d1d2aa202803dada164cb378a4485fbba59f9e76,pymc3/distributions/timeseries.py,AR,logp,#AR#Any#,113

Before Change

    def logp(self, value):

        y = value[self.p:]
        results, _ = scan(lambda l, obs, p: obs[p - l:-l],
                          outputs_info=None, sequences=[tt.arange(1, self.p + 1)],
                          non_sequences=[value, self.p])
        x = tt.stack(results)

        if self.constant:
            y = y - self.rho[0]

After Change

    def logp(self, value):
        if self.constant:
            x = tt.add(*[self.rho[i + 1] * value[self.p - (i + 1):-(i + 1)] for i in range(self.p)])
            eps = value[self.p:] - self.rho[0] - x
        else:
            x = tt.add(*[self.rho[i] * value[self.p - (i + 1):-(i + 1)] for i in range(self.p)])
            eps = value[self.p:] - x

        innov_like = Normal.dist(mu=0.0, tau=self.tau).logp(eps)

Instances