K_means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # K_means 聚类算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *\n",
    "def loadDataSet(filename):\n",
    "    dataMat=[]\n",
    "    with open(filename,'r') as f:\n",
    "        lines=f.readlines()\n",
    "        for line in lines:\n",
    "            curline=line.strip().split('\\t')\n",
    "            fltLine=list(map(float,curline))\n",
    "            dataMat.append(fltLine)\n",
    "        return dataMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1.658985,  4.285136],\n",
       "        [-3.453687,  3.424321],\n",
       "        [ 4.838138, -1.151539],\n",
       "        [-5.379713, -3.362104],\n",
       "        [ 0.972564,  2.924086],\n",
       "        [-3.567919,  1.531611],\n",
       "        [ 0.450614, -3.302219],\n",
       "        [-3.487105, -1.724432],\n",
       "        [ 2.668759,  1.594842],\n",
       "        [-3.156485,  3.191137],\n",
       "        [ 3.165506, -3.999838],\n",
       "        [-2.786837, -3.099354],\n",
       "        [ 4.208187,  2.984927],\n",
       "        [-2.123337,  2.943366],\n",
       "        [ 0.704199, -0.479481],\n",
       "        [-0.39237 , -3.963704],\n",
       "        [ 2.831667,  1.574018],\n",
       "        [-0.790153,  3.343144],\n",
       "        [ 2.943496, -3.357075],\n",
       "        [-3.195883, -2.283926],\n",
       "        [ 2.336445,  2.875106],\n",
       "        [-1.786345,  2.554248],\n",
       "        [ 2.190101, -1.90602 ],\n",
       "        [-3.403367, -2.778288],\n",
       "        [ 1.778124,  3.880832],\n",
       "        [-1.688346,  2.230267],\n",
       "        [ 2.592976, -2.054368],\n",
       "        [-4.007257, -3.207066],\n",
       "        [ 2.257734,  3.387564],\n",
       "        [-2.679011,  0.785119],\n",
       "        [ 0.939512, -4.023563],\n",
       "        [-3.674424, -2.261084],\n",
       "        [ 2.046259,  2.735279],\n",
       "        [-3.18947 ,  1.780269],\n",
       "        [ 4.372646, -0.822248],\n",
       "        [-2.579316, -3.497576],\n",
       "        [ 1.889034,  5.1904  ],\n",
       "        [-0.798747,  2.185588],\n",
       "        [ 2.83652 , -2.658556],\n",
       "        [-3.837877, -3.253815],\n",
       "        [ 2.096701,  3.886007],\n",
       "        [-2.709034,  2.923887],\n",
       "        [ 3.367037, -3.184789],\n",
       "        [-2.121479, -4.232586],\n",
       "        [ 2.329546,  3.179764],\n",
       "        [-3.284816,  3.273099],\n",
       "        [ 3.091414, -3.815232],\n",
       "        [-3.762093, -2.432191],\n",
       "        [ 3.542056,  2.778832],\n",
       "        [-1.736822,  4.241041],\n",
       "        [ 2.127073, -2.98368 ],\n",
       "        [-4.323818, -3.938116],\n",
       "        [ 3.792121,  5.135768],\n",
       "        [-4.786473,  3.358547],\n",
       "        [ 2.624081, -3.260715],\n",
       "        [-4.009299, -2.978115],\n",
       "        [ 2.493525,  1.96371 ],\n",
       "        [-2.513661,  2.642162],\n",
       "        [ 1.864375, -3.176309],\n",
       "        [-3.171184, -3.572452],\n",
       "        [ 2.89422 ,  2.489128],\n",
       "        [-2.562539,  2.884438],\n",
       "        [ 3.491078, -3.947487],\n",
       "        [-2.565729, -2.012114],\n",
       "        [ 3.332948,  3.983102],\n",
       "        [-1.616805,  3.573188],\n",
       "        [ 2.280615, -2.559444],\n",
       "        [-2.651229, -3.103198],\n",
       "        [ 2.321395,  3.154987],\n",
       "        [-1.685703,  2.939697],\n",
       "        [ 3.031012, -3.620252],\n",
       "        [-4.599622, -2.185829],\n",
       "        [ 4.196223,  1.126677],\n",
       "        [-2.133863,  3.093686],\n",
       "        [ 4.668892, -2.562705],\n",
       "        [-2.793241, -2.149706],\n",
       "        [ 2.884105,  3.043438],\n",
       "        [-2.967647,  2.848696],\n",
       "        [ 4.479332, -1.764772],\n",
       "        [-4.905566, -2.91107 ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataMat=mat(loadDataSet('testSet.txt'))\n",
    "dataMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distEclud(vecA,vecB):\n",
    "    return sqrt(sum(power(vecA-vecB,2)))\n",
    "#生成最大值与最小值之间的数\n",
    "def randCent(dataSet, k):\n",
    "    n = shape(dataSet)[1]\n",
