Lab10- Kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1 3.5 1.4 0.2] 0\n",
      "[4.9 3.  1.4 0.2] 0\n",
      "[4.7 3.2 1.3 0.2] 0\n",
      "[4.6 3.1 1.5 0.2] 0\n",
      "[5.  3.6 1.4 0.2] 0\n",
      "[5.4 3.9 1.7 0.4] 0\n",
      "[4.6 3.4 1.4 0.3] 0\n",
      "[5.  3.4 1.5 0.2] 0\n",
      "[4.4 2.9 1.4 0.2] 0\n",
      "[4.9 3.1 1.5 0.1] 0\n",
      "[5.4 3.7 1.5 0.2] 0\n",
      "[4.8 3.4 1.6 0.2] 0\n",
      "[4.8 3.  1.4 0.1] 0\n",
      "[4.3 3.  1.1 0.1] 0\n",
      "[5.8 4.  1.2 0.2] 0\n",
      "[5.7 4.4 1.5 0.4] 0\n",
      "[5.4 3.9 1.3 0.4] 0\n",
      "[5.1 3.5 1.4 0.3] 0\n",
      "[5.7 3.8 1.7 0.3] 0\n",
      "[5.1 3.8 1.5 0.3] 0\n",
      "[5.4 3.4 1.7 0.2] 0\n",
      "[5.1 3.7 1.5 0.4] 0\n",
      "[4.6 3.6 1.  0.2] 0\n",
      "[5.1 3.3 1.7 0.5] 0\n",
      "[4.8 3.4 1.9 0.2] 0\n",
      "[5.  3.  1.6 0.2] 0\n",
      "[5.  3.4 1.6 0.4] 0\n",
      "[5.2 3.5 1.5 0.2] 0\n",
      "[5.2 3.4 1.4 0.2] 0\n",
      "[4.7 3.2 1.6 0.2] 0\n",
      "[4.8 3.1 1.6 0.2] 0\n",
      "[5.4 3.4 1.5 0.4] 0\n",
      "[5.2 4.1 1.5 0.1] 0\n",
      "[5.5 4.2 1.4 0.2] 0\n",
      "[4.9 3.1 1.5 0.2] 0\n",
      "[5.  3.2 1.2 0.2] 0\n",
      "[5.5 3.5 1.3 0.2] 0\n",
      "[4.9 3.6 1.4 0.1] 0\n",
      "[4.4 3.  1.3 0.2] 0\n",
      "[5.1 3.4 1.5 0.2] 0\n",
      "[5.  3.5 1.3 0.3] 0\n",
      "[4.5 2.3 1.3 0.3] 0\n",
      "[4.4 3.2 1.3 0.2] 0\n",
      "[5.  3.5 1.6 0.6] 0\n",
      "[5.1 3.8 1.9 0.4] 0\n",
      "[4.8 3.  1.4 0.3] 0\n",
      "[5.1 3.8 1.6 0.2] 0\n",
      "[4.6 3.2 1.4 0.2] 0\n",
      "[5.3 3.7 1.5 0.2] 0\n",
      "[5.  3.3 1.4 0.2] 0\n",
      "[5.1 3.5 1.4 0.2] 0\n",
      "[4.9 3.  1.4 0.2] 0\n",
      "[4.7 3.2 1.3 0.2] 0\n",
      "[4.6 3.1 1.5 0.2] 0\n",
      "[5.  3.6 1.4 0.2] 0\n",
      "[5.4 3.9 1.7 0.4] 0\n",
      "[4.6 3.4 1.4 0.3] 0\n",
      "[5.  3.4 1.5 0.2] 0\n",
      "[4.4 2.9 1.4 0.2] 0\n",
      "[4.9 3.1 1.5 0.1] 0\n",
      "[5.4 3.7 1.5 0.2] 0\n",
      "[4.8 3.4 1.6 0.2] 0\n",
      "[4.8 3.  1.4 0.1] 0\n",
      "[4.3 3.  1.1 0.1] 0\n",
      "[5.8 4.  1.2 0.2] 0\n",
      "[5.7 4.4 1.5 0.4] 0\n",
      "[5.4 3.9 1.3 0.4] 0\n",
      "[5.1 3.5 1.4 0.3] 0\n",
      "[5.7 3.8 1.7 0.3] 0\n",
      "[5.1 3.8 1.5 0.3] 0\n",
      "[5.4 3.4 1.7 0.2] 0\n",
      "[5.1 3.7 1.5 0.4] 0\n",
      "[4.6 3.6 1.  0.2] 0\n",
      "[5.1 3.3 1.7 0.5] 0\n",
      "[4.8 3.4 1.9 0.2] 0\n",
      "[5.  3.  1.6 0.2] 0\n",
      "[5.  3.4 1.6 0.4] 0\n",
      "[5.2 3.5 1.5 0.2] 0\n",
      "[5.2 3.4 1.4 0.2] 0\n",
      "[4.7 3.2 1.6 0.2] 0\n",
