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Bayesian GPLVM can now take either a likelihood or data matrix as first argument

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This commit is contained in:
James Hensman 2013-05-20 16:49:07 +01:00
parent 2569240095
commit 255a9bbd73
1 changed files with 6 additions and 3 deletions
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9
GPy/models/Bayesian_GPLVM.py
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@ -19,17 +19,20 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
""" """
Bayesian Gaussian Process Latent Variable Model Bayesian Gaussian Process Latent Variable Model
:param Y: observed data :param Y: observed data (np.ndarray) or GPy.likelihood
:type Y: np.ndarray :type Y: np.ndarray| GPy.likelihood instance
:param Q: latent dimensionality :param Q: latent dimensionality
:type Q: int :type Q: int
:param init: initialisation method for the latent space :param init: initialisation method for the latent space
:type init: 'PCA'|'random' :type init: 'PCA'|'random'
""" """
def __init__(self, likelihood, Q, X=None, X_variance=None, init='PCA', M=10, def __init__(self, likelihood_or_Y, Q, X=None, X_variance=None, init='PCA', M=10,
Z=None, kernel=None, oldpsave=10, _debug=False, Z=None, kernel=None, oldpsave=10, _debug=False,
**kwargs): **kwargs):
if type(likelihood_or_Y) is np.ndarray:
likelihood = Gaussian(likelihood_or_Y)
if X == None: if X == None:
X = self.initialise_latent(init, Q, likelihood.Y) X = self.initialise_latent(init, Q, likelihood.Y)