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BGPLVM working

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Nicolo Fusi 2013-01-29 15:57:03 +00:00
parent 936d08723e
commit ff1b64022e
2 changed files with 1 additions and 7 deletions
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2
GPy/examples/oil_flow_demo.py
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@ -31,7 +31,7 @@ N, D = Y.shape
Y -= Y.mean(axis=0) Y -= Y.mean(axis=0)
#Y /= Y.std(axis=0) #Y /= Y.std(axis=0)
Q = 7 Q = 10
k = GPy.kern.rbf_ARD(Q) + GPy.kern.white(Q) k = GPy.kern.rbf_ARD(Q) + GPy.kern.white(Q)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M = 12) m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M = 12)
m.constrain_positive('(rbf|bias|S|white|noise)') m.constrain_positive('(rbf|bias|S|white|noise)')

6
GPy/models/BGPLVM.py
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@ -57,12 +57,6 @@ class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
return np.hstack((dL_dmu.flatten(), dL_dS.flatten())) 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))) return np.hstack((self.dL_dmuS().flatten(), sparse_GP_regression.log_likelihood_gradients(self)))
def plot(self):
GPLVM.plot(self)
#passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
mu, var = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
pb.plot(mu[:, 0] , mu[:, 1], 'ko')