def main():
distribution = Banana(dimension=8)
sigma = 5
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
mcmc_sampler = Kameleon(distribution, kernel,
distribution.sample(100).samples)
start = zeros(distribution.dimension)
mcmc_params = MCMCParams(start=start, num_iterations=20000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(plot_times=True))
chain.run()
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()
