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optional plotting of inducing inputs added

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Max Zwiessele 2013-06-28 11:01:31 +01:00
parent 7325e319b4
commit c0d514b6c0
1 changed files with 24 additions and 35 deletions
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23
GPy/util/plot_latent.py
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@ -2,7 +2,7 @@ import pylab as pb
import numpy as np import numpy as np
from .. import util from .. import util
def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, fignum=None): def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, fignum=None, plot_inducing=False, legend=True):
""" """
: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
@ -67,26 +67,15 @@ def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None,
ax.set_xlabel('latent dimension %i' % input_1) ax.set_xlabel('latent dimension %i' % input_1)
ax.set_ylabel('latent dimension %i' % input_2) ax.set_ylabel('latent dimension %i' % input_2)
if not np.all(labels==1.): if not np.all(labels == 1.) and legend:
ax.legend(loc=0, numpoints=1) ax.legend(loc=0, numpoints=1)
ax.set_xlim(xmin[0], xmax[0]) ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(xmin[1], xmax[1]) ax.set_ylim(xmin[1], xmax[1])
ax.grid(b=False) # remove the grid if present, it doesn't look good ax.grid(b=False) # remove the grid if present, it doesn't look good
ax.set_aspect('auto') # set a nice aspect ratio ax.set_aspect('auto') # set a nice aspect ratio
return ax
if plot_inducing:
ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
def plot_latent_indices(Model, which_indices=None, *args, **kwargs):
if which_indices is None:
try:
input_1, input_2 = np.argsort(Model.input_sensitivity())[:2]
except:
raise ValueError, "cannot Automatically determine which dimensions to plot, please pass 'which_indices'"
else:
input_1, input_2 = which_indices
ax = plot_latent(Model, which_indices=[input_1, input_2], *args, **kwargs)
# TODO: Here test if there are inducing points...
ax.plot(Model.Z[:, input_1], Model.Z[:, input_2], '^w')
return ax return ax