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, 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:])
