예제 #1
0
 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
예제 #2
0
    def get_linvac(self, epsilon):
        """Convert probability into a linear vacuous mixture:

        .. math::

           \underline{E}(f)=(1-\epsilon)E(f)+\epsilon\inf f"""
        epsilon = self.make_number(epsilon)
        return LinVac(self.pspace,
                      lprob=[(1 - epsilon) * self[{
                          omega: 1
                      }, True][0] for omega in self.pspace],
                      number_type=self.number_type)
예제 #3
0
파일: prob.py 프로젝트: gnustats/improb
 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
예제 #4
0
    def get_precise(self, gamble, event=True, algorithm='linear'):
        r"""Calculate the conditional expectation,

        .. math::

           E(f|A)=
           \frac{
           \sum_{\omega\in A}p(\omega)f(\omega)
           }{
           \sum_{\omega\in A}p(\omega)
           }

        where :math:`p(\omega)` is simply the probability of the
        singleton :math:`\omega`::

            self[{omega: 1}, True][0]
        """
        # default algorithm
        if algorithm is None:
            algorithm = 'linear'
        # other algorithms?
        if algorithm != 'linear':
            return LinVac.get_lower(self, gamble, event, algorithm)
        self.is_valid(raise_error=True)
        gamble = self.make_gamble(gamble)
        if event is True or (isinstance(event, Event) and event.is_true()):
            # self[{omega: 1}, True][0] is the lower probability ([0])
            # of the indicator gamble of omega ({omega: 1}),
            # unconditionally (True).
            return sum(self[{
                omega: 1
            }, True][0] * gamble[omega] for omega in self.pspace)
        else:
            event = self.pspace.make_event(event)
            event_prob = self.get_precise(event.indicator(self.number_type))
            if event_prob == 0:
                raise ZeroDivisionError(
                    "cannot condition on event with zero probability")
            return self.get_precise(gamble * event) / event_prob
예제 #5
0
파일: prob.py 프로젝트: gnustats/improb
    def get_precise(self, gamble, event=True, algorithm='linear'):
        r"""Calculate the conditional expectation,

        .. math::

           E(f|A)=
           \frac{
           \sum_{\omega\in A}p(\omega)f(\omega)
           }{
           \sum_{\omega\in A}p(\omega)
           }

        where :math:`p(\omega)` is simply the probability of the
        singleton :math:`\omega`::

            self[{omega: 1}, True][0]
        """
        # default algorithm
        if algorithm is None:
            algorithm = 'linear'
        # other algorithms?
        if algorithm != 'linear':
            return LinVac.get_lower(self, gamble, event, algorithm)
        self.is_valid(raise_error=True)
        gamble = self.make_gamble(gamble)
        if event is True or (isinstance(event, Event) and event.is_true()):
            # self[{omega: 1}, True][0] is the lower probability ([0])
            # of the indicator gamble of omega ({omega: 1}),
            # unconditionally (True).
            return sum(self[{omega: 1}, True][0] * gamble[omega]
                       for omega in self.pspace)
        else:
            event = self.pspace.make_event(event)
            event_prob = self.get_precise(event.indicator(self.number_type))
            if event_prob == 0:
                raise ZeroDivisionError(
                    "cannot condition on event with zero probability")
            return self.get_precise(gamble * event) / event_prob