CH10 K-means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH10 K-means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 发现K个簇，每个类的中心采用簇中所含值的均值构成。\n",
    "- Unsupervised Classification\n",
    "- 可以通过肘部法则确定k\n",
    "\n",
    "聚类评价指标\n",
    "- 轮廓系数(SC)\n",
    "- 互信息(MI)\n",
    "- 兰德指数(RI)，调整兰德指数(ARI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLiA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### K-means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadDataSet(fileName):      #general function to parse tab -delimited floats\n",
    "    dataMat = []                #assume last column is target value\n",
    "    fr = open(fileName)\n",
    "    for line in fr.readlines():\n",
    "        curLine = line.strip().split('\\t')\n",
    "        dataMat.append(curLine)\n",
    "    return dataMat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SSE \n",
    "def distEclud(vecA, vecB):\n",
    "    return np.sqrt(sum(np.power(vecA - vecB, 2).A[0])) #la.norm(vecA-vecB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def randCent(dataSet, k):\n",
    "    n = np.shape(dataSet)[1]\n",
    "    centroids = np.mat(np.zeros((k,n)))#create centroid mat\n",
    "    for j in range(n):#create random cluster centers, within bounds of each dimension\n",
    "        minJ = min(dataSet[:,j]) \n",
    "        rangeJ = float(max(dataSet[:,j]) - minJ)\n",
    "        centroids[:,j] = np.mat(minJ + rangeJ * np.random.rand(k,1))\n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):\n",
    "    m = np.shape(dataSet)[0]\n",
    "    clusterAssment = np.mat(np.zeros((m,2)))#create mat to assign data points \n",
    "                                      #to a centroid, also holds SE of each point\n",
    "    centroids = createCent(dataSet, k)\n",
    "    clusterChanged = True\n",
    "    while clusterChanged:\n",
    "        clusterChanged = False\n",
    "        for i in range(m):#for each data point assign it to the closest centroid\n",
    "            minDist = np.inf; minIndex = -1\n",
    "            for j in range(k):\n",
    "                distJI = distEclud(centroids[j,:],dataSet[i,:])\n",
    "                if distJI < minDist:\n",
    "                    minDist = distJI; minIndex = j\n",
    "            if clusterAssment[i,0] != minIndex: clusterChanged = True\n",
    "            # SE距离平方\n",
    "            clusterAssment[i,:] = minIndex,minDist**2\n",
    "        print (centroids)\n",
    "        for cent in range(k):#recalculate centroids\n",
    "            ptsInClust = dataSet[np.nonzero(clusterAssment[:,0].A==cent)[0]]#get all the point in this cluster\n",
    "            centroids[cent,:] = np.mean(ptsInClust, axis=0) #assign centroid to mean \n",
    "    return centroids, clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datMat = np.mat(loadDataSet(\"./Data/CH10/testSet.txt\")).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-5.379713]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(datMat[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-4.232586]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(datMat[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[5.1904]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(datMat[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[4.838138]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(datMat[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-1.3146886 , -2.5030694 ],\n",