    "    centroids = mat(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] = mat(minJ + rangeJ * random.rand(k,1))\n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 3.80441664, -0.23754842],\n",
       "        [ 1.69653101, -0.40583024]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randCent(dataMat,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.184632816681332"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distEclud(dataMat[0],dataMat[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# k_MEANS算法会创建k个质心，然后将每个点分配到最近的质心，重新计算质心\n",
    "def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):\n",
    "    m = shape(dataSet)[0]\n",
    "    clusterAssment = mat(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 = inf; minIndex = -1\n",
    "            for j in range(k):\n",
    "                distJI = distMeas(centroids[j,:],dataSet[i,:])\n",
    "                if distJI < minDist:\n",
    "                    minDist = distJI; minIndex = j\n",
    "            if clusterAssment[i,0] != minIndex: clusterChanged = True\n",
    "            clusterAssment[i,:] = minIndex,minDist**2\n",
    "        print(centroids)\n",
    "        for cent in range(k):#recalculate centroids\n",
    "            ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]#get all the point in this cluster\n",
    "            centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean \n",
    "    return centroids, clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.40697609  4.47989262]\n",
      " [-3.94828585  2.59397044]\n",
      " [-2.86961095  3.8432152 ]\n",
      " [ 2.33613657  4.52999062]]\n",
      "[[ 3.48493595 -0.62303267]\n",
      " [-3.09539062 -2.26219085]\n",
      " [-2.18799937  3.01824781]\n",
      " [ 2.00090788  1.64428112]]\n",
      "[[ 3.11052774 -2.61694289]\n",
      " [-3.19984738 -2.96423548]\n",
      " [-2.46154315  2.78737555]\n",
      " [ 2.4519287   3.02837225]]\n",
      "[[ 2.80293085 -2.7315146 ]\n",
      " [-3.38237045 -2.9473363 ]\n",
      " [-2.46154315  2.78737555]\n",
      " [ 2.6265299   3.10868015]]\n"
     ]
    }
   ],
   "source": [
    "myCentroids, clustAssing = kMeans(dataMat, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def showPlt(datMat, alg=kMeans, numClust=4):\n",
    "    myCentroids, clustAssing = alg(datMat, numClust)\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",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-3.24109513  1.70235875]\n",
      " [-3.31781127 -2.30638316]\n",
      " [ 0.55544564  3.90980657]\n",
      " [-2.50506184 -0.36074292]]\n",
      "[[-2.768021    2.65028438]\n",
      " [-2.74312074 -3.05531939]\n",
      " [ 2.26956248  2.65844463]\n",
      " [ 2.70091921 -2.82615093]]\n",
      "[[-2.46154315  2.78737555]\n",
      " [-3.38237045 -2.9473363 ]\n",
      " [ 2.6265299   3.10868015]\n",
      " [ 2.80293085 -2.7315146 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(dataMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 二分 K——means\n",
    "def biKmeans(dataSet, k, distMeas=distEclud):\n",
    "    m = shape(dataSet)[0]\n",
    "    clusterAssment = mat(zeros((m,2)))\n",
    "    centroid0 = 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] = distMeas(mat(centroid0), dataSet[j,:])**2\n",
    "    while (len(centList) < k):\n",
    "        lowestSSE = inf\n",
    "        for i in range(len(centList)):\n",
    "            ptsInCurrCluster = dataSet[nonzero(clusterAssment[:,0].A==i)[0],:]#get the data points currently in cluster i\n",
    "            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)\n",
    "            sseSplit = sum(splitClustAss[:,1])#compare the SSE to the currrent minimum\n",
    "            sseNotSplit = sum(clusterAssment[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[nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) #change 1 to 3,4, or whatever\n",
    "        bestClustAss[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[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE\n",
    "    return mat(centList), clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.67340297  0.35986225]\n",
      " [ 1.90103912 -1.87560137]]\n",
      "[[-2.8815949   3.10812271]\n",
      " [ 1.30897764  0.21151872]]\n",
      "[[-2.94737575  3.3263781 ]\n",
      " [ 1.23710375  0.17480612]]\n",
      "sseSplit, and notSplit:  570.722757425 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "[[-2.94148471  2.73874082]\n",
