def getConfidenceInterval(self, value_list, confidenceLevel): from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures BSM = BasicStatisticalMeasures() lb, ub = Intervals().ConfidIntervals(value_list, confidenceLevel) return {"lb": lb, "ub": ub, "avg": BSM.mean(value_list)}
def getConfidenceIntervals(value_list): from Globals import G if len(set(value_list)) == 1: # All values are same, no need to perform statistical analysis return { 'lb': value_list[0], 'ub': value_list[0], 'avg': value_list[0], } from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures BSM=BasicStatisticalMeasures() lb, ub = Intervals().ConfidIntervals(value_list, G.confidenceLevel) return {'lb': lb, 'ub': ub, 'avg': BSM.mean(value_list) }
def main(): # add all the objects in a list objectList = [S, M1, M2, E, Q, R, F1, F2] # set the length of the experiment maxSimTime = 1440.0 # call the runSimulation giving the objects and the length of the experiment runSimulation(objectList, maxSimTime, numberOfReplications=10, seed=1) print 'The exit of each replication is:' print E.Exits # calculate confidence interval using the Knowledge Extraction tool from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures BSM = BasicStatisticalMeasures() lb, ub = Intervals().ConfidIntervals(E.Exits, 0.95) print 'the 95% confidence interval for the throughput is:' print 'lower bound:', lb print 'mean:', BSM.mean(E.Exits) print 'upper bound:', ub
def main(): # add all the objects in a list objectList=[S,M1,M2,E,Q,R,F1,F2] # set the length of the experiment maxSimTime=1440.0 # call the runSimulation giving the objects and the length of the experiment runSimulation(objectList, maxSimTime, numberOfReplications=10, seed=1) print 'The exit of each replication is:' print E.Exits # calculate confidence interval using the Knowledge Extraction tool from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures BSM=BasicStatisticalMeasures() lb, ub = Intervals().ConfidIntervals(E.Exits, 0.95) print 'the 95% confidence interval for the throughput is:' print 'lower bound:', lb print 'mean:', BSM.mean(E.Exits) print 'upper bound:', ub
def getConfidenceInterval(self, value_list, confidenceLevel): from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures BSM = BasicStatisticalMeasures() lb, ub = Intervals().ConfidIntervals(value_list, confidenceLevel) return {'lb': lb, 'ub': ub, 'avg': BSM.mean(value_list)}