(self, theta)
return np.log(np.log(1 + 3.0 * (self.scale / np.exp(theta)) ** 2))
Returns the log probability of theta. Note: theta should
        be on a log scale.

        Parameters
        ----------
        theta : (D,) numpy array
            A hyperparameter configuration in log space.

        Returns
        -------
        float
            The log probability of theta
Returns the log probability of theta. Note: theta should
        be on a log scale.
Returns the log probability of theta . Note : theta should be on a log scale .
def lnprob(self, theta):
        """
        Returns the log probability of theta. Note: theta should
        be on a log scale.

        Parameters
        ----------
        theta : (D,) numpy array
            A hyperparameter configuration in log space.

        Returns
        -------
        float
            The log probability of theta
        """
        # We computed it exactly as in the original spearmint code
        if np.any(theta == 0.0):
            return np.inf
        return np.log(np.log(1 + 3.0 * (self.scale / np.exp(theta)) ** 2))
def lnprob ( self , theta ) : # We computed it exactly as in the original spearmint code if np . any ( theta == 0.0 ) : return np . inf return np . log ( np . log ( 1 + 3.0 * ( self . scale / np . exp ( theta ) ) ** 2 ) )