Clusters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                                                   Algoritmos de Agrupamiento\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from math import dist\n",
    "import mpl_toolkits.mplot3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = pd.read_excel(\"Datos IRIS.xlsx\", index_col=None, header=None)\n",
    "iris.plot(2, 3, kind=\"scatter\", xlabel=\"X2\", ylabel=\"X3\")\n",
    "plt.title('Descriptores seleccionados')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## División de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "# División de dataset\n",
    "\n",
    "df_T = iris.iloc[0:35] # Datos de Entrenamiento\n",
    "df_T = df_T.append(iris.iloc[50:85])\n",
    "df_T = df_T.append(iris.iloc[100:135])\n",
    "\n",
    "#print(\"Entrenamiento \\n\", df_T)\n",
    "\n",
    "\n",
    "df_P = iris.iloc[35:50]          #Datos de prueba\n",
    "df_P = df_P.append(iris.iloc[85:100])\n",
    "df_P = df_P.append(iris.iloc[135:150])\n",
    "\n",
    "#print(\"Prueba \\n\", df_P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalización de los conjuntos de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mínimos y máximos\n",
    "def minimo(dfin):\n",
    "    min_0 = dfin[0].min()  #Columna 0\n",
    "    min_1 = dfin[1].min()  #Columna 1\n",
    "    min_2 = dfin[2].min()  #Columna 2\n",
    "    min_3 = dfin[3].min()  #Columna 3\n",
    "\n",
    "    return [min_0, min_1, min_2, min_3]\n",
    "\n",
    "def maximo(dfin):\n",
    "    max_0 = dfin[0].max()  #Columna 0\n",
    "    max_1 = dfin[1].max()  #Columna 1\n",
    "    max_2 = dfin[2].max()  #Columna 2\n",
    "    max_3 = dfin[3].max()  #Columna 3\n",
    "\n",
    "    return [max_0, max_1, max_2, max_3]\n",
    "\n",
    "# Normalización por columna\n",
    "def minmax_norm(dfin , min, max):\n",
    "    return (dfin - min) / (max - min)\n",
    "\n",
    "# Normalización del dataframe\n",
    "def norm_df(dfin, min = [0,0,0,0], max = [0,0,0,0]):\n",
    "\n",
    "    # Normalización de Dataframe de entrenamiento\n",
    "    df_norm_0 = minmax_norm(dfin[0], min[0], max[0])  #Columna 0\n",
    "    df_norm_1 = minmax_norm(dfin[1], min[1], max[1])  #Columna 1\n",
    "    df_norm_2 = minmax_norm(dfin[2], min[2], max[2])  #Columna 2\n",
    "    df_norm_3 = minmax_norm(dfin[3], min[3], max[3])  #Columna 3\n",
    "\n",
    "    return pd.DataFrame([df_norm_0,df_norm_1, df_norm_2, df_norm_3, dfin[5]] ).transpose()  # Dataframe con las columnas normalizadas\n",
    "\n",
    "#Mínimos y máximos para cada Dataframe\n",
    "global_min = minimo(df_T)    # Mínimo global de los datos\n",
    "global_max = maximo(df_T)    # Máximo global de los datos\n",
    "\n",
    "minimo_P = minimo(df_P)\n",
    "maximo_P = maximo(df_P)\n",
    "\n",
    "\n",
    "#Actualización de mínimo y máximo global en caso de que sea necesario\n",
    "for i in range(len(minimo_P)):\n",
    "    if minimo_P[i] < global_min[i]:\n",
    "        global_min[i] = minimo_P[i]\n",
    "    if maximo_P[i] > global_max[i]:\n",
    "        global_max[i] = maximo_P[i]\n",
    "\n",
    "#print(global_min, global_max)\n",
    "\n",
    "#Normalización Dataframe de entrenamiento\n",
    "# Normalización de Dataframe de validación\n",
    "df_norm_0 = minmax_norm(df_T[0], global_min[0], global_max[0])  #Columna 0\n",
    "df_norm_1 = minmax_norm(df_T[1], global_min[1], global_max[1])  #Columna 1\n",
    "df_norm_2 = minmax_norm(df_T[2], global_min[2], global_max[2])  #Columna 2\n",
    "df_norm_3 = minmax_norm(df_T[3], global_min[3], global_max[3])  #Columna 3\n",
    "\n",
    "df_TN = pd.DataFrame([df_norm_0,df_norm_1, df_norm_2, df_norm_3]).transpose()  # Dataframe con las columnas normalizadas\n",
    "\n",
    "#print(\"Entrenamiento Normalizado \\n\", df_TN)\n",
    "\n",
    "#Normalización Dataframe de prueba\n",
    "df_PN = norm_df(df_P, global_min, global_max)\n",
    "#print(\"Prueba Normalizado \\n\",df_PN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcular Error\n",
    "etiquetas = df_T.iloc[:, -1].values\n",
    "etiquetas_prueba = df_P.iloc[:, -1].values\n",
    "\n",
    "\n",
    "def error(output, labels):\n",
    "    count_error = 0      #contador de errores\n",
    "    total_data = len(output)  #Total de datos\n",
    "\n",
    "    for i in range(len(output)):\n",
    "        if output[i] != labels[i]:\n",
    "            count_error += 1\n",
    "    \n",
    "    percentage = count_error * 100/total_data           # Porcentaje de error\n",
    "    percentage = round(percentage, 2)\n",
    "\n",
    "    return percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Para 3 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pipe-\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:984: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
      "  self._check_params(X)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 2 2\n",
      " 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2]\n",
      "Error de entrenamiento 5.71\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [2, 3]].values\n",
    "\n",
    "centros = np.array([[0.1, 0.1], [0.3, 0.5], [0.6, 0.6]]) #Matriz que contiene las coordenadas de los centros\n",
    "\n",
    "kmeans = KMeans(n_clusters = 3, init = centros, max_iter = 300, random_state = 0)     #Entrenamiento para hallar los centros\n",
    "y_kmeans = kmeans.fit_predict(x)      #Obtener la clasificación de los datos de entrenamiento\n",
    "print(y_kmeans)                       # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centroides de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"3 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in y_kmeans:\n",
    "    if i == 0:\n",
    "        salida.append(1)\n",
    "    elif i == 1:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(\"Error de entrenamiento\", error(salida, etiquetas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_kmeans = kmeans.predict(prueba)    #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_kmeans)                      #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(prueba[r_kmeans == 0, 0], prueba[r_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_kmeans == 1, 0], prueba[r_kmeans == 1, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_kmeans == 2, 0], prueba[r_kmeans == 2, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centroides de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 3 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_kmeans:\n",
    "    if i == 0:\n",
    "        salida.append(1)\n",
    "    elif i == 1:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(error(salida, etiquetas_prueba))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Para 4 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2\n",
      " 2 1 2 2 2 1 2 1 1 2 1 2 1 2 2 1 2 1 2 1 2 2 1 2 2 2 2 1 1 1 1 2 2 3 3 3 3\n",
      " 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 2 2 3 2 3 3 3 2 2]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pipe-\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:984: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
      "  self._check_params(X)\n"
     ]
    },
    {
     "data": {