       "        [-0.52429231,  4.06990024]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randCent(datMat,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1.658985, 4.285136]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datMat[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.184632816681332"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distEclud(datMat[0],datMat[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.2609484  -1.07910327]\n",
      " [ 2.17916804 -0.32325098]\n",
      " [-5.27730102  1.00962993]\n",
      " [-0.90832273 -4.16026687]]\n",
      "[[-2.48539592 -0.12335985]\n",
      " [ 2.82102866  0.39129192]\n",
      " [-3.21690935  1.95590853]\n",
      " [-2.37421167 -3.51431367]]\n",
      "[[-3.083058   -0.4696565 ]\n",
      " [ 2.82102866  0.39129192]\n",
      " [-2.45009747  2.89275747]\n",
      " [-2.9890561  -3.0737179 ]]\n"
     ]
    }
   ],
   "source": [
    "myCentroids, clustAssing = kMeans(datMat.A,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.        ,  5.79840698, -5.379713  , -3.362104  ],\n",
       "       [ 3.        , 11.88354311,  0.450614  , -3.302219  ],\n",
       "       [ 3.        ,  0.04154977, -2.786837  , -3.099354  ],\n",
       "       [ 3.        ,  7.53485393, -0.39237   , -3.963704  ],\n",
       "       [ 3.        ,  0.66654862, -3.195883  , -2.283926  ],\n",
       "       [ 3.        ,  0.25893235, -3.403367  , -2.778288  ],\n",
       "       [ 3.        ,  1.0545148 , -4.007257  , -3.207066  ],\n",
       "       [ 3.        , 16.33585298,  0.939512  , -4.023563  ],\n",
       "       [ 3.        ,  1.13010303, -3.674424  , -2.261084  ],\n",
       "       [ 3.        ,  0.34754263, -2.579316  , -3.497576  ],\n",
       "       [ 3.        ,  0.75293189, -3.837877  , -3.253815  ],\n",
       "       [ 3.        ,  2.09566528, -2.121479  , -4.232586  ],\n",
       "       [ 3.        ,  1.00914283, -3.762093  , -2.432191  ],\n",
       "       [ 3.        ,  2.52877341, -4.323818  , -3.938116  ],\n",
       "       [ 3.        ,  1.0500355 , -4.009299  , -2.978115  ],\n",
       "       [ 3.        ,  0.28190627, -3.171184  , -3.572452  ],\n",
       "       [ 3.        ,  1.30620868, -2.565729  , -2.012114  ],\n",
       "       [ 3.        ,  0.11499622, -2.651229  , -3.103198  ],\n",
       "       [ 3.        ,  3.38226924, -4.599622  , -2.185829  ],\n",
       "       [ 3.        ,  0.89214155, -2.793241  , -2.149706  ],\n",
       "       [ 3.        ,  3.69946456, -4.905566  , -2.91107   ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clustAssing[:,0] index\n",
    "# clustAssing[:,1] error\n",
    "data_ = np.hstack([clustAssing.A,datMat.A])\n",
    "data_[data_[:,0]==3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-0.4696565 ],\n",
       "        [ 0.39129192],\n",
       "        [ 2.89275747],\n",
       "        [-3.0737179 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myCentroids[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visulization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2275f5a21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(data_[data_[:,0]==0,:][:,2],data_[data_[:,0]==0,:][:,3],marker=\"*\",c=\"red\")\n",
    "plt.scatter(data_[data_[:,0]==1,:][:,2],data_[data_[:,0]==1,:][:,3],marker=\"v\",c=\"blue\")\n",
    "plt.scatter(data_[data_[:,0]==2,:][:,2],data_[data_[:,0]==2,:][:,3],marker=\".\",c=\"green\")\n",
    "plt.scatter(data_[data_[:,0]==3,:][:,2],data_[data_[:,0]==3,:][:,3],marker=\"+\",c=\"black\")\n",
    "plt.scatter(myCentroids.A[:,0],myCentroids.A[:,1],marker=\"+\",c=\"black\",s=100)\n",
    "plt.xlim(-6,6)\n",
    "plt.ylim(-6,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Postprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 合并最近质心\n",
    "- 合并使得SSE增幅最小的质心\n",
