import numpy as np
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 Norm normalizer: [False]
    :param noise_var: the noise variance for Gaussian likelhood, defaults to 1.

        Normalize Y with the norm given.
        If normalizer is False, no normalization will be done
        If it is None, we use GaussianNorm(alization)

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

    """

    def __init__(self, X, Y, kernel=None, Y_metadata=None, normalizer=None, noise_var=1., mean_function=None):

        if kernel is None:
            kernel = kern.RBF(X.shape[1])

        likelihood = likelihoods.Gaussian(variance=noise_var)

        super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer, mean_function=mean_function)

