{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/hernansalinas/Curso_aprendizaje_estadistico/blob/main/Sesiones/Sesion_01a_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 408,
     "status": "ok",
     "timestamp": 1638158532809,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "GXsXvSsWrntr",
    "outputId": "676a61af-f3ca-46c1-da30-28bce4a25d3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nEste notebook es reproducido con base al curso del profesor \\nRaul Pollar\\ngithub:https://github.com/rramosp/20182.mldl\\n# Machine Learning\\n\\nPara mas detalles y complemento ver:\\n\\nNotas: https://rramosp.github.io/ai4eng.v1/intro.html\\n#Machine Learning online\\n\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy import optimize\n",
    "from scipy.stats import norm\n",
    "#plt.rcParams['text.usetex'] = True\n",
    "\n",
    "\"\"\"\n",
    "Este notebook es reproducido con base al curso del profesor \n",
    "Raul Pollar\n",
    "github:https://github.com/rramosp/20182.mldl\n",
    "# Machine Learning\n",
    "\n",
    "Para mas detalles y complemento ver:\n",
    "\n",
    "Notas: https://rramosp.github.io/ai4eng.v1/intro.html\n",
    "#Machine Learning online\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qtYPk-HxsmKb"
   },
   "source": [
    "\n",
    "# 1 Dimension \n",
    "\n",
    "\\begin{equation}\n",
    "f(x)=\\frac{e^{-x^2/2}}{\\sqrt(2\\pi)}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1638158533139,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "kexHJqEltrsa"
   },
   "outputs": [],
   "source": [
    "#Supongamos que tenemos datos los cuales siguen una distribucion de probabilidad\n",
    "# normal, emplemos la libreria stats norm de scipy:\n",
    "\n",
    "# https://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.norm.html\n",
    "\n",
    "#The location (loc) keyword specifies the mean. \n",
    "#The scale (scale) keyword specifies the standard deviation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1638158533140,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "BRcizH1Os7tL"
   },
   "outputs": [],
   "source": [
    "def pdf(x, mean1 = 10, mean2=12, desv1=2, desv2=2):\n",
    "  fig, ax = plt.subplots(1, 1)\n",
    "  \n",
    "  f1 = stats.norm(loc = mean1, scale = desv1)\n",
    "  f2 = stats.norm(loc = mean2, scale = desv2)\n",
    "  ax.plot(x, f1.pdf(x),'g-', lw = 3, alpha = 0.6, label='pdf 1')\n",
    "  ax.plot(x, f2.pdf(x),'b-', lw = 3, alpha = 0.6, label='pdf 2')  \n",
    "  ax.set_xlabel(\"x\")\n",
    "  ax.set_ylabel(\"PDF(X)\")\n",
    "  return \"Done\", f1, f2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "executionInfo": {
     "elapsed": 420,
     "status": "ok",
     "timestamp": 1638158533552,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "tbmkmohUw4o3",
    "outputId": "abb94435-61de-487d-c42f-7dec8fc0cecb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e64a1c3d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x=np.linspace(5, 30,100)\n",
    "_, f1, f2 = pdf(x, mean1 = 12, mean2 = 17, desv1 = 1, desv2 = 2)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MmW3Liomz_Mn"
   },
   "source": [
    "¿como se puede encontrar la frontera en este caso?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "executionInfo": {
     "elapsed": 303,
     "status": "ok",
     "timestamp": 1638158533852,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "MkAKXPXo4VNu",
    "outputId": "f71121c5-357a-4b81-9880-54bf598de2a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e6496efd0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Determinemos el minimo\n",
    "plt.plot(abs(f2.pdf(x)-f1.pdf(x))[25:60])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1638158533852,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "L29cYqrY0V07",
    "outputId": "b2e75f93-3d53-4e6a-cb9d-c50f494f8cf9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index min :10, function min : 0.01644600381255973\n"
     ]
    }
   ],
   "source": [
    "index_min = np.argmin(abs(f2.pdf(x)-f1.pdf(x))[25:60])\n",
    "f_min = min(abs(f2.pdf(x)-f1.pdf(x))[25:60])\n",
    "print(f\"index min :{index_min}, function min : {f_min}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1638158533853,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "Wtcc6Xygz-hy",
    "outputId": "c4b91b9a-9eba-4c72-c94a-67ae3ef8fb0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e64409e90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#x = np.linspace(5, 30,1000)\n",
    "_, f1, f2 = pdf(x, mean1 = 12, mean2 = 17, desv1 = 1, desv2 = 2)\n",
    "plt.plot(x,abs(f2.pdf(x)-f1.pdf(x)), \"r-\", alpha=0.5, label=\"(f2-f1)\")\n",
    "xmin_=x[25+index_min]\n",
    "plt.vlines(xmin_, 0, 1, alpha=0.8)\n",
    "plt.ylim(0,0.4)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oM6YQNKnDmy-"
   },
   "source": [
    "La función de distribucion cumulativa, viene dada por:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 431,
     "status": "ok",
     "timestamp": 1638158534280,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "5wphIxbksWtC",
    "outputId": "702a3394-8474-4dc6-9254-b68bdb424c32"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, f1.cdf(x))\n",
    "plt.plot(xmin_, f1.cdf(xmin_),\"ro\", alpha=0.8)\n",
    "\n",
    "plt.plot(x,f2.cdf(x))\n",
    "plt.plot(xmin_,f2.cdf(xmin_),\"bo\", alpha=0.8)\n",
    "\n",
    "plt.plot(x, f1.pdf(x), color=\"red\", label=\"pop 1\", alpha=0.8)\n",
    "plt.plot(x, f2.pdf(x), color=\"blue\", label=\"pop 2\",alpha=0.8)\n",
    "plt.xlim()\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1638158534281,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "O-AKYles7o0p",
    "outputId": "972c257b-179c-46c2-eb92-6479c9637290"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.30 % 5.7 % \n"
     ]
    }
   ],
   "source": [
    "# El error puede ser calculado como :\n",
    "e1 = 1-f1.cdf(xmin_)\n",
    "e2 = f2.cdf(xmin_)\n",
    "\n",
    "#Error asociado a cada distribucion de probabilidad\n",
    "print(f\"{e1*100:.2f} % {e2*100:.1f} % \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A-matvz5Du96"
   },
   "source": [
    "Analicemos otra situación, aplicando un algoritmo de machine learning:\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1638158534281,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "vyyNUlGu8lJv"
   },
   "outputs": [],
   "source": [
    "# Supongamos que tenemos un sistema que puede arrojar valores binario, \n",
    "# de esta manera tenemos que dada una variable aleatoria r,el sistema \n",
    "# valor determinado. # Volviendo a las distrubicione normales del principio, tenemos que:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1638158534281,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "egz8Pyq8FKxa",
    "outputId": "9290b366-1abf-4a80-9978-0b04b69fb10f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(5, 30,100)\n",
    "_, f1, f2 = pdf(x, mean1 = 12, mean2 = 17, desv1 = 1, desv2 = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z4Ii3nXlGui5"
   },
   "source": [
    "Elijamos un conjunto de valores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 338,
     "status": "ok",
     "timestamp": 1638158534615,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "cm2ZKhb5E749",
    "outputId": "43c23881-6282-4647-836a-8475e5948611"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_1 = f1.rvs(1000)\n",
    "random_2 = f2.rvs(1000)\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(random_1, density=True)\n",
    "plt.plot(x, f1.pdf(x))\n",
    "plt.hist(random_2, density=True)\n",
    "plt.plot(x, f2.pdf(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1638158534616,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "iApk51QqFFmQ"
   },
   "outputs": [],
   "source": [
    "#Generamos N datos para cada distribucion de probabilidad a partir de variables\n",
    "# aleatorias\n",
