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

input_sensitivity and ard plotting

Browse source
This commit is contained in:
Max Zwiessele 2014-02-24 14:47:43 +00:00
parent d90d67a8c1
commit b200b9fa90
11 changed files with 108 additions and 83 deletions
Download patch file Download diff file Expand all files Collapse all files

23
GPy/models/gplvm.py
View file

@ -28,28 +28,20 @@ class GPLVM(GP):
:type init: 'PCA'|'random'
"""
if X is None:
X = self.initialise_latent(init, input_dim, Y)
from ..util.initialization import initialize_latent
X = initialize_latent(init, input_dim, Y)
if kernel is None:
kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
kernel = kern.RBF(input_dim, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
likelihood = Gaussian()
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
self.X = Param('X', X)
self.X = Param('latent_mean', X)
self.add_parameter(self.X, index=0)
def initialise_latent(self, init, input_dim, Y):
Xr = np.random.randn(Y.shape[0], input_dim)
if init == 'PCA':
PC = PCA(Y, input_dim)[0]
Xr[:PC.shape[0], :PC.shape[1]] = PC
else:
pass
return Xr
def parameters_changed(self):
GP.parameters_changed(self)
self.X.gradient = self.kern.gradients_X(self.posterior.dL_dK, self.X)
super(GPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self._dL_dK, self.X, None)
def _getstate(self):
return GP._getstate(self)
@ -79,7 +71,8 @@ class GPLVM(GP):
pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
def plot_latent(self, *args, **kwargs):
return util.plot_latent.plot_latent(self, *args, **kwargs)
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_latent(self, *args, **kwargs)
def plot_magnification(self, *args, **kwargs):
return util.plot_latent.plot_magnification(self, *args, **kwargs)