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 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)}