mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-04 09:12:38 +02:00
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
# 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
|
|
|
|
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
|
|
|
|
.. Note:: Multiple independent outputs are allowed using columns of Y
|
|
|
|
"""
|
|
|
|
def __init__(self, X, Y, kernel=None,Y_metadata=None, mean_function=None):
|
|
if kernel is None:
|
|
kernel = kern.RBF(X.shape[1])
|
|
|
|
likelihood = likelihoods.Bernoulli()
|
|
|
|
GP.__init__(self, X=X, Y=Y, kernel=kernel, likelihood=likelihood, inference_method=EP(), mean_function=mean_function, name='gp_classification')
|
|
|
|
@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')
|
|
|
|
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
|
|
|
|
@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)
|
|
|
|
def save_model(self, output_filename, compress=True, save_data=True):
|
|
self._save_model(output_filename, compress=True, save_data=True)
|