    "- 以下不停的运行，随机初始化质心的结果会影响最后的结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.2603625   1.4431184 ]\n",
      " [ 3.11181716 -0.51945266]\n",
      " [-1.55698796  1.22551704]]\n",
      "[[ 2.16902454  3.50190077]\n",
      " [ 1.942066   -0.59411112]\n",
      " [-2.3558602   1.26983287]]\n",
      "[[ 2.95977168  3.26903847]\n",
      " [-0.03056976 -2.84165565]\n",
      " [-2.71580587  2.48817592]]\n",
      "[[ 2.93386365  3.12782785]\n",
      " [-0.45965615 -2.7782156 ]\n",
      " [-2.94737575  3.3263781 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2275f5a0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datMat3 = loadDataSet(\"./Data/CH10/testSet2.txt\")\n",
    "datMat3 = np.mat(datMat3).astype(float)\n",
    "myCentroids, clustAssing = kMeans(datMat3.A,3)\n",
    "data_ = np.hstack([clustAssing.A,datMat3.A])\n",
    "data_[data_[:,0]==3,:]\n",
    "plt.figure()\n",
    "plt.scatter(data_[data_[:,0]==0,:][:,2],data_[data_[:,0]==0,:][:,3],marker=\"*\",c=\"red\")\n",
    "plt.scatter(data_[data_[:,0]==1,:][:,2],data_[data_[:,0]==1,:][:,3],marker=\"v\",c=\"blue\")\n",
    "plt.scatter(data_[data_[:,0]==2,:][:,2],data_[data_[:,0]==2,:][:,3],marker=\".\",c=\"green\")\n",
    "plt.scatter(data_[data_[:,0]==3,:][:,2],data_[data_[:,0]==3,:][:,3],marker=\"+\",c=\"black\")\n",
    "plt.scatter(myCentroids.A[:,0],myCentroids.A[:,1],marker=\"+\",c=\"black\",s=100)\n",
    "plt.xlim(-6,6)\n",
    "plt.ylim(-6,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bisecting K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def biKmeans(dataSet, k, distMeas=distEclud):\n",
    "    m = np.shape(dataSet)[0]\n",
    "    clusterAssment = np.mat(np.zeros((m,2)))\n",
    "    centroid0 = np.mean(dataSet, axis=0).tolist()[0]\n",
    "    centList =[centroid0] #create a list with one centroid\n",
    "    for j in range(m):#calc initial Error\n",
    "        clusterAssment[j,1] = distEclud(np.mat(centroid0), dataSet[j,:])**2\n",
    "    while (len(centList) < k):\n",
    "        lowestSSE = np.inf\n",
    "        for i in range(len(centList)):\n",
    "            ptsInCurrCluster = dataSet[np.nonzero(clusterAssment[:,0].A==i)[0],:]#get the data points currently in cluster i\n",
    "            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distEclud)\n",
    "            sseSplit = sum(splitClustAss[:,1])#compare the SSE to the currrent minimum\n",
    "            sseNotSplit = sum(clusterAssment[np.nonzero(clusterAssment[:,0].A!=i)[0],1])\n",
    "            print (\"sseSplit, and notSplit: \",sseSplit,sseNotSplit)\n",
    "            if (sseSplit + sseNotSplit) < lowestSSE:\n",
    "                bestCentToSplit = i\n",
    "                bestNewCents = centroidMat\n",
    "                bestClustAss = splitClustAss.copy()\n",
    "                lowestSSE = sseSplit + sseNotSplit\n",
    "        bestClustAss[np.nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) #change 1 to 3,4, or whatever\n",
    "        bestClustAss[np.nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit\n",
    "        print ('the bestCentToSplit is: ',bestCentToSplit)\n",
    "        print ('the len of bestClustAss is: ', len(bestClustAss))\n",
    "        centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]#replace a centroid with two best centroids \n",
    "        centList.append(bestNewCents[1,:].tolist()[0])\n",
    "        clusterAssment[np.nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE\n",
    "    return np.mat(centList), clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.13187197  0.69436805]\n",
      " [ 0.9513127  -0.54106196]]\n",
      "[[-0.67283794  3.1722195 ]\n",