      "[4.8 3.1 1.6 0.2] 0\n",
      "[5.4 3.4 1.5 0.4] 0\n",
      "[5.2 4.1 1.5 0.1] 0\n",
      "[5.5 4.2 1.4 0.2] 0\n",
      "[4.9 3.1 1.5 0.2] 0\n",
      "[5.  3.2 1.2 0.2] 0\n",
      "[5.5 3.5 1.3 0.2] 0\n",
      "[4.9 3.6 1.4 0.1] 0\n",
      "[4.4 3.  1.3 0.2] 0\n",
      "[5.1 3.4 1.5 0.2] 0\n",
      "[5.  3.5 1.3 0.3] 0\n",
      "[4.5 2.3 1.3 0.3] 0\n",
      "[4.4 3.2 1.3 0.2] 0\n",
      "[5.  3.5 1.6 0.6] 0\n",
      "[5.1 3.8 1.9 0.4] 0\n",
      "[4.8 3.  1.4 0.3] 0\n",
      "[5.1 3.8 1.6 0.2] 0\n",
      "[4.6 3.2 1.4 0.2] 0\n",
      "[5.3 3.7 1.5 0.2] 0\n",
      "[5.  3.3 1.4 0.2] 0\n",
      "[5.1 3.5 1.4 0.2] 0\n",
      "[4.9 3.  1.4 0.2] 0\n",
      "[4.7 3.2 1.3 0.2] 0\n",
      "[4.6 3.1 1.5 0.2] 0\n",
      "[5.  3.6 1.4 0.2] 0\n",
      "[5.4 3.9 1.7 0.4] 0\n",
      "[4.6 3.4 1.4 0.3] 0\n",
      "[5.  3.4 1.5 0.2] 0\n",
      "[4.4 2.9 1.4 0.2] 0\n",
      "[4.9 3.1 1.5 0.1] 0\n",
      "[5.4 3.7 1.5 0.2] 0\n",
      "[4.8 3.4 1.6 0.2] 0\n",
      "[4.8 3.  1.4 0.1] 0\n",
      "[4.3 3.  1.1 0.1] 0\n",
      "[5.8 4.  1.2 0.2] 0\n",
      "[5.7 4.4 1.5 0.4] 0\n",
      "[5.4 3.9 1.3 0.4] 0\n",
      "[5.1 3.5 1.4 0.3] 0\n",
      "[5.7 3.8 1.7 0.3] 0\n",
      "[5.1 3.8 1.5 0.3] 0\n",
      "[5.4 3.4 1.7 0.2] 0\n",
      "[5.1 3.7 1.5 0.4] 0\n",
      "[4.6 3.6 1.  0.2] 0\n",
      "[5.1 3.3 1.7 0.5] 0\n",
      "[4.8 3.4 1.9 0.2] 0\n",
      "[5.  3.  1.6 0.2] 0\n",
      "[5.  3.4 1.6 0.4] 0\n",
      "[5.2 3.5 1.5 0.2] 0\n",
      "[5.2 3.4 1.4 0.2] 0\n",
      "[4.7 3.2 1.6 0.2] 0\n",
      "[4.8 3.1 1.6 0.2] 0\n",
      "[5.4 3.4 1.5 0.4] 0\n",
      "[5.2 4.1 1.5 0.1] 0\n",
      "[5.5 4.2 1.4 0.2] 0\n",
      "[4.9 3.1 1.5 0.2] 0\n",
      "[5.  3.2 1.2 0.2] 0\n",
      "[5.5 3.5 1.3 0.2] 0\n",
      "[4.9 3.6 1.4 0.1] 0\n",
      "[4.4 3.  1.3 0.2] 0\n",
      "[5.1 3.4 1.5 0.2] 0\n",
      "[5.  3.5 1.3 0.3] 0\n",
      "[4.5 2.3 1.3 0.3] 0\n",
      "[4.4 3.2 1.3 0.2] 0\n",
      "[5.  3.5 1.6 0.6] 0\n",
      "[5.1 3.8 1.9 0.4] 0\n",
      "[4.8 3.  1.4 0.3] 0\n",
      "[5.1 3.8 1.6 0.2] 0\n",
      "[4.6 3.2 1.4 0.2] 0\n",
      "[5.3 3.7 1.5 0.2] 0\n",
      "[5.  3.3 1.4 0.2] 0\n",
      "--------------------------------------------------\n",
      "****************************************************************************************************\n",
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    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "iris =datasets.load_iris()\n",
    "\n",
    "setosa=iris.data[0:50]\n",
