def one_loop(crits, lbda_min, coalitions):
cw = generate_random_criteria_weights(crits)
lbda = random.uniform(lbda_min, 1)
t = test_result("")
t["ncoalitions"] = 0
for i in range(len(crits) + 1):
t["%d_criteria" % i] = 0
for coalition in coalitions:
w = sum_weights(cw, coalition)
if w >= lbda:
t[pprint_coalition(coalition)] = 1
t["ncoalitions"] += 1
t["%d_criteria" % len(coalition)] += 1
else:
t[pprint_coalition(coalition)] = 0
return t
if __name__ == "__main__":
import random
from pymcda.generate import generate_criteria
from pymcda.generate import generate_random_criteria_weights
from pymcda.generate import generate_categories
from pymcda.generate import generate_categories_profiles
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.generate import generate_random_plinear_preference_function
random.seed(123)
criteria = generate_criteria(5)
crit_weights = generate_random_criteria_weights(criteria)
categories = generate_categories(5)
cat_profiles = generate_categories_profiles(categories)
a = generate_alternatives(100)
pt = generate_random_performance_table(a, criteria)
ap_best = pt.get_best(criteria)
ap_worst = pt.get_worst(criteria)
b = cat_profiles.get_ordered_profiles()
bpt = generate_random_profiles(b, criteria)
pf = generate_random_plinear_preference_function(criteria, ap_worst,
ap_best)
print(crit_weights)
print(categories)
print(cat_profiles)
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_criteria_weights
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.types import AlternativePerformances
from pymcda.ui.graphic import display_electre_tri_models
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(7, 2, 5)
worst = AlternativePerformances("worst", {c.id: 0 for c in model.criteria})
best = AlternativePerformances("best", {c.id: 1 for c in model.criteria})
# Add veto
vpt = generate_random_profiles(model.profiles, model.criteria, None, 3,
worst, model.bpt['b1'])
model.veto = PerformanceTable([model.bpt['b1'] - vpt['b1']])
model.veto_weights = generate_random_criteria_weights(model.criteria)
model.veto_lbda = random.random()
# Generate a set of alternatives
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
nmeta = 20
nloops = 10
print('Original model')
print('==============')
cids = model.criteria.keys()
model.bpt.display(criterion_ids=cids)
model.cv.display(criterion_ids=cids)
if __name__ == "__main__":
import random
from pymcda.generate import generate_criteria
from pymcda.generate import generate_random_criteria_weights
from pymcda.generate import generate_categories
from pymcda.generate import generate_categories_profiles
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.generate import generate_random_plinear_preference_function
random.seed(123)
criteria = generate_criteria(5)
crit_weights = generate_random_criteria_weights(criteria)
categories = generate_categories(5)
cat_profiles = generate_categories_profiles(categories)
a = generate_alternatives(100)
pt = generate_random_performance_table(a, criteria)
ap_best = pt.get_best(criteria)
ap_worst = pt.get_worst(criteria)
b = cat_profiles.get_ordered_profiles()
bpt = generate_random_profiles(b, criteria)
pf = generate_random_plinear_preference_function(criteria, ap_worst,
ap_best)
print(crit_weights)
print(categories)
print(cat_profiles)
self.setItem(row, col, item)
self.row_ap[row] = ap
def add_pt(self, alts, pt):
for a in alts:
self.add_alternative_performances(a, pt[a.id])
if __name__ == "__main__":
from pymcda.generate import generate_criteria
from pymcda.generate import generate_random_criteria_weights
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
c = generate_criteria(5)
cw = generate_random_criteria_weights(c)
a = generate_alternatives(100)
pt = generate_random_performance_table(a, c)
app = QtGui.QApplication(sys.argv)
layout = QtGui.QVBoxLayout()
tabs = QtGui.QTabWidget()
layout.addWidget(tabs)
# Criteria table
table = qt_criteria_table()
table.add_criteria(c, cw)
tab = QtGui.QWidget()