    "m = 100 # training values \n",
    "\n",
    "random_1 = f1.rvs(m)  #m variables aleatorias pertenecientes a la clase 1\n",
    "random_2 = f2.rvs(m)  #m variables aleatorias pertenecientes a la clase 2\n",
    "\n",
    "X = np.zeros(2*m)\n",
    "X[0:m] = random_1\n",
    "X[m:2*m] = random_2  # Construccion de vector con la caracteristica 1\n",
    "\n",
    "Y = np.zeros(2*m)    # Valor asociada a la caracteristica 1\n",
    "Y[m:2*m] = np.ones(m) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xWl98nrRK4yU"
   },
   "source": [
    "El dataset anterior puede ser escrito como:\n",
    "\n",
    "|Y         | X_1       |\n",
    "|----------|----------|\n",
    "|$Y^{1}$ | $X_1^{1}$|\n",
    "|$Y^{2}$ | $X_1^{2}$|\n",
    "|.         | .        |\n",
    "|.         | .        |\n",
    "|.         | .        |\n",
    "|$Y^{m}$ | $X_1^{m}$  |\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1638158534616,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "QOR1Ac02KFNQ",
    "outputId": "a60f465e-ccd2-43d7-ec66-6e97406a9837"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#De forma grafica puede ser entendido asi:\n",
    "\n",
    "plt.figure(figsize=(14,1))\n",
    "plt.plot(X[Y==0],np.zeros(m),\"go\", alpha=0.4)\n",
    "plt.plot(X[Y==1],np.zeros(m),\"bo\", alpha=0.4)\n",
    "plt.yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZUCjPHihNs8r"
   },
   "source": [
    "¿Como podemos determinar la frontera?\n",
    "- Empleemos diferentes algoritmos para hacerlo:\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 453,
     "status": "ok",
     "timestamp": 1638158535066,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "Xd5G29pGKP4Q",
    "outputId": "e3b681d9-3a62-4370-ec7d-52b305aae12b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.945\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "X = X.reshape(-1, 1) \n",
    "clf = DecisionTreeClassifier(max_depth = 1)  # Estimador \n",
    "clf = clf.fit(X, Y)                          # Fit \n",
    "print(f\"Score: {clf.score(X, Y)}\")           # Score \n",
    "clf.predict(f2.rvs(1).reshape(1, -1))        # prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1638158535067,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "jwIrQByzPASA",
    "outputId": "ca94266b-3806-4e06-c083-751ebae5376d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.,\n",
       "       1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ya se pueden realizar predicciones.\n",
    "# Supongamos un nuevo valor perteneciente a al distrubcion 2, asi:\n",
    "x_test = f2.rvs(100).reshape(1, -1)  # Valor para probar\n",
    "# Lo anterior implica que de la clase f2, los valores que se deberian esperar\n",
    "# son iguales al valor 1, la prediccion que tenemos nos dice que:\n",
    "clf.predict(x_test.T) # La prediccion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_owVgDpEV6qY"
   },
   "source": [
    " Analicemos gráficamente las fronteras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1638158535067,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "uTYTsg4CYDwJ",
    "outputId": "0378de54-b0e5-4829-d9d5-1284faa86290"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e5fab86d0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dado un nuevo conjunto de puntos, ¿que puede estimar el algoritmo?\n",
    "Xr = np.linspace(8, 25, 100).reshape(-1,1)   # Para un nuevo conjunto de datos, tentemos que  la prediccion es:\n",
    "yr = clf.predict(Xr) \n",
    "plt.figure(figsize=(14,1))\n",
    "plt.plot(Xr[yr==0], np.zeros(len(Xr[yr==0]) ),color=\"g\" ,alpha=0.1, lw=3 )  # GRaficando la prediccion para el conjunto de datos, para los de tipo 0\n",
    "plt.plot(Xr[yr==1], np.zeros(len(Xr[yr==1]) ), color=\"b\",alpha=0.1, lw =3 ) #  GRaficando la prediccion para el conjunto de datos, para los de tipo 1\n",
    "plt.plot(X[Y==0],np.zeros(m),\"go\", alpha = 0.4) # Valores aleatorios # Valores de entrenamiento\n",
    "plt.plot(X[Y==1],np.zeros(m),\"bo\", alpha = 0.4)                      # Valores de test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rBpf-ii3Z40M"
   },
   "source": [
    "# Tarea\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-XnPkuq5bsdf"
   },
   "source": [
    "1. Definir una función que permita determinar m  valores de entrenamiento dado un m, Para  f1, f2  definidos como:\n",
    "\n",
    "f1 = stats.norm(loc = mean1, scale = desv1)\n",
    "\n",
    "f2 = stats.norm(loc = mean2, scale = desv2)\n",
    "\n",
    "Emplee los valores de mean1=12, mean1=17, con desv1=1, desv2=2\n",
    "\n",
    "Con base en ello,  construya una función de python, que permita determinar la predicción empleando un DesicionTreeClasifier \n",
    "\n",
    "```\n",
    "def train_values(m)\n",
    "  \"\"\"\n",
    "  Params m \n",
    "  Return X, y\n",
    "  \"\"\"\n",
    " \n",
    "  return X, y\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "def main(X, y, Xr, m):    \n",
    "  \"\"\"\n",
    "  Params:\n",
    "      X : Features values\n",
    "      y : training values for each features\n",
    "      Xr : np.linspace(5, 25, 100).reshape(-1, 1) New Values for prediction\n",
    "      m  : len of value of training \n",
    "  Return :\n",
    "      yr: Prediction\n",
    "  \"\"\"\n",
    "\n",
    "\n",
    "  return yr\n",
    "```\n",
    "\n",
    "\n",
    "2. Con la función construida realice los siguientes experimentos:\n",
    "  - Elija diferentes valores m entre  (10 , 100). ¿Cómo se compara la frontera Bayesiana respecto a los valores determinados?\n",
    "  - Eliga m=100, realice un muestreo para 10 experimentos, ¿Cómo se compara la frontera Bayesiana respecto a los valores determinados?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1638158535067,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "NzesHxuaXqCW"
   },
   "outputs": [],
   "source": [
    "#En resumen\n",
    "def train_values(m = 100):\n",
    "  \n",
    "  random_1 = f1.rvs(m)  #m variables aleatorias pertenecientes a la clase 1\n",
    "  random_2 = f2.rvs(m)  #m variables aleatorias pertenecientes a la clase 2\n",
    "\n",
    "  X = np.zeros(2*m)\n",
    "  X[0:m] = random_1\n",
    "  X[m:2*m] = random_2  # Construccion de vector con la caracteristica 1\n",
    "\n",
    "  y = np.zeros(2*m)    # Valor asociada a la caracteristica 1\n",
    "  y[m:2*m] = np.ones(m) \n",
    "\n",
    "  return X, y\n",
    "\n",
    "\n",
    "def main(X, y, Xr, m):    \n",
    "  #X, y = train_values(100)\n",
    "  clf = DecisionTreeClassifier(max_depth = 1)\n",
    "  clf = clf.fit(X.reshape(-1, 1), y)\n",
    "\n",
    " # Xr = np.linspace(8,25, 100 ).reshape(-1, 1)\n",
    "  yr = clf.predict(Xr)\n",
    "\n",
    "\n",
    "  plt.figure(figsize=(14,1))\n",
    "  plt.plot(Xr[yr==0], np.zeros(len(Xr[yr==0]) ), color=\"g\", alpha=0.6, lw=3 )  # GRaficando la prediccion para el conjunto de datos, para los de tipo 0\n",
    "  plt.plot(Xr[yr==1], np.zeros(len(Xr[yr==1]) ), color=\"b\", alpha=0.6, lw =3 ) #  GRaficando la prediccion para el conjunto de datos, para los de tipo 1\n",
    "  plt.plot(X[y==0],np.zeros(m),\"g.\", alpha = 0.4) # Valores aleatorios # Valores de entrenamiento\n",
    "  plt.plot(X[y==1],np.zeros(m),\"b.\", alpha = 0.4)                      # Valores de test\n",
    "  plt.yticks([])\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 898,
     "status": "ok",
     "timestamp": 1638158535960,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "jjmnC8vFaYms",
    "outputId": "a0779603-a3e2-4f87-b713-b122a5e0d938"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8RY7C5ae2/K/rc0/DtpxD4fPzsJJLql5reE17gnkoPbcOOWIuSsGtQ3527TzUlz+1CwAAAADN+A/nAAAAAIKg+AAAAAAQBMUHAAAAgCAoPgAAAAAEQfEBAAAAIAiKDwAAAABBUHwAAAAACOL/AQmZ0Hl1M+ZmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para diferentes valores de entrenamiento, el resultado es el siguiente:\n",
    "m = np.arange(10, 60, 10)\n",
