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

Browse source 
Conflicts:
	GPy/core/gp.py
	GPy/plotting/matplot_dep/models_plots.py
This commit is contained in:
Ricardo 2014-03-12 12:57:04 +00:00
commit 4ce6d2d72c
34 changed files with 800 additions and 710 deletions
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20
GPy/plotting/matplot_dep/models_plots.py
View file

@ -68,7 +68,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#work out what the inputs are for plotting (1D or 2D)
fixed_dims = np.array([i for i,v in fixed_inputs])
free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims)
plots = {}
#one dimensional plotting
if len(free_dims) == 1:
@ -96,20 +96,20 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
m, v, lower, upper = model.predict(Xgrid,full_cov=False)
Y = Y
for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples)
for yi in Ysim.T:
ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
#add error bars for uncertain (if input uncertainty is being modelled)
if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs():
ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
plots['xerrorbar'] = ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
@ -125,7 +125,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
Zu = Z[:,free_dims]
z_height = ax.get_ylim()[0]
ax.plot(Zu, np.zeros_like(Zu) + z_height, 'r|', mew=1.5, markersize=12)
plots['inducing_inputs'] = ax.plot(Zu, np.zeros_like(Zu) + z_height, 'r|', mew=1.5, markersize=12)
@ -150,8 +150,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
Y = Y
for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T
ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
#set the limits of the plot to some sensible values
ax.set_xlim(xmin[0], xmax[0])
@ -164,11 +164,11 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
if hasattr(model,"Z"):
#Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
Zu = Z[:,free_dims]
ax.plot(Zu[:,free_dims[0]], Zu[:,free_dims[1]], 'wo')
plots['inducing_inputs'] = ax.plot(Zu[:,free_dims[0]], Zu[:,free_dims[1]], 'wo')
else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
return plots
def plot_fit_f(model, *args, **kwargs):
"""

45
GPy/plotting/matplot_dep/variational_plots.py
View file

@ -44,3 +44,48 @@ def plot(parameterized, fignum=None, ax=None, colors=None):
pb.draw()
fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return fig
def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None):
"""
Plot latent space X in 1D:
- if fig is given, create input_dim subplots in fig and plot in these
- if ax is given plot input_dim 1D latent space plots of X into each `axis`
- if neither fig nor ax is given create a figure with fignum and plot in there
colors:
colors of different latent space dimensions input_dim
"""
if ax is None:
fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
if colors is None:
colors = pb.gca()._get_lines.color_cycle
pb.clf()
else:
colors = iter(colors)
plots = []
means, variances, gamma = param_to_array(parameterized.mean, parameterized.variance, parameterized.binary_prob)
x = np.arange(means.shape[0])
for i in range(means.shape[1]):
# mean and variance plot
a = fig.add_subplot(means.shape[1]*2, 1, 2*i + 1)
a.plot(means, c='k', alpha=.3)
plots.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
a.fill_between(x,
means.T[i] - 2 * np.sqrt(variances.T[i]),
means.T[i] + 2 * np.sqrt(variances.T[i]),
facecolor=plots[-1].get_color(),
alpha=.3)
a.legend(borderaxespad=0.)
a.set_xlim(x.min(), x.max())
if i < means.shape[1] - 1:
a.set_xticklabels('')
# binary prob plot
a = fig.add_subplot(means.shape[1]*2, 1, 2*i + 2)
a.bar(x,gamma[:,i],bottom=0.,linewidth=0,align='center')
a.set_xlim(x.min(), x.max())
a.set_ylim([0.,1.])
pb.draw()
fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return fig