apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-15 06:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Basic framework for serializing GPy models

Browse source
This commit is contained in:
Moreno 2017-04-11 11:42:58 +01:00
parent d529da3e6c
commit e572bfb746
26 changed files with 828 additions and 64 deletions
Download patch file Download diff file Expand all files Collapse all files

21
GPy/models/gp_classification.py
View file

@ -4,6 +4,7 @@
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):
@ -27,3 +28,23 @@ class GPClassification(GP):
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)

20
GPy/models/gp_regression.py
View file

@ -35,3 +35,23 @@ class GPRegression(GP):
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer, mean_function=mean_function)
@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)
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)
def save_model(self, output_filename, compress=True, save_data=True):
self._save_model(output_filename, compress=True, save_data=True)