      " [ 0.61495004 -1.69500396]]\n",
      "[[-0.06953469  3.29844341]\n",
      " [-0.32150057 -2.62473743]]\n",
      "[[-0.00675605  3.22710297]\n",
      " [-0.45965615 -2.7782156 ]]\n",
      "sseSplit, and notSplit:  [[453.03348958]] 0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "[[-0.44678883  2.49181206]\n",
      " [-4.31171965  3.03134218]]\n",
      "[[ 1.71544126  3.19785696]\n",
      " [-3.58362738  3.28784469]]\n",
      "[[ 2.76275171  3.12704005]\n",
      " [-3.06779095  3.33769884]]\n",
      "[[ 2.93386365  3.12782785]\n",
      " [-2.94737575  3.3263781 ]]\n",
      "sseSplit, and notSplit:  [[77.59224932]] [[29.15724944]]\n",
      "[[-0.14501058 -2.3794826 ]\n",
      " [ 0.17095419 -2.09091422]]\n",
      "[[-0.54562988 -2.99100569]\n",
      " [-0.11576125 -1.92705525]]\n",
      "[[-0.39994281 -3.1000235 ]\n",
      " [-0.6985095  -1.490984  ]]\n",
      "[[-0.15366667 -3.15354   ]\n",
      " [-1.3776246  -1.6522424 ]]\n",
      "[[-0.05200457 -3.16610557]\n",
      " [-1.41084317 -1.873139  ]]\n",
      "[[-7.11923077e-04 -3.21792031e+00]\n",
      " [-1.31198114e+00 -1.96162114e+00]]\n",
      "[[ 0.07973025 -3.24942808]\n",
      " [-1.26873575 -2.07139688]]\n",
      "[[ 0.19848727 -3.24320436]\n",
      " [-1.26405367 -2.209896  ]]\n",
      "[[ 0.2642961 -3.3057243]\n",
      " [-1.1836084 -2.2507069]]\n",
      "[[ 0.35496167 -3.36033556]\n",
      " [-1.12616164 -2.30193564]]\n",
      "sseSplit, and notSplit:  [[12.75326314]] [[423.87624014]]\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  40\n"
     ]
    }
   ],
   "source": [
    "datMat3 = loadDataSet(\"./Data/CH10/testSet2.txt\")\n",
    "datMat3 = np.mat(datMat3).astype(float)\n",
    "centList, myNewAssments = biKmeans(datMat3,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visulization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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idUUgn8rWsbv7h2b6fTNbI+kCSSvd3UM1DPnX0yPdcYc0MnLkNVZXBLJXbVXM+ZKul9Tj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2275f5a2128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_ = np.hstack([myNewAssments.A,datMat3.A])\n",
    "data_[data_[:,0]==3,:]\n",
    "plt.figure()\n",
    "plt.scatter(data_[data_[:,0]==0,:][:,2],data_[data_[:,0]==0,:][:,3],marker=\"*\",c=\"red\")\n",
    "plt.scatter(data_[data_[:,0]==1,:][:,2],data_[data_[:,0]==1,:][:,3],marker=\"v\",c=\"blue\")\n",
    "plt.scatter(data_[data_[:,0]==2,:][:,2],data_[data_[:,0]==2,:][:,3],marker=\".\",c=\"green\")\n",
    "plt.scatter(centList.A[:,0],centList.A[:,1],marker=\"+\",c=\"black\",s=100)\n",
    "plt.xlim(-6,6)\n",
    "plt.ylim(-6,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EX : Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def distSLC(vecA, vecB):#Spherical Law of Cosines\n",
    "    a = np.sin(vecA[0,1]*pi/180) * np.sin(vecB[0,1]*np.pi/180)\n",
    "    b = np.cos(vecA[0,1]*pi/180) * np.cos(vecB[0,1]*np.pi/180) * \\\n",
    "                      np.cos(pi * (vecB[0,0]-vecA[0,0]) /180)\n",
    "    return arccos(a + b)*6371.0 #pi is imported with numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clusterClubs(numClust=5):\n",
    "    datList = []\n",
    "    for line in open('./Data/CH10/places.txt').readlines():\n",
    "        lineArr = line.split('\\t')\n",
    "        datList.append([float(lineArr[4]), float(lineArr[3])])\n",
    "    datMat = np.mat(datList)\n",
    "    myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    imgP = plt.imread('./Data/CH10/Portland.png')\n",
    "    ax0.imshow(imgP)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[np.nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-122.7676579    45.46153218]\n",
