{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algoritmo K-Means Exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 5, 1.5, 8, 1, 9]\n",
    "y = [2, 8, 1.8, 8, 0.6, 11]\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(x,y, color='g', edgecolor='black')\n",
    "plt.ylabel('y')\n",
    "plt.xlabel('x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.   2. ]\n",
      " [ 5.   8. ]\n",
      " [ 1.5  1.8]\n",
      " [ 8.   8. ]\n",
      " [ 1.   0.6]\n",
      " [ 9.  11. ]]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[1,2], [5,8], [1.5, 1.8], [8, 8], [1, 0.6], [9, 11]]) # Convertendo nossos dados para um array numpy\n",
    "\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vamos inicilizar o algoritmo K-Means com os parâmetros necessários e utilizar a função fit() para ajustarmos os dados\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.16666667 1.46666667]\n",
      " [7.33333333 9.        ]]\n",
      "[0 1 0 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "# Obtendo os valores das centróides e rótulos baseados nos ajustes\n",
    "centroids = kmeans.cluster_centers_\n",
    "labels = kmeans.labels_\n",
    "\n",
    "print(centroids)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordenada: [1. 2.] rótulo: 0\n",
      "Coordenada: [5. 8.] rótulo: 1\n",
      "Coordenada: [1.5 1.8] rótulo: 0\n",
      "Coordenada: [8. 8.] rótulo: 1\n",
      "Coordenada: [1.  0.6] rótulo: 0\n",
      "Coordenada: [ 9. 11.] rótulo: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cores = [\"g.\", \"r.\", \"c.\", \"y.\"]\n",
    "\n",
    "for i in range(len(X)):\n",
    "    print(\"Coordenada:\", X[i], \"rótulo:\", labels[i])\n",
    "    plt.plot(X[i][0], X[i][1], cores[labels[i]], markersize=10)\n",
    "\n",
    "plt.scatter(centroids[:,0], centroids[:,1], marker=\"x\", s=150, linewidths=5, zorder=10, edgecolor='black')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}