      "image/png": 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fvz8DBgzg1Vdf5eabb/aab/z48SxYsIDx48dXpS1cuJDnnnuO/v3706dPH/7+97973XfOnDlVDcEul4sLLrigxvuTJ0+mS5cu9OvXj/79+/Pyyy/TvHlznn/+eS677DL69u1LUlIS119/fY39TjrpJO6//36GDx9O//79ycrKYuzYsQDcf//9XHjhhQwZMoSTTjopmI/ImBr87Tsfr2MLYqbc9a1hGauvYNcs1vV/UF13s2pFhbNdUeFsr/+D/8cwIWFrFpuGLCxcqMxEXy58OST5Yk0ky42PNYsTcj0CVGu2CdTeNhFh6xGY+ngugFNWUUZKUgrHJR9XZwEcf/PFmmiU29d6BIlVNVSp9k3fgoAxMcXfvvPxOrYg1sqdmIHAGBPT/O07H69jC2Kt3BYIjDExyd++8/E6tiCWyp2YbQQmJtjfzfiSuyuXLm260DGtI/859B++/u/XXrtN+psv1kS63LZmsTEm7vi7AE68LpQTS+W2qqEQsWmojWm8+vrTe0uvTDu4uQCmTYPWrSEpyfk5bRps2+bzmNESa+XxZIEgjGwa6mo2DbXxpb659r2lr9iygi4fbqZF1unw7LNw8KDTBfzgQWe7Xz9YtSrm1i2ItfJ4SshAsHAhpKc7XyLS053tULFpqG0aauO/+ubaT5+TXic9ZVYKKbNSuGfe1Sx5DVKOHIXS0poHLC2FkhJ+GDuKe+ZdXeOY0Vq3IB7WUUi4NoKFCyEnB0pKnO2dO51tgIkTQ3OO/Px8Nm7cWGcG0sppqO+66y7Ky8spqSyEm+c01MOHD68zDfVTTz1Fz549+fjjj5k2bRpvv/02UD3Vc3JyMhdddBGPP/44Z555JocOHaoz4ZznNNQpKSns37+/ahrqNWvW0KtXL66++mqefPJJbrnllqr9KqehzsvLo127dpx33nksW7aMiy++2Odn4Vk2Y2qbNXwWBd8WsOPADsoqyqr60z8y8hFuXHVjjfSTW52Moty6Ygeuct/HbVYO0z+EqRdEv49+fdcYS2MdEu6J4K67qoNApZISJz1UbBrqajYNtfGlvv70I7qPqJN+3zn3cf859zNhfTnNKnwf11UBEwrKY6KPfqyNGfAm4QLBV18Flt4YDU1DffLJJzNp0iReeuklli5dWlWVs27dOsaMGcObb75ZYxrqioqKqmmoK1+ffvqp1/PdeeedPPvssxw+fJgzzzyzzuyj4WDTUJtg1Nef3lv6a5teI61uraVXrY4RE330IbbGDHiTcFVDXbo41UHe0sNt586ddOrUiSlTpnD06FHy8/OZM2cOl1xySY18vqahvuyyy1BVCgsL6d+/f51zVE5D3bdvX3Jzc/nss89qrIRWOQ318OHDq6qGPKehPuWUU+qdhvqmm25i7969tGvXjkWLFnHjjTcC1dNQn3rqqSxdutTrk44x9alvrn1v6aoKrVbBwUMNHzitFbcNuS0m1i2I9XUUwvpEICIjReRzEdkqInd6eb+LiLwjIv8WkUIRGRXO8gDMng2pqTXTUlOd9HCzaaiNqau+ufa9pQ86eRBJV14FLpfvg7pcJF19dZ1jRkvMr6NQ37Skwb6AZGAb0B1oBqwHetfKMxeY6v69N7CjoeMGPQ21qi5YoNq1q6qI83PBgoB2NyFi01DHlgOHD2jvx3rrgcMHGkyvL29EbN2qmpqq6nQa9f5KTXXyRUAwn0UkP0d8TEMdzieCwcBWVd2uqseAV4CxteMQ0Nr9extgdxjLU2XiRNixAyoqnJ+h6i1kTDwLtC9/1PrE9+gBS5Y4j/K1nwxcLid9yRInXwQE81nEytiCsM01JCLjgJGqOtm9fRVwmqre4JHnJOAtoB3QEjhHVfO8HCsHyAHo0qVL1s5alfw2Z018sr9bbKhvbvz2qe3ZW7K3Rnrl/UJEoj///7Zt8NBDMH8+HDoEaWlw1VUwfXpEgkAwawrYegQ1TQBeUNVOwChgvojUKZOqzlXVbFXN7tChQ8QLaUxTVt/c+M+Nfa5Oere23Uhvmx4b8+j36AGPPQbFxVBe7vx87LGIPQkEs6ZAIq1HsAvo7LHdyZ3m6VfAawCq+iHQHGgfxjIZY2qpty9/t/r78sdyn/hICWZ8QKyNLQhnIMgFeopINxFpBlwOLK+V5ytgBICI/BQnEOwJY5mMMV4E2pc/lvvER1Iwn0UsfY5hXY/A3R10Dk4PonmqOltEZuG0Xi8Xkd7AM0AaTsPxHar6lq9j2noETYf93WJHfXPje0tX1bic/z8cgllTIJbWI7CFaUIkLS2NQ4e8D3IZMmRI0DOQ3n333Zx11lmcc845fu+zfPlyNm/ezJ131hnCUWX37t3cdNNNUZmdNBb+bsYkCgsEEeAtEJSVldWZ6yfUysvL43Yun1j4uxmTKGK511DUhGuRiHBNQz1p0qSqb+3p6enMmDGDgQMHsnjxYlauXMlPfvITsrKyuOmmm7jwwgsBeOGFF7jhBqe37qRJk7jpppsYMmQI3bt3rzqWP1NUz5o1i0GDBpGRkUFOTg7x9uXBhJ6//39ieTGWSvFQxnBL2EAQzoEc+fn5PPzww3zxxRc10iunoS4oKGD9+vU15gCCmtNQAzWmoa7thBNOID8/n4svvpjrrruOVatWkZeXx5499be1f/PNN/zrX//ijTfe8Fpd5DlFdWFhIRPdI+1uuOEGcnNz2bhxI4cPH+aNN94I9CMxTYy//39iZcCUL/FQxnBLuEAQiUUiQj0NtTeV6Z999hndu3evOt+ECRPqLdfFF19MUlISvXv35j//+U+d971NUQ3wzjvvcNppp9G3b1/efvttNm3a5OvyTRPm7/+feFiMJR7KGCkJFwgiMZAj1NNQB3IOX4477riq3/2t3jly5AjTpk1jyZIlbNiwgSlTptSZatokDn///8TagClv4qGMkZJwgSCaAzl27txJx44dmTJlCpMnTyY/P59LLrmkao2B7Oxs0tLS6kxD7cupp57K9u3b2bFjB0DV00RjVE5RXbm+cOXqZQDt27fn0KFDUV/72ESXv/9/Ym3AlDfxUMZISbhAANEbyBHMNNT1adGiBU888UTVesatWrWiTZs2jSqftymq27Zty5QpU8jIyOD8889n0KBBjTq2aTr8/f8TSwOm6hMPZYyEhOw+GumBHOF26NAh0tLSUFV+/etf07NnT6ZPnx7tYjXIuo/GJ3///8TD/7N4KGOo2DiCJu6hhx7ixRdf5NixYwwYMIBnnnmG1Nqr78SgRP+7RU1FBSQl1dyGumlJCVlh0GTZOIImbvr06RQUFLB582YWLlwYF0HARMn/DIPVWdU3/4oKWNzaeXmmrc5y8kZIfX35Qz1eIdgxA011zIEFAmMSRUUFlBXD9wXVwWDVQCj/wXm9ObA6CHxf4OStDA5hFsiiOIHs39h84do/VlnVkIka+7tFgeeNvlKb/iDAgfXVae0y4fy8sFcPBbIojreFW/xd4CXYhWCisZBMqFnVkDHGkZTk3OA9XZAPI/NrpkUgCEBgi+IEM14h2DEDTX3MgQUCYxJJ5ROBp1UDnWohT57tCGEUyKI4wYxXCHbMQFMfc5B4gWDbNpg2DVq3dr7xtG7tbG/bFvShv/32Wy6//HJ69OhBVlYWo0aNqjPfkD9eeOEFdu/eHXR5jKnBs1qoXSZcXu5UCxWvd6qF2vZ30tpl1mxHCLNAFsUJZP/G5gu0nE2CqsbVKysrS2vbvHlznTSvVq5UTU1VdblUofrlcjnpK1f6dxwvKioq9PTTT9cnn3yyKq2goEDXrl0b8LHOPvtszc3N9fpeWVlZo8sYa/z+u5nQeets1ZWZquXlznZ5ueorLZ2XZ9rKTCdvBHxS9Il+e/BbVVX99uC3mrsr12e6v/s3Nl+g5YwXOAuCeb2vRv3GHuir0YFg61bnZu8ZAGq/UlOdfI2wZs0aHTp0qNf3/vKXv2h2drb27dtX7777blVV/fLLL/UnP/mJTp48WXv37q3nnnuulpSU6OLFi7Vly5baq1cv7d+/v5aUlGjXrl31jjvu0AEDBuiiRYv05Zdf1oyMDO3Tp4/ecccdquoEiGuuuUb79OmjGRkZ+uCDDzbqOiLJAkEQKip8b/tSecP33PaWFux5TEzxFQgSp2rob3+D0lLfeUpL4aGHGnX4jRs3kpWVVSf9rbfeYsuWLXzyyScUFBSQl5fH2rVrAdiyZQu//vWv2bRpE23btuX1119n3LhxZGdns3DhQgoKCmjRogVQPe30WWedxYwZM3j77bcpKCggNzeXZcuWUVBQwK5du9i4cSMbNmzg2muvbdR1mDhQOBPypztfX8D5mT/dSfdH7UbgpCTvacGex8SNxAkECxb4Fwjmzw/pad966y3eeustBgwYwMCBA/nss8/YsmULAN26datakyArK6tq4jhvKucdys3NZdiwYXTo0IGUlBQmTpzI2rVr6d69O9u3b+fGG2/kzTffpHXr1iG9DhMjVKH0AHz+cPVNOn+6s116oPqmHS/nMTEhvOsoxpJ61hNudL5a+vTp43VmTlXlt7/9Ldddd12N9B07dtSYFjo5OZnDhw/Xe/yGpp1u164d69evZ/Xq1Tz11FO89tprzJs3L8CrMDFPBAa6n1o/f9h5AZx6s5MuEl/nMTEhcZ4I3MtFhixfLT/72c84evQoc+fOrUorLCykdevWzJs3r2o94127dvHdd9/5PFarVq04ePCg1/cGDx7Me++9x969eykvL2fRokWcffbZ7N27l4qKCn7+859z7733kp+f73V/0wR43qQrhePmHKnzmKhLnCeCK6+EZ5/1XT3kcsFVVzXq8CLC0qVLueWWW/jzn/9M8+bNSU9PZ86cObRt25YzzjgDcNYvXrBggc91BiZNmsT1119PixYt+PDDD2u8d9JJJ3H//fczfPhwVJXRo0czduxY1q9fz7XXXlu13vF9993XqOswcaCymsZT/vTQ36QjdR4TffW1IsfqK1Z7DZnAWa+hRqioUF13s+pCnJ/etuPpPCZi8NFrKHGeCHr0gCVLYNw456nA88nA5XJeS5Y4+YyJVSLgaluzrr6y+sbVNrRtBJE4j4kJiRMIAC64AAoLnS6i8+c7DcNpaU510PTpFgRMfOg303mGrbwZV96kvd2cPfN5227MeRo6R6QFc40GaEKBQFURf/74PXrAY485LxM1at0Pg1P737q3f/uFM52unpVBorLO39XWuck35jwb7gn+mKFUODO2yhOnmkSvoebNm7Nv3z67ucQJVWXfvn00b9482kVpusIxDiDWxhbEWnniWJNYj6C0tJSioiKOHDkSpVKZQDVv3pxOnTrhcrmiXZSmy/PGWCnYcQDhOGYwYq08MazJr1lsjKmHKizyePCfUBH8DTIcx2xK5YlRtjCNMYmovnEAwXz5C8cxgxFr5YlTYQ0EIjJSRD4Xka0icmc9eX4hIptFZJOIxMeab8bEOs8qk1NvhsFbYGlfGPowJDdyHY7ax5xQ4fz0rKOPpFgrTxwLW68hEUkGHgfOBYqAXBFZrqqbPfL0BH4LnKmq34vIj8JVHmMSiuc4gP+cB2f1d4+fcb9/8KAz0v7FF53xMxdcENgxY2FsQayVJ46FrY1ARM4AZqrq+e7t3wKo6n0eef4CfKGqz/p7XGsjMCYAW7dC//5QUlJ/ntRUZ3yNv+NoYq3ffqyVJ0ZFq43gZOBrj+0id5qnXkAvEflfEflIREZ6O5CI5IjIOhFZt2fPnjAV15gIqv0FLNAvZLXnzCot9X7MBx/0b/r1Bx+smVa5RGWw5YwEf8ZUGJ+i3VicAvQEhgETgGdEpG3tTKo6V1WzVTW7Q4cOkS2hMaFWODO4BV8WNYclLapv8KWlsPg4WJRS95gvPedfIHjhqeqbf+XaxsvS65bzrTOcV2PLbmJSOAPBLqCzx3Ynd5qnImC5qpaq6pfAFziBwZimKdhBUKWlQBloeXUwWNwCUKACPr6h5jFLjvlXrsMV1YvVVy5wX/5DzXLm3QL7PnZeebfYAK4mJJxtBCk4N/YROAEgF7hCVTd55BkJTFDVa0SkPfBvIFNV99V3XGsjMHEv2EFQpaVOENByj8Rk6H4dbH+i5jGHz3MahhuSmgTPVFRvt8uE89ZBwW01y9nrJufnF480ruwmaqLSRqCqZcANwGrgU+A1Vd0kIrNEZIw722pgn4hsBt4BbvcVBIxpEoJd8MXlgnG1VrO77DCcVmv+rIEPOetwNDR62+WCSdfXTDs/D5KT65Yza47zamzZTUwKaxuBqq5U1V6q2kNVZ7vT7lbV5e7fVVVvVdXeqtpXVV8JZ3mMiQnBDoKqfCLwtLiFUy1U+5i33upfIMh6p2ba6iwoL69bzrxbnFdjy25iUrQbi41JLMEOgvKsFpJkuOwYkAyUO9VC3afVPGbxY7B4sdNFtHZAcLmc9BknQvNPneqgy8udn98XwLITa5az101OldAXjzi/2wCuJqPJTENtTFwIdhCUywWkgOBUD7lcTrXQ4uMAcaqHah9z1Cjf63Bs/xWUtXaqg5KSnJ+rs+Do93DqxOpyZs1xGorB+d0GcDUZNumcMdEQyCCoigrnBu25XV5e8xt+aSmkpNQ9pmrdfUX8y5eU5L2cEJkBXDZQLKRs0jljwmDhQkhPd+6X6enOtt/8HQT1P8Oqu3VCdffOd8+tmc/lqnuMfw6vu++yH8OyznXHAWycVXPfyqDgrZyRGMBVODO4sRYmIBYIjGmEhQshJwd27nTuUTt3OtsBBYOGVFRAWbFTX1+7j39ZcfUN3t993xwIR/4Dh3fF9jgAW3Am4qxqyJhGSE93bv61de0KO3aE8ESeN/9K7TKr6/MD3bdtf/jR2bE/DsAWnAk5W5jGmBCrrD6vTcT3F/VGqaiAV5Krty8vbzgI+NpXJD4WcrEFZ0LK2giMCbEuXQJLb7TKb/WePOv9A933zYHxMQ7AFpyJKAsExjTC7NlOF3xPqalOesh4Vu3U7uPfUDDwtm/b/nBgfeyPA7AFZyLOxhEY0wgTJzo/77oLvvrKeRKYPbs6PSSSkiClTc02gco+/iltfFcPedt3ZL7Ta0hSYnscgC04E3E+2whE5EQAVf1WRDoAQ4HPPSeOizRrIzAJx9s4Am9jAaDx4whi8eZq4whCqlFtBCJyHfAh8JGITAXeAEYD/09EfhWWkhoTQUGNA6jHtGnV47pSUpztoMtU+5v/xln+rxPgbXxAvCzkEi/lbAJ8VQ3dAPQBWgA7gVPcTwbtcGYKfS4C5TMmLCrHAVSu4Fg5DgAaX70zbRo8+WT1dnl59fYTT3jfJ+AyefaxB6fKpHKdAHB+z5pTs47dvkmbBtRbNSQi/1bVAe7f16tqf2/vRZpVDZlQCMc4gJQU5+ZfW3IylJWFsEze+tjbOgGmAY3tPlohIpWTmYz2OFjzBvYzJuZ99VVg6f7wFgR8pft77jrp3tYzsHUCTBB83dBvwVn/DlUt8kg/AVgSxjIZE3bhGAeQnBxYur/nrpPurY+9rRNgguArELwI3CoiVf+MRaQj8GdgbLgLZkw4hWMcQGV9vr/pjSqTtz72tk6ACZKvQJAF9AAKRORnInIz8AlOT6LBkSicMeEycSLMnevUv4s4P+fODW4cwBNPwNSp1U8AycnOtj8NxX6XyVsf+6w5cMJpzstzfMCpN1u/e+OXBucacgeAh4DdwOm1qokizhqLjcH/dQK8pVlgSEiNHUfQVkSeBq4FRuK0C6wSkZ+Fp5jGxJdwjEPwmz/rBGy4x+b0N37xVTWUD2wBslX1LVW9BbgKuFdEFkWicMbEqoisRxAMm9PfBMDXOIJO9VUDicgUVX0mrCWrh1UNmVgQsfUIgmFz+hsPjaoa8tUWEK0gYEzUbNvmDB1u3RqSkijc2ZrHmEZ3ttXIFsw4hJDzNt7AgoDxwgaGGdOQVaugXz949lk4eBBUac1BpvAshfRjJKuqsoZ8PYJg2Jz+xk8WCIzxZds2GDfOmQCotLTGW80opSUlLGEc3dkW+vUIgmFz+psA2HoExvjyt7/VCQC1uSjl7rSHSHnqsdCuRxAMm9PfBMDWLDbGl9atneogf/IVF4e/PIGyOf2Nm61ZbExjHToU2nyRZnP6Gz9YIDDGl7Q0v7Id1LTYGUNgTIAsEBjjy5VXUp7s8pnlGC5e1Ktia0CZMQGwQGCML7fdxtEK34GgFBcPMZ2SEmcxe2PiTVgDgYiMFJHPRWSriNzpI9/PRURFxGtDhjFR06MH43QJP5DKMWoGhGO4+IFUxrGE7fQAYmxAmTF+ClsgcK9j8DhwAdAbmCAivb3kawXcDHwcrrIYE4zNXS+gH4XMJYdiWlNOEsW0Zi459KOQN7mgKm9MDSgzxk/hfCIYDGxV1e2qegx4Be8L2vwRZ7GbI2EsizGNNns2fJvagxt5jLYUk0I5P2pWzK2ux6qeBCD4hW2MiZZwBoKTga89tovcaVVEZCDQWVVX+DqQiOSIyDoRWbdnz57Ql9QYH7wtGDNvHjz/fGgXtjEmWqI2slhEkoAHgUkN5VXVucBccAaUhbdkxtQ1caL3m7zd+E1TEM4ngl1AZ4/tTu60Sq2ADOBdEdkBnA4stwZjEwh/F4eZNg1SUpxv7ykpznawx/R336guYGOMP1Q1LC+cp43tQDegGbAe6OMj/7s4i+D4PG5WVpYao6q6YIFqaqqqM2+C80pNddI9TZ1aM0/la+rUxh/T3/K4XKrNmjXueMaEErBO67mvhnWuIREZBcwBkoF5qjpbRGa5C7S8Vt53gd+oqs+JhGyuIVPJ38VhUlKgvLxuvuRkKCtr3DEDKY83MbWAjUkIvuYasknnTNxKSvI+m7IIVFTU3K5P7f39PWYg5fHGn+MZE0o26Zxpkurrs187PTnZez5v6f4es7F5GpPXmHCzQGDi1uzZTt99T9768ufkeN/fW7q/x/S3PC4XNGvWuOMZEzH1NR7E6ssai42nBQtUu3ZVFXF+1tcIO3WqanKy01ibnOy9oTjQY/q7bzDHMyZUiFZjcThYG4ExxgTO2giMMcbUywKBMcYkOAsExhiT4CwQGGNMgrNAYIwxCc4CgTHGJDgLBMYYk+AsEBhjTIKzQGCMMQnOAoExxiQ4CwTGGJPgLBAYY0yCs0BgjDEJzgKBMcYkOAsExhiT4CwQGGNMgrNAYIwxCc4CgTHGJDgLBMYYk+AsEBhjTIKzQGCMMQnOAoExxiQ4CwTGGJPgLBAYY0yCs0BgjDEJzgKBMcYkuLAGAhEZKSKfi8hWEbnTy/u3ishmESkUkTUi0jWc5THGGFNX2AKBiCQDjwMXAL2BCSLSu1a2fwPZqtoPWAL8JVzlMcYY4104nwgGA1tVdbuqHgNeAcZ6ZlDVd1S1xL35EdApjOUxxhjjRTgDwcnA1x7bRe60+vwKWOXtDRHJEZF1IrJuz549ISyiMcaYmGgsFpErgWzgr97eV9W5qpqtqtkdOnSIbOGMMaaJSwnjsXcBnT22O7nTahCRc4C7gLNV9WgYy2OMMcaLcD4R5AI9RaSbiDQDLgeWe2YQkQHA08AYVf0ujGUxxhhTj7A9EahqmYjcAKwGkoF5qrpJRGYB61R1OU5VUBqwWEQAvlLVMeEqU6zYsHADa+5aQ/FXxbTp0oYRs0fQd2LfaBfLGJOgwlk1hKquBFbWSrvb4/dzwnn+WLRh4Qb+kfMPSktKASjeWcw/cv4BYMHAGBMVMdFYnEjW3LWmKghUKi0pZc1da6JUImNMorNAEGHFXxUHlG6MMeFmgSDC2nRpE1C6McaEmwWCCBsxewSuVFeNNFeqixGzR0SpRMaYRBfWxmJTV2WDsPUaMsbECgsEUdB3Yl+78RtjYoYFAj/42+//pXNe4ss1X1ZtdxvRjQHXDqizL9gTgTEmdoiqRrsMAcnOztZ169ZF7Hy1+/2DU6d/0dyLaty8aweBKgJ4fMTJzZJRVSpKK3wezxhjQklE8lQ129t71ljcAH/7/XsNAlAjCACUHyuvEQTqO54xxkSKBYIGRKrfv40jMMZEiwWCBkSq37+NIzDGRIsFggb42++/24hu3g8gNTeTmyWT5Kr5sds4AmNMNFkgaEDfiX25aO5FtOnaBgTadG3jtWH36n9eXScYdBvRjUvnX1pj37HzxnLx8xc3eDxjjIkU6zVkjDEJwFevIRtH0Egrpq0gb24eWq5IspCVk0WXM7v4NT7A1iMwxsQSeyJohBXTVrDuybplSEpJoqLM9/gAf8clGGNMKNk4ghDLm5vnNd0zCID38QG2HoExJtZY1VAjaHnNp6h27OcMPqAfhTTjGMdoRiH9+JAhfP9VzX1tPQJjTKyxQNAIkixVweAUtnAZr5FMOck4TwTHcYyB5NOf9azscG2Nfdt0aUPxzro3fRtHYIyJFqsaaoSsnCzAeRK4jNdoRmlVEKiUTAXNKGXMgZdg27aqdFuPwBgTaywQNMLoJ0aTPTWbM/iQZMp95k3Scnjooaptf8clGGNMpFivoWC0bg0HD/qXr9jaAIwx0WO9hsLl0KHQ5jPGmChIiMbiQAZwecsLdReSWXHDCqari+M41uD5j1S4+LPcA0D73u0563dnBVUeq0YyxoRSk68aCmQAl7e83haSqTSKNxhIfp2GYk/lJJFHFqsYXW+eQMpjg8+MMY2R0FVDgQzg8pbX20IylT5kCOUk+zx/Ocl8xBk+8wRSHht8ZowJtSYfCAIZwBXooK7vOZ7F/IJjuCiv9VGWk8QxXCzmF3zP8Y0qpw0+M8ZEQpMPBIEsLNOYQV1b6clTTCWPLI5wHBUIRziOPLJ4iqlspWejyxmpRXGMMYmtyQeCQAZwecvrbSGZ2r7neFYxmj/zW/7IH/gzv2UVo/16Egi0PDb4zBgTak0+EAQygMtbXm8LyVy64FKOa3tcjX2Pa3sc7Xu3r5HWvnd7r2mXLri00eWxhmJjTKg1+V5DxhhjorgwjYiMBB4GkoFnVfX+Wu8fB7wEZAH7gPGquiOcZaoUbP98b/v/+/l/8+WaL6vydBvRjav/eXU4im+MMSETticCEUkGvgDOBYqAXGCCqm72yDMN6Keq14vI5cAlqjre13FD8UQQbP98b/sjgJeP0oKBMSYWRGscwWBgq6puV9VjwCvA2Fp5xgIvun9fAowQEQljmYDg++d7299bEABqPCEYY0wsCmcgOBn42mO7yJ3mNY+qlgHFwAm1DyQiOSKyTkTW7dmzJ+iCBds/3/rxG2OakrjoNaSqc1U1W1WzO3ToEPTxgu2fb/34jTFNSTgDwS6gs8d2J3ea1zwikgK0wWk0Dqtg++d72596KrS6jejWmCIaY0zEhDMQ5AI9RaSbiDQDLgeW18qzHLjG/fs44G2NQH/WYPvne9v/0vmX1rnpW0OxMSYehHUcgYiMAubgdB+dp6qzRWQWsE5Vl4tIc2A+MADYD1yuqtt9HdPGERhjTOCiNo5AVVcCK2ul3e3x+xHgsnCWwRhjjG9x0VhsjDEmfCwQGGNMgrNAYIwxCc4CgTHGJLi4m31URPYAOxu5e3tgbwiLEw/smhODXXNiCOaau6qq1xG5cRcIgiEi6+rrPtVU2TUnBrvmxBCua7aqIWOMSXAWCIwxJsElWiCYG+0CRIFdc2Kwa04MYbnmhGojMMYYU1eiPREYY4ypxQKBMcYkuCYZCERkpIh8LiJbReROL+8fJyKvut//WETSo1DMkPLjmm8Vkc0iUigia0SkazTKGUoNXbNHvp+LiIpI3Hc19OeaReQX7r/1JhF5OdJlDDU//m13EZF3ROTf7n/fo6JRzlARkXki8p2IbKznfRGRR9yfR6GIDAz6pKrapF44U15vA7oDzYD1QO9aeaYBT7l/vxx4NdrljsA1DwdS3b9PTYRrdudrBawFPgKyo13uCPydewL/Btq5t38U7XJH4JrnAlPdv/cGdkS73EFe81nAQGBjPe+PAlbhLId1OvBxsOdsik8Eg4GtqrpdVY8BrwBja+UZC7zo/n0JMEJE6lljLC40eM2q+o6qlrg3P8JZMS6e+fN3Bvgj8GfgSCQLFyb+XPMU4HFV/R5AVb+LcBlDzZ9rVqC1+/c2wO4Ili/kVHUtzvos9RkLvKSOj4C2InJSMOdsioHgZOBrj+0id5rXPKpaBhQDJ0SkdOHhzzV7+hXON4p41uA1ux+ZO6vqikgWLIz8+Tv3AnqJyP+KyEciMjJipQsPf655JnCliBThrH9yY2SKFjWB/n9vUFgXpjGxR0SuBLKBs6NdlnASkSTgQWBSlIsSaSk41UPDcJ761opIX1U9EM1ChdkE4AVV/ZuInAHMF5EMVa2IdsHiRVN8ItgFdPbY7uRO85pHRFJwHif3RaR04eHPNSMi5wB3AWNU9WiEyhYuDV1zKyADeFdEduDUpS6P8wZjf/7ORcByVS1V1S+BL3ACQ7zy55p/BbwGoKofAs1xJmdrqvz6/x6IphgIcoGeItJNRJrhNAYvr5VnOXCN+/dxwNvqboWJUw1es4gMAJ7GCQLxXm8MDVyzqharantVTVfVdJx2kTGqGs8LXvvzb3sZztMAItIep6rI5zrgMc6fa/4KGAEgIj/FCQR7IlrKyFoOXO3uPXQ6UKyq3wRzwCZXNaSqZSJyA7Aap8fBPFXdJCKzgHWquhx4DufxcStOo8zl0Stx8Py85r8CacBid7v4V6o6JmqFDpKf19yk+HnNq4HzRGQzUA7crqpx+7Tr5zXfBjwjItNxGo4nxfMXOxFZhBPM27vbPf4AuABU9SmcdpBRwFagBLg26HPG8edljDEmBJpi1ZAxxpgAWCAwxpgEZ4HAGGMSnAUCY4xJcBYIjDEmwVkgMCZAItJZRL4UkePd2+3c25ki8qF71s9CERkf7bIa4w/rPmpMI4jIHcApqpojIk8DO4DXAVXVLSLyYyAP+GkTn97BNAEWCIxpBBFx4dzo5+HM+JmpqqW18qwHxqnqligU0Ri/NbmRxcZEgqqWisjtwJvAeV6CwGCc+fO3RaN8xgTC2giMabwLgG9wJrer4p4bfj5wrc2AaeKBBQJjGkFEMoFzcWY1nV65MIiItAZWAHe5Fw0xJuZZIDAmQO7V7J4EblHVr3Am9HvAPTvmUpzVo5ZEs4zGBMIai40JkIjkACNUdbx7OxlnuuS/A78HNnlkn6SqBREvpDEBsEBgjDEJzqqGjDEmwVkgMMaYBGeBwBhjEpwFAmOMSXAWCIwxJsFZIDDGmARngcAYYxLc/wdD2qouzqYbYwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 2, 3, 3, 3, 2, 2]\n",
      "Error Entrenamiento: 7.62 %\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [2, 3]].values\n",
    "\n",
    "centros = np.array([[0.1, 0.1], [0.2, 0.4], [0.6, 0.6], [0.8, 0.7]]) #Matriz que contiene las coordenadas de los centros\n",
    "\n",
    "kmeans = KMeans(n_clusters = 4, init = centros , max_iter = 300, n_init = 10, random_state = 0) #Entrenamiento para hallar los centros\n",
    "y_kmeans = kmeans.fit_predict(x)       #Obtener la clasificación de los datos de entrenamiento\n",
    "print(y_kmeans)                        # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], c='blue', marker='o', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[y_kmeans == 3, 0], x[y_kmeans == 3, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"4 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in y_kmeans:\n",
    "    if i == 0:\n",
    "        salida.append(1)\n",
    "    elif i == 2 or i == 1:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "print(\"Error Entrenamiento:\",error(salida, etiquetas), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 1 1 1 2 1 1 1 1 1 1 1 1 3 3 3 2 3 3 3\n",
      " 3 3 3 3 3 3 3 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2]\n",
      "4.44\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_kmeans = kmeans.predict(prueba)    #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_kmeans)                      #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(prueba[r_kmeans == 0, 0], prueba[r_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_kmeans == 2, 0], prueba[r_kmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_kmeans == 1, 0], prueba[r_kmeans == 1, 1], c='blue', marker='o', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_kmeans == 3, 0], prueba[r_kmeans == 3, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 4 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_kmeans:\n",
    "    if i == 0:\n",
    "        salida.append(1)\n",
    "    elif i == 2 or i == 1:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "print(error(salida, etiquetas_prueba))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Para 5 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3\n",
      " 3 2 3 3 3 2 3 2 2 3 2 3 2 3 3 2 3 2 3 2 3 3 3 3 3 3 3 2 2 2 2 3 3 1 4 4 4\n",
      " 4 1 3 4 4 1 4 4 4 4 4 4 4 1 1 3 4 4 1 3 4 4 3 3 4 4 4 1 4 3 3]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pipe-\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:984: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
