def testRandomWeightsLaws():
print('*------- test random laws for stochastic outranking ------*')
t = RandomCBPerformanceTableau(numberOfActions=5,\
numberOfCriteria=7,\
weightDistribution='equiobjectives',
)
t.saveXMCDA2('test')
t = XMCDA2PerformanceTableau('test')
g = BipolarOutrankingDigraph(t)
g.recodeValuation(-1,1)
g.showRelationTable()
print('Triangular')
gmc = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='triangular',\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc.showRelationTable()
gmc.recodeValuation(-100,100)
gmc.showRelationStatistics('medians')
gmc.showRelationStatistics('likelihoods')
print('Uniform')
gmc1 = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='uniform',\
spread = 0.5,\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc1.showRelationTable()
gmc1.recodeValuation(-100,100)
gmc1.showRelationStatistics('medians')
gmc1.showRelationStatistics('likelihoods')
print('Beta(2,2)')
gmc2 = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='beta(2,2)',\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc2.showRelationTable()
gmc2.recodeValuation(-100,100)
gmc2.showRelationStatistics('medians')
gmc2.showRelationStatistics('likelihoods')
print('Beta(12,12)')
gmc3 = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='beta(4,4)',\
spread = 0.5,\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc3.showRelationTable()
gmc3.recodeValuation(-100,100)
gmc3.showRelationStatistics('medians')
gmc3.showRelationStatistics('likelihoods')
def testRandomWeightsLaws():
print('*------- test random laws for stochastic outranking ------*')
t = RandomCBPerformanceTableau(numberOfActions=5,\
numberOfCriteria=7,\
weightDistribution='equiobjectives',
)
t.saveXMCDA2('test')
t = XMCDA2PerformanceTableau('test')
g = BipolarOutrankingDigraph(t)
g.recodeValuation(-1, 1)
g.showRelationTable()
print('Triangular')
gmc = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='triangular',\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc.showRelationTable()
gmc.recodeValuation(-100, 100)
gmc.showRelationStatistics('medians')
gmc.showRelationStatistics('likelihoods')
print('Uniform')
gmc1 = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='uniform',\
spread = 0.5,\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc1.showRelationTable()
gmc1.recodeValuation(-100, 100)
gmc1.showRelationStatistics('medians')
gmc1.showRelationStatistics('likelihoods')
print('Beta(2,2)')
gmc2 = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='beta(2,2)',\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc2.showRelationTable()
gmc2.recodeValuation(-100, 100)
gmc2.showRelationStatistics('medians')
gmc2.showRelationStatistics('likelihoods')
print('Beta(12,12)')
gmc3 = StochasticBipolarOutrankingDigraph(t,Normalized=True,\
distribution='beta(4,4)',\
spread = 0.5,\
sampleSize=100,likelihood=0.1,\
Debug=False,samplingSeed=1)
gmc3.showRelationTable()
gmc3.recodeValuation(-100, 100)
gmc3.showRelationStatistics('medians')
gmc3.showRelationStatistics('likelihoods')
