def _getp(self):
if self._p is not None:<EOL><INDENT>return self._p<EOL><DEDENT>pmean = _gvar.mean(self.palt).flat<EOL>buf = (<EOL>self.y.flat if self.prior is None else<EOL>numpy.concatenate((self.y.flat, self.prior.flat))<EOL>)<EOL>D = numpy.zeros((self.cov.shape[<NUM_LIT:0>], len(buf)), float)<EOL>for i, chivw_i in enumerate(self._chivw(_gvar.valder(pmean))):<EOL><INDENT>for a in range(D.shape[<NUM_LIT:0>]):<EOL><INDENT>D[a, i] = chivw_i.dotder(self.cov[a])<EOL><DEDENT><DEDENT>p = []<EOL>for a in range(D.shape[<NUM_LIT:0>]): <EOL><INDENT>p.append(_gvar.gvar(pmean[a], _gvar.wsum_der(D[a], buf),<EOL>buf[<NUM_LIT:0>].cov))<EOL><DEDENT>self._p = _reformat(self.p0, p)<EOL>return self._p<EOL>
Build :class:`gvar.GVar`\s for best-fit parameters.