      " [-122.65564076   45.3973652 ]]\n",
      "[[-122.7255474    45.51717564]\n",
      " [-122.5783403    45.50954025]]\n",
      "[[-122.7142141    45.51492203]\n",
      " [-122.56818551   45.5102949 ]]\n",
      "[[-122.70981637   45.51478609]\n",
      " [-122.56409551   45.51016235]]\n",
      "[[-122.70766576   45.51319176]\n",
      " [-122.56201911   45.51149539]]\n",
      "sseSplit, and notSplit:  [[0.35591468]] 0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  69\n",
      "[[-122.81014647   45.45580239]\n",
      " [-122.67141903   45.41779491]]\n",
      "[[-122.77132655   45.52464809]\n",
      " [-122.67583536   45.50746359]]\n",
      "[[-122.769877     45.51281758]\n",
      " [-122.67211648   45.51340557]]\n",
      "[[-122.76779508   45.50019   ]\n",
      " [-122.6685817    45.5216429 ]]\n",
      "[[-122.77486818   45.48294173]\n",
      " [-122.67406455   45.52831677]]\n",
      "sseSplit, and notSplit:  [[0.10156322]] [[0.16473616]]\n",
      "[[-122.6216444    45.41368975]\n",
      " [-122.3861237    45.50549409]]\n",
      "[[-122.57898855   45.51397297]\n",
      " [-122.4568086    45.4961344 ]]\n",
      "sseSplit, and notSplit:  [[0.099093]] [[0.19117852]]\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  33\n"
     ]
    },
    {
     "data": {
      "image/png": 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pDEWWYsWnaepaj+Y0bNvG970FzczJuT5q391rm/iez/7hMYVCFj94ehLmF1Ye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\nyCL0mIasu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cFfJXV9mebhhXCa3nZIJhPYtk33/OV9jrx9TGjhBLqRz7Ht2zhO+IkKvk9T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\nfEe4gS8worrhC/BZ4zo/GT9MK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9TueRhr3CGaOiReruORpLy4GSBtvs4Pfuheet0A3w8L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XaTrWwBkE8rSJFHuX6FQlbB8UJyKZmhFVDIKkhqhtE0AGzMVI4bLZmVWgEUmbEbUitoDK2A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\nH7tIK7K2A1PfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x227616d4da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterClubs(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EX0 : TestSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datMat = loadDataSet(\"./Data/CH10/testSet.txt\")\n",
    "X = np.mat(datMat).astype(float).A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "est = KMeans(n_clusters=4)\n",
    "est.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5c4cWAS3JSMrCNN2KfMtstJZ5HDl+ROdL/lZUpTbGLklSS+AwcA74/7Js78my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\nrC7sLcUv/csAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22763a22550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1],c=est.labels_.astype(np.float), edgecolor='k')\n",
    "plt.scatter(est.cluster_centers_[:,0],est.cluster_centers_[:,1],marker=\"+\",c=\"red\",s=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怎么确定多少个质心？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149.95430467642635"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.inertia_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EX1 : TestSet2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datMat = loadDataSet(\"./Data/CH10/testSet2.txt\")\n",
    "X = np.mat(datMat).astype(float).A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "est = KMeans(n_clusters=3)\n",
    "est.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xrc3dCVWRBKtwWuZ1IXnfcWbPvuB1QoVwmBSMOs7Hx4dZs39mn+8W9vgmkuSz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x227639a34e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1],c=est.labels_.astype(np.float), edgecolor='k')\n",
