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integrated sparse GP regression and BGPLVM classes

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Nicolo Fusi 2013-01-30 15:51:36 +00:00
parent 079a20620a
commit 2493e2d336
3 changed files with 25 additions and 23 deletions
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14
GPy/models/BGPLVM.py
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@ -25,12 +25,12 @@ class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
S = np.ones_like(X) * 1e-2#
sparse_GP_regression.__init__(self, X, Y, X_uncertainty = S, **kwargs)
def get_param_names(self):
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)],[])
return (X_names + S_names + sparse_GP_regression.get_param_names(self))
return (X_names + S_names + sparse_GP_regression._get_param_names(self))
def get_param(self):
def _get_params(self):
"""
Horizontally stacks the parameters in order to present them to the optimizer.
The resulting 1-D array has this structure:
@ -40,13 +40,13 @@ class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
===============================================================
"""
return np.hstack((self.X.flatten(), self.X_uncertainty.flatten(), sparse_GP_regression.get_param(self)))
return np.hstack((self.X.flatten(), self.X_uncertainty.flatten(), sparse_GP_regression._get_params(self)))
def set_param(self,x):
def _set_params(self,x):
N, Q = self.N, self.Q
self.X = x[:self.X.size].reshape(N,Q).copy()
self.X_uncertainty = x[(N*Q):(2*N*Q)].reshape(N,Q).copy()
sparse_GP_regression.set_param(self, x[(2*N*Q):])
sparse_GP_regression._set_params(self, x[(2*N*Q):])
def dL_dmuS(self):
dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi1_dmuS(self.dL_dpsi1,self.Z,self.X,self.X_uncertainty)
@ -57,6 +57,6 @@ class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
return np.hstack((dL_dmu.flatten(), dL_dS.flatten()))
def log_likelihood_gradients(self):
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dmuS().flatten(), sparse_GP_regression.log_likelihood_gradients(self)))