      "  self._check_params(X)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2]\n",
      "Error de entrenamiento: 6.67\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [2, 3]].values\n",
    "\n",
    "centros = np.array([[0.1, 0.1], [0.2, 0.2], [0.4, 0.4], [0.6,0.6], [0.8,0.8]]) #Matriz que contiene las coordenadas de los centros\n",
    "\n",
    "kmeans = KMeans(n_clusters = 5, init = centros, max_iter = 300, n_init = 10, random_state = 0)  #Entrenamiento para hallar los centros\n",
    "y_kmeans = kmeans.fit_predict(x)            #Obtener la clasificación de los datos de entrenamiento\n",
    "print(y_kmeans)                             # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[y_kmeans == 3, 0], x[y_kmeans == 3, 1], c='black', marker='+', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "ax1.scatter(x[y_kmeans == 4, 0], x[y_kmeans == 4, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"5 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in y_kmeans:\n",
    "    if i == 0 : \n",
    "        salida.append(1)\n",
    "    elif i == 3 or i == 2:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "print(\"Error de entrenamiento:\",error(salida, etiquetas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 2 2 2 3 2 2 2 2 2 3 2 2 1 4 4 3 4 4 4\n",
      " 4 1 1 4 4 4 4 4]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "2.22\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_kmeans = kmeans.predict(prueba)       #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_kmeans)                         #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(prueba[r_kmeans == 0, 0], prueba[r_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_kmeans == 3, 0], prueba[r_kmeans == 3, 1], c='black', marker='+', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_kmeans == 2, 0], prueba[r_kmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_kmeans == 1, 0], prueba[r_kmeans == 1, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "ax1.scatter(prueba[r_kmeans == 4, 0], prueba[r_kmeans == 4, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 5 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_kmeans:\n",
    "    if i == 0 : \n",
    "        salida.append(1)\n",
    "    elif i == 3 or i == 2:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(error(salida, etiquetas_prueba))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Para 6 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3\n",
      " 3 2 3 3 3 2 3 2 2 3 2 3 2 3 3 2 3 2 4 2 3 3 3 3 3 4 3 2 2 2 2 4 3 5 4 5 4\n",
      " 5 1 3 1 4 5 4 4 5 4 5 5 4 1 1 3 5 4 1 4 5 1 4 4 5 4 1 1 5 3 3]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pipe-\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:984: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
      "  self._check_params(X)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2]\n",
      "Error de entrenamiento 6.67\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [2, 3]].values\n",
    "\n",
    "centros = np.array([[0.1, 0.1],[0.2, 0.2], [0.3, 0.4], [0.5, 0.5], [0.7, 0.7], [0.8, 0.9]]) #Matriz que contiene las coordenadas de los centros\n",
    "\n",
    "kmeans = KMeans(n_clusters = 6, init = centros, max_iter = 300, n_init = 10, random_state = 0)    #Entrenamiento para hallar los centros\n",
    "y_kmeans = kmeans.fit_predict(x)             #Obtener la clasificación de los datos de entrenamiento\n",
    "print(y_kmeans)                              # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], c='black', marker='+', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[y_kmeans == 3, 0], x[y_kmeans == 3, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[y_kmeans == 4, 0], x[y_kmeans == 4, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "ax1.scatter(x[y_kmeans == 5, 0], x[y_kmeans == 5, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "ax1.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], c='black', marker='x', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"6 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in y_kmeans:\n",
    "    if i == 0: \n",
    "        salida.append(1)\n",
    "    elif i == 2 or i == 3:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "print(\"Error de entrenamiento\",error(salida, etiquetas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 2 2 2 3 2 2 3 2 3 3 2 2 5 5 4 4 5 5 5\n",
      " 4 5 5 5 4 4 5 4]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_kmeans = kmeans.predict(prueba)      #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_kmeans)                        #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(prueba[r_kmeans == 0, 0], prueba[r_kmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_kmeans == 2, 0], prueba[r_kmeans == 2, 1], c='black', marker='+', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_kmeans == 3, 0], prueba[r_kmeans == 3, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_kmeans == 4, 0], prueba[r_kmeans == 4, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "ax1.scatter(prueba[r_kmeans == 5, 0], prueba[r_kmeans == 5, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "ax1.scatter(prueba[r_kmeans == 1, 0], prueba[r_kmeans == 1, 1], c='black', marker='x', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 6 Grupos\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_kmeans:\n",
    "    if i == 0: \n",
    "        salida.append(1)\n",
    "    elif i == 2 or i == 3:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(error(salida, etiquetas_prueba))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Punto 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fcmeans import FCM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Para 3 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1\n",
      " 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2]\n",
      "Error de entrenamiento 4.76\n",
      "[[100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [ 98.   1.   1.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [ 99.   0.   1.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [ 99.   0.   1.]\n",
      " [ 98.   1.   1.]\n",
      " [ 99.   0.   0.]\n",
      " [ 99.   0.   1.]\n",
      " [ 98.   1.   1.]\n",
      " [100.   0.   0.]\n",
      " [ 99.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [ 99.   0.   0.]\n",
      " [ 99.   0.   1.]\n",
      " [ 98.   1.   1.]\n",
      " [ 95.   1.   4.]\n",
      " [ 98.   1.   1.]\n",
      " [100.   0.   0.]\n",
      " [ 98.   0.   1.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [ 99.   0.   1.]\n",
      " [ 99.   0.   1.]\n",
      " [100.   0.   0.]\n",
      " [ 99.   0.   1.]\n",
      " [  0.   2.  97.]\n",
      " [  1.   3.  96.]\n",
      " [  1.  12.  87.]\n",
      " [  1.   3.  96.]\n",
      " [  1.   4.  95.]\n",
      " [  0.   1.  99.]\n",
      " [  2.  15.  84.]\n",
      " [ 21.  11.  68.]\n",
      " [  0.   2.  98.]\n",
      " [  2.   4.  94.]\n",
      " [ 16.  11.  73.]\n",
      " [  1.   3.  96.]\n",
      " [  9.   9.  83.]\n",
      " [  0.   2.  97.]\n",
      " [  5.   8.  87.]\n",
      " [  0.   0. 100.]\n",
      " [  1.   3.  96.]\n",
      " [  8.   9.  84.]\n",
      " [  1.   3.  96.]\n",
      " [  6.   7.  87.]\n",
      " [  2.  49.  49.]\n",
      " [  1.   3.  96.]\n",
      " [  1.  12.  87.]\n",
      " [  2.   5.  94.]\n",
      " [  0.   1.  99.]\n",
      " [  0.   0. 100.]\n",
      " [  1.   4.  95.]\n",
      " [  2.  43.  55.]\n",
      " [  1.   3.  96.]\n",
      " [ 16.  11.  73.]\n",
      " [  7.   8.  85.]\n",
      " [ 13.  10.  78.]\n",
      " [  4.   5.  91.]\n",
      " [  2.  32.  66.]\n",
