def plot_proposal(self, ys):
# evaluate density itself
Visualise.visualise_distribution(self.distribution, Z=self.Z, Xs=self.Xs, Ys=self.Ys)
# precompute constants of proposal
mcmc_hammer = Kameleon(self.distribution, self.kernel, self.Z, \
self.nu2, self.gamma)
# plot proposal around each y
for y in ys:
mu, L_R = mcmc_hammer.compute_constants(y)
gaussian = Gaussian(mu, L_R, is_cholesky=True)
hold(True)
Visualise.contour_plot_density(gaussian)
hold(False)
draw()
def update(self, mcmc_chain, step_output):
if mcmc_chain.iteration > self.plot_from and mcmc_chain.iteration%self.lag==0:
if mcmc_chain.mcmc_sampler.distribution.dimension==2:
subplot(2, 3, 1)
if self.distribution is not None:
Visualise.plot_array(self.Xs, self.Ys, self.P)
# only plot a number of random samples otherwise this is too slow
if self.num_samples_plot>0:
num_plot=min(mcmc_chain.iteration-1,self.num_samples_plot)
indices=permutation(mcmc_chain.iteration)[:num_plot]
else:
num_plot=mcmc_chain.iteration-1
indices=arange(num_plot)
samples=mcmc_chain.samples[0:mcmc_chain.iteration]
samples_to_plot=mcmc_chain.samples[indices]
# still plot all likelihoods
likelihoods=mcmc_chain.log_liks[0:mcmc_chain.iteration]
likelihoods_to_plot=mcmc_chain.log_liks[indices]
proposal_1d=step_output.proposal_object.samples[0,:]
y = samples[len(samples) - 1]
# plot samples, coloured by likelihood, or just connect
if self.colour_by_likelihood:
likelihoods_to_plot=likelihoods_to_plot.copy()
likelihoods_to_plot=likelihoods_to_plot-likelihoods_to_plot.min()
likelihoods_to_plot=likelihoods_to_plot/likelihoods_to_plot.max()
cm=get_cmap("jet")
for i in range(len(samples_to_plot)):
color = cm(likelihoods_to_plot[i])
plot(samples_to_plot[i,0], samples_to_plot[i,1] ,"o",
color=color, zorder=1)
else:
plot(samples_to_plot[:,0], samples_to_plot[:,1], "m", zorder=1)
plot(y[0], y[1], 'r*', markersize=15.0)
plot(proposal_1d[0], proposal_1d[1], 'y*', markersize=15.0)
if self.distribution is not None:
Visualise.contour_plot_density(mcmc_chain.mcmc_sampler.Q, self.Xs, \
self.Ys, log_domain=False)
else:
Visualise.contour_plot_density(mcmc_chain.mcmc_sampler.Q)
# axis('equal')
xlabel("$x_1$")
ylabel("$x_2$")
if self.num_samples_plot>0:
title(str(self.num_samples_plot) + " random samples")
subplot(2, 3, 2)
plot(samples[:, 0], 'b')
title("Trace $x_1$")
subplot(2, 3, 3)
plot(samples[:, 1], 'b')
title("Trace $x_2$")
subplot(2, 3, 4)
plot(mcmc_chain.log_liks[0:mcmc_chain.iteration], 'b')
title("Log-likelihood")
if len(samples) > 2:
subplot(2, 3, 5)
hist(samples[:, 0])
title("Histogram $x_1$")
subplot(2, 3, 6)
hist(samples[:, 1])
title("Histogram $x_2$")
else:
# if target dimension is not two, plot traces
num_plots=mcmc_chain.mcmc_sampler.distribution.dimension
samples=mcmc_chain.samples[0:mcmc_chain.iteration]
likelihoods=mcmc_chain.log_liks[0:mcmc_chain.iteration]
num_y=round(sqrt(num_plots))
num_x=num_plots/num_y+1
for i in range(num_plots):
subplot(num_y, num_x, i+1)
plot(samples[:, i], 'b')
title("Trace $x_" +str(i) + "$")
subplot(num_y, num_x, num_plots+1)
plot(likelihoods)
title("Log-Likelihood")
suptitle(mcmc_chain.mcmc_sampler.__class__.__name__)
show()
draw()
clf()