    "plt.scatter(est.cluster_centers_[:,0],est.cluster_centers_[:,1],marker=\"+\",c=\"red\",s=100)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EX3 :  Place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Dolphin II',\n",
       "  '10860 SW Beaverton-Hillsdale Hwy',\n",
       "  'Beaverton, OR',\n",
       "  '45.486502',\n",
       "  '-122.788346'],\n",
       " ['Hotties',\n",
       "  '10140 SW Canyon Rd.',\n",
       "  'Beaverton, OR',\n",
       "  '45.493150',\n",
       "  '-122.781021'],\n",
       " ['Pussycats',\n",
       "  '8666a SW Canyon Road',\n",
       "  'Beaverton, OR',\n",
       "  '45.498187',\n",
       "  '-122.766147'],\n",
       " ['Stars Cabaret',\n",
       "  '4570 Lombard Ave',\n",
       "  'Beaverton, OR',\n",
       "  '45.485943',\n",
       "  '-122.800311'],\n",
       " ['Sunset Strip',\n",
       "  '10205 SW Park Way',\n",
       "  'Beaverton, OR',\n",
       "  '45.508203',\n",
       "  '-122.781853'],\n",
       " ['Vegas VIP Room',\n",
       "  '10018 SW Canyon Rd',\n",
       "  'Beaverton, OR',\n",
       "  '45.493398',\n",
       "  '-122.779628'],\n",
       " ['Full Moon Bar and Grill',\n",
       "  '28014 Southeast Wally Road',\n",
       "  'Boring, OR',\n",
       "  '45.430319',\n",
       "  '-122.376304'],\n",
       " ['505 Club', '505 Burnside Rd', 'Gresham, OR', '45.507621', '-122.425553'],\n",
       " ['Dolphin',\n",
       "  '17180 McLoughlin Blvd',\n",
       "  'Milwaukie, OR',\n",
       "  '45.399070',\n",
       "  '-122.618893'],\n",
       " ['Dolphin III',\n",
       "  '13305 SE McLoughlin BLVD',\n",
       "  'Milwaukie, OR',\n",
       "  '45.427072',\n",
       "  '-122.634159'],\n",
       " ['Acropolis',\n",
       "  '8325 McLoughlin Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.462173',\n",
       "  '-122.638846'],\n",
       " ['Blush',\n",
       "  '5145 SE McLoughlin Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.485396',\n",
       "  '-122.646587'],\n",
       " ['Boom Boom Room',\n",
       "  '8345 Barbur Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.464826',\n",
       "  '-122.699212'],\n",
       " ['Bottoms Up',\n",
       "  '16900 Saint Helens Rd',\n",
       "  'Portland, OR',\n",
       "  '45.646831',\n",
       "  '-122.842918'],\n",
       " ['Cabaret II', '17544 Stark St', 'Portland, OR', '45.519142', '-122.482480'],\n",
       " ['Cabaret Lounge',\n",
       "  '503 W Burnside',\n",
       "  'Portland, OR',\n",
       "  '45.523094',\n",
       "  '-122.675528'],\n",
       " ['Carnaval', '330 SW 3rd Avenue', 'Portland, OR', '45.520682', '-122.674206'],\n",
       " ['Casa Diablo',\n",
       "  '2839 NW St. Helens Road',\n",
       "  'Portland, OR',\n",
       "  '45.543016',\n",
       "  '-122.720828'],\n",
       " ['Chantilly Lace',\n",
       "  '6723 Killingsworth St',\n",
       "  'Portland, OR',\n",
       "  '45.562715',\n",
       "  '-122.593078'],\n",
       " ['Club 205', '9939 Stark St', 'Portland, OR', '45.519052', '-122.561510'],\n",
       " ['Club Rouge', '403 SW Stark', 'Portland, OR', '45.520561', '-122.675605'],\n",
       " [\"Dancin' Bare\",\n",
       "  '8440 Interstate Ave',\n",
       "  'Portland, OR',\n",
       "  '45.584124',\n",
       "  '-122.682725'],\n",
       " [\"Devil's Point\",\n",
       "  '5305 SE Foster Rd',\n",
       "  'Portland, OR',\n",
       "  '45.495365',\n",
       "  '-122.608366'],\n",
       " ['Double Dribble',\n",
       "  '13550 Southeast Powell Boulevard',\n",
       "  'Portland, OR',\n",
       "  '45.497750',\n",