      " [  1.   3.  96.]\n",
      " [  2.  87.  11.]\n",
      " [  1.  79.  20.]\n",
      " [  0.  99.   1.]\n",
      " [  1.  86.  13.]\n",
      " [  0.  97.   2.]\n",
      " [  1.  90.   9.]\n",
      " [  2.  22.  76.]\n",
      " [  2.  86.  13.]\n",
      " [  1.  88.  11.]\n",
      " [  2.  86.  11.]\n",
      " [  1.  86.  13.]\n",
      " [  1.  88.  11.]\n",
      " [  0.  98.   2.]\n",
      " [  1.  82.  17.]\n",
      " [  2.  83.  14.]\n",
      " [  1.  90.   9.]\n",
      " [  1.  84.  15.]\n",
      " [  2.  88.  10.]\n",
      " [  3.  84.  13.]\n",
      " [  2.  16.  83.]\n",
      " [  1.  94.   5.]\n",
      " [  2.  77.  21.]\n",
      " [  2.  87.  11.]\n",
      " [  2.  55.  43.]\n",
      " [  0. 100.   0.]\n",
      " [  1.  88.  11.]\n",
      " [  2.  49.  49.]\n",
      " [  2.  55.  43.]\n",
      " [  0.  99.   1.]\n",
      " [  3.  64.  34.]\n",
      " [  1.  94.   5.]\n",
      " [  1.  92.   7.]\n",
      " [  0.  97.   3.]\n",
      " [  2.  20.  78.]\n",
      " [  4.  35.  62.]] 2\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [ 2, 3]].values\n",
    "\n",
    "\n",
    "fcmean = FCM(n_clusters=3,m = 2.0, n_init=10, random_state=0)  #Parámetros para el algoritmo\n",
    "y_fcmeans = fcmean.fit(x)               #Entrenamiento para hallar los centros\n",
    "r_fcmeans = fcmean.predict(x)           #Obtener la clasificación de los datos de entrenamiento\n",
    "\n",
    "print(r_fcmeans)                        # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(x[r_fcmeans == 0, 0], x[r_fcmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[r_fcmeans == 2, 0], x[r_fcmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[r_fcmeans == 1, 0], x[r_fcmeans == 1, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"3 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if i == 0:\n",
    "        salida.append(1)\n",
    "    elif i == 2:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(\"Error de entrenamiento\", error(salida, etiquetas))\n",
    "\n",
    "pert_fcmeans = fcmean.soft_predict(x)\n",
    "print(np.round(pert_fcmeans * 100), 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "0.0\n",
      "[[ 99.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [ 99.   0.   1.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [ 91.   2.   6.]\n",
      " [ 96.   1.   3.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [100.   0.   0.]\n",
      " [  1.  10.  88.]\n",
      " [  1.   6.  93.]\n",
      " [  0.   1.  99.]\n",
      " [  1.   2.  97.]\n",
      " [  1.   3.  96.]\n",
      " [  1.   3.  96.]\n",
      " [  0.   1.  99.]\n",
      " [  3.   4.  93.]\n",
      " [ 21.  11.  68.]\n",
      " [  1.   1.  98.]\n",
      " [  2.   3.  95.]\n",
      " [  1.   1.  98.]\n",
      " [  0.   1.  99.]\n",
      " [ 23.  12.  65.]\n",
      " [  1.   2.  97.]\n",
      " [  1.  93.   6.]\n",
      " [  2.  89.   9.]\n",
      " [  1.  84.  15.]\n",
      " [  2.  49.  49.]\n",
      " [  0.  97.   3.]\n",
      " [  2.  89.   9.]\n",
      " [  2.  86.  12.]\n",
      " [  1.  79.  20.]\n",
      " [  1.  94.   5.]\n",
      " [  2.  86.  12.]\n",
      " [  2.  88.  10.]\n",
      " [  2.  74.  24.]\n",
      " [  1.  90.   9.]\n",
      " [  1.  91.   7.]\n",
      " [  2.  66.  32.]] 2\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_fcmeans = fcmean.predict(prueba)  #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_fcmeans)                    #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(prueba[r_fcmeans == 0, 0], prueba[r_fcmeans == 0, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_fcmeans == 2, 0], prueba[r_fcmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_fcmeans == 1, 0], prueba[r_fcmeans == 1, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 3 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if i == 0:\n",
    "        salida.append(1)\n",
    "    elif i == 2:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(error(salida, etiquetas_prueba))\n",
    "\n",
    "# Matriz de grados de pertenecia\n",
    "pert_fcmeans = fcmean.soft_predict(prueba)\n",
    "print(np.round(pert_fcmeans * 100), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Para 4 Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1\n",
      " 1 3 1 3 1 3 1 3 3 1 3 1 3 1 1 3 1 3 1 3 1 3 3 1 1 1 1 3 3 3 3 1 1 0 1 0 0\n",
      " 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 2, 3, 3, 3, 2, 2]\n",
      "Error de entrenamiento 8.57\n",
      "[[  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0.  99.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  1.   1.  96.   2.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0.  98.   1.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0.  98.   1.]\n",
      " [  1.   1.  97.   2.]\n",
      " [  0.   0.  99.   1.]\n",
      " [  0.   1.  97.   2.]\n",
      " [  0.   1.  97.   2.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0.  99.   1.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0.  99.   1.]\n",
      " [  0.   1.  97.   2.]\n",
      " [  1.   1.  97.   2.]\n",
      " [  1.   3.  91.   6.]\n",
      " [  0.   1.  97.   2.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   1.  97.   2.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   1.  97.   2.]\n",
      " [  0.   0.  98.   1.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0.  98.   1.]\n",
      " [  3.  82.   1.  14.]\n",
      " [  2.  88.   1.   9.]\n",
      " [  1.  97.   0.   2.]\n",
      " [  2.  11.   1.  86.]\n",
      " [  2.  93.   0.   5.]\n",
      " [  4.  47.   1.  48.]\n",
      " [  1.  98.   0.   1.]\n",
      " [  3.  10.   6.  81.]\n",
      " [  4.  54.   2.  40.]\n",
      " [  4.  26.   2.  69.]\n",
      " [  2.   7.   3.  87.]\n",
      " [  5.  64.   1.  30.]\n",
      " [  1.   6.   1.  92.]\n",
      " [  3.  82.   1.  14.]\n",
      " [  2.  10.   2.  86.]\n",
      " [  4.  63.   1.  32.]\n",
      " [  2.  88.   1.   9.]\n",
      " [  2.   7.   2.  90.]\n",
      " [  2.  88.   1.   9.]\n",
      " [  0.   1.   0.  99.]\n",
      " [ 15.  76.   1.   8.]\n",
      " [  2.  11.   1.  86.]\n",
      " [  1.  97.   0.   2.]\n",
      " [  5.  42.   2.  51.]\n",
      " [  3.  29.   1.  66.]\n",
      " [  4.  63.   1.  32.]\n",
      " [  3.  85.   1.  11.]\n",
      " [  8.  87.   1.   4.]\n",
      " [  2.  88.   1.   9.]\n",
      " [  2.   7.   3.  87.]\n",
      " [  0.   1.   0.  98.]\n",
      " [  1.   5.   2.  91.]\n",
      " [  0.   1.   0.  99.]\n",
      " [  4.  92.   0.   3.]\n",
      " [  2.  88.   1.   9.]\n",
      " [ 82.  11.   1.   5.]\n",
      " [ 43.  48.   1.   8.]\n",
      " [100.   0.   0.   0.]\n",
      " [ 55.  37.   1.   7.]\n",
      " [ 98.   1.   0.   1.]\n",
      " [ 86.   9.   1.   4.]\n",
      " [  6.  85.   1.   8.]\n",
      " [ 71.  21.   1.   7.]\n",
      " [ 64.  28.   1.   6.]\n",
      " [ 82.  11.   2.   5.]\n",
      " [ 60.  32.   1.   7.]\n",
      " [ 57.  36.   1.   6.]\n",
      " [ 92.   6.   0.   2.]\n",
      " [ 53.  37.   1.   8.]\n",
      " [ 72.  18.   2.   7.]\n",
      " [ 82.  13.   1.   4.]\n",
      " [ 49.  43.   1.   7.]\n",
      " [ 84.  10.   1.   4.]\n",
      " [ 79.  13.   2.   6.]\n",
      " [  2.  94.   0.   3.]\n",
      " [ 92.   5.   1.   2.]\n",
      " [ 48.  42.   2.   9.]\n",
      " [ 81.  13.   1.   5.]\n",
      " [ 17.  75.   1.   7.]\n",
      " [ 98.   1.   0.   0.]\n",
      " [ 69.  23.   1.   6.]\n",
      " [ 15.  76.   1.   8.]\n",
      " [ 17.  75.   1.   7.]\n",
      " [ 96.   3.   0.   1.]\n",
      " [ 34.  53.   2.  11.]\n",
      " [ 85.  11.   1.   3.]\n",
      " [ 88.   8.   1.   3.]\n",
