apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-13 05:52:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Changed all M's for num_inducing

Browse source
This commit is contained in:
Alan Saul 2013-06-05 15:29:45 +01:00
parent aac4f6a237
commit 3475b52b6c
21 changed files with 142 additions and 142 deletions
Download patch file Download diff file Expand all files Collapse all files

8
GPy/models/mrd.py
View file

@ -33,7 +33,7 @@ class MRD(model):
:param X_variance:
Initial latent space variance
:param init: [cooncat|single|random]
initialization method to use:
initialization method to use:
*concat: PCA on concatenated outputs
*single: PCA on each output
*random: random
@ -44,7 +44,7 @@ class MRD(model):
:param kernels: list of kernels or kernel shared for all BGPLVMS
:type kernels: [GPy.kern.kern] | GPy.kern.kern | None (default)
"""
def __init__(self, likelihood_or_Y_list, input_dim, M=10, names=None,
def __init__(self, likelihood_or_Y_list, input_dim, num_inducing=10, names=None,
kernels=None, initx='PCA',
initz='permute', _debug=False, **kw):
if names is None:
@ -61,13 +61,13 @@ class MRD(model):
assert not ('kernel' in kw), "pass kernels through `kernels` argument"
self.input_dim = input_dim
self.num_inducing = M
self.num_inducing = num_inducing
self._debug = _debug
self._init = True
X = self._init_X(initx, likelihood_or_Y_list)
Z = self._init_Z(initz, X)
self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, M=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, num_inducing=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
del self._init
self.gref = self.bgplvms[0]