Module_7_Lab_1_KMeansClustering.ipynb

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  "metadata": {
    "colab": {
      "name": "Module_7_Lab_1_KMeansClustering.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GVGunasekhar/FMML_COURSE_ASSIGNMENT/blob/main/Module_7_Lab_1_KMeansClustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSVIRwShuG11"
      },
      "source": [
        "Lab by Nikunj Nawal\n",
        "\n",
        "nikunj.nawal@research.iiit.ac.in"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XWsDgHdl5dOG"
      },
      "source": [
        "# K-Means\n",
        "\n",
        "K-Means algorithm is a centroid based clustering algorithm where the sum of distances of points from the centroid of each cluster is minimized. The final output is a set of K clusters .ie. the cluster assigned to each point and the K centroids of the clusters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smmi2Hm_6dtJ"
      },
      "source": [
        "### The Algorithm\n",
        "> 1.   Initialize K centroids to K points randomly and set each point's initial cluster as the centroid closest to it.\n",
        "2.   While the clusers are changing - \\\\\n",
        "a. Assign the new centroids as the centroids of the points which have the same assigned cluster. \\\\\n",
        "b. Assign the new clusters to the points as the closest centroid.\n",
        "3. Return the assignments and the centroids.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQWLS_wq_WVt"
      },
      "source": [
        "### Part 1 - Implementation of KMeans and testing on Synthetic Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYTNMyeL8rKH"
      },
      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_blobs"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGIO_edJ4uhB",
        "outputId": "a56b686a-fbda-40d5-f838-9392a7fbd2c8"
      },
      "source": [
        "# Generating Synthetic Data\n",
        "\n",
        "k=2\n",
        "color=[\"darkturquoise\",\"darkorange\",\"teal\",\"darkviolet\",\"tomato\", \"yellowgreen\",\"hotpink\",\"gold\"]\n",
        "\n",
        "X,_=make_blobs(n_samples=500, n_features=2, centers=k, random_state=10)\n",
        "print(\"Shape = \" + str(X.shape))\n",
        "\n",
        "print(\"Sample:\")\n",
        "print(X[:5])"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape = (500, 2)\n",
            "Sample:\n",
            "[[ 5.83801528 -9.29715058]\n",
            " [ 5.67102739 -9.76225984]\n",
            " [ 3.15598083  5.95298368]\n",
            " [ 3.87586762  5.94663718]\n",
            " [ 3.94478532  4.85652467]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHfSomXy91rb"
      },
      "source": [
        "#DEFINE ALL THE FUNCTIONS & VARIABLES WE NEED TO DO CLUSTERING\n",
        "#Create an empty dictionary to store the cluster name, centroid and points associated with the cluster.\n",
        "clusters={}\n",
        "\n",
        "def initializeClusterCentroids():\n",
        "  #For each cluster, initialize its properties\n",
        "  for i in range(k):\n",
        "      center = np.random.randint(-8, 10, size= (X.shape[1],), )\n",
        "      points = []\n",
        "      \n",
        "      cluster = {\n",
        "          'center' : center,\n",
        "          'points' : points,\n",
        "          'color' : color[i]\n",
        "      }\n",
        "      clusters[i] = cluster #looping over clusters dictionary \n",
        "\n",
        "#Calculate euclidean distance between v1 and v2\n",
        "def distance(v1, v2):\n",
        "    return np.sum((v2-v1)**2)**0.5\n",
        "\n",
        "def assignPointsToCluster(clusters, X):\n",
        "    for ix in range(X.shape[0]):#iterate for all datapoints\n",
        "\n",
        "        #Foe each datapoint, find the distance to k centroids\n",
        "        distance_of_i = [] \n",
        "        for kx in range(k):\n",
        "            d = distance(X[ix], clusters[kx]['center']) #we have find distance b/w \n",
        "            #all the point with all the five (k) cluster centers\n",
        "            distance_of_i.append(d)\n",
        "          \n",
        "        #Based on the distances to k centroid, pick which cluster to assign it to\n",
        "        #here we want find the minimum distance of cluster centers\n",
        "        cluster_to_choose = np.argmin(distance_of_i)\n",
        "        clusters[cluster_to_choose]['points'].append(X[ix]) \n",
        "\n",
        "#STEP 3: UPDATE CLUSTER CENTROIDS\n",
        "def updateCluster(clusters):\n",
        "    for kx in range(k):\n",
        "        cluster_points = clusters[kx]['points']\n",
        "        \n",
        "        cluster_points = np.array(cluster_points)\n",
        "        #here pts are array of list but we want array of array so\n",
        "        \n",
        "        if len(cluster_points)>0:\n",
        "            new_center = np.mean(cluster_points, axis=0) #axis is along rows so will \n",
        "            #find mean of all the feature seprately it will give 2,0 when we have cluster pts 50,2\n",
        "            clusters[kx]['center'] = new_center\n",
        "            clusters[kx]['points'] = [] # clear the points in a cluster list (emptying pts)\n",
        "            #as we have to do step 2 after step 3 again till not converge"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H95Y7_Es-5pl"
      },
      "source": [
