def _log_prior_gradients(self):
if self.priors.size == <NUM_LIT:0>:<EOL><INDENT>return <NUM_LIT:0.><EOL><DEDENT>x = self.param_array<EOL>ret = np.zeros(x.size)<EOL>[np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]<EOL>priored_indexes = np.hstack([i for p, i in self.priors.items()])<EOL>for c,j in self.constraints.items():<EOL><INDENT>if not isinstance(c, Transformation):continue<EOL>for jj in j:<EOL><INDENT>if jj in priored_indexes:<EOL><INDENT>ret[jj] += c.log_jacobian_grad(x[jj])<EOL><DEDENT><DEDENT><DEDENT>return ret<EOL>
evaluate the gradients of the priors