       "  '-122.524073'],\n",
       " ['Dream on Saloon',\n",
       "  '15920 Stark St',\n",
       "  'Portland, OR',\n",
       "  '45.519142',\n",
       "  '-122.499672'],\n",
       " ['DV8', '5003 Powell Blvd', 'Portland, OR', '45.497498', '-122.611177'],\n",
       " ['Exotica', '240 Columbia Blvd', 'Portland, OR', '45.583048', '-122.668350'],\n",
       " ['Frolics', '8845 Sandy Blvd', 'Portland, OR', '45.555384', '-122.571475'],\n",
       " ['G-Spot Airport',\n",
       "  '8654 Sandy Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.554263',\n",
       "  '-122.574167'],\n",
       " ['G-Spot Northeast',\n",
       "  '3400 NE 82nd Ave',\n",
       "  'Portland, OR',\n",
       "  '45.547229',\n",
       "  '-122.578746'],\n",
       " ['G-Spot Southeast',\n",
       "  '5241 SE 72nd Ave',\n",
       "  'Portland, OR',\n",
       "  '45.484823',\n",
       "  '-122.589208'],\n",
       " ['Glimmers', '3532 Powell Blvd', 'Portland, OR', '45.496918', '-122.627920'],\n",
       " ['Golden Dragon Exotic Club',\n",
       "  '324 SW 3rd Ave',\n",
       "  'Portland, OR',\n",
       "  '45.520714',\n",
       "  '-122.674189'],\n",
       " ['Heat',\n",
       "  '12131 SE Holgate Blvd.',\n",
       "  'Portland, OR',\n",
       "  '45.489637',\n",
       "  '-122.538196'],\n",
       " [\"Honeysuckle's Lingerie\",\n",
       "  '3520 82nd Ave',\n",
       "  'Portland, OR',\n",
       "  '45.548651',\n",
       "  '-122.578730'],\n",
       " ['Hush Playhouse',\n",
       "  '13560 Powell Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.497765',\n",
       "  '-122.523985'],\n",
       " [\"JD's Bar &amp; Grill\",\n",
       "  '4523 NE 60th Ave',\n",
       "  'Portland, OR',\n",
       "  '45.555811',\n",
       "  '-122.600881'],\n",
       " [\"Jody's Bar And Grill\",\n",
       "  '12035 Glisan St',\n",
       "  'Portland, OR',\n",
       "  '45.526306',\n",
       "  '-122.538833'],\n",
       " ['Landing Strip',\n",
       "  '6210 Columbia Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.595042',\n",
       "  '-122.728825'],\n",
       " ['Lucky Devil Lounge',\n",
       "  '633 SE Powell Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.501585',\n",
       "  '-122.659310'],\n",
       " ['Lure', '11051 Barbur Blvd', 'Portland, OR', '45.445233', '-122.732606'],\n",
       " ['Magic Garden', '217 4th Ave', 'Portland, OR', '45.524692', '-122.674466'],\n",
       " [\"Mary's Club\", '129 Broadway', 'Portland, OR', '45.535101', '-122.667390'],\n",
       " [\"Montego's\",\n",
       "  '15826 SE Division',\n",
       "  'Portland, OR',\n",
       "  '45.504448',\n",
       "  '-122.500034'],\n",
       " ['Mr. Peeps', '709 122nd Ave', 'Portland, OR', '45.527863', '-122.537726'],\n",
       " [\"Mynt Gentlemen's Club\",\n",
       "  '3390 NE Sandy Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.532426',\n",
       "  '-122.628865'],\n",
       " ['Mystic', '9950 SE Stark St.', 'Portland, OR', '45.519037', '-122.561283'],\n",
       " ['Nicolai Street Clubhouse',\n",
       "  '2460 24th Ave',\n",
       "  'Portland, OR',\n",
       "  '45.540098',\n",
       "  '-122.641114'],\n",
       " ['Oh Zone', '6218 Columbia Blvd', 'Portland, OR', '45.595069', '-122.728961'],\n",
       " ['Pallas Club',\n",
       "  '13639 Powell Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.497990',\n",
       "  '-122.522849'],\n",
       " ['Pirates Cove',\n",
       "  '7427 Sandy Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.549288',\n",
       "  '-122.586505'],\n",
       " ['Private Pleasures',\n",
       "  '10931 53rd Ave',\n",
       "  'Portland, OR',\n",