        "#We need to visualize this too. Lets define a function that can do that!\n",
        "def plotclusters(clusters, plot):\n",
        "    for kx in range(k):\n",
        "        cluster_points = clusters[kx]['points']\n",
        "        cluster_color = clusters[kx]['color']\n",
        "        cluster_center = clusters[kx]['center']\n",
        "        cluster_points = np.array(cluster_points)\n",
        "        \n",
        "        #plotting points associated /nearest to the cluster centers\n",
        "        if len(cluster_points) > 0:\n",
        "          plot.scatter(cluster_points[:, 0], cluster_points[:, 1], s = 2, c = cluster_color)\n",
        "        \n",
        "        plot.scatter(cluster_center[0], cluster_center[1], s = 250, c = cluster_color, marker=\"o\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxdYCXL9_2Ol"
      },
      "source": [
        "We can now run and visualize how the clusters evolve."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "gcTPXjLe-7nC",
        "outputId": "f000030a-d4ef-4e83-9469-e1dc72e752e0"
      },
      "source": [
        "fig, axs = plt.subplots(2, 3)\n",
        "fig.set_size_inches(15, 10)\n",
        "axs[0, 0].scatter(X[:,0],X[:,1], s = 2, c = 'black')\n",
        "axs[0, 0].set_title('Dataset')\n",
        "\n",
        "np.random.seed(1)\n",
        "initializeClusterCentroids()\n",
        "\n",
        "#Now, lets plot the centroids it has initalized\n",
        "axs[0, 1].set_title('Centroid Initialization')\n",
        "axs[0, 1].scatter(X[:, 0], X[:, 1], s = 2, c = 'black')\n",
        "for i in range(k):#for all the clusters\n",
        "    center = clusters[i]['center'] #its cluster's center\n",
        "    axs[0, 1].scatter(center[0], center[1], c=clusters[i]['color'], s=250, marker=\"o\")\n",
        "\n",
        "#CLUSTERING EPOCH 1\n",
        "axs[0, 2].set_title('Iteration 1')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[0, 2])\n",
        "updateCluster(clusters)\n",
        "\n",
        "#CLUSTERING EPOCH 2\n",
        "axs[1, 0].set_title('Iteration 2')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[1, 0])\n",
        "updateCluster(clusters)\n",
        "\n",
        "#CLUSTERING EPOCH 3\n",
        "axs[1, 1].set_title('Iteration 3')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[1, 1])\n",
        "updateCluster(clusters)\n",
        "\n",
        "#CLUSTERING EPOCH 4\n",
        "axs[1, 2].set_title('Iteration 4')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[1, 2])\n",
        "updateCluster(clusters)\n",
        "\n",
        "# Hide x labels and tick labels for top plots and y ticks for right plots.\n",
        "for ax in axs.flat:\n",
        "    ax.label_outer()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 6 Axes>"
            ],
            "image/png": 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skKQNvb06pWOrq5xJ1KYhZ4DSwKOdiJDq8/VWRZ/Kwpn1VSNDU6clcyq12+nBTlOeU1340j6VeeWUOrU1Neqpxun60FAZT9q0Oe3n/uOVJdXp0wAwXOUrZ6yssKTziKd91D3dcm8L9urpxulqa2qUYRhq3tSheRs7FBgcTHocJ+QMACTnts62152J1gHLZs5ImbdpcpkzkvP9I2eAMDrSEOJ2d8xtwaDaegLqCAT12tCfqSyc2dnbpxdmztAcr1n5O61FE0/0mgdS4sZVsmtt3tShpTt2qiMQVGsgIElqDZjXlKp4989NKBbTLjjFVBYAw0s6ixX3G4ZWdx9MOWfWzDxGbU2Nmu/zSXKfNU5r66Rbbus9p3RslaRQzmwIBrWgPfVBm0xyJpXX5VqxlAPA8JRqnW3VSYnaNNnMGSk7bZpc5Ez0cVPdzKBY6vdiKQeGLzrSEBJvEcn9/f364d63dWL7Zo1+eYOmv9auOZs6NHNju5o3dWhB+2YdXWk+Jez0zLzTiIuvokIvzWo0R048HjVv6nAc+Uk26p7qwpfxgvKNvr6k70lnJx63nZPWa05q36LXegIFq/TZkQdALqVSZ/cbhl7rCeikTZu1eNvrGjU0Au42Zyo9Hs32+/R804yUsyZ61D3VcjvljKTQAJIkbQiEZ2PnOmesc1ivm7cps5kKmSBnAORaKrtfWjkzc2O7lmzv1Pw4i/1nO2ek9Ns06eRMonIku39uNzMoljYNOYN8oCMNIdEBUVc1Ure8tUdHbNio/3jzLa091KOgQ0V0yDD0Zn+/Rnqkc2rGKPoV8XbOibcWjeS+AnS7u030MZfs2BkaPVrg9+uMMaNDwTnH5414lMdtRexUFjehaH9Na0+PYwDni1NwAkC2uK2z7bOGrdH1Q4ahVQ3THHdgS7RDWz6zJlHONPq8eqFphpqHHsVpsT3Kk+uckaJ2pwsEtSCDZQwyQc4AyDU3dbZTzrQGArp3Sl3cLCnVnEmlHE5lsa4p+tqiFUubJjpnVncfpDMNWUdHGkLsAfHgtHqd2L5FN+/eo17DiOkci2ZI6jOkW3bv1fxNm9XZG1nB2p/jj7fN81yvN7TbTaKGgX00xe321tHHbO3p0a+m1GlVwzQ91ThdvooKPdM4XXO8Xm0IBHVKx9ahx1jdjw45lcVNKNpfYynUttIN3qpQIEvSkh07CR4AWZOozrbX7fa617LA79epYw4LzcJyOrZTzkiRdVuLzxeqi+PV8dGj9m6zJlHOWA0tn8z3GkPHzkfOWPcg3fVJs4mcAZBr8epsNzmTaFA+ej21QrRponPm3qn1amtq1IopdaEyRudMsnIku3+NPq/rnCmGNo29HKM8Hi3e9joz05B1dKQhQqXHI1+FR6ds3qqNgUDSDrRohqSNgYAWdmyJ6UwLDA5q3tAjoVZlVunx6OnG6Zrj8+rVYLgDK9Fiz9GjKW4XvrQfs8Xn0xWdb2jxttdD5+zs7dOGoDkyblX69VUjQ1Ok3Wz1HF0WN0Fuvca+xkKhtpWu9Hh079T60OetBerQAzB8OdXZ0Y8dHjmyMlxf+/2hTWFO6diacDTdKWdChs5n2M4bb7Fnp1F7N1mTLGe2BXsjZj5Yu585dfK5vX9ucsZ6nX19UnIGwHAWXVdmK2dCj4M6tEfy0aZxOt7SHTtDs7+i13226tdM2jRuB8GKpU1jlWNVw7TQDt6FmqSA4auy0AVAcekzDH1063bt7etXf5rH6Je0t69fH926Xa1NMzRyaNSmZWOH2oY616zHOawRnQ2ByA6sRp83Zltnq5J22iraDfu2zf2GuaON/ThWMK3r6dECv1/1VSN1SsdWbQgGNcfnDc0oSFX0NtZWcFpbVFvbZFtrLFgdeOluL2011tLdmtoadbLuQyEaWgDKS/Rjhwvbt+iFphnq7O1TfdVIdfb2OY6mR9et0Tlj1YWruw+GHrlptb3XngtWvZvPnAnVrw6dfKlwmzPW+qTkDIByk62cOWnT5lBHlfW6fLZp7Dnj9Hi/pJj6td8wMm7TROeMdT8StWkyeXQ/05yp9Hh0ZvVosgY5Q0daGUpUMS3bvVcb0piJFnMOSRsCAS3bvVc3HjFJHYFgqHEjSc3eKi3Z3qnWQEDzfT7N9/vV2tMTMRXaemSlIxDUkh071drTo/l+v+b7fGoNBFKuEO3XLcWGTKJg2hAIhoL2yffe16ljDpOvoiKtSj5RSFvX7BRKbq8x3fdaou8DW1wDSFWqdaP12KHVANkQNOtce31ozwqn+j86Z+Z4q1RfNTL0/lEejw4ZRkTO2Mv7oY6tGedM9DUly5lKj8ecPWDr5HutJ6C3+vrJGQBIoFA5Y+9Ek8z2hL1Nc7zPpxcDgYgZxtlu00Rflz1rojvurJxJ1KaxNjtItT5O1vG4dMfOguWMRNYgt+hIKzOJKqb9/f367p69GXeiWQxJ392zV1+cOCHme7cdeYQ+/voOSea045dnztAlOzr16tD6ZGts20Db1y9o7elRW1NjymWxX/d8n0/3Tq3X043TQwFqf0TGCoDoYDpyZKUmvNKmQ4ahUR6P9sydrdM3b0u5ko87I2FIslBKJJP32jmNOgGAG+n8Amw9drhg02ZtCAYdF9JvDQTU1tQYapAkO+YD06aos7cv9P5DhqEZVVWhR26S5UylxxMxa0uSOnt71TNoyF/hUX1VbBns1z7HazZmdvX1x80ZKTIT5vt8WtixRYcMQ36PR7vmzNK/bHmdnAEAm2LJmUcapmpqVVVo9nFrIBBa6N8+w9heXku8No253Ez8rInOmReaZjh2FsXLmeg2jV/S7KHOw1Q7rRJlTTHkjETWIHfoSCsziSqmFfvfVV+WF2HsMwyt3P+urpl4eGiEp8Xv19nVYyIq3kqPJ2YqtPV3u7k+cyZBdAeWdW3xQi86JJs3dUS8tyMQjHlv9CjGX7q6Q8/ZHzIM/X/7D2RlSnZ0eZM1gBLJ5L0AkA1uHo2xP1oomb/w2x87tOpGe502d2hHZV+F8/KujT5vRM7MstYds97vNdetsZfL+ruddZ5Kj0cLNm3W+kBAh3k86pcidq72ejw6zu/Tp8aN1WXjx2l8ZWXkTOZgUAs7tuiloYaSU85IkZnQHgjoX4cGmXoMQx/Y2KEd/f1x72U85AyA4cxNR4u1dteADI2QJyc5c3b1GElROTPUnrEvI+C0sYG1o+YpHVu1rqdHR1dW6oiqkXq5J6DeBFnzTv9ARM4saN+sl5oaQ52CyXKmvmqkfr3v3VCbpmeorInuZTyJsoacwXDnMYws95yUgO7ubtXU1Kirq0vV1dWFLk5e9RuGTmrfotaeHs3xebWmMTxafnLHFr1wqCf5QVJ0wii//jlzRswU7OhHLa3RlQV+v56Lmikw3+tVQFLb0LP9G2zP3Lc1NSadOuw0EpTovU5l/cDGDrUFw+dtbTxGV+18M6LMiUZwUpmCnsm6AJmuKQCko5zrVSflfD/s9W1jVZX+OfMY7e0fiKnrrUctJWm+z1xPRYodFAkMDoZmECQbLXeq/+wdd1aDJVnOHO/zaeHow/TTd/YlvV6PpJEej745qVZfmzRRJwyV1ZJKzlj5bGdlnpuciXcPMnldtt8LpKuc69V4yvWeRMzK8nm1sr5OvoqKiB01o9cys3LGqf7NZc5Y57PK65fZeTXf59M9U+r0gfbNrq7ZypobJk3U/+w/ELGUQaK2ULz2ll2z16u2IDkDSO7rVTrSyih0JLNSOnHTZr04FCz2yvzVYDBiBCRbfB6PDhzbrB1Rj1E6lS1eMNkXbZakOV5vaFr2iil1Ed9rnzXTcSTFGpmy1iaI916ntWO2BXs1c2N76HXNI0dqfXOTpMQz4eznzsaz/kCxKud61Um534+DAwOa+EqbAjJ/+Tdkjr6vjKpz7eI1BDoCwYj6N1Edn6w+TiVnrHK75ZE0x+fT/0yr1/mv7wh1fqWbM5K529lTDssQxEPWYDgr93rVSTnfk8DgoFo2bY4Y5J7v9+v5OPWp5L7+zXbOWF9f3X1Qi7e9HvraDG+VNqe4k6RHUrPPq17DUEewN2FbKDoTnDJ4vterZ4Y2XSBnAPf1qvO8VQxb24K9oU40yexEk8zHHXPRiSZJAcPQgk2bE24lHY/1XHuDt0qjhirpUZ7ILZit3b+kxFssh3aRSfLe6Oniq7sPqr5qZOh1c31erZs9U5UeT6h8yQLEaQq6nX3raAAodc8ePCQraaxazb6bmKRQnS5JLUOPYTrVk9YjHlL8Ot76xT7TnPHZvp5qbWxI2hgI6PQt2/THhqkZ5UyL36+2pkY9N/OYiFkWySTKGnIGwHDS2dsX0YkmhR+nbPBWab7PF/E9a/F/p3oy1zkjhXeRnD90Hkkpd6JJZtZsCgR1cGBQTxzTkLAtFH2t/YYRkzPPz2okZ4A0sEZaEcnHFNYGb1Xo2X4pPCPNmuGVK9Hr0lijPE5ToZ1GNzp7+yLWJ9vV1x8xUpTKjizRi05Gv9f+XP4oj0eLt72uFr9fTzdOD4VFqj+fRM/6M7IDIJ/ykTWnjjks9OimNbMrejex6DXSpNjdlCV3u24lWi/Hbc5sDfYqEHPk1PRL2tvXr/O27VDr0CNEUmo5Y+2m1tnbl/L542UNOQMgnwrRppHC645Vejx6vmlGzBpp0fVvoh2Vo7lZ/9P+5ItTXVvp8eie+qNdP84ZT7+kvf39+tIbu9TaNEMj45Tf6lC0HnG9ovMNPUPOAFlR8BlpN998szweT8RHU1NTwvf89re/VVNTk3w+n+bOnatHH300T6XNnUxHOdyq9Hj0/Mxj1NbUqLamRu0/bo7aZ83U/2uYmpPzWeZUmZWsU4U7c2O7WjZtjhmdt98D+0hRi9+vfsOI+L69cyzeSEgqoyQrp9TpkYapoc67dT092hwM6lOv71Dzpo7Qz8jtMa2AtmYo2IMl2Ww1AMiWfGWNr6JC+45t1qqGaeo+tjmi7rPqa19FhWb7fZrt94W+Hq+eTDT716qLrdkHbnOmIxCMqL9/e+BAVq69X9KGQEDLdu+N+5pKj0dPN07X/z366IicsR4x/ZDtZxQYHHSdXfHuITkDIF8K0aZ5eeYMtTU1RnTeWE+izPX7Qzljfd2pnkz2lEl91UjN8ZptjXiD4s2bOiIW7o/OGUn6fVdXVq7fTdZI0i1HTg79vbWnJ9SJFv20UKZtGnIG5abgHWmS1NzcrLfeeiv08cwzz8R97XPPPaeLLrpIn/nMZ/TSSy/pvPPO03nnnacNGzbkrby5mLaaz8rHCpbZfl9oKm+Dt0reHI4aVFVUhB5Tcapw7VOzrdF5e/halXZbU6OMoXVsTtq0Wa/1BEKviQ7uwOCgXjnUo0e7unVwYMAx1K3FnWdubNdJ7Vv0yqEenTQUhDfu2h2aft3i9+ui13eEZu2t6+nRaz0BzdvYEfcXheh/J/EC2s108nQxxRooTbn6v5vPrPFVVGhRzRiNHjHC9SMjbh+Xt9gbL/J4XOfMfL9fS7Z3hurvvX19Wrbn7TSu0pkh6bt79mp/f39MzrzWE1BgcFCndGzVZ994I/SIa8tQmZo3dYRmD6zr6dGxG9td54zkfA/JGQDRhkPOWG2aY0dFdpa5eV+qWXNKx1ZtGNr07KnG6XEHxS2jPB592pYz/Yah/f39OcmavX19oXbOBzZ16JVDPQoMDmph+xadu217KGcW+P1mJ1r7ZtdtGnIGcFYUHWmVlZWaPHly6OPwww+P+9of//jHWrx4sb72ta9p1qxZuvXWW3X88cfrJz/5SV7KmqtRllxWPolYFZMkHef3JXl1+l4MBEIzDiz2a7aLHp23ymn93WpgtAYCEbPDooN7/mvtOq59s87dtl0TX2lzDPWOQDA0ctTa06Pj2jeHPw8EdO+UOrU1Neo7R0xSm+0XgTlVVbp4R2dECEWvEZDo34k9EBLNwshEvkYEUykPIQgkl8v/u4XOmlw22Fp7elznzGfHj4vorPrR3nfUl+Wy9RmGVu5/NyZnmjd1aIFtdtwhw9Aj06bqO0dMithhTpL8Ho82Dz16Q864LxNZAyRGzqTGnjUbAsGYRyKdsuaQYYTWpbbq73v27c9J1ty5953woFEgqOPaN0fMwrZyZsWUOm0L9mpDIDywlKhNQ87ELxM5g6LoSNu8ebOOPPJINTQ06JJLLlFnZ2fc165Zs0ZnnXVWxNcWLVqkNWvW5LqYkjIbZUn0ny5XlU+y8tgrpv81tka5OqtTkNpnmVmP5NgjaJTHo/qqkRHlXLJjp+ZVRR4n3oYAr/WFQy4gaYbt8dL6qpFJK0CrzEt37IwYzZnr8+qBhilqs4XQXK834vqSLcRpXc+8TR0KDA6mPDLmRjFNsS7GEASKVa5yRiqOrEnlEZJkkjXYnHJmlMejL7y5K/SaFr9fqw8eTHlzgWQMSSv2vet4jdasBslcAPvG3XsicqbF79cjDVPVY3svOZMcWQO4k+n/3WJv02QzZ6TUsuZ4b3jdNOvKR3k8OnJkpb7z1p6cZM3jBw+GMsXS5pAzzZs6tGTHzlAeJmvTkDOxyBlYCt6RduKJJ2rFihVatWqV7r77br3++us65ZRT9N577zm+fvfu3Zo0aVLE1yZNmqTdu3fHPUcwGFR3d3fER7rSHWVx858uF5VPItEV06mjR2tkls9d5fFobeMxcYPUmpL9zMxj9H+PPlr2SdGHDENPvve+OgLBiBkHX6idGHOcxdte18kbO3RP/dFqa2rU2pkzIoJslMejF5tmqH3WTP19RoMWtJvrAlzR+UbodVYn3pyqKr08c4aem3mMOnv7IkZzVjVM04tNjZrl84Ue+5zj82qtbVFpKfG/k+hRrQWbNuekEo4ug5vOw2jZ+iWk2EIQyLZSyRmp8FnTEQhm/EuofTZ1sgZbdM4cijrfjZNq9XJPptsMOHslENDlO3bG5Mxcr1drGs1yr5xaH5qxZuXMmpnH6OzqMeRMisgaDHfZyppMZo2VQpsmGzkjpZc1K6bVh75mnfWQYeiJ7oN6P0edLusO9eiZGdPVbJtsMCdOzrT29OjeqfVqnzUzaZsmlZxp2dhBzqCsFLwj7SMf+YguuOACHXvssVq0aJEeffRRHThwQL/5zW+ydo5ly5appqYm9FFXV5f2sdIdZSnEf7pklUZ0xXT8KL+umjA+q2W4YdJELThsVML7ZK07YF8rRgqvlWYfOVng9+ui8WPl9BDqS729Oq59s5bu2KlKj0f/nGUuPvpIw1TtO7ZZvooK9RuGTty0OTSlubWnR7+eNkXts2Zq77HNmuPzakNvrz7T+Yak2Ht0ZvVoVXo86jcMBQYGJEleeWKuL9G/k/qqkfLbPt8QDDr+e3BT6bsdEXyqcbpO6dia0i8U2RxxKdQ0fyBfyjVnpNSzxiqfUznd1nv2uklS0gZbopz51+071JvDEeUXA4GYnHk1GNTpm7epwVulRp+XnMnSyD5Zg+EuW1mTyayxUmjTWGVzKqPbTpV0s+by7TtDn9uftPnqrrcSni8ThqTn3z+k9bNn6uWZM9Ts82pDgpxp9HkjNmkzBgclxWZNon8nDd4qNdtmwbX19oY6He3IGQxXBe9IizZ27Fg1NjZqy5Ytjt+fPHmy9uzZE/G1PXv2aPLkyY6vl6QbbrhBXV1doY+dO3fGfa0b6Yyy5Ps/ndvRoujK8Y6jjoj45TtTF4wdm/Q19kA+ZBh6pGGq/jhtSmjWQGtPj5ZPrQtVoLv6+vXW3NmhRzWjWYFZ6fHo2FF+nVNTrUqPJ7Qg9Wu9trXOhoKk0efVrr7+UAeb/RjR96jfMNTSvlltveE125x+iYj376Szty/h4zqSu59fKiOC9pl1bn/pyeYvSoWY5g/kUznmjJRe1kT/Qu+0w2aiX3bTqZuS5UwuHVM1kpyJI9sNcrIGw102sybdWWOl0KbJNGek9LPGvtblg9OmhP6+uTe3HY7BoXXKfBUVoUc1k+WMtcHai0ProzllTbx/J5Uejx6YUq9EyBkMZ0XXkXbw4EFt3bpVRxxxhOP3Fy5cqNWrV0d87bHHHtPChQvjHtPr9aq6ujriI9/y/Z/ObaURXTn6Kyq0YVajaitHqDLDMhzr9Wpm1PP6TuyB3OL3a2pVlaZGdZKNkEcN3qrQKMS/bHldr