def main():
distribution = Banana()
# distribution = Flower(amplitude=6, frequency=6, variance=1, radius=10, dimension=8)
# Visualise.visualise_distribution(distribution)
show()
#
sigma = 5
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
mcmc_sampler = KameleonWindowLearnScale(distribution,
kernel,
stop_adapt=inf)
start = asarray([0, -5.])
mcmc_params = MCMCParams(start=start, num_iterations=30000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(
PlottingOutput(distribution,
plot_from=3000,
colour_by_likelihood=False,
num_samples_plot=0))
chain.append_mcmc_output(StatisticsOutput(plot_times=False))
chain.run()
print distribution.emp_quantiles(chain.samples[10000:])
def main():
dist=Ring(dimension=50)
X=dist.sample(10000).samples
#print X[:,2:dist.dimension]
print dist.emp_quantiles(X)
dist2=Banana(dimension=50)
X2=dist2.sample(10000).samples
print dist2.emp_quantiles(X2)
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 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 main():
distribution = Banana(dimension=8, bananicity=0.1, V=100.0)
sigma = 5
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
mcmc_sampler = KameleonWindow(distribution, kernel)
start = zeros(distribution.dimension)
mcmc_params = MCMCParams(start=start, num_iterations=80000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
# chain.append_mcmc_output(PlottingOutput(distribution, plot_from=3000))
chain.append_mcmc_output(StatisticsOutput(plot_times=True))
chain.run()
print distribution.emp_quantiles(chain.samples)
def main():
distribution = Banana(dimension=8, bananicity=0.1, V=100.0)
sigma = 5
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
mcmc_sampler = KameleonWindow(distribution, kernel)
start = zeros(distribution.dimension)
mcmc_params = MCMCParams(start=start, num_iterations=80000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
# chain.append_mcmc_output(PlottingOutput(distribution, plot_from=3000))
chain.append_mcmc_output(StatisticsOutput(plot_times=True))
chain.run()
print distribution.emp_quantiles(chain.samples)
def main():
distribution = Banana()
# distribution = Flower(amplitude=6, frequency=6, variance=1, radius=10, dimension=8)
# Visualise.visualise_distribution(distribution)
show()
#
sigma = 5
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=inf)
start = asarray([0,-5.])
mcmc_params = MCMCParams(start=start, num_iterations=30000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(PlottingOutput(distribution, plot_from=3000, colour_by_likelihood=False, num_samples_plot=0))
chain.append_mcmc_output(StatisticsOutput(plot_times=False))
chain.run()
print distribution.emp_quantiles(chain.samples[10000:])
def main():
distribution = Banana(dimension=2, bananicity=0.03, V=100.0)
mcmc_sampler = StandardMetropolis(distribution)
start = zeros(distribution.dimension)
start = asarray([0., -2.])
