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[core] updating system, security branching

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This commit is contained in:
Max Zwiessele 2015-09-02 09:06:17 +01:00
parent 938cc49aed
commit 70a9a26d7e
15 changed files with 366 additions and 65 deletions
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14
GPy/models/bayesian_gplvm.py
View file

@ -137,6 +137,20 @@ class BayesianGPLVM(SparseGP_MPI):
fignum, plot_inducing, legend,
plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
def plot_magnification(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_magnification(self, labels, which_indices,
resolution, ax, marker, s,
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)
def do_test_latents(self, Y):
"""
Compute the latent representation for a set of new points Y

40
GPy/models/gplvm.py
View file

@ -43,19 +43,19 @@ 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 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 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"
@ -82,5 +82,17 @@ class GPLVM(GP):
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)
def plot_magnification(self, *args, **kwargs):
return util.plot_latent.plot_magnification(self, *args, **kwargs)
def plot_magnification(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_magnification(self, labels, which_indices,
resolution, ax, marker, s,
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)