8xq1CtNM0KjPdYddQp1p910JOn+KfVJpy9Xesxzbwv2hkZM7GsJzKmqcmygxBtZsZ9nrsPjOtHljffzSyUY0vmlJ9u/KOV7mj+QT+WYM1J6WROvnE6P5jjVo5nWZ/FyJld+fNRROckZKX7WlGvOSGQNhrdyzJpc5oy9zs1V1jS7aAtlQ9PQ0zuJcsY+C21jIBB63NPidJ2J2jSz/eHHQluGZrrZkTMYzjyGUdgV8v7jP/5DH/vYxzRlyhTt2rVLN910k9avX6/XXntNEydO1JIlS3TUUUdp2bJlkqTnnntOp556qm6//Xade+65evDBB3XbbbfpxRdf1Jw5c1yds7u7WzU1Nerq6ipIAOWD1bu/rqdHC/z+lIOus7dXH926XRsCgZQWxfTInOX1f44+Sh8afZjrc1qV9JLtnWoNBDTf55Mh85GYFp9Pa5pmaFuwVzM3tofe09bUqMqhxTufOvi+JldWyldR4Vix2e+Hz+NRwDBCx7W/tt8wd/+0KtnXegLa0dur7+zeq9aeHrX4/bqn/mgd17459J6XZ87QsaP8judq8fu1xuHe28/jdP76qpH6UPsWtdquP9E1ufkZxztnIum8B+WpHOrVVJTL/cg0a+Idq8Xnk+HxhOrd6Ho03fosXs54pKwvAC2Zmdhz3Bx5K8LjltE50xEIKjA4qCWdO9UWCLrKGes4ibKGnMFwUy71airK4Z7kKmcW+P2hRwVTrUeTncOeNcd7vQrKXPw/V1nj9Xj0/nFzNGKojE45Y3WEXdH5hrmDtLdKbbZOq0emTdXZNWNirj+dNg05g1Lmtl7NdNJRxt544w1ddNFF2rdvnyZOnKgPfehDev755zVxormgfGdnpypsv4CefPLJeuCBB/Stb31L//mf/6kZM2boD3/4g+tOtHJhjcSkW2nUV1WptWmGlu3eq+/u2as+w0hY8XskjfR49M1Jtbphcm3cTQviVWTW6JE1Hbo1EAh1lFmvtUYUrEbWkh071drTY66fFtXgcjrf043TtWDT5tBuaU/bpjbby9To85prqW0MT3W2rOvpUaXHo+O9Xr0YDGq+16vZ/sgV25xGVqJHaOyjQvayWkFiXZMkGXHuZaXHo6cbp+vJ997XqWMOSxr8TudMJp33ACgfmWZN9LGsOu2IkZWhjiSnejRR3ZROzly6o1MvHIqdtZwpv8ejEQly5qRNmyMeA7KuN1nOSMmzJls5s2bmMXHXvSFnAORatnPGXqelU49aEmWNpFDd/mIwmPOsOdbvi8maRDkjSW3BXjVXVamtt1dzqqp0xtD6nHbptGnIGZSLgnekPfjggwm//49//CPmaxdccIEuuOCCHJVo+Mi00hjp8ejGIybpixMn6Ff73tXNb+2O2W3GI6lllF8XjRury8aP07hK539SodGZoc4vp1GN2soRqpLUK3MB6AZvlXy2TlR7kPYbhpo3dUhSRDjYK/noUZSVU+q0YahjbEMgqM7ePjV4qxxHWrYFe2M60aTwlGCPVa6K2Kej7R1+qUwhtoeV/Zpa4wSXtXh2dNndjB6lipEcAPFk6xdUe5023+/XfJ9PrYFASvVoYHBQC9rNDWVSyZkLx43VukM9WZ8pcGgo+5bu2OmYM06Nm7k+b9KckdLLmlRzxhJdfknkDIC8yXZHiFWnpZM1ydo0gcFBXbD19dDrrUceKz2enGXNWaNHO/7+Hy9nJLNN8/cZDVrYsUUbAkGd0rE1pi4nZ4D4im6NNOSO211qoo2vrNS/1lRHdKL9cdoUPT59mnqOm6O1M2fo2tqJCTvRrIX+W22jGqu7D4bKEhgc1JEbNsqaYHzIMNTZ2xdzLCtI7YuI2ndgm29bUDl6FEVSzDPy8dZK6DcMzbOtn+OX9ErTDD0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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqWdqALtALKg"
      },
      "source": [
        "Let us now try the same thing with different number of clusters. We will also see how initialization matters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDwkJxaP_BVA",
        "outputId": "108bfa09-2ecd-485c-b793-5f89d3f3ef88"
      },
      "source": [
        "# Generating Synthetic Data\n",
        "\n",
        "k=5\n",
        "color=[\"darkturquoise\",\"darkorange\",\"teal\",\"darkviolet\",\"tomato\", \"yellowgreen\",\"hotpink\",\"gold\"]\n",
        "\n",
        "X,_=make_blobs(n_samples=500, n_features=2, centers=k, random_state=10)\n",
        "print(\"Shape = \" + str(X.shape))\n",
        "\n",
        "print(\"Sample:\")\n",
        "print(X[:5])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape = (500, 2)\n",
            "Sample:\n",
            "[[-7.32786741  4.23641465]\n",
            " [ 1.12565931  6.32088416]\n",
            " [-0.49239887 -5.28550826]\n",
            " [-7.12432134  6.16236021]\n",
            " [ 2.38696269  4.11331935]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "2_HmX5BaAfjq",
        "outputId": "aec46abe-419f-46ab-fb44-9e30818ba3d4"
      },
      "source": [
        "fig, axs = plt.subplots(2, 3)\n",
        "fig.set_size_inches(15, 10)\n",
        "axs[0, 0].scatter(X[:,0],X[:,1], s = 2, c = 'black')\n",
        "axs[0, 0].set_title('Dataset')\n",
        "\n",
        "## Change initialization here to 'np.random.seed(0)'\n",
        "np.random.seed(3)\n",
        "initializeClusterCentroids()\n",
        "\n",
        "#Now, lets plot the centroids it has initalized\n",
        "axs[0, 1].set_title('Centroid Initialization')\n",
        "axs[0, 1].scatter(X[:, 0], X[:, 1], s = 2, c = 'black')\n",
        "for i in range(k):#for all the clusters\n",
        "    center = clusters[i]['center'] #its cluster's center\n",
        "    axs[0, 1].scatter(center[0], center[1], c=clusters[i]['color'], s=250, marker=\"o\")\n",
        "\n",
        "#CLUSTERING EPOCH 1\n",
        "axs[0, 2].set_title('Iteration 1')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[0, 2])\n",
        "updateCluster(clusters)\n",
        "\n",
        "#CLUSTERING EPOCH 2\n",
        "axs[1, 0].set_title('Iteration 2')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[1, 0])\n",
        "updateCluster(clusters)\n",
        "\n",
        "#CLUSTERING EPOCH 3\n",
        "axs[1, 1].set_title('Iteration 3')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[1, 1])\n",
        "updateCluster(clusters)\n",
        "\n",
        "#CLUSTERING EPOCH 4\n",
        "axs[1, 2].set_title('Iteration 4')\n",
        "assignPointsToCluster(clusters, X)\n",
        "plotclusters(clusters, axs[1, 2])\n",
        "updateCluster(clusters)\n",
        "\n",
        "# Hide x labels and tick labels for top plots and y ticks for right plots.\n",
        "for ax in axs.flat:\n",
        "    ax.label_outer()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlqe_wvnAyvp"
      },
      "source": [
        "**As you can see, changing the random seed and hence changing the initialization has a large effect on the performance of KMeans.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uVyJPKobClt8"
      },
      "source": [
        "Hence our KMeans algo can be run by calling the following function with appropriate X (dataset)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PHM6J-lAg6E"
      },
      "source": [
        "from copy import deepcopy\n",
        "\n",
        "def run_kmeans(k, X):\n",
        "  initializeClusterCentroids()\n",
        "  assignPointsToCluster(clusters, X)\n",
        "  while (True):\n",
        "    prev_clusters = deepcopy(clusters)\n",
        "    updateCluster(clusters)\n",
        "    assignPointsToCluster(clusters, X)\n",
        "    if prev_clusters == clusters:\n",
        "      break\n",
        "  return clusters"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUxLYV43D99i"
      },
      "source": [
        "### Kmeans in scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVCkiXmdEcNg"
      },
      "source": [
        "Even though implementation of KMeans is easy, using libraries like sklearn allows our code to  be much simpler and faster. The next two cells show how to use it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WT-WZDaikBD1"
      },
      "source": [
        "Check out all functionalities at https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "hP0Isa2gD7n3",
        "outputId": "2142f768-7998-48f9-d3f8-e273d8f2ba7f"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "kmeans_demo = KMeans(n_clusters=5) #random centroid intialization\n",
        "#kmeans_demo = KMeans() #What if we dont initialize the number of clusters?\n",
        "#kmeans_demo = KMeans(n_clusters=5, init='k-means++') #Does smart centroid initialization help?\n",
        "\n",
        "#Let us fit our synthetic datapoints into the kmeans object\n",
        "kmeans_demo.fit(X)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "xHll4yqNFCSn",
        "outputId": "e2315b04-de4b-406e-be56-b523ed085d00"
      },
      "source": [
        "#Lets inspect the clustering model\n",
        "print(\"ITERATIONS TO CONVERGE = \" + str(kmeans_demo.n_iter_))\n",
        "print(\"CLUSTER CENTROIDS = \")\n",
        "print(str(kmeans_demo.cluster_centers_))\n",
        "print()\n",
        "\n",
        "#using the colours we defined for the clusters at the start\n",
        "colors_toplot = []\n",
        "for label in kmeans_demo.labels_:\n",
        "  colors_toplot.append(color[label])\n",
        "\n",
        "plt.scatter(X[:,0], X[:, 1], c = colors_toplot, s=2)\n",
        "plt.scatter(kmeans_demo.cluster_centers_[:,0], kmeans_demo.cluster_centers_[:,1], c = 'black', s=250, marker = \"o\")\n",
        "plt.gcf().set_size_inches(8,6)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ITERATIONS TO CONVERGE = 2\n",
            "CLUSTER CENTROIDS = \n",
            "[[ 5.56873506 -9.60861515]\n",
            " [-6.06533341  5.2400571 ]\n",
            " [-6.78567723 -8.26282547]\n",
            " [ 2.78561142  4.93182424]\n",
            " [-0.06909909 -5.56399319]]\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBe2HWoNFWTO"
      },
      "source": [
        "### KMeans on Digits Dataset (KMeans for labelling)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhekZruK9KNr"
      },
      "source": [
        "Here we will attempt to use k-means to try to identify similar digits without using the original label information; this might be similar to a first step in extracting meaning from a new dataset about which you don't have any a priori label information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4yV9wx9FVun",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e633b196-434a-4a4c-b51e-938f16c1dd80"
      },
      "source": [
        "from sklearn.datasets import load_digits\n",
        "\n",
        "digits = load_digits()\n",
        "digits.data.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUBgYLeN9Sa-",
        "outputId": "ce06a1c7-6f14-4ca8-bed2-7e3ccbe59369"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Perform KMeans\n",
        "kmeans = KMeans(n_clusters=10, random_state=0)\n",
        "clusters = kmeans.fit_predict(digits.data)\n",
        "kmeans.cluster_centers_.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "lSVbUHUi9n18",
        "outputId": "727b4697-7554-40a9-c064-229b4ae0ced7"
      },
      "source": [
        "# We can now see how the centers of the clusters formed are\n",
        "\n",
        "fig, ax = plt.subplots(2, 5, figsize=(8, 3))\n",
        "centers = kmeans.cluster_centers_.reshape(10, 8, 8)\n",
        "for axi, center in zip(ax.flat, centers):\n",
        "    axi.set(xticks=[], yticks=[])\n",
        "    axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 10 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAD7CAYAAAD0MpkiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAUoklEQVR4nO3dW4yVZ7nA8WeYYTjNDJBCsHSmQNGgSDxFrW2CtNUaUBtJbLCtAU0bQ9ULxRKTxii08dJqPDahXNjUeEHEhNparDZKS7SmRaOpmFAtbaFjqZbDrBkYDjNrX9Ho3jvueV5Zs2d4f7/r77/eb9b7rW8e1oR8bc1msxkAAFz0pvx/nwAAAOPD4AcAUAmDHwBAJQx+AACVMPgBAFTC4AcAUAmDHwBAJQx+AACV6BjLQaOjo9Hf3x/d3d3R1tbW6nOiQLPZjEajEQsXLowpUy7sPG//J75W7n+Ea2AycA+om/2vW2b/xzT49ff3R19f3wU5OVrr0KFD0dvbe0Ff0/5PHq3Y/wjXwGTiHlA3+1+3sez/mAa/7u7u116wp6fnPz+zf+Ps2bPp5hvf+Ea6efDBB9NNRMQPfvCDdHP55Zenm+y/2AYGBqKvr++1vbqQSvf/5MmT6bW+8pWvpJvvf//76WbmzJnpJiJi06ZN6eb2229PNzNmzEgd38r9jyi/Bo4ePZpe6/Of/3y6efLJJ9PN0NBQuomIuPTSS9PNV7/61XSzevXq1PET8R4wMjKSXuvee+9NNxER9913X7qZNWtWutm4cWO6+ehHP5puIiK6urrGfOxE3P/R0dH0Wrt27Uo3X/7yl9PN3//+93QTEfHOd74z3dx2223p5rrrrksd32g0YsWKFWPa/zENfue/2u3p6ZmQg9/06dPTTXt7e7qJiKIPVcl7VvpVfSu+hi/d/46OMV1e/6KzszPdlPzMpe9TybVWsv/Zwe+8Vv0ZpvQaOHfuXHqtqVOnppuSz0vpe1Vy7yj5h0bpvXYi3QNKBr+Sz1hE2TVQspcln83SvcwMfudNpP0vGfxKPivj+fkv+b020T7//nMHAEAlDH4AAJUw+AEAVMLgBwBQCYMfAEAlDH4AAJUw+AEAVMLgBwBQCYMfAEAlDH4AAJXIP3ukxfbt25du7rnnnnSzfv36dBMRMW3atHQzMDCQbrKP6il5NFarPfHEE+nmscceSzcbNmxIN88++2y6iYjYsWNHurnpppvSzaJFi9LNRLR3795086Mf/SjdvPGNb0w3a9euTTcRZc/evuKKK4rWmuz279+fbr70pS8VrXXjjTemm5JHypVcnzfccEO6iSh7ZNtEcuzYsXSzffv2dPO6170u3SxfvjzdRJT97ih5bn121sgc7xs/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEh2tfPGhoaF0s3Xr1nRT8oD21atXp5uIiF/96lfppuQB0itXrkwd32w202u0Wsm+3HfffS04k//pjjvuKOpe//rXp5u5c+cWrXUx6O/vTzednZ3pZtOmTenm+uuvTzcREXPmzEk3PT09RWtNdi+88EK6mTdvXtFaN954Y7rZv39/unn44YfTzcmTJ9PNxaDk99Itt9ySbq688sp08+CDD6abiIhXX3013ZT83pg+fXrq+DNnzoz5WN/4AQBUwuAHAFAJgx8AQCUMfgAAlTD4AQBUwuAHAFAJgx8AQCUMfgAAlTD4AQBUwuAHAFAJgx8AQCUMfgAAleho5Yv/7Gc/G5dm27Zt6eaZZ55JNxERjz32WLq54YYb0s11112XOn7q1KnpNVpt0aJF6WbOnDnp5q677ko3f/nLX9JNRMS6devSzYwZM4rWuhj09PSkm/b29nTz7W9/O908//zz6SYi4pOf/GS6mT17dtFak13Jw+lXrFhRtNaWLVvSzcsvv5xu5s2bl26GhobSzcWg5LM8ffr0dPPII4+kmwceeCDdlDpy5Ei6aWtra9nxvvEDAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAq0dHKF9+9e3crX/41e/fuTTdPPPFE0Vr9/f3p5uMf/3jRWpNds9lMNx0d+UvymmuuSTe//vWv001ExP33359uPvzhD6ebZcuWpZuJ6Kqrrko3n/3sZ9PNX//613Szf//+dBMR8eSTT6ab3t7edDNz5sx0M9EsXbo03WzZsqVoraeffjrdDA4Opptdu3almxdffDHdRESsWLGiqJsohoeH082ePXvSzU9+8pN0c/jw4XQTEXH99denm9mzZxet1Sq+8QMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACph8AMAqERH5uBmsxnNZnPMxy9fvjx9Qu9+97vTzb59+9LNwYMH001ExAc+8IF08773va9orcnuzJkz6abRaKSbkvd32rRp6SYiYvPmzenm5z//ebpZtmxZupmIent7003Je7x37950s2PHjnQTEdHf359uRkZGitaa7Nra2tLN7Nmzi9Zas2ZNuunoSP0KjIiIn/70p+nmwIED6SYi4oMf/GBRN1GU3Gff+973ppvf//736Wbp0qXpJiLizjvvTDfvec97itZqFd/4AQBUwuAHAFAJgx8AQCUMfgAAlTD4AQBUwuAHAFAJgx8AQCUMfgAAlTD4AQBUwuAHAFAJgx8AQCUMfgAAlcg/oTph/fr16WblypXpZufOnenmhRdeSDcREV/4whfSzWWXXVa01mQ3PDycbrZv355uurq60s1zzz2XbiIijhw5km5KHgQ/MjLS0uPHS8k1sGfPnnTz0EMPpZsDBw6km4iIj3zkI+mms7OzaK3J7tSpU+lm27ZtRWu9+c1vTjdz5sxJN6+88kq6mTZtWrqJiGg2my05drzMnTs33bzhDW9INydOnEg3GzduTDcREVdffXW6Kd3/VvGNHwBAJQx+AACVMPgBAFTC4AcAUAmDHwBAJQx+AACVMPgBAFTC4AcAUAmDHwBAJQx+AACVMPgBAFTC4AcAUInU0+Pb2tqira1tzMdfcskl6RPq7u5ONzt27Eg3ixYtSjcREW9605uKuhqVPJj6wIED6eaBBx5INz09PekmIuKWW25JN2vWrEk3586da+nx4+Xs2bPpZvv27elmz5496eaOO+5INxERq1evTjcT7SHt42XWrFnppvTe/MUvfjHdHDt2LN2sXbs23Vx77bXp5mLQbDbTzW9/+9t009nZmW6uueaadBMR0d7eXtRNJL7xAwCohMEPAKASBj8AgEoY/AAAKmHwAwCohMEPAKASBj8AgEoY/AAAKmHwAwCohMEPAKASBj8AgEqM6Vm955+3NzAw0NKTiYg4c+ZMujl9+nS6GRkZSTcREY1GI92Mx/t2fo2SZyP+X0r3f3h4OL1Wyf6XKH2fSs6v5JqZOXNm0Rqt2P9/ft3sNVBy7Zc8d7jk5y65b0SU/UwdHanHoheZiPeA0dHR9Fol942I1l37/13J86cHBweL1sq83xfL/p86dSrdlPw+H489OW+iff7bmmM46vDhw9HX1/efnxktd+jQoejt7b2gr2n/J49W7H+Ea2AycQ+om/2v21j2f0yD3+joaPT390d3d3e0tbVdsBPkwmk2m9FoNGLhwoUxZcqF/Qu+/Z/4Wrn/Ea6BycA9oG72v26Z/R/T4AcAwOTnP3cAAFTC4AcAUAmDHwBAJQx+AACVMPgBAFTC4AcAUAmDHwBAJQx+AACVMPgBAFTC4AcAUAmDHwBAJQx+AACVMPgBAFTC4AcAUAmDHwBAJQx+AACVMPgBAFSiYywHjY6ORn9/f3R3d0dbW1urz4kCzWYzGo1GLFy4MKZMubDzvP2f+Fq5/xGugcnAPaBu9r9umf0f0+DX398ffX19F+TkaK1Dhw5Fb2/vBX1N+z95tGL/I1wDk4l7QN3sf93Gsv9jGvy6u7tfe8Genp7//Mz+jd27d6eb22+/Pd0sWbIk3URE3HPPPenmHe94R9FaGQMDA9HX1/faXl1I47n/JV588cV084lPfKJorZdeeindfOtb30o373//+1PHDwwMxJIlS1qy/xHl10Cz2Uyv9ctf/jLdbNmyJd0MDw+nm4iIzZs3p5u1a9emm2nTpqWOn4j3gOPHj6fX+tSnPpVuIiIeffTRdPO5z30u3WzatCndzJ07N91kTcT9L3H06NF0s2HDhnRz2WWXpZuIiLvvvjvdLFiwoGitjMz+j2nwO//Vbk9PT8s3febMmemm5Kvn9vb2dBMR0dXVlW7Gc1hqxdfw47n/JUpudKX7X/InlJJruvR9btWfYUqvgZLBb9asWemmZD9Lr4EZM2akm5L9zA5+502ke8Do6Gh6ralTp6abUiXvccle+h0wdmfPnk03HR1jGmX+RWdnZ7qJKPt9M9H233/uAACohMEPAKASBj8AgEoY/AAAKmHwAwCohMEPAKASBj8AgEoY/AAAKmHwAwCohMEPAKAS+eecJJw4cSLdfPrTn043IyMj6ebcuXPpJiJi48aN6eYXv/hFuhmPZzu2Wsnjug4ePJhutm7dmm6eeuqpdBMRRQ8/P3bsWLrJvncl7/V4aDQa6eaRRx5JN4cPH043pZ+xXbt2pZtrr7023ZQ+S3Qieeihh9LN448/XrTW4sWL003J831Lnrt81VVXpZuLQcl9adu2benmN7/5TbpZt25duokoewTnROMbPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIdrXzxp59+Ot2UPGz9/vvvTzelD83+0Ic+lG7++Mc/pptVq1alm4mmZC/vvvvudLN79+50c/nll6ebiIhGo5FuFixYkG7a29tbevx4OX36dLoZHR1NN729velmeHg43URETJ8+Pd1cDA92L9HV1ZVubrvttqK1li5dmm6++93vppuzZ8+mm1oNDg6mmx/+8Ifp5mMf+1i6ufnmm9NNRER3d3dRN5H4xg8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKhERytf/PDhw+mm5GHrV199dbpZvHhxuomIuPLKK9PNU089lW5WrVqVbiaaZ599Nt3s2bMn3cyZMyfdHDlyJN1ERJw+fTrdXHrppelmypTcv8myx4+XqVOnpptLLrkk3ZRcayMjI+kmIuKuu+5KNz09PUVrTXYl97FFixYVrfXwww+nm5LP89y5c9NNrf72t7+lm3/84x/pZsGCBenmd7/7XbqJiFiyZEm6Kbmm29ra0s1YTczfFgAAXHAGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKhERytf/OTJk+lm3rx56Wb+/PnppqOj7Ecveej84OBg0VqTXcmDqW+99dZ0U/Kg9Z07d6abiIjjx4+nm66urqK1Lgbt7e3pptFopJuSz9jixYvTTUTE2972tnRT8j5cDObOnZtuSn5vRETs27cv3ZRcAyW/o2r1yiuvpJtjx46lmx//+Mfp5tFHH003ERFLlixJN1u3bk03b3nLW9LNWPnGDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEh2tfPHly5enm+effz7dDAwMpJuhoaF0ExHxhz/8Id28613vKlprsps/f366ufnmm9PNwYMH083OnTvTTUREd3d3upk1a1bRWheD4eHhdPPMM8+km7e+9a3pZnR0NN1ElN2jrrjiinTT3t6ebiaac+fOpZt9+/YVrfXyyy+nm1WrVqWb48ePp5vSe0BPT09RN1H09fWlm7a2tnSzbNmydLNu3bp0ExGxa9eudPOd73wn3dx7772p40dGRsZ8rG/8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCAShj8AAAqYfADAKiEwQ8AoBIGPwCASnS08sXf/va3p5spU/Kz6Gc+85l0c/r06XQTEfHcc8+lm5UrVxatNdl1d3enmxkzZqSbY8eOpZsTJ06km4iI2bNnp5vh4eGitS4GnZ2d6WbOnDnp5ujRo+nm1KlT6SYi4qWXXko3JfebmTNnppuJpuSz+bWvfa1orT/96U/pZnBwMN08/vjj6WbVqlXpJiJi8+bNYz620WgUrdFKfX196WbNmjXp5s9//nO6KZk1IiJeffXVdDM0NJRusveMzPG+8QMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACph8AMAqITBDwCgEgY/AIBKGPwAACrR0coX7+npSTf3339/urnpppvSTUdH2Y/+9a9/Pd0sW7asaK3Jrq2tLd1MnTo13cyfPz/dLFiwIN1ERMyYMSPdjI6OFq11MSi5B2zYsCHd3Hnnnenm1KlT6SYioqurK92MjIwUrTXZlXyeZ82aVbRWo9FIN4cPH043JfebI0eOpJuIiIMHD4752MHBwaI1WmnKlPx3S9/73vfSza233ppu1q9fn24iIhYvXpxuvvnNb6ab6dOnp44/c+bMmI/1jR8AQCUMfgAAlTD4AQBUwuAHAFAJgx8AQCUMfgAAlTD4AQBUwuAHAFAJgx8AQCUMfgAAlTD4AQBUYkwPrG02mxERMTAw0NKTiYg4efJkujl/fq1uIsqe71nyvmWfcXl+jdKf698Zz/0vUfKMztJnp547dy7dlJxf9r1u5f7/8+tmz6vkfIaGhtJNyX6WXgMl96iSz072vZuI94CSn7vkM1aq5L0quW4yz1H9Z5nn757/3Eyk/S9Rcr88e/ZsC87kf1fy7PXxuGdkPv9tzTEcdfjw4ejr60udBP8/Dh06FL29vRf0Ne3/5NGK/Y9wDUwm7gF1s/91G8v+j2nwGx0djf7+/uju7o62trYLdoJcOM1mMxqNRixcuDCmTLmwf8G3/xNfK/c/wjUwGbgH1M3+1y2z/2Ma/AAAmPz85w4AgEoY/AAAKmHwAwCohMEPAKASBj8AgEoY/AAAKmHwAwCoxH8BbW3whnbqYlQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAMCCXBR-ARJ"
