clustering-kmeans.ipynb

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   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "cluster1 = np.random.uniform(0.5, 1.5, (2, 10))\n",
    "cluster2 = np.random.uniform(3.5, 4.5, (2, 10))\n",
    "X = np.hstack((cluster1, cluster2)).T\n",
    "X = np.vstack((x, y)).T\n",
    "K = range(1, 10)\n",
    "meandistortions = []\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(X)\n",
    "    meandistortions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "plt.plot(K, meandistortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Average distortion')\n",
    "plt.title('Selecting k with the Elbow Method')\n",
    "plt.show()"
   ]
  }
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