# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)

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=True, 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)

    @staticmethod
    def from_gp(gp):
        from copy import deepcopy
        gp = deepcopy(gp)
        return GPRegression(gp.X, gp.Y, gp.kern, gp.Y_metadata, gp.normalizer, gp.likelihood.variance.values, gp.mean_function)

    def to_dict(self, save_data=True):
        model_dict = super(GPRegression,self).to_dict(save_data)
        model_dict["class"] = "GPy.models.GPRegression"
        return model_dict

    @staticmethod
    def _from_dict(input_dict, data=None):
        import GPy
        input_dict["class"] = "GPy.core.GP"
        m = GPy.core.GP.from_dict(input_dict, data)
        return GPRegression.from_gp(m)

    def save_model(self, output_filename, compress=True, save_data=True):
        self._save_model(output_filename, compress=True, save_data=True)
