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BGPLVM still failing, doesn't seem to be numerical : (

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Max Zwiessele 2013-04-17 15:45:20 +01:00
parent 0ed7f8dfdd
commit 865e9df255
3 changed files with 117 additions and 84 deletions
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92
GPy/models/mrd.py
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@ -271,14 +271,31 @@ class MRD(model):
self.Z = Z
return Z
def plot_X_1d(self, colors=None):
fig = pylab.figure(num="MRD X 1d", figsize=(min(8, (3 * len(self.bgplvms))), min(12, (2 * self.X.shape[1]))))
def _handle_plotting(self, fig_num, axes, plotf):
if axes is None:
fig = pylab.figure(num=fig_num, figsize=(4 * len(self.bgplvms), 3 * len(self.bgplvms)))
for i, g in enumerate(self.bgplvms):
if axes is None:
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
else:
ax = axes[i]
plotf(i, g, ax)
pylab.draw()
if axes is None:
fig.tight_layout()
return fig
else:
return pylab.gcf()
def plot_X_1d(self, fig_num="MRD X 1d", axes=None, colors=None):
fig = pylab.figure(num=fig_num, figsize=(min(8, (3 * len(self.bgplvms))), min(12, (2 * self.X.shape[1]))))
if colors is None:
colors = pylab.gca()._get_lines.color_cycle
pylab.clf()
plots = []
for i in range(self.X.shape[1]):
ax = fig.add_subplot(self.X.shape[1], 1, i + 1)
if axes is None:
ax = fig.add_subplot(self.X.shape[1], 1, i + 1)
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)))
ax.fill_between(numpy.arange(self.X.shape[0]),
@ -289,72 +306,41 @@ class MRD(model):
ax.legend(borderaxespad=0.)
if i < self.X.shape[1] - 1:
ax.set_xticklabels('')
# ax1.legend(plots, [r"$\mathbf{{X_{}}}$".format(i + 1) for i in range(self.X.shape[1])],
# bbox_to_anchor=(0., 1 + .01 * self.X.shape[1],
# 1., 1. + .01 * self.X.shape[1]), loc=3,
# ncol=self.X.shape[1], mode="expand", borderaxespad=0.)
pylab.draw()
fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return fig
def plot_X(self):
fig = pylab.figure("MRD X", figsize=(4 * len(self.bgplvms), 3))
fig.clf()
for i, g in enumerate(self.bgplvms):
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
ax.imshow(g.X)
pylab.draw()
fig.tight_layout()
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
def plot_predict(self):
fig = pylab.figure("MRD Predictions", figsize=(4 * len(self.bgplvms), 3))
fig.clf()
for i, g in enumerate(self.bgplvms):
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
ax.imshow(g.predict(g.X)[0])
pylab.draw()
fig.tight_layout()
def plot_predict(self, fig_num="MRD Predictions", axes=None):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: ax.imshow(g.predict(g.X)[0]))
return fig
def plot_scales(self, *args, **kwargs):
fig = pylab.figure("MRD Scales", figsize=(4 * len(self.bgplvms), 3))
fig.clf()
for i, g in enumerate(self.bgplvms):
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
g.kern.plot_ARD(ax=ax, *args, **kwargs)
pylab.draw()
fig.tight_layout()
def plot_scales(self, fig_num="MRD Scales", axes=None, *args, **kwargs):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs))
return fig
def plot_latent(self, *args, **kwargs):
fig = pylab.figure("MRD Latent Spaces", figsize=(4 * len(self.bgplvms), 3))
fig.clf()
for i, g in enumerate(self.bgplvms):
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
g.plot_latent(ax=ax, *args, **kwargs)
pylab.draw()
fig.tight_layout()
def plot_latent(self, fig_num="MRD Latent Spaces", axes=None, *args, **kwargs):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
return fig
def _debug_plot(self):
self.plot_X()
self.plot_X_1d()
self.plot_latent()
self.plot_scales()
fig = pylab.figure("MRD DEBUG PLOT", figsize=(4 * len(self.bgplvms), 9))
fig.clf()
axes = [fig.add_subplot(3, len(self.bgplvms), i + 1) for i in range(len(self.bgplvms))]
self.plot_X(axes=axes)
axes = [fig.add_subplot(3, len(self.bgplvms), i + len(self.bgplvms) + 1) for i in range(len(self.bgplvms))]
self.plot_latent(axes=axes)
axes = [fig.add_subplot(3, len(self.bgplvms), i + 2 * len(self.bgplvms) + 1) for i in range(len(self.bgplvms))]
self.plot_scales(axes=axes)
pylab.draw()
fig.tight_layout()
def _debug_optimize(self, opt='scg', maxiters=500, itersteps=10):
def _debug_optimize(self, opt='scg', maxiters=5000, itersteps=10):
iters = 0
import multiprocessing
class M(multiprocessing.Process):
def __init__(self, q, *args, **kw):
self.q = q
super(M, self).__init__(*args, **kw)
pass
def run(self):
pass
optstep = lambda: self.optimize(opt, messages=1, max_f_eval=itersteps)
self._debug_plot()
raw_input("enter to start debug")