Mean_Shift.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# A demo of the mean-shift clustering algorithm\n",
    "\n",
    "\n",
    "Reference:\n",
    "\n",
    "Dorin Comaniciu and Peter Meer, \"Mean Shift: A robust approach toward\n",
    "feature space analysis\". IEEE Transactions on Pattern Analysis and\n",
    "Machine Intelligence. 2002. pp. 603-619.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.17621263, -1.05230526],\n",
       "       [ 1.38661833,  0.78309966],\n",
       "       [-1.79999411, -1.1015103 ],\n",
       "       ...,\n",
       "       [ 1.3845992 , -0.60765548],\n",
       "       [ 0.79484718, -0.49796634],\n",
       "       [ 1.69400467, -1.11591235]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Generate sample data\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]\n",
    "X, _ = make_blobs(n_samples=1000, centers=centers, cluster_std=0.6)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       0, 0, 2, 2, 2, 2, 1, 2, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0413940137030107"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Compute clustering with MeanShift\n",
    "\n",
    "# The following bandwidth can be automatically detected using\n",
    "bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=5000)\n",
    "bandwidth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeanShift(bandwidth=1.0413940137030107, bin_seeding=True, cluster_all=True,\n",
       "          min_bin_freq=1, n_jobs=None, seeds=None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
    "ms.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ms.labels_\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.86347926, -1.05600368],\n",
       "       [ 0.95862182, -0.94429671],\n",
       "       [ 1.01094202,  1.01664378]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_centers = ms.cluster_centers_\n",
    "cluster_centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of estimated clusters : 3\n"
     ]
    }
   ],
   "source": [
    "labels_unique = np.unique(labels)\n",
    "n_clusters_ = len(labels_unique)\n",
    "\n",
    "print(\"number of estimated clusters : %d\" % n_clusters_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "\n",
    "# #############################################################################\n",
    "# Plot result\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "\n",
    "plt.figure(1)\n",
    "plt.clf()\n",
    "\n",
    "colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n",
    "for k, col in zip(range(n_clusters_), colors):\n",
    "    my_members = labels == k\n",
    "    cluster_center = cluster_centers[k]\n",
    "    plt.plot(X[my_members, 0], X[my_members, 1], col + '.')\n",
    "    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n",
    "             markeredgecolor='k', markersize=14)\n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}