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 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 test_predict(self):
# define some easy training data and predict predictive distribution
circle1 = Ring(variance=1, radius=3)
circle2 = Ring(variance=1, radius=10)
n = 100
X = circle1.sample(n / 2).samples
X = vstack((X, circle2.sample(n / 2).samples))
y = ones(n)
y[:n / 2] = -1.0
# plot(X[:n/2,0], X[:n/2,1], 'ro')
# hold(True)
# plot(X[n/2:,0], X[n/2:,1], 'bo')
# hold(False)
# show()
covariance = SquaredExponentialCovariance(1, 1)
likelihood = LogitLikelihood()
gp = GaussianProcess(y, X, covariance, likelihood)
# predict on mesh
n_test = 20
P = linspace(X[:, 0].min() - 1, X[:, 1].max() + 1, n_test)
Q = linspace(X[:, 1].min() - 1, X[:, 1].max() + 1, n_test)
X_test = asarray(list(itertools.product(P, Q)))
# Y_test = exp(LaplaceApproximation(gp).predict(X_test).reshape(n_test, n_test))
Y_train = exp(LaplaceApproximation(gp).predict(X))
print Y_train
print Y_train>0.5
print y
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')
)[-1] + " /nfs/home1/ucabhst/kameleon_experiments 3 5000 2000"
exit()
experiment_dir_base = str(sys.argv[1])
n = int(str(sys.argv[2]))
num_iterations = int(str(sys.argv[3]))
burnin = int(str(sys.argv[4]))
# loop over parameters here
experiment_dir = experiment_dir_base + os.sep + str(
os.path.abspath(sys.argv[0])).split(os.sep)[-1].split(".")[0] + os.sep
print "running experiments", n, "times at base", experiment_dir
for i in range(n):
distribution = Ring()
mcmc_samplers = []
kernel = GaussianKernel(sigma=1)
# mcmc_samplers.append(KameleonWindow(distribution, kernel))
mcmc_samplers.append(KameleonWindowLearnScale(distribution, kernel))
mean_est = array([-2.0, -2.0])
cov_est = 0.05 * eye(2)
# mcmc_samplers.append(AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est))
mcmc_samplers.append(
AdaptiveMetropolisLearnScale(distribution,
mean_est=mean_est,
cov_est=cov_est))
