apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 12:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[plotting] restructuring more and more

Browse source
This commit is contained in:
mzwiessele 2015-10-04 12:31:22 +01:00
parent c07f3dbe98
commit 831e032ade
18 changed files with 330 additions and 272 deletions
Download patch file Download diff file Expand all files Collapse all files

25
GPy/models/gplvm.py
View file

@ -44,31 +44,6 @@ class GPLVM(GP):
super(GPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
#def jacobian(self,X):
# J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
# for i in range(self.output_dim):
# J[:,:,i] = self.kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1], X, self.X)
# return J
#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(np.linalg.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
# return target
def plot(self):
assert self.Y.shape[1] == 2, "too high dimensional to plot. Try plot_latent"
from matplotlib import pyplot as plt
plt.scatter(self.Y[:, 0],
self.Y[:, 1],
40, self.X[:, 0].copy(),
linewidth=0, cmap=plt.cm.jet)
Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
mu, _ = self.predict(Xnew)
plt.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,