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Implemented MLP gradients with respect to X.

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This commit is contained in:
Neil Lawrence 2013-08-28 01:19:43 +02:00
parent b127c96bf2
commit 84b7156d23
4 changed files with 32 additions and 46 deletions
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18
GPy/examples/dimensionality_reduction.py
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@ -363,18 +363,18 @@ def stick_play(range=None, frame_rate=15):
GPy.util.visualize.data_play(Y, data_show, frame_rate)
return Y
def stick():
def stick(kernel=None):
data = GPy.util.datasets.osu_run1()
# optimize
m = GPy.models.GPLVM(data['Y'], 2)
m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
m.optimize(messages=1, max_f_eval=10000)
m._set_params(m._get_params())
plt.clf
ax = m.plot_latent()
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
if GPy.util.visualize.visual_available:
plt.clf
ax = m.plot_latent()
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
return m