# Copyright (c) 2012, James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)


from ..core import GP
from .. import likelihoods
from .. import kern

class GPRegression(GP):
    """
    Gaussian Process model for regression

    This is a thin wrapper around the models.GP class, with a set of sensible defaults

    :param X: input observations
    :param Y: observed values
    :param kernel: a GPy kernel, defaults to rbf
    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
    :type normalize_X: False|True
    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
    :type normalize_Y: False|True

    .. Note:: Multiple independent outputs are allowed using columns of Y

    """

    def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, likelihood=None):
        if kernel is None:
            kernel = kern.rbf(X.shape[1])

        if likelihood is None:
            likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)

        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
        self.ensure_default_constraints()

    def getstate(self):
        return GP.getstate(self)

    def setstate(self, state):
        return GP.setstate(self, state)
