy\n9VeLZcEvS+XnBV/KWp03lsOPGqdRzPVlw35rSLfuXaV9e+yLMlQuTdgyKpKxs9IDakc/TiQToolm\nsvH2GMsYnUEetUebxU6xFfPRzVgs9OQmlhjHcguNWI4exHEsC72eef4kQALFQ4BiWTx1ySshARIg\nARCgWDa5D6xn2+avTTabXmH9+r6jiWRaMfNBa+rU1Zz3IyOmp1V0vFOikUh9E1MlFMHBd2dUyaQn\nntdeZGfL3K9XycIlXxmB3HPPHeT4E8fJBhu0lz4brC0V7XzG+UyTVy21od8giKV+TO+wWqJPC46l\nBmvEiJz432cimjiynijez7TJZsWquNV2cyFOG4klLksv2Y5jyYhlIdYoz5kESKDYCVAsi72GeX0k\nQAJtjQDFsiU1HkEnO8bcEuyNRqwQPsQdHW9DZzvoERZNXbGlVpu93n3Hh3L77bfKwt+XS83KkFTV\nVsnA/v3lvHNPk82GrCm91+sg7dtVqDuGdcxLbXqrpaGzIHwcr9QCS/AOpp4JDqBH1X6FNJcOTKJD\nl4RC9VJeip5lYb/O+awmmEUilXqBOrKnM3CK6XRX5+PFkhFLEGIiARIggfwiUIhiefHFFyeF6LYt\n6U7cQAIkQAJFRIBi2aLKXBbdC8KGD3rRwRSRQZ1oCsdJm6N9Jo6oW1T8dGPVSpGunXzy0is/yg3X\n3yqffjxXKss6yk+/zpOuHdaUI446UI46ekfp16+bRh61Wa0GQEvKfGZMzIi2vw2UQiB1qs1tIZVI\nYY2YhrWdrukxVo/jNKfFdnxwjnYeson54kmNxFK5+JSLjVhOnD5JhmhTWL5jWTz1zSshARIofAKF\nKJbJWjOhNmyLp8KvGV4BCZAACbSMAMWyRdwW6V7xUhntsRWyphbZqNWpdhmLXmHxXqR27aMfRDsh\ndl2RVWp0uJIO2pvsF58tk+uvvVOeeW66dGm3gSxdVC21wXrZfY8x8rczRul4mCqYOkZJSZlGLTWK\nGdF2sv74bmH10PXhWl1fb2TT6fxHD2BSvFDivO2ynUazFfDEVSzd3rFEfUX/HoBaYSIBEiABEsgc\ngVV335y0sCqXbe1OODXpfu1dtiXdKUMbKJYZAsliSIAEipIAxbJF1bpC94KGWLmMzsMUbVJnU/3T\nJbx3iQ/Ux85jqVRLQBSxREspk6pVEW366pPlSyJy683T5K7bntR19drLbK1UhZbJsM03lqOPPUD2\n2GsrWadXiWn26o8ezx7WeaUS0Ut8ILOYxrbqPM4X5wqhRMI8PoWfXMWymYiljS4XB4nCr0teAQmQ\nQPEQgFi6CWS6VwrhpFimS435SYAESCA3BCiWLeEc72pmf6yANGJq3nJUdcO8jQjq1IYxtScebAlG\nqnWdX0r9lbqEzoDMriqS2gVQSKOY7f3y+GP/k2uuvkNmffq59jDbSUoDHWTdXhvIwYccLMccPUJ6\n9w7IsqVh6dhZG72qFZlOfnBInUdkFD3UOoKpE5OiG816rIBKFYdOuYolI5am9vlFAiRAAl4QcGvy\nmu75rPXht+nuktH8jFhmFCcLIwESKDICFMsWVCg64jHRQngaPkYVMUwIooT1uqQZ8J4fem2NaFQy\ngql+kBcCqc1jw37kdSKIGLIEHe8gYTM++KoNanNXf1i++Px3ue3mSdqj7H+kujog5eVrSrdu5XLM\n4TvomJgHygYbBSSow5qUlmoDWOysZQXMK5U2eokCmyYcEFIZPXDTzQW27CqWjFgWWG3ydEmABIqJ\nQKaill5HK1EnFMtiujN5LSRAApkmQLFsAVGIJZIdBsRRM2f8SacBrAod8qhM+lQsIzZaadZhR/0f\n4hdNdfVhKSnRqKU2XUX/r85YmM7GVdpEFuNllgRC8uWcxTJhwnSZMuVpqV1VI13bd5Cua3ZUuRwv\nhxz6Z+naw689zEakohIH0P3txzSJtUeLrbQrimLqKpaMWBZFHfMiSIAECpdAJqKWXkcrQZ9iWbj3\nIM+cBEgg+wQoli1gjEavSFA0JGcKJUSEUH0S7VrNR+0xHNfUFJuxXo3Up/39IFnXFD869cEySgtI\nnb74Z8RVMwSDGgPVKZq7VlcH5cuvf5P773hJHpj4mHQsbSfr9OwqnTuXyFHHjJPDj9xTOnSxZ2aK\nbBNfjcRSr7jRcCPNRCzh/aiHuJpqE8x4kSRAAiSQKwKtjVrmQ7QSrCiWubpjeBwSIIFCJECxbEGt\nQUKQoG+Yb9A4uwVbkaLG4iw0/m7YKbre2df5bnhDExutp6K5LGQTzV1/+22FfPLJPJk29U15cMLT\n8seS5bJBjyHSp+/GMnLktnLmWdtJp24aBVULRq+nGhQ1pwPhwqHNB4aMD9Zp81wjt75aXYHM0CyE\nVXWcExVd57yc6xXTs22drtOhTaSdlhW1ZM3pVXIVS7eIpZ4wxdKrWuNxSYAE2hKB1kQt8yFaibqi\nWLalO5bXSgIkkC4BimW6xPIof60GORcsWCK//rJQXn7hGx2u5F6pqquRjTfYREraLZNDxx8gp/x1\nVx2eRPVQhzQpgSNGE8bA9GlUNKDGaCKjEEtYph+miXlVTzPBe6AQS6io0zGRI554l7RexbJct8S1\n69VcXqRGYolrixvHcoKK5dDeHMfSi3rhMUmABEjAEmhp1DJfopW4DoqlrU1OSYAESGB1AhTL1ZkU\nxBqjgRpYhBQikvnTj0H55usv5JGH39DP00YE+/UdID17dpZ999tLjv/LtlJW7pM69caAuqIJVEZq\n0ehWrxfjcCLqqFFKhEdtgksicGnG4LTCCcmEbOpGzYpfsvjP6+QmlhMhlr36yphBw3DK5mMuK3qp\njFh6XXs8PgmQQFsh0JKoZb5EK1FHFMu2cqfyOkmABFpCgGLZEmp5sA+atobU9UrVB9E0FmqnQTr5\nZPbv8sN3c+SlFz6ViROekXDIr8OS9JQthvWRXUaNlAMO2km6ruGIYFiq1BuhZBVaiH60nHivhHf5\nTTASzWPR9BWpXD+6MmpjTrTTbPD0q1mx1IjlmIEUS08riQcnARJo8wTSjVrmU7QSlUexbPO3MAGQ\nAAm4EKBYusDJ502QSYgl3pkMmPBb2InGaWdB2sGszPzoV/ls9hfy+vSvZMrkV2R5zW+y8Yb9ZPOh\n68nW2wyUgw7ZXdZdp6vU1wc1gglRbBhL08iiXjxEEuU78U1HK80LmyayGc2A7d4HLI0ew4F9GMWl\nSVNYRiyVCRMJkAAJ5AmBdKKW+RStBD6KZZ7cRDwNEiCBvCRAsczLamn+pKB54UhY6vUT0EE1G9zO\n6JWENIRXpu9UfvT+Qpk180v53/9myVNPTZZVwYXSZ/0+st12m8tBBx0oO+88RMp1eBKIKnqdxVuU\n6N3Wb965RKlRc8QBo24Zm+I0C0UsGbFEbTGRAAmQgOcEUo1a5lu0EuAolp7fPjwBEiCBPCZAsczj\nynE7tYh577HGaCBU0OmZVd99RBNV896j7q0iWK8RPDSX/ezTpfLKK/+Vt9/6SN7637vyx9I/ZIsh\n28puo0fJjjsOlREj15P27fQdTB000++r0yJCplSnXLS3dcqLbyqL80PHQJrR89RsU1i+Y+l5HfEE\nSIAESMASSCVqmW/RSpw7xdLWIKckQAIksDoBiuXqTApiTVhq1fWWaMCwUs+3XB2yTMLaDBaOh6as\nEMBwJCQl2lMPtDOoffSUqx/OnrVAnnz8dZn18ffyxluzZEVVley0/Z9k7/22lN33HCp9+66puUNa\nnsqlHsHp8VVDn9F3KjVA2ihRLBvh4AIJkAAJkEAKBJqLWuZjtBKXRbFMoXKZhQRIoM0SoFgWaNU7\nYrksKpba8Y727KrBRtNDrNqgvjsJuayRkhInpKgDi5jtAe1CNqzhvU8+XiKTn35L/jPtQ5k9e6aU\nlAZl/wNHy1777CA7/3modO6oehlWMUWXs5rCYRSI5rLO+5iYh7xasYxEQ5luv3RNQVn6ahSx1GPg\n3dBq/SzRczTvWLIpbJbIs1gSIAESaBkBt6hlPkYrcZVuv+Ps78GW0eBeJEACJFD4BCiWBVuHGEcS\nQ4VAHPFRi1KJMsm4IBrIBnUVerPBCg1XojdXHbsSzVp9mNVN/31rvky473F5+aV3ZNHiBbJhn/5y\n3HEHye5jN5FNh/SWkOYpKalXyawVX0THrQxg3Eo9VliHHdEXMzFeJEKkYVitJj9e1PQgNRLLqPA2\nEks2hfWgVnhIEiABEkhOIFnUMl+jlbgSimXy+uQWEiABEqBYFvA9EHvf0YgkLgRyB7u0U40yeOPM\n5QAAQABJREFUGqmE7OGjAmqHCdGlGpXGCm3l+uvvQbnrjmnywrPTZd63v8ji5T/LqJ3GyDnn7iUb\n9hsk6/dBh0BIGHKkXktEU1mfRHTME5+/Un/R2u1OLi++G4ulI7xWLCdgHEuNWI7lcCNeVA2PSQIk\nQAJJCSSKWuZrtBIXQbFMWpXcQAIkQAIahGLbjcK8DdQf9RVKZzgQOKMPMumMN+nIJaKYao0mUqnm\naaOZmDE9vkbMG5TaVY8EgxrXVDP7cd4q+feNj8mr02bKwt8Xyaq6xXLdFZfKqDHry0YDemrkUmOV\npRDLKu2NdoU2u9UIpqylx0BTXG+Tm1iiKewWGrEcPYjjWHpbSzw6CZAACTQm0DRqmc/RSpw5xbJx\n/XGJBEiABOIJUCzjaRTSvHqktkx1xBIOaZrFVulUbdNEKdH0VTv2iWg00UolIptGQKFh6PynXEKR\nCv1FqfFMzYNWrbVVEXnowc/knrsfkt9/WyHzf/lJNt9ssPzjvL9I/wFry6DNOmh+RC5XaRmrVCzR\n2U+Fvm9pD+L+i1d3ykpyE0tGLLOCnIWSAAmQQEYIxEct8zlaiYulWGakylkICZBAkRKgWBZqxarH\nxcQSVqjvU2rjVv0gcgnTtGKps9b5YmKJyGatRh0rtJMfJ9pYAv/UfCF9bbNqVcR8rrvmMXnk8Snq\nrBWyfNkyOfrYA+WMs3eVdXv3kPJ2KFY7B9Lj+PHuJg6ToAMfrHP7RWx2zMCXm1gyYpkBwCyCBEiA\nBLJEwEYt8z1aict3+31mfwdmCROLJQESIIG8J0CxzPsqSnaCMEv9QBaNTEIo8U4lLBJiiY+NVmKd\nJoQmTV7kC0ldqERC9SWmiauOSmKKQ5Gx/nc028yPQnLMsSdJbU1Yvv72a1m/97ryz8vOlzFjB0i7\njiVSHtCBTvQwPnTD6mFyFcvpk2SINoXlO5YeVhAPTQIkQAIuBBC1zPdoJU6fYulSidxEAiTQ5glQ\nLAv2FoBKLdMP3qOMvkuJZq/xyTR7hXDiExXOCAzS2KiRUnTmCpkM6K7ayatofzxiJBN76PIKjV52\naO+Tq654WO6843GNknaUJYtDMnbsoXLi8TvKkC0D0rGTX8rLKxP2CJv3EUu9xog6sU408spEAiRA\nAiTgBYG6j96T0mHDvTh0WsekWKaFi5lJgATaGAGKZcFWON6nXKgffY9S2jtTHQLEGJIumUimH+9b\nIjqJYUmQVEAj2kRWxTKiAmmCjFGbwjIkE6OROGKpCyqk6NwHyoXvmTNXylln3CAfzJgvleW9pXpV\nJ7nx1k1l3AEDpFPHNaW8olxzafFqpG6/fE2mDH81ilhq2Y3GsWwmYolrRqJYOhz4TQIkQAIkkJiA\n2+82NoVNzIxrSYAE2g4BimXbqevEV4pQnU1GsGxzWqx0pNLIquaDePrVXe+76yW5/vrbZPnSJbJg\nUT/ZY/ThctkV28vmW2hnQJpZR7V0mtWq6EJSfSq3Ye3CNmCa4mqZscgqurXFcTSTM4OFFiVXsXTr\nFZYRyxbx5k4kQAIk0BYJUCzbYq3zmkmABFIlQLFMlVSx5nMVS1y0854mIpoQTMglxHHutzVy3bU3\nyisv/SpV1SUybJt1tGOffWTUqI0NKRQbRttazVuiLhmqr5MStUyfEUjIpW7DZgx94suwWCJiql3c\n2nEsJzJiaeqEXyRAAiRAAq0jQLFsHT/uTQIkUNwEKJbFXb/NXx3kzqbVIpbY4Igl5tQNYx37BGsj\nuhyWO++dJtdce7ssXVIjW2+1nZx62jg5YPym2uNsWD91mj+s8Ug011XZw7udZgxNxBexCk1zdR0C\no61MjSKWTcWSEctW0uXuJEACJEACIECx5H1AAiRAAskJUCyTs2kbW5oRy7C+gOhH+1fNZ97LVCoI\nNiKhZWuttpx96NGX5dorHtUo5mLZuP+Gcs65+8mRR++osUonzOkzTV2jTmn21HEwtTCfz3kns5Wt\nYE2JrmLZTMQSbotLyoDfmnPhFwmQAAmQQHESoFgWZ73yqkiABDJDgGKZGY6FW0ozYhlBpBFRS9gf\n8urHNIuNXrFPN/2ycIW8Pm2O3H7LJHn3vU9kw/V6ycmnjpPTz9xd363U/c27lfFiGT1obPiT1iud\nq1i6RSxxSRTLaG1yQgIkQAIk4EbA/j5LlIed9ySiwnUkQAJtiQDFsi3VdqJrbSSWWIi+TNkor5qX\nimVEx+WIoG8fXTRr9CusTog9li0Jywfvfic3XveATHv9DeneZS05+LDRcs11x2oPrT4pxYgoSDhE\n9JgQOvRa6zPvWGK+5clNLCeoWA7tzXEsW06Xe5IACZAACYAAxZL3AQmQAAkkJ0CxTM6mbWxpJJa4\nZKyAPSJpY1YTcfSrSMICA9HOe3TJSKEjlWaTbl2lY15+/ulCueWmx+XRSU9LrzX7yOFH7iGnnrmv\njnXpk/YdVE41H+TUDHWiZeAvvH5bmG5raXITy4kQy159ZcygYTGvNTHSOMHFbOvjpi09e+5HAiRA\nAiRQCAQoloVQSzxHEiABrwhQLL0iny/HjcqVOR0ji1jRIJZoCov4pL5pGT1jTJHRMUvT8auZ1SFG\ndF19yCdzZi+Tm298SiY+PFnWaN9BDjpkZzn5rwfJwMGdjb2hKa1fi4l/VzNaeIsnjcVSz05fC431\nChuNWI4ZSLFsMWDuSAIkQAIkwIgl7wESIAEScCFAsXSB0yY2uYolNtoPhgRBcoTSymVE27OaIkxv\nr7oZ7VtVHOd8tlxuu/lpmTTpRamqqpLxB+4uJ566t2y9TU+prcNolyEpLakwpWlL2VanRmKJs1Rx\nbSSWjFi2mjELIAESIIG2ToARy7Z+B/D6SYAE3AhQLN3otIVtVixjcocV8e9ZrpYhSsXZQRuyauTR\naShrNuhqbKkPiXynY10+8uBUuVU79flj5a8yZtfhcu4Fh8ifth8kdWZcy1IJab5SddbWtoZtJJZo\nvhs/jiUjltE644QESIAESKA1BCiWraHHfUmABIqdAMWy2GvY7fqSOWNMLG0Gt0JULNFMVrMiaImA\nJSQRzVzD2qJ24S8heejB1+TeCc/IN/M+kV12HCEPPHK5rN2jzDSHDWkeI5Zuh0hhW7NiyYhlChSZ\nhQRIgARIwI0AxdKNDreRAAm0dQIUy7Z+ByS8fiuUTacJM+tKiCVs0tluJlHBxJpVKyLy7JQP5W+n\n3yCLly6RE048SK685ihp3xGZnE5zdNKqRLFsFT7uTAIkQAIkkAIBimUKkJiFBEigzRKgWLbZqk/n\nwq1gJtgnZpE6Y3rjUUVUyTSrTSATXfr4pLa6Xi795zNy9XV3SEVJpTz06A2y7wEbSbBOpFzHwmRT\n2ARsuYoESIAESCCvCFAs86o6eDIkQAJ5RoBimWcVkvvTsdLYwpghdsfHry9LortX03usmqImE8TU\ndaFwUIcUqZCFv4bksIMvlPdnfC2bDBkkd9//f7L+BmtJp/Y6nEkLD28OpF+MWFoSnJIACZAACWSL\nAMUyW2RZLgmQQDEQoFgWQy22+BpghJBBWJ39xBVmnTNulZmNl0Cbx6diaQwTGwMmeAmxhGyiB1if\nr8x08vPprMUydswxsvC3pXL4YcfIlVeNl569yk2xrflyE8sJ0c57xnK4kdYg5r4kQAIk0OYJUCzb\n/C1AACRAAi4EKJYucIp/E6TSRhlxtdp21SYrjHY56RT7I7NapAk7OmWatboarWPrw9jml4D2/orl\nq656QK667E4pD2wuN1x9sRxx/DpSWg4LbXlyE8uJKpZbaOc9owdxHMuWE+aeJEACJEACFEveAyRA\nAiSQnADFMjmbNrDFkcCGaCXkLip4cMVUkk+7dUUT2AjGDFEx9aFMJ3ppeouVEglr6LIO3b/6a6Us\nUCnLV4gcd9TFMmXKxzJwg91l0vMHS/+NO0nAvGup+XAOEW0ei+ObL5wM1iNBfqPnaJad9zn1VU09\nkm7BoTHVbHYcS0YsHSb8JgESIAESaB0BimXr+HFvEiCB4iZAsSzu+s3+1cULaEwCTbxSj40VKn5o\nE6tRSxPQ1PzBmoj89GON7LffePnsy1my/34T5K+njZQhw0ulvHyZ7qFNY0OV4te8fvVVCKnIUszo\np71+sDKisdZSLRvzqp16iGRiyYilQcQvEiABEiCBVhKgWLYSIHcnARIoagIUy6Ku3hxcXLxY4nAY\nzNI0r7UbIJf6zmXUNbGE5rDzf66XV6e9KWf9/Wz5Y2kfufWWS2T84QOlS+eQhEIh3aNMSo1V6g5+\nNHRdqR/sXaqfEtVKLRNiqUv4aiSWegBfwBeLWE6cPkmGaFNYvmMJWEwkQAIkQAItJUCxbCk57kcC\nJNAWCFAs20ItZ/MarT/aYyQRy3C0JStijua9y5CqocrfMUefK0889Y3UBKvk5anXyi67bGJKgkL6\nbeATPc76anRNtJmsSqcOUqI+6TSDxSlgSyxi2VQs3d6x1J211a0RVJwbEwmQAAmQAAkkI0CxTEaG\n60mABEgA8SVNBEECLSbQ9O5ZTSxRsl9l0i/16oclTitWI5erVkXku3m/6XiW58iSJctkq602kWuu\nPkE2H7KeiXniC8X5YJjm5Uk0iYVtQiorjA2aIU10jatYNhOxRN9CSBRLhwO/SYAESIAEEhOgWCbm\nwrUkQAIkAAIUS94HrSOQkljiENA2JzSI6CVcDu9c1tVF5PHJH8q5514u83/6VW649hY55vitpHMX\nfScTu6F8fIxhooseyKU2h41UmNVOpjqp03WMWCoaJhIgARIggawRoFhmDS0LJgESKAICFMsiqERP\nLyFFsYyoBvpMpzsavUTQUVM0UGh8c4utDpI5n9bI2FF7y7kX7i5bbr2245NOVkcuEZf0RQcWiUTf\nrzSRzJCKZUVysWTE0lLklARIgARIoBUEKJatgMddSYAEip4AxbLoqzjLF7iaWOJ4MEd8bLgRc4hA\nQiUbIpe6YFJQs8145wc5/phr5Ztv5snNN/1Tjj1huJRV+MSP9yt1u99oo2Y3TW21HHvcqFgG3cSS\n71hGSXNCAiRAAiTQGgIUy9bQ474kQALFToBiWew1nO3rs4JnjxNrv4q3HrExPoPzFqMZfsTkd5br\ntU0sdttv9+tk2sufy9bD+8lV1x0k2/6pn4TCQY1wav+vAR2CBEWZ8u00OmilRjKD+t5lrCksNmvR\ndhxL9gqrQJhIgARIgARaTYBi2WqELIAESKCICVAsi7hyc3Jp8d6IAxrxsxFLrIiXS0cksdZJznKd\nduxTqvu9+doqOfHkf8qcr76QKy4+Q04+dRfptEZ0fx2v0gQrzfHCEgnUahQTx1HhDPsl6HdimiaA\nqWsbiaVbxFLz2g6Amp6dc478JgESIAESIAGHAMWSdwIJkAAJJCdAsUzOhltSIRAvljaaaGTSNoVF\nIcgU27haqfX67mUo5JNSzXbIwTfIM1NmyODB/eTK6w6T3UYPlhWrqqR9+/am61fIZcQXFn9gpZaK\nZrIdVThLVSyTDzcyQcVyaG+OY7kaeK4gARIgARJIiwDFMi1czEwCJNDGCFAs21iFZ/xyrVg28kas\njP+4HzWsjVjD6qElKoevvPi9nHP27fLpnBlywQWnyAUXHaQSqerp13FKNI86pUYjtZmtf5kjlqEu\neqgyqdOOYmNNYROMYzm0V18ZM2hY7KxMdDJ67oxYutcPt5IACZAACTgEKJa8E0iABEggOQGKZXI2\n3NIqAtY4UYiVzMQFBus0WqlWGdRefEr8PvnryffKHfdOkc03XV+uvv44GT1qmPb66kilX4vyQyx9\nS3WNNokNraHFlzQvlhqxHDOQYpm4BriWBEiABEggFQIUy1QoMQ8JkEBbJUCxbKs1n/PrjhfNxgcP\noUWrbg5rc9lyjTz+99X5csH5d8nb770ip556nFxz3fFSVuYzeZw9gxLwVeushknrO+p6n9RpuJIR\ny8ZcuUQCJEACJJBZAhTLzPJkaSRAAsVFgGJZXPXpwdU0FcZGbWKdYGWis4rPpkWEtOfXQMAnK1cF\nNXpZKv+88GG59rqJssngTeXyK46XvfcerG9URrTDHsQu63SqTWO1Q59IveokxLK5prCMWCaqBa4j\nARIgARJIgwDFMg1YzEoCJNDmCFAs21yVZ/qCbSc9MEX7iR6jqXPGH9qKJfJEx6YMhmqkNuiTju3K\n5aWXPpfLLnlS3n1/ppx40l5y++3Ha0SzTo+wQj/4r5NGKyGWzv512q0sI5bxgDlPAiRAAiSQaQIU\ny0wTZXkkQALFRIBiWUy16cm12PEqrVTGDdrhJpbx56piGayrldq6Gqms1N5ffaVSvSoiZ5/xiNx1\nz4Oy7757yV33nSJrrBHUSOVvKpWl6qLr6PuVWgiOoe9c1pUEKJbxTDlPAiRAAiSQcQIUy4wjZYEk\nQAJFRIBiWUSV6c2lNI1Y4iyi4ciUxbJGqms0GukLSGlppYTVGEsDpXLLzS/Jhf94WrbedjO58JKD\nZMSfukp9/RIpCWikMtLe9BAbCZdpZz5hCQbcx7HEcCPsvMebO4RHJQESIIFiIUCxLJaa5HWQAAlk\ngwDFMhtUWWaaBBZp/g7atLVcIhhOpGS5vjZZJ++8+7tcc9nL8uGH38ppp+0p55w3WmpqIlJeoR39\nRJaoiGrj11Ano7GNO+/RMvQVzGoNni5RuZ2IcSw53EiadcLsJEACJEACTQlQLJsS4TIJkAAJNBCg\nWDaw4JxnBDB0iNoixBKDSpasknqplR+/D8gF5z0qjz32iBx52EEy8aH/k3oTBQ2J31el+wS0Saw2\nnVUZDapIJnvHcgLEUiOWYznciGc1zAOTAAmQQDEQoFgWQy3yGkiABLJFgGKZLbIsNw0CyzQvxLJM\npzqsiL9Go5HYvUI78HlWLrr4Gtn+T7vIxAn/lPU3DGi0sk7Hu6xTydSeZEXFUmUzqNHJZGKJiOUW\nGrEcPYjjWIIqEwmQAAmQQMsIUCxbxo17kQAJtA0CFMu2Uc95fZURWaY6ifatZeqIKpbaDBbtW/3a\nSc8TT74jZ51+r5SXdZTzLzxaDjtqiHmn0q+Dj8A+fREdZ6QZsWTEMq+rnydHAiRAAgVDgGJZMFXF\nEyUBEvCAAMXSA+g8ZFMCaNZaqu9XlqgoImKppqjSGFS//OijBXLjtU/Is0+/IcefsJvcfteJGrGM\nSH1Ix7ss1RiljmXZnFgyYtmUN5dJgARIgARaQoBi2RJq3IcESKCtEKBYtpWazuvrrNWzQ0+v+CBi\nabzSvE+5amVExfIFuezya2TnP/9Jpr96tWkCWxcMSUW5RivxgqWKZlB760naFHb6JBmiTWH5jmVe\n3wQ8ORIgARLICwI777yzvPnmmwnPJRTCOFeJU0kJfgutnnbYYQeZPn366hu4hgRIgASKjADFssgq\ntDAvR0OTaPuK6COawmrAMhyVS4yKec89/5MTTrhGNtust9x79/kyZGgv0dFFxG+yhyVcXyehQHly\nsXR7x1KPFdGydKJNb5lIgARIgATaOoHXXntNIJeZSq+//rrsuOOOmSqO5ZAACZBA3hKgWOZt1bSd\nE6sP1agkag+vOnxIfb3PRCr9sDwdn7JEde+JSTPk76c/LN17dJbDjtxKTj11XzMsCQj5/CHdp0bq\nAx0axNKsjxtupJmIJSQWiWLpcOA3CZAACbR1AogyJotapsNm++23lzfeeCOdXZiXBEiABAqWAMWy\nYKuumE7cNi1yopaIWCKCGPEHtYGsX+Z8VS2XXjhZnp40WY48ZqTcdd/fJaK7hDVXJFIjJaXaFFba\nJRdLRiyL6WbhtZAACZBA1glkKmrJaGXWq4oHIAESyCMCFMs8qoy2eyr1eulQSW0GqyJp3rPEGl8t\nRqqUn3/xy1WXPi133HWnHLjfTvLE5AtMxDKiBuoPBDVnSMWyfYNY6npfwCfVGoJcosVOZMRSGTGR\nAAmQAAmkQ6C1UUtGK9OhzbwkQALFQIBiWQy1WPDXgIgl2qOqCaLzHkQsEbxUsfRpXLKqukJuun6a\nXHTR+bLLyN3kpZcvk9JKNJnVcSwD6PinTsWyY3KxZMSy4O8QXgAJkAAJ5JpAa6OW2H+nnXbK9Wnz\neCRAAiTgGQGKpWfoeeAGAlGxRK+wuhIfeGZYII31GrVsJ3feOV1OPukc2Wrz3eS55y6XHr19EqoP\nSkkJcgf1v07JxbKZiKWRWC2F71gqBCYSIAESIIEYgZZGLRmtjCHkDAmQQBsiQLFsQ5Wdv5faIJaw\nSidaCcGs1U+9CmM7efLJN2X8+Etkkw1HygMPXCBbjAjoOJfVUoYRR1RBgxLXeU/TprBuEUvdm2KZ\nv3cGz4wESIAEvCTQ0qglo5Ve1hqPTQIk4BUBiqVX5HncOAJxYqlrdWTKaMQyaMSyVCpl6vSZcsxh\nt0iFv49cculxcuhxPSUcqVIrrBe/diHr9o7lBBXLob05jmUccM6SAAmQAAmkSCDdqCWjlSmCZTYS\nIIGiI0CxLLoqLcQLwjiWeL9SB6aMRSy1x1fVRXyXSIV8MHOenHPaozLn4zo58ugd5epbdtItq6Qu\nGJSysnLNGdcrbIKI5dBefWXMoGG6jzmE0+wVC1jG+5w6ZVNYg4NfJEACJEACcQTSjVoyWhkHj7Mk\nQAJtigDFsk1Vd75eLHp2VakM60eTGVfSFzYNXCGWiFjO+eZXufzCF+X5J7+XXUdtJJNfOVxzrpDq\n6hqprGzfRCw14KlFxXqFjUYsxwykWBrA/CIBEiABEkiLQKpRS0Yr08LKzCRAAkVGgGJZZBVakJcT\nQeNXDRuauGG9RhCd4UcQq0Qc0afC+duvIbnnjlfl4stvlp1GDJWpr12um+p0D+3cp0Tft1T9RG4f\nWtViquHHRmLJiKUDht8kQAIkQAJpE0g1asloZdpouQMJkEAREaBYFlFlFuylWK80QolmsegNFqLZ\nTl2zRMK6qmZVRB5+6B35y2lHyPCh28pbbz8kJWWaxwQ5MY5lSYNYJmoKq+9YMmJZsHcIT5wESIAE\nPCfQXNSS0UrPq4gnQAIk4DEBiqXHFcDDK4FGYomQI5rGIlUascR27aNHnnx8hhx05FHSr08/+ejD\nF6RDJ5/4EaY041jGRSwTiSUjlgDFRAIkQAIk0EICzUUtGa1sIVjuRgIkUDQEKJZFU5UFfCGNxBLN\nYK1YlqtR6ngi6NBH87z9xjdyzjnXSruOAXns8dula1e/+EuxM8axrHCPWFIsC/gG4amTAAmQQH4Q\nSBa1ZLQyP+qHZ0ECJOAtAYqlt/x5dBCIiSVmELFEc1j00wqx1JCk9uZTr6vn/7xKPvzgS31/slbG\n7rGtlFdqU1gf8kIs27uLJZvCKicmEiABEiCB1hBIFrVktLI1VLkvCZBAsRCgWBZLTRbydcTEEjKJ\niCVkESvLVCw1YolZTWHdFAxGpAJCibFB8Bqm1OgHYtnJXSwZsQQsJhIgARIggVYSaBq1ZLSylUC5\nOwmQQNEQoFgWTVUW8IWsJpaIWkIyVSrNMCRqkFiMprDm96PTHj92rNIPOu/pklQsJ0SHGxnL4Uai\nBDkhARIgARJoKYGmUUtGK1tKkvuRAAkUGwGKZbHVaCFej5VGn41YQhgxj7CkGmREp1jUDyZh/fL5\nI/qp06AlIpZhV7GcqGK5hUYsRw/iOJYKi4kESIAESKCVBGzUcuTIkfLmm2+2sjTuTgIkQALFQYBi\nWRz1WNhXAVtEMmIJqbRiibauJjQZE0tkQ3afRisjqpO+aLPZoHRuiFia7Q3jWDJiCWpMJEACJEAC\nmSJgo5aMVmaKKMshARIoBgIUy2KoxUK/BmOK9iLwjqU1TaxD1DIascQiUiyyad/FrG/8jiWy6C7V\n+lmiRTFiaajxiwRIgARIIIMELrjgArn88sszWCKLIgESIIHCJkCxLOz6K7KzR6QSCWJpbRNRS/ux\n22xUExLq7BOUdg0Ry6bjWE6fJEO0KSzfsQQ/JhIgARIgARIgARIgARLIPAGKZeaZssQWEbBS6baz\nFc74PFYsy5KLpds7llqkfYUTsVEmEiABEiABEiABEiABEiCB9AlQLNNnxj0yTgC9wCJZtUsUoYRU\nJk9B1Uod8VJ8pkNZ7dgn4GtoCttMxFKHyTTJHj35UbiFBEiABEiABEiABEiABEggEQGKZSIqXJdj\nAlYscVh01hM1PXMWEEpEJROJJVRQP7opqLskFUtGLA1JfpEACZAACZAACZAACZBAtghQLLNFluWm\nQQCd8CBBKKNiCY80fhkvlpjHJyqUcZ36uIolI5bKjIkESIAESIAESIAESIAEskeAYpk9tiw5ZQK1\n0ZyIOWLcyuhiLHCJiKWNWlqx1LzWM3VrUF2TEcsoN05IgARIgARIgARIgARIIMcEKJY5Bs7DJSLQ\nSrFUwQyqj8bEUg/RaLiRZiKWERVYq6uJzo7rSIAESIAESIAESIAESIAE3AlQLN35cGtOCEDrbIqF\nKe2K6DQ+D1bF5YNY6mJSsXR7x1JLolhGEXNCAiRAAiRAAiRAAiRAAi0kQLFsITjull8Egno6MbFs\nMo7lBBXLob05jmV+1RjPhgRIgARIgARIgARIoJgIUCyLqTbb8LW4ieVEiGWvvjJm0LDYa5no/se+\ny8mIZRu+cXjpJEACJEACJEACJEACGSFAscwIRhbiNYFmxVIjlmMGUiy9ricenwRIgARIgARIgARI\noDgJUCyLs17b3FU1K5aMWLa5e4IXTAIkQAIkQAIkQAIkkDsCFMvcseaRskigWbFkxDKL9Fk0CZAA\nCZAACZAACZBAWydAsWzrd0CRXH+zYsmIZZHUNC+DBEiABEiABEiABEggHwlQLPOxVnhOaRNoLJY6\nGImOa1mtPfQs0aFITOc9jFimzZQ7kAAJkAAJkAAJkAAJkECqBCiWqZJivrwmENQuXgM6tqU/5Jym\nT6XSiuX9U5+ULTDcyKAt2StsXtciT44ESIAESIAESIAESKBQCVAsC7XmeN6NCDhiKSqWPpFwRHwl\nPqnyR2SpjiVy33+ekGEQy8FbUSwbUeMCCZAACZAACZAACZAACWSGAMUyMxxZiscEYk1h67TtK8Sy\n1B+LWD7w6lMyZN0+HG7E4zri4UmABEiABEiABEiABIqXAMWyeOu2TV0ZxFJfqxS/EctwVCx95h3L\nB1+dIkN7bSC7DdiCEcs2dVfwYkmABEiABEiABEiABHJFgGKZK9I8TlYJQCz9kbAEbFPYaMRysQYw\nJ0x9Qob23lB25zuWWa0DFk4CJEACJEACJEACJNB2CVAs227dF9WVx8SyTi/L7ze9wlb5IrII71i+\n9IhstX5/2X2TrRmxLKpa58WQAAmQAAmQAAmQAAnkCwGKZb7UBM+jVQRiYlmv3cFqQq+wq/xhWRzx\ny4PTNGLZq5/2CjuMYtkqytyZBEiABEiABEiABEiABBIToFgm5sK1BUbAeccy4vQKq+duxXKJiuXk\nd16STXv0lj9vuOnqYqlNZTWLWe8oaYFdOE+XBEiABEiABEiABEiABPKAAMUyDyqBp9B6AqtHLCMm\nYrkkEpCXP3lDBnRbR/7UayNzIHVJMRKJGU1hHaEEiWLpcOA3CZAACZAACZAACZAACaRLgGKZLjHm\nJwESIAESIAESIAESIAESIAESaESAYtkIBxdIgARIgARIgARIgARIgARIgATSJUCxTJcY85MACZAA\nCZAACZAACZAACZAACTQiQLFshIMLJEACJEACJEACJEACJEACJEAC6RKgWKZLjPlJgARIgARIgARI\ngARIgARIgAQaEaBYNsLBBRIgARIgARIgARIgARIgARIggXQJUCzTJcb8JEACJEACJEACJEACJEAC\nJEACjQhQLBvh4AIJkEAhEIgEf5XwytmFcKpFeY7+DpuKr6yH1sEnEgkuLMprLISLCnTd1Zwm68Hb\n2mI9eMvfHp31YEl4O2U9eMsfR/eVrS3+Dpt7ciIUS0+w86AkQAKtIQCxrF88vTVFcN9WEAh03SUm\nlhT8VoBs5a4lPQ4zJUAsWQ+thNmK3VkPrYCXwV1tPdQvnqZ/8PotgyWzqHQIsB7SoZWdvL6y7mIF\nPztHSF4qxTI5G24hARLIUwIUS28rhmLpLX97dPsAR7G0RLyZsh684d70qLYeKJZNyeR2mfWQW96J\njkaxTESF60iABEggCQGKZRIwOVpNscwR6GYOYx/gKJbNgMryZtZDlgGnWLytB4plisCylI31kCWw\naRRLsUwDFrOSAAmQAMXS23uAYuktf3t0+wBHsbREvJmyHrzh3vSoth4olk3J5HaZ9ZBb3omORrFM\nRIXrSIAESCAJAYplEjA5Wk2xzBHoZg5jH+Aols2AyvJm1kOWAadYvK0HimWKwLKUjfWQJbBpFEux\nTAMWs5IACZAAxdLbe4Bi6S1/e3T7AEextES8mbIevOHe9Ki2HiiWTcnkdpn1kFveiY5GsUxEhetI\ngARIIAkBimUSMDlaTbHMEehmDmMf4CiWzYDK8mbWQ5YBp1i8rQeKZYrAspSN9ZAlsGkUS7FMAxaz\nkgAJkADF0tt7gGLpLX97dPsAR7G0RLyZsh684d70qLYeKJZNyeR2mfWQW96JjkaxTESF60iABEgg\nCQGKZRIwOVpNscwR6GYOYx/gKJbNgMryZtZDlgGnWLytB4plisCylI31kCWwaRRLsUwDFrOSAAmQ\nAMXS23uAYuktf3t0+wBHsbREvJmyHrzh3vSoth4olk3J5HaZ9ZBb3omORrFMRIXrSIAESCAJAYpl\nEjA5Wk2xzBHoZg5jH+Aols2AyvJm1kOWAadYvK0HimWKwLKUjfWQJbBpFEuxTAMWs5IACZAAxdLb\ne4Bi6S1/e3T7AEextES8mbIevOHe9Ki2HiiWTcnkdpn1kFveiY5GsUxEhetIgARIIAkBimUSMDla\nTbHMEehmDmMf4CiWzYDK8mbWQ5YBp1i8rQeKZYrAspSN9ZAlsGkUS7FMAxazkgAJkADF0tt7gGLp\nLX97dPsAR7G0RLyZsh684d70qLYeKJZNyeR2mfWQW96JjkaxTESF60iABEggCQGKZRIwOVpNscwR\n6GYOYx/gKJbNgMryZtZDlgGnWLytB4plisCylI31kCWwaRRLsUwDFrOSAAmQAMXS23uAYuktf3t0\n+wBHsbREvJmyHrzh3vSoth4olk3J5HaZ9ZBb3omORrFMRIXrSIAESCAJAYplEjA5Wk2xzBHoZg5j\nH+Aols2AyvJm1kOWAadYvK0HimWKwLKUjfWQJbBpFEuxTAMWs5IACZAAxdLbe4Bi6S1/e3T7AEex\ntES8mbIevOHe9Ki2HiiWTcnkdpn1kFveiY5GsUxEhetIgARIIAkBimUSMDlaTbHMEehmDmMf4CiW\nzYDK8mbWQ5YBp1i8rQeKZYrAspSN9ZAlsGkUS7FMAxazkgAJkADF0tt7gGLpLX97dPsAR7G0RLyZ\nsh684f7/7d0J3A1VH8Dxv/WVJZRQKkqR7ZWylYgIUZEixRtJ9sguSUhJIpElsqV9kaXSpqQiCUX2\nNXtFyU6W95zJjLsvz3Nnuc/zm88nz8yZuTNnvv+Z7vzvzJwTuFUzDiSWgTLOThMHZ71DbY3EMpQK\nZQgggEAYARLLMDAOFZNYOgQdZTPmBRyJZRQom2cTB5uBY1y9GQcSyxjBbFqMONgEG8dqSSzjwGJR\nBBBAgMTS3WOAxNJdf3Pr5gUciaUp4s5f4uCOe+BWzTiQWAbKODtNHJz1DrU1EstQKpQhgAACYQTS\nS2J59Ohx2bnnLzl58rRcWfgiyZo1SxgRZ4vTY2J55Mhx+XXHH5IxY0a54nJvxMK8gHMqsTxz5oz8\n889JyZIls2TIkCGug86rx3JcOxFm4WSKg+8uHD9+QrZu3yuZM2eSgvlzS47s2XxnJ924GQevJZZp\n+dgPdZB4NQ6h6qrL9v15UPb88bfkuyCXFLgod7jFUlV++Mgx2fzrH2r950v+fPZsw7eCJJa+Gowj\ngAACUQS8klj++NNmadZujOza86eoa265UH0xTR3dVmrcVCrKHoSf/ce+AzJ+yhcyc+5SWbF6m7Vg\n5kwZ5coi+eX+RlWkZ6f6riaZbiSW78xaJI899bblEetInRr/lbHDWsW6uN9yq9ftkIHDZsj3SzfK\nbpXgqxAbgxmLDi1rSZsWNY1Ey++DDk2YF3B2J5anT5+WsZM+lwHD3pe/Dx6V7Odllc0/jjSO90i7\n6uaxbMe5GW5fvR4H33ovXrpBnhv9ofyydods3faHnNb/41KD/pmg2FUXy8PNa0iXtrcZZbH+48a5\nGapuZhy8kFgm4thfs36H+n4ZK38fOBJqd/3KLsibUxZ+PMC1/xf5VsbpOASe6zpBnBLle1ibDh7+\ngbw54zv5be9Bq/qXF7pQri97hdxVv4Lc1+hGqzwlI8tWbJEhI2fLyjXbZfPW36zvj1w5s0m9mmVl\nYJ/GUrRIgZSsOupnSCyjErEAAgggcE7AC4nlqVOnpHLdJ+WnX349VzE1Nk4lMa3VxVlKhjfe/046\n9Jwsh4+eiPjxYkULyqfv9JFLL7kw4nJ2zXQjsew98A0ZMX5u3Lt0benCsuTzwXF/bsjIWTLgufet\nC+9wKyiuLsa//fBJyZM7R7hFbCs3L+DsTCx1ct2m2yuyeNkmv/34dfkouaRgXr8y3wk3j2U7zk3f\nfQsc93IczLru//uwdOk7TV1IL7IucM15vn91gnl0x1TJlCmTb3HEcafPzXCVMePgdmKZqGN/3OTP\npfPjr4bb3aDybT+NlosL5Akqd7rAyTik5Fz/869DclvTobJsxdaINE/1uUf6dGkQcZlQM3Wdho6a\nI4NHzJR/Tp4KtYhRlkU9KTBrene5tXqZsMukdAaJZUrl+BwCCKRLAS8kli9PnSedHpsa5J+axPLK\n8o/K9p37rHXmVclK4cvyGWX71Jeh71C/1rUyU30puTG4kVhOf/cbadV5Qty7W179+rzok0Fxfe6r\nb1dJ3SZDraQyz/nZ5Z47K8mlF18gv+/9W2Z9slR27v7LWmfThpVl+riO1rRTI+YFnB2J5YkT/xi/\ntg8dPSfkxVG0xNLNY9mOczNSTL0cB11vfaF7Z/Ph8tn8ldZu6IvacmWKSIVyVxqP2q9Y/av8oH48\n0Dcwj2yfEldi6eS5ae1AiBEzDm4nlok69sdO+ky69JseYk+DiwrmzyO/Ln/ReFQ/eK6zJU7GYfy0\nefJIn6lBOxjue1jfSa7T+FnjLqL5oQvyZJfqVUoZj/l//vVKOXb8pDlLBva6W/p2bWhNxzLy4stz\npceAN6xF8+TOLtUqX6MeN88jP6/61e9Huty5ssvizwYl/M4liaXFzwgCCCAQXcDtxFK/k1GySk/5\nc//hoMqG+0ILWjBEQZHrOhsJy511rjMed61cvpi11OQ35kvnx6bJ8RPnvvT0o08VyhW1lnFqxI3E\nUu+bfr9Pv+cXadCPal5VoascOXvXt6/6xXmg+uU5nuGWhoPlm8XrjI/oO5ILZvcX/aiZORw7dkLu\nbT1aPp73k1GUUb1v+NfGiZI9+3/MRRz5a17AJTqx1MlzzbuelrUbd1v7caHaf98fN6Illm4dy3ad\nmxZEiBEvx0FXt+/gt2TYmI+smt931w0y/vmHgo5XbbdN/bClE854B6fOzUj1MuPgdmKZqGPfN7F8\n5vEm0r1D/bC7r995jve957ArS+UMp+Lw77neQ30PBz8qHO57OPBc0Ms9eF8164eULdt+l/r3PScb\nNv9mKOTK8R/ZuXKMnHdebP9v13UqfkMP6/Hl6/5bRD6Y1s3v6Q6dDHdWybD5Tdb2gZry0tCWqVT3\n/ziJpb8HUwgggEBEAbcTy/bqcdVXXvvKqOO9DSoZF2OLftxoTIf7Qou4Q2dnTn3rayl8ab6w72gO\nGPq+PD1yprWqcc89KK3/d4s17dSIW4llLPv3wviPpdfAN41F9buQG5e8IIXUncZYB5245ivWRg4c\nOmZ85K2JneTu2ysFfXzjlj1S4saeVrkbSb55AZfoxPKjz5dLwwdGGPum75oP6n2PVL+ppJSp1tva\n32iJpVvHsl3nprXjIUa8HIe9+w7KFdd3tu7CtFH/v9AXsW4kIak9N0PQ+xWZcXA7sUzUse+bWD77\nxL0qsbzdb3+9OuFUHOI91/W74lde/6jRIJ620+fCGPUdGjjoR2Qr1XnCKn5zQie5547g7wBrAZ+R\nEeM+kt6D3jJK9PfP6oXDVGNv+X2W+He0XY9JMun1+cZEDvWD5M6VLyW08SwSyyByChBAAIHwAm4m\nlvqF/BvUu5W60YscqhGTVd8Nk/vajJZEJJbh9/jfOfp9t7LVH7MW696+njzb/z5r2qkRryaWJ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+ZPOvjRODvldiWU8il7EjDnGf6+r7oEXTasYPjeb3cOBjw+OGtZLWzWsE7fqS5ZvkJnU30ey+\n6vtPBsr1Zf27GonnfAjcgH7s/C71mK3ZsM97kx+VBmFeswn8bKzTJJaxSrEcAgggoAScTCxjAY/W\naIC5Dv3La0vVr9xfqiVT/XjNuu/977B16TtNxk75wlxcOqq7CyPVXQavDV5ILH0vqHSLl7/+NDpk\nIh/KLlIc+g95V4aMmm18TN97fHNiJ7n79kpBq9H9J1a7Y5AcOHTMmNf8nioyZXS7oOXsLDAv4LyY\nWCbiWI4Up1hdYz03Y11fqOW8HAffO5G67kP63Ss9Ot5u7YZ+F7Jj76l+feR+9cHjclPla6xl4o1D\nas5Na6MpGDHj4HZimYhj/8PPlsm0txZIB/UdUOOmUkEahw4flQFD35cXJ35qzWt1f3V5efhD1rRb\nI27FIZZz/ab6A2Txsk0GjU7EP32nj9xY8Vx3UVu3/yF3NhsuazbsNJa55qqLZeU3z/lR6vOhRadx\nRquugd/jZ86cUQ0E7VKN9BQImeDrp1w6qpaadZ/Yeihb6nKjBWb9Q3QiBxLLRGqyLgQQSPMCyZpY\ntusxSSa9Pt+Kz09fPSOlrrnMmP7pl19FP4bpO5RW86I9WTm4b5O4H7f13UZKxt1OLNdv2i2l1R2W\nM2crH29T7ZHioBNG3RLvkaMnLJpa1UpLrZtLScECeWT/X9bWyKwAABeGSURBVIdlyU+b5e2Z31sX\nBzqxXag60Hb6ETTzAi7RiaW+aG3TbZKcONvqrYbQXUd8vWitZVKzainVr965hmDaqvd/zcYnEnUs\nR4qTVZEoI7FcbEZZRdTZXo2DrrhukbR01d6yV/01B308V1Qthx5Q3erohsfWqC6NzOHu2yvKWxMf\nMSeNv/HEIbXnpt+G45ww4+BmYpmoY9/3B65CF+eV6jeWkEsvuVA14pZRdPLz2fyVfjHVferqLmLC\nNc4UJ2WqFncrDrGc6+/O/l6atR1jfXfoltZvUv0QF1cJ5P6/j8rMuUvk4NkfC3WL4O+oluDr31rO\nzyPwfFiuvsf1d7Uelq/cKvox8AvyZJdbbiotZUpeJv9RCezyFVtl0Y8brS599LIF1FNLC+cOksvV\naxyJHkgsEy3K+hBAIE0LJGti2frRiTLt7QVWbPSFgO4zTg++rdAZBTH+48b7l24nlo/0mWr0J6mJ\n9F3Flapv0eJXXRKjmEikOOiVvP/hYrnv4Zesi49IK86oMv/XxneQxndWjrSYLfPMC7hEJ5b6UTCd\nXMcz9OhQT4Y88e/7qIk6lqPFKZb6xXKxGct6Ii3j1TiYdf72+7VSp8mzcuKfU2ZRyL+6tc2Xh7cO\nakQknjik9twMWbEYC804uJlYJurYH/nyx9JzwJsx7XmZEpfJ9LHtrR8pY/qQjQu5FYdYz/VX3/lG\n2nSdaD2KGopC3818Vz2iWle19h04tH50gvoe/8Yq9v0eD2wwy1ooYES3HD99bIegR2wDFkvxJIll\niun4IAIIpEcBryWWvu/l6S/4pnfdGDIs781ZbPxaqt/d0I/Y/Pz1s1ZjM7oD7cp1npDDPnfKQq4k\noHD88w9ZfSwGzLJt0u3E0ve9scZ3VpI3Xu4U175GioO5Ih2P4WM/kjdnLLT6qzTn6b/6l24d567t\nbpNr1PuVbgzmBVyiE0vdOfg1N/SImoj47vPQ/k2lW/v6RlGijuVY4uRbh1DjsZ6boT4ba5lX4+Bb\n/5XqPct+z7wrH6v3u3wHfVemRLFC0v7BW8P+fySeOKT23PStW7zjZhzcTCwTdezrhpdenjZPvW+/\nUH5WT7OY7+OZJrrxmatUdxo91Dv4DzSpan2PmPPd/OtWHOI512fNXSovvfKpLP15s9VVmGl2Q/mr\n5OnHm0jVyiXMIr+/kc4H/Wh5m26vGA3z+D71olegf4S8onB+6dT6Vmn7QE3JkiWz33oTOUFimUhN\n1oUAAmlewGuJpW4BcJ96RFJfpOXNkzOi/959B+WY6k5EP9aUrIPbieWJE/+o91SPSOZMGeXCC3Kl\niDHWOOhGGnb/tl+279ynYnxICubPox5dulAuuvB8R1uADbWT5gVcohPLUNtyqyzWOIWrXzznZrh1\nRCtPpjgcVI+/6kZ6du7+Uy675AIjqYzlAjfWOCTi3IzmHW6+GQc3E8twdUtNuX40ff2mPeqx5kPq\n8ftTqsuY/MZ/scQtNdtN6WfdikNKznXd0JJ+J3KzOifyqe+SwpddJBerVx6iDdHOB51grlOvbOze\ns1/9QHdSilx+kVxVJL96LDZrtFUnZD6JZUIYWQkCCKQXAa8llunF3dxPtxNLsx7p/a95AZeWE8tk\niDFx8EaUzDiktcTSG7qx14I4xG5l15IklnbJsl4EEEiTAiSW7oaVxNJdf3Pr5gUciaUp4s5f4uCO\ne+BWzTiQWAbKODtNHJz1DrU1EstQKpQhgAACYQRILMPAOFRMYukQdJTNmBdwJJZRoGyeTRxsBo5x\n9WYcSCxjBLNpMeJgE2wcqyWxjAOLRRFAAAESS3ePARJLd/3NrZsXcCSWpog7f4mDO+6BWzXjQGIZ\nKOPsNHFw1jvU1kgsQ6lQhgACCIQRILEMA+NQMYmlQ9BRNmNewJFYRoGyeTZxsBk4xtWbcSCxjBHM\npsWIg02wcayWxDIOLBZFAAEESCzdPQZILN31N7duXsCRWJoi7vwlDu64B27VjAOJZaCMs9PEwVnv\nUFsjsQylQhkCCCAQRoDEMgyMQ8Uklg5BR9mMeQFHYhkFyubZxMFm4BhXb8aBxDJGMJsWIw42wcax\nWhLLOLBYFAEEECCxdPcYILF019/cunkBR2Jpirjzlzi44x64VTMOJJaBMs5OEwdnvUNtjcQylApl\nCCCAQBgBEsswMA4Vk1g6BB1lM+YFHIllFCibZxMHm4FjXL0ZBxLLGMFsWow42AQbx2pJLOPAYlEE\nEECAxNLdY4DE0l1/c+vmBRyJpSnizl/i4I574FbNOJBYBso4O00cnPUOtTUSy1AqlCGAAAJhBEgs\nw8A4VExi6RB0lM2YF3AkllGgbJ5NHGwGjnH1ZhxILGMEs2kx4mATbByrJbGMA4tFEUAAARJLd48B\nEkt3/c2tmxdwJJamiDt/iYM77oFbNeNAYhko4+w0cXDWO9TWSCxDqVCGAAIIhBEgsQwD41AxiaVD\n0FE2Y17AkVhGgbJ5NnGwGTjG1ZtxILGMEcymxYiDTbBxrJbEMg4sFkUAAQRILN09Bkgs3fU3t25e\nwJFYmiLu/CUO7rgHbtWMA4lloIyz08TBWe9QWyOxDKVCGQIIIBBGgMQyDIxDxSSWDkFH2Yx5AUdi\nGQXK5tnEwWbgGFdvxoHEMkYwmxYjDjbBxrFaEss4sFgUAQQQILF09xggsXTX39y6eQFHYmmKuPOX\nOLjjHrhVMw4kloEyzk4TB2e9Q22NxDKUCmUIIIBAGAESyzAwDhWTWDoEHWUz5gUciWUUKJtnEweb\ngWNcvRkHEssYwWxajDjYBBvHakks48BiUQQQQIDE0t1jgMTSXX9z6+YFHImlKeLOX+LgjnvgVs04\nkFgGyjg7TRyc9Q61NRLLUCqUIYAAAmEESCzDwDhUTGLpEHSUzZgXcCSWUaBsnk0cbAaOcfVmHEgs\nYwSzaTHiYBNsHKslsYwDi0URQAABBBBAAAEEEEAAAQS8JZDhjBq8VSVqgwACCCCAAAIIIIAAAggg\nkEwCJJbJFC3qigACCCCAAAIIIIAAAgh4UIDE0oNBoUoIIIAAAggggAACCCCAQDIJkFgmU7SoKwII\nIIAAAggggAACCCDgQQESSw8GhSohgAACCCCAAAIIIIAAAskkQGKZTNGirggggAACCCCAAAIIIICA\nBwVILD0YFKqEAAIIIIAAAggggAACCCSTAIllMkWLuiKAAAIIIIAAAggggAACHhQgsfRgUKgSAggg\ngAACCCCAAAIIIJBMAiSWyRQt6ooAAggggAACCCCAAAIIeFCAxNKDQaFKCCCAAAIIIIAAAggggEAy\nCZBYJlO0qCsCCCCAAAIIIIAAAggg4EEBEksPBoUqIYAAAggggAACCCCAAALJJEBimUzRoq4IIIAA\nAggggAACCCCAgAcFSCw9GBSqhAACCCCAAAIIIIAAAggkkwCJZTJFi7oigAACCCCAAAIIIIAAAh4U\nILH0YFCoEgIIIIAAAggggAACCCCQTAIklskULeqKAAIIIIAAAggggAACCHhQgMTSg0GhSggggAAC\nCCCAAAIIIIBAMgmQWCZTtKgrAggggAACCCCAAAIIIOBBARJLDwaFKiGAAAIIIIAAAggggAACySRA\nYplM0aKuCCCAAAIIIIAAAggggIAHBUgsPRgUqoQAAggggAACCCCAAAIIJJMAiWUyRYu6IoAAAggg\ngAACCCCAAAIeFCCx9GBQqBICCCCAAAIIIIAAAgggkEwCJJbJFC3qigACCCCAAAIIIIAAAgh4UIDE\n0oNBoUoIIIAAAggggAACCCCAQDIJkFgmU7SoKwIIIIAAAggggAACCCDgQQESSw8GhSohgAACCCCA\nAAIIIIAAAskkQGKZTNGirggggAACCCCAAAIIIICABwVILD0YFKqEAAIIIIAAAggggAACCCSTAIll\nMkWLuiKAAAIIIIAAAggggAACHhQgsfRgUKgSAggggAACCCCAAAIIIJBMAiSWyRQt6ooAAggggAAC\nCCCAAAIIeFCAxNKDQaFKCCCAAAIIIIAAAggggEAyCZBYJlO0qCsCCCCAAAIIIIAAAggg4EEBEksP\nBoUqIYAAAggggAACCCCAAALJJEBimUzRoq4IIIAAAggggAACCCCAgAcFSCw9GBSqhAACCCCAAAII\nIIAAAggkkwCJZTJFi7oigAACCCCAgKsCZ86cEf1fxowZo9bj1KlTkilTpqjLsQACCCCQFgRILNNC\nFNkHBBBAAAEE0qnA7t275ZNPPpEDBw5I9uzZpXbt2lK4cOGYNXTyN3PmTNm1a5dkyJBBypYtK1Wr\nVg36vF6uadOm8uWXX8r+/fulTJky8tVXX0nevHmDlp0wYYL06tVL/v77b8mTJ4+89NJL0qxZs6Dl\nKEAAAQTSkgCJZVqKJvuCAAIIIIBAOhPo3r27jBgxwtrr5s2by/Tp063paCNLly6V8uXLW4tly5ZN\njh49ak2bIwsWLJCbb77ZnDT+vvPOO9K4cWO/Mj1x3XXXyfLly63yGjVqGAmpVcAIAgggkAYFSCzT\nYFDZJQQQQAABBNKLQNeuXWXkyJHW7t5///3y+uuvW9PRRpYsWSIVK1a0FsuaNascP37cmjZHpkyZ\nIq1atTInjb9jxoyRDh06+JXpiVy5csmhQ4es8tKlS8vKlSutaUYQQACBtChAYpkWo8o+IYAAAggg\nkE4EnEos165dKyVKlPBTnTdvntxyyy1+ZXqiVq1aoueZg76rqe9uMiCAAAJpWYDEMi1Hl31DAAEE\nEEAgjQs4lVhqxvHjxxuPtOr3LatXry7t2rWTLFmyBAl/9913xl1U/Y6lfiy2RYsWQUlp0IcoQAAB\nBJJcgMQyyQNI9RFAAAEEEEjPAk4mlunZmX1HAAEEogmQWEYTYj4CCCCAAAIIeFaAxNKzoaFiCCCQ\nzgRILNNZwNldBBBAAAEE0pKA04nlsWPH5PTp00bXJpEcT548KXpZ3cps5syZIy0aNE8/arthwwZZ\nvXq15MyZU3TjP5dccknQchQggAACXhIgsfRSNKgLAggggAACCMQl4GRiqRvv0Y346CF//vyyefNm\nyZEjR1B9hw0bJn369DESUJ1UTpw4UVq2bBm0nG/Bzp07ZfDgwaK7P/nll1+CujzR/WWWKlVKmjRp\nIp06dTL63PT9POMIIICA2wIklm5HgO0jgAACCCCAQIoFnEwszzvvPOMupFnZ3377zUgwzWnzr078\ndFck5jBw4EDp37+/ORn0d9asWfLQQw/Jvn37guaFKtCtzk6dOlUKFSoUajZlCCCAgCsCJJausLNR\nBBBAAAEEEEiEQLInlp07d5bRo0fHTaHvYOouTcqVKxf3Z/kAAgggYIcAiaUdqqwTAQQQQAABBBwR\nSObEcs6cOXLnnXf6OWXKlEnuv/9+qVy5svHo67Zt22Tx4sXywQcfyK5du/yWrVGjhtH9iV8hEwgg\ngIBLAiSWLsGzWQQQQAABBBBIvUBgYnnxxRdLnTp1Yl7x9u3bjTt/5geyZs0qx48fNyf9/ibyUVjd\nQE/ZsmVl1apV1jZ0Qz1vvfWW1K9f3yozR3bs2CG1a9eWNWvWmEXG37lz50rdunX9yphAAAEE3BAg\nsXRDnW0igAACCCCAQEIEAhPL1K7UqcRyypQp0qpVK7/qfvHFF1KzZk2/Mt8J/Q5myZIl5ffff7eK\n9aOwy5Yts6YZQQABBNwSILF0S57tIoAAAggggECqBZI1saxatap8++231v5XqFBBfvjhB2s63Eiv\nXr1EtzrrOxw6dChk67S+yzCOAAII2C1AYmm3MOtHAAEEEEAAAdsEkjWx1I/s7tmzx3J57bXXpFmz\nZtZ0uJF169bJNddc4zd7xYoVUqZMGb8yJhBAAAGnBUgsnRZnewgggAACCCCQMIHAxFK/nzh+/PiY\n179kyRJp1KiRtbwTj8LqO4y5cuWytqlH9LuW+jHXWAb9Wb0Oc5g5c6Y0aNDAnOQvAggg4IoAiaUr\n7GwUAQQQQAABBBIhEJhY6hZVX3/99ZhXrRPLihUrWss7kVj+/PPPcu2111rb1CMHDx4U3XhPLEOx\nYsVkw4YN1qLDhw+Xbt26WdOMIIAAAm4IkFi6oc42EUAAAQQQQCAhAsmYWM6ePdvvDmO2bNnk6NGj\nMXsUL15c1q9fby1PYmlRMIIAAi4KkFi6iM+mEUAAAQQQQCB1AsmYWM6fP190H5S+g04sdYIZy3D1\n1VfLxo0brUUnTpworVu3tqYZQQABBNwQILF0Q51tIoAAAggggEBCBJIxsdy2bZsULlzYb/+3bNki\nRYoU8SsLNxHYn6ZOVG+++eZwi1OOAAIIOCJAYukIMxtBAAEEEEAAATsEkjGxPH36tOjk8MSJExbJ\njBkz5K677rKmw43odyv1O5a+w44dO6RQoUK+RYwjgAACjguQWDpOzgYRQAABBBBAIFECyZhY6n0P\nbICnXr168tFHH0Vl6dKli4waNcpaLnv27EYLsRkyZLDKGEEAAQTcECCxdEOdbSKAAAIIIIBAQgSS\nNbF84IEHZPr06ZZBxowZjS5HAvuotBZQI3v37pUrrrjCr6uRdu3aybhx43wXYxwBBBBwRYDE0hV2\nNooAAggggAACiRBI1sRSN75TokQJOXnypMVQoEABmTt3rpQrV84qM0f0I7C33367X2uwOXLkMBrx\nKViwoLkYfxFAAAHXBEgsXaNnwwgggAACCCCQWoFkTSz1fnfo0CHobuP5558vzZo1k0qVKhmJ59at\nW0X3tTl58mT5888//bieeOIJGTRokF8ZEwgggIBbAiSWbsmzXQQQQAABBBBItUAyJ5Z79uyR66+/\nXnbt2hW3g+6uZNasWZIrV664P8sHEEAAATsESCztUGWdCCCAAAIIIOCIQDInlhpI34Vs27atvPfe\nezF5Zc6cWQYOHCh9+vQR/V4mAwIIIOAVARJLr0SCeiCAAAIIIIBA3AJDhgyRvn37Wp/TraaOHDnS\nmo42sn79eilTpozV9YfutkN33xFq0PPMu4u6NVZ9xzHUHcOOHTvK2LFjrVXoRLB///7WdKiRV199\nVcaMGWM04HP48OGgRbJkySLVqlWTwYMHS+XKlYPmU4AAAgi4LUBi6XYE2D4CCCCAAAIIpEpg3759\ncvDgQcmZM6fky5cv7nUdOXLEaHE1U6ZMohvQ0XcFQw3//POPkUzqeXo7ui/KUEPLli1l2rRp1qyn\nnnpK+vXrZ01HGjlz5ozo9ypXrVplJLF58+Y16lS2bFnJnTt3pI8yDwEEEHBVgMTSVX42jgACCCCA\nAAJpTaBBgwYye/Zsa7eGDx8u3bp1s6YZQQABBNKiAIllWowq+4QAAggggAACrgjoO45FixaVLVu2\nWNufOnWqtGjRwppmBAEEEEiLAiSWaTGq7BMCCCCAAAIIuCIwceJEadOmjd+2Fy9eLBUrVvQrYwIB\nBBBIawIklmktouwPAggggAACCDgmcOjQIaldu7boxnX27t0rq1ev9tt28eLFZc2aNZIhQwa/ciYQ\nQACBtCZAYpnWIsr+IIAAAggggIBjArqRndKlS4fd3oQJE+Thhx8OO58ZCCCAQFoRILFMK5FkPxBA\nAAEEEEDAcQF9N7JkyZJB29V3KHv37m10D6Jbm2VAAAEE0roAiWVajzD7hwACCCCAAAK2CZw6dUq6\ndu0qixYtEt3/ZOHChY1+MevWrSu33HKLbdtlxQgggIDXBEgsvRYR6oMAAggggAACCCCAAAIIJJkA\niWWSBYzqIoAAAggggAACCCCAAAJeEyCx9FpEqA8CCCCAAAIIIIAAAgggkGQCJJZJFjCqiwACCCCA\nAAIIIIAAAgh4TYDE0msRoT4IIIAAAggggAACCCCAQJIJkFgmWcCoLgIIIIAAAggggAACCCDgNQES\nS69FhPoggAACCCCAAAIIIIAAAkkmQGKZZAGjuggggAACCCCAAAIIIICA1wRILL0WEeqDAAIIIIAA\nAggggAACCCSZAIllkgWM6iKAAAIIIIAAAggggAACXhMgsfRaRKgPAggggAACCCCAAAIIIJBkAiSW\nSRYwqosAAggggAACCCCAAAIIeE3g/wnK0gXi6OAyAAAAAElFTkSuQmCC\n"
    }
   },
   "cell_type": "markdown",
   "id": "9f8aa07a-7b9e-4390-b0b9-61df385eaebb",
   "metadata": {},
   "source": [
    "![Gauss.png](attachment:3285c96e-9f38-49c1-a707-c8e8da282c0a.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba69989b-8b40-44eb-9653-74dac682d687",
   "metadata": {},
   "source": [
    "En el caso de la mutación, se deben definir dos probabilidades:\n",
    "* La probabilidad de mutar un individuo (lo veremos más adelante)\n",
    "* La probabilidad  de mutar cada uno de los genes del individuo (`indpb`)\n",
    "Este último valor, en general, debe ser bajo para que la mutación no modifique en exceso al individuo. Valores muy altos de esta probabilidad pueden hacer que el algoritmo no converja correctamente, o que no se intensifiquen ciertas zonas del espacio de búsqueda."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec8d27a-f2a3-4b4a-8539-5b369e46870c",
   "metadata": {},
   "source": [
    "**SELECCIÓN** ---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e71fdc0-26ae-4a0d-90df-c94620196272",
   "metadata": {},
   "outputs": [],
   "source": [
    "toolbox.register(\"select\", tools.selTournament, tournsize=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd02472-145b-428a-9b87-5641c02d812f",
   "metadata": {},
   "source": [
    "Hemos usado el operador `selTournament(individuals, k, tournsize)` que nos permite realizar una **selección mediante torneo**. En este caso fijamos un tamaño igual a 3. Se ha demostrado que este tamaño funciona relativamente bien para la mayoría de los casos [Lones 2011](https://cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa27f6c7-1ec0-42c6-a33f-2d666e90a7fc",
   "metadata": {},
   "source": [
    "`selTournament` funciona de la siguiente forma [Detalle de SelTournament](https://deap.readthedocs.io/en/master/api/tools.html#deap.tools.selTournament):\n",
    "1. Se selecciona de manera aleatoria un número `tournsize` de individuos de la población `individuals`\n",
    "2.  En cada selección existen reemplazos por lo que un individuo puede ser seleccionado varias veces en un mismo torneo\n",
    "3. Los individuos seleccionados compiten entre ellos para ganar el torneo utilizando el desempeño obtenido por cada uno al evaluar la función de `fitness`\n",
    "4. El individuo con mejor desempeño es el que ganará el torneo, y será quien participe en las operaciones genéticas\n",
    "5. Este proceso se repite hasta seleccionar un total de `k` individuos"
   ]
  },
  {
   "attachments": {
    "4b618212-6d43-411c-aabf-cb428cf3876d.png": {
     "image/png": 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lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4xMTk4PC9leGlmOlBp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\nzuUSHGgRTxkAAAAASUVORK5CYII=\n"
    }
   },
   "cell_type": "markdown",
   "id": "f58405d1-4577-4522-9567-215b579350f4",
   "metadata": {},
   "source": [
    "![tournament.png](attachment:4b618212-6d43-411c-aabf-cb428cf3876d.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c339156-3036-4098-8848-59f040fec409",
   "metadata": {},
   "source": [
    "El algoritmo realiza **tantos torneos como individuos tiene la población**. Aunque 3 es válido para la mayoría de los casos, cuando la población crece mucho, se deben utilizar **tamaños más altos** para **hacer más rápida la convergencia** del algoritmo. La **selección con torneo** es **muy elitista** y hace que el algoritmo converja más rapidamente que, por ejemplo, con el algoritmo de la ruleta. Las **funciones** se han registrado con `register` con los `alias`: `mate`, `mutate` y `select` que **no deben modificarse** ya que son usados por la función del módulo `algorithms` que implementa el Algortimo Genético. Pasa lo mismo con la función objetivo `evaluate`. **No debe cambiarse el alias**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b979849f-fbc3-40dc-a40f-7d09fdeb1fc4",
   "metadata": {},
   "source": [
    "#### 4. Últimos pasos: Algoritmo genético como una caja negra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe73e5c-e42a-4c2d-9871-7b5515a1298d",
   "metadata": {},
   "source": [
    "Ahora veremos como **configurar el algoritmo genético** y la **representación de los resultados** del algoritmo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376050df-c03d-4607-8a38-a11880ca7980",
   "metadata": {},
   "source": [
    "##### 4.1 Configuración del Algoritmo Genético ---------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee971dcd-ab6a-4e6d-aa8d-e1026a963301",
   "metadata": {},
   "source": [
    "Definimos la función `main()` que realizará toda la **configuración** del algoritmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ec41bae-097f-439a-a635-6786ae49933b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos la semilla del generador de números aleatorios\n",
    "# Se hace así para tener resultados reproducibles\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0ecb69df-1053-4b17-b839-c460a363e9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos:\n",
    "#  CXPB: Probabilidad de Cruce\n",
    "#  MUTPB: Probabilidad de Mutación\n",
    "#  NGEN: Número de Generaciones\n",
    "# Los valores son de prueba para obtener unos resultados preeliminares\n",
    "CXPB, MUTPB, NGEN = 0.5, 0.2, 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3460399a-2356-40ce-9739-73f8ba30cb71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[27.885359691576753, -94.99784895546661],\n",
       " [-44.99413632617615, -55.35785237023545],\n",
       " [47.29424283280247, 35.33989748458225],\n",
       " [78.43591354096907, -82.61223347411678],\n",
       " [-15.61563606294591, -94.04055611238593],\n",
       " [-56.27240503927933, 1.0710576206724767],\n",
       " [-94.69280606322728, -60.2324698626703],\n",
       " [29.976887555904653, 8.98829612064334],\n",
       " [-55.911875591860664, 17.85313677518174],\n",
       " [61.88609133556534, -98.7002480643878],\n",
       " [61.163850366561576, 39.62787899764538],\n",
       " [-31.949896696401623, -68.90410003764369],\n",
       " [91.44261444135623, -32.68109097747465],\n",
       " [-81.45083132397042, -80.65672463330719],\n",
       " [69.49887326949195, 20.745206273378216],\n",
       " [61.42565465487604, 45.94635733876359],\n",
       " [7.245618290940143, 94.62315279587412],\n",
       " [-24.29312455832931, 10.408126254645396],\n",
       " [65.88093285059898, 23.703950472849215],\n",
       " [72.34138006215545, 15.470429051352411],\n",
       " [40.914367242984696, -90.83512326886756],\n",
       " [-54.42034486969063, -42.12240727957857],\n",
       " [-84.04160461527451, -53.44182272779396],\n",
       " [-79.79971411805417, -44.40527937798158],\n",
       " [27.136888852880034, -27.033564205983154],\n",
       " [-25.96380657662347, -58.09859384570246],\n",
       " [-46.60443559017733, 87.33091754249881],\n",
       " [29.60707704931872, 21.826201133397632],\n",
       " [-65.7722703603806, 45.82535959006984],\n",
       " [-67.31950124761431, -24.10891164847044],\n",
       " [97.90467012731904, 27.99995197081857],\n",
       " [11.38994875492925, 36.922850197974924]]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generamos la Población Inicial\n",
    "pop = toolbox.population()\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "49fd0cba-05ee-42ca-84a5-e206a1eb1011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<deap.tools.support.HallOfFame at 0x7fe32805b0a0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definimos el objeto hof que almacenará el mejor individuo \n",
    "# encontrado a lo largo de las generaciones del Algoritmo G.\n",
    "hof = tools.HallOfFame(1)\n",
    "hof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9140d938-c470-4ee5-b3c7-960b97a174cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(hof)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad882a4e-8dbe-457f-b865-ae95c6d81518",
   "metadata": {},
   "source": [
    "El método `HallOfFame` [Clase HallOfFame](https://deap.readthedocs.io/en/master/api/tools.html#hall-of-fame) recibe dos parámetros:\n",
    "* `maxsize`: Nº individuos a almacenar\n",
    "* `similar`: Función para comparar si dos individuos son iguales. Si no se pone nada, utilizará el método `operator.eq` del módulo `operator` [Módulo Operator](https://docs.python.org/3/library/operator.html#operator.eq)\n",
    "\n",
    "Este es el **mecanismo** que implementa `deap` **para no perder al mejor individuo** a lo largo de la evolución del algoritmo, ya que con el algoritmo`eaSimple` se podría perder al mejor individuo debido a los operadores genéticos.\n",
    "\n",
    "La clase `HallOfFame` está definida en el submódulo `tools` y **en cada generación** del algoritmo genético el objeto `hof` es **actualizado** mediante el método `update` (transparente a nosotros), que recibe como **entrada** la **población actual** y actualiza el contenido del objeto `hof`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "71a5a9a6-d7e8-4263-8470-31a6937fef37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<deap.tools.support.Statistics object at 0x7fe32805b370>\n"
     ]
    }
   ],
   "source": [
    "# Definimos un objeto para generar las estadísticas de la \n",
    "# población a lo largo de las generaciones del algoritmo\n",
    "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
    "print(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0388302-1fe9-4ecd-aa37-83404a45e6ad",
   "metadata": {},
   "source": [
    "La clase `Statistics` se encuentra definido en el submódulo `tools` [Clase Statistics](https://deap.readthedocs.io/en/master/api/tools.html#statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d3a993-b36e-4237-9f79-d50cb23a691f",
   "metadata": {},
   "source": [
    "Ahora registramos en el objeto `stats` las funciones estadísticas a utilizar junto con su alias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd1f7271-054e-4bfb-ad59-9e90e1be6efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.register(\"avg\", np.mean)\n",
    "stats.register(\"std\", np.std)\n",
    "stats.register(\"min\", np.min)\n",
    "stats.register(\"max\", np.max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efaad2b8-9930-4a01-9aaa-4148a57dee47",
   "metadata": {},
   "source": [
    "[Función media](https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html)\n",
    "\n",
    "[Función desviación estandar](https://docs.scipy.org/doc/numpy/reference/generated/numpy.std.html?highlight=numpy%20std#numpay.std)\n",
    "\n",
    "[Función mínimo](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.min.html?highlight=numpy%20min#numpay.ndarray.min)\n",
    "\n",
    "[Función máximo](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.max.html?)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e78e4c-feaf-453a-a6e0-70c589410f20",
   "metadata": {},
   "source": [
    "El objeto `stats` tiene un método `compile` [Método Compile](https://deap.readthedocs.io/en/master/tutorials/basic/part3.html), que recibe como entrada la población que permite generar las estadísticas. A este método se le llama internamente en cada generación del algoritmo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9730a451-8129-48e8-a0e6-7aad9fab4557",
   "metadata": {},
   "source": [
    "**ESTAMOS EN DISPOSICIÓN DE PODER EJECUTAR EL ALGORITMO GENÉTICO** ---------------------------------------------\n",
    "\n",
    "En `deap` tenemos una ámplia gama de algoritmos en el submódulo `algorithms` [Submódulo Algorithms](https://deap.readthedocs.io/en/master/api/algo.html), en nuestro caso, usaremos `eaSimple` que es una de las versiones más simples y que realizará lo siguiente:\n",
    "\n",
    "1. Evaluará la población inicial\n",
    "2. Mientras no se alcance el límite de generaciones:\n",
    "    1. Selección de individuos para participar en las operaciones genéticas\n",
    "    2. Creación de la siguiente generación mediante cruce y/o mutación de los individuos seleccionados\n",
    "    3. Evaluación de la generación creada\n",
    "\n",
    "Además, el algoritmo `eaSimple`, devolverá la **población final** y el **registro de evolución** y tendrá como **parámetros de entrada**:\n",
    "* `population` : Población inicial\n",
    "* `toolbox` : La caja de herramientas con todas las funciones necesarias\n",
    "* `cxpb` : Probabilidad de cruce en tanto por uno\n",
    "* `mutpb` : Probabilidad de mutación en tanto por uno\n",
    "* `ngen` : Nº de generaciones del algoritmo\n",
    "* `stats` : Objeto estadístico con las funciones registradas (opcional)\n",
    "* `halloffame` : Objeto del tipo HallOfFame con los mejores individuos por generación, opcional\n",
    "* `verbose` : Si se deben mostrar o no las estadísticas de cada generación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "021b8956-500e-478a-ba7f-88ed20e587c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loogbook contendrá todo el registro de evolución\n",
    "logbook = tools.Logbook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "795ee81d-b126-432e-bd15-f6588935368b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t77.4361\t25.1119\t26.4289\t116.497\n",
      "1  \t19    \t96.4883\t22.7128\t31.9797\t135.753\n",
      "2  \t27    \t112.689\t17.4287\t66.3743\t139.022\n",
      "3  \t17    \t126.311\t7.47741\t107.958\t139.022\n",
      "4  \t20    \t133.989\t3.86947\t123.191\t139.022\n",
      "5  \t19    \t136.773\t2.2418 \t131.216\t139.022\n",
      "6  \t18    \t138.523\t0.984614\t135.518\t139.022\n",
      "7  \t17    \t139.048\t0.143654\t139.022\t139.847\n",
      "8  \t20    \t138.835\t1.15547 \t132.457\t139.847\n",
      "9  \t13    \t134.645\t24.3715 \t-1     \t139.847\n",
      "10 \t20    \t139.228\t0.357508\t139.022\t139.847\n",
      "11 \t21    \t135.033\t24.4356 \t-1     \t139.847\n",
      "12 \t18    \t139.641\t0.357508\t139.022\t139.847\n",
      "13 \t23    \t139.598\t0.824322\t135.476\t139.847\n",
      "14 \t18    \t135.314\t24.4937 \t-1     \t139.847\n",
      "15 \t18    \t139.847\t0       \t139.847\t139.847\n",
      "16 \t19    \t135.446\t24.5064 \t-1     \t139.847\n",
      "17 \t17    \t139.847\t0       \t139.847\t139.847\n",
      "18 \t28    \t135.277\t24.4941 \t-1     \t139.847\n",
      "19 \t18    \t135.446\t24.5064 \t-1     \t139.847\n",
      "20 \t22    \t130.794\t34.0467 \t-1     \t139.847\n"
     ]
    }
   ],
   "source": [
    "pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=CXPB,\n",
    "                                       mutpb=MUTPB, ngen=NGEN, stats=stats,\n",
    "                                       halloffame=hof, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6a153cfc-2b17-4849-a202-894056da9689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [100.69014625521163, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -102.12728477675302], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -91.15745011240782], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -99.85981097553889], [97.90467012731904, -97.10376792589386]]\n"
     ]
    }
   ],
   "source": [
    "print(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec94ddad-2cf9-4c60-89c2-ed199725c83e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg    \tstd     \tmin    \tmax    \n",
      "0  \t32    \t77.4361\t25.1119 \t26.4289\t116.497\n",
      "1  \t19    \t96.4883\t22.7128 \t31.9797\t135.753\n",
      "2  \t27    \t112.689\t17.4287 \t66.3743\t139.022\n",
      "3  \t17    \t126.311\t7.47741 \t107.958\t139.022\n",
      "4  \t20    \t133.989\t3.86947 \t123.191\t139.022\n",
      "5  \t19    \t136.773\t2.2418  \t131.216\t139.022\n",
      "6  \t18    \t138.523\t0.984614\t135.518\t139.022\n",
      "7  \t17    \t139.048\t0.143654\t139.022\t139.847\n",
      "8  \t20    \t138.835\t1.15547 \t132.457\t139.847\n",
      "9  \t13    \t134.645\t24.3715 \t-1     \t139.847\n",
      "10 \t20    \t139.228\t0.357508\t139.022\t139.847\n",
      "11 \t21    \t135.033\t24.4356 \t-1     \t139.847\n",
      "12 \t18    \t139.641\t0.357508\t139.022\t139.847\n",
      "13 \t23    \t139.598\t0.824322\t135.476\t139.847\n",
      "14 \t18    \t135.314\t24.4937 \t-1     \t139.847\n",
      "15 \t18    \t139.847\t0       \t139.847\t139.847\n",
      "16 \t19    \t135.446\t24.5064 \t-1     \t139.847\n",
      "17 \t17    \t139.847\t0       \t139.847\t139.847\n",
      "18 \t28    \t135.277\t24.4941 \t-1     \t139.847\n",
      "19 \t18    \t135.446\t24.5064 \t-1     \t139.847\n",
      "20 \t22    \t130.794\t34.0467 \t-1     \t139.847\n"
     ]
    }
   ],
   "source": [
    "print(logbook)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d42738b-38a2-4bf3-ae06-524c8c696e97",
   "metadata": {},
   "source": [
    "Agrupamos todo lo anterior en la función `main()` que realizará toda la **configuración** del algoritmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8dadb2a6-6214-437d-ae51-3a01b7ae77df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    # Definimos la semilla del generador de números aleatorios\n",
    "    # Se hace así para tener resultados reproducibles\n",
    "    random.seed(42)\n",
    "    \n",
    "    # Definimos:\n",
    "    #  CXPB: Probabilidad de Cruce\n",
    "    #  MUTPB: Probabilidad de Mutación\n",
    "    #  NGEN: Número de Generaciones\n",
    "    # Los valores son de prueba para obtener unos resultados preeliminares\n",
    "    CXPB, MUTPB, NGEN = 0.5, 0.2, 20\n",
    "    \n",
    "    #Generamos la Población Inicial\n",
    "    pop = toolbox.population()\n",
    "    \n",
    "    # Definimos el objeto hof que almacenará el mejor individuo \n",
    "    # encontrado a lo largo de las generaciones del Algoritmo G.\n",
    "    hof = tools.HallOfFame(1)\n",
    "    \n",
    "    # Definimos un objeto para generar las estadísticas de la \n",
    "    # población a lo largo de las generaciones del algoritmo\n",
    "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
    "    \n",
    "    # Registramos en el objeto stats \n",
    "    # las funciones estadísticas a usar\n",
    "    stats.register(\"avg\", np.mean)\n",
    "    stats.register(\"std\", np.std)\n",
    "    stats.register(\"min\", np.min)\n",
    "    stats.register(\"max\", np.max)\n",
    "    \n",
    "    # loogbook contendrá todo el registro de evolución\n",
    "    logbook = tools.Logbook()\n",
    "    \n",
    "    # Algoritmo eaSimple, devuelve la población final y el registro de evolución\n",
    "    # y tiene como entrada:\n",
    "    # population – población inicial\n",
    "    # toolbox – La caja de herramientas con todas las funciones necesarias\n",
    "    # cxpb – Probabilidad de cruce en tanto por uno\n",
    "    # mutpb – Probabilidad de mutación en tanto por uno\n",
    "    # ngen – Nº de generaciones del algoritmo\n",
    "    # stats – Objeto estadístico con las funciones registradas (opcional)\n",
    "    # halloffame – Objeto del tipo HallOfFame con los mejores individuos por generación, opcional\n",
    "    # verbose – Si se deben mostrar o no las estadísticas de cada generación\n",
    "    pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=CXPB,\n",
    "                                       mutpb=MUTPB, ngen=NGEN, stats=stats,\n",
    "                                       halloffame=hof, verbose=True)\n",
    "   \n",
    "    # Devolvemos el HallOfFame y el registro de evolución\n",
    "    return hof, logbook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb232bd-fc36-4cd7-b1bc-f84db42fe094",
   "metadata": {},
   "source": [
    "##### 4.2 Resultados del Algoritmo Genético ---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "35ca1ba8-df59-400c-8839-eeb1cf2e80e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t77.4361\t25.1119\t26.4289\t116.497\n",
      "1  \t19    \t96.4883\t22.7128\t31.9797\t135.753\n",
      "2  \t27    \t112.689\t17.4287\t66.3743\t139.022\n",
      "3  \t17    \t126.311\t7.47741\t107.958\t139.022\n",
      "4  \t20    \t133.989\t3.86947\t123.191\t139.022\n",
      "5  \t19    \t136.773\t2.2418 \t131.216\t139.022\n",
      "6  \t18    \t138.523\t0.984614\t135.518\t139.022\n",
      "7  \t17    \t139.048\t0.143654\t139.022\t139.847\n",
      "8  \t20    \t138.835\t1.15547 \t132.457\t139.847\n",
      "9  \t13    \t134.645\t24.3715 \t-1     \t139.847\n",
      "10 \t20    \t139.228\t0.357508\t139.022\t139.847\n",
      "11 \t21    \t135.033\t24.4356 \t-1     \t139.847\n",
      "12 \t18    \t139.641\t0.357508\t139.022\t139.847\n",
      "13 \t23    \t139.598\t0.824322\t135.476\t139.847\n",
      "14 \t18    \t135.314\t24.4937 \t-1     \t139.847\n",
      "15 \t18    \t139.847\t0       \t139.847\t139.847\n",
      "16 \t19    \t135.446\t24.5064 \t-1     \t139.847\n",
      "17 \t17    \t139.847\t0       \t139.847\t139.847\n",
      "18 \t28    \t135.277\t24.4941 \t-1     \t139.847\n",
      "19 \t18    \t135.446\t24.5064 \t-1     \t139.847\n",
      "20 \t22    \t130.794\t34.0467 \t-1     \t139.847\n",
      "Mejor fitness: 139.847439\n",
      "Mejor individuo [97.90467012731904, -99.85981097553889]\n"
     ]
    }
   ],
   "source": [
    "best, log = main()\n",
    "print(\"Mejor fitness: %f\" %best[0].fitness.values)\n",
    "print(\"Mejor individuo %s\" %best[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22dcc461-adab-410b-83b7-06567cb7fef5",
   "metadata": {},
   "source": [
    "**CUESTIONES** ---------------------------------------------\n",
    "\n",
    "Datos que corresponden a las funciones registradas en el objeto `stats`\n",
    "* `gen`: Generación en la que me encuentro\n",
    "* `nevals`: Número de individuos que han sido evaluados respecto a su desempeño\n",
    "    * No es igual al número de individuos porque los operadores de cruce y mutación son probabilísticos (un individuo seleccionado puede no haberse modificado y no será, por tanto, evaluado, ahorrando operaciones redundantes). Cuando un individuo **es modificado** debido a un operación de genética (cruce o mutación), su `fitness` se invalida, poniendo el atributo `valid=False`. En cada generación, todos los individuos que tienen un `fitness` **inválido** serán evaluados. \n",
    "* `avg`: Desempeño medio de la población\n",
    "    * Se incrementa a lo largo de las generaciones. **Positivo** porque los **individuos** que forman la población son, en media, **mejores**\n",
    "* `std`: Desviación típica del desempeño medio de la población\n",
    "    * Disminuye, en general, a lo largo de las generaciones. **Positivo** porque los **individuos** que forman la población son, **cada vez más parecidos** y que el algoritmo es **elitista** por el proceso de selección\n",
    "* `min`: Mínimo desempeño de la población\n",
    "* `max`: Máximo desempeño de la población\n",
    "    * Aumenta a lo largo de las generaciones. **Muy positivo** porque indica que el algoritmo funciona correctamente\n",
    "    \n",
    "\n",
    "Aunque `hof` lo hemos definido para que almecene solo un individuo, el objeto es una secuencia (como una lista en Python) por lo que para obtener el individuo o el `fitness` debemos usar el índice cero.\n",
    "\n",
    "\n",
    "Al A.G. nos ha proporcionado en tan solo unos pasos una solución que es bastante buena - no es la mejor, pero está muy cerca.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1a0ff0f7-5dcf-4c0c-85d6-525e9a916925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg    \tstd     \tmin    \tmax    \n",
      "0  \t32    \t77.4361\t25.1119 \t26.4289\t116.497\n",
      "1  \t19    \t96.4883\t22.7128 \t31.9797\t135.753\n",
      "2  \t27    \t112.689\t17.4287 \t66.3743\t139.022\n",
      "3  \t17    \t126.311\t7.47741 \t107.958\t139.022\n",
      "4  \t20    \t133.989\t3.86947 \t123.191\t139.022\n",
      "5  \t19    \t136.773\t2.2418  \t131.216\t139.022\n",
      "6  \t18    \t138.523\t0.984614\t135.518\t139.022\n",
      "7  \t17    \t139.048\t0.143654\t139.022\t139.847\n",
      "8  \t20    \t138.835\t1.15547 \t132.457\t139.847\n",
      "9  \t13    \t134.645\t24.3715 \t-1     \t139.847\n",
      "10 \t20    \t139.228\t0.357508\t139.022\t139.847\n",
      "11 \t21    \t135.033\t24.4356 \t-1     \t139.847\n",
      "12 \t18    \t139.641\t0.357508\t139.022\t139.847\n",
      "13 \t23    \t139.598\t0.824322\t135.476\t139.847\n",
      "14 \t18    \t135.314\t24.4937 \t-1     \t139.847\n",
      "15 \t18    \t139.847\t0       \t139.847\t139.847\n",
      "16 \t19    \t135.446\t24.5064 \t-1     \t139.847\n",
      "17 \t17    \t139.847\t0       \t139.847\t139.847\n",
      "18 \t28    \t135.277\t24.4941 \t-1     \t139.847\n",
      "19 \t18    \t135.446\t24.5064 \t-1     \t139.847\n",
      "20 \t22    \t130.794\t34.0467 \t-1     \t139.847\n"
     ]
    }
   ],
   "source": [
    "print(log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776c2097-8f93-4d5c-8739-00055d30a607",
   "metadata": {},
   "source": [
    "#### 5. ¿Cómo conseguir resultados consistentes?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "955beae5-7f78-4bd8-b202-94769b07a825",
   "metadata": {},
   "source": [
    "Buenas prácticas a llevar a cabo para mejorar el modelo:\n",
    "* Aumentar la población hasta que no veamos mejoras significativas probando con pocas generaciones e ir aumentando el número de individuos\n",
    "* Aumentar el número de generaciones y comprobar que el algoritmo converja mostrando una gráfica de convergencia del algoritmo\n",
    "* Hacer un barrido de valores de probabilidades de cruce y mutación, y mostrar algunas estadísticas para ver de qué manera afectan dichas probabilidades"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7431a729-2c95-400d-9a93-a977df52db41",
   "metadata": {},
   "source": [
    "Ahora lanzamos el Algoritmo Genético 20 veces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5f20aa56-9a70-41c6-8724-798b20295c34",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    CXPB, MUTPB, NGEN = 0.5, 0.2, 20\n",
    "    pop = toolbox.population()\n",
    "    hof = tools.HallOfFame(1)\n",
    "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
    "    stats.register(\"avg\", np.mean)\n",
    "    stats.register(\"std\", np.std)\n",
    "    stats.register(\"min\", np.min)\n",
    "    stats.register(\"max\", np.max)\n",
    "    logbook = tools.Logbook()\n",
    "    pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=CXPB,\n",
    "                                       mutpb=MUTPB, ngen=NGEN, stats=stats,\n",
    "                                       halloffame=hof, verbose=True)\n",
    "    return hof, logbook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "eeab0a59-a372-4671-bfd3-32cde5ba0aa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t74.4806\t26.1253\t17.3208\t126.645\n",
      "1  \t21    \t88.0266\t22.4865\t21.376 \t126.645\n",
      "2  \t17    \t106.006\t16.9455\t79.778 \t127.551\n",
      "3  \t26    \t114.558\t20.6497\t37.2162\t139.286\n",
      "4  \t21    \t127.228\t4.66593\t118.291\t139.286\n",
      "5  \t15    \t130.741\t5.02507\t124.196\t139.286\n",
      "6  \t16    \t131.424\t24.081 \t-1     \t139.286\n",
      "7  \t18    \t133.774\t24.3027\t-1     \t139.722\n",
      "8  \t26    \t139.326\t0.200697\t138.844\t139.722\n",
      "9  \t20    \t135.079\t24.4414 \t-1     \t139.722\n",
      "10 \t13    \t135.256\t24.4729 \t-1     \t139.722\n",
      "11 \t21    \t139.722\t0       \t139.722\t139.722\n",
      "12 \t16    \t135.324\t24.4846 \t-1     \t139.722\n",
      "13 \t15    \t139.722\t0       \t139.722\t139.722\n",
      "14 \t15    \t139.722\t0       \t139.722\t139.722\n",
      "15 \t21    \t135.324\t24.4846 \t-1     \t139.722\n",
      "16 \t23    \t139.722\t0       \t139.722\t139.722\n",
      "17 \t23    \t135.288\t24.4789 \t-1     \t139.722\n",
      "18 \t23    \t139.74 \t0.0983789\t139.722\t140.287\n",
      "19 \t13    \t139.729\t0.213297 \t138.817\t140.287\n",
      "20 \t19    \t139.863\t0.244834 \t139.722\t140.287\n",
      "gen\tnevals\tavg    \tstd    \tmin   \tmax    \n",
      "0  \t32    \t76.5531\t30.4214\t10.143\t124.484\n",
      "1  \t27    \t97.3145\t21.7656\t48.2925\t137.069\n",
      "2  \t15    \t117.45 \t12.8644\t88.6674\t137.069\n",
      "3  \t17    \t125.428\t8.68544\t105.457\t137.069\n",
      "4  \t23    \t132.137\t3.04594\t124.484\t137.069\n",
      "5  \t12    \t134.301\t1.88516\t129.896\t137.069\n",
      "6  \t22    \t135.515\t1.64499\t132.533\t138.404\n",
      "7  \t20    \t136.794\t1.0067 \t133.305\t138.404\n",
      "8  \t22    \t133.065\t24.0863\t-1     \t138.404\n",
      "9  \t22    \t137.772\t1.0326 \t133.32 \t138.404\n",
      "10 \t23    \t138.309\t0.531056\t135.352\t138.404\n",
      "11 \t19    \t138.321\t0.990611\t133.326\t140.82 \n",
      "12 \t19    \t138.631\t0.704262\t138.404\t140.82 \n",
      "13 \t24    \t134.903\t24.4411 \t-1     \t140.82 \n",
      "14 \t18    \t140.216\t1.04622 \t138.404\t140.82 \n",
      "15 \t23    \t136.313\t24.6657 \t-1     \t140.82 \n",
      "16 \t25    \t140.82 \t0       \t140.82 \t140.82 \n",
      "17 \t14    \t140.82 \t0       \t140.82 \t140.82 \n",
      "18 \t23    \t140.82 \t0       \t140.82 \t140.82 \n",
      "19 \t20    \t140.748\t0.40623 \t138.486\t140.82 \n",
      "20 \t15    \t140.82 \t0       \t140.82 \t140.82 \n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t76.6477\t31.2833\t9.82021\t132.465\n",
      "1  \t22    \t104.714\t18.6455\t66.1966\t133.466\n",
      "2  \t18    \t121.213\t10.6687\t98.3793\t136.243\n",
      "3  \t28    \t125.32 \t23.3314\t-1     \t136.243\n",
      "4  \t19    \t132.741\t3.02477\t123.399\t137.066\n",
