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dimensionalityreduction plotting adjusted to new syntax

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This commit is contained in:
Max Zwiessele 2013-06-05 11:21:02 +01:00
parent 312cfebcb1
commit ffb6eb414b
3 changed files with 17 additions and 20 deletions
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9
GPy/examples/dimensionality_reduction.py
View file

@ -263,7 +263,7 @@ def bgplvm_simulation(optimize='scg',
# m.constrain('variance|noise', logexp_clipped()) # m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints() m.ensure_default_constraints()
m['noise'] = Y.var() / 100. m['noise'] = Y.var() / 100.
m['linear_variance'] = .001 m['linear_variance'] = .01
if optimize: if optimize:
print "Optimizing model:" print "Optimizing model:"
@ -271,11 +271,8 @@ def bgplvm_simulation(optimize='scg',
max_f_eval=max_f_eval, max_f_eval=max_f_eval,
messages=True, gtol=1e-6) messages=True, gtol=1e-6)
if plot: if plot:
import pylab m.plot_X_1d("BGPLVM Latent Space 1D")
m.plot_X_1d() m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
pylab.figure('BGPLVM Simulation ARD Parameters');
pylab.axis();
m.kern.plot_ARD()
return m return m
def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw): def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):

16
GPy/models/Bayesian_GPLVM.py
View file

@ -241,22 +241,22 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
x = np.arange(self.X.shape[0]) x = np.arange(self.X.shape[0])
for i in range(self.X.shape[1]): for i in range(self.X.shape[1]):
if ax is None: if ax is None:
ax = fig.add_subplot(self.X.shape[1], 1, i + 1) a = fig.add_subplot(self.X.shape[1], 1, i + 1)
elif isinstance(ax, (tuple, list)): elif isinstance(ax, (tuple, list)):
ax = ax[i] a = ax[i]
else: else:
raise ValueError("Need one ax per latent dimnesion Q") raise ValueError("Need one ax per latent dimnesion Q")
ax.plot(self.X, c='k', alpha=.3) a.plot(self.X, c='k', alpha=.3)
plots.extend(ax.plot(x, self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i))) plots.extend(a.plot(x, self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
ax.fill_between(x, a.fill_between(x,
self.X.T[i] - 2 * np.sqrt(self.X_variance.T[i]), self.X.T[i] - 2 * np.sqrt(self.X_variance.T[i]),
self.X.T[i] + 2 * np.sqrt(self.X_variance.T[i]), self.X.T[i] + 2 * np.sqrt(self.X_variance.T[i]),
facecolor=plots[-1].get_color(), facecolor=plots[-1].get_color(),
alpha=.3) alpha=.3)
ax.legend(borderaxespad=0.) a.legend(borderaxespad=0.)
ax.set_xlim(x.min(), x.max()) a.set_xlim(x.min(), x.max())
if i < self.X.shape[1] - 1: if i < self.X.shape[1] - 1:
ax.set_xticklabels('') a.set_xticklabels('')
pylab.draw() pylab.draw()
fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95)) fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return fig return fig

10
GPy/models/mrd.py
View file

@ -261,12 +261,12 @@ class MRD(model):
fig = pylab.figure(num=fignum, figsize=(4 * len(self.bgplvms), 3)) fig = pylab.figure(num=fignum, figsize=(4 * len(self.bgplvms), 3))
for i, g in enumerate(self.bgplvms): for i, g in enumerate(self.bgplvms):
if axes is None: if axes is None:
axes = fig.add_subplot(1, len(self.bgplvms), i + 1) ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
elif isinstance(axes, (tuple, list)): elif isinstance(axes, (tuple, list)):
axes = axes[i] ax = axes[i]
else: else:
raise ValueError("Need one axes per latent dimension Q") raise ValueError("Need one axes per latent dimension Q")
plotf(i, g, axes) plotf(i, g, ax)
pylab.draw() pylab.draw()
if axes is None: if axes is None:
fig.tight_layout() fig.tight_layout()
@ -286,11 +286,11 @@ class MRD(model):
return fig return fig
def plot_scales(self, fignum="MRD Scales", ax=None, *args, **kwargs): def plot_scales(self, fignum="MRD Scales", ax=None, *args, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(axes=ax, *args, **kwargs)) fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs))
return fig return fig
def plot_latent(self, fignum="MRD Latent Spaces", ax=None, *args, **kwargs): def plot_latent(self, fignum="MRD Latent Spaces", ax=None, *args, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(axes=ax, *args, **kwargs)) fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
return fig return fig
def _debug_plot(self): def _debug_plot(self):