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)
Example #2
0
 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)
Example #3
0
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')
Example #4
0
 def __init__(self, variance=0.05, radius=3.5, dimension=2):
     Flower.__init__(self, 0, 1, variance, radius, dimension)