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Mods to visualize and dimensionality to make stick demos work for summer school.

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Neil Lawrence 2013-06-13 06:38:11 +01:00
parent d3a4f99b89
commit 7071dff030
2 changed files with 37 additions and 7 deletions
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39
GPy/examples/dimensionality_reduction.py
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@ -261,6 +261,7 @@ def bgplvm_simulation(optimize='scg',
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, _debug=True)
# m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints()
m['noise'] = Y.var() / 100.
@ -327,28 +328,56 @@ def brendan_faces():
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
lvm_visualizer.close()
return m
def stick_play(range=None, frame_rate=15):
data = GPy.util.datasets.stick()
# optimize
if range==None:
Y = data['Y'].copy()
else:
Y = data['Y'][range[0]:range[1], :].copy()
y = Y[0, :]
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
GPy.util.visualize.data_play(Y, data_show, frame_rate)
return Y
def stick():
data = GPy.util.datasets.stick()
m = GPy.models.GPLVM(data['Y'], 2)
# optimize
m = GPy.models.GPLVM(data['Y'], 2)
m.ensure_default_constraints()
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')
lvm_visualizer.close()
return m
def stick_bgplvm(model=None):
data = GPy.util.datasets.stick()
Q = 6
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20,kernel=kernel)
# optimize
m.ensure_default_constraints()
m.optimize(messages=1, max_f_eval=3000,xtol=1e-300,ftol=1e-300)
m._set_params(m._get_params())
plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
plt.sca(latent_axes)
m.plot_latent()
y = m.likelihood.Y[0, :].copy()
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
raw_input('Press enter to finish')
return m
def cmu_mocap(subject='35', motion=['01'], in_place=True):
data = GPy.util.datasets.cmu_mocap(subject, motion)