def main():
#############################################################
# Real Code
# # initialization
startTime = time.time()
process = Preprocess()
relation = Relationship()
comparison = Comparison()
clustering = Clustering()
#--------------------------------Preprocess Data-----------------------------------------------------------
# data = P.load('cleandata.txt')
# cleandata = P.preprocess(data,'19+ranked')
# randomData = P.createRandomData(cleandata,50000)
# P.save(cleandata,'prepro_data_19_ranked.txt')
# cleandata = P.preprocess(data,'19+free')
# P.save(cleandata,'prepro_data_19_free.txt')
# cleandata = P.preprocess(data,'9+ranked')
# P.save(cleandata,'prepro_data_9_ranked.txt')
# cleandata = P.preprocess(data,'9+free')
# P.save(cleandata,'prepro_data_9_free.txt')
# # create random data
# cleandata = P.load('prepro_data_19_ranked.txt')
# randomData = P.getSubset(cleandata,50000,'Random')
# P.save(randomData,'prepro_random_data_19_ranked.txt')
# cleandata = P.load('prepro_data_19_free.txt')
# randomData = P.getSubset(cleandata,50000,'Random')
# P.save(randomData,'prepro_random_data_19_free.txt')
# cleandata = P.load('prepro_data_9_free.txt')
# randomData = P.getSubset(cleandata,50000,'Random')
# P.save(randomData,'prepro_random_data_9_free.txt')
#-----------------------------------Degree Distribution-----------------------------------------------------------
# data = process.load('prepro_data_19_ranked.txt')
# relationship = relation.findRelationship(data)
# gameCount = len(data)
# degreeList = relation.cleanRelationship(relationship,'DegreeDistribution')
# relation.plotDegreeDistribution(degreeList,gameCount, 'Real Data', '19+ranked')
# data = process.load('prepro_data_19_free.txt')
# relationship = relation.findRelationship(data)
# gameCount = len(data)
# degreeList = relation.cleanRelationship(relationship,'DegreeDistribution')
# relation.plotDegreeDistribution(degreeList,gameCount,'Real Data','19+free')
# # can not find records match "border size: 9 & game type: free"
# data = process.load('prepro_data_9_free.txt')
# relationship = relation.findRelationship(data)
# gameCount = len(data)
# degreeList = relation.cleanRelationship(relationship,'DegreeDistribution')
# relation.plotDegreeDistribution(degreeList,gameCount,'Real Data','9+free')
# # # random data
# data = process.load('prepro_random_data_19_ranked.txt')
# relationship = relation.findRelationship(data)
# gameCount = len(data)
# degreeList = relation.cleanRelationship(relationship,'DegreeDistribution')
# relation.plotDegreeDistribution(degreeList,gameCount,'Random Data','19+ranked')
# data = process.load('prepro_random_data_19_free.txt')
# relationship = relation.findRelationship(data)
# gameCount = len(data)
# degreeList = relation.cleanRelationship(relationship,'DegreeDistribution')
# relation.plotDegreeDistribution(degreeList,gameCount,'Random Data','19+free')
# # # can not find records match "border size: 9 & game type: free"
# data = process.load('prepro_random_data_9_free.txt')
# relationship = relation.findRelationship(data)
# gameCount = len(data)
# degreeList = relation.cleanRelationship(relationship,'DegreeDistribution')
# relation.plotDegreeDistribution(degreeList,gameCount,'Random Data','9+free')
#------------------------------------Shortest Path-------------------------------------------------------------------
