parameter_inference.py 文件源码

def _compute_weights_and_cov(self, pop):
        params = np.column_stack(tuple([pop.outputs[p] for p in self.parameter_names]))

        if self._populations:
            q_logpdf = GMDistribution.logpdf(params, *self._gm_params)
            p_logpdf = self._prior.logpdf(params)
            w = np.exp(p_logpdf - q_logpdf)
        else:
            w = np.ones(pop.n_samples)

        if np.count_nonzero(w) == 0:
            raise RuntimeError("All sample weights are zero. If you are using a prior "
                               "with a bounded support, this may be caused by specifying "
                               "a too small sample size.")

        # New covariance
        cov = 2 * np.diag(weighted_var(params, w))

        if not np.all(np.isfinite(cov)):
            logger.warning("Could not estimate the sample covariance. This is often "
                           "caused by majority of the sample weights becoming zero."
                           "Falling back to using unit covariance.")
            cov = np.diag(np.ones(params.shape[1]))

        return w, cov