# Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from ..core import GP
from .. import likelihoods
from .. import kern
import numpy as np
from ..inference.latent_function_inference.expectation_propagation import EP
[docs]class GPClassification(GP):
"""
Gaussian Process classification
This is a thin wrapper around the models.GP class, with a set of sensible defaults
:param X: input observations
:param Y: observed values, can be None if likelihood is not None
:param kernel: a GPy kernel, defaults to rbf
:param likelihood: a GPy likelihood, defaults to Bernoulli
:param inference_method: Latent function inference to use, defaults to EP
:type inference_method: :class:`GPy.inference.latent_function_inference.LatentFunctionInference`
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self, X, Y, kernel=None,Y_metadata=None, mean_function=None, inference_method=None,
likelihood=None, normalizer=False):
if kernel is None:
kernel = kern.RBF(X.shape[1])
if likelihood is None:
likelihood = likelihoods.Bernoulli()
if inference_method is None:
inference_method = EP()
super(GPClassification, self).__init__(X=X, Y=Y, kernel=kernel, likelihood=likelihood, inference_method=inference_method,
mean_function=mean_function, name='gp_classification', normalizer=normalizer)
[docs] @staticmethod
def from_gp(gp):
from copy import deepcopy
gp = deepcopy(gp)
GPClassification(gp.X, gp.Y, gp.kern, gp.likelihood, gp.inference_method, gp.mean_function, name='gp_classification')
[docs] def to_dict(self, save_data=True):
model_dict = super(GPClassification,self).to_dict(save_data)
model_dict["class"] = "GPy.models.GPClassification"
return model_dict
[docs] @staticmethod
def from_dict(input_dict, data=None):
import GPy
m = GPy.core.model.Model.from_dict(input_dict, data)
return GPClassification.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)
@staticmethod
def _build_from_input_dict(input_dict, data=None):
input_dict = GPClassification._format_input_dict(input_dict, data)
input_dict.pop('name', None) # Name parameter not required by GPClassification
return GPClassification(**input_dict)