def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
from GPy.models import BayesianGPLVM
from matplotlib import pyplot as plt
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))
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
# 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 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.util.visualize.stick_show(y[None, :],
connect=data['connect'])
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 stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
"""Interactive visualisation of the Stick Man data from Ohio State University with the Bayesian GPLVM."""
from GPy.models import BayesianGPLVM
from matplotlib import pyplot as plt
import numpy as np
import GPy
import pods
data = pods.datasets.osu_run1()
Q = 6
kernel = GPy.kern.RBF(Q, lengthscale=np.repeat(.5, Q), ARD=True)
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
m.data = data
m.likelihood.variance = 0.001
# optimize
try:
if optimize: m.optimize('bfgs', messages=verbose, max_iters=5e3, bfgs_factor=10)
except KeyboardInterrupt:
print("Keyboard interrupt, continuing to plot and return")
if plot:
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
plt.sca(latent_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'])
dim_select = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean[:1, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
fig.canvas.draw()
# Canvas.show doesn't work on OSX.
#fig.canvas.show()
raw_input('Press enter to finish')
return m
def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
"""Interactive visualisation of the Stick Man data from Ohio State University with the Bayesian GPLVM."""
from GPy.models import BayesianGPLVM
from matplotlib import pyplot as plt
import numpy as np
import GPy
import pods
data = pods.datasets.osu_run1()
Q = 6
kernel = GPy.kern.RBF(Q, lengthscale=np.repeat(0.5, Q), ARD=True)
m = BayesianGPLVM(data["Y"], Q, init="PCA", num_inducing=20, kernel=kernel)
m.data = data
m.likelihood.variance = 0.001
# optimize
try:
if optimize:
m.optimize("bfgs", messages=verbose, max_iters=5e3, bfgs_factor=10)
except KeyboardInterrupt:
print("Keyboard interrupt, continuing to plot and return")
if plot:
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
plt.sca(latent_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"])
dim_select = GPy.plotting.matplot_dep.visualize.lvm_dimselect(
m.X.mean[:1, :].copy(),
m,
data_show,
latent_axes=latent_axes,
sense_axes=sense_axes,
)
fig.canvas.draw()
# Canvas.show doesn't work on OSX.
# fig.canvas.show()
input("Press enter to finish")
return m
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
import pods
data = pods.datasets.osu_run1()
Q = 6
kernel = GPy.kern.RBF(Q, lengthscale=np.repeat(.5, Q), ARD=True)
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
m.data = data
m.likelihood.variance = 0.001
# optimize
try:
if optimize:
m.optimize('bfgs', messages=verbose, max_iters=5e3, bfgs_factor=10)
except KeyboardInterrupt:
print "Keyboard interrupt, continuing to plot and return"
if plot:
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
plt.sca(latent_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'])
dim_select = GPy.plotting.matplot_dep.visualize.lvm_dimselect(
m.X.mean[:1, :].copy(),
m,
data_show,
latent_axes=latent_axes,
sense_axes=sense_axes)
fig.canvas.draw()
fig.canvas.show()
raw_input('Press enter to finish')
return m
def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
from GPy.models import BayesianGPLVM
from matplotlib import pyplot as plt
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))
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
# 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 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.util.visualize.stick_show(y[None, :], connect=data['connect'])
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