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

Update GPLVM class to use metadata and output normalizers.

Browse source
This commit is contained in:
Neil Lawrence 2021-05-19 09:45:21 +01:00 • committed by Neil Lawrence
parent 8b098ec59b
commit 5c71aa45c7
1 changed files with 7 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

9
GPy/models/gplvm.py
View file

@ -14,7 +14,7 @@ class GPLVM(GP):
"""
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, name="gplvm"):
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, name="gplvm", Y_metadata=None, normalizer=False):
"""
:param Y: observed data
@ -23,6 +23,11 @@ class GPLVM(GP):
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
:param normalizer:
normalize the outputs Y.
If normalizer is True, we will normalize using Standardize.
If normalizer is False (the default), no normalization will be done.
:type normalizer: bool
"""
if X is None:
from ..util.initialization import initialize_latent
@ -34,7 +39,7 @@ class GPLVM(GP):
likelihood = Gaussian()
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM', Y_metadata=Y_metadata, normalizer=normalizer)
self.X = Param('latent_mean', X)
self.link_parameter(self.X, index=0)