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Added olivetti faces data set. It required adding netpbmfile.py a bsd licensed pgm file reader from Christoph Gohlke, which doesn't seem to have a spearate installer. Also modified image_show to assume by default that array ordering is python instead of fortran. Modified brendan_faces demo to explicilty force fortran ordering. Notified Teo of change.

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This commit is contained in:
Neil Lawrence 2013-10-14 05:59:15 +01:00
parent de0a5d0e70
commit a92780cb89
5 changed files with 458 additions and 54 deletions
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31
GPy/examples/dimensionality_reduction.py
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@ -327,31 +327,52 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
m.plot_scales("MRD Scales")
return m
def brendan_faces():
from GPy import kern
data = GPy.util.datasets.brendan_faces()
Q = 2
Y = data['Y'][0:-1:10, :]
# Y = data['Y']
Y = data['Y']
Yn = Y - Y.mean()
Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, Q)
# m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
# optimize
m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
m.optimize('scg', messages=1, max_f_eval=10000)
m.optimize('scg', messages=1, max_iters=10)
ax = m.plot_latent(which_indices=(0, 1))
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
return m
def olivetti_faces():
from GPy import kern
data = GPy.util.datasets.olivetti_faces()
Q = 2
Y = data['Y']
Yn = Y - Y.mean()
Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, Q)
m.optimize('scg', messages=1, max_iters=1000)
ax = m.plot_latent(which_indices=(0, 1))
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
return m
def stick_play(range=None, frame_rate=15):
data = GPy.util.datasets.osu_run1()
# optimize
if range == None: