apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-02 14:45:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

merge dim reduction

Browse source
This commit is contained in:
Max Zwiessele 2013-08-29 15:16:29 +01:00
parent 59139f01f0
commit 3ea85c6fa6
1 changed files with 52 additions and 8 deletions
Download patch file Download diff file Expand all files Collapse all files

60
GPy/examples/dimensionality_reduction.py
View file

@ -352,7 +352,7 @@ def brendan_faces():
return m return m
def stick_play(range=None, frame_rate=15): def stick_play(range=None, frame_rate=15):
data = GPy.util.datasets.stick() data = GPy.util.datasets.osu_run1()
# optimize # optimize
if range == None: if range == None:
Y = data['Y'].copy() Y = data['Y'].copy()
@ -363,23 +363,67 @@ def stick_play(range=None, frame_rate=15):
GPy.util.visualize.data_play(Y, data_show, frame_rate) GPy.util.visualize.data_play(Y, data_show, frame_rate)
return Y return Y
def stick(): def stick(kernel=None):
data = GPy.util.datasets.stick() data = GPy.util.datasets.osu_run1()
# optimize
m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
m.optimize(messages=1, max_f_eval=10000)
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
def bcgplvm_linear_stick(kernel=None):
data = GPy.util.datasets.osu_run1()
# optimize
mapping = GPy.mappings.Linear(data['Y'].shape[1], 2)
m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
m.optimize(messages=1, max_f_eval=10000)
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
def bcgplvm_stick(kernel=None):
data = GPy.util.datasets.osu_run1()
# optimize
back_kernel=GPy.kern.rbf(data['Y'].shape[1], lengthscale=10.)
mapping = GPy.mappings.Kernel(X=data['Y'], output_dim=2, kernel=back_kernel)
m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
m.optimize(messages=1, max_f_eval=10000)
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
def robot_wireless():
data = GPy.util.datasets.robot_wireless()
# optimize # optimize
m = GPy.models.GPLVM(data['Y'], 2) m = GPy.models.GPLVM(data['Y'], 2)
m.optimize(messages=1, max_f_eval=10000) m.optimize(messages=1, max_f_eval=10000)
m._set_params(m._get_params()) m._set_params(m._get_params())
plt.clf plt.clf
ax = m.plot_latent() 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 return m
def stick_bgplvm(model=None): def stick_bgplvm(model=None):
data = GPy.util.datasets.stick() data = GPy.util.datasets.osu_run1()
Q = 6 Q = 6
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2)) 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) m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)