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plotting returns

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Max Zwiessele 2014-03-12 09:51:26 +00:00
parent 01f5d789c5
commit 5f2b383510
1 changed files with 10 additions and 10 deletions
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20
GPy/plotting/matplot_dep/models_plots.py
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@ -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) #work out what the inputs are for plotting (1D or 2D)
fixed_dims = np.array([i for i,v in fixed_inputs]) fixed_dims = np.array([i for i,v in fixed_inputs])
free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims) free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims)
plots = {}
#one dimensional plotting #one dimensional plotting
if len(free_dims) == 1: if len(free_dims) == 1:
@ -89,20 +89,20 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
m, v, lower, upper = model.predict(Xgrid) m, v, lower, upper = model.predict(Xgrid)
Y = Y Y = Y
for d in which_data_ycols: for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol) plots['gpplot'] = 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['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
#optionally plot some samples #optionally plot some samples
if samples: #NOTE not tested with fixed_inputs if samples: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples) Ysim = model.posterior_samples(Xgrid, samples)
for yi in Ysim.T: 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. #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) #add error bars for uncertain (if input uncertainty is being modelled)
if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs(): 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()), xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5) ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
@ -118,7 +118,7 @@ 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 = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
Zu = Z[:,free_dims] Zu = Z[:,free_dims]
z_height = ax.get_ylim()[0] 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)
@ -143,8 +143,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
Y = Y Y = Y
for d in which_data_ycols: for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T 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) plots['contour'] = 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['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 #set the limits of the plot to some sensible values
ax.set_xlim(xmin[0], xmax[0]) ax.set_xlim(xmin[0], xmax[0])
@ -157,11 +157,11 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
if hasattr(model,"Z"): if hasattr(model,"Z"):
#Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims] #Zu = model.Z[:,free_dims] * model._Xscale[:,free_dims] + model._Xoffset[:,free_dims]
Zu = Z[:,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: else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions" raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
return plots
def plot_fit_f(model, *args, **kwargs): def plot_fit_f(model, *args, **kwargs):
""" """