from kameleon_mcmc.mcmc.MCMCParams import MCMCParams
from kameleon_mcmc.mcmc.output.StatisticsOutput import StatisticsOutput
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolis import AdaptiveMetropolis
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import \
KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
if __name__ == '__main__':
experiment_dir = str(os.path.abspath(sys.argv[0])).split(
os.sep)[-1].split(".")[0] + os.sep
distribution = Flower(amplitude=6,
frequency=6,
variance=1,
radius=10,
dimension=8)
sigma = 5
kernel = GaussianKernel(sigma=sigma)
burnin = 60000
num_iterations = 120000
#mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin)
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
#mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
mcmc_sampler = StandardMetropolis(distribution)
def get_plotting_bounds(self):
if self.dimension == 2:
value = self.radius + self.amplitude + 3 * sqrt(self.variance)
return [(-value, value) for _ in range(2)]
else:
return Flower.get_plotting_bounds(self)
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')
def __init__(self, variance=0.05, radius=3.5, dimension=2):
Flower.__init__(self, 0, 1, variance, radius, dimension)
