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Some bugfixes that have affected GPLVM/sparseGPLVM since the hetero noise change

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This commit is contained in:
Nicolas 2013-02-21 15:21:37 +00:00
parent a166187231
commit 3f924bea8f
2 changed files with 6 additions and 5 deletions
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6
GPy/models/GPLVM.py
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@ -53,10 +53,10 @@ class GPLVM(GP):
return np.hstack((dL_dX.flatten(),GP._log_likelihood_gradients(self)))
def plot(self):
assert self.Y.shape[1]==2
pb.scatter(self.Y[:,0],self.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)
assert self.likelihood.Y.shape[1]==2
pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)
Xnew = np.linspace(self.X.min(),self.X.max(),200)[:,None]
mu, var = self.predict(Xnew)
mu, var, upper, lower = self.predict(Xnew)
pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
def plot_latent(self):

5
GPy/models/sparse_GPLVM.py
View file

@ -42,7 +42,8 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM):
return sparse_GP_regression.log_likelihood(self)
def dL_dX(self):
dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(self.dL_dpsi2,self.psi1)
#dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(self.dL_dpsi2,self.psi1)
dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(self.dL_dpsi2[0,:,:],self.psi1)
dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0,self.X)
dL_dX += self.kern.dK_dX(dL_dpsi1.T,self.X,self.Z)
@ -55,5 +56,5 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM):
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)
mu, var, upper, lower = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
pb.plot(mu[:, 0] , mu[:, 1], 'ko')