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[ploting] dim reduction

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Max Zwiessele 2014-05-16 11:20:50 +01:00
parent 382645ff37
commit efc1f4413c
2 changed files with 5 additions and 5 deletions
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6
GPy/plotting/matplot_dep/dim_reduction_plots.py
View file

@ -31,7 +31,7 @@ def plot_latent(model, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40, resolution=50, ax=None, marker='o', s=40,
fignum=None, plot_inducing=False, legend=True, fignum=None, plot_inducing=False, legend=True,
plot_limits=None, plot_limits=None,
aspect='auto', updates=False, **kwargs): aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
""" """
:param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc) :param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
:param resolution: the resolution of the grid on which to evaluate the predictive variance :param resolution: the resolution of the grid on which to evaluate the predictive variance
@ -60,7 +60,7 @@ def plot_latent(model, labels=None, which_indices=None,
def plot_function(x): def plot_function(x):
Xtest_full = np.zeros((x.shape[0], model.X.shape[1])) Xtest_full = np.zeros((x.shape[0], model.X.shape[1]))
Xtest_full[:, [input_1, input_2]] = x Xtest_full[:, [input_1, input_2]] = x
_, var = model.predict(Xtest_full) _, var = model.predict(Xtest_full, **predict_kwargs)
var = var[:, :1] var = var[:, :1]
return np.log(var) return np.log(var)
@ -81,7 +81,7 @@ def plot_latent(model, labels=None, which_indices=None,
view = ImshowController(ax, plot_function, view = ImshowController(ax, plot_function,
(xmin, ymin, xmax, ymax), (xmin, ymin, xmax, ymax),
resolution, aspect=aspect, interpolation='bilinear', resolution, aspect=aspect, interpolation='bilinear',
cmap=pb.cm.binary, **kwargs) cmap=pb.cm.binary, **imshow_kwargs)
# make sure labels are in order of input: # make sure labels are in order of input:
ulabels = [] ulabels = []

4
GPy/plotting/matplot_dep/models_plots.py
View file

@ -97,7 +97,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
for d in which_data_ycols: for d in which_data_ycols:
plots['gpplot'] = 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)
plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5) if not plot_raw: plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, 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
@ -151,7 +151,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
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
plots['contour'] = 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)
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.) if not plot_raw: 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])