    "\n",
    "for m_ in m:\n",
    "  X, y = train_values(m_)\n",
    "  Xr = np.linspace(8,25, 100 ).reshape(-1, 1)\n",
    "  main(X, y, Xr,m_)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 265,
     "status": "ok",
     "timestamp": 1638158536222,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "ubffzz2FcnBi",
    "outputId": "1db0b480-4dbf-471d-b460-218307678a37"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Realicemos un muestreo para  m valores 10 experimentos\n",
    "m = 100*np.ones(5)\n",
    "for m_ in m:\n",
    "  m_=int(m_)\n",
    "  X, y = train_values(m_)\n",
    "  Xr = np.linspace(8,25, 100 ).reshape(-1, 1)\n",
    "  main(X, y, Xr,m_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AuOJWLtCk47f"
   },
   "source": [
    "# 2 Dimensiones\n",
    "\n",
    "Caso Bivariante\n",
    "\n",
    "\\begin{equation}\n",
    "f(x, y) = \\frac{1}{2\\pi \\sigma_x \\sigma_y \\sqrt{1-\\rho^2}} \\exp \\left( -\\frac{1}{2(1-\\rho^2)} \\left(\\frac{x^2}{\\sigma_x^2}+\\frac{y^2}{\\sigma_y^2}-\\frac{2\\rho x y}{(\\sigma_x \\sigma_y)} \\right) \\right) \n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "$\\rho$ coeficiente de correlación, media es $(0, 0)$\n",
    "\n",
    "\n",
    "La matriz de covarianzas viene dada por:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Sigma =  \n",
    "\\begin{bmatrix}\n",
    "\\sigma_x^2 & \\rho \\sigma_x \\sigma_y  \\\\\n",
    "\\rho \\sigma_x \\sigma_y & \\sigma_y^2\\\\\n",
    "\\end{bmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "Otra forma de escribirla puede ser como sigue\n",
    "\n",
    "\n",
    "\n",
    "### El caso más general viene dado por:\n",
    "\n",
    "En el caso más general tenemos que :\n",
    "\\begin{equation}\n",
    "f_X(x_1,x_2, ... x_n) = \\frac{1}{2\\pi^{n/2}|\\Sigma|^{1/2}} \\exp\\left( -\\frac{1}{2}(x-\\mu)^T\\Sigma^{-1}(x-\\mu)\\right)\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "$|\\Sigma|$ es el determinante de la matrix de covarianza, \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1638158536222,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "tTUUsgbIk7YG"
   },
   "outputs": [],
   "source": [
    "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.multivariate_normal.html\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import multivariate_normal\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YTnJXHqoI1HB"
   },
   "source": [
    "## Funcion de distribucion de  probabilidad  1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 250,
     "status": "ok",
     "timestamp": 1638158536469,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "_lbZ9b0XmrsA",
    "outputId": "2825a0fb-5885-45df-9481-1d1a786c7d53"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PDF(X)')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rJzDd5GUhGA1Ut9uuiew73/tFZIuIPCsiYzs6kYjcJyIlIlJSV1fnRVYTY0K7/8wJBjJ+6nzXUTyXWzibY6TCnjdcRzH9lOvO4ueBHFWdBrwM/LKjg1R1maoWqWpRVlZWnwY0/dOoY8XsHjid+IRE11E8F4iLY0/KDMY2lKChkOs4ph/yshDUAu0/4Y+J7DtHVY+qanNk83Fglod5jE8c3FfOGD1I85gFrqP0mdbsaxhBHfurdrqOYvohLwtBMVAgIrkikggsAVa0P0BERrbbvAOwv2LTY9Ubwm3lw6+8yXGSvjNyerifoHaD9ROYrvOsEKhqG3A/sIrwG/wzqrpdRB4RkTsih31RRLaLyGbgi8C9XuUxPrLnDY4xmHGTilwn6TNj86dxmAwCe235StN18V6eXFVXAivP2/dwu9tfA77mZQbjLxoKkX28hMqUmcyKi3Mdp89IIMC+wbPIOVGMhkJIwHX3n+lP7K/FxJSayu0M5yht2Ve7jtLnNOcaMmlgX9lG11FMP2OFwMSU/RvDbeSjZtzsOEnfGz0zvBTnwc0vOU5i+hsrBCamxO/9C4fJYEzeVNdR+tyonInsl+EkVls/gekaKwQmZmgoRO7JDexNK/JtG3nNkCLGn9pIKBh0HcX0I/58tZiYVFW6ngxOoDnXuo7iTGD8daRxispta11HMf1IpwuBiAREZIaI3CYi7xGRYV4GM6arDkXaxsfO9F//wFk5s8L9BEe2vuw4ielPLjt8VETygK8CC4FyoA5IBiaIyGngMeCXqmrXthunkqrfpFaGM3rcRNdRnMkcNY69gTEMqH3LdRTTj3TmG8G/Ar8C8lT1FlW9W1U/EJkf6A4gDbjHy5DGXE6wrY2805uoHTLbdRTnDmbMIf/0Flpbmi9/sDF0ohCo6lJVfUNVtYP7Dqvqf6hqh5PFGdNXKre+zWBOE8iL3dXIOish/zoGSRO7N//FdRTTT1y2EIjIgyJywXEiMlREnvAmljFdczTSJp5TtMhxEvfGR/4Njm2zfgLTOZ1pGpoIbBCRc1M5isjngPXAVq+CGdMVA/e/RVVgLJkjsl1HcW5I5gh2x41n8IE1rqOYfuKyncWqep+IXAU8KiLbgEmEO43nq+oBrwMacznNTacpOLOVLcPuIMd1mChRlzmXGQefpen0SZIHpriOY6JcZ4ePbgPWAdcCI4CfWxEw0aJiw+sMkBYSC25wHSVqDJh0I0nSSnnJq66jmH6gM30E9wCbgEogD3gf8G0RecquJTDRoHHHKwRVGD/b+gfOyi+6iVaN42TpK66jmH6gM98IPgDcoKrfUtU2VV0PzAfWAHb5onFuyMG3qUiYQFp6pusoUWNQ6hAqEieRedj6CczldWb46J2quve8faqqPwPmepbMmE5oPF5PfmsZ9cNjf5H6rmoYcRV5rRUcr69zHcVEuc40Dd3d0fBRAFWtE5E8Eelw8ncRWSQiZSJSISIPXeIx3i8iKiL+WVLK9IrdJS8RLyFSJ9/oOkrUSbtiIQFRdhe/6DqKiXKdWaFsKLBRRNYTHjJ6doqJfOA64AhwwZu8iMQBjwI3ATVAsYisUNUd5x2XCjwAvNOD52F8qqnsNZo0gfxZVgjOlz/jek6/kERr+Wtwi138by6uM01DPwRmAr8FsoAbI9u1wD2q+n5VLe/gV+cAFapaqaotwHLgzg6O+wbwLaCpe0/B+NnwI+9QkXwFyQMGuY4SdRKTkqkYMI0R9etcRzFRrlPDR1U1qKovq+rXVfUzqvolVX1MVfdd4tdGA9Xttmsi+84RkZnAWFX906UeX0TuE5ESESmpq7P2ThN25GA1uaEqGkctuPzBPnV6zALGhWo4XLvHdRQTxTrTR/BSu9u9ttB8pN/h+8CDlztWVZepapGqFmVlZfVWBNPPVZW8AMDQqf6ddvpysqbdAsDe4pWOk5ho1plvBO3feT/YhXPXAmPbbY+J7DsrFZgCvC4iVcA8YIV1GJvOCu1+nRMMJG+a/xaq76zcK+ZyjMGw58+uo5go1plCcMGso51UDBSISK6IJAJLgBXnTqp6XFUzVTVHVXMIX5Nwh6qWdPPxjI9oKET2sXXsHjSTuPjOjHnwp0BcHJWps8g5vg4N2ZIhpmOdKQTjRWSFiDzf7va5n4v9kqq2AfcDq4CdwDOqul1EHhGRO3onvvGr6ootjKCOlnHXu44S9YI515PFMapK17uOYqJUZz5KtR/p892unFxVVwIrz9v38EWOvb4r5zb+dmDDC2QDY4pudx0l6mXPvg22/jOHNr5AbqEt3GMu1JnZR881LopIVmSfDd0xTiXt+3N4Wcrxk11HiXojsgvYFxjNwOo3XEcxUaozo4ZERP5ZRI4AZcAuEakTkQ4/2RvjtdaWZgpObaQmw6aV6KwDQ+dTcGYzTWdOuY5iolBn+gi+DFwNzFbVDFVNJzzH0AIR+bKn6YzpQPmG1QySJhInvMd1lH4jadJNDJAWKtbbtNTmQp0pBPcAS1X13BUpqloJ3A181KtgxlzMiW0vhaednnOb6yj9Rv7sW2jVOBq32/KV5kKdKQQJqnrk/J2RfoKE3o9kzKWlH3yTioSJNu10F6QMTqc8qZCsw2+5jmKiUGcKQUs37zOm1x2vryO/dRf1I+0isq46Pupq8oO7qT9ce/mDja90phBcKSInRKQx8nPi7DYw1euAxrS3e92fiBMlfcotrqP0O0OnhVdwq1x3yam9jA91ZvbROFUdrKqpkZ/B7batacj0qdZdr3JSB5A34zrXUfqdvGlXc5xBaMVrrqOYKNOZ4aPJIvIlEflJZBZQu57fOKGhEOPq11A+aAYJiUmu4/Q7cfHxVKTMJqdhrU03Yd6lM01DvwSKgK3ArcD3PE1kzEXsK9sYnlYid6HrKP1WMG8hWRyjcpstN27+qjOFoFBV71bVxwgvZH+Nx5mM6dCB9eGprcbN62h9I9MZ4+eH/+3qNvzRcRITTTpTCFrP3ohMJGeMEynVr7MnMI4RY/NdR+m3MkdkUxGXR1rt666jmCjSlVFDZ0cKTWs3iuiE1wGNATh54hgTmrZyaJgNG+2puhHXUtCyk+P1NmWYCevKqKGzI4Xi290e3BchjSlf+ycSJUjqVLuauKfSr7yNeAlRsfais8gbn+nUmsXGuNZa+iIndQATZltHcU8VzLyB4wwitMummzBhnhYCEVkkImUiUiEiD3Vw/2dFZKuIbBKRN0Wk0Ms8pn8KDxt9m10pRTZstBfExcdTkTqX3IY1hIJB13FMFPCsEIhIHPAosBgoBJZ28Eb/G1WdqqrTgW8TXszemHep2lnMcI7SNv5G11FiRihvIZk0ULltjesoJgp4+Y1gDlChqpWq2gIs592rnaGq7TubB9H99ZFNDDu0PjzUcdxcW+G0t+TOC/9b1m20YaSmc0tVdtdooLrddg3hdQzeRUQ+D3wFSARsgnlzgdSa1ewJ5JA7Js91lJiROWIs5fEFpNe87jqKiQLOO4tV9VFVzQO+Cvzfjo6JTG1RIiIldXU25M1PGo4cZGLzdg6OvN51lJhzZNQNTGgt5eihGtdRjGNeFoJaYGy77TGRfRezHHhfR3eo6jJVLVLVoqysrF6MaKJd+Vu/J15CDJ11l+soMWdY0V0ERKl86znXUYxjXhaCYqBARHJFJBFYArxr4LKIFLTbvA0o9zCP6Yfiyl+gjnTyr7SZTXrb+CnzOEgW8RUvuo5iHPOsEESmo7gfWAXsBJ5R1e0i8oiInO31u19EtovIJsL9BB/zKo/pf5qbTjOxcR2VGdcQiItzHSfmSCDA3sxrmXSqhDOnGl3HMQ55OqW0qq4EVp637+F2tx/w8vFN/1a2ZiXTpInkKe91HSVmDZz2Xga89hyb3n6e6Td9xHUc44jzzmJjLubMtuc5rUlMnG/TSnhl4tzFNOoAWnbYMFI/s0JgopKGQuQefYPSlDkkDxjkOk7MSkxKZtfg+eQde5Ngm00u7FdWCExUqtj8JsOoJ1iw2HWUmKcTFzOU45RvWO06inHECoGJSkfX/4GgCgVXv991lJhXsOD/0KpxHNtos5H6lRUCE5WG73+VsqQpDMkc4TpKzEtLz6QseSqjD71qaxn7lBUCE3X27dpEbqiKEzmLXEfxjVN5t5EdqqWqdL3rKMYBKwQm6tS+9VsAxl/3N46T+EfetUsJqnDo7d+6jmIcsEJgos7w6hfZmVDIsNG5rqP4RuaIsZQmTWXk/lXWPORDVghMVNm3axPjQ1Ucz7VrB/rayfz3Mi5UY81DPmSFwEQVaxZyx5qH/MsKgYkqI6xZyJlw89A0ax7yISsEJmrsLQuPFrJmIXdO5t9uzUM+ZIXARI39b1uzkGvWPORPVghM1LBmIffONg+N2v+iNQ/5iBUCExV2b10bvogszxaod+1kwZ1kh2qp2PKW6yimj1ghMFGh7s1f0KJxTLjxXtdRfG/Swo/RrAkcfeuXrqOYPuJpIRCRRSJSJiIVIvJQB/d/RUR2iMgWEXlVRMZ5mcdEp7bWFvIPvcD2QfNIzxrpOo7vpaVnsi11ARPrVtHS3OQ6jukDnhUCEYkDHgUWA4XAUhEpPO+wjUCRqk4DngW+7VUeE722v/m/ZNKAXrnUdRQTET9zKemcYMcbv3cdxfQBL78RzAEqVLVSVVuA5cCd7Q9Q1dWqejqyuRYY42EeE6XaNvyGBlK44jqbcjpaFF59F0dJI7TJRg/5gZeFYDRQ3W67JrLvYj4JvNDRHSJyn4iUiEhJXV1dL0Y0rp1oOMoVJ/5CWebNJCUPdB3HRCQkJlE+bBFTTr7N8aOHXMcxHouKzmIRuRsoAr7T0f2qukxVi1S1KCsrq2/DGU+Vvvo0ydJK+lUfcx3FnCdzwcdIlDZKX33KdRTjMS8LQS0wtt32mMi+dxGRhcA/AneoarOHeUwUSil7ln2B0RRMv9Z1FHOevKnz2RMYR9qu51xHMR7zshAUAwUikisiicAS4F1r4YnIDOAxwkXgsIdZTBSqLt9MYctW9me/DwlExZdT044EAhwafxeT2nZStbPEdRzjIc9efaraBtwPrAJ2As+o6nYReUREzl419B0gBfidiGwSEVs01UdqX/kprRpH/qLPuo5iLmLCzffRovEcevWnrqMYD8V7eXJVXQmsPG/fw+1uL/Ty8U30OnOqkcmHnmdL6jXMGpHtOo65iIxhoylJu57CupWcamxgUOoQ15GMB+z7uHFi60v/RRqnSJ5/n+so5jJSFnyGVDnDthefcB3FeMQKgXFiyPan2RsYS+H8xa6jmMuYOHshlYEcMkt/ZRPRxSgrBKbPlW/6CxPadnGg4CPWSdwPSCBA3aS7yQtWUrZhtes4xgP2KjR97tiff8ZpTaJw8WdcRzGddMWiT3FKk2n8y2OuoxgPWCEwfer40UNMrX+ZbRk3MXjIUNdxTCelDE5nW+YipjW8Rv3hCy4HMv2cFQLTp3as+D4DpIWshV90HcV00YibHiBJWtm14nuuo5heZoXA9JkzpxqZtPc3bB4wh9wr5rqOY7po3KSZbBx4FZNrlnOqscF1HNOLrBCYPrPlj4+SzgkSrv2K6yimmwbc8CBpnGLr8z92HcX0IisEpk+0tbYwtvQJyuInMXnuLa7jmG6aNHshOxKmkLPrF7S22NRgscIKgekTm1b9glF6mNNzvmBDRvu51vlfYARH2LTycddRTC+xV6TxnIZCpG/8KXsDY7jyRluFrL+bdv2H2BMYR9aWnxMKBl3HMb3ACoHx3OZXl5MX3MOhqZ8hEBfnOo7pIQkEODr9c+SE9rHpJVurIBZYITCeCra1MWTNv1Mto5h5u80yGitm3PopqgJjySz+Dm2tLa7jmB6yQmA8tf75n5ET2kfdnH8gPiHRdRzTS+Li4zk27yGyQ7Vs+N+fuI5jesgKgfFM05lTZG/+IeXxBcy4xZaijDXTF36E0vjJ5Gz7MU2nT7qOY3rACoHxzKbff48R1NF83T/ZSKEYJIEAoRv/mWHUs+nZb7mOY3rA01eniCwSkTIRqRCRhzq4/1oR2SAibSLyAS+zmL51ouEoE8uXsTVpBlOuudN1HOORwvmL2Zw8m8LKJzheX+c6jukmzwqBiMQBjwKLgUJgqYgUnnfYPmWRHeYAAAzhSURBVOBe4Dde5TBu7Pz1P5CmJ0le/A3XUYzHUm//NwbpaUp//Xeuo5hu8vIbwRygQlUrVbUFWA6866Ohqlap6hbAVruIIeUb36Do8HMUZ91FwfRrXMcxHhs/ZS7Fwz/E7CP/S2nJq67jmG7wshCMBqrbbddE9nWZiNwnIiUiUlJXZ18/o1mwrQ3++GWOSRqT7/6u6zimj0y5+1sckXQSXnjQhpP2Q/2iB09Vl6lqkaoWZWVluY5jLqH4d9+iIFjB3tn/ZOsN+EjK4HRq53+dvOAeSv77/7mOY7rIy0JQC4xttz0mss/EqEM1u5lS+mO2JBcxc/EnXMcxfWz6TfewacA8ppX/lAN7y1zHMV3gZSEoBgpEJFdEEoElwAoPH884FGxr48hTHyNAiKEf+pENF/UhCQQYvuRHhAjQ8Kt7rYmoH/Hs1aqqbcD9wCpgJ/CMqm4XkUdE5A4AEZktIjXAB4HHRGS7V3mMt9Y9/Y9c0bKVbdP/idHjr3AdxzgyctxESov+hcmtOyh+6muu45hOElV1naFLioqKtKSkxHUM007pOy+Rv/LDbBp8A7O+/Kx9GzAU/+DDzGxYRektv+GKq251HccAIrJeVYs6us9esaZHjtfXMeSFz3EokMXETz1uRcAAUPipx9gfGEnWS5/nWN0B13HMZdir1nRbS3MT1Y99gAyt59R7l5GaluE6kokSg1KH0Py+xxmiJzjwnx+kuem060jmEqwQmG7RUIjNP/0oU5o3sXnGN5gw83rXkUyUyb9yAVtm/zuFLVvZ+tN7bBGbKGaFwHTL2l98ldnHV7Em+zPMft/nXccxUaro9vtYk/t5ik68wjtPPug6jrkIKwSmy9753XeZv28ZxUMWM+/eb7qOY6LcvHv+lXUZ72V+7X/xzvJ/dx3HdMAKgemStb9+hLnbv8HmAXO58m9/YZ3D5rIkEGDGZ59g48CrmFv6TdY+/bDrSOY89io2naKhEGv+66vMK/8eGwZdy+QvrSAxKdl1LNNPJCQmMeVLf2B96g3M2/1D1jzxIBqyuSajhRUCc1mtLc2s+9mnmb/35xSn3cy0Lz1nRcB0WUJiEtMfeJZ1Q25lfvXjrHv047Q0N7mOZbBCYC7jyP69VHzneubWPcva4UuY9cXltvaw6ba4+HiKvvAr1oy8m7lH/8Ce71zH4do9rmP5nhUCc1Hb3lwBy65lXMtuSmZ/l3l/+xiBuDjXsUw/F4iLY/5nHmXD3P9gTGsVcf95HVvf+B/XsXzNCoG5wPH6Otb98CNMeeUezshADn34TxTd9mnXsUyMmbn44xxZ+gInA6lMfe1ein/wYRqOHHQdy5esEJhzgm1tFP/hUVp/VMTM+hdYM/KjZP7dOnILZ7uOZmLUuEkzGf5377Bm9L1Mb3gZ/clsin//Q5u5tI/ZpHOGUDDIhhefZNj6H5AdqqU8voDAHT8ib9pVrqMZH6nc9g6tf/gCE9vKqJZRHJrxADNu/RRx8fGuo8WES006Z4XAx47VHaBs1c8ZvfsZxup+9gTG0TD3Qa5ceLf1BRgnNBRi0yu/IW3tdxgfqqJGRlCT+yEmLPosGcO6tdKtibBCYM5pPF5P2ZvPEdj5PFMa3yJR2tiZUMjp6Z9kxqKPWwEwUSEUDLLppadI3vgEhS1badF4tqVcRWjye5lw9fttGdRusELgYy3NTVRueoOGna8z8OBaJp3ZTKK0cZQ0KjIXMuw9f2t9ACaq7d25ngOv/Yz8upfJpIEWjaMseRonR85jyOQbGH/lNSQlD3QdM+o5KwQisgj4IRAHPK6q3zzv/iTgKWAWcBT4sKpWXeqcVgg61trSzMG9pdRXl3Hm4C4Ch7eT0VhGdtteEqUNgKpANgezFpA28y4mzLrR2l5NvxJsa6N8w2oaNvwPww+/RW6oCoAWjaM6fhz1qRMJZhUyYOQE0sdMYsS4SXbhYztOCoGIxAG7gJuAGsJrGC9V1R3tjvkcME1VPysiS4C7VPXDlzpvLBaCUDBIS0sTrS3NtDafoaX5DK1Np2g+fZLWppO0njlJ6+kTBM80EDrTgJ6uJ+7MURKaj5HSUkd6Wx0ZepyA/PX/ZT2DqUku4FT6ZJJy5pE7cyHpWSMdPktjelfDkYPs2fAKTXveYdCxHYxuKmcox8/dH1KhXtI4FpfJyaRhtCalE0weigzKIJCcRtzANBIGDiFhQAoJySkkDUwhIXkQCYnJJCYPJCExicTE5JhpLr1UIfDyI+EcoEJVKyMhlgN3AjvaHXMn8PXI7WeBn4iIqAfVqfj3P2TYtv/sxJEdP7S02y/t4oX3K4Iikd8XDd8WQpH94Z84QgghAoSI1yBxhIinjThRkoHOfnZp0TiOy2AaA2mcTMziWOoEylNGEj80l9RRE8kaN5mMrFFk2IRwJoYNyRzBjJvvBu4+t6/+cC2H9+6ksbaMtqN7iDt5gOQzh0hr2k/K6VKG6HESpWvrIgRVaCOeIAHaJI4QAZQAQQLtXt2CEgi/G0j41X/2nYF27w6cuz9yu93+d+t4/5FZX2LWbZ/qUv7O8LIQjAaq223XAHMvdoyqtonIcWAocKT9QSJyH3AfQHZ2drfCJKRmcXTg+E4de9H/OdJ+v/z12HP75dwfQfh2IHLf2dsBkLjw7UA8GkiAQBzEJUBcAhKXiCQkI/HJBBKSCCRFPqEkp5CUms6A1HQGDc4gJXUIWYEAWd36lzAmdmUMGx0eXTR7YYf3ayhEY2MDp07Uc6bxGM2Nx2htOklb82mCTScJtTahbU1oaxMabIFgGwRbIBREQq0QakM0BBoEDYVvo6D619uRD4PnPlSqtvsg2e6D5kU+78pFPowCJKZ4swpgv2gkVtVlwDIINw115xzTb/oI3PSRXs1ljOlfJBAgNS3DllU9j5dtB7XA2HbbYyL7OjxGROKBNMKdxsYYY/qIl4WgGCgQkVwRSQSWACvOO2YF8LHI7Q8Ar3nRP2CMMebiPGsairT53w+sIjx89ElV3S4ijwAlqroCeAJ4WkQqgHrCxcIYY0wf8rSPQFVXAivP2/dwu9tNwAe9zGCMMebSbHyhMcb4nBUCY4zxOSsExhjjc1YIjDHG5/rd7KMiUgfs7eavZ3LeVcs+4cfn7cfnDP583n58ztD15z1OVTuckKDfFYKeEJGSi026FMv8+Lz9+JzBn8/bj88Zevd5W9OQMcb4nBUCY4zxOb8VgmWuAzjix+ftx+cM/nzefnzO0IvP21d9BMYYYy7kt28ExhhjzmOFwBhjfM43hUBEFolImYhUiMhDrvP0BRF5UkQOi8g211n6ioiMFZHVIrJDRLaLyAOuM3lNRJJFZJ2IbI48539xnakviUiciGwUkT+6ztIXRKRKRLaKyCYR6ZUF3H3RRyAiccAu4CbCS2YWA0tVdcclf7GfE5FrgZPAU6o6xXWeviAiI4GRqrpBRFKB9cD7Yvn/tYgIMEhVT4pIAvAm8ICqrnUcrU+IyFeAImCwqt7uOo/XRKQKKFLVXruIzi/fCOYAFapaqaotwHLgTseZPKeqbxBe58E3VPWAqm6I3G4EdhJeGztmadjJyGZC5Cf2P+EBIjIGuA143HWW/swvhWA0UN1uu4YYf3MwICI5wAzgHbdJvBdpHtkEHAZeVtWYf84R/wH8AxByHaQPKfCSiKwXkft644R+KQTGZ0QkBXgO+JKqnnCdx2uqGlTV6YTXBp8jIjHfFCgitwOHVXW96yx97GpVnQksBj4faQLuEb8UglpgbLvtMZF9JgZF2smfA36tqr93nacvqWoDsBpY5DpLH1gA3BFpM18OvEdEfuU2kvdUtTby38PA/xBu+u4RvxSCYqBARHJFJJHw2sgrHGcyHoh0nD4B7FTV77vO0xdEJEtEhkRuDyA8KKLUbSrvqerXVHWMquYQfk2/pqp3O47lKREZFBkEgYgMAm4Gejwq0BeFQFXbgPuBVYQ7D59R1e1uU3lPRH4LrAEmikiNiHzSdaY+sAC4h/Cnw02Rn1tdh/LYSGC1iGwh/KHnZVX1xVBKHxoOvCkim4F1wJ9U9cWentQXw0eNMcZcnC++ERhjjLk4KwTGGONzVgiMMcbnrBAYY4zPWSEwxhifs0JgTA9EZjvdIyIZke30yHaO22TGdJ4VAmN6QFWrgZ8B34zs+iawTFWrnIUypovsOgJjeigypcV64Eng08B0VW11m8qYzot3HcCY/k5VW0Xk74EXgZutCJj+xpqGjOkdi4EDQMzP+mlijxUCY3pIRKYTnuhtHvDlyCppxvQbVgiM6YHIbKc/I7zuwT7gO8B33aYypmusEBjTM58G9qnqy5HtnwKTReQ6h5mM6RIbNWSMMT5n3wiMMcbnrBAYY4zPWSEwxhifs0JgjDE+Z4XAGGN8zgqBMcb4nBUCY4zxuf8PLtLg7xMd4owAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 5, 100, endpoint=False)\n",
    "y = multivariate_normal.pdf(x, mean = 2.5, cov = 0.25);  \n",
    "\n",
    "f1 = stats.norm(loc = 2.5, scale = np.sqrt((0.25))) \n",
    "\n",
    "fig1 = plt.figure()\n",
    "ax = fig1.add_subplot(111)\n",
    "ax.plot(x, y)\n",
    "ax.plot(x,f1.pdf(x))\n",
    "ax.set_xlabel(\"X\")\n",
    "ax.set_ylabel(\"PDF(X)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BvfqpgEcI5YJ"
   },
   "source": [
    "## Funcion de distribucion de  probabilidad  2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BE-NkfYHJkP7"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1638158536470,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "FEg-2Zwkvwdr"
   },
   "outputs": [],
   "source": [
    "x, y = np.mgrid[-4:4:.1, -4:4:.1]\n",
    "pos = np.dstack((x, y))\n",
    "mu = [0.0, 0.0]                 # Centrada en el origen \n",
    "cov = [[1.0, 0.0], [0.0, 1.0]]  # Matriz de covarianzas identidad\n",
    "rv = multivariate_normal(mu, cov)\n",
    "\n",
    "x1, y1 = np.mgrid[-4:4:.1, -4:4:.1]\n",
    "pos1 = np.dstack((x1, y1))\n",
    "mu1 = [0.0, 0.0]                 # Centrada en el origen \n",
    "cov1 = [[1.0, -0.5], [-0.5, 1.0]]  # Matriz de covarianzas identidad\n",
    "rv1 = multivariate_normal(mu1, cov1)\n",
    "\n",
    "x2, y2 = np.mgrid[-4:4:.1, -4:4:.1]\n",
    "pos2 = np.dstack((x2, y2))\n",
    "mu2 = [0.0, 0.0]                 # Centrada en el origen \n",
    "cov2 = [[1.0, 0.8], [0.8, 1]]  # Matriz de covarianzas identidad\n",
    "rv2 = multivariate_normal(mu2, cov2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 2367,
     "status": "ok",
     "timestamp": 1638158538829,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "zY6RNoZBwUMm",
    "outputId": "e2994a10-3d8e-4235-b203-ab19c0452d75"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$PDF(X_1, X_2)$')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up a figure twice as tall as it is wide\n",
    "fig = plt.figure(figsize = (12,10))\n",
    "\n",
    "ax = fig.add_subplot(2, 3, 1, projection='3d')\n",
    "#fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "surf = ax.plot_surface(x,y, rv.pdf(pos), cmap = cm.coolwarm,\n",
    "                       linewidth = 0, antialiased = False)\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_zlabel(\"$PDF(X_1,X_2)$\")\n",
    "\n",
    "ax = fig.add_subplot(2, 3, 2, projection='3d')\n",
