62859ba05a9dff7677b6946077544bb9a224c8cc,pymc3/variational/opvi.py,Approximation,logp,#Approximation#Any#,960

Before Change

        return self.logq(z) / self.normalizing_constant

    def logp(self, z):
        factors = ([tt.sum(var.logpt)for var in self.model.basic_RVs] +
                   [tt.sum(var) for var in self.model.potentials])
        p = self.to_flat_input(tt.add(*factors))
        p = theano.clone(p, {self.input: z})
        return p

    def logp_norm(self, z):
        t = self.normalizing_constant
        factors = ([tt.sum(var.logpt) / t for var in self.model.basic_RVs] +

After Change

    def logp(self, nmc=None):
        if nmc is None:
            _logp = self.single_symbolic_logp
            nmc = 1
        else:
            _logp = self.sized_symbolic_logp
        _logp = _logp / pm.floatX(self._n_samples)
        return self.set_size_deterministic(_logp, nmc, 0)

    def logp_norm(self, nmc):
        return self.logp(nmc) / self.normalizing_constant

Instances