def loglike(self, y, f, var=None):
        r"""
        Gaussian log likelihood.

        Parameters
        ----------
        y: ndarray
            array of 0, 1 valued integers of targets
        f: ndarray
            latent function from the GLM prior (:math:`\mathbf{f} =
            \boldsymbol\Phi \mathbf{w}`)
        var: float, ndarray, optional
            The variance of the distribution, if not input, the initial value
            of variance is used.

        Returns
        -------
        logp: ndarray
            the log likelihood of each y given each f under this
            likelihood.
        """
        # way faster than calling norm.logpdf
        var = self._check_param(var)
        y, f = np.broadcast_arrays(y, f)
        ll = - 0.5 * (np.log(2 * np.pi * var) + (y - f)**2 / var)
        return ll
def loglike ( self , y , f , var = None ) : var = self . _check_param ( var ) y , f = np . broadcast_arrays ( y , f ) ll = - 0.5 * ( np . log ( 2 * np . pi * var ) + ( y - f ) ** 2 / var ) return ll
r"""
        Gaussian log likelihood.

        Parameters
        ----------
        y: ndarray
            array of 0, 1 valued integers of targets
        f: ndarray
            latent function from the GLM prior (:math:`\mathbf{f} =
            \boldsymbol\Phi \mathbf{w}`)
        var: float, ndarray, optional
            The variance of the distribution, if not input, the initial value
            of variance is used.

        Returns
        -------
        logp: ndarray
            the log likelihood of each y given each f under this
            likelihood.