    "#fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "surf = ax.plot_surface(x1,y1, rv1.pdf(pos1), cmap = cm.coolwarm,\n",
    "                       linewidth = 0, antialiased = False)\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_zlabel(\"$PDF(X_1,X_2)$\")\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(2, 3, 3, projection='3d')\n",
    "#fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "surf = ax.plot_surface(x2,y2, rv2.pdf(pos2), cmap = cm.coolwarm,\n",
    "                       linewidth = 0, antialiased = False)\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_zlabel(\"$PDF(X_1,X_2)$\")\n",
    "\n",
    "#=============================================================\n",
    "ax = fig.add_subplot(2, 3, 4)\n",
    "cs = ax.contourf(x, y, rv.pdf(pos))\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_xlim(-2.5,2.5)\n",
    "ax.set_ylim(-2.5,2.5)\n",
    "cbar = fig.colorbar(cs)\n",
    "#cbar.ax.set_ylabel('PDF(X_1, X_2)$')\n",
    "\n",
    "ax = fig.add_subplot(2, 3, 5)\n",
    "cs = ax.contourf(x1, y1, rv1.pdf(pos1))\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "#ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_xlim(-2.5,2.5)\n",
    "ax.set_ylim(-2.5,2.5)\n",
    "cbar = fig.colorbar(cs)\n",
    "#cbar.ax.set_ylabel('PDF(X_1, X_2)$')\n",
    "\n",
    "ax = fig.add_subplot(2, 3, 6)\n",
    "cs = ax.contourf(x2, y2, rv2.pdf(pos2))\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "#ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_xlim(-2.5,2.5)\n",
    "ax.set_ylim(-2.5,2.5)\n",
    "cbar = fig.colorbar(cs)\n",
    "cbar.ax.set_ylabel('$PDF(X_1, X_2)$')\n",
    "#=============================================================\n",
    "\n",
    "\n",
    "# Add the contour line levels to the colorbar\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tw_xW8twMw4e"
   },
   "source": [
    "\\begin{equation}\n",
    "\\mu =  \n",
    "\\begin{bmatrix}\n",
    "0 \\\\\n",
    "0 \\\\\n",
    "\\end{bmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\Sigma =  \n",
    "\\begin{bmatrix}\n",
    "1 & 0  \\\\\n",
    "0 & 1\\\\\n",
    "\\end{bmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "\\Sigma =  \n",
    "\\begin{bmatrix}\n",
    "1 & 0.5  \\\\\n",
    "0.5 & 1\\\\\n",
    "\\end{bmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "\\Sigma =  \n",
    "\\begin{bmatrix}\n",
    "1 & 0.8  \\\\\n",
    "0.8 & 1\\\\\n",
    "\\end{bmatrix}\n",
    "\\end{equation}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wStX93ghQvNN"
   },
   "source": [
    "Tomemos dos funcion de distribucion de probabilidad y realicemos un procedimiento analogo al descrito en 1D, en este caso, nos podemos preguntar\n",
    "por ¿cual es el plano que permite separar dos distrubuciones?, es importante resaltar que cuando se esta trabajando con datos, generalmente no se conoce la distribucion de probablidad de los datos, solo hay un pequeña muestra  para la cual se desea minimar el error respecto a la frontera Bayesiana. \n",
    "\n",
    "Supongamos dos distrubuciones de datos,respresentados de la siguiente forma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1638158538829,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "kG6C58YqoTP_"
   },
   "outputs": [],
   "source": [
    "# Caso mas visible\n",
    "x, y = np.mgrid[-4:4:.1, -4:4:.1]\n",
    "pos = np.dstack((x, y))\n",
    "mu = [1, 1]                 # Centrada en el origen \n",
    "cov = [[1.0, 0.0], [0.0, 1.0]]  # Matriz de covarianzas identidad\n",
    "rv = multivariate_normal(mu, cov)\n",
    "\n",
    "x1, y1 = np.mgrid[-4:4:.1, -4:4:.1]\n",
    "pos1 = np.dstack((x1, y1))\n",
    "mu1 = [-2, -2]                 # Centrada en el origen \n",
    "cov1 = [[1.0, -0.5], [-0.5, 1.0]]  # Matriz de covarianzas identidad\n",
    "rv1 = multivariate_normal(mu1, cov1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 868,
     "status": "ok",
     "timestamp": 1638158539695,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "s7ngjqCAQuFA",
    "outputId": "f294c599-856c-449a-a2bf-ad7cb675e7e2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,6))\n",
    "ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "surf1 = ax.plot_surface(x,y, rv.pdf(pos) + rv1.pdf(pos1), cmap = cm.coolwarm,\n",
    "                       linewidth = 0, antialiased = False)\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_zlabel(\"$PDF(X_1,X_2)$\")\n",
    "#=============================================================\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "cs1 = ax.contourf(x, y, rv.pdf(pos) + rv1.pdf(pos1)  )\n",
    "#cs2 = ax.contourf(x1, y1, rv1.pdf(pos1) )\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "#ax.set_xlim(-2.5,2.5)\n",
    "#ax.set_ylim(-2.5,2.5)\n",
    "cbar = fig.colorbar(cs1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 870,
     "status": "ok",
     "timestamp": 1638158540564,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "raOXfqVyTig3",
    "outputId": "3a219cdb-8356-4504-916b-501bccc2be78"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Caso \n",
    "#=======================================================\n",
    "x, y = np.mgrid[-4:4:.1, -4:4:.1]\n",
    "pos = np.dstack((x, y))\n",
    "mu = [1, 1]                 # Centrada en el origen \n",
    "cov = [[1.0, 0.0], [0.0, 1.0]]  # Matriz de covarianzas identidad\n",
    "rv = multivariate_normal(mu, cov)\n",
    "\n",
    "x1, y1 = np.mgrid[-4:4:.1, -4:4:.1]\n",
    "pos1 = np.dstack((x1, y1))\n",
    "mu1 = [-1, -1]                 # Centrada en el origen \n",
    "cov1 = [[1.0, -0.5], [-0.5, 1.0]]  # Matriz de covarianzas identidad\n",
    "rv1 = multivariate_normal(mu1, cov1)\n",
    "#=======================================================\n",
    "\n",
    "fig = plt.figure(figsize = (12,6))\n",
    "ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "surf1 = ax.plot_surface(x,y, rv.pdf(pos) + rv1.pdf(pos1), cmap = cm.coolwarm,\n",
    "                       linewidth = 0, antialiased = False)\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "ax.set_zlabel(\"$PDF(X_1,X_2)$\")\n",
    "#=============================================================\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "cs1 = ax.contourf(x, y, rv.pdf(pos) + rv1.pdf(pos1)  )\n",
    "#cs2 = ax.contourf(x1, y1, rv1.pdf(pos1) )\n",
    "ax.set_xlabel(\"$X_1$\")\n",
    "ax.set_ylabel(\"$X_2$\")\n",
    "#ax.set_xlim(-2.5,2.5)\n",
    "#ax.set_ylim(-2.5,2.5)\n",
    "cbar = fig.colorbar(cs1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qBN775PMUcn9"
   },
   "source": [
    "Supongamos, que podemos tener una muestra del  dataset(análogo a uando tiene un  problema NO ideal).\n",
    "\n",
    "El dataset anterior puede ser escrito como:\n",
    "\n",
    "|Y         | X_1       |X_2       |\n",
    "|----------|----------|-----------|\n",
    "|$Y^{1}$ | $X_1^{1}$|  $X_2^{1}$| \n",
    "|$Y^{2}$ | $X_1^{2}$|$X_2^{1}$| \n",
    "|.         | .        |.        |\n",
    "|.         | .        |.        |\n",
    "|.         | .        |.        |\n",
    "|$Y^{m}$ | $X_1^{m}$  |$X_2^{1}$| \n",
    "\n",
    "\n",
    "y, desde python puede ser generado como sigue:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 451,
     "status": "ok",
     "timestamp": 1638158541012,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "c_Jcae4AWr56",