      " [-3.2070967   2.61680536]]\n",
      "[[-2.43131608  3.45250362]\n",
      " [-3.90577229  3.092145  ]]\n",
      "[[-2.1459026  3.421808 ]\n",
      " [-3.7488489  3.2309482]]\n",
      "[[-1.9062885   3.42271988]\n",
      " [-3.64143392  3.26215025]]\n",
      "[[-1.76576557  3.39794014]\n",
      " [-3.58362738  3.28784469]]\n",
      "sseSplit, and notSplit:  22.9717718963 532.659806789\n",
      "[[ 0.82212346 -1.38427742]\n",
      " [-0.82286464  2.26602609]]\n",
      "[[-0.14333495 -2.41561436]\n",
      " [ 2.92430661  3.34087561]]\n",
      "[[-0.45965615 -2.7782156 ]\n",
      " [ 2.93386365  3.12782785]]\n",
      "sseSplit, and notSplit:  68.6865481262 38.0629506357\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  40\n"
     ]
    }
   ],
   "source": [
    "dataMat=mat(loadDataSet('testSet2.txt'))\n",
    "centList,myNewAssments=biKmeans(dataMat,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对地图上的点进行聚类\n",
    "import urllib\n",
    "import json\n",
    "def geoGrab(stAddress, city):\n",
    "    apiStem = 'http://where.yahooapis.com/geocode?'  #create a dict and constants for the goecoder\n",
    "    params = {}\n",
    "    params['flags'] = 'J'#JSON return type\n",
    "    params['appid'] = 'aaa0VN6k'\n",
    "    params['location'] = '%s %s' % (stAddress, city)\n",
    "    url_params = urllib.parse.urlencode(params)\n",
    "    yahooApi = apiStem + url_params      #print url_params\n",
    "    print(yahooApi)\n",
    "    c=urllib.request.urlopen(yahooApi)\n",
    "    return json.loads(c.read())\n",
    "\n",
    "from time import sleep\n",
    "def massPlaceFind(fileName):\n",
    "    fw = open('places.txt', 'w')\n",
    "    for line in open(fileName).readlines():\n",
    "        line = line.strip()\n",
    "        lineArr = line.split('\\t')\n",
    "        retDict = geoGrab(lineArr[1], lineArr[2])\n",
    "        if retDict['ResultSet']['Error'] == 0:\n",
    "            lat = float(retDict['ResultSet']['Results'][0]['latitude'])\n",
    "            lng = float(retDict['ResultSet']['Results'][0]['longitude'])\n",
    "            print(\"%s\\t%f\\t%f\" % (lineArr[0], lat, lng))\n",
    "            fw.write('%s\\t%f\\t%f\\n' % (line, lat, lng))\n",
    "        else: print(\"error fetching\")\n",
    "        sleep(1)\n",
    "    fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distSLC(vecA, vecB):#Spherical Law of Cosines\n",
    "    a = sin(vecA[0,1]*pi/180) * sin(vecB[0,1]*pi/180)\n",
    "    b = cos(vecA[0,1]*pi/180) * cos(vecB[0,1]*pi/180) * \\\n",
    "                      cos(pi * (vecB[0,0]-vecA[0,0]) /180)\n",
    "    return arccos(a + b)*6371.0 #pi is imported with numpy\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def clusterClubs(numClust=5):\n",
    "    datList = []\n",
    "    for line in open('places.txt').readlines():\n",
    "        lineArr = line.split('\\t')\n",
    "        datList.append([float(lineArr[4]), float(lineArr[3])])\n",
    "    datMat = mat(datList)\n",
    "    print(\"ss\",datMat)\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('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[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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://where.yahooapis.com/geocode?location=1+VA+Center+Augusts%2CME&appid=aaa0VN6k&flags=J\n"
     ]
    },
    {
     "ename": "URLError",
     "evalue": "<urlopen error [Errno 11001] getaddrinfo failed>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1253\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1254\u001b[1;33m                 \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1255\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers)\u001b[0m\n\u001b[0;32m   1106\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1107\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers)\u001b[0m\n\u001b[0;32m   1151\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1152\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body)\u001b[0m\n\u001b[0;32m   1102\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1103\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body)\u001b[0m\n\u001b[0;32m    933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 934\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    935\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    876\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 