mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-13 05:52:38 +02:00
BGPLVM MRD Examples and plotting adjustments
This commit is contained in:
Max Zwiessele
parent
2d9ecaa042
commit
65ead17ff5
3 changed files with 90 additions and 53 deletions
|
|
@ -54,6 +54,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
self._savedgradients = []
|
||||
self._savederrors = []
|
||||
self._savedpsiKmm = []
|
||||
self._savedABCD = []
|
||||
|
||||
sparse_GP.__init__(self, X, Gaussian(Y), kernel, Z=Z, X_variance=X_variance, **kwargs)
|
||||
|
||||
|
|
@ -135,6 +136,17 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
self._savedparams.append([self.f_call, self._get_params()])
|
||||
self._savedgradients.append([self.f_call, self._log_likelihood_gradients()])
|
||||
self._savedpsiKmm.append([self.f_call, [self.Kmm, self.dL_dKmm]])
|
||||
sf2 = self.scale_factor ** 2
|
||||
if self.likelihood.is_heteroscedastic:
|
||||
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
|
||||
B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
|
||||
else:
|
||||
A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
|
||||
B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
|
||||
C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5 * self.M * np.log(sf2))
|
||||
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
|
||||
self._savedABCD.append([self.f_call, A, B, C, D])
|
||||
|
||||
# print "\nkl:", kl, "ll:", ll
|
||||
return ll - kl
|
||||
|
||||
|
|
@ -169,7 +181,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
ax.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
|
||||
return ax
|
||||
|
||||
def plot_X_1d(self, fig=None, axes=None, fig_num="MRD X 1d", colors=None):
|
||||
def plot_X_1d(self, fig=None, axes=None, fig_num="LVM mu S 1d", colors=None):
|
||||
"""
|
||||
Plot latent space X in 1D:
|
||||
|
||||
|
|
@ -196,7 +208,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
else:
|
||||
ax = axes[i]
|
||||
ax.plot(self.X, c='k', alpha=.3)
|
||||
plots.extend(ax.plot(self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{}}}$".format(i)))
|
||||
plots.extend(ax.plot(self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
|
||||
ax.fill_between(np.arange(self.X.shape[0]),
|
||||
self.X.T[i] - 2 * np.sqrt(self.X_variance.T[i]),
|
||||
self.X.T[i] + 2 * np.sqrt(self.X_variance.T[i]),
|
||||
|
|
@ -251,6 +263,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
gradient_dict = dict(self._savedgradients)
|
||||
kmm_dict = dict(self._savedpsiKmm)
|
||||
iters = np.array(param_dict.keys())
|
||||
ABCD_dict = np.array(self._savedABCD)
|
||||
self.showing = 0
|
||||
|
||||
# ax2 = pylab.subplot2grid(splotshape, (1, 0), 2, 4)
|
||||
|
|
@ -281,14 +294,26 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
ha='center', va='center')
|
||||
figs[-1].canvas.draw()
|
||||
figs[-1].tight_layout(rect=(.15, 0, 1, .86))
|
||||
# figs.append(pylab.figure("BGPLVM DEBUG Kmm", figsize=(12, 6)))
|
||||
# fig = figs[-1]
|
||||
# ax6 = fig.add_subplot(121)
|
||||
# ax6.text(.5, .5, r"${\mathbf{K}_{mm}}$", color='magenta', alpha=.5, transform=ax6.transAxes,
|
||||
# ha='center', va='center')
|
||||
# ax7 = fig.add_subplot(122)
|
||||
# ax7.text(.5, .5, r"${\frac{dL}{dK_{mm}}}$", color='magenta', alpha=.5, transform=ax7.transAxes,
|
||||
# ha='center', va='center')
|
||||
figs.append(pylab.figure("BGPLVM DEBUG Kmm", figsize=(12, 6)))
|
||||
fig = figs[-1]
|
||||
ax6 = fig.add_subplot(121)
|
||||
ax6.text(.5, .5, r"${\mathbf{K}_{mm}}$", color='magenta', alpha=.5, transform=ax6.transAxes,
|
||||
ha='center', va='center')
|
||||
ax7 = fig.add_subplot(122)
|
||||