      "5  \t16    \t134.874\t1.85689\t130.132\t137.066\n",
      "6  \t17    \t136.259\t0.611463\t133.466\t137.625\n",
      "7  \t22    \t132.163\t23.9228 \t-1     \t137.647\n",
      "8  \t13    \t136.814\t0.644518\t135.678\t137.647\n",
      "9  \t18    \t137.356\t0.642989\t134.647\t137.647\n",
      "10 \t17    \t133.305\t24.122  \t-1     \t137.647\n",
      "11 \t20    \t137.646\t0.00380115\t137.625\t137.647\n",
      "12 \t13    \t137.697\t0.28045   \t137.647\t139.259\n",
      "13 \t23    \t133.516\t24.1656   \t-1     \t139.259\n",
      "14 \t19    \t133.717\t24.2059   \t-1     \t139.259\n",
      "15 \t21    \t134.277\t24.3085   \t-1     \t139.259\n",
      "16 \t12    \t134.711\t24.3798   \t-1     \t139.259\n",
      "17 \t27    \t139.048\t1.175     \t132.506\t139.259\n",
      "18 \t13    \t138.958\t1.6739    \t129.638\t139.259\n",
      "19 \t17    \t139.264\t0.0293559 \t139.259\t139.428\n",
      "20 \t24    \t139.275\t0.0491784 \t139.259\t139.428\n",
      "gen\tnevals\tavg    \tstd   \tmin    \tmax    \n",
      "0  \t32    \t78.3268\t24.499\t22.8727\t121.341\n",
      "1  \t28    \t96.0546\t22.8725\t28.1999\t131.538\n",
      "2  \t20    \t112.409\t23.587 \t-1     \t135.313\n",
      "3  \t17    \t118.073\t23.9541\t-1     \t139.533\n",
      "4  \t19    \t132.362\t6.80291\t121.341\t139.533\n",
      "5  \t22    \t127.957\t33.4296\t-1     \t139.533\n",
      "6  \t20    \t137.572\t2.00828\t131.97 \t139.533\n",
      "7  \t20    \t139.029\t0.57079\t138.286\t139.533\n",
      "8  \t22    \t139.455\t0.30176\t138.286\t139.533\n",
      "9  \t22    \t135.096\t24.4448\t-1     \t139.533\n",
      "10 \t21    \t130.749\t34.0176\t-1     \t139.533\n",
      "11 \t16    \t139.533\t0      \t139.533\t139.533\n",
      "12 \t21    \t135.141\t24.4517\t-1     \t139.541\n",
      "13 \t19    \t135.066\t24.4418\t-1     \t139.541\n",
      "14 \t22    \t139.496\t0.472517\t137.138\t140.738\n",
      "15 \t17    \t139.498\t0.47267 \t137.138\t140.738\n",
      "16 \t22    \t139.764\t0.469239\t139.533\t140.747\n",
      "17 \t17    \t140.217\t0.596407\t139.541\t140.747\n",
      "18 \t14    \t136.158\t24.644  \t-1     \t140.747\n",
      "19 \t21    \t140.746\t0.00143975\t140.738\t140.747\n",
      "20 \t24    \t131.887\t34.3114   \t-1     \t140.747\n",
      "gen\tnevals\tavg    \tstd   \tmin    \tmax    \n",
      "0  \t32    \t73.7405\t28.401\t17.2921\t116.612\n",
      "1  \t15    \t94.7733\t23.3984\t-1     \t118.306\n",
      "2  \t22    \t109.567\t11.9392\t65.5434\t128.984\n",
      "3  \t16    \t117.517\t7.0322 \t101.795\t131.152\n",
      "4  \t9     \t122.602\t6.17287\t112.802\t131.152\n",
      "5  \t15    \t128.331\t4.69856\t116.612\t135.043\n",
      "6  \t22    \t131.135\t3.53573\t121.918\t135.043\n",
      "7  \t17    \t133.964\t1.82011\t131.152\t137.142\n",
      "8  \t21    \t135.153\t1.02707\t131.152\t138.234\n",
      "9  \t20    \t126.933\t33.0596\t-1     \t138.38 \n",
      "10 \t23    \t136.155\t1.30149\t135.043\t138.38 \n",
      "11 \t25    \t137.144\t1.26676\t134.977\t138.38 \n",
      "12 \t26    \t137.979\t0.903212\t133.871\t138.38 \n",
      "13 \t16    \t138.352\t0.0566323\t138.234\t138.38 \n",
      "14 \t23    \t129.668\t33.7384  \t-1     \t138.38 \n",
      "15 \t20    \t138.38 \t0        \t138.38 \t138.38 \n",
      "16 \t25    \t134.024\t24.251   \t-1     \t138.38 \n",
      "17 \t16    \t138.259\t0.81598  \t134.21 \t140.008\n",
      "18 \t21    \t138.43 \t0.283404 \t138.38 \t140.008\n",
      "19 \t20    \t125.16 \t40.5944  \t-1     \t140.008\n",
      "20 \t15    \t133.886\t24.259   \t-1     \t140.008\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t86.4545\t28.2871\t17.1841\t134.667\n",
      "1  \t21    \t109.727\t17.6388\t54.2402\t135.704\n",
      "2  \t20    \t112.887\t32.9705\t-1     \t135.704\n",
      "3  \t22    \t129.197\t5.83418\t112.259\t137.737\n",
      "4  \t21    \t132.718\t2.61367\t125.478\t137.737\n",
      "5  \t20    \t135.179\t2.62536\t129.918\t137.737\n",
      "6  \t16    \t137.247\t1.47005\t130.921\t137.737\n",
      "7  \t19    \t133.401\t24.1392\t-1     \t137.737\n",
      "8  \t17    \t137.737\t0      \t137.737\t137.737\n",
      "9  \t24    \t137.502\t0.964177\t132.696\t137.737\n",
      "10 \t15    \t133.386\t24.1366 \t-1     \t137.737\n",
      "11 \t14    \t137.737\t0       \t137.737\t137.737\n",
      "12 \t26    \t137.748\t0.447402\t136.554\t139.887\n",
      "13 \t18    \t138.054\t1.00846 \t135.429\t140.304\n",
      "14 \t21    \t129.849\t33.8015 \t-1     \t140.304\n",
      "15 \t19    \t138.939\t1.23726 \t135.7  \t140.304\n",
      "16 \t23    \t139.75 \t0.744285\t137.737\t140.456\n",
      "17 \t21    \t135.679\t24.5493 \t-1     \t140.456\n",
      "18 \t20    \t140.264\t0.133112\t139.887\t140.456\n",
      "19 \t18    \t140.361\t0.0734918\t140.304\t140.456\n",
      "20 \t24    \t135.913\t24.5947  \t-1     \t140.456\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t76.5288\t20.8907\t37.2076\t113.184\n",
      "1  \t22    \t93.1236\t20.7605\t43.1403\t118.619\n",
      "2  \t19    \t110.088\t7.13609\t91.3674\t126.297\n",
      "3  \t16    \t114.592\t4.64653\t108.347\t126.297\n",
      "4  \t13    \t118.632\t4.18532\t108.347\t126.992\n",
      "5  \t18    \t121.942\t3.92425\t114.292\t126.992\n",
      "6  \t23    \t124.404\t3.42087\t118.619\t126.992\n",
      "7  \t20    \t126.729\t0.698536\t123.149\t126.992\n",
      "8  \t25    \t127.066\t0.505437\t126.297\t129.783\n",
      "9  \t18    \t127.31 \t1.08968 \t126.992\t132.736\n",
      "10 \t23    \t127.458\t1.37469 \t124.498\t132.736\n",
      "11 \t25    \t128.305\t1.81916 \t126.992\t132.999\n",
      "12 \t18    \t130.091\t2.20582 \t126.992\t132.999\n",
      "13 \t21    \t131.928\t1.41486 \t129.783\t132.999\n",
      "14 \t27    \t128.718\t23.3038 \t-1     \t133.452\n",
      "15 \t20    \t133.028\t0.109525\t132.999\t133.452\n",
      "16 \t23    \t124.387\t32.4103 \t-1     \t134.349\n",
      "17 \t21    \t133.226\t0.828416\t129.441\t134.349\n",
      "18 \t20    \t133.852\t0.88561 \t129.623\t134.379\n",
      "19 \t13    \t134.237\t0.622011\t130.774\t134.349\n",
      "20 \t17    \t134.349\t0       \t134.349\t134.349\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t74.2718\t27.2687\t25.7836\t131.515\n",
      "1  \t21    \t100.03 \t20.532 \t62.487 \t132.084\n",
      "2  \t19    \t119.318\t13.8552\t76.9814\t132.084\n",
      "3  \t22    \t126.911\t6.54942\t101.018\t134.558\n",
      "4  \t19    \t131.074\t2.70234\t119.7  \t134.558\n",
      "5  \t25    \t132.224\t1.43013\t129.017\t134.731\n",
      "6  \t22    \t124.976\t32.551 \t-1     \t134.731\n",
      "7  \t20    \t134.139\t1.0641 \t132.084\t134.842\n",
      "8  \t17    \t134.72 \t0.0417494\t134.558\t134.731\n",
      "9  \t22    \t130.527\t23.6276  \t-1     \t137.125\n",
      "10 \t24    \t130.431\t23.6418  \t-1     \t137.125\n",
      "11 \t23    \t135.034\t0.802899 \t134.731\t137.256\n",
      "12 \t17    \t135.424\t1.48081  \t130.195\t137.256\n",
      "13 \t20    \t136.456\t1.07092  \t134.731\t137.256\n",
      "14 \t10    \t137.1  \t0.541649 \t134.106\t137.256\n",
      "15 \t16    \t137.31 \t0.371747 \t137.125\t139.369\n",
      "16 \t22    \t137.454\t0.615739 \t137.256\t139.369\n",
      "17 \t20    \t133.379\t24.1515  \t-1     \t139.369\n",
      "18 \t12    \t138.228\t1.07639  \t136.663\t139.369\n",
      "19 \t15    \t130.134\t33.8696  \t-1     \t139.369\n",
      "20 \t26    \t134.806\t24.3978  \t-1     \t139.369\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t78.4685\t30.2003\t6.06864\t117.689\n",
      "1  \t12    \t96.7661\t18.9313\t40.2863\t140.115\n",
      "2  \t15    \t113.945\t15.1289\t71.8471\t140.115\n",
      "3  \t17    \t125.112\t13.5783\t96.3728\t140.115\n",
      "4  \t19    \t130.935\t24.6266\t-1     \t140.115\n",
      "5  \t28    \t139.248\t1.87959\t133.878\t140.115\n",
      "6  \t18    \t140.115\t0      \t140.115\t140.115\n",
      "7  \t19    \t140.115\t0      \t140.115\t140.115\n",
      "8  \t19    \t135.615\t24.5418\t-1     \t140.115\n",
      "9  \t23    \t139.896\t1.25247\t132.925\t140.306\n",
      "10 \t11    \t135.705\t24.5529\t-1     \t140.115\n",
      "11 \t20    \t135.686\t24.5497\t-1     \t140.115\n",
      "12 \t23    \t140.122\t0.0395301\t140.115\t140.342\n",
      "13 \t27    \t135.733\t24.5581  \t-1     \t140.342\n",
      "14 \t19    \t131.366\t34.1769  \t-1     \t140.342\n",
      "15 \t21    \t140.219\t0.222467 \t139.139\t140.342\n",
      "16 \t21    \t140.301\t0.0980424\t139.949\t140.342\n",
      "17 \t18    \t135.925\t24.5924  \t-1     \t140.342\n",
      "18 \t16    \t140.086\t1.42332  \t132.161\t140.342\n",
      "19 \t9     \t135.925\t24.5924  \t-1     \t140.342\n",
      "20 \t18    \t140.335\t0.175038 \t139.541\t140.926\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t79.7036\t27.7418\t24.2933\t127.311\n",
      "1  \t21    \t101.712\t16.066 \t75.8095\t129.51 \n",
      "2  \t17    \t112.427\t22.1965\t-1     \t132.939\n",
      "3  \t17    \t123.948\t6.54429\t112.226\t132.939\n",
      "4  \t16    \t129.65 \t4.74936\t119.474\t135.843\n",
      "5  \t19    \t128.413\t23.2821\t-1     \t135.843\n",
      "6  \t25    \t133.081\t0.571706\t132.939\t135.843\n",
      "7  \t18    \t133.223\t0.78325 \t132.939\t135.843\n",
      "8  \t18    \t133.653\t1.43235 \t131.64 \t135.843\n",
      "9  \t19    \t134.997\t1.33501 \t131.64 \t135.843\n",
      "10 \t12    \t135.843\t0       \t135.843\t135.843\n",
      "11 \t15    \t131.567\t23.8097 \t-1     \t135.843\n",
      "12 \t18    \t123.014\t39.8871 \t-1     \t135.843\n",
      "13 \t17    \t135.935\t0.531052\t135.727\t138.889\n",
      "14 \t23    \t136.129\t0.887899\t135.843\t138.889\n",
      "15 \t22    \t136.605\t1.31903 \t135.843\t138.889\n",
      "16 \t23    \t132.978\t24.1083 \t-1     \t138.889\n",
      "17 \t15    \t138.396\t1.09225 \t135.843\t138.889\n",
      "18 \t19    \t138.889\t0       \t138.889\t138.889\n",
      "19 \t25    \t134.545\t24.345  \t-1     \t139.751\n",
      "20 \t20    \t134.527\t24.3462 \t-1     \t139.751\n",
      "gen\tnevals\tavg    \tstd   \tmin    \tmax    \n",
      "0  \t32    \t75.5836\t29.672\t20.1545\t134.759\n",
      "1  \t16    \t100.956\t22.8981\t36.0423\t134.759\n",
      "2  \t26    \t114.475\t18.3167\t81.6869\t136.659\n",
      "3  \t20    \t130.363\t8.14645\t100.5  \t136.659\n",
      "4  \t14    \t135.187\t1.31589\t130.342\t136.659\n",
      "5  \t23    \t136.125\t0.854523\t134.759\t136.659\n",
      "6  \t23    \t132.347\t23.9526 \t-1     \t138.208\n",
      "7  \t21    \t132.364\t23.9615 \t-1     \t138.208\n",
      "8  \t20    \t136.758\t0.532114\t135.183\t138.208\n",
      "9  \t20    \t137.095\t0.696288\t136.659\t138.208\n",
      "10 \t18    \t137.413\t1.49798 \t130.717\t139.253\n",
      "11 \t26    \t138.242\t0.534043\t136.659\t139.253\n",
      "12 \t22    \t138.552\t0.482372\t138.208\t139.253\n",
      "13 \t24    \t138.777\t1.15222 \t132.868\t139.253\n",
      "14 \t18    \t139.177\t0.448798\t136.682\t139.398\n",
      "15 \t18    \t134.763\t24.3963 \t-1     \t139.398\n",
      "16 \t18    \t139.33 \t0.0715634\t139.253\t139.398\n",
      "17 \t20    \t139.338\t0.165262 \t138.722\t139.398\n",
      "18 \t22    \t139.41 \t0.0679585\t139.398\t139.788\n",
      "19 \t21    \t139.434\t0.226292 \t138.784\t140.397\n",
      "20 \t26    \t130.276\t33.9275  \t-1     \t140.397\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t79.5745\t30.9934\t15.2846\t140.481\n",
      "1  \t14    \t108.187\t17.0158\t79.7324\t140.736\n",
      "2  \t20    \t117.166\t27.077 \t-1     \t140.736\n",
      "3  \t15    \t136.821\t5.94325\t115.537\t140.736\n",
      "4  \t24    \t139.936\t1.85928\t133.942\t140.736\n",
      "5  \t22    \t136.204\t24.6492\t-1     \t140.736\n",
      "6  \t17    \t140.736\t0      \t140.736\t140.736\n",
      "7  \t16    \t140.736\t0      \t140.736\t140.736\n",
      "8  \t19    \t140.592\t0.799247\t136.142\t140.736\n",
      "9  \t14    \t140.606\t0.745134\t136.46 \t140.867\n",
      "10 \t20    \t140.74 \t0.022727\t140.736\t140.867\n",
      "11 \t16    \t136.331\t24.6655 \t-1     \t140.867\n",
      "12 \t18    \t140.774\t0.265332\t139.337\t140.867\n",
      "13 \t21    \t136.336\t24.6704 \t-1     \t140.867\n",
      "14 \t15    \t136.433\t24.6837 \t-1     \t140.867\n",
      "15 \t19    \t140.703\t0.756364\t136.598\t140.867\n",
      "16 \t14    \t136.128\t24.6804 \t-1     \t140.867\n",
      "17 \t17    \t136.43 \t24.6833 \t-1     \t140.867\n",
      "18 \t19    \t140.867\t0       \t140.867\t140.867\n",
      "19 \t25    \t140.767\t0.552513\t137.691\t140.867\n",
      "20 \t16    \t140.355\t2.07567 \t130.192\t140.867\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax   \n",
      "0  \t32    \t75.2414\t32.7905\t14.4766\t132.19\n",
      "1  \t18    \t105.106\t21.0665\t29.9465\t136.973\n",
      "2  \t14    \t122.056\t12.4942\t81.4423\t136.973\n",
      "3  \t12    \t131.468\t3.81179\t120.857\t136.973\n",
      "4  \t21    \t134.6  \t2.26755\t132.19 \t137.259\n",
      "5  \t16    \t136.624\t1.02366\t132.485\t137.259\n",
      "6  \t24    \t137.009\t0.0944516\t136.973\t137.259\n",
      "7  \t20    \t137.018\t0.103697 \t136.973\t137.259\n",
      "8  \t17    \t136.91 \t0.584803 \t133.764\t137.259\n",
      "9  \t21    \t132.779\t24.0305  \t-1     \t137.69 \n",
      "10 \t23    \t137.25 \t0.185618 \t136.973\t137.974\n",
      "11 \t22    \t133.081\t24.0836  \t-1     \t137.974\n",
      "12 \t21    \t128.962\t33.5576  \t-1     \t137.974\n",
      "13 \t18    \t138.009\t0.25335  \t137.69 \t139.393\n",
      "14 \t21    \t138.107\t0.413603 \t137.974\t139.393\n",
      "15 \t17    \t129.464\t33.6946  \t-1     \t139.393\n",
      "16 \t14    \t134.441\t24.3467  \t-1     \t140.798\n",
      "17 \t20    \t139.479\t0.444868 \t137.974\t140.798\n",
      "18 \t18    \t126.405\t40.9798  \t-1     \t140.798\n",
      "19 \t18    \t135.371\t24.4979  \t-1     \t140.798\n",
      "20 \t18    \t131.209\t34.1395  \t-1     \t140.798\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t73.5291\t22.7604\t19.0505\t107.367\n",
      "1  \t20    \t92.7506\t14.0998\t55.7272\t119.705\n",
      "2  \t19    \t105.426\t10.3849\t75.2526\t124.798\n",
      "3  \t20    \t113.37 \t9.57379\t78.1935\t130.115\n",
      "4  \t22    \t120.775\t6.00293\t107.012\t130.115\n",
      "5  \t21    \t125.441\t4.36313\t114.689\t130.115\n",
      "6  \t16    \t128.986\t2.57269\t122.069\t130.115\n",
      "7  \t23    \t130.051\t0.355227\t128.073\t130.115\n",
      "8  \t18    \t130.174\t0.330792\t130.115\t132.016\n",
      "9  \t14    \t130.534\t0.930214\t130.115\t134.018\n",
      "10 \t19    \t126.616\t22.9577 \t-1     \t135.865\n",
      "11 \t25    \t131.943\t2.08003 \t125.502\t135.865\n",
      "12 \t25    \t133.277\t1.91034 \t130.115\t135.865\n",
      "13 \t20    \t134.807\t1.6131  \t130.115\t135.865\n",
      "14 \t25    \t135.691\t0.538241\t134.018\t135.865\n",
      "15 \t25    \t135.789\t0.422768\t133.435\t135.865\n",
      "16 \t18    \t131.685\t23.8371 \t-1     \t138.982\n",
      "17 \t26    \t136.25 \t1.27154 \t133.099\t138.982\n",
      "18 \t23    \t136.644\t1.3498  \t135.865\t138.982\n",
      "19 \t18    \t133.177\t24.1674 \t-1     \t138.982\n",
      "20 \t20    \t138.69 \t0.908612\t135.865\t138.982\n",
      "gen\tnevals\tavg    \tstd    \tmin   \tmax    \n",
      "0  \t32    \t73.7991\t24.9588\t11.357\t104.095\n",
      "1  \t21    \t90.4841\t20.7347\t24.7605\t123.183\n",
      "2  \t14    \t104.523\t17.665 \t62.1214\t127.227\n",
      "3  \t19    \t117.886\t9.14726\t90.2013\t129.984\n",
      "4  \t22    \t116.21 \t30.6701\t-1     \t136.134\n",
      "5  \t20    \t128.039\t4.50101\t119.178\t136.282\n",
      "6  \t15    \t131.99 \t4.41777\t122.894\t137.497\n",
      "7  \t19    \t135.875\t1.37782\t130.851\t137.497\n",
      "8  \t21    \t136.402\t0.568221\t134.833\t137.899\n",
      "9  \t20    \t136.939\t0.669153\t136.282\t137.899\n",
      "10 \t21    \t132.835\t24.0804 \t-1     \t137.899\n",
      "11 \t20    \t137.767\t0.338181\t136.097\t137.899\n",
      "12 \t22    \t137.899\t0       \t137.899\t137.899\n",
      "13 \t21    \t133.543\t24.1649 \t-1     \t137.899\n",
      "14 \t23    \t137.899\t0       \t137.899\t137.899\n",
      "15 \t19    \t137.899\t0       \t137.899\t137.899\n",
      "16 \t25    \t137.615\t1.02258 \t132.675\t137.899\n",
      "17 \t17    \t137.731\t0.653453\t134.869\t137.899\n",
      "18 \t16    \t137.857\t0.233097\t136.559\t137.899\n",
      "19 \t20    \t137.849\t0.275552\t136.315\t137.899\n",
      "20 \t29    \t137.899\t0       \t137.899\t137.899\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t82.9111\t29.2761\t22.5692\t137.103\n",
      "1  \t18    \t103.762\t23.3827\t45.3492\t138.812\n",
      "2  \t19    \t122.349\t24.8285\t-1     \t138.812\n",
      "3  \t17    \t134.476\t3.85782\t124.299\t138.812\n",
      "4  \t19    \t132.75 \t24.0736\t-1     \t139.476\n",
      "5  \t21    \t138.315\t1.209  \t135.335\t139.476\n",
      "6  \t15    \t139.096\t0.394909\t137.842\t139.476\n",
      "7  \t22    \t139.341\t0.53918 \t136.475\t139.476\n",
      "8  \t19    \t135.065\t24.4383 \t-1     \t139.476\n",
      "9  \t17    \t135.087\t24.4419 \t-1     \t139.476\n",
      "10 \t17    \t139.278\t1.105   \t133.126\t139.476\n",
      "11 \t22    \t139.476\t0       \t139.476\t139.476\n",
      "12 \t16    \t139.335\t0.788762\t134.943\t139.476\n",
      "13 \t19    \t139.476\t0       \t139.476\t139.476\n",
      "14 \t21    \t135.057\t24.438  \t-1     \t139.945\n",
      "15 \t13    \t139.55 \t0.169989\t139.476\t139.945\n",
      "16 \t25    \t135.248\t24.4718 \t-1     \t139.945\n",
      "17 \t18    \t130.951\t34.0713 \t-1     \t139.945\n",
      "18 \t17    \t135.357\t24.5015 \t-1     \t139.945\n",
      "19 \t20    \t139.945\t0       \t139.945\t139.945\n",
      "20 \t23    \t139.945\t0       \t139.945\t139.945\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t68.3121\t29.3137\t19.7296\t119.086\n",
      "1  \t19    \t94.6145\t18.7624\t55.8023\t133.88 \n",
      "2  \t16    \t112.918\t21.5052\t29.8815\t133.88 \n",
      "3  \t17    \t129.374\t5.97145\t115.788\t133.88 \n",
      "4  \t22    \t132.743\t2.89145\t121.119\t133.88 \n",
      "5  \t15    \t133.88 \t0      \t133.88 \t133.88 \n",
      "6  \t15    \t133.774\t0.593672\t130.468\t133.88 \n",
      "7  \t17    \t133.777\t0.576717\t130.566\t133.88 \n",
      "8  \t10    \t133.88 \t0       \t133.88 \t133.88 \n",
      "9  \t17    \t133.675\t0.922656\t129.958\t134.752\n",
      "10 \t24    \t133.527\t1.53377 \t125.551\t134.752\n",
      "11 \t23    \t134.117\t0.399717\t133.617\t134.752\n",
      "12 \t22    \t134.546\t0.986932\t133.88 \t139.51 \n",
      "13 \t22    \t135.144\t1.42007 \t133.88 \t139.51 \n",
      "14 \t21    \t135.793\t1.96698 \t134.752\t139.51 \n",
      "15 \t11    \t132.906\t24.1657 \t-1     \t140.065\n",
      "16 \t24    \t135.051\t24.448  \t-1     \t140.065\n",
      "17 \t14    \t139.447\t0.97245 \t134.168\t140.065\n",
      "18 \t26    \t139.591\t0.843062\t135.674\t140.065\n",
      "19 \t19    \t135.64 \t24.5414 \t-1     \t140.065\n",
      "20 \t20    \t140.065\t0       \t140.065\t140.065\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t81.9453\t25.7656\t15.4215\t121.366\n",
      "1  \t24    \t96.9791\t22.9094\t37.3241\t130.108\n",
      "2  \t22    \t111.546\t13.9815\t77.475 \t129.181\n",
      "3  \t22    \t121.249\t9.30188\t100.19 \t135.36 \n",
      "4  \t25    \t127.821\t4.78866\t115.01 \t137.439\n",
      "5  \t21    \t131.009\t3.5323 \t126.341\t137.439\n",
      "6  \t23    \t129.794\t23.732 \t-1     \t138.76 \n",
      "7  \t18    \t137.032\t1.0822 \t135.003\t139.112\n",
      "8  \t24    \t129.232\t33.6371\t-1     \t141.057\n",
      "9  \t22    \t138.238\t1.42135\t131.881\t141.057\n",
      "10 \t22    \t134.732\t24.3894\t-1     \t141.057\n",
      "11 \t21    \t126.369\t40.9739\t-1     \t141.057\n",
      "12 \t22    \t135.576\t24.5728\t-1     \t141.057\n",
      "13 \t19    \t136.284\t24.6672\t-1     \t141.057\n",
      "14 \t11    \t140.911\t0.80815\t136.412\t141.057\n",
      "15 \t16    \t140.921\t0.753169\t136.728\t141.057\n",
      "16 \t19    \t141.057\t0       \t141.057\t141.057\n",
      "17 \t18    \t141.057\t0       \t141.057\t141.057\n",
      "18 \t24    \t140.965\t0.509486\t138.128\t141.057\n",
      "19 \t15    \t136.617\t24.7168 \t-1     \t141.057\n",
      "20 \t24    \t141.028\t0.157974\t140.149\t141.057\n",
      "gen\tnevals\tavg    \tstd   \tmin    \tmax    \n",
      "0  \t32    \t66.6002\t32.624\t2.41258\t137.062\n",
      "1  \t16    \t85.6662\t20.6342\t32.7259\t137.062\n",
      "2  \t24    \t101.73 \t14.4469\t64.5642\t137.062\n",
      "3  \t12    \t112.095\t9.36356\t95.3884\t137.062\n",
      "4  \t24    \t118.214\t9.8756 \t92.6851\t137.062\n",
      "5  \t18    \t121.054\t22.8288\t-1     \t137.062\n",
      "6  \t16    \t122.34 \t32.2238\t-1     \t137.062\n",
      "7  \t12    \t134.492\t3.64749\t127.304\t137.062\n",
      "8  \t23    \t136.701\t1.46375\t129.731\t137.176\n",
      "9  \t17    \t136.853\t0.880029\t132.735\t137.176\n",
      "10 \t15    \t137.094\t0.0510747\t137.062\t137.176\n",
      "11 \t20    \t137.031\t0.368999 \t135.337\t137.176\n",
      "12 \t21    \t137.119\t0.258619 \t135.691\t137.176\n",
      "13 \t22    \t132.858\t24.0416  \t-1     \t137.176\n",
      "14 \t19    \t137.168\t0.0462256\t136.91 \t137.176\n",
      "15 \t16    \t137.176\t0        \t137.176\t137.176\n",
      "16 \t23    \t137.176\t0        \t137.176\t137.176\n",
      "17 \t18    \t132.858\t24.0416  \t-1     \t137.176\n",
      "18 \t21    \t137.134\t0.235962 \t135.82 \t137.176\n",
      "19 \t14    \t137.195\t0.103878 \t137.176\t137.773\n",
      "20 \t15    \t137.206\t0.232441 \t136.346\t137.773\n",
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t32    \t71.1623\t27.3787\t4.69435\t107.941\n",
      "1  \t23    \t95.1199\t21.0426\t32.5942\t137.348\n",
      "2  \t20    \t112.032\t14.5003\t75.5335\t137.348\n",
      "3  \t24    \t124.824\t11.8149\t105.218\t137.348\n",
      "4  \t20    \t135.547\t4.3948 \t115.251\t137.348\n",
      "5  \t18    \t119.865\t45.6949\t-1     \t137.348\n",
      "6  \t13    \t137.348\t0      \t137.348\t137.348\n",
      "7  \t21    \t137.348\t0      \t137.348\t137.348\n",
      "8  \t23    \t137.348\t0      \t137.348\t137.348\n",
      "9  \t25    \t137.348\t0      \t137.348\t137.348\n",
      "10 \t23    \t137.326\t0.12331\t136.64 \t137.348\n",
      "11 \t20    \t132.945\t24.0614\t-1     \t137.348\n",
      "12 \t14    \t137.348\t0      \t137.348\t137.348\n",
      "13 \t18    \t132.909\t24.0594\t-1     \t137.348\n",
      "14 \t17    \t137.248\t0.560423\t134.127\t137.348\n",
      "15 \t18    \t137.371\t0.124161\t137.348\t138.062\n",
      "16 \t31    \t133.192\t24.1127 \t-1     \t140.441\n",
      "17 \t21    \t133.411\t24.1563 \t-1     \t140.441\n",
      "18 \t24    \t133.612\t24.1927 \t-1     \t140.441\n",
      "19 \t25    \t138.558\t1.04501 \t137.348\t140.441\n",
      "20 \t23    \t135.143\t24.4755 \t-1     \t140.441\n",
      "media: 139.719641\n",
      "mejor: 141.056541\n"
     ]
    }
   ],
   "source": [
    "# Inicializamos la lista de las soluciones. Almacenaremos el fitness\n",
    "# del mejor individuo de cada intento del algoritmo genético\n",
    "lista_mejores = list()\n",
    "\n",
    "# Lancamos el algoritmo 20 veces\n",
    "for i in range(20):\n",
    "    # Fijamos la semilla\n",
    "    random.seed(i)\n",
    "    best, log = main()\n",
    "    lista_mejores.append(best[0].fitness.values)\n",
    "\n",
    "# Mostramos los resultados. Al terminar el bucle, calculamos\n",
    "# la mediay el mejor resultado de todos los intentos\n",
    "media = np.mean(lista_mejores)\n",
    "mejor_total = max(lista_mejores)\n",
    "print(\"media: %f\" %media)\n",
    "print(\"mejor: %f\" %mejor_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58544482-5ddf-43bb-a675-bd391f28cb26",
