apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-12 21:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Redundant code commented

Browse source
This commit is contained in:
Ricardo 2013-09-17 16:54:31 +01:00
parent 299f17ee46
commit a1a8486bc3
1 changed files with 34 additions and 27 deletions
Download patch file Download diff file Expand all files Collapse all files

61
GPy/core/sparse_gp.py
View file

@ -351,38 +351,45 @@ class SparseGP(GPBase):
which_data = slice(None) which_data = slice(None)
GPBase.plot(self, samples=0, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax, output=output) GPBase.plot(self, samples=0, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax, output=output)
if self.X.shape[1] == 1 and not hasattr(self,'multioutput'):
if self.has_uncertain_inputs:
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
Zu = self.Z * self._Xscale + self._Xoffset
ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
elif self.X.shape[1] == 2 and not hasattr(self,'multioutput'): if not hasattr(self,'multioutput'):
Zu = self.Z * self._Xscale + self._Xoffset
ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
elif self.X.shape[1] == 2 and hasattr(self,'multioutput'): if self.X.shape[1] == 1:
Xu = self.X[self.X[:,-1]==output,:] if self.has_uncertain_inputs:
if self.has_uncertain_inputs: Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
Zu = self.Z * self._Xscale + self._Xoffset
ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
Xu = self.X[self.X[:,-1]==output ,0:1] #?? elif self.X.shape[1] == 2:
Zu = self.Z * self._Xscale + self._Xoffset
ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0], ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
Zu = self.Z[self.Z[:,-1]==output,:]
Zu = self.Z * self._Xscale + self._Xoffset
Zu = self.Z[self.Z[:,-1]==output ,0:1] #??
ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
#ax.set_ylim(ax.get_ylim()[0],)
else: else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions" pass
"""
if self.X.shape[1] == 2 and hasattr(self,'multioutput'):
Xu = self.X[self.X[:,-1]==output,:]
if self.has_uncertain_inputs:
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
Xu = self.X[self.X[:,-1]==output ,0:1] #??
ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
Zu = self.Z[self.Z[:,-1]==output,:]
Zu = self.Z * self._Xscale + self._Xoffset
Zu = self.Z[self.Z[:,-1]==output ,0:1] #??
ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
#ax.set_ylim(ax.get_ylim()[0],)
else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
"""
def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False): def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
""" """