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added jacobian and magnification factor

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Alessandra Tosi 2013-07-30 15:58:18 +01:00
parent 632dda8397
commit dc637e30cb
1 changed files with 17 additions and 0 deletions
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17
GPy/models/gplvm.py
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@ -63,6 +63,20 @@ class GPLVM(GP):
return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self))) return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self)))
def jacobian(self,X):
target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
for i in range(self.output_dim):
target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
return target
def magnification(self,X):
target=np.zeros(X.shape[0])
J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
J=self.jacobian(X)
for i in range(X.shape[0]):
target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
return target
def plot(self): def plot(self):
assert self.likelihood.Y.shape[1] == 2 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) pb.scatter(self.likelihood.Y[:, 0], self.likelihood.Y[:, 1], 40, self.X[:, 0].copy(), linewidth=0, cmap=pb.cm.jet)
@ -72,3 +86,6 @@ class GPLVM(GP):
def plot_latent(self, *args, **kwargs): def plot_latent(self, *args, **kwargs):
return util.plot_latent.plot_latent(self, *args, **kwargs) return util.plot_latent.plot_latent(self, *args, **kwargs)
def plot_magnification(self, *args, **kwargs):
return util.plot_latent.plot_magnification(self, *args, **kwargs)