def test_optimization():
# load the data.
cdir = os.path.abspath(os.path.dirname(demo.__file__))
data = np.load(os.path.join(cdir, 'xy.npz'))
X = data['X']
y = data['y']
# create the model and add data.
gp = pygp.BasicGP(sn=.1, sf=1, ell=.1, mu=0)
gp.add_data(X, y)
# optimize the model
pygp.optimize(gp, {'sn': None})
# make sure our constraint is satisfied
nt.assert_equal(gp.get_hyper()[0], np.log(0.1))
import pygp.plotting as pp
if __name__ == '__main__':
# load the data.
cdir = os.path.abspath(os.path.dirname(__file__))
data = np.load(os.path.join(cdir, 'xy.npz'))
X = data['X']
y = data['y']
# create the model and add data to it.
model = pygp.BasicGP(sn=.1, sf=1, ell=.1)
model.add_data(X, y)
# find the ML hyperparameters and plot the predictions.
pygp.optimize(model)
# create a prior structure.
priors = {
'sn': pygp.priors.Uniform(0.01, 1.0),
'sf': pygp.priors.Uniform(0.01, 5.0),
'ell': pygp.priors.Uniform(0.01, 1.0),
'mu': pygp.priors.Uniform(-2, 2)}
# create sample-based models.
mcmc = pygp.meta.MCMC(model, priors, n=200, burn=100)
smc = pygp.meta.SMC(model, priors, n=200)
pl.figure(1)
pl.clf()
optimize its hyperparameters.
"""
import os
import numpy as np
import matplotlib.pyplot as pl
import pygp
import pygp.plotting as pp
if __name__ == '__main__':
# load the data.
cdir = os.path.abspath(os.path.dirname(__file__))
data = np.load(os.path.join(cdir, 'xy.npz'))
X = data['X']
y = data['y']
# create the model, add data, and optimize it.
gp = pygp.BasicGP(sn=.1, sf=1, ell=.1, mu=0)
gp.add_data(X, y)
pygp.optimize(gp)
# plot the posterior.
pl.figure(1)
pl.clf()
pp.plot_posterior(gp)
pl.legend(loc=2)
pl.draw()
pl.show()
cdir = os.path.abspath(os.path.dirname(__file__))
data = np.load(os.path.join(cdir, "xy.npz"))
X = data["X"]
y = data["y"]
# create a basic GP.
gp1 = pygp.BasicGP(sn=0.1, sf=1, ell=0.1)
gp1.add_data(X, y)
# create a sparse GPs.
U = np.linspace(-1.3, 2, 10)[:, None]
gp2 = pygp.inference.FITC.from_gp(gp1, U)
gp3 = pygp.inference.DTC.from_gp(gp1, U)
# find the ML parameters
pygp.optimize(gp1)
pygp.optimize(gp2)
pygp.optimize(gp3)
# plot the dense gp.
pl.figure(1)
pl.clf()
pl.subplot(131)
pp.plot_posterior(gp1)
pl.title("Full GP")
# grab the axis limits.
axis = pl.axis()
# plot the FITC sparse gp.
pl.subplot(132)
import numpy as np
import matplotlib.pyplot as pl
import pygp
import pygp.plotting as pp
if __name__ == '__main__':
# load the data.
cdir = os.path.abspath(os.path.dirname(__file__))
data = np.load(os.path.join(cdir, 'xy.npz'))
X = data['X']
y = data['y']
# create the model, add data, and optimize it.
gp = pygp.BasicGP(sn=.1, sf=1, ell=.1, mu=0)
gp.add_data(X, y)
priors = {"sf": pygp.priors.LogNormal(1e-15, 1, 0.1, 1),
"ell": pygp.priors.Uniform(1e-15, 1e2),
"sn": pygp.priors.Horseshoe(1e-9, 1, 0.1)}
pygp.optimize(gp, priors)
print(gp)
# plot the posterior.
pl.figure(1)
pl.clf()
pp.plot_posterior(gp)
pl.legend(loc=2)
pl.draw()
pl.show()
import pygp.priors
import pygp.plotting as pp
if __name__ == '__main__':
# load the data.
cdir = os.path.abspath(os.path.dirname(__file__))
data = np.load(os.path.join(cdir, 'xy.npz'))
X = data['X']
y = data['y']
# create the model and add data to it.
model = pygp.BasicGP(sn=.1, sf=1, ell=.1)
model.add_data(X, y)
# find the ML hyperparameters and plot the predictions.
pygp.optimize(model)
# create a prior structure.
priors = {
'sn': pygp.priors.Uniform(0.01, 1.0),
'sf': pygp.priors.Uniform(0.01, 5.0),
'ell': pygp.priors.Uniform(0.01, 1.0),
'mu': pygp.priors.Uniform(-2, 2)
}
# create sample-based models.
mcmc = pygp.meta.MCMC(model, priors, n=200, burn=100)
smc = pygp.meta.SMC(model, priors, n=200)
pl.figure(1)
pl.clf()
Basic demo showing how to instantiate a simple GP model, add data to it, and
optimize its hyperparameters.
"""
import os
import numpy as np
import matplotlib.pyplot as pl
import pygp
import pygp.plotting as pp
if __name__ == '__main__':
# load the data.
cdir = os.path.abspath(os.path.dirname(__file__))
data = np.load(os.path.join(cdir, 'xy.npz'))
X = data['X']
y = data['y']
# create the model, add data, and optimize it.
gp = pygp.BasicGP(sn=.1, sf=1, ell=.1, mu=0)
gp.add_data(X, y)
pygp.optimize(gp)
# plot the posterior.
pl.figure(1)
pl.clf()
pp.plot_posterior(gp)
pl.legend(loc=2)
pl.draw()
pl.show()