877\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    878\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    848\u001b[0m         self.sock = self._create_connection(\n\u001b[1;32m--> 849\u001b[1;33m             (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[0;32m    850\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    693\u001b[0m     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 694\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    695\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    732\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 733\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    734\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-7326426f4463>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgeoGrab\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'1 VA Center'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Augusts,ME'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-30-95086ffc26d4>\u001b[0m in \u001b[0;36mgeoGrab\u001b[1;34m(stAddress, city)\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0myahooApi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapiStem\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0murl_params\u001b[0m      \u001b[1;31m#print url_params\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myahooApi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myahooApi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    161\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    162\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 163\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    464\u001b[0m             \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    482\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    483\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 484\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    485\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    442\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    443\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 444\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    445\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1281\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1284\u001b[0m     \u001b[0mhttp_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\xujia\\appdata\\local\\programs\\python\\python35\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1254\u001b[0m                 \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1255\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1256\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1257\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1258\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
     ]
    }
   ],
   "source": [
    "geoGrab('1 VA Center','Augusts,ME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ss [[-122.788346   45.486502]\n",
      " [-122.781021   45.49315 ]\n",
      " [-122.766147   45.498187]\n",
      " [-122.800311   45.485943]\n",
      " [-122.781853   45.508203]\n",
      " [-122.779628   45.493398]\n",
      " [-122.376304   45.430319]\n",
      " [-122.425553   45.507621]\n",
      " [-122.618893   45.39907 ]\n",
      " [-122.634159   45.427072]\n",
      " [-122.638846   45.462173]\n",
      " [-122.646587   45.485396]\n",
      " [-122.699212   45.464826]\n",
      " [-122.842918   45.646831]\n",
      " [-122.48248    45.519142]\n",
      " [-122.675528   45.523094]\n",
      " [-122.674206   45.520682]\n",
      " [-122.720828   45.543016]\n",
      " [-122.593078   45.562715]\n",
      " [-122.56151    45.519052]\n",
      " [-122.675605   45.520561]\n",
      " [-122.682725   45.584124]\n",
      " [-122.608366   45.495365]\n",
      " [-122.524073   45.49775 ]\n",
      " [-122.499672   45.519142]\n",
      " [-122.611177   45.497498]\n",
      " [-122.66835    45.583048]\n",
      " [-122.571475   45.555384]\n",
      " [-122.574167   45.554263]\n",
      " [-122.578746   45.547229]\n",
      " [-122.589208   45.484823]\n",
      " [-122.62792    45.496918]\n",
      " [-122.674189   45.520714]\n",
      " [-122.538196   45.489637]\n",
      " [-122.57873    45.548651]\n",
      " [-122.523985   45.497765]\n",
      " [-122.600881   45.555811]\n",
      " [-122.538833   45.526306]\n",
      " [-122.728825   45.595042]\n",
      " [-122.65931    45.501585]\n",
      " [-122.732606   45.445233]\n",