    "outputId": "f9723748-b8d1-4432-9702-92f1d0e739ad"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c/3FbfUEegwsb4ZXW+bfppLGPFxvfhjbJzV2KAwmzprPaxrIs9h7X+RevyRVARFw+N19Ikq6RdFCLOACgZ9NQA3i9pMtaBwEAvVmyJSDbV0h62phDGyPikrk/s1HSQ1I+7N32BkkbRl+tGjxOAOjVkiWAiHhJ6bjt10k6XtKLo9CMEBFbJG0ZnXPA8mlaGETFOmbVWm9h/bW4cXnJ4usNu2YXv5nNqhMKH4lk3xRJ0iXjH96qk9NT1irvUyjdt59txrKttLFq4Vg6DK40uO8zhWOZqrV8SSqcl6rtOcCj0aQIbPtYSWdI+sOI+HGLGACgd61qAB+QtK+kHbZvtH1hozgAoFtNrgAi4rdavC4A4Bem4S4gAEADDIPrysBNZ4PHUFEIrG1Uy3bIWl84p/RaNTunleIrfl9JAbZUtK1RfQNAydAFXRq+9gzD4AAA85AAAKBTJAAA6FSrYXBooqIRrGgKNt0oNr4VZGv9NWvvknRKxfp7VoeQFtjcJXnfS3WIUm1jsa8jafi/X9byW+AKAAA6RQIAgE6RAACgU9QAoPr115rzJvlaFdJNcxZQuyF7YvXs4jfHyTbhWdikNmpfquccZwrqU8sAVwAA0CkSAAB0igQAAJ0iAQBApygCY4lMsrA4pMr4qgan5c1ju9aXtjN7ZsVrLWc1Bd1p/5xNB64AAKBTJAAA6BQJAAA6xYYwK860N8AMvSFIybRsZjPtr4WVjw1hAADzkAAAoFMkAADoFH0AK860rw9Pe3wlyzn2GoU6RO3GOcu2P2Rl4goAADpFAgCATpEAAKBTJAAA6BSNYMAgaNxqg6LynqERDAAwDwkAADpFAgCATtEIBmAZY63/0Wh6BWD7dNthe/+WcQBAj5olANsHS3qZpDtaxQAAPWt5BXC+pDMkLZ/7UAFgBWlSA7C9XtLdEXGT7RYhAANjLboN+gAejSVLALavkPS0MYc2SjpLo+WfPXmeDZI2jL5aNVB0AIAlSwAR8ZJxj9t+rqRDJT38r/+DJO20fWREfGfM82yRtGV07gEsFwHAQCa+BBQRX5X0lIe/tv0tSUdExPcnHQsA9IxGMADoVPNGsIg4pHUMAJYrir2PBlcAANApEgAAdIoEAACdIgEAQKdIAADQKRIAAHSKBAAAnSIBAECnSAAA0CkSAAB0igQAAJ0iAQBAp0gAANApEgAAdIoEAACdIgEAQKdIAADQKRIAAHSKBAAAnSIBAECnSAAA0CkSAAB0igQAAJ0iAQBAp0gAANApEgAAdIoEAACdIgEAQKdIAADQKRIAAHSKBAAAnSIBAECnmiUA239t+zbbt9j+u1ZxAECvHtPiRW2/SNJ6Sc+PiJ/YfkqLOACgZ62uAE6VdG5E/ESSIuLeRnEAQLdaJYBnS/p921+2/e+2X9goDgDo1pItAdm+QtLTxhzaOPe6T5S0TtILJX3C9jMiIsY8zwZJG0ZfrVqqcAGgO0uWACLiJdkx26dK+tTcL/xrbf9c0v6SvjfmebZI2jI674BHJAgAQJ0mRWBJn5b0IklX2n62pL0lfb9RLACae2vh2PkTi6I3rRLARZIusv01SQ9K+vNxyz8AgKXTJAFExIOSXtPitQEAI3QCA0CnvJxWXmx/T9K3K07dX8ujxkCcwyLOYRHnsCYZ529GxJN3f3BZJYBatq+LiCNax7EQ4hwWcQ6LOIc1DXGyBAQAnSIBAECnekkAW1oHsIeIc1jEOSziHFbzOLuoAQAAHqmXKwAAwG5IAADQqe4SgO3TbYft/VvHMo7tzbZvtn2j7cttH9A6pnFsv3duR7ebbW+3vV/rmMax/adzu8793PbU3Rpo+1jbX7d9u+13tI5nHNsX2b53bnTL1LJ9sO0rbd8693d+WuuYxrH9a7avtX3TXJzvahVLVwnA9sGSXibpjtaxFLw3Ip4XEYdJulTS2a0DSuyQ9JyIeJ6k/5J0ZuN4Ml+T9ApJX2wdyO5s7yXpg5KOk7RG0qttr2kb1VgflXRs6yD2wEOSTo+INRqNmn/TlL6fP5F0TEQ8X9Jhko61va5FIF0lAI3GCp4haWor3xHxo3lf/rqmNNaIuDwiHpr78hpJB7WMJxMRuyLi663jSBwp6faI+ObcfKyPa7RV6lSJiC9K+t/WcSwkIu6JiJ1z/3+/pF2SDmwb1SPFyP/NffnYuf+a/Jx3kwBsr5d0d0Tc1DqWhdh+j+07JZ2s6b0CmO/1ki5rHcQydKCkO+d9fZem8BfWcmT7EEmHS/py20jGs72X7Rsl3StpR0Q0ibPVOOglscAuZGdptPzTXCnOiLgkIjZK2mj7TElvlvTOiQY4Z6E45/7MRo0uvbdOMrb59iRO9MP24yV9UtJbdruinhoR8TNJh83Vzrbbfk5ETLzGsqISQLYLme3nSjpU0k22pdFyxU7bR0bEdyYYoqTybmm72Srps2qUABaK0/brJB0v6cUt93NYxPs5be6WdPC8rw+aewyVbD9Wo1/+WyPiU63jWUhE/ND2lRrVWCaeALpYAoqIr0bEUyLikIg4RKNL7bUtfvkvxPaz5n25XtJtrWIpsX2sRvWUmYj4cet4lqmvSHqW7UNt7y3pVZJmG8e0bHn0r7uPSNoVEe9vHU/G9pMfvmvO9j6SXqpGP+ddJIBl5lzbX7N9s0ZLVlN5K5ukD0jaV9KOuVtWL2wd0Di2T7R9l6TflfSvtj/fOqaHzRXR3yzp8xoVLD8REbe0jeqRbP+zpC9J+m3bd9n+y9YxJY6S9FpJx8x9Jm+0/fLWQY3xdI22w71Zo38E7IiIS1sEwigIAOgUVwAA0CkSAAB0igQAAJ0iAQBAp0gAANApEgCwCHMTJ//b9hPnvv6Nua8Psf052z+03eSWPmCxSADAIkTEnZIukHTu3EPnStoSEd+S9F6N7kMHlgUSALB450taZ/stko6W9D5Jioh/k3R/y8CAxVhRs4CASYiIn9r+G0mfk/SyiPhp65iAGlwBAHWOk3SPpOe0DgSoRQIAFsn2YRoN8Fon6a22n944JKAKCQBYhLmJkxdoNGv+Do0Kv+9rGxVQhwQALM4bJN0RETvmvv6QpNW2/9D2f0j6F0kvnpua+cfNogT2ANNAAaBTXAEAQKdIAADQKRIAAHSKBAAAnSIBAECnSAAA0CkSAAB06v8Bfbh42n2AKE0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Histograma de los datos sinteticos a estudiar\n",
    "N = 1000\n",
    "def fig_2histogram(N1,N2):\n",
    "  X_data1 = np.concatenate([rv.rvs(N1), rv1.rvs(N2)]) # Todos los datos en la misma distribución, \n",
    "  #pertenecientes a dos clases\n",
    "  plt.figure()\n",
    "  plt.hist2d(X_data1[:,0], X_data1[:,1], bins=(50, 50), cmap=plt.cm.jet)\n",
    "  #plt.figure()\n",
    "  #plt.hist2d(X_data2[:,0], X_data2[:,1], bins=(50, 50), cmap=plt.cm.jet)\n",
    "  plt.xlabel(\"X1\")\n",
    "  plt.ylabel(\"X2\")\n",
    "  plt.show()\n",
    "\n",
    "fig_2histogram(N1 = 100,  N2 = 50)\n",
    "fig_2histogram(N1 = 1000, N2 = 1000)\n",
    "fig_2histogram(N1 = 10000,N2 = 10000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1638158541012,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "iqFYpGBlemlB"
   },
   "outputs": [],
   "source": [
    "def sample(N1= 1000, N2 = 100):\n",
    "  X_t = np.concatenate([rv.rvs(N1), rv1.rvs(N2)]) # Todos los datos en la misma distribución, \n",
    "  y   = np.concatenate([np.zeros(N1), np.ones(N2) ]  )\n",
    "  return X_t, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 568,
     "status": "ok",
     "timestamp": 1638158541578,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "qV6c3h4gZUJC",
    "outputId": "123b651b-4edb-46b9-a038-1a1c1ec19185"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'X2')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "X_t, y = sample()\n",
    "plt.plot(X_t[y==0][:,0],X_t[y==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_t[y==1][:,0],X_t[y==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.xlabel(\"X1\")\n",
    "plt.ylabel(\"X2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t_dER0N7fJ2S"
   },
   "source": [
    "# Tarea\n",
    "Dadas que las  distribuciones de probabilidad son conocidas, ¿Cómo se puede aproximar la frontera Bayesiana en este caso?. Implemente un algoritmo que permita determinarla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yxr4hPs4tIXD"
   },
   "source": [
    "## Boundary \n",
    "Librerias para dibujar la frontera:\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1638158541579,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "Xv8UyykztM32"
   },
   "outputs": [],
   "source": [
    "def make_meshgrid(x, y, h=0.02):\n",
    "    \"\"\"Create a mesh of points to plot in\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: data to base x-axis meshgrid on\n",
    "    y: data to base y-axis meshgrid on\n",
    "    h: stepsize for meshgrid, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    xx, yy : ndarray\n",
    "    \"\"\"\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    return xx, yy\n",
    "\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out\n",
    "\n",
    "\n",
    "def plot_contoursExact(ax, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZIDLDLLN0NM8"
   },
   "source": [
    "# Generation of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1638158541579,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "vZf2tAdUmPGI"
   },
   "outputs": [],
   "source": [
    "#Un poco mas ordena, la generacion de data\n",
    "def data( mu=[1,1], mu1=[-2,2], cov=[[1.0, 0.0], [0.0, 1.0]] , cov1= [[1.0, -0.5], [-0.5, 1.0]]   ):# Caso mas visible  \n",
    "  rv = multivariate_normal(mu, cov)\n",
    "  rv1 = multivariate_normal(mu1, cov1)\n",
    "  return rv, rv1\n",
    "\n",
    "def sample(N1= 1000, N2 = 100):\n",
    "  X_t = np.concatenate([rv.rvs(N1), rv1.rvs(N2)]) # Todos los datos en la misma distribución, \n",
    "  y   = np.concatenate([np.zeros(N1), np.ones(N2) ]  )\n",
    "  return X_t, y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1638158541579,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "mjk247bmnCMM",
    "outputId": "f4c790e8-b131-4b85-89b8-57940c563aa2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'X2')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "rv, rv1 = data(mu=[-1.2, 0.2], mu1=[-1.5, 1.5],\n",
    "               cov=[[1.0, 0.0], [0.0, 1.0]] , \n",
    "               cov1= [[1.0, -0.5], [-0.5, 1.0]] )\n",
    "\n",
    "X_t, y = sample(N1 = 100, N2 = 100)\n",
    "X_p, y_ = sample(N1 = 100, N2 = 100)\n",
    "\n",
    "plt.plot(X_t[y==0][:,0],X_t[y==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_t[y==1][:,0],X_t[y==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.xlabel(\"X1\")\n",
    "plt.ylabel(\"X2\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1638158541580,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "IZnTSrEz1ZVC"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_t, y, \n",
    "                                                    test_size=0.33, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rjFFNUFEefmK"
   },
   "source": [
    "# SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1638158541580,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "-PuvdR1Mg9CA"
   },
   "outputs": [],
   "source": [
    "X_t, y = X_train, y_train # Datos de entrenamiento \n",
    "X_p, y_ = X_test, y_test  # Datos para la prediccion \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "executionInfo": {
     "elapsed": 425,
