Source code for GPy.models.gp_classification

# 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)