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[plotting] latent plotting had dimension mix up in it

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mzwiessele 2015-11-30 16:35:30 +00:00
parent 8878353fdb
commit cb3b4ca08d
14 changed files with 49 additions and 25 deletions
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2
GPy/plotting/gpy_plot/gp_plots.py
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@ -393,7 +393,7 @@ def plot_f(self, plot_limits=None, fixed_inputs=None,
apply_link, which_data_ycols, which_data_rows,
visible_dims, levels, samples, 0,
lower, upper, plot_data, plot_inducing,
plot_density, predict_kw, projection, legend)
plot_density, predict_kw, projection, legend, **kwargs)

7
GPy/plotting/gpy_plot/latent_plots.py
View file

@ -123,13 +123,16 @@ def plot_latent_inducing(self,
:param kwargs: the kwargs for the scatter plots
"""
input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
if input_3 is None: zlabel=None
else: zlabel = 'latent dimension %i' % input_3
if 'color' not in kwargs:
kwargs['color'] = 'white'
canvas, kwargs = pl().new_canvas(projection=projection,
xlabel='latent dimension %i' % input_1,
ylabel='latent dimension %i' % input_2,
zlabel='latent dimension %i' % input_3, **kwargs)
zlabel=zlabel, **kwargs)
Z = self.Z.values
labels = np.array(['inducing'] * Z.shape[0])
scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
@ -195,7 +198,7 @@ def plot_magnification(self, labels=None, which_indices=None,
labels = np.ones(self.num_data)
legend = False
scatters = _plot_latent_scatter(canvas, X, which_indices, labels, marker, num_samples, projection='2d', **scatter_kwargs or {})
view = _plot_magnification(self, canvas, which_indices[:2], Xgrid, xmin, xmax, resolution, updates, mean, covariance, kern, **imshow_kwargs)
view = _plot_magnification(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, updates, mean, covariance, kern, **imshow_kwargs)
retval = pl().add_to_canvas(canvas, dict(scatter=scatters, imshow=view),
legend=legend,
)

6
GPy/plotting/gpy_plot/plot_util.py
View file

@ -131,6 +131,8 @@ def helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resol
Xnew, x, y, xmin, xmax = x_frame2D(X[:,free_dims], plot_limits, resolution)
Xgrid = np.zeros((Xnew.shape[0], self.input_dim))
Xgrid[:,free_dims] = Xnew
#xmin = Xgrid.min(0)[free_dims]
#xmax = Xgrid.max(0)[free_dims]
for i,v in fixed_inputs:
Xgrid[:,i] = v
else:
@ -305,7 +307,7 @@ def get_free_dims(model, visible_dims, fixed_dims):
visible_dims = np.arange(model.input_dim)
dims = np.asanyarray(visible_dims)
if fixed_dims is not None:
dims = np.setdiff1d(dims, fixed_dims)
dims = [dim for dim in dims if dim not in fixed_dims]
return np.asanyarray([dim for dim in dims if dim is not None])
@ -357,7 +359,7 @@ def x_frame2D(X,plot_limits=None,resolution=None):
"""
assert X.shape[1]==2, "x_frame2D is defined for two-dimensional inputs"
if plot_limits is None:
xmin, xmax = X.min(0),X.max(0)
xmin, xmax = X.min(0), X.max(0)
xmin, xmax = xmin-0.075*(xmax-xmin), xmax+0.075*(xmax-xmin)
elif len(plot_limits) == 2:
xmin, xmax = plot_limits