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parameterized now supports deleting of parameters

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This commit is contained in:
Max Zwiessele 2014-02-21 17:53:44 +00:00
parent 2da256fa93
commit 659643038f
12 changed files with 113 additions and 83 deletions
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23
GPy/plotting/matplot_dep/models_plots.py
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@ -56,7 +56,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
if ax is None:
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
#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)
@ -66,7 +69,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#define the frame on which to plot
resolution = resolution or 200
Xnew, xmin, xmax = x_frame1D(model.X[:,free_dims], plot_limits=plot_limits)
Xnew, xmin, xmax = x_frame1D(X[:,free_dims], plot_limits=plot_limits)
Xgrid = np.empty((Xnew.shape[0],model.input_dim))
Xgrid[:,free_dims] = Xnew
for i,v in fixed_inputs:
@ -77,13 +80,13 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
m, v = model._raw_predict(Xgrid)
lower = m - 2*np.sqrt(v)
upper = m + 2*np.sqrt(v)
Y = model.Y
Y = Y
else:
m, v, lower, upper = model.predict(Xgrid)
Y = model.Y
Y = Y
for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
ax.plot(model.X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
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
@ -95,8 +98,8 @@ 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(model.X[which_data_rows, free_dims], model.Y[which_data_rows, which_data_ycols],
xerr=2 * np.sqrt(model.X_variance[which_data_rows, free_dims]),
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)
@ -120,7 +123,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#define the frame for plotting on
resolution = resolution or 50
Xnew, _, _, xmin, xmax = x_frame2D(model.X[:,free_dims], plot_limits, resolution)
Xnew, _, _, xmin, xmax = x_frame2D(X[:,free_dims], plot_limits, resolution)
Xgrid = np.empty((Xnew.shape[0],model.input_dim))
Xgrid[:,free_dims] = Xnew
for i,v in fixed_inputs:
@ -130,14 +133,14 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#predict on the frame and plot
if plot_raw:
m, _ = model._raw_predict(Xgrid)
Y = model.Y
Y = Y
else:
m, _, _, _ = model.predict(Xgrid)
Y = model.data
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(model.X[which_data_rows, free_dims[0]], model.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.)
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])