#-*- coding：utf-8

import sys

sys.path.append("kMeans.py")

import kMeans

from numpy import *

# datMat = mat(kMeans.loadDataSet('testSet.txt'))

# mindata = min(datMat[:, 0])

# print(mindata)

#

#

# ranCentK = kMeans.randCent(datMat, 2)

# print(ranCentK)

#

# dis = kMeans.distEclud(datMat[0], datMat[1])

# print(dis)

# datMat3 = mat(kMeans.loadDataSet('testSet2.txt'))

# centList, myNewAssments = kMeans.biKmeans(datMat3, 3)

# print(centList)

geoResults = kMeans.geoGrab('1 VA Center', 'Augusta, ME')

print(geoResults)

res = geoResults['ResultSet']['Error']

print(res)

print('over!!!')

'''

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 = list(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.parse.urlencode(params)

    yahooApi = apiStem + url_params      #print url_params

    print(yahooApi)

    c = urllib.request.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', '^', '', '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()