def test_plot_samples():
# create a gp model
model = pygp.BasicGP(sn=1, sf=1, ell=1)
# create a prior datastructure
priors1 = {'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 a prior datastructure which holds fixed all but one parameter
priors2 = {'sn': pygp.priors.Uniform(0.01, 1.0),
'sf': None,
'ell': None,
'mu': None}
# sample and plot the models
pg.plot_samples(pygp.meta.SMC(model, priors1, 200))
pg.plot_samples(pygp.meta.SMC(model, priors2, 200))
mcmc = pygp.meta.MCMC(model, priors, n=200, burn=100)
smc = pygp.meta.SMC(model, priors, n=200)
pl.figure(1)
pl.clf()
pl.subplot(131)
pp.plot_posterior(model)
pl.title('Type-II ML')
pl.legend(loc='best')
axis = pl.axis()
pl.subplot(132)
pp.plot_posterior(mcmc)
pl.axis(axis)
pl.title('MCMC')
pl.subplot(133)
pp.plot_posterior(mcmc)
pl.axis(axis)
pl.title('SMC')
pl.draw()
pl.figure(2)
pp.plot_samples(mcmc)
pl.suptitle('Samples generated by MCMC')
pl.draw()
pl.show()
def callback(model, bounds, info, x, index, ftrue):
"""
Plot the current posterior and the index.
"""
xx0, xx1 = np.meshgrid( # define grid
np.linspace(bounds[0, 0], bounds[0, 1], 200),
np.linspace(bounds[1, 0], bounds[1, 1], 200))
xx = np.c_[xx0.flat, xx1.flat]
ff = ftrue(xx).reshape(xx0.shape) # compute true function
acq = index(xx).reshape(xx0.shape) # compute acquisition
mu, _ = model.posterior(xx) # compute the posterior
mu = mu.reshape(xx0.shape)
X = info['x'] # get observations and
xbest = info['xbest'] # recommendations
fig = pl.figure(1)
pl.clf()
pl.subplot(221) # top left subplot
pl.contour(xx0, xx1, ff, 20) # plot true function
pl.scatter(x[0], x[1], color='k', zorder=2) # plot current selection
pl.scatter(X[:, 0], X[:, 1], # plot previous data
facecolors='none', color='k', zorder=2)
pl.scatter(xbest[-1, 0], xbest[-1, 1], marker='+', # plot recommendation
s=60, lw=2, color='g', zorder=2)
pl.axis(bounds.flatten())
pl.title('true function')
pl.subplot(222) # top right subplot
pl.contour(xx0, xx1, mu, 20) # plot posterior
pl.scatter(xbest[-1, 0], xbest[-1, 1], marker='+', # plot recommendation
s=60, lw=2, color='g', zorder=2)
pl.axis(bounds.flatten())
pl.title('posterior mean')
pl.subplot(223) # bottom left subplot
pl.contour(xx0, xx1, acq, 20) # plot acquisition
pl.scatter(x[0], x[1], color='k', zorder=2) # plot current selection
pl.axis(bounds.flatten())
pl.title('acquisition')
if not np.all(np.isnan(xbest)):
pl.subplot(224) # bottom right subplot
pl.plot(ftrue(xbest), 'g', lw=2, alpha=0.5) # plot value of recom.
pl.axis('tight')
pl.title('value of recommendation')
for ax in fig.axes: # remove tick labels
ax.set_xticklabels([])
ax.set_yticklabels([])
pl.draw()
pl.show(block=False)
pl.figure(2) # plot hyperparameter
pp.plot_samples(model) # samples
pl.draw()
pl.show(block=False)
mcmc = pygp.meta.MCMC(model, priors, n=200, burn=100)
smc = pygp.meta.SMC(model, priors, n=200)
pl.figure(1)
pl.clf()
pl.subplot(131)
pp.plot_posterior(model)
pl.title('Type-II ML')
pl.legend(loc='best')
axis = pl.axis()
pl.subplot(132)
pp.plot_posterior(mcmc)
pl.axis(axis)
pl.title('MCMC')
pl.subplot(133)
pp.plot_posterior(mcmc)
pl.axis(axis)
pl.title('SMC')
pl.draw()
pl.figure(2)
pp.plot_samples(mcmc)
pl.suptitle('Samples generated by MCMC')
pl.draw()
pl.show()