    "versicolor=iris.data[50:100]\n",
    "virginica=iris.data[100:150]\n",
    "\n",
    "setosa_sepal_length_avg=np.mean(setosa[:,0])\n",
    "setosa_sepal_width_avg=np.mean(setosa[:,1])\n",
    "setosa_petal_length_avg=np.mean(setosa[:,2])\n",
    "setosa_petal_width_avg=np.mean(setosa[:,3])\n",
    "\n",
    "versicolor_sepal_length_avg=np.mean(versicolor[:,0])\n",
    "versicolor_sepal_width_avg=np.mean(versicolor[:,1])\n",
    "versicolor_petal_length_avg=np.mean(versicolor[:,2])\n",
    "versicolor_petal_width_avg=np.mean(versicolor[:,3])\n",
    "\n",
    "virginica_sepal_length_avg=np.mean(virginica[:,0])\n",
    "virginica_sepal_width_avg=np.mean(virginica[:,1])\n",
    "virginica_petal_length_avg=np.mean(virginica[:,2])\n",
    "virginica_petal_width_avg=np.mean(virginica[:,3])\n",
    "\n",
    "#eğer veriler sınıflara ayrılmışsa elimizdeki veri setinde sınıf sayısını bulabiliriz.\n",
    "def sinifsayisibul():\n",
    "    h=[]\n",
    "    for i in range(len(iris.target)):\n",
    "        if (iris.target[i] in h):\n",
    "            continue\n",
    "        else:\n",
    "            h.append(iris.target[i])\n",
    "    return len(h)\n",
    "    \n",
    "sinifsayisibul()\n",
    "\n",
    "\n",
    "def sinifdondur(sinif):\n",
    "    if(sinifsayisibul()<sinif):\n",
    "        print(\"Böyle bir sınıf yok\")\n",
    "    else:\n",
    "        data = iris.data\n",
    "        classId = iris.target[:]\n",
    "        number_of_class = 3\n",
    "        class_counter = 1\n",
    "        for j in range(number_of_class):\n",
    "            for i in range(len(classId)):\n",
    "                if(classId[i] == sinif):\n",
    "                    print(data[i],classId[i])\n",
    "        print(\"-\"*50)\n",
    "        class_counter+=1\n",
    "        \n",
    "sinifdondur(0) #0. sınıfın verileri gelir kaç tane sınıf olduğunuda sinifsayisibul() fonk'nu kullanılarak bulunabilir\n",
    "\n",
    "print(\"*\"*100)\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "fig=plt.figure()\n",
    "ax=plt.axes(projection='3d')\n",
    "\n",
    "xdata=iris.data[:,0]\n",
    "ydata=iris.data[:,2]\n",
    "zdata=iris.data[:,3]\n",
    "label=iris.target\n",
    "\n",
    "ax.scatter3D(xdata[100:149], ydata[100:149], zdata[100:149],marker='o')\n",
    "ax.scatter3D(xdata[50:99], ydata[50:99], zdata[50:99],marker='x')\n",
    "ax.scatter3D(xdata[0:49], ydata[0:49], zdata[0:49],marker='x')\n",
    "\n",
    "\n",
    "def get_mu_s():\n",
    "    mu_0=[5,2,0]\n",
