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fixed inconsistent naming of parameters in LVM models

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Nicolo Fusi 2013-02-19 14:36:55 +00:00
parent 7cd1c426f4
commit dfe500f8eb
4 changed files with 5 additions and 5 deletions
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4
GPy/models/BGPLVM.py
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@ -40,8 +40,8 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
sparse_GP.__init__(self, X, Gaussian(Y), kernel, Z=Z, X_uncertainty=S, **kwargs)
def _get_param_names(self):
X_names = sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
S_names = sum([['S_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
X_names = sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
S_names = sum([['S_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
return (X_names + S_names + sparse_GP._get_param_names(self))
def _get_params(self):

2
GPy/models/GPLVM.py
View file

@ -38,7 +38,7 @@ class GPLVM(GP):
return np.random.randn(Y.shape[0], Q)
def _get_param_names(self):
return sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[]) + GP._get_param_names(self)
return sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[]) + GP._get_param_names(self)
def _get_params(self):
return np.hstack((self.X.flatten(), GP._get_params(self)))

2
GPy/models/sparse_GP.py
View file

@ -148,7 +148,7 @@ class sparse_GP(GP):
return np.hstack([self.Z.flatten(),GP._get_params(self)])
def _get_param_names(self):
return sum([['iip_%i_%i'%(i,j) for i in range(self.Z.shape[0])] for j in range(self.Z.shape[1])],[]) + GP._get_param_names(self)
return sum([['iip_%i_%i'%(i,j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])],[]) + GP._get_param_names(self)
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """

2
GPy/models/sparse_GPLVM.py
View file

@ -28,7 +28,7 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM):
sparse_GP_regression.__init__(self, X, Y, **kwargs)
def _get_param_names(self):
return (sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
return (sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
+ sparse_GP_regression._get_param_names(self))
def _get_params(self):