(self, kernel, prior=None,
                 noise=1e-3, use_gradients=False,
                 normalize_output=False,
                 normalize_input=True,
                 lower=None, upper=None, rng=None)
Interface to the george GP library. The GP hyperparameter are obtained
        by optimizing the marginal log likelihood.

        Parameters
        ----------
        kernel : george kernel object
            Specifies the kernel that is used for all Gaussian Process
        prior : prior object
            Defines a prior for the hyperparameters of the GP. Make sure that
            it implements the Prior interface.
        noise : float
            Noise term that is added to the diagonal of the covariance matrix
            for the Cholesky decomposition.
        use_gradients : bool
            Use gradient information to optimize the negative log likelihood
        lower : np.array(D,)
            Lower bound of the input space which is used for the input space normalization
        upper : np.array(D,)
            Upper bound of the input space which is used for the input space normalization
        normalize_output : bool
            Zero mean unit variance normalization of the output values
        normalize_input : bool
            Normalize all inputs to be in [0, 1]. This is important to define good priors for the
            length scales.
        rng: np.random.RandomState
            Random number generator
Interface to the george GP library. The GP hyperparameter are obtained
        by optimizing the marginal log likelihood.
Interface to the george GP library . The GP hyperparameter are obtained by optimizing the marginal log likelihood .
def __init__(self, kernel, prior=None,
                 noise=1e-3, use_gradients=False,
                 normalize_output=False,
                 normalize_input=True,
                 lower=None, upper=None, rng=None):
        """
        Interface to the george GP library. The GP hyperparameter are obtained
        by optimizing the marginal log likelihood.

        Parameters
        ----------
        kernel : george kernel object
            Specifies the kernel that is used for all Gaussian Process
        prior : prior object
            Defines a prior for the hyperparameters of the GP. Make sure that
            it implements the Prior interface.
        noise : float
            Noise term that is added to the diagonal of the covariance matrix
            for the Cholesky decomposition.
        use_gradients : bool
            Use gradient information to optimize the negative log likelihood
        lower : np.array(D,)
            Lower bound of the input space which is used for the input space normalization
        upper : np.array(D,)
            Upper bound of the input space which is used for the input space normalization
        normalize_output : bool
            Zero mean unit variance normalization of the output values
        normalize_input : bool
            Normalize all inputs to be in [0, 1]. This is important to define good priors for the
            length scales.
        rng: np.random.RandomState
            Random number generator
        """

        if rng is None:
            self.rng = np.random.RandomState(np.random.randint(0, 10000))
        else:
            self.rng = rng

        self.kernel = kernel
        self.gp = None
        self.prior = prior
        self.noise = noise
        self.use_gradients = use_gradients
        self.normalize_output = normalize_output
        self.normalize_input = normalize_input
        self.X = None
        self.y = None
        self.hypers = []
        self.is_trained = False
        self.lower = lower
        self.upper = upper
def __init__ ( self , kernel , prior = None , noise = 1e-3 , use_gradients = False , normalize_output = False , normalize_input = True , lower = None , upper = None , rng = None ) : if rng is None : self . rng = np . random . RandomState ( np . random . randint ( 0 , 10000 ) ) else : self . rng = rng self . kernel = kernel self . gp = None self . prior = prior self . noise = noise self . use_gradients = use_gradients self . normalize_output = normalize_output self . normalize_input = normalize_input self . X = None self . y = None self . hypers = [ ] self . is_trained = False self . lower = lower self . upper = upper