content: '''
  Created on Feb 16, 2011
  k Means Clustering for Ch10 of Machine Learning in Action
  @author: Peter Harrington
  '''
  from numpy import *
  
  def loadDataSet(fileName):      #general function to parse tab -delimited floats
      dataMat = []                #assume last column is target value
      fr = open(fileName)
      for line in fr.readlines():
          curLine = line.strip().split('\t')
          fltLine = map(float,curLine) #map all elements to float()
          dataMat.append(fltLine)
      return dataMat
  
  def distEclud(vecA, vecB):
      return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)
  
  def randCent(dataSet, k):
      n = shape(dataSet)[1]
      centroids = mat(zeros((k,n)))#create centroid mat
      for j in range(n):#create random cluster centers, within bounds of each dimension
          minJ = min(dataSet[:,j]) 
          rangeJ = float(max(dataSet[:,j]) - minJ)
          centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1))
      return centroids
      
  def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
      m = shape(dataSet)[0]
      clusterAssment = mat(zeros((m,2)))#create mat to assign data points 
                                        #to a centroid, also holds SE of each point
      centroids = createCent(dataSet, k)
      clusterChanged = True
      while clusterChanged:
          clusterChanged = False
          for i in range(m):#for each data point assign it to the closest centroid
              minDist = inf; minIndex = -1
              for j in range(k):
                  distJI = distMeas(centroids[j,:],dataSet[i,:])
                  if distJI < minDist:
                      minDist = distJI; minIndex = j
              if clusterAssment[i,0] != minIndex: clusterChanged = True
              clusterAssment[i,:] = minIndex,minDist**2
          print centroids
          for cent in range(k):#recalculate centroids
              ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]#get all the point in this cluster
              centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean 
      return centroids, clusterAssment
  
  def biKmeans(dataSet, k, distMeas=distEclud):
      m = shape(dataSet)[0]
      clusterAssment = mat(zeros((m,2)))
      centroid0 = mean(dataSet, axis=0).tolist()[0]
      centList =[centroid0] #create a list with one centroid
      for j in range(m):#calc initial Error
          clusterAssment[j,1] = distMeas(mat(centroid0), dataSet[j,:])**2
      while (len(centList) < k):
          lowestSSE = inf
          for i in range(len(centList)):
              ptsInCurrCluster = dataSet[nonzero(clusterAssment[:,0].A==i)[0],:]#get the data points currently in cluster i
              centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)
              sseSplit = sum(splitClustAss[:,1])#compare the SSE to the currrent minimum
              sseNotSplit = sum(clusterAssment[nonzero(clusterAssment[:,0].A!=i)[0],1])
              print "sseSplit, and notSplit: ",sseSplit,sseNotSplit
              if (sseSplit + sseNotSplit) < lowestSSE:
                  bestCentToSplit = i
                  bestNewCents = centroidMat
                  bestClustAss = splitClustAss.copy()
                  lowestSSE = sseSplit + sseNotSplit
          bestClustAss[nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) #change 1 to 3,4, or whatever
          bestClustAss[nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit
          print 'the bestCentToSplit is: ',bestCentToSplit
          print 'the len of bestClustAss is: ', len(bestClustAss)
          centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]#replace a centroid with two best centroids 
          centList.append(bestNewCents[1,:].tolist()[0])
          clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE
      return mat(centList), clusterAssment
  
  import urllib
  import json
  def geoGrab(stAddress, city):
      apiStem = 'http://where.yahooapis.com/geocode?'  #create a dict and constants for the goecoder
      params = {}
      params['flags'] = 'J'#JSON return type
      params['appid'] = 'aaa0VN6k'
      params['location'] = '%s %s' % (stAddress, city)
      url_params = urllib.urlencode(params)
      yahooApi = apiStem + url_params      #print url_params
      print yahooApi
      c=urllib.urlopen(yahooApi)
      return json.loads(c.read())
  
  from time import sleep
  def massPlaceFind(fileName):
      fw = open('places.txt', 'w')
      for line in open(fileName).readlines():
          line = line.strip()
          lineArr = line.split('\t')
          retDict = geoGrab(lineArr[1], lineArr[2])
          if retDict['ResultSet']['Error'] == 0:
              lat = float(retDict['ResultSet']['Results'][0]['latitude'])
              lng = float(retDict['ResultSet']['Results'][0]['longitude'])
              print "%s\t%f\t%f" % (lineArr[0], lat, lng)
              fw.write('%s\t%f\t%f\n' % (line, lat, lng))
          else: print "error fetching"
          sleep(1)
      fw.close()
      
  def distSLC(vecA, vecB):#Spherical Law of Cosines
      a = sin(vecA[0,1]*pi/180) * sin(vecB[0,1]*pi/180)
      b = cos(vecA[0,1]*pi/180) * cos(vecB[0,1]*pi/180) * \
                        cos(pi * (vecB[0,0]-vecA[0,0]) /180)
      return arccos(a + b)*6371.0 #pi is imported with numpy
  
  import matplotlib
  import matplotlib.pyplot as plt
  def clusterClubs(numClust=5):
      datList = []
      for line in open('places.txt').readlines():
          lineArr = line.split('\t')
          datList.append([float(lineArr[4]), float(lineArr[3])])
      datMat = mat(datList)
      myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC)
      fig = plt.figure()
      rect=[0.1,0.1,0.8,0.8]
      scatterMarkers=['s', 'o', '^', '8', 'p', \
                      'd', 'v', 'h', '>', '<']
      axprops = dict(xticks=[], yticks=[])
      ax0=fig.add_axes(rect, label='ax0', **axprops)
      imgP = plt.imread('Portland.png')
      ax0.imshow(imgP)
      ax1=fig.add_axes(rect, label='ax1', frameon=False)
      for i in range(numClust):
          ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:]
          markerStyle = scatterMarkers[i % len(scatterMarkers)]
          ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)
      ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)
      plt.show()
  