posteriors.py 文件源码

def _unnormalized_loglikelihood(self, x):
        x = np.asanyarray(x)
        ndim = x.ndim
        x = x.reshape((-1, self.dim))

        logpdf = -np.ones(len(x)) * np.inf

        logi = self._within_bounds(x)
        x = x[logi, :]
        if len(x) == 0:
            if ndim == 0 or (ndim == 1 and self.dim > 1):
                logpdf = logpdf[0]
            return logpdf

        mean, var = self.model.predict(x)
        logpdf[logi] = ss.norm.logcdf(self.threshold, mean, np.sqrt(var)).squeeze()

        if ndim == 0 or (ndim == 1 and self.dim > 1):
            logpdf = logpdf[0]

        return logpdf