# data = P.load('prepro_data_19_ranked.txt')
# relationship = R.findRelationship(data)
# playerList = relationship.keys()
# playerList.sort()
# P.save(playerList,'playerList_19_ranked.txt')
# pathMatrix = R.createPathMatrix(relationship,playerList)
# print pathMatrix
# data = P.load('prepro_data_19_free.txt')
# relationship = R.findRelationship(data)
# playerList = relationship.keys()
# playerList.sort()
# P.save(playerList,'playerList_19_free.txt')
# pathMatrix = R.createPathMatrix(relationship,playerList)
# print pathMatrix
# data = P.load('prepro_data_9_free.txt')
# relationship = R.findRelationship(data)
# playerList = relationship.keys()
# playerList.sort()
# P.save(playerList,'playerList_9_free.txt')
# pathMatrix = R.createPathMatrix(relationship,playerList)
# print pathMatrix
# # random data
# data = P.load('prepro_random_data_19_ranked.txt')
# relationship = R.findRelationship(data)
# playerList = relationship.keys()
# playerList.sort()
# P.save(playerList,'playerList__random_19_ranked.txt')
# pathMatrix = R.createPathMatrix(relationship,playerList)
# print pathMatrix
# P.save(pathMatrix,'pathMatrix_19_ranked.txt')
# data = P.load('prepro_random_data_19_free.txt')
# relationship = R.findRelationship(data)
# playerList = relationship.keys()
# playerList.sort()
# P.save(playerList,'playerList_random_19_free.txt')
# pathMatrix = R.createPathMatrix(relationship,playerList)
# print pathMatrix
# P.save(pathMatrix,'pathMatrix_19_free.txt')
# data = P.load('prepro_random_data_9_free.txt')
# relationship = R.findRelationship(data)
# playerList = relationship.keys()
# playerList.sort()
# P.save(playerList,'playerList_random_9_free.txt')
# pathMatrix = R.createPathMatrix(relationship,playerList)
# print pathMatrix
# P.save(pathMatrix,'pathMatrix_9_free.txt')
#------------------------------------Clustering-------------------------------------------------------------------
# data = P.load('prepro_random_data_19_ranked.txt')
# relationship = R.findRelationship(data)
# gameCount = len(data)
# playerList = relationship.keys()
# C = Clustering()
# clusterDict = C.cluster(playerList,relationship)
# P.save(clusterDict,'Clustering Result_random_data_19_ranked.txt')
# data = P.load('prepro_random_data_19_free.txt')
# relationship = R.findRelationship(data)
# gameCount = len(data)
# playerList = relationship.keys()
# C = Clustering()
# clusterDict = C.cluster(playerList,relationship)
# P.save(clusterDict,'Clustering Result_random_data_19_free.txt')
# data = P.load('prepro_random_data_9_free.txt')
# relationship = R.findRelationship(data)
# gameCount = len(data)
# playerList = relationship.keys()
# C = Clustering()
# clusterDict = C.cluster(playerList,relationship)
# P.save(clusterDict,'Clustering Result_random_data_9_free.txt')
# data = P.load('test_data.txt')
# relationship = R.findRelationship(data)
# gameCount = len(data)
# playerList = relationship.keys()
# C = Clustering()
# clusterDict = C.cluster(playerList,relationship)
# print clusterDict
# print type(clusterDict)
# C.getIntelView(clusterDict)
#------------------------------------Comparison-------------------------------------------------------------------
# P.save(clusterDict,'Clustering Result_test_data.txt')
# data = process.load('/home/yotoo/Project/comparison/Degree Distribution(Border size: 9; Game Type: Free).txt')
data = process.load('/home/yotoo/Project/comparison/Degree Distribution(Border size: 19; Game Type: Ranked).txt')
flag = '19 + Ranked'
comparison.linearRegression(data,flag)
data = process.load('/home/yotoo/Project/comparison/Degree Distribution(Border size: 19; Game Type: Free).txt')
flag = '19 + Free'
comparison.linearRegression(data,flag)
data = process.load('/home/yotoo/Project/comparison/Degree Distribution(Border size: 9; Game Type: Free).txt')
flag = '9 + Free'
comparison.linearRegression(data,flag)
endTime = time.time()
print 'totally use ' + str(endTime-startTime) + ' seconds!'
