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_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 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