      " [ 95.   4.   0.   1.]\n",
      " [  4.  91.   0.   5.]\n",
      " [ 17.  65.   2.  16.]] 2\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [2, 3]].values\n",
    "\n",
    "fcmean = FCM(n_clusters=4,m = 2.0, n_init=10, random_state=0)      #Parámetros para el algoritmo \n",
    "y_fcmeans = fcmean.fit(x)               #Entrenamiento para hallar los centros\n",
    "r_fcmeans = fcmean.predict(x)           #Obtener la clasificación de los datos de entrenamiento\n",
    "\n",
    "print(r_fcmeans)                # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(x[r_fcmeans == 2, 0], x[r_fcmeans == 2, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[r_fcmeans == 1, 0], x[r_fcmeans == 1, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[r_fcmeans == 3, 0], x[r_fcmeans == 3, 1], c='black', marker='*', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[r_fcmeans == 0, 0], x[r_fcmeans == 0, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"4 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if i == 2:\n",
    "        salida.append(1)\n",
    "    elif i == 1 or i == 3:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "print(\"Error de entrenamiento\", error(salida, etiquetas))\n",
    "\n",
    "# Matriz de grados de pertenecia\n",
    "pert_fcmeans = fcmean.soft_predict(x)\n",
    "print(np.round(pert_fcmeans * 100), 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 3 3 3 3 1 3 3 3 3 3 3 3 3 0 0 0 1 0 0 0\n",
      " 1 0 0 0 1 0 0 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2]\n",
      "8.89\n",
      "[[  0.   0.  99.   1.]\n",
      " [  0.   0.  99.   0.]\n",
      " [  0.   0.  98.   1.]\n",
      " [  0.   0.  99.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0.  99.   0.]\n",
      " [  0.   0.  99.   0.]\n",
      " [  0.   0.  99.   0.]\n",
      " [  2.   4.  84.  10.]\n",
      " [  1.   2.  93.   4.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  0.   0. 100.   0.]\n",
      " [  3.  91.   0.   6.]\n",
      " [  1.  97.   0.   2.]\n",
      " [  4.  38.   1.  57.]\n",
      " [  2.  15.   1.  82.]\n",
      " [  2.  11.   1.  86.]\n",
      " [  3.  21.   1.  75.]\n",
      " [  3.  78.   1.  18.]\n",
      " [  0.   2.   0.  98.]\n",
      " [  3.  10.   6.  81.]\n",
      " [  2.  22.   1.  75.]\n",
      " [  1.   8.   1.  90.]\n",
      " [  2.  22.   1.  75.]\n",
      " [  3.  29.   1.  66.]\n",
      " [  4.  13.   9.  74.]\n",
      " [  2.  15.   1.  82.]\n",
      " [ 93.   5.   1.   2.]\n",
      " [ 85.  10.   1.   4.]\n",
      " [ 49.  43.   1.   7.]\n",
      " [ 15.  76.   1.   8.]\n",
      " [ 88.   9.   1.   3.]\n",
      " [ 85.  10.   1.   4.]\n",
      " [ 74.  18.   2.   6.]\n",
      " [ 43.  48.   1.   8.]\n",
      " [ 93.   4.   0.   2.]\n",
      " [ 81.  12.   2.   5.]\n",
      " [ 78.  15.   1.   5.]\n",
      " [ 37.  53.   1.   8.]\n",
      " [ 66.  27.   1.   6.]\n",
      " [ 85.  10.   1.   4.]\n",
      " [ 24.  67.   1.   7.]] 2\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_fcmeans = fcmean.predict(prueba)      #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_fcmeans)                        #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(prueba[r_fcmeans == 2, 0], prueba[r_fcmeans == 2, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_fcmeans == 1, 0], prueba[r_fcmeans == 1, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_fcmeans == 3, 0], prueba[r_fcmeans == 3, 1], c='black', marker='*', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_fcmeans == 0, 0], prueba[r_fcmeans == 0, 1], c='green', marker='*', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 4 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if i == 2:\n",
    "        salida.append(1)\n",
    "    elif i == 1 or i == 3:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(error(salida, etiquetas_prueba))\n",
    "\n",
    "# Matriz de grados de pertenecia\n",
    "pert_fcmeans = fcmean.soft_predict(prueba)\n",
    "print(np.round(pert_fcmeans * 100), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Para 5 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4\n",
      " 4 2 4 4 4 2 4 4 2 4 2 4 2 4 4 2 4 2 0 2 4 4 4 4 4 0 4 2 2 2 2 4 4 1 0 1 0\n",
      " 1 1 4 1 0 1 0 0 1 0 1 1 0 1 1 4 1 0 1 0 1 0 0 0 1 0 1 1 1 4 4]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2]\n",
      "Error de entrenamiento 5.71\n",
      "[[  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  1.   1.   3.  95.   1.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   1.  98.   1.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   1.  98.   1.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  0.   0.   1.  98.   0.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   1.  98.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   1.  99.   0.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  2.   1.   6.  88.   3.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  1.   0.   2.  96.   1.]\n",
      " [  0.   0.   1.  98.   1.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   1.  98.   1.]\n",
      " [  2.   0.   2.   0.  96.]\n",
      " [  2.   0.   2.   0.  96.]\n",
      " [  8.   2.   3.   0.  87.]\n",
      " [  5.   2.  61.   1.  31.]\n",
      " [  1.   0.   1.   0.  98.]\n",
      " [  5.   2.  18.   1.  75.]\n",
      " [ 13.   3.   4.   0.  80.]\n",
      " [  3.   2.  83.   4.   8.]\n",
      " [  5.   2.  14.   1.  78.]\n",
      " [  8.   3.  44.   2.  44.]\n",
      " [  2.   1.  90.   2.   5.]\n",
      " [  8.   2.  13.   1.  76.]\n",
      " [  2.   1.  91.   1.   6.]\n",
      " [  2.   0.   2.   0.  96.]\n",
      " [  5.   2.  74.   2.  17.]\n",
      " [  3.   1.   7.   0.  88.]\n",
      " [  2.   0.   2.   0.  96.]\n",
      " [  2.   1.  87.   1.   8.]\n",
      " [  2.   0.   2.   0.  96.]\n",
      " [  0.   0.  99.   0.   1.]\n",
      " [ 69.   6.   4.   1.  21.]\n",
      " [  5.   2.  61.   1.  31.]\n",
      " [  8.   2.   3.   0.  87.]\n",
      " [  8.   3.  28.   2.  59.]\n",
      " [  6.   2.  32.   1.  59.]\n",
      " [  3.   1.   7.   0.  88.]\n",
      " [  3.   1.   3.   0.  93.]\n",
      " [ 61.   6.   4.   1.  29.]\n",
      " [  2.   0.   2.   0.  96.]\n",
      " [  2.   1.  90.   2.   5.]\n",
      " [  0.   0. 100.   0.   0.]\n",
      " [  1.   0.  95.   1.   3.]\n",
      " [  1.   1.  90.   1.   7.]\n",
      " [ 37.   6.   5.   1.  51.]\n",
      " [  2.   0.   2.   0.  96.]\n",
      " [ 16.  74.   3.   1.   6.]\n",
      " [ 96.   2.   0.   0.   2.]\n",
      " [  6.  92.   0.   0.   1.]\n",
      " [ 77.  13.   2.   0.   8.]\n",
      " [  6.  93.   0.   0.   1.]\n",
      " [ 13.  80.   2.   1.   4.]\n",
      " [ 29.   6.   8.   1.  57.]\n",
      " [ 36.  46.   5.   1.  12.]\n",
      " [ 61.  24.   3.   1.  11.]\n",
      " [ 16.  75.   3.   1.   6.]\n",
      " [ 84.   9.   2.   0.   5.]\n",
      " [ 98.   1.   0.   0.   1.]\n",
      " [ 41.  51.   2.   0.   5.]\n",
      " [ 81.  10.   2.   0.   7.]\n",
      " [ 34.  49.   5.   1.  11.]\n",
      " [ 32.  56.   3.   1.   8.]\n",
      " [ 85.   8.   1.   0.   6.]\n",
      " [ 13.  80.   2.   1.   5.]\n",
      " [ 16.  73.   3.   1.   7.]\n",
      " [ 13.   3.   5.   1.  79.]\n",
      " [ 13.  82.   1.   0.   3.]\n",
      " [ 77.  11.   3.   1.   9.]\n",
      " [ 19.  70.   3.   1.   7.]\n",
      " [ 78.   5.   3.   0.  14.]\n",
      " [ 20.  76.   1.   0.   3.]\n",
      " [ 48.  36.   4.   1.  12.]\n",
      " [ 69.   6.   4.   1.  21.]\n",
      " [ 78.   5.   3.   0.  14.]\n",
      " [ 31.  63.   1.   0.   4.]\n",
      " [ 49.  18.   7.   1.  25.]\n",
      " [ 33.  56.   3.   1.   8.]\n",
      " [ 16.  76.   2.   1.   5.]\n",