mcmc_params = MCMCParams(start=start, num_iterations=10000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(plot_times=True, lag=1000))
# chain.append_mcmc_output(PlottingOutput(distribution, plot_from=1, num_samples_plot=0,
# colour_by_likelihood=False))
chain.run()
f = open("std_metropolis_chain_gaussian.bin", 'w')
dump(chain, f)
f.close()
from kameleon_mcmc.kernel.GaussianKernel import GaussianKernel
from kameleon_mcmc.mcmc.MCMCChain import MCMCChain
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 = Banana(dimension=8, bananicity=0.03, V=100)
sigma = GaussianKernel.get_sigma_median_heuristic(
distribution.sample(1000).samples)
sigma = 10
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
burnin = 20000
num_iterations = 40000
mcmc_sampler = KameleonWindowLearnScale(distribution,
kernel,
stop_adapt=burnin)
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
cov_est[0, 0] = distribution.V
from kameleon_mcmc.kernel.GaussianKernel import GaussianKernel
from kameleon_mcmc.mcmc.MCMCChain import MCMCChain
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 = Banana(dimension=8, bananicity=0.1, V=100)
sigma = GaussianKernel.get_sigma_median_heuristic(distribution.sample(1000).samples)
sigma = 10
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
burnin = 40000
num_iterations = 80000
#mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin)
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
cov_est[0, 0] = distribution.V
#mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
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 kameleon_mcmc.kernel.GaussianKernel import GaussianKernel
from kameleon_mcmc.mcmc.MCMCChain import MCMCChain
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 = Banana(dimension=8, bananicity=0.03, V=100)
sigma = GaussianKernel.get_sigma_median_heuristic(distribution.sample(1000).samples)
sigma = 10
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
burnin = 20000
num_iterations = 40000
#mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin)
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
cov_est[0, 0] = distribution.V
#mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
import sys
from kameleon_mcmc.distribution.Banana import Banana
from kameleon_mcmc.experiments.SingleChainExperiment import SingleChainExperiment
from kameleon_mcmc.kernel.GaussianKernel import GaussianKernel
from kameleon_mcmc.mcmc.MCMCChain import MCMCChain
from kameleon_mcmc.mcmc.MCMCParams import MCMCParams
from kameleon_mcmc.mcmc.output.StatisticsOutput import StatisticsOutput
from kameleon_mcmc.mcmc.samplers.GMMMetropolis import GMMMetropolis
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 = Banana(dimension=8, bananicity=0.1, V=100)
burnin = 40000
num_iterations = 80000
mcmc_sampler = GMMMetropolis(distribution,
num_components=10,
num_sample_discard=500,
num_samples_gmm=1000,
num_samples_when_to_switch=10000,
num_runs_em=10)
#mean_est = zeros(distribution.dimension, dtype="float64")
#cov_est = 1.0 * eye(distribution.dimension)
#cov_est[0, 0] = distribution.V
#mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
def gradient(self, x, Y):
assert (len(shape(x)) == 1)
assert (len(shape(Y)) == 2)
assert (len(x) == shape(Y)[1])
if self.nu == 1.5 or self.nu == 2.5:
x_2d = reshape(x, (1, len(x)))
lower_order_rho = self.rho * sqrt(2 * (self.nu - 1)) / sqrt(
2 * self.nu)
lower_order_kernel = MaternKernel(lower_order_rho, self.nu - 1,
self.sigma)
k = lower_order_kernel.kernel(x_2d, Y)
differences = Y - x
G = (1.0 / lower_order_rho**2) * (k.T * differences)
return G
else:
raise NotImplementedError()
if __name__ == '__main__':
distribution = Banana()
Z = distribution.sample(50).samples
Z2 = distribution.sample(50).samples
kernel = MaternKernel(5.0, nu=1.5, sigma=2.0)
K = kernel.kernel(Z, Z2)
imshow(K, interpolation="nearest")
#G = kernel.gradient(Z[0],Z2)
#print G
show()
else:
raise NotImplementedError()
return K
def gradient(self, x, Y):
assert(len(shape(x))==1)
assert(len(shape(Y))==2)
assert(len(x)==shape(Y)[1])
if self.nu==1.5 or self.nu==2.5:
x_2d=reshape(x, (1, len(x)))
lower_order_rho = self.rho * sqrt(2*(self.nu-1)) / sqrt(2*self.nu)
lower_order_kernel = MaternKernel(lower_order_rho,self.nu-1,self.sigma)
k = lower_order_kernel.kernel(x_2d, Y)
differences = Y - x
G = ( 1.0 / lower_order_rho ** 2 ) * (k.T * differences)
return G
else:
raise NotImplementedError()
if __name__ == '__main__':
distribution = Banana()
Z = distribution.sample(50).samples
Z2 = distribution.sample(50).samples
kernel = MaternKernel(5.0, nu=1.5, sigma=2.0)
K = kernel.kernel(Z, Z2)
imshow(K, interpolation="nearest")
#G = kernel.gradient(Z[0],Z2)
#print G
show()