       "  '45.446442',\n",
       "  '-122.731034'],\n",
       " ['Pussycats',\n",
       "  '3414 Northeast 82nd Avenue',\n",
       "  'Portland, OR',\n",
       "  '45.547337',\n",
       "  '-122.578744'],\n",
       " ['Riverside Corral',\n",
       "  '545 Tacoma St',\n",
       "  'Portland, OR',\n",
       "  '45.464338',\n",
       "  '-122.660285'],\n",
       " [\"Rooster's\",\n",
       "  '605 Columbia Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.583693',\n",
       "  '-122.672462'],\n",
       " ['Rose City Strip',\n",
       "  '3620 35th Pl',\n",
       "  'Portland, OR',\n",
       "  '45.496601',\n",
       "  '-122.627688'],\n",
       " ['Safari Show Club',\n",
       "  '3000 SE Powell Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.497091',\n",
       "  '-122.634581'],\n",
       " [\"Sassy's Bar &amp; Grill\",\n",
       "  '927 Morrison St',\n",
       "  'Portland, OR',\n",
       "  '45.517225',\n",
       "  '-122.656367'],\n",
       " ['Secret Rendezvous',\n",
       "  '12503 Division St',\n",
       "  'Portland, OR',\n",
       "  '45.504087',\n",
       "  '-122.534481'],\n",
       " ['Shimmers', '7944 Foster Rd', 'Portland, OR', '45.483836', '-122.581608'],\n",
       " [\"Soobie's\", '333 SE 122nd Ave', 'Portland, OR', '45.520162', '-122.537787'],\n",
       " [\"Spyce Gentleman's Club\",\n",
       "  '33 NW 2nd Ave',\n",
       "  'Portland, OR',\n",
       "  '45.523370',\n",
       "  '-122.672388'],\n",
       " ['Sugar Shack',\n",
       "  '6732 Killingsworth St',\n",
       "  'Portland, OR',\n",
       "  '45.562699',\n",
       "  '-122.593048'],\n",
       " ['The Hawthorne Strip',\n",
       "  '1008 Hawthorne Blvd',\n",
       "  'Portland, OR',\n",
       "  '45.512220',\n",
       "  '-122.655527'],\n",
       " [\"Tommy's Too\",\n",
       "  '10335 Foster Rd',\n",
       "  'Portland, OR',\n",
       "  '45.476721',\n",
       "  '-122.557005'],\n",
       " ['Union Jacks',\n",
       "  '938 Burnside St',\n",
       "  'Portland, OR',\n",
       "  '45.522902',\n",
       "  '-122.656249'],\n",
       " ['Video Visions',\n",
       "  '6723 Killingsworth St',\n",
       "  'Portland, OR',\n",
       "  '45.562715',\n",
       "  '-122.593078'],\n",
       " ['Stars Cabaret Bridgeport',\n",
       "  '17939 SW McEwan Rd',\n",
       "  'Tigard, OR',\n",
       "  '45.425788',\n",
       "  '-122.765754'],\n",
       " ['Jiggles', '7455 SW Nyberg St', 'Tualatin, OR', '45.382682', '-122.753932']]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "datMat = loadDataSet(\"./Data/CH10/places.txt\")\n",
    "X = np.mat(datMat).A[:,3:5].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=5, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "est = KMeans(n_clusters=5)\n",
    "est.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  45.49920908, -122.65078825],\n",
       "       [  45.52437896, -122.55535315],\n",
       "       [  45.4665528 , -122.7680632 ],\n",
       "       [  45.46897   , -122.4009285 ],\n",
       "       [  45.59011757, -122.72072414]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xrc3dCVWRBKtwWuZ1IXnfcWbPvuB1QoVwmBSMOs7Hx4dZs39mn+8W9vgmkuSz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22764602438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1],\n",
    "           c=est.labels_.astype(np.float), edgecolor='k')\n",
    "plt.scatter(est.cluster_centers_[:,0],est.cluster_centers_[:,1],marker=\"+\",c=\"red\",s=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means++"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "275px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}