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2d plotting

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Max Zwiessele 2014-02-24 11:34:22 +00:00
parent c2750ae719
commit 8dbb65ab50
4 changed files with 50 additions and 49 deletions
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18
GPy/plotting/matplot_dep/models_plots.py
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@ -57,8 +57,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
X, Y = param_to_array(model.X, model.Y)
if model.has_uncertain_inputs(): X_variance = model.X_variance
X, Y, Z = param_to_array(model.X, model.Y, model.Z)
if model.has_uncertain_inputs(): X_variance = param_to_array(model.q.variance)
#work out what the inputs are for plotting (1D or 2D)
fixed_dims = np.array([i for i,v in fixed_inputs])
@ -97,10 +97,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#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(),
xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
#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(),
# xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
# ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
#set the limits of the plot to some sensible values
@ -112,7 +112,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#add inducing inputs (if a sparse model is used)
if hasattr(model,"Z"):
#Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
Zu = param_to_array(model.Z[:,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)
@ -136,7 +136,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
Y = Y
else:
m, _, _, _ = model.predict(Xgrid)
Y = model.data
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)
@ -152,7 +152,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#add inducing inputs (if a sparse model is used)
if hasattr(model,"Z"):
#Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
Zu = model.Z[:,free_dims]
Zu = Z[:,free_dims]
ax.plot(Zu[:,free_dims[0]], Zu[:,free_dims[1]], 'wo')
else: