def testStochasticOutrankingDigraphs():
print('*------ test stochastic bipolar outranking digraphs ----*')
from outrankingDigraphs import StochasticBipolarOutrankingDigraph
from weakOrders import RankingByChoosingDigraph
t = RandomCBPerformanceTableau(numberOfActions=13,\
numberOfCriteria=13,\
weightDistribution='equiobjectives')
t.save('test')
t = PerformanceTableau('test')
g = BipolarOutrankingDigraph(t)
g.recodeValuation(-1,1)
g.showRelationTable()
gmc = StochasticBipolarOutrankingDigraph(t,Normalized=False,\
sampleSize=50,\
likelihood=0.05,\
Debug=False,\
samplingSeed=1)
gmc.showRelationTable()
gmc.showRelationStatistics('medians')
gmc.showRelationStatistics('likelihoods')
for x in gmc.actions:
for y in gmc.actions:
print('==>>',x,y)
print('Q4',gmc.relationStatistics[x][y]['Q4'])
print('Q3',gmc.relationStatistics[x][y]['Q3'])
print('probQ3',gmc.computeCDF(x,y,gmc.relationStatistics[x][y]['Q3']))
print('Q2',gmc.relationStatistics[x][y]['median'])
print('mean',gmc.relationStatistics[x][y]['mean'])
print('Q1',gmc.relationStatistics[x][y]['Q1'])
print('probQ1',gmc.computeCDF(x,y,gmc.relationStatistics[x][y]['Q1']))
print('Q0',gmc.relationStatistics[x][y]['Q0'])
print('pv',gmc.relationStatistics[x][y]['likelihood'])
print('prob0',gmc.computeCDF(x,y,0.0))
print('sd',gmc.relationStatistics[x][y]['sd'])
grbc = RankingByChoosingDigraph(g)
grbc.showWeakOrder()
gmcrbc = RankingByChoosingDigraph(gmc)
gmcrbc.showWeakOrder()
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')
def testStochasticOutrankingDigraphs():
print('*------ test stochastic bipolar outranking digraphs ----*')
from outrankingDigraphs import StochasticBipolarOutrankingDigraph
from transitiveDigraphs import RankingByChoosingDigraph
t = RandomCBPerformanceTableau(numberOfActions=13,\
numberOfCriteria=13,\
weightDistribution='equiobjectives')
t.save('test')
t = PerformanceTableau('test')
g = BipolarOutrankingDigraph(t)
g.recodeValuation(-1, 1)
g.showRelationTable()
gmc = StochasticBipolarOutrankingDigraph(t,Normalized=False,\
sampleSize=50,\
likelihood=0.05,\
Debug=False,\
samplingSeed=1)
gmc.showRelationTable()
gmc.showRelationStatistics('medians')
gmc.showRelationStatistics('likelihoods')
for x in gmc.actions:
for y in gmc.actions:
print('==>>', x, y)
print('Q4', gmc.relationStatistics[x][y]['Q4'])
print('Q3', gmc.relationStatistics[x][y]['Q3'])
print('probQ3',
gmc.computeCDF(x, y, gmc.relationStatistics[x][y]['Q3']))
print('Q2', gmc.relationStatistics[x][y]['median'])
print('mean', gmc.relationStatistics[x][y]['mean'])
print('Q1', gmc.relationStatistics[x][y]['Q1'])
print('probQ1',
gmc.computeCDF(x, y, gmc.relationStatistics[x][y]['Q1']))
print('Q0', gmc.relationStatistics[x][y]['Q0'])
print('pv', gmc.relationStatistics[x][y]['likelihood'])
print('prob0', gmc.computeCDF(x, y, 0.0))
print('sd', gmc.relationStatistics[x][y]['sd'])
grbc = RankingByChoosingDigraph(g)
grbc.showWeakOrder()
gmcrbc = RankingByChoosingDigraph(gmc)
gmcrbc.showWeakOrder()