# 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)