# load some data. Will change to batch work later
dr.loadSalesData(gs._project_entry_point_ + "/../preprocess/output/Monthly_Sold_Waste/10 October_Week 3.csv","October Week 3","efficiency")
dr.loadSalesData(gs._project_entry_point_ + "/../preprocess/output/Monthly_Sold_Waste/10 October_Week 4.csv","October Week 4","efficiency")
# create your algorithm prototype here
for i in range(1):
sys.stdout.write(str(i)+' ')
evolutionAlgorithmController = evolutionAlgorithmGroupItem()
#evolutionAlgorithmController = evolutionAlgorithm()
# get data from data loader
evolutionAlgorithmController.getData(dr)
resultForEvolutionAlgorithm = evolutionAlgorithmController.generateRecommendation()
# calculate the objective function value
objectiveFunctionObj = objectiveFunction()
objectiveFunctionObj.recommendation = resultForEvolutionAlgorithm
score = objectiveFunctionObj.assess()
print "Final result:"
for item in resultForEvolutionAlgorithm.itemList:
print str(item.itemName)+"x"+str(resultForEvolutionAlgorithm.itemList[item])
# do comparison and output the best (when we have more than 1)
print "Test: score = "+str(score)
print('Under Construction')
print dr.itemData.__dict__['idToItem'][3084].__dict__['category'][4].__dict__
def getObjectiveFunctionValue(self):
objF = objectiveFunction()
objF.recommendation = self.generateRecommendation()
self.objectiveFunctionValue = objF.assess()