apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-15 06:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

plotting, allot of plotting

Browse source
This commit is contained in:
mzwiessele 2014-03-20 16:20:39 +00:00
parent 6f9c97ee72
commit ce728d8465
9 changed files with 97 additions and 39 deletions
Download patch file Download diff file Expand all files Collapse all files

45
GPy/plotting/matplot_dep/dim_reduction_plots.py
View file

@ -30,7 +30,8 @@ def most_significant_input_dimensions(model, which_indices):
def plot_latent(model, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, plot_inducing=False, legend=True,
aspect='auto', updates=False):
plot_limits=None,
aspect='auto', updates=False, **kwargs):
"""
: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
@ -38,6 +39,8 @@ def plot_latent(model, labels=None, which_indices=None,
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
else:
fig = ax.figure
Tango.reset()
if labels is None:
@ -57,15 +60,28 @@ def plot_latent(model, labels=None, which_indices=None,
def plot_function(x):
Xtest_full = np.zeros((x.shape[0], model.X.shape[1]))
Xtest_full[:, [input_1, input_2]] = x
mu, var, low, up = model.predict(Xtest_full)
_, var = model.predict(Xtest_full)
var = var[:, :1]
return np.log(var)
#Create an IMshow controller that can re-plot the latent space shading at a good resolution
if plot_limits is None:
xmin, ymin = X[:, [input_1, input_2]].min(0)
xmax, ymax = X[:, [input_1, input_2]].max(0)
x_r, y_r = xmax-xmin, ymax-ymin
xmin -= .1*x_r
xmax += .1*x_r
ymin -= .1*y_r
ymax += .1*y_r
else:
try:
xmin, xmax, ymin, ymax = plot_limits
except (TypeError, ValueError) as e:
raise e.__class__, "Wrong plot limits: {} given -> need (xmin, xmax, ymin, ymax)".format(plot_limits)
view = ImshowController(ax, plot_function,
tuple(X[:, [input_1, input_2]].min(0)) + tuple(X[:, [input_1, input_2]].max(0)),
(xmin, ymin, xmax, ymax),
resolution, aspect=aspect, interpolation='bilinear',
cmap=pb.cm.binary)
cmap=pb.cm.binary, **kwargs)
# make sure labels are in order of input:
ulabels = []
@ -99,18 +115,31 @@ def plot_latent(model, labels=None, which_indices=None,
if not np.all(labels == 1.) and legend:
ax.legend(loc=0, numpoints=1)
#ax.set_xlim(xmin[0], xmax[0])
#ax.set_ylim(xmin[1], xmax[1])
ax.grid(b=False) # remove the grid if present, it doesn't look good
ax.set_aspect('auto') # set a nice aspect ratio
if plot_inducing:
Z = param_to_array(model.Z)
ax.plot(Z[:, input_1], Z[:, input_2], '^w')
ax.set_xlim((xmin, xmax))
ax.set_ylim((ymin, ymax))
try:
fig.canvas.draw()
fig.tight_layout()
fig.canvas.draw()
except Exception as e:
print "Could not invoke tight layout: {}".format(e)
pass
if updates:
ax.figure.canvas.show()
try:
ax.figure.canvas.show()
except Exception as e:
print "Could not invoke show: {}".format(e)
raw_input('Enter to continue')
view.deactivate()
return ax
def plot_magnification(model, labels=None, which_indices=None,
@ -186,7 +215,7 @@ def plot_magnification(model, labels=None, which_indices=None,
ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
if updates:
ax.figure.canvas.show()
fig.canvas.show()
raw_input('Enter to continue')
pb.title('Magnification Factor')