Clear clutter. No more test/data/ directory

1 files changed, 0 insertions, 361 deletions

diff --git a/test/GA.ipynb b/test/GA.ipynb
deleted file mode 100644
index 9d73164..0000000
--- a/test/GA.ipynb
+++ /dev/null
@@ -1,361 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2fd45b3a-9a24-4782-812c-08223edb750e",
-   "metadata": {},
-   "source": [
-    "# Prueba del algoritmo genetico"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "45972b70-b2a6-48f2-aafa-9f660548079a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34eab22f-a400-4a14-9ac4-047d87dff69f",
-   "metadata": {},
-   "source": [
-    "Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "a5736dba-4c38-4b7f-9a1a-963bdb006236",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Ciudad:\n",
-    "    regiones = {\n",
-    "        'costa': {\n",
-    "            'plazoentrega': 1\n",
-    "        },\n",
-    "        'sierra': {\n",
-    "            'plazoentrega': 2\n",
-    "        },\n",
-    "        'selva': {\n",
-    "            'plazoentrega': 3\n",
-    "        }\n",
-    "    }\n",
-    "    def __init__(self, nombre, region, longitud, latitud):\n",
-    "        self.nombre = nombre\n",
-    "        self.region = region\n",
-    "        self.x = longitud\n",
-    "        self.y = latitud"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "8ee9610f-d128-4d1b-9458-ca9048073f20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Road_network:\n",
-    "    def __init__(self, cities, distances):\n",
-    "        \"\"\"Grafo completo del pais\n",
-    "        \n",
-    "        Params\n",
-    "        ------\n",
-    "        \n",
-    "        cities: list\n",
-    "            Lista de objetos Ciudad\n",
-    "            \n",
-    "        routes: dict\n",
-    "            (Aun no se como implementar esto)\n",
-    "        \"\"\"\n",
-    "        \n",
-    "        self.cities = cities\n",
-    "        self.routes = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "7dd72c93-acba-46f9-ac99-8c3d1a8cfa67",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Vehiculo:\n",
-    "    tipos = {\n",
-    "        1: {\n",
-    "            'cargamax': 50\n",
-    "        },\n",
-    "        2: {\n",
-    "            'cargamax': 100\n",
-    "        },\n",
-    "        3: {\n",
-    "            'cargamax': 200\n",
-    "        }\n",
-    "    }\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "474a3596-f75d-411e-bf4c-20b8b8259434",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Pedido:\n",
-    "    def __init__(self, cliente, cantidad):\n",
-    "        self.cliente = cliente\n",
-    "        self.cantidad = cantidad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "7602722f-9026-44dc-b922-e17a7d3af45b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Cliente:\n",
-    "    def __init__(self, nombre, ciudad):\n",
-    "        self.nombre = nombre\n",
-    "        self.ciudad = ciudad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "f6b4829a-9001-410c-b20c-01c65c777d8a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class VRP:\n",
-    "    def __init__(self):\n",
-    "        # Conjuntos\n",
-    "        self.I = range(2)\n",
-    "        self.J = range(3)\n",
-    "        self.T = range(5)       # en horas\n",
-    "        self.V = range(3)       # 3 tipos de vehiculos\n",
-    "    \n",
-    "    def init_data(self):\n",
-    "        \"\"\"\n",
-    "        Lista los parametros iniciales, definidos en \"Modelo Matematico\" en ISA v02\n",
-    "        \n",
-    "        i: almacen grande (depot)\n",
-    "        j: almacen pequeño (customer)\n",
-    "        v: tipo de vehiculo\n",
-    "        t: tiempo\n",
-    "        \"\"\"\n",
-    "        # Parametros\n",
-    "        # Nombres cortos confunden, pero matrices de varias dimensiones \n",
-    "        # sin etiquetas confunden mas\n",
-    "        \n",
-    "        # Demanda\n",
-    "        self.D_jt = [ [ random.choice([0,1,2,3]) for _ in self.I ] for _ in self.T ]\n",
-    "        # Capacidades de vehiculos\n",
-    "        self.VL_v = [ random.choice([10, 15, 20]) for _ in self.V ]\n",
-    "        # distancia entre almacen i, j\n",
-    "        self.d_ij = [ random.choice([25,50,100]) for _ in self.V ]\n",
-    "        #self.r_ijvt = [ [ 0 for _ in self.V ] for "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "ab559513-5c14-4dd2-a51d-7737114dfba1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10, 10, 15])"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = VRP()\n",
-    "p.init_data()\n",
-    "np.array(p.VL_v)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "2c3c85e0-a90c-4fda-86f7-778d7328c74d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[25, 50, 50]"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.d_ij"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "511ff788-0d1a-4ac7-9575-de182d236574",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "078280b5-70ef-4691-8798-a686d85d188c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "611c9a0d-bb1a-48eb-af37-f033abe8ed66",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64c68216-b9f1-45f9-a0fe-3862ab106c24",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9cb6bb08-1547-4b64-993f-b2c453535264",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b83d9e98-db8f-45cd-9eff-07d4194f7e07",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b8c06031-9c55-4e13-a27b-91b0886902e6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9483c22-243f-44e5-9a6d-09da92354554",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "12952643-4a10-40bf-8af3-41319c744732",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b0306c4f-eb68-4009-9390-0f881c6a8cb4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b3ad92de-b2ed-4f21-a696-1fa2981f89dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def genetic_algorithm(population, fitness_fn, ngen=100, pmut=0.1):\n",
-    "    \"Algoritmo Genetico \"\n",
-    "    \n",
-    "    popsize = len(population)\n",
-    "    evaluate_population(population, fitness_fn)  # evalua la poblacion inicial\n",
-    "    ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
-    "    bestfitness = [population[ibest[0]].fitness]\n",
-    "    print(\"Poblacion inicial, best_fitness = {}\".format(population[ibest[0]].fitness))\n",
-    "    \n",
-    "    for g in range(ngen):   # Por cada generacion\n",
-    "        \n",
-    "        ## Selecciona las parejas de padres para cruzamiento \n",
-    "        mating_pool = []\n",
-    "        for i in range(int(popsize/2)): mating_pool.append(select_parents_roulette(population)) \n",
-    "        \n",
-    "        ## Crea la poblacion descendencia cruzando las parejas del mating pool con Recombinación de 1 punto\n",
-    "        offspring_population = []\n",
-    "        for i in range(len(mating_pool)): \n",
-    "            #offspring_population.extend( mating_pool[i][0].crossover_onepoint(mating_pool[i][1]) )\n",
-    "            offspring_population.extend( mating_pool[i][0].crossover_uniform(mating_pool[i][1]) )\n",
-    "\n",
-    "        ## Aplica el operador de mutacion con probabilidad pmut en cada hijo generado\n",
-    "        for i in range(len(offspring_population)):\n",
-    "            if random.uniform(0, 1) < pmut: \n",
-    "                offspring_population[i] = offspring_population[i].mutate_position()\n",
-    "        \n",
-    "        ## Evalua la poblacion descendencia\n",
-    "        evaluate_population(offspring_population, fitness_fn)  # evalua la poblacion inicial\n",
-    "        \n",
-    "        ## Selecciona popsize individuos para la sgte. generación de la union de la pob. actual y  pob. descendencia\n",
-    "        population = select_survivors(population, offspring_population, popsize)\n",
-    "\n",
-    "        ## Almacena la historia del fitness del mejor individuo\n",
-    "        ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
-    "        bestfitness.append(population[ibest[0]].fitness)\n",
-    "        print(\"generacion {}, best_fitness = {}\".format(g, population[ibest[0]].fitness))\n",
-    "    \n",
-    "    return population[ibest[0]], bestfitness  "
-   ]
-  }
- ],
- "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": "3.9.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}