def solve_cplex(self, aa, pt):
self.lp.solve()
status = self.lp.solution.get_status()
if status != self.lp.solution.status.optimal:
raise RuntimeError("Solver status: %s" % status)
obj = self.lp.solution.get_objective_value()
cfs = CriteriaFunctions()
cvs = CriteriaValues()
for cs in self.cs:
cv = CriterionValue(cs.id, 1)
cvs.append(cv)
nseg = cs.value
x_points = range(nseg)
p1 = Point(self.points[cs.id][0], 0)
ui = 0
f = PiecewiseLinear([])
for i in x_points:
uivar = 'w_' + cs.id + "_%d" % (i + 1)
ui += self.lp.solution.get_values(uivar)
x = self.points[cs.id][i + 1]
p2 = Point(x, ui)
s = Segment("s%d" % (i + 1), p1, p2)
f.append(s)
p1 = p2
s.p1_in = True
s.p2_in = True
cf = CriterionFunction(cs.id, f)
cfs.append(cf)
cat = {v: k for k, v in self.cat.items()}
catv = CategoriesValues()
ui_a = 0
for i in range(1, len(cat)):
ui_b = self.lp.solution.get_values("u_%d" % i)
catv.append(CategoryValue(cat[i], Interval(ui_a, ui_b)))
ui_a = ui_b
catv.append(CategoryValue(cat[i + 1], Interval(ui_a, 1)))
return obj, cvs, cfs, catv
def solve_glpk(self, aa, pt):
self.lp.solve()
status = self.lp.status()
if status != 'opt':
raise RuntimeError("Solver status: %s" % self.lp.status())
obj = self.lp.vobj()
cfs = CriteriaFunctions()
cvs = CriteriaValues()
for cid, points in self.points.items():
cv = CriterionValue(cid, 1)
cvs.append(cv)
p1 = Point(self.points[cid][0], 0)
ui = 0
f = PiecewiseLinear([])
for i in range(len(points) - 1):
uivar = 'w_' + cid + "_%d" % (i + 1)
ui += self.w[cid][i].primal
p2 = Point(self.points[cid][i + 1], ui)
s = Segment(p1, p2)
f.append(s)
p1 = p2
s.p2_in = True
cf = CriterionFunction(cid, f)
cfs.append(cf)
cat = {v: k for k, v in self.cat.items()}
catv = CategoriesValues()
ui_a = 0
for i in range(0, len(cat) - 1):
ui_b = self.u[i].primal
catv.append(CategoryValue(cat[i + 1], Interval(ui_a, ui_b)))
ui_a = ui_b
catv.append(CategoryValue(cat[i + 2], Interval(ui_a, 1)))
return obj, cvs, cfs, catv
def generate_random_categories_values(cats, k = 3):
ncats = len(cats)
r = [round(random.random(), k) for i in range(ncats - 1)]
r.sort()
v0 = 0
catvs = CategoriesValues()
for i, cat in enumerate(cats.get_ordered_categories()):
if i == ncats - 1:
v1 = 1
else:
v1 = r[i]
catv = CategoryValue(cat, Interval(v0, v1))
catvs.append(catv)
v0 = v1
return catvs