Source code for GPy.models.gp_regression

# 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

[docs]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)
[docs] @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)
[docs] 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)
[docs] def save_model(self, output_filename, compress=True, save_data=True): self._save_model(output_filename, compress=True, save_data=True)