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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Ricardo 2013-06-04 18:56:18 +01:00
commit eb5f2ff5f0
7 changed files with 86 additions and 67 deletions
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9
GPy/models/Bayesian_GPLVM.py
View file

@ -218,7 +218,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
return means, covars
def plot_X_1d(self, ax=None, fignum=None, colors=None):
def plot_X_1d(self, fignum=None, ax=None, colors=None):
"""
Plot latent space X in 1D:
@ -230,7 +230,8 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
colors of different latent space dimensions Q
"""
import pylab
fig = pylab.figure(num=fignum, figsize=(8, min(12, (2 * self.X.shape[1]))))
if ax is None:
fig = pylab.figure(num=fignum, figsize=(8, min(12, (2 * self.X.shape[1]))))
if colors is None:
colors = pylab.gca()._get_lines.color_cycle
pylab.clf()
@ -241,8 +242,10 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
for i in range(self.X.shape[1]):
if ax is None:
ax = fig.add_subplot(self.X.shape[1], 1, i + 1)
else:
elif isinstance(ax, (tuple, list)):
ax = ax[i]
else:
raise ValueError("Need one ax per latent dimnesion Q")
ax.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)))
ax.fill_between(x,

34
GPy/models/mrd.py
View file

@ -256,15 +256,17 @@ class MRD(model):
self.Z = Z
return Z
def _handle_plotting(self, fig_num, axes, plotf):
def _handle_plotting(self, fignum, axes, plotf):
if axes is None:
fig = pylab.figure(num=fig_num, figsize=(4 * len(self.bgplvms), 3))
fig = pylab.figure(num=fignum, figsize=(4 * len(self.bgplvms), 3))
for i, g in enumerate(self.bgplvms):
if axes is None:
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
axes = fig.add_subplot(1, len(self.bgplvms), i + 1)
elif isinstance(axes, (tuple, list)):
axes = axes[i]
else:
ax = axes[i]
plotf(i, g, ax)
raise ValueError("Need one axes per latent dimension Q")
plotf(i, g, axes)
pylab.draw()
if axes is None:
fig.tight_layout()
@ -275,20 +277,20 @@ class MRD(model):
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))
def plot_X(self, fignum="MRD Predictions", ax=None):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X))
return fig
def plot_predict(self, fig_num="MRD Predictions", axes=None, **kwargs):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: ax.imshow(g.predict(g.X)[0], **kwargs))
def plot_predict(self, fignum="MRD Predictions", ax=None, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.predict(g.X)[0], **kwargs))
return fig
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))
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))
return fig
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))
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))
return fig
def _debug_plot(self):
@ -296,11 +298,11 @@ class MRD(model):
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)
self.plot_X(ax=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)
self.plot_latent(ax=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)
self.plot_scales(ax=axes)
pylab.draw()
fig.tight_layout()

4
GPy/models/sparse_GP_regression.py
View file

@ -28,7 +28,7 @@ class sparse_GP_regression(sparse_GP):
"""
def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10, X_variance=None):
#kern defaults to rbf (plus white for stability)
if kernel is None:
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
@ -43,5 +43,5 @@ class sparse_GP_regression(sparse_GP):
#likelihood defaults to Gaussian
likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
self._set_params(self._get_params())