homeworks

1_Modulos_Ejercicios.ipynb

@@ -0,0 +1,70 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "norwegian-standard",
+   "metadata": {},
+   "source": [
+    "# Ejercicios sobre Modulos\n",
+    "\n",
+    "Utilizando documentación de libreria `datetime` (https://docs.python.org/3/library/datetime.html) construir una función `name_day(yyyy,mm,dd)` que para dado ano, mes y dia (formato: yyyy,mm,dd) devuelve una cadena con el nombre del dia de la semana.\n",
+    "\n",
+    "*pista: para devolver el nombre de la semana en español, uno puede usar la condición \"if\" múltiple o, más elegantemente, crear dos listas con nombres de la semana en inglés y español y usar metodo `find` con indexacion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advance-humidity",
+   "metadata": {},
+   "source": [
+    "#### Por ejemplo:\n",
+    "`name_day(1969,1,30)` tiene que devolver: `Jueves`\n",
+    "\n",
+    "`name_day(2023,10,20)` tiene que devolver: `Viernes`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "laden-fighter",
+   "metadata": {},
+   "source": [
+    "# Reto\n",
+    "Utilizando documentación de libreria `datetime` (https://docs.python.org/3/library/datetime.html) construir una función `name_day2(fecha)` que recibe una cadena con fecha en formato: `fecha='yyyy-mm-dd'` y devuelve una cadena con nombre del dia (en Espanol) que corresponde a esa fecha. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "general-airfare",
+   "metadata": {},
+   "source": [
+    "#### Por ejemplo:\n",
+    "`fecha='1969-01-30'`  \n",
+    "`name_day2(fecha)` tiene que devolver: `Jueves`\n",
+    "\n",
+    "`fecha='2023-10-20'`  \n",
+    "`name_day2(fecha)` tiene que devolver: `Viernes`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Chapter 1/.ipynb_checkpoints/ch1_affinity_create-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7d30ba50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "65515366",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.zeros((100, 5), dtype='bool')\n",
+    "features = [\"bread\", \"milk\", \"cheese\", \"apples\", \"bananas\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ec3c1990",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(X.shape[0]):\n",
+    "    if np.random.random() < 0.3:\n",
+    "        # A bread winner\n",
+    "        X[i][0] = 1\n",
+    "        if np.random.random() < 0.5:\n",
+    "            # Who likes milk\n",
+    "            X[i][1] = 1\n",
+    "        if np.random.random() < 0.2:\n",
+    "            # Who likes cheese\n",
+    "            X[i][2] = 1\n",
+    "        if np.random.random() < 0.25:\n",
+    "            # Who likes apples\n",
+    "            X[i][3] = 1\n",
+    "        if np.random.random() < 0.5:\n",
+    "            # Who likes bananas\n",
+    "            X[i][4] = 1\n",
+    "    else:\n",
+    "        # Not a bread winner\n",
+    "        if np.random.random() < 0.5:\n",
+    "            # Who likes milk\n",
+    "            X[i][1] = 1\n",
+    "            if np.random.random() < 0.2:\n",
+    "                # Who likes cheese\n",
+    "                X[i][2] = 1\n",
+    "            if np.random.random() < 0.25:\n",
+    "                # Who likes apples\n",
+    "                X[i][3] = 1\n",
+    "            if np.random.random() < 0.5:\n",
+    "                # Who likes bananas\n",
+    "                X[i][4] = 1\n",
+    "        else:\n",
+    "            if np.random.random() < 0.8:\n",
+    "                # Who likes cheese\n",
+    "                X[i][2] = 1\n",
+    "            if np.random.random() < 0.6:\n",
+    "                # Who likes apples\n",
+    "                X[i][3] = 1\n",
+    "            if np.random.random() < 0.7:\n",
+    "                # Who likes bananas\n",
+    "                X[i][4] = 1\n",
+    "    if X[i].sum() == 0:\n",
+    "        X[i][4] = 1  # Must buy something, so gets bananas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68c98923",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[False False  True  True  True]\n",
+      " [ True  True False  True False]\n",
+      " [ True False  True  True False]\n",
+      " [False False  True  True  True]\n",
+      " [False  True False False  True]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X[:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "07806f75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"affinity_dataset.txt\", X, fmt='%d')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "446d49ba",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}