      },
      "source": [
        "Because k-means knows nothing about the identity of the cluster, the 0–9 labels may be permuted. We can fix this by matching each learned cluster label with the true labels found in them:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_E4d3Jkc-BMY",
        "outputId": "282f1a94-6fc1-40a1-94b1-5a4fb6d3cb83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from scipy.stats import mode\n",
        "\n",
        "labels = np.zeros_like(clusters)\n",
        "for i in range(10):\n",
        "    mask = (clusters == i)\n",
        "    labels[mask] = mode(digits.target[mask])[0]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-42b7ab3e164b>:6: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
            "  labels[mask] = mode(digits.target[mask])[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0c2izOVb-Ezq"
      },
      "source": [
        "Now we can check how accurate our unsupervised clustering was in finding similar digits within the data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZ2HJXhT-IJn",
        "outputId": "bc0116ed-b484-43c9-cf62-9301b8b5aa7e"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(digits.target, labels)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7935447968836951"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwjdViYU-QRE"
      },
      "source": [
        "**Hence clustering algorithms like KMeans are highly effective for identifying labels for groups apriori and even a simple KMeans acheives a ~80% accuracy on digit classification.**\n",
        "\n",
        "The confusion matrix for this looks like:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "1idrQbsg-OQZ",
        "outputId": "a38a3582-7e59-4700-df1e-be9bbf14175c"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns; sns.set()\n",
        "\n",
        "mat = confusion_matrix(digits.target, labels)\n",
        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
        "            xticklabels=digits.target_names,\n",
        "            yticklabels=digits.target_names)\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label');"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MV0GTurvFkji"
      },
      "source": [
        "### KMeans on Iris Dataset (Elbow Method)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNrXyKIjAp2L"
      },
      "source": [
        "We will perform KMeans on the famous Iris Dataset which has four features: sepal length, sepal width, petal length, and petal width. The fifth column is for species, which holds the value for these types of plants."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOcJbmeaFFhF"
      },
      "source": [
        "#Import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "from sklearn import datasets"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wJWkYsYtA9Bi",
        "outputId": "6d2b4b89-cabd-4672-e222-0db82b5599c2"
      },
      "source": [
        "#import the dataset\n",
        "iris = datasets.load_iris()\n",
        "# print(iris.data[:10])\n",
        "df = pd.DataFrame(iris.data, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])\n",
        "x = df.iloc[:, [0,1,2,3]].values\n",
        "df.head(5)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width\n",
              "0           5.1          3.5           1.4          0.2\n",
              "1           4.9          3.0           1.4          0.2\n",
              "2           4.7          3.2           1.3          0.2\n",
              "3           4.6          3.1           1.5          0.2\n",
              "4           5.0          3.6           1.4          0.2"
            ],
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQAJZu30D9yx"
      },
      "source": [
        "Let us start with assigning the initial number of clusters as k=5."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZKt6zzmD1e6",
        "outputId": "fdd7be33-892a-45dc-d20f-1685cd0e2ca9"
      },
      "source": [
        "kmeans5 = KMeans(n_clusters=5)\n",
        "y_kmeans5 = kmeans5.fit_predict(x)\n",
        "print(y_kmeans5)\n",
        "\n",
        "kmeans5.cluster_centers_"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 2 3 3 3 2 3 2 2 3 2 3 2 3 3 2 3 2 3 2 3 3\n",
            " 3 3 3 3 3 2 2 2 2 3 2 3 3 3 2 2 2 3 2 2 2 2 2 3 2 2 0 3 4 0 0 4 2 4 0 4 0\n",
            " 0 0 3 0 0 0 4 4 3 0 3 4 3 0 4 3 3 0 4 4 4 0 3 3 4 0 0 3 0 0 0 3 0 0 0 3 0\n",
            " 0 3]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.52916667, 3.05833333, 5.50833333, 2.1625    ],\n",
              "       [5.006     , 3.428     , 1.462     , 0.246     ],\n",
              "       [5.508     , 2.6       , 3.908     , 1.204     ],\n",
              "       [6.20769231, 2.85384615, 4.74615385, 1.56410256],\n",
              "       [7.475     , 3.125     , 6.3       , 2.05      ]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xL3AAUdCEGsA"
      },
      "source": [
