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_Xmean is now Xoffset and _Xstd is now _Xscale

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Ricardo 2013-06-05 19:18:29 +01:00
parent 0a51407528
commit 8d8408dee0
3 changed files with 14 additions and 14 deletions
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2
GPy/core/gp.py
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@ -142,7 +142,7 @@ class GP(GPBase):
"""
# normalize X values
Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
# now push through likelihood

12
GPy/core/gp_base.py
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@ -21,12 +21,12 @@ class GPBase(Model):
self.num_data, self.output_dim = self.likelihood.data.shape
if normalize_X:
self._Xmean = X.mean(0)[None, :]
self._Xstd = X.std(0)[None, :]
self.X = (X.copy() - self._Xmean) / self._Xstd
self._Xoffset = X.mean(0)[None, :]
self._Xscale = X.std(0)[None, :]
self.X = (X.copy() - self._Xoffset) / self._Xscale
else:
self._Xmean = np.zeros((1, self.input_dim))
self._Xstd = np.ones((1, self.input_dim))
self._Xoffset = np.zeros((1, self.input_dim))
self._Xscale = np.ones((1, self.input_dim))
super(GPBase, self).__init__()
# All leaf nodes should call self._set_params(self._get_params()) at
@ -107,7 +107,7 @@ class GPBase(Model):
if self.X.shape[1] == 1:
Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
m, _, lower, upper = self.predict(Xnew, which_parts=which_parts)

14
GPy/core/sparse_gp.py
View file

@ -43,11 +43,11 @@ class SparseGP(GPBase):
self.X_variance = X_variance
if normalize_X:
self.Z = (self.Z.copy() - self._Xmean) / self._Xstd
self.Z = (self.Z.copy() - self._Xoffset) / self._Xscale
# normalize X uncertainty also
if self.has_uncertain_inputs:
self.X_variance /= np.square(self._Xstd)
self.X_variance /= np.square(self._Xscale)
def _compute_kernel_matrices(self):
# kernel computations, using BGPLVM notation
@ -269,9 +269,9 @@ class SparseGP(GPBase):
"""
# normalize X values
Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
if X_variance_new is not None:
X_variance_new = X_variance_new / self._Xstd ** 2
X_variance_new = X_variance_new / self._Xscale ** 2
# here's the actual prediction by the GP model
mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
@ -292,13 +292,13 @@ class SparseGP(GPBase):
GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax)
if self.X.shape[1] == 1:
if self.has_uncertain_inputs:
Xu = self.X * self._Xstd + self._Xmean # 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._Xstd + self._Xmean
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:
Zu = self.Z * self._Xstd + self._Xmean
Zu = self.Z * self._Xscale + self._Xoffset
ax.plot(Zu[:, 0], Zu[:, 1], 'wo')