     "status": "ok",
     "timestamp": 1638158541999,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "Ajfc9Jk0iKTJ",
    "outputId": "cc7b0552-a8be-4841-883b-137e0e355436"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.753731343283582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Valores Esperados')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC( gamma = 0.2) \n",
    "clf.fit(X_t,y)\n",
    "print(clf.score(X_t, y))\n",
    "y_p = clf.predict(X_p)\n",
    "\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X_p[y_p==0][:,0],X_p[y_p==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_p==1][:,0],X_p[y_p==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.title(\"Valores predichos\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_p[y_==0][:,0],X_p[y_==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_==1][:,0],X_p[y_==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.title(\"Valores Esperados\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 520,
     "status": "ok",
     "timestamp": 1638158542517,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "3aSJ6bFcopHv",
    "outputId": "c1c8cfa0-e7d9-4c6a-e0ca-ef95d60e100b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f8e54946210>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4ZCiUXSu5lrpoInXkvvFdF9obr/3m9hl/ycwu/PcK+HDx6yIa+ZhjT+3/Tpr+HYxgH/iaY8+x6eCjNPadordhJftv+BAn2m4MuqyiU5RBPpcDKf24B2YF/DVLEz+ze3emD3itSA3MXHPVi70c3WLn2LMN++Ty+dQFPK8fH4/8Ksw1x55j296vMVjVSE9jK1XD/Wzb+zWeAIV5nvnVfvhPwLuBpWZ2Eviic+4BP44dBgdeOM2B5I2UgL99OyQHV7MDHmaO4pNBX9V+koajxykbGmS8uoa+tVcxvHpVLssveF4vorGYOfZ97et4YN9NXDV6hGsmT/PH3hYe6L5pxvMnzQ5uSPR9/89fNifCu0AW8Gw6+CiDVY0MVy8BmP5z08FHFeR55lfXykf8OE7UzFiQMatzJrnlQFLHfd+l4uXDLD1ygnh5GWOlpcQG+1n6yxc507eGkfXX5alq/wXR+jebl4toLGaO/clD69gw9BK1ZSOMllazbPIsVUMv8eShdbyj5lmYnJgR3K2fuosn3bYL3/L2AGQ2AnfHOuk/2MNQn1Hd4Ki/oQlrW5HVa8yFxr5T9MxqXBiuqqeptzOgioqXplZyZHrLgaRNu9nxyN0MLatnuHoJ65f3JO7vP8+KiiqOpzvhGovNuB3GqZugWv/8tJg59vi5captmHHKYHKSMUqptmHOdI/iJiYuDu59ANmPvN2xTjr2nqOyCmoaJxkbNjr2nqMVQhPmvQ0rqRrunx6JA1QN99PbEL7f00KnIM+j1BHMK11NiTtdA02dnTz0bx+e8djkfjOpZq9mTUoN/HyHfVCtf4vR0V7F/qOXcWaogWXVfWxa+watq4cvelzaOfbBMj529aPT02VXTJ7gbMlyllaOAjBeVUd7XxUrJk8nTlZ6DO7Z+g/2UFkFFdUANvWno/9gDw0hCfL9N3yIbXu/BiRG4lXD/dQM9/Lc5k8EXFnxUZDnUSYjmOn9ZlJtuuhh0/vQpJor8MH/BVFh29Y1qaO9ij0HrqO+YohLas5xfixxezuHLwrzt1fu5W/XnOHx9m2cOreUS6rP8qkdv+WmdeA2JFZUXnfo6zzaeQ2D4+PUlQwx0F/NyHgZb1/xOr/mPb7XP9Rn1DROAhdOtJRXOQZ7S2jw/dmyc6LtRp4gMSfe1NtJb0MLz23+hObHA6Agz6NcjGDSXgkmTeDD3KtdMzH7g8CP1j+/xbs62P/yO6mzHmoZgjGoZRhnI+x/eRn/svKXMx7f+qm7+M2+67gGuAaABvZxJfsGmF5ROXjTJu7c89/YM/peTo61sirWwfban3Pypltz8hqqGxxjw8mReMLYcGKuPExOtN04Z3CrNTF/FOR5FPQIxus1HNN9ENza8jjfOXI7bixGfel5+uO19I+V8dF1j3j+0MjWqluu59WDb6emeZIzKa1DziVGtN2b/jVwIWjOffQ/sWOBoDnRdiNshzsPPkpj32l6G1pyGkz1NzTRsfcc4CivSoT6yLDRvHlJ2seHLTTVmphf5mavTsiDllUb3Ef/7qd5f17Jjd8fq+C1gzUM9JVS1xDnmhsGuaJtdFH/ba4CqO+R1xgfYcaIdnQIyiqhYcc1M4Im9dvRE1s/E5qgWWzXShhfy45H7qZiZGDmNOLQOUYr63hkx7cDqakQfP1zlx10zm2cfb9G5DKnxYbsFW2jiw7u2cfftvdrMDFB3fluLjl9nKuOP8ezW/6GX9xyl6faFxrRztcDfZw1oWj7s7YVNLStWHBOPIz93GpNzC8FuaSVj6/Gmw4+ChMTNJ97k3hpOcOVdZSPDXHT89/j1Kr1np7H2lbQSqL7Y7C3hOoGR/PmJdOBPFfQDHaM03EufdvfVZwI1fRF0uzXUtf3R5Z2/4Hq0X52PHJ3TutM92EP0HDuFJecPs5IVR1nl1zGQMMlak3MIQW5pJWPUV5j3ynqzncTLy1nIpbYu3usopqqkQFfnme+Ee1cHURPjN1MZZO7qO3PPXuUbRPfDOWcb+prqev7IytPv47DGKqsp2JkIGd1pvuwv3XPF8CMgdpllI8NUTY6xMrTr9M9OgCxmFoTc6Rk4YdIMWrsO8VwVf2M+4ar6mns869XurdhJZWj55kouXA1ndjEOCMVtb4+Tzr7b/gQNcO9VA2dAzdB1dA5aoZ7+X1FG+VVM88blVc5JrsGLnywWYzh6iUMVjUmvlUELPW1LO3+Aw7DgO7my3Na54wP+6n3pGp0gKqRfs4uv5LOldcwXlFNiZuk7vzZUJ1/KDQKckkrOcpL5fdX4/03fIjJkhLKx4YAR2xijNL4OAM1S3P+FfxE2408sfUzjFbW0dTbyWhlHU9s/QwlK+oYG7YZjx0bNlrpzPkHW7ZSX0v1aD/j5VV0rlhHf/0lQO7qTPdhH5uIE5tIbNnbX38Jv798E0fa3k3fklUK8RzS1IqklY9VeyfabuTZLX/DTc9/j6qRAUYqaule0gKxkum51lxK1wNdT2fak6TXLH8j1MvRk68lbbdIjupMNz01Ebs4UsL0PhUqjcglrblGrH6Pqn5xy108/OFvc+ytf0JfYys9Sy8L9Cu4ta2gdesSyiphsLeEskpo3bqE9pvek3YqJh8fOJmYa8ooF3Wme67hijqGK+tD/z4VGvWRiyzShQ6N3C8I8iKfdaZ7LiAS71MUqY9cxIOwrZycz3zL5vP1XGF9bwqVplZEFpBss6sYGaCnsXW6pW/NseeCLk0EUJCLLChdm11YWg9FwL9LvW0D/jMQA/7ROfcVP44rMp98TXdoubmEnecRuZnFgO8A7wGuBj5iZld7Pa7IfPI53ZGPnnoRL/wYkW8CTjjnfgdgZj8AbgVe9+HYkqUwnpzzs6Z8bCGQrLel8xh157voXnIpZ5ddoSvhSOj4MUfeCryZcvvk1H0SkDCenPO7plxvIZBa76mVa+lesprmc2+ysuP1nPXUi2Qrb+2HZrYT2AlQ16icz6Uwbms6u6ay+BiN5zr4V4/+HcfXbMl4dJ7rC//Orvfs8isZrG3SftoSSn4EeQdwacrtVVP3zeCc2w3shsSCIB+eV+YQxpNzqTXV9/+RFZ1HiMdKcY6sdujL9RYCuXwPF3vBiFwK49SbZM+PIH8JuMrMriAR4B8GdvhwXMlSrkerXmtq7nmDeGlix8PxiuqsvjHk+rJ5uXoP3bHEXi7p9jvPNMyzDWNdhq3weA5y51zczO4GniTRfvigc+41z5VJ1vKx4VWmIZJaU/noEPFYKaUT45xauma6zkxHu7lcwZir97D/YA+VVVy033n/wR4aMghyL2Ecxqk38caXBUHOuZ87597qnLvSOfdlP44p2cv1hlfZnLhMrckZGHCq5WoGGhJbrQb9jWG2XL2HQ32Wdr/zoT6b479Iz8sipXzsNS/5pb1WClQuR6vZjuiSNSU/COJl5Ykd8kLazpeL97C6IbE1bupFoceGE3PlmfAyhx/GqTfxRkv0JWNeR3T52iI3jOpvaGJk2BgdAucco0MwMmzU39CU0XG8LFLK51a3kh8akUvG/BjR5XOHvjBZ6KLQi+VlDj/XJ4ol/xTkkrF8nEwtZPNdFHqxvIZxsX6QFioFuWRMI7pwUBhLkoJcsqIQEQkPBbnIHPK1+jEMKz0l2hTkImnka/Wjnys9/aZl/NGh9kORNPJ1VaDESk9HRTWYJfrLK6sSKz2DFMYdNGVuCnKRNPK1+tGvlZ5+0+XtokVBLpJGvq4KlFzpmSqblZ5+0zL+aFGQi6SRr9WPfq309JsubxctCnKRNPK1jYC1raB16xLKKmGwt4SySmjdmvlKT79pGX+0qGtFZA756pX3stIzV50lWvQVLQpykYjKdYukFn1Fh6ZWRCJKnSWSpCAXiSh1lkiSglwkotRZIkmegtzM/sLMXjOzSTPb6FdRIrIwdZZIkteTna8CtwHf86EWEU+KbW8QdZZIkqcgd84dgcQeESJBytcmV2GjzhKBPM6Rm9lOMztgZgeGBoPdEEgKjzo4pJgtOCI3s71AurMnu5xzP1nsEznndgO7AVpWbQh2IwkpOF6uKh+UYpsKktxZMMidc1vzUYjkV6GFiB8XhM6nYp0KktxQ+2ERKsS9pqPWweHnVNCaY8+x45G7ueu/3saOR+6O9P9HyY6nk51m9gHgvwDLgJ+Z2WHn3J/5UpnkzIwQgek/Nx18NLKjwcV2cITlm4hfU0Ea2Qt471p5HHjcp1okT6I4n7wYC3VwhCn0/JoKKsQPZcmcplaKULGuCAxTZ4tfU0Fapi+gIC9KUZtP9kuYQs+v/c6L9UNZZtI2tkWoWFcEhq2zxY/FPPtv+BDb9n4NSHwoVQ33UzPcy3ObP+FHiRIRCvIiVYwrAgsx9Ir1Q1lmUpBL0SjU0CvGD2WZSUEuRUWhJ4VIJztFRCJOQS4iEnGaWpEFhWU1pIikpxG5zKsQ92URKTQKcplXmFZDikh6CnKZV5hWQ4pIegpymZeWgIuEn4Jc5lWs+7KIRIm6VmRehboaUqSQKMhlQVoNKRJumloREYk4T0FuZl81s6Nm9rKZPW5mjX4VJiIii+N1RP40cK1zbgPwG+Dz3ksSEZFMeApy59xTzrn41M0XgVXeSxIRkUz4OUf+ceB/z/VDM9tpZgfM7MDQYI+PTysiUtwW7Foxs71AutUfu5xzP5l6zC4gDjw813Gcc7uB3QAtqza4rKoVEZGLLBjkzrmt8/3czO4A3gfc4pxTQIuI5JmnPnIz2wZ8FvgT59yQPyWJiEgmvM6RfxuoA542s8Nmdr8PNYmISAY8jcidc2v8KkRERLKjlZ0iIhGnIBcRiTgFuYhIxCnIRUQiTkEuIhJxCnIRkYhTkIuIRJyCXEQk4hTkIiIRpyAXEYk4BbmISMQpyEVEIk5BLiIScQpyEZGIU5CLiEScglxEJOIU5CIiEecpyM3sH8zs5anLvD1lZiv9KkxERBbH64j8q865Dc6564CfAl/woSYREcmApyB3zvWn3KwBnLdyREQkU54uvgxgZl8G/groA26a53E7gZ0AdY2tXp9WRESmLDgiN7O9ZvZqmn9uBXDO7XLOXQo8DNw913Gcc7udcxudcxura5r8ewUiIkVuwRG5c27rIo/1MPBz4IueKhIRkYx47Vq5KuXmrcBRb+WIiEimvM6Rf8XM2oBJ4A3gTu8liYhIJjwFuXPug34VIiIi2dHKThGRiFOQi4hEnIJcRCTiFOQiIhGnIBcRiTgFuYhIxCnIRUQiTkEuIhJxCnIRkYgz5/K/hbiZnSGxpN9PS4GzPh8zCHod4aLXES7F/jouc84tm31nIEGeC2Z2wDm3Meg6vNLrCBe9jnDR60hPUysiIhGnIBcRibhCCvLdQRfgE72OcNHrCBe9jjQKZo5cRKRYFdKIXESkKCnIRUQiriCD3Mw+bWbOzJYGXUs2zOwfzOxlMztsZk+Z2cqga8qUmX3VzI5OvY7Hzawx6JqyYWZ/YWavmdmkmUWu7c3MtpnZMTM7YWafC7qebJnZg2bWZWavBl1LtszsUjN71sxen/qd+qRfxy64IDezS4F/AbQHXYsHX3XObXDOXQf8FPhC0AVl4WngWufcBuA3wOcDridbrwK3Ab8IupBMmVkM+A7wHuBq4CNmdnWwVWXtIWBb0EV4FAc+7Zy7GngH8Ld+/f8ouCAH7gM+C0T2LK5zrj/lZg0RfC3Ouaecc/Gpmy8Cq4KsJ1vOuSPOuWNB15GlTcAJ59zvnHNjwA+AWwOuKSvOuV8APUHX4YVzrtM5d2jq3weAI0CrH8f2dPHlsDGzW4EO59yvzSzocjwxsy8DfwX0ATcFXI5XHwceDbqIItQKvJly+yTw9oBqkRRmdjnwNuBXfhwvckFuZnuBljQ/2gX8exLTKqE33+twzv3EObcL2GVmnwfuBr6Y1wIXYaHXMPWYXSS+Uj6cz9oysZjXIeIXM6sFfgTcM+vbd9YiF+TOua3p7jez9cAVQHI0vgo4ZGabnHOn81jiosz1OtJ4GPg5IQzyhV6Dmd0BvA+4xYV4wUIG/y+ipgO4NOX2qqn7JCBmVkYixB92zj3m13EjF+Rzcc69AixP3jazPwAbnXOR2ynNzK5yzh2funkrcDTIerJhZttInKv4E+fcUND1FKmXgKvM7AYQ7vIAAACsSURBVAoSAf5hYEewJRUvS4wwHwCOOOe+4eexC/FkZyH4ipm9amYvk5gq8q1NKY++DdQBT0+1Ud4fdEHZMLMPmNlJ4J3Az8zsyaBrWqypk813A0+SOLH2Q+fca8FWlR0z+yfgl0CbmZ00s78OuqYsbAY+Btw89XfisJm9148Da4m+iEjEaUQuIhJxCnIRkYhTkIuIRJyCXEQk4hTkIiIRpyAXEYk4BbmISMT9f+dv9FW0i4cLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# title for the plots\n",
    "title = ('Decision surface of linear SVC ')\n",
    "# Set-up grid for plotting.\n",
    "X0, X1 = X_t[:, 0], X_t[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "plt.plot(X_p[y_p==0][:,0],X_p[y_p==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_p==1][:,0],X_p[y_p==1][:,1],\"bo\", alpha=0.4)\n",
    "\n",
    "plt.plot(X_p[y_==0][:,0],X_p[y_==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_==1][:,0],X_p[y_==1][:,1],\"bo\", alpha=0.4)\n",
    "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A_Dceyg0h___"
   },
   "source": [
    "Empleando pipelines\n",
    "\n",
    "```\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "X_t, y = sample()\n",
    "clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))\n",
    "clf.fit(X_t, y)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yTQmvqx_e7Qs"
   },
   "source": [
    "## LogisticRegresion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1638158543046,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "KWH3uQJVfAyM",
    "outputId": "18e53764-29e5-411e-dc7f-7d47e42f6099"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7388059701492538\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Valores Esperados')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression( ) \n",
    "clf.fit(X_t,y)\n",
    "print(clf.score(X_t,y))\n",
    "y_p = clf.predict(X_p)\n",
    "\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X_p[y_p==0][:,0],X_p[y_p==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_p==1][:,0],X_p[y_p==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.title(\"Valores predichos\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_p[y_==0][:,0],X_p[y_==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_==1][:,0],X_p[y_==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.title(\"Valores Esperados\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 209,
     "status": "ok",
     "timestamp": 1638158543251,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "I6gcu-y8pSID",
    "outputId": "17d771ce-706e-45f4-a612-51dd758945f5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f8e54769610>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "# title for the plots\n",
    "title = ('Decision surface of linear SVC ')\n",
    "# Set-up grid for plotting.\n",
    "X0, X1 = X_t[:, 0], X_t[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "plt.plot(X_p[y_p==0][:,0],X_p[y_p==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_p==1][:,0],X_p[y_p==1][:,1],\"bo\", alpha=0.4)\n",
    "\n",
    "plt.plot(X_p[y_==0][:,0],X_p[y_==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_==1][:,0],X_p[y_==1][:,1],\"bo\", alpha=0.4)\n",
    "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UPL78E5kfEnR"
   },
   "source": [
    "## RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "executionInfo": {
     "elapsed": 751,
     "status": "ok",
     "timestamp": 1638158543999,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "oywUFQbXe6Pw",
    "outputId": "97886b88-b51f-4361-92ad-556be32c79fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Valores Esperados')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier() \n",
    "clf.fit(X_t,y)\n",
    "print(clf.score(X_t,y))\n",
    "y_p = clf.predict(X_p)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X_p[y_p==0][:,0],X_p[y_p==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_p==1][:,0],X_p[y_p==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.title(\"Valores predichos\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_p[y_==0][:,0],X_p[y_==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_==1][:,0],X_p[y_==1][:,1],\"bo\", alpha=0.4)\n",
    "plt.title(\"Valores Esperados\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 906,
     "status": "ok",
     "timestamp": 1638158544904,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "dsffAu37sjYH",
    "outputId": "45b015d7-cc15-4fc6-822a-3aba4c6f950c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f8e54c43f10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "# title for the plots\n",
    "title = ('Decision surface of linear SVC ')\n",
    "# Set-up grid for plotting.\n",
    "X0, X1 = X_t[:, 0], X_t[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "plt.plot(X_p[y_p==0][:,0],X_p[y_p==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_p==1][:,0],X_p[y_p==1][:,1],\"bo\", alpha=0.4)\n",
    "\n",
    "plt.plot(X_p[y_==0][:,0],X_p[y_==0][:,1],\"ro\", alpha=0.4)\n",
    "plt.plot(X_p[y_==1][:,0],X_p[y_==1][:,1],\"bo\", alpha=0.4)\n",
    "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ElVLuS6Auhi0"
   },
   "source": [
    "# Apendice\n",
    "\n",
    " Empleando un flujo de trabajo existente de trabajo en  [sklearn](https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html) para los datos de estudio, tenemos que:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "executionInfo": {
     "elapsed": 3480,
     "status": "ok",
     "timestamp": 1638158576343,
     "user": {
      "displayName": "HERNAN DAVID SALINAS JIMENEZ",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgEk3gwBIYwvdknZYL8kugGy8_RY0b8a2OdwkKqPWJWp-YbaIa2A4t1CORdHmNw-_lfxh3bMyOGtb5cdHBQAC2PD5qTOBqBoHybUtihtHXHa86Je3ks8kiM_DQC7oWOvRKkJX6GPSrkYQMSye0O5WjtWYrQ8MquV8Poqy4b9hPTdEYeul1_3Urfi76qHz20MU4BIzzUBw2ci4vN1PvXoPsyDl7E5zzN0tP3AEtRYVVJDwEHkHaObz_nf5Iw3slkD3Pj6uT-ZXIwmqlgsBVKsHa4i5jEGL4GIY2Ei5ZXAx0xNeUINMMKP9zHTfR7gNpNFw3C72rxwwKVTvlimdXECR0aBr_4k2YpxliB_7ROPkgV_6IHDqxc2m8Jo3fu3SpeFFQlDh16fZqwD1Db1hH78BfmfHIGUzTgeRqsFdqMFsPfVwzlkf2Fh3gp7b81iV7Dy0vAeqUA0tBc4x7kiZETftwvC6gNKlHL8BHHrsgnyxT5JMQ04oYMgSCyEmhpEjhoulRYR4kxPQZeCdaWXIQkzF04Ys8B84AL_DxRYgjPt1RS3_15zIffLktZZLbVxddGpePnLAmsvgzwCMGWHH5U3eR5NgxxxOPmhEDEleYJ4k9xi0t0uJNYei2hf5u43XGkmpZ9gk7ulQPVQ3H-lwsMEKTzgCgesQA1O6RUI2i4YEHY-r71GpE-25Cc67yQbKBs2-z1BVTNntmScoMHq03IRuI8yuA7w5d4ZTZ6e-BlhGRAVfryrRJuwaxJURxcEqgSBUfAlWE=s64",
      "userId": "00408651407692255291"
     },
     "user_tz": 300
    },
    "id": "4dzlzgMnthgn",
    "outputId": "b6aba955-6181-4285-fe0c-142567a7bb48"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "\n",
    "\n",
    "\n",
    "# Take the first two features. We could avoid this by using a two-dim dataset\n",
    "rv, rv1 = data(mu=[1,1], mu1=[-1.5,1.5],\n",
    "               cov=[[1.0, 0.0], [0.0, 1.0]] , \n",
    "               cov1= [[1.0, -0.5], [-0.5, 1.0]] )\n",
    "\n",
    "X_t, y = sample()\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_t, y, \n",
    "                                                    test_size=0.33, \n",
    "                                                    random_state=42)\n",
    "\n",
    "\n",
    "X = X_train\n",
    "y = y_train\n",
    "# we create an instance of SVM and fit out data. We do not scale our\n",
    "# data since we want to plot the support vectors\n",
    "C = 1.0  # SVM regularization parameter\n",
    "models = (\n",
    "    svm.SVC(kernel=\"linear\", C=C),\n",
    "    svm.LinearSVC(C=C, max_iter=10000),\n",
    "    svm.SVC(kernel=\"rbf\", gamma=0.7, C=C),\n",
    "    svm.SVC(kernel=\"poly\", degree=3, gamma=\"auto\", C=C),\n",
    ")\n",
    "models = (clf.fit(X, y) for clf in models)\n",
    "\n",
    "# title for the plots\n",
    "titles = (\n",
    "    \"SVC with linear kernel\",\n",
    "    \"LinearSVC (linear kernel)\",\n",
    "    \"SVC with RBF kernel\",\n",
    "    \"SVC with polynomial (degree 3) kernel\",\n",
    ")\n",
    "\n",
    "# Set-up 2x2 grid for plotting.\n",
    "fig, sub = plt.subplots(2, 2)\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "X0, X1 = X[:, 0], X[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "for clf, title, ax in zip(models, titles, sub.flatten()):\n",
    "    plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors=\"k\")\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xlabel(\"Sepal length\")\n",
    "    ax.set_ylabel(\"Sepal width\")\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMOHCzKOOYHWCYSOvapE6ld",
   "collapsed_sections": [],
   "name": "Sesion_02_intuicion_estadistica.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}