        "How do we choose the optimal number of clusters? There’s a method called the Elbow method, which is designed to help find the optimal number of clusters in a dataset. So let’s use this method to calculate the optimum value of k. To implement the Elbow method, we need to create some Python code (shown below), and we’ll plot a graph between the number of clusters and the corresponding error value.\n",
        "\n",
        "This graph generally ends up shaped like an elbow, hence its name:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8VSBRjiLEQ7V",
        "outputId": "c04b7183-9b92-45ce-bb77-49db29a9da31"
      },
      "source": [
        "Error =[]\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters = i).fit(x)\n",
        "    kmeans.fit(x)\n",
        "    Error.append(kmeans.inertia_)\n",
        "    \n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(range(1, 11), Error)\n",
        "plt.title('Elbow method')\n",
        "plt.xlabel('No of clusters')\n",
        "plt.ylabel('Error')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEhXXxe6Em4E"
      },
      "source": [
        "The output graph of the Elbow method shows that the shape of elbow is approximately formed at k=3. The optimal value of k is between 2 and 4, as the elbow-like shape is formed at k=3 in the above graph."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTpdV03VE79J",
        "outputId": "d33ac323-35ca-4ea0-82fd-0e3f87931eb7"
      },
      "source": [
        "kmeans3 = KMeans(n_clusters=3)\n",
        "y_kmeans3 = kmeans3.fit_predict(x)\n",
        "print(y_kmeans3)\n",
        "\n",
        "kmeans3.cluster_centers_"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 0 0 2 0 0 0 0\n",
            " 0 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0 0 2 0\n",
            " 0 2]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.85      , 3.07368421, 5.74210526, 2.07105263],\n",
              "       [5.006     , 3.428     , 1.462     , 0.246     ],\n",
              "       [5.9016129 , 2.7483871 , 4.39354839, 1.43387097]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_wPmjOSFP5y"
      },
      "source": [
        "We can now visualize the clustering in two dimensions instead of the original four."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "GOPhGuicFBkV",
        "outputId": "11ef903e-b370-46fb-cc8f-cb28b44776d9"
      },
      "source": [
        "plt.scatter(x[:,0], x[:,1], c=y_kmeans3, cmap='rainbow')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4dbef10c40>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJz6aYt6RRRl"
      },
      "source": [
        "## Points to think about"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFLzASTIRaNc"
      },
      "source": [
        "1. Is feature scaling essential for KMeans as it is for most ML algos? Explain.\n",
        "**Answer**\\\n",
        "Yes, the scaling has significant impact on clustering algorithm. If the features are incompatible(different units in measuments eg height and weight), keeping values not scaled will make the cluster decided by the feature having higher dimension. \n",
        "\\\n",
        "[Reference](https://stackoverflow.com/questions/57507584/feature-scaling-for-kmeans-algorithm)\n",
        "\n",
        "2. What are ways to prevent initialization variation in KMeans?\n",
        "**Answer**\n",
        "  - **Forgy Initialization**: One of the faster initialization methods for k-Means. If we choose to have k clusters, the Forgy method chooses any k points from the data at random as the initial points.\n",
        "  - **Random Partition Initialization**: We randomly assign each point in the data to a random cluster ID. Then, we group the points by their cluster ID and take the average (per cluster ID) to yield the initial points. Random Partition method is known to yield initial points close to the mean of the Data.\n",
        "  - **kmeans++ Initialization**: This is a standard method and which generally works better than Forgy’s method and the Random Partition method for initializing k-Means. Select a random data point, then take next point farthest from the first. We do so by sampling a point from a probability distribution that is proportional to the squared distance of a point from the first center.\n",
        "  \\\n",
        "  \\\n",
        "**Even with these algoritm it is recommended to consider other points as the KMeans algorithm assumes many things.**\n",
        "\\\n",
        "[Reference](https://medium.com/analytics-vidhya/comparison-of-initialization-strategies-for-k-means-d5ddd8b0350e)\n",
        "\n",
        "3. What is the training and testing complexity of KMeans?\n",
        "**Answer**\\\n",
        "- **Training**\n",
        "$$O(t*k*n*d)\\\\ where,\\ t=No.of\\ iterations, k=No.of\\  centroids(clusters)\\ d=No.of\\ dimensions\\ and\\ n=No.of\\ points$$\n",
        "- **Testing**\n",
        "$$O(k*n*d)\\\\ where,\\ k=No.of\\  centroids(clusters)\\ d=No.of\\ dimensions\\ and\\ n=No.of\\ points$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uZrTx2nHSSZn"
      },
      "source": [
        "## References and resources\n",
        "\n",
        "1. https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a\n",
        "2. https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/\n",
        "3. https://en.wikipedia.org/wiki/K-means_clustering\n",
        "4. https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html\n",
        "5. https://heartbeat.fritz.ai/k-means-clustering-using-sklearn-and-python-4a054d67b187"
      ]
    }
  ]
}