      " [ 19.  76.   1.   0.   3.]\n",
      " [ 19.   4.   6.   1.  70.]\n",
      " [ 30.  12.  12.   2.  45.]] 2\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [ 2, 3]].values\n",
    "\n",
    "fcmean = FCM(n_clusters=5, m = 2.0, n_init=10, random_state=0)      #Parámetros para el algoritmo \n",
    "y_fcmeans = fcmean.fit(x)               #Entrenamiento para hallar los centros\n",
    "r_fcmeans = fcmean.predict(x)           #Obtener la clasificación de los datos de entrenamiento\n",
    "\n",
    "print(r_fcmeans)                        # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(x[r_fcmeans == 3, 0], x[r_fcmeans == 3, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[r_fcmeans == 2, 0], x[r_fcmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[r_fcmeans == 4, 0], x[r_fcmeans == 4, 1], c='green', marker='*', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[r_fcmeans == 1, 0], x[r_fcmeans == 1, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "ax1.scatter(x[r_fcmeans == 0, 0], x[r_fcmeans == 0, 1], c='black', marker='+', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"5 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if  i == 3: \n",
    "        salida.append(1)\n",
    "    elif i == 2 or i == 4:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "print(\"Error de entrenamiento\", error(salida, etiquetas))\n",
    "\n",
    "# Matriz de grados de pertenecia\n",
    "pert_fcmeans = fcmean.soft_predict(x)\n",
    "print(np.round(pert_fcmeans * 100), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 2 2 2 4 2 2 4 2 4 4 2 2 1 1 0 0 0 1 1\n",
      " 0 1 1 1 0 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "0.0\n",
      "[[  0.   0.   1.  98.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   1.  98.   1.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   1.  99.   0.]\n",
      " [  0.   0.   1.  99.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  3.   2.  10.  80.   5.]\n",
      " [  1.   1.   5.  91.   2.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   0.  99.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [  0.   0.   0. 100.   0.]\n",
      " [ 11.   2.   5.   1.  81.]\n",
      " [  1.   0.   1.   0.  98.]\n",
      " [  5.   2.  24.   1.  68.]\n",
      " [  5.   2.  52.   1.  40.]\n",
      " [  5.   2.  61.   1.  31.]\n",
      " [  6.   2.  47.   1.  43.]\n",
      " [  1.   0.   2.   0.  96.]\n",
      " [  2.   1.  85.   1.  11.]\n",
      " [  3.   2.  83.   4.   8.]\n",
      " [  6.   2.  42.   1.  50.]\n",
      " [  4.   2.  68.   1.  26.]\n",
      " [  6.   2.  42.   1.  50.]\n",
      " [  6.   2.  32.   1.  59.]\n",
      " [  5.   3.  72.   7.  13.]\n",
      " [  5.   2.  52.   1.  40.]\n",
      " [  5.  92.   1.   0.   2.]\n",
      " [ 20.  71.   3.   1.   6.]\n",
      " [ 85.   8.   1.   0.   6.]\n",
      " [ 69.   6.   4.   1.  21.]\n",
      " [ 51.  41.   2.   0.   6.]\n",
      " [ 20.  71.   3.   1.   6.]\n",
      " [ 39.  46.   4.   1.  10.]\n",
      " [ 96.   2.   0.   0.   2.]\n",
      " [  7.  90.   1.   0.   2.]\n",
      " [ 20.  69.   3.   1.   7.]\n",
      " [ 36.  50.   4.   1.   9.]\n",
      " [ 91.   4.   1.   0.   4.]\n",
      " [ 84.  10.   1.   0.   4.]\n",
      " [ 27.  62.   3.   1.   6.]\n",
      " [ 92.   3.   1.   0.   5.]] 2\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_fcmeans = fcmean.predict(prueba)          #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_fcmeans)                            #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización delos grupos\n",
    "ax1.scatter(prueba[r_fcmeans == 3, 0], prueba[r_fcmeans == 3, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_fcmeans == 2, 0], prueba[r_fcmeans == 2, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_fcmeans == 4, 0], prueba[r_fcmeans == 4, 1], c='green', marker='*', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_fcmeans == 1, 0], prueba[r_fcmeans == 1, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "ax1.scatter(prueba[r_fcmeans == 0, 0], prueba[r_fcmeans == 0, 1], c='black', marker='+', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 5 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if  i == 3: \n",
    "        salida.append(1)\n",
    "    elif i == 2 or i == 4:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(error(salida, etiquetas_prueba))\n",
    "\n",
    "# Matriz de grados de pertenecia\n",
    "pert_fcmeans = fcmean.soft_predict(prueba)\n",
    "print(np.round(pert_fcmeans * 100), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Para 6 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 5 5 0 0 0 0 0 0 0 0 5 5 5 5 5 0 5 0 5 0 0 5 0 0 0 0 5 0 0 0 1 1\n",
      " 1 3 1 1 1 3 1 3 3 1 3 1 3 1 1 3 1 3 2 3 1 1 1 1 1 2 1 3 3 3 3 1 1 4 2 4 2\n",
      " 4 4 1 4 2 4 2 2 4 2 4 4 2 4 4 1 4 2 4 2 4 2 2 2 4 2 4 4 4 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2]\n",
      "Error de entrenamiento 5.71\n",
      "[[ 98.   0.   0.   0.   0.   2.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 90.   0.   0.   0.   0.   9.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [  8.   0.   0.   0.   0.  91.]\n",
      " [ 39.   0.   0.   0.   0.  60.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 91.   0.   0.   0.   0.   9.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 89.   0.   0.   0.   0.  10.]\n",
      " [ 91.   0.   0.   0.   0.   9.]\n",
      " [ 78.   1.   0.   1.   0.  20.]\n",
      " [ 81.   0.   0.   0.   0.  18.]\n",
      " [  2.   0.   0.   0.   0.  98.]\n",
      " [ 16.   0.   0.   1.   0.  83.]\n",
      " [ 39.   0.   0.   0.   0.  60.]\n",
      " [ 30.   0.   0.   1.   0.  68.]\n",
      " [ 31.   0.   0.   0.   0.  68.]\n",
      " [ 77.   0.   0.   1.   0.  22.]\n",
      " [  2.   0.   0.   0.   0.  98.]\n",
      " [ 68.   1.   0.   1.   0.  30.]\n",
      " [ 15.   1.   0.   1.   0.  83.]\n",
      " [ 60.   1.   0.   1.   0.  37.]\n",
      " [ 89.   0.   0.   0.   0.  10.]\n",
      " [  2.   0.   0.   0.   0.  98.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 89.   0.   0.   0.   0.  10.]\n",
      " [ 89.   0.   0.   0.   0.  10.]\n",
      " [  2.   0.   0.   0.   0.  98.]\n",
      " [ 91.   0.   0.   0.   0.   9.]\n",
      " [ 98.   0.   0.   0.   0.   2.]\n",
      " [ 91.   0.   0.   0.   0.   9.]\n",
      " [  0.  95.   2.   2.   1.   0.]\n",
      " [  0.  96.   2.   2.   0.   0.]\n",
      " [  0.  87.   7.   3.   2.   0.]\n",
      " [  1.  30.   5.  61.   2.   1.]\n",
      " [  0.  98.   1.   1.   0.   0.]\n",
      " [  1.  73.   5.  18.   2.   1.]\n",
      " [  0.  79.  13.   4.   3.   0.]\n",
      " [  3.   8.   3.  79.   2.   5.]\n",
      " [  1.  77.   5.  14.   2.   1.]\n",
      " [  2.  42.   8.  43.   3.   2.]\n",
      " [  2.   5.   2.  88.   1.   2.]\n",
      " [  1.  75.   8.  13.   2.   1.]\n",
      " [  1.   5.   2.  90.   1.   1.]\n",
      " [  0.  95.   2.   2.   1.   0.]\n",
      " [  2.  17.   4.  73.   2.   2.]\n",
      " [  0.  87.   3.   8.   1.   0.]\n",
      " [  0.  96.   2.   2.   0.   0.]\n",
      " [  1.   8.   2.  86.   1.   1.]\n",
      " [  0.  96.   2.   2.   0.   0.]\n",
      " [  0.   1.   0.  99.   0.   0.]\n",
      " [  1.  21.  68.   4.   6.   1.]\n",
      " [  1.  30.   5.  61.   2.   1.]\n",
      " [  0.  87.   7.   3.   2.   0.]\n",
      " [  1.  57.   8.  28.   3.   2.]\n",
      " [  1.  58.   6.  32.   2.   1.]\n",
      " [  0.  87.   3.   8.   1.   0.]\n",
      " [  0.  92.   3.   3.   1.   0.]\n",
      " [  1.  29.  60.   4.   6.   1.]\n",
      " [  0.  96.   2.   2.   0.   0.]\n",
      " [  2.   5.   2.  88.   1.   2.]\n",
      " [  0.   0.   0. 100.   0.   0.]\n",