    "    mu_1=[4,4,0]\n",
    "    mu_2=[3,2,5]\n",
    "    \n",
    "    return mu_0,mu_1,mu_2\n",
    "\n",
    "def get_distance(mu,point):\n",
    "    x=mu[0]-point[0]\n",
    "    y=mu[1]-point[1]\n",
    "    z=mu[2]-point[2]\n",
    "    return (x**2+y**2+z**2)**0.5\n",
    "\n",
    "\n",
    "def get_class_for_one_instance(flower):\n",
    "    mu_s = get_mu_s()\n",
    "    d_0 = get_distance(mu_s[0], flower)\n",
    "    d_1 = get_distance(mu_s[1], flower)\n",
    "    d_2 = get_distance(mu_s[2], flower)\n",
    "    \n",
    "    if((d_0 < d_1) and (d_0 < d_2)):\n",
    "        return \"0\"\n",
    "    if((d_1 < d_0) and (d_1 < d_2)):\n",
    "        return \"1\"\n",
    "    if((d_2 < d_1) and (d_2 < d_0)):\n",
    "        return \"2\"\n",
    "    \n",
    "\n",
    "def my_f_1(s_1=125):\n",
    "    x=iris.data[s_1][0]\n",
    "    y=iris.data[s_1][2]\n",
    "    z=iris.data[s_1][3]\n",
    "    my_f_1=[x,y,z]\n",
    "    r=get_class_for_one_instance(my_f_1)\n",
    "    print(r)\n",
    "    \n",
    "for i in range(150):\n",
    "    my_f_1(i)\n",
    "\n",
    "    \n",
    "def get_flower(i): \n",
    "    #iris data üzerinde bana i değerinde gelen datanın 0 2 3 sütunun donduruyorum\n",
    "    x=iris.data[i][0]\n",
    "    y=iris.data[i][2]\n",
    "    z=iris.data[i][3]\n",
    "    return [x,y,z]\n",
    "    \n",
    "    \n",
    "def update_mu(): \n",
    "    #150 ya kadar her bir çicek için bana class bilgisi ver güncelle ve geriye mu'leri ver\n",
    "    hata=\"yok\"\n",
    "    mu_0_counter=0.0001\n",
    "    mu_0_sum=0\n",
    "    \n",
    "    mu_1_counter=0.0001\n",
    "    mu_1_sum=0\n",
    "    \n",
    "    mu_2_counter=0.0001\n",
    "    mu_2_sum=0\n",
    "    \n",
    "    c_1=[]\n",
    "    c_2=[]\n",
    "    c_3=[]\n",
    "    \n",
    "    for i in range(150):\n",
    "        my_flower_data=get_flower(i)\n",
    "        f_class = get_class_for_one_instance(my_flower_data)\n",
    "        hata=\"var\"\n",
    "        if(f_class==\"0\"):\n",
    "            c_1.append(my_flower_data)\n",
    "        if(f_class==\"1\"):\n",
    "            c_2.append(my_flower_data)\n",
    "        if(f_class==\"2\"):\n",
    "            c_3.append(my_flower_data)\n",
    "    return c_1,c_2,c_3\n",
    "    \n",
    "    \n",
    "c_1,c_2,c_3 = update_mu()\n",
    "print(len(c_1))\n",
    "print(len(c_2))\n",
    "print(len(c_3))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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