   "metadata": {},
   "source": [
    "#### 6. Convergencia del algoritmo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a83e7b8-bb3e-447c-8459-97a963632f6c",
   "metadata": {},
   "source": [
    "A partir del objeto `Logbook` [Objeto Logbook](https://deap.readthedocs.io/en/master/api/tools.html#logbook) que contiene todos los datos de evolución del algoritmo, desarrollamos la función `plot_evolution` para analizar la convergencia representando la evolución del Algoritmo Genético:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d0b9017e-7242-40d8-9561-e3bbc2af2dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_evolucion(log):\n",
    "    \"\"\"\n",
    "    Representa la evolución del mejor individuo en cada generación\n",
    "    \"\"\"\n",
    "    gen = log.select(\"gen\")\n",
    "    fit_mins = log.select(\"min\")\n",
    "    fit_maxs = log.select(\"max\")\n",
    "    fit_ave = log.select(\"avg\")\n",
    "\n",
    "    fig, ax1 = plt.subplots()\n",
    "    ax1.plot(gen, fit_mins, \"b\")\n",
    "    ax1.plot(gen, fit_maxs, \"r\")\n",
    "    ax1.plot(gen, fit_ave, \"--k\")\n",
    "    # ax1.fill_between(gen, fit_mins, fit_maxs, where=fit_maxs >= fit_mins, facecolor='g', alpha = 0.2)\n",
    "    ax1.set_xlabel(\"Generation\")\n",
    "    ax1.set_ylabel(\"Fitness\")\n",
    "    ax1.set_ylim([-10, 160])\n",
    "    ax1.legend([\"Min\", \"Max\", \"Avg\"], loc=\"lower center\")\n",
    "    plt.grid(True)\n",
    "    plt.savefig(\"Convergencia.eps\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587d8ead-61fd-49f1-8fcb-288953cf40c8",
   "metadata": {},
   "source": [
    "Pintamos la Gráfica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e4b0ef3a-d346-42c8-82c0-41ba68263f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg    \tstd    \tmin    \tmax    \n",
      "0  \t8     \t79.8039\t27.9145\t31.2954\t113.917\n",
      "1  \t2     \t106.772\t8.01216\t95.3282\t113.917\n",
      "2  \t4     \t113.296\t6.74022\t98.8946\t125.664\n",
      "3  \t4     \t118.271\t5.72858\t113.506\t125.664\n",
      "4  \t4     \t124.196\t3.8852 \t113.917\t125.664\n",
      "5  \t4     \t125.664\t0      \t125.664\t125.664\n",
      "6  \t6     \t126.382\t1.89989\t125.664\t131.409\n",
      "7  \t4     \t125.64 \t2.92056\t119.728\t131.409\n",
      "8  \t3     \t127.819\t2.78115\t125.664\t131.409\n",
      "9  \t3     \t130.691\t1.89989\t125.664\t131.409\n",
      "10 \t3     \t131.409\t0      \t131.409\t131.409\n",
      "11 \t6     \t131.409\t0      \t131.409\t131.409\n",
      "12 \t6     \t131.409\t0      \t131.409\t131.409\n",
      "13 \t5     \t131.409\t0      \t131.409\t131.409\n",
      "14 \t6     \t132.05 \t1.69633\t131.409\t136.538\n",
      "15 \t6     \t133.625\t3.0365 \t128.617\t136.538\n",
      "16 \t4     \t135.772\t1.6813 \t131.409\t136.538\n",
      "17 \t2     \t136.538\t0      \t136.538\t136.538\n",
      "18 \t6     \t136.538\t0      \t136.538\t136.538\n",
      "19 \t4     \t136.538\t0      \t136.538\t136.538\n",
      "20 \t4     \t136.538\t0      \t136.538\t136.538\n",
      "Mejor fitness: 136.538209\n",
      "Mejor individuo [-94.69280606322728, -98.36643159028789]\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": [
    "# Generación de población\n",
    "toolbox.register(\"population\", tools.initRepeat, list,\n",
    "                 toolbox.individual, 8)\n",
    "random.seed(42)\n",
    "best, log = main()\n",
    "print(\"Mejor fitness: %f\" %best[0].fitness.values)\n",
    "print(\"Mejor individuo %s\" %best[0])\n",
    "plot_evolucion(log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0f535b-91ba-4510-9ee4-34bd4d63761f",
   "metadata": {},
   "source": [
    "Dilema de la **exploración** vs la **explotación**: ¿Qué mecanismo u operadores genéticos favorecen la exploración cuales la explotación?. La respuesta a esta cuestión no está clara y es un **tema en discusión** en la literatura especializada. **La mezcla de selección, cruce y mutación generan exploración y explotación**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71bfb789-6d4c-4838-b447-5e6155a44f91",
   "metadata": {},
   "source": [
    "**CÓDIGO COMPLETO** ----------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40c28bfa-094b-4c2a-a68c-3692350f719d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tnevals\tavg    \tstd   \tmin    \tmax    \n",
      "0  \t30    \t77.9162\t24.544\t26.4289\t116.497\n",
      "1  \t15    \t98.1576\t15.6516\t58.7221\t123.194\n",
      "2  \t18    \t108.641\t17.0172\t47.071 \t131.857\n",
      "3  \t24    \t122.041\t7.64215\t99.9797\t134.549\n",
      "4  \t19    \t128.584\t3.92487\t122.005\t134.549\n",
      "5  \t11    \t132.005\t2.13284\t125.135\t134.549\n",
      "6  \t22    \t133.419\t1.60997\t131.169\t136.779\n",
      "7  \t20    \t130.449\t24.4622\t-1     \t136.779\n",
      "8  \t17    \t136.143\t1.12918\t133.455\t137.464\n",
      "9  \t16    \t136.686\t0.662231\t134.132\t137.486\n",
      "10 \t14    \t137.114\t0.611792\t136.779\t139.908\n",
      "11 \t15    \t137.235\t1.30432 \t131.674\t139.908\n",
      "12 \t23    \t137.979\t0.981347\t137.464\t140.041\n",
      "13 \t21    \t133.762\t25.0534 \t-1     \t140.101\n",
      "14 \t17    \t138.776\t1.53284 \t133.594\t140.101\n",
      "15 \t15    \t130.265\t35.0922 \t-1     \t140.101\n",
      "16 \t15    \t140.042\t0.0666659\t139.908\t140.101\n",
      "17 \t15    \t140.024\t0.334088 \t138.242\t140.101\n",
      "18 \t18    \t140.101\t5.68434e-14\t140.101\t140.101\n",
      "19 \t20    \t140.021\t0.347706   \t138.217\t140.101\n",
      "20 \t20    \t140.067\t0.183959   \t139.076\t140.101\n",
      "Mejor fitness: 140.100794\n",
      "Mejor individuo [-99.34638537370458, -98.78526358100852]\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": [
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "from deap import base\n",
    "from deap import creator\n",
    "from deap import tools\n",
    "from deap import algorithms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Creamos los objetos para definir el problema y el tipo de individuo\n",
    "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n",
    "creator.create(\"Individual\", list, fitness=creator.FitnessMax)\n",
    "\n",
    "def funcion_objetivo(x):\n",
    "    \"\"\"\n",
    "    Función objetivo de nuestro problema\n",
    "    \"\"\"\n",
    "    for i in range(len(x)):\n",
    "        if x[i] > 100 or x[i] < -100:\n",
    "            return -1,\n",
    "    res = math.sqrt(x[0]**2 + x[1]**2)\n",
    "    return res,\n",
    "\n",
    "toolbox = base.Toolbox()\n",
    "\n",
    "# Generación de genes\n",
    "toolbox.register(\"attr_uniform\", random.uniform, -100, 100)\n",
    "\n",
    "# Generación de inviduos y población\n",
    "toolbox.register(\"individual\", tools.initRepeat, creator.Individual,\n",
    "                 toolbox.attr_uniform, 2)\n",
    "toolbox.register(\"population\", tools.initRepeat, list,\n",
    "                 toolbox.individual, 30)\n",
    "\n",
    "# Registro de operaciones genéticas\n",
    "toolbox.register(\"evaluate\", funcion_objetivo)\n",
    "toolbox.register(\"mate\", tools.cxOnePoint)\n",
    "toolbox.register(\"mutate\", tools.mutGaussian, mu=0,\n",
    "                 sigma= 5, indpb=0.1)\n",
    "toolbox.register(\"select\", tools.selTournament, tournsize=3)\n",
    "\n",
    "def plot_evolucion(log):\n",
    "    \"\"\"\n",
    "    Representa la evolución del mejor individuo en cada generación\n",
    "    \"\"\"\n",
    "    gen = log.select(\"gen\")\n",
    "    fit_mins = log.select(\"min\")\n",
    "    fit_maxs = log.select(\"max\")\n",
    "    fit_ave = log.select(\"avg\")\n",
    "\n",
    "    fig, ax1 = plt.subplots()\n",
    "    ax1.plot(gen, fit_mins, \"b\")\n",
    "    ax1.plot(gen, fit_maxs, \"r\")\n",
    "    ax1.plot(gen, fit_ave, \"--k\")\n",
    "    #ax1.fill_between(gen, fit_mins, fit_maxs, where=fit_maxs >= fit_mins, facecolor='g', alpha = 0.2)\n",
    "    ax1.set_xlabel(\"Generation\")\n",
    "    ax1.set_ylabel(\"Fitness\")\n",
    "    ax1.set_ylim([-10, 160])\n",
    "    ax1.legend([\"Min\", \"Max\", \"Avg\"], loc=\"lower center\")\n",
    "    plt.grid(True)\n",
    "    plt.savefig(\"Convergencia.eps\", dpi = 300)\n",
    "\n",
    "def main():\n",
    "    random.seed(42)\n",
    "    CXPB, MUTPB, NGEN = 0.5, 0.2, 20\n",
    "    pop = toolbox.population()\n",
    "    hof = tools.HallOfFame(1)\n",
    "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
    "    stats.register(\"avg\", np.mean)\n",
    "    stats.register(\"std\", np.std)\n",
    "    stats.register(\"min\", np.min)\n",
    "    stats.register(\"max\", np.max)\n",
    "    logbook = tools.Logbook()\n",
    "    pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=CXPB,\n",
    "                                       mutpb=MUTPB, ngen=NGEN, stats=stats,\n",
    "                                       halloffame=hof, verbose=True)\n",
    "    return hof, logbook\n",
    "\n",
    "\n",
    "best, log = main()\n",
    "print(\"Mejor fitness: %f\" %best[0].fitness.values)\n",
    "print(\"Mejor individuo %s\" %best[0])\n",
    "plot_evolucion(log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56189680-d960-4905-ba52-71da33dc2e2d",
   "metadata": {},
   "source": [
    "Si definimos `N` como el número de generaciones, `P` como el número de individuos de la población y `T` como el tiempo que tarda en ejecutarse la función objetivo, el tiempo total para obtener la solución al problema es `Ttotal = N x P x T` (calcula 100 individuos con 100 generaciones y 1 segundo en procesar la función objetivo) -> Existen mecanismos para evaluar los individuos de la población en paralelo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533c50b2-f4e2-410e-b0c8-f9e93aa98a74",
   "metadata": {},
   "source": [
    "#### 7. Para seguir aprendiendo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829cbe85-79b4-412e-b529-f1d843b2eaf6",
   "metadata": {},
   "source": [
    "[Holland 1992](https://github.com/yyccR/papers/blob/master/genetic%20algorithm/%E3%80%8AAdaptation%20in%20Natural%20and%20Artificial%20Systems%E3%80%8BJohn%20Holland%201992.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f658bedc-59ad-4b57-8b7c-7b305d9e250b",
   "metadata": {},
   "source": [
    "[Goldberg 2006](http://www2.fiit.stuba.sk/~kvasnicka/Free%20books/Goldberg_Genetic_Algorithms_in_Search.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9921b1a3-334c-4222-a99a-72a7d66794d8",
   "metadata": {},
   "source": [
    "[Ser, J.D. et al. 2019](https://www.sciencedirect.com/science/article/pii/S2210650218310277)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff06e92-58ad-4283-91d6-b778927af9f0",
   "metadata": {},
   "source": [
    "1.- Encontrar los máximos de la función \"picos\"\n",
    "\n",
    "$$z = f(x,y) = 3(1-x)^2e^{-(x^2)-(y+1)^2}-10(x/5-x^3-y^5)e^{-x^2-y^2}-1/3e^{-(x+1)^2-y^2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9386ded-6ef8-45e2-9ced-0f4932497ba5",
   "metadata": {},
   "source": [
    "En el dominio $$\\{x,y\\} \\in [-100,100]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56526327-714a-4d16-ae23-90306b07ffca",
   "metadata": {},
   "source": [
    "2.- Dada la [Función de Rastringin](https://en.wikipedia.org/wiki/Rastrigin_function#:~:text=In%20mathematical%20optimization%2C%20the%20Rastrigin,has%20been%20generalized%20by%20Rudolph.), con A = 10 y  $$\\{xi\\} \\in [-5.12,5.12]$$\n",
    "\n",
    "Implementa dicha función en Python y minimízala para n=10\n",
    "\n",
    "Compara el funcionamiento del Algoritmo Genético para cruce con un punto con cruce con dos puntos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b08d616f-069a-4780-9a20-3403b1e3b66f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
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 },
 "nbformat": 4,
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}