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()