distributions.py 文件源码

def _get_statistics(self,data=[]):
        n, tot = self._fixedr_distns[0]._get_statistics(data)
        if n > 0:
            data = flattendata(data)
            alphas_n, betas_n = self.alphas_0 + tot, self.betas_0 + self.r_support*n
            log_marg_likelihoods = \
                    special.betaln(alphas_n, betas_n) \
                        - special.betaln(self.alphas_0, self.betas_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 log_marg_likelihoods