apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-11 21:12:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[examples] stick man example corrected

Browse source
This commit is contained in:
mzwiessele 2014-05-13 08:36:28 +01:00
parent 42fe64cec9
commit 0acb196b26
1 changed files with 17 additions and 10 deletions
Download patch file Download diff file Expand all files Collapse all files

27
GPy/examples/dimensionality_reduction.py
View file

@ -408,13 +408,13 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True):
data = GPy.util.datasets.osu_run1()
# optimize
m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
if optimize: m.optimize('bfgs', messages=verbose, max_f_eval=10000)
if plot:
plt.clf
ax = m.plot_latent()
y = m.Y[0, :]
data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
vis = GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, latent_axes=ax)
vis = GPy.plotting.matplot_dep.visualize.lvm(m.X[:1, :].copy(), m, data_show, latent_axes=ax)
raw_input('Press enter to finish')
return m
@ -475,23 +475,30 @@ def robot_wireless(optimize=True, verbose=True, plot=True):
def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
from GPy.models import BayesianGPLVM
from matplotlib import pyplot as plt
import numpy as np
import GPy
data = GPy.util.datasets.osu_run1()
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, lengthscale=np.repeat(.5, Q), ARD=True) + GPy.kern.Bias(Q, _np.exp(-2))
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
m.data = data
m.X.mean -= m.X.mean.mean(0); m.X.mean /= m.X.mean.var(0)
m.X.variance /= 100
m.likelihood.variance = 0.001
m.Z.randomize()
# optimize
m.ensure_default_constraints()
if optimize: m.optimize('scg', messages=verbose, max_iters=200, xtol=1e-300, ftol=1e-300)
m._set_params(m._get_params())
if optimize: m.optimize('bfgs', messages=verbose, max_iters=1500, xtol=1e-300, ftol=1e-300)
if plot:
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.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
m.plot_latent(ax=latent_axes)
y = m.Y[:1, :].copy()
data_show = GPy.plotting.matplot_dep.visualize.stick_show(y, connect=data['connect'])
GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean[:1, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
raw_input('Press enter to finish')
return m