distributions.py 文件源码

def _get_statistics(self,data):
        # NOTE: since this isn't really in exponential family, this method needs
        # to look at hyperparameters. form posterior hyperparameters for the p
        # parameters here so we can integrate them out and get the r statistics
        n, tot = super(NegativeBinomialIntegerR,self)._get_statistics(data)
        if n > 0:
            alpha_n, betas_n = self.alpha_0 + tot, self.beta_0 + self.r_support*n
            data = flattendata(data)
            log_marg_likelihoods = \
                    special.betaln(alpha_n, betas_n) \
                        - special.betaln(self.alpha_0, self.beta_0) \
                    + (special.gammaln(data[:,na]+self.r_support)
                        - special.gammaln(data[:,na]+1) \
                        - special.gammaln(self.r_support)).sum(0)
        else:
            log_marg_likelihoods = np.zeros_like(self.r_support)

        return n, tot, log_marg_likelihoods