def generate_random_piecewise_linear(gi_min=0,
gi_max=1,
n_segments=3,
ui_min=0,
ui_max=1,
k=3):
d = ui_max - ui_min
r = [ui_min + d * round(random.random(), k) for i in range(n_segments - 1)]
r.append(ui_min)
r.append(ui_max)
r.sort()
interval = (gi_max - gi_min) / n_segments
f = PiecewiseLinear([])
for i in range(n_segments):
a = Point(round(gi_min + i * interval, k), r[i])
b = Point(round(gi_min + (i + 1) * interval, k), r[i + 1])
s = Segment("s%d" % (i + 1), a, b)
f.append(s)
s.p1_in = True
s.p2_in = True
return f
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_plinear_preference_function(crits, ap_worst, ap_best):
cfs = CriteriaFunctions()
for crit in crits:
worst = ap_worst.performances[crit.id]
best = ap_best.performances[crit.id]
if crit.direction == -1:
worst, best = best, worst
r = sorted([random.uniform(worst, best) for i in range(2)])
a = Point(float("-inf"), 0)
b = Point(r[0],0)
c = Point(r[1],1)
d = Point(float("inf"), 1)
f = PiecewiseLinear([Segment('s1', a, b),
Segment('s2', b, c),
Segment('s3', c, d)])
cf = CriterionFunction(crit.id, f)
cfs.append(cf)
return cfs
from pymcda.types import CriterionValue, CriteriaValues
from pymcda.types import PiecewiseLinear, Segment, Point
from pymcda.types import CriterionFunction, CriteriaFunctions
c1 = Criterion("c1")
c2 = Criterion("c2")
c3 = Criterion("c3")
c = Criteria([c1, c2, c3])
cv1 = CriterionValue("c1", 0.5)
cv2 = CriterionValue("c2", 0.25)
cv3 = CriterionValue("c3", 0.25)
cvs = CriteriaValues([cv1, cv2, cv3])
f1 = PiecewiseLinear([
Segment('s1', Point(0, 0), Point(2.5, 0.2)),
Segment('s2', Point(2.5, 0.2), Point(5, 1), True, True)
])
f2 = PiecewiseLinear([
Segment('s1', Point(0, 0), Point(2.5, 0.8)),
Segment('s2', Point(2.5, 0.8), Point(5, 1), True, True)
])
f3 = PiecewiseLinear([
Segment('s1', Point(0, 0), Point(2.5, 0.5)),
Segment('s2', Point(2.5, 0.5), Point(5, 1), True, True)
])
cf1 = CriterionFunction("c1", f1)
cf2 = CriterionFunction("c2", f2)
cf3 = CriterionFunction("c3", f3)
cfs = CriteriaFunctions([cf1, cf2, cf3])
from pymcda.types import Criterion, Criteria
from pymcda.types import CriterionValue, CriteriaValues
from pymcda.types import PiecewiseLinear, Segment, Point
from pymcda.types import CriterionFunction, CriteriaFunctions
c1 = Criterion("c1")
c2 = Criterion("c2")
c3 = Criterion("c3")
c = Criteria([c1, c2, c3])
cv1 = CriterionValue("c1", 0.5)
cv2 = CriterionValue("c2", 0.25)
cv3 = CriterionValue("c3", 0.25)
cvs = CriteriaValues([cv1, cv2, cv3])
f1 = PiecewiseLinear([Segment('s1', Point(0, 0), Point(2.5, 0.2)),
Segment('s2', Point(2.5, 0.2), Point(5, 1),
True, True)])
f2 = PiecewiseLinear([Segment('s1', Point(0, 0), Point(2.5, 0.8)),
Segment('s2', Point(2.5, 0.8), Point(5, 1),
True, True)])
f3 = PiecewiseLinear([Segment('s1', Point(0, 0), Point(2.5, 0.5)),
Segment('s2', Point(2.5, 0.5), Point(5, 1),
True, True)])
cf1 = CriterionFunction("c1", f1)
cf2 = CriterionFunction("c2", f2)
cf3 = CriterionFunction("c3", f3)
cfs = CriteriaFunctions([cf1, cf2, cf3])
app = QtGui.QApplication(sys.argv)
