def getModel(self, userID:int, argsDict:dict):
status:float = argsDict[ASequentialSimulation.ARG_STATUS]
print("status: " + str(status))
mGlobal:DataFrame = self.getModelGlobal()
if self.modeSkip:
print("aaaaaaaaaaaaaaaaa")
numberOfClick:int = self.getModelPersonAllUsers().getNumberOfClick(userID)
print("numberOfClick: " + str(numberOfClick))
if numberOfClick < self.skipClickThreshold:
return mGlobal
mGlobal.linearNormalizing()
#print("GLOBAL:")
#print(mGlobal.head(10))
mGlobal = PModelDHondt.multiplyModel(mGlobal, 1.0 - status)
mPerson:DataFrame = self.getModelPerson(userID)
mPerson.linearNormalizing()
mPerson = PModelDHondt.multiplyModel(mPerson, status)
rPModel:DataFrame = PModelDHondt.sumModels(mGlobal, mPerson)
rPModel.linearNormalizing()
#print("VYSLEDNY:")
#print(rPModel.head(10))
return rPModel
def test02():
print("Test 02")
# methods parametes
portfolioModelData1: List[tuple] = [['metoda1', 100], ['metoda2', 80],
['metoda3', 60]]
portfolioModel1: DataFrame = pd.DataFrame(portfolioModelData1,
columns=["methodID", "votes"])
portfolioModel1.set_index("methodID", inplace=True)
portfolioModel1.__class__ = PModelDHondt
#portfolioModel1.linearNormalizing()
portfolioModelData2: List[tuple] = [['metoda2', 20], ['metoda3', 40],
['metoda1', 0]]
portfolioModel2: DataFrame = pd.DataFrame(portfolioModelData2,
columns=["methodID", "votes"])
portfolioModel2.set_index("methodID", inplace=True)
portfolioModel2.__class__ = PModelDHondt
#print(portfolioModel1.head())
#print(portfolioModel2.head())
rModel = PModelDHondt.sumModels(portfolioModel1, portfolioModel2)
print(rModel)
print()
PModelDHondt.linearNormalizingPortfolioModelDHondt(rModel)
print(rModel)