      " [  1.   3.   1.  94.   0.   1.]\n",
      " [  1.   6.   1.  90.   1.   1.]\n",
      " [  1.  51.  37.   5.   6.   1.]\n",
      " [  0.  96.   2.   2.   0.   0.]\n",
      " [  1.   6.  16.   3.  73.   1.]\n",
      " [  0.   2.  96.   0.   2.   0.]\n",
      " [  0.   1.   6.   0.  92.   0.]\n",
      " [  0.   8.  77.   2.  13.   0.]\n",
      " [  0.   1.   6.   0.  92.   0.]\n",
      " [  1.   4.  13.   2.  80.   1.]\n",
      " [  1.  57.  28.   8.   5.   1.]\n",
      " [  1.  12.  35.   5.  46.   1.]\n",
      " [  1.  10.  61.   3.  24.   1.]\n",
      " [  1.   6.  16.   3.  74.   1.]\n",
      " [  0.   5.  84.   1.   9.   0.]\n",
      " [  0.   1.  98.   0.   1.   0.]\n",
      " [  0.   5.  41.   2.  51.   0.]\n",
      " [  0.   7.  81.   2.  10.   0.]\n",
      " [  1.  10.  34.   5.  48.   2.]\n",
      " [  1.   8.  32.   3.  56.   1.]\n",
      " [  0.   6.  84.   1.   8.   0.]\n",
      " [  1.   5.  13.   2.  79.   1.]\n",
      " [  1.   7.  16.   3.  72.   1.]\n",
      " [  0.  78.  13.   5.   3.   1.]\n",
      " [  0.   3.  13.   1.  82.   0.]\n",
      " [  1.   9.  76.   3.  11.   1.]\n",
      " [  1.   7.  19.   3.  69.   1.]\n",
      " [  0.  14.  77.   3.   5.   0.]\n",
      " [  0.   3.  20.   1.  75.   0.]\n",
      " [  1.  12.  47.   4.  35.   1.]\n",
      " [  1.  21.  68.   4.   6.   1.]\n",
      " [  0.  14.  77.   3.   5.   0.]\n",
      " [  0.   4.  30.   1.  63.   0.]\n",
      " [  1.  25.  48.   7.  18.   1.]\n",
      " [  1.   8.  33.   3.  56.   1.]\n",
      " [  1.   5.  16.   2.  76.   1.]\n",
      " [  0.   3.  19.   1.  76.   0.]\n",
      " [  1.  70.  19.   6.   4.   1.]\n",
      " [  2.  45.  29.  11.  11.   2.]] 2\n"
     ]
    }
   ],
   "source": [
    "#Datos de entrenamiento normalizados con los dos descriptores seleccionados\n",
    "x = df_TN.iloc[:, [ 2, 3]].values\n",
    "\n",
    "fcmean = FCM(n_clusters=6, m = 2.0, n_init=10, random_state=0)      #Parámetros para el algoritmo \n",
    "y_fcmeans = fcmean.fit(x)                                           #Entrenamiento para hallar los centros\n",
    "r_fcmeans = fcmean.predict(x)                                       #Obtener la clasificación de los datos de entrenamiento\n",
    "\n",
    "print(r_fcmeans)                                    # Se muestra la clasificación resultante de los datos de entrenamiento\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización de los grupos\n",
    "ax1.scatter(x[r_fcmeans == 5, 0], x[r_fcmeans == 5, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(x[r_fcmeans == 0, 0], x[r_fcmeans == 0, 1], c='gray', marker='+', label = 'Iris-setosa')\n",
    "ax1.scatter(x[r_fcmeans == 1, 0], x[r_fcmeans == 1, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[r_fcmeans == 3, 0], x[r_fcmeans == 3, 1], c='green', marker='*', label = 'Iris-versicolour')\n",
    "ax1.scatter(x[r_fcmeans == 4, 0], x[r_fcmeans == 4, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "ax1.scatter(x[r_fcmeans == 2, 0], x[r_fcmeans == 2, 1], c='black', marker='x', label = 'Iris-virginica')\n",
    "\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"6 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if i == 5 or i == 0: \n",
    "        salida.append(1)\n",
    "    elif i == 1 or i == 3:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "print(\"Error de entrenamiento\", error(salida, etiquetas))\n",
    "\n",
    "# Matriz de grados de pertenecia\n",
    "pert_fcmeans = fcmean.soft_predict(x)\n",
    "print(np.round(pert_fcmeans * 100), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 5 5 0 5 5 5 0 0 0 0 1 1 1 3 3 3 1 3 3 1 3 1 1 3 3 4 4 2 2 2 4 4\n",
      " 2 4 4 4 2 2 4 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "0.0\n",
      "[[81.  0.  0.  0.  0. 18.]\n",
      " [90.  0.  0.  0.  0.  9.]\n",
      " [91.  0.  0.  0.  0.  9.]\n",
      " [90.  0.  0.  0.  0.  9.]\n",
      " [98.  0.  0.  0.  0.  2.]\n",
      " [46.  0.  0.  0.  0. 53.]\n",
      " [46.  0.  0.  0.  0. 53.]\n",
      " [90.  0.  0.  0.  0.  9.]\n",
      " [21.  2.  1.  3.  1. 73.]\n",
      " [21.  1.  0.  2.  0. 76.]\n",
      " [39.  0.  0.  0.  0. 60.]\n",
      " [89.  0.  0.  0.  0. 10.]\n",
      " [98.  0.  0.  0.  0.  2.]\n",
      " [98.  0.  0.  0.  0.  2.]\n",
      " [98.  0.  0.  0.  0.  2.]\n",
      " [ 0. 80. 11.  5.  2.  1.]\n",
      " [ 0. 98.  1.  1.  0.  0.]\n",
      " [ 1. 67.  5. 24.  2.  1.]\n",
      " [ 1. 38.  5. 52.  2.  1.]\n",
      " [ 1. 30.  5. 61.  2.  1.]\n",
      " [ 1. 42.  6. 47.  2.  2.]\n",
      " [ 0. 96.  1.  2.  0.  0.]\n",
      " [ 1. 10.  2. 86.  1.  1.]\n",
      " [ 3.  8.  3. 79.  2.  5.]\n",
      " [ 1. 48.  5. 42.  2.  1.]\n",
      " [ 1. 24.  4. 68.  2.  1.]\n",
      " [ 1. 48.  5. 42.  2.  1.]\n",
      " [ 1. 58.  6. 32.  2.  1.]\n",
      " [ 6. 12.  5. 66.  3.  8.]\n",
      " [ 1. 38.  5. 52.  2.  1.]\n",
      " [ 0.  2.  5.  1. 92.  0.]\n",
      " [ 1.  6. 19.  3. 71.  1.]\n",
      " [ 0.  6. 84.  1.  8.  0.]\n",
      " [ 1. 21. 68.  4.  6.  1.]\n",
      " [ 0.  6. 51.  2. 41.  1.]\n",
      " [ 1.  6. 19.  3. 71.  1.]\n",
      " [ 1. 10. 39.  4. 45.  1.]\n",
      " [ 0.  2. 96.  0.  2.  0.]\n",
      " [ 0.  2.  7.  1. 90.  0.]\n",
      " [ 1.  7. 20.  3. 68.  1.]\n",
      " [ 1.  9. 36.  4. 50.  1.]\n",
      " [ 0.  4. 91.  1.  4.  0.]\n",
      " [ 0.  4. 84.  1. 10.  0.]\n",
      " [ 1.  6. 27.  3. 62.  1.]\n",
      " [ 0.  5. 91.  1.  3.  0.]] 2\n"
     ]
    }
   ],
   "source": [
    "###############     Prueba   ################\n",
    "\n",
    "#Datos de prueba normalizadoscon los dos descriptores seleccionados\n",
    "prueba = df_PN.iloc[:, [2, 3]].values\n",
    "\n",
    "r_fcmeans = fcmean.predict(prueba)          #Clasificación de los datos de prueba en base a los centros obtenidos\n",
    "print(r_fcmeans)                            #Se muestra el resultado de la clasificación\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "# Visualización de los grupos\n",
    "ax1.scatter(prueba[r_fcmeans == 5, 0], prueba[r_fcmeans == 5, 1], c='purple', marker='o', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_fcmeans == 0, 0], prueba[r_fcmeans == 0, 1], c='gray', marker='+', label = 'Iris-setosa')\n",
    "ax1.scatter(prueba[r_fcmeans == 1, 0], prueba[r_fcmeans == 1, 1], c='orange', marker='x', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_fcmeans == 3, 0], prueba[r_fcmeans == 3, 1], c='green', marker='*', label = 'Iris-versicolour')\n",
    "ax1.scatter(prueba[r_fcmeans == 4, 0], prueba[r_fcmeans == 4, 1], c='blue', marker='o', label = 'Iris-virginica')\n",
    "ax1.scatter(prueba[r_fcmeans == 2, 0], prueba[r_fcmeans == 2, 1], c='black', marker='x', label = 'Iris-virginica')\n",
    "\n",
    "# Visualización de los centros de cada grupo\n",
    "ax1.scatter(fcmean.centers[:, 0], fcmean.centers[:, 1], s = 100, c = 'red', label = 'Centros')\n",
    "ax1.set_xlabel('X2')\n",
    "ax1.set_ylabel('X3')\n",
    "ax1.set_title(\"Prueba 6 Grupos FCM\")\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Interpretación de la clasificación (El algoritmo le asigna en orden diferente)\n",
    "salida = []\n",
    "for i in r_fcmeans:\n",
    "    if i == 5 or i == 0: \n",
    "        salida.append(1)\n",
    "    elif i == 1 or i == 3:\n",
    "        salida.append(2)\n",
    "    else:\n",
    "        salida.append(3)\n",
    "\n",
    "print(salida)\n",
    "\n",
    "print(error(salida, etiquetas_prueba))\n",
    "\n",
    "# Matriz de grados de pertenecia\n",
    "pert_fcmeans = fcmean.soft_predict(prueba)\n",
    "print(np.round(pert_fcmeans * 100), 2)"
   ]
  }
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