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Placed back in examples for motion capture! Added spheres to visualization of figure.

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Neil Lawrence 2013-06-06 06:41:02 +01:00
parent 6b8821df9a
commit 642dbfc764
1 changed files with 52 additions and 55 deletions
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107
GPy/examples/dimensionality_reduction.py
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@ -304,75 +304,72 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
m.plot_scales("MRD Scales")
return m
# # Commented out because dataset is missing
# def brendan_faces():
# from GPy import kern
# data = GPy.util.datasets.brendan_faces()
# Q = 2
# Y = data['Y'][0:-1:10, :]
# # Y = data['Y']
# Yn = Y - Y.mean()
# Yn /= Yn.std()
def brendan_faces():
from GPy import kern
data = GPy.util.datasets.brendan_faces()
Q = 2
Y = data['Y'][0:-1:10, :]
# 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)
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())
# optimize
m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
# m.ensure_default_constraints()
# m.optimize('scg', messages=1, max_f_eval=10000)
m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=10000)
# 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)
# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
# raw_input('Press enter to finish')
# plt.close('all')
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)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
plt.close('all')
# return m
return m
# # Commented out because dataset is missing
# def stick():
# data = GPy.util.datasets.stick()
# m = GPy.models.GPLVM(data['Y'], 2)
def stick():
data = GPy.util.datasets.stick()
m = GPy.models.GPLVM(data['Y'], 2)
# # optimize
# m.ensure_default_constraints()
# m.optimize(messages=1, max_f_eval=10000)
# m._set_params(m._get_params())
# optimize
m.ensure_default_constraints()
m.optimize(messages=1, max_f_eval=10000)
m._set_params(m._get_params())
# 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')
# plt.close('all')
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')
plt.close('all')
# return m
return m
# # Commented out because dataset is missing
# def cmu_mocap(subject='35', motion=['01'], in_place=True):
def cmu_mocap(subject='35', motion=['01'], in_place=True):
# data = GPy.util.datasets.cmu_mocap(subject, motion)
# Y = data['Y']
# if in_place:
# # Make figure move in place.
# data['Y'][:, 0:3] = 0.0
# m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
data = GPy.util.datasets.cmu_mocap(subject, motion)
Y = data['Y']
if in_place:
# Make figure move in place.
data['Y'][:, 0:3] = 0.0
m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
# # optimize
# m.ensure_default_constraints()
# m.optimize(messages=1, max_f_eval=10000)
# optimize
m.ensure_default_constraints()
m.optimize(messages=1, max_f_eval=10000)
# ax = m.plot_latent()
# y = m.likelihood.Y[0, :]
# data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
# raw_input('Press enter to finish')
# plt.close('all')
ax = m.plot_latent()
y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
plt.close('all')
# return m
return m
# def BGPLVM_oil():
# data = GPy.util.datasets.oil()