def main():
d = 5
ps = rand(d)
ps /= norm(ps)
distribution = Bernoulli(ps)
num_history = 100
Z = distribution.sample(num_history).samples
threshold = 0.8
spread = 0.2
gamma = 0.2
kernel = HypercubeKernel(gamma)
mcmc_sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
start = zeros(distribution.dimension, dtype=numpy.bool8)
mcmc_params = MCMCParams(start=start, num_iterations=1000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(plot_times=True))
chain.append_mcmc_output(DiscretePlottingOutput(plot_from=0, lag=100))
chain.run()
print "ps", ps
print "empirical", mean(chain.samples, 0)
def main():
d = 5
ps = rand(d)
ps /= norm(ps)
distribution = Bernoulli(ps)
num_history = 100
Z = distribution.sample(num_history).samples
threshold = 0.8
spread = 0.2
gamma = 0.2
kernel = HypercubeKernel(gamma)
mcmc_sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
start = zeros(distribution.dimension, dtype=numpy.bool8)
mcmc_params = MCMCParams(start=start, num_iterations=1000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(plot_times=True))
chain.append_mcmc_output(DiscretePlottingOutput(plot_from=0, lag=100))
chain.run()
print "ps", ps
print "empirical", mean(chain.samples, 0)
def test_chain_bernoulli(self):
# runs the sampler on a distribution of infdependent bernoulli variables
# and compares the mean
d = 5
ps = rand(d)
ps /= norm(ps)
distribution = Bernoulli(ps)
num_history = 100
Z = distribution.sample(num_history).samples
threshold = 0.8
spread = 0.2
gamma = 0.2
kernel = HypercubeKernel(gamma)
mcmc_sampler = DiscreteKameleon(distribution, kernel, Z, threshold,
spread)
start = zeros(distribution.dimension, dtype=numpy.bool8)
mcmc_params = MCMCParams(start=start, num_iterations=1000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.run()
self.assertAlmostEqual(norm(mean(chain.samples, 0) - ps), 0, delta=0.2)
def test_sample_dim(self):
n = 3
d = 2
p = asarray(rand(d))
b = Bernoulli(p)
s = b.sample(n)
self.assertEqual(s.samples.shape, (n, d))
def test_sample_mean_values(self):
n = 10000
d = 3
runs = 100
for _ in range(runs):
ps = asarray(rand(d))
b = Bernoulli(ps)
s = b.sample(n)
for i in range(d):
self.assertAlmostEqual(mean(s.samples[:, i]),
ps[i],
delta=0.05)
def test_chain_bernoulli(self):
# runs the sampler on a distribution of infdependent bernoulli variables
# and compares the mean
d = 5
ps = rand(d)
ps /= norm(ps)
distribution = Bernoulli(ps)
num_history = 100
Z = distribution.sample(num_history).samples
threshold = 0.8
spread = 0.2
gamma = 0.2
kernel = HypercubeKernel(gamma)
mcmc_sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
start = zeros(distribution.dimension, dtype=numpy.bool8)
mcmc_params = MCMCParams(start=start, num_iterations=1000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.run()
self.assertAlmostEqual(norm(mean(chain.samples, 0) - ps), 0, delta=0.2)
def test_sample_samples_