def extend(self, keys=None, lower=True, upper=True, algorithm='linear'):
# default algorithm
if algorithm is None:
algorithm = 'linear'
# other algorithms?
if algorithm != 'linear':
LinVac.extend(self, keys, lower, upper, algorithm)
return
# number of undefined singletons?
num_undefined = len(self.pspace) - len(self)
if num_undefined == 0:
# nothing to do
return
# sum probabilities over all defined singletons
mass = sum(
self.get(({
omega: 1
}, True), (0, 0))[0] for omega in self.pspace)
# distribute remaining probability over remaining events
remaining_mass = (1 - mass) / num_undefined
value = (remaining_mass, remaining_mass)
for omega in self.pspace:
key = ({omega: 1}, True)
if key not in self:
self[key] = value
def extend(self, keys=None, lower=True, upper=True, algorithm='linear'):
# default algorithm
if algorithm is None:
algorithm = 'linear'
# other algorithms?
if algorithm != 'linear':
LinVac.extend(self, keys, lower, upper, algorithm)
return
# number of undefined singletons?
num_undefined = len(self.pspace) - len(self)
if num_undefined == 0:
# nothing to do
return
# sum probabilities over all defined singletons
mass = sum(self.get(({omega: 1}, True), (0, 0))[0]
for omega in self.pspace)
# distribute remaining probability over remaining events
remaining_mass = (1 - mass) / num_undefined
value = (remaining_mass, remaining_mass)
for omega in self.pspace:
key = ({omega: 1}, True)
if key not in self:
self[key] = value