ax7.text(.5, .5, r"${\frac{dL}{dK_{mm}}}$", color='magenta', alpha=.5, transform=ax7.transAxes,
|
||||
ax8 = fig.add_subplot(121)
|
||||
ax8.text(.5, .5, r"${\mathbf{A,B,C,D}}$", color='k', alpha=.5, transform=ax8.transAxes,
|
||||
ha='center', va='center')
|
||||
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 1], label='A')
|
||||
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 2], label='B')
|
||||
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 3], label='C')
|
||||
ax8.plot(ABCD_dict[:, 0], ABCD_dict[:, 4], label='D')
|
||||
ax8.legend()
|
||||
figs[-1].canvas.draw()
|
||||
figs[-1].tight_layout(rect=(.15, 0, 1, .86))
|
||||
|
||||
X, S, Z, theta = self._debug_filter_params(param_dict[self.showing])
|
||||
Xg, Sg, Zg, thetag = self._debug_filter_params(gradient_dict[self.showing])
|
||||
|
|
@ -330,16 +355,16 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
ax5.set_xticks(np.arange(len(theta)))
|
||||
ax5.set_xticklabels(self._get_param_names()[-len(theta):], rotation=17)
|
||||
|
||||
imkmm = ax6.imshow(kmm_dict[self.showing][0])
|
||||
from mpl_toolkits.axes_grid1 import make_axes_locatable
|
||||
divider = make_axes_locatable(ax6)
|
||||
caxkmm = divider.append_axes("right", "5%", pad="1%")
|
||||
cbarkmm = pylab.colorbar(imkmm, cax=caxkmm)
|
||||
|
||||
imkmmdl = ax7.imshow(kmm_dict[self.showing][1])
|
||||
divider = make_axes_locatable(ax7)
|
||||
caxkmmdl = divider.append_axes("right", "5%", pad="1%")
|
||||
cbarkmmdl = pylab.colorbar(imkmmdl, cax=caxkmmdl)
|
||||
# imkmm = ax6.imshow(kmm_dict[self.showing][0])
|
||||
# from mpl_toolkits.axes_grid1 import make_axes_locatable
|
||||
# divider = make_axes_locatable(ax6)
|
||||
# caxkmm = divider.append_axes("right", "5%", pad="1%")
|
||||
# cbarkmm = pylab.colorbar(imkmm, cax=caxkmm)
|
||||
#
|
||||
# imkmmdl = ax7.imshow(kmm_dict[self.showing][1])
|
||||
# divider = make_axes_locatable(ax7)
|
||||
# caxkmmdl = divider.append_axes("right", "5%", pad="1%")
|
||||
# cbarkmmdl = pylab.colorbar(imkmmdl, cax=caxkmmdl)
|
||||
|
||||
# Qleg = ax1.legend(Xlatentplts, [r"$Q_{}$".format(i + 1) for i in range(self.Q)],
|
||||
# loc=3, ncol=self.Q, bbox_to_anchor=(0, 1.15, 1, 1.15),
|
||||
|
|
@ -420,13 +445,13 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
thetagrads.set_offsets(np.array([xtheta.ravel(), theta.ravel()]).T)
|
||||
thetagrads.set_UVC(Utheta, thetag)
|
||||
|
||||
imkmm.set_data(kmm_dict[self.showing][0])
|
||||
imkmm.autoscale()
|
||||
cbarkmm.update_normal(imkmm)
|
||||
|
||||
imkmmdl.set_data(kmm_dict[self.showing][1])
|
||||
imkmmdl.autoscale()
|
||||
cbarkmmdl.update_normal(imkmmdl)
|
||||
# imkmm.set_data(kmm_dict[self.showing][0])
|
||||
# imkmm.autoscale()
|
||||
# cbarkmm.update_normal(imkmm)
|
||||
#
|
||||
# imkmmdl.set_data(kmm_dict[self.showing][1])
|
||||
# imkmmdl.autoscale()
|
||||
# cbarkmmdl.update_normal(imkmmdl)
|
||||
|
||||
ax2.relim()
|
||||
# ax3.relim()
|
||||
|
|
@ -452,4 +477,4 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
|
|||
cidp = figs[0].canvas.mpl_connect('button_press_event', onclick)
|
||||
cidd = figs[0].canvas.mpl_connect('motion_notify_event', motion)
|
||||
|
||||
return ax1, ax2, ax3, ax4, ax5, ax6, ax7
|
||||
return ax1, ax2, ax3, ax4, ax5 # , ax6, ax7
|
||||
|
|
|
|||
|
|
@ -287,6 +287,9 @@ class MRD(model):
|
|||
else:
|
||||
return pylab.gcf()
|
||||
|
||||
def plot_X_1d(self):
|
||||
return self.gref.plot_X_1d()
|
||||
|
||||
def plot_X(self, fig_num="MRD Predictions", axes=None):
|
||||
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: ax.imshow(g.X))
|
||||
return fig
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue