def partitionUsers(expertModels, expertise):
for userData in UsersData._usersData:
pValues = []
for expertModel in expertModels:
userLocation = (userData[2], userData[3])
isExpert = userData[4] == expertise
p = Utility.getModelValue(expertModel, userLocation, isExpert)
pValues.append((p, expertModel['regionName']))
maxPRegion = max(pValues, key = itemgetter(0))[1]
if len(userData) == 5:
userData.append(maxPRegion)
else:
userData[5] = maxPRegion
UsersData._isPartitioned = True
def getRankedExperts(self, expertise, userLocation):
if len(self._expertModelsDict) == 0:
self._modelGenerator.loadCachedModels()
self._expertModelsDict = self._modelGenerator.getExpertImpactModels()[expertise]
expertImpactList = []
for expert in self._expertModelsDict:
models = self._expertModelsDict[expert]
modelValue = 0
prevModelValue = 0
for model in models:
modelValue = Utility.getModelValue(model, userLocation, True)
modelValue = max(modelValue, prevModelValue)
prevModelValue = modelValue
expertImpactList.append((modelValue, expert))
rankedExpertsData = sorted(expertImpactList, key = itemgetter(0), reverse = True)
rankedExperts = []
print rankedExpertsData
for rankedExpertData in rankedExpertsData:
rankedExperts.append(rankedExpertData[1])
return rankedExperts
