cv25 = CriterionValue(CriteriaSet(['c2', 'c5']), 0.1)
cv34 = CriterionValue(CriteriaSet(['c3', 'c4']), 0.1)
cv35 = CriterionValue(CriteriaSet(['c3', 'c5']), -0.1)
cv45 = CriterionValue(CriteriaSet(['c4', 'c5']), -0.1)
cvs = CriteriaValues([cv1, cv2, cv3, cv4, cv5, cv12, cv13, cv14, cv15,
cv23, cv24, cv25, cv34, cv35, cv45])
lbda = 0.6
model = MRSort(c, cvs, bpt, lbda, cps)
print(model.lbda, model.cv)
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.get_assignments(pt)
model.cv = None
model.lbda = None
mip = MipMRSortMobius(model, pt, aa)
mip.solve()
print(model.lbda, model.cv)
aa2 = model.get_assignments(pt)
for a in aa:
a2 = aa2[a.id]
if a.category_id != a2.category_id:
print(a, a2)
from pymcda.types import AlternativePerformances, PerformanceTable
from pymcda.types import AlternativeAssignment, AlternativesAssignments
# Generate a random ELECTRE TRI BM model
random.seed(127890123456789)
ncriteria = 5
model = MRSort()
model.criteria = generate_criteria(ncriteria)
model.cv = CriteriaValues([CriterionValue('c%d' % (i + 1), 0.2)
for i in range(ncriteria)])
b1 = AlternativePerformances('b1', {'c%d' % (i + 1): 0.5
for i in range(ncriteria)})
model.bpt = PerformanceTable([b1])
cat = generate_categories(2)
model.categories_profiles = generate_categories_profiles(cat)
model.lbda = 0.6
vb1 = AlternativePerformances('b1', {'c%d' % (i + 1): random.uniform(0,0.4)
for i in range(ncriteria)})
model.veto = PerformanceTable([vb1])
model.veto_weights = model.cv.copy()
model.veto_lbda = 0.4
# Generate a set of alternatives
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
worst = pt.get_worst(model.criteria)
best = b1
print('Original model')