cjauvin/dbscan_bug.json

## dbscan_bug.json
{
 "metadata": {
  "name": "",
  "signature": "sha256:5d88cc5ee3abf75f4705adfae917ca4a4fa214cbf4a09025e81166a248935557"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.spatial\n",
      "\n",
      "def DBSCAN(points, eps, min_pts):    \n",
      "    tree = scipy.spatial.cKDTree(points)\n",
      "    neighbors = tree.query_ball_point(points, eps)\n",
      "    clusters = [] # list of (set, set)'s, to distinguish \n",
      "                  # core/reachable points\n",
      "    visited = set()\n",
      "    for i in xrange(len(points)):\n",
      "        if i in visited: \n",
      "            continue\n",
      "        visited.add(i)\n",
      "        if len(neighbors[i]) >= min_pts:\n",
      "            clusters.append(({i}, set())) # core\n",
      "            to_merge_in_cluster = set(neighbors[i])\n",
      "            while to_merge_in_cluster:\n",
      "                j = to_merge_in_cluster.pop()\n",
      "                if j not in visited:\n",
      "                    visited.add(j)\n",
      "                    if len(neighbors[j]) >= min_pts:\n",
      "                        to_merge_in_cluster |= set(neighbors[j])\n",
      "                if not any([j in c[0] | c[1] for c in clusters]):\n",
      "                    if len(neighbors[j]) >= min_pts:\n",
      "                        clusters[-1][0].add(j) # core\n",
      "                    else:\n",
      "                        clusters[-1][1].add(j) # reachable\n",
      "    return clusters\n",
      "\n",
      "points = [(22.214376738504338, -117.58984077972329), (25.329918859114954, -113.19578768859782),\n",
      "          (24.570980585891803, -100.62882174079044), (26.961834845479476, -85.395310220483253),\n",
      "          (40.158080954865667, -110.44261354568215), (22.377185307252855, -98.45407770820762),\n",
      "          (32.182597246654062, -105.90973973759604), (31.435841636030801, -105.48877980002629),\n",
      "          (27.184915156073007, -119.74217954260372), (33.383480635382092, -109.9155493519732),\n",
      "          (26.178255373795441, -86.417157561363084), (43.389753626484776, -105.85267062621378),\n",
      "          (41.789964270198986, -92.916361921685592), (44.304756170519966, -121.28713141520021),\n",
      "          (39.466033622547755, -82.459478872651005), (40.708254734787019, -113.13252229896504),\n",
      "          (40.266033588400276, -102.43408100586714), (25.657894774168501, -109.25943663112676),\n",
      "          (33.998714618471269, -119.26529690876269), (30.780927929393414, -84.656376314787451)]\n",
      "points = asarray(points)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clusters = DBSCAN(points, 7, 4)\n",
      "print('cluster 0:', sorted(clusters[0][0] | clusters[0][1]))\n",
      "print('cluster 1:', sorted(clusters[1][0] | clusters[1][1]))\n",
      "colors = 'rbgycm' * 10\n",
      "scatter(*zip(*points), color=[.75] * 3, alpha=.5, s=20)\n",
      "for i, (core, reachable) in enumerate(clusters):\n",
      "    core = list(core); reachable = list(reachable)\n",
      "    scatter(*zip(*points[core]), color=colors[i], alpha=1, s=20)\n",
      "    scatter(*zip(*points[reachable]), color=colors[i], alpha=.4, s=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "cluster 0: [0, 1, 8, 17, 18]\n",
        "cluster 1: [4, 6, 7, 9, 11, 15]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Xp8qAMyN4AEA/ZRiGDMOQaZoR7aZpyjAMvvQRCYmrEgD6KZfLpeTkZPl8vnD4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\nW3zFNHgUFhZq3bp1+uUvf6mvfe1rZ+z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       "text": [
        "<matplotlib.figure.Figure at 0x1113d0dd0>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.cluster\n",
      "\n",
      "clusters = sklearn.cluster.DBSCAN(7, 4).fit_predict(points)\n",
      "print('cluster 0:', where(clusters == 0)[0])\n",
      "print('cluster 1:', where(clusters == 1)[0])\n",
      "colors = 'rbgycm' * 10\n",
      "scatter(points[:,0], points[:,1], color=[.75] * 3, alpha=.5, s=20)\n",
      "for i in set(clusters):\n",
      "    if i < 0: continue\n",
      "    scatter(*zip(*points[where(clusters == i)[0]]), \n",
      "            color=colors[int(i)], alpha=1, s=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "cluster 0: [ 0  1  8 17 18]\n",
        "cluster 1: [ 4  6  7  9 11 15]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6vXGqDOgYwQMAeijDMGQYhkzTjGg3TVOGYfClj0hIXJUA0EO5XC4lJyfL6/WG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\nugAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111071b90>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clusters = sklearn.cluster.DBSCAN(7, 4).fit_predict(points)\n",
      "print('cluster 0:', where(clusters == 0)[0])\n",
      "print('cluster 1:', where(clusters == 1)[0])\n",
      "colors = 'rbgycm' * 10\n",
      "scatter(points[:,0], points[:,1], color=[.75] * 3, alpha=.5, s=20)\n",
      "for i in set(clusters):\n",
      "    if i < 0: continue\n",
      "    scatter(*zip(*points[where(clusters == i)[0]]), \n",
      "            color=colors[int(i)], alpha=1, s=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "cluster 0: [ 4  6  7  9 11 15 17]\n",
        "cluster 1: [ 0  1  8 18]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/Xz6fL0GVAcdG8ACAbsowDBmGIdM0o9pN05RhGHzpI5ISZyUAdFNut1spKSny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\n1NToV7/6VdRdLX369Gmz4PTKK6/UTTf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       "text": [
        "<matplotlib.figure.Figure at 0x111342a10>"
       ]
      }
     ],
     "prompt_number": 40
    }
   ],
   "metadata": {}
  }
 ]
}