def main():
# define the MCMC target distribution
# possible distributions are in kameleon_mcmc.distribution: Banana, Flower, Ring
# distribution = Banana(dimension=2, bananicity=0.03, V=100.0)
distribution = Ring()
# create instance of kameleon sampler that learns the length scale
# can be replaced by any other sampler in kameleon_mcmc.mcmc.samplers
kernel = GaussianKernel(sigma=5)
mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=inf, nu2=0.05)
# mcmc chain and its parameters
start = asarray([0,-3])
mcmc_params = MCMCParams(start=start, num_iterations=30000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
# plot every iteration and print some statistics
chain.append_mcmc_output(PlottingOutput(distribution, plot_from=2000))
chain.append_mcmc_output(StatisticsOutput())
# run cmcm
chain.run()
# print empirical quantiles
burnin=10000
print distribution.emp_quantiles(chain.samples[burnin:])
Visualise.visualise_distribution(distribution, chain.samples)
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()
from matplotlib.pyplot import axis, savefig
from numpy import linspace
from kameleon_mcmc.distribution.Banana import Banana
from kameleon_mcmc.distribution.Flower import Flower
from kameleon_mcmc.distribution.Ring import Ring
from kameleon_mcmc.tools.Visualise import Visualise
if __name__ == '__main__':
distributions = [Ring(), Banana(), Flower()]
for d in distributions:
Xs, Ys = d.get_plotting_bounds()
resolution = 250
Xs = linspace(Xs[0], Xs[1], resolution)
Ys = linspace(Ys[0], Ys[1], resolution)
Visualise.visualise_distribution(d, Xs=Xs, Ys=Ys)
axis("Off")
savefig("heatmap_" + d.__class__.__name__ + ".eps",
bbox_inches='tight')
from matplotlib.pyplot import axis, savefig
from numpy import linspace
from kameleon_mcmc.distribution.Banana import Banana
from kameleon_mcmc.distribution.Flower import Flower
from kameleon_mcmc.distribution.Ring import Ring
from kameleon_mcmc.tools.Visualise import Visualise
if __name__ == '__main__':
distributions = [Ring(), Banana(), Flower()]
for d in distributions:
Xs, Ys = d.get_plotting_bounds()
resolution = 250
Xs = linspace(Xs[0], Xs[1], resolution)
Ys = linspace(Ys[0], Ys[1], resolution)
Visualise.visualise_distribution(d, Xs=Xs, Ys=Ys)
axis("Off")
savefig("heatmap_" + d.__class__.__name__ + ".eps", bbox_inches='tight')