      " [-122.674466   45.524692]\n",
      " [-122.66739    45.535101]\n",
      " [-122.500034   45.504448]\n",
      " [-122.537726   45.527863]\n",
      " [-122.628865   45.532426]\n",
      " [-122.561283   45.519037]\n",
      " [-122.641114   45.540098]\n",
      " [-122.728961   45.595069]\n",
      " [-122.522849   45.49799 ]\n",
      " [-122.586505   45.549288]\n",
      " [-122.731034   45.446442]\n",
      " [-122.578744   45.547337]\n",
      " [-122.660285   45.464338]\n",
      " [-122.672462   45.583693]\n",
      " [-122.627688   45.496601]\n",
      " [-122.634581   45.497091]\n",
      " [-122.656367   45.517225]\n",
      " [-122.534481   45.504087]\n",
      " [-122.581608   45.483836]\n",
      " [-122.537787   45.520162]\n",
      " [-122.672388   45.52337 ]\n",
      " [-122.593048   45.562699]\n",
      " [-122.655527   45.51222 ]\n",
      " [-122.557005   45.476721]\n",
      " [-122.656249   45.522902]\n",
      " [-122.593078   45.562715]\n",
      " [-122.765754   45.425788]\n",
      " [-122.753932   45.382682]]\n",
      "[[-122.39675368   45.42536208]\n",
      " [-122.80450077   45.57504012]]\n",
      "[[-122.53228195   45.49259215]\n",
      " [-122.67224529   45.52035345]]\n",
      "[[-122.54110004   45.50418828]\n",
      " [-122.68313993   45.51691943]]\n",
      "[[-122.55095081   45.5149589 ]\n",
      " [-122.69753113   45.51014305]]\n",
      "[[-122.54868607   45.51882187]\n",
      " [-122.69551477   45.50729503]]\n",
      "sseSplit, and notSplit:  3043.26331611 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  69\n",
      "[[-122.42097291   45.496289  ]\n",
      " [-122.4785743    45.49766264]]\n",
      "[[-122.4009285    45.46897   ]\n",
      " [-122.55924018   45.52238271]]\n",
      "sseSplit, and notSplit:  501.32878822 2191.82442752\n",
      "[[-122.71895457   45.54803451]\n",
      " [-122.63048994   45.54958844]]\n",
      "[[-122.73609976   45.50972281]\n",
      " [-122.64816561   45.50446261]]\n",
      "[[-122.76009173   45.50068747]\n",
      " [-122.65515417   45.51142475]]\n",
      "sseSplit, and notSplit:  1561.38802465 851.438888582\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  39\n",
      "[[-122.53675127   45.43669188]\n",
      " [-122.38030519   45.4964839 ]]\n",
      "[[-122.56208315   45.52250274]\n",
      " [-122.42811233   45.485694  ]]\n",
      "sseSplit, and notSplit:  511.806537619 1561.38802465\n",
      "[[-122.77775183   45.50194261]\n",
      " [-122.80626433   45.44360599]]\n",
      "[[-122.76527727   45.528197  ]\n",
      " [-122.7458315    45.42503625]]\n",
      "[[-122.7718838   45.5345341]\n",
      " [-122.7365076   45.4329942]]\n",
      "sseSplit, and notSplit:  513.45763679 1449.06404157\n",
      "[[-122.68271661   45.52848084]\n",
      " [-122.65801215   45.52589681]]\n",
      "[[-122.67443544   45.54266422]\n",
      " [-122.6435854    45.49268107]]\n",
      "[[-122.66642592   45.53957731]\n",
      " [-122.641833     45.47815355]]\n",
      "[[-122.66502873   45.53626333]\n",
      " [-122.63869656   45.47002711]]\n",
      "sseSplit, and notSplit:  268.803355642 1815.20176024\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  15\n",
      "[[-122.39109321   45.52042996]\n",
      " [-122.55320489   45.49149013]]\n",
      "[[-122.4009285    45.46897   ]\n",
      " [-122.55924018   45.52238271]]\n",
      "sseSplit, and notSplit:  501.32878822 1111.08278978\n",
      "[[-122.73012984   45.55948606]\n",
      " [-122.76657627   45.5205224 ]]\n",
      "[[-122.755383     45.5949895 ]\n",
      " [-122.78288433   45.4942305 ]]\n",
      "sseSplit, and notSplit:  136.742055209 1513.41879359\n",
      "[[-122.63096882   45.40120075]\n",
      " [-122.65953338   45.50010709]]\n",
      "[[-122.626526     45.413071  ]\n",
      " [-122.65775673   45.520366  ]]\n",
      "[[-122.63063267   45.42943833]\n",
      " [-122.65865724   45.5231371 ]]\n",
      "[[-122.63804575   45.43816325]\n",
      " [-122.65857585   45.52607705]]\n",
      "sseSplit, and notSplit:  270.542186251 1364.89652537\n",
      "[[-122.72573666   45.4316156 ]\n",
      " [-122.75819853   45.3920934 ]]\n",
      "[[-122.7321515    45.44557225]\n",
      " [-122.753932     45.382682  ]]\n",
      "sseSplit, and notSplit:  22.9181823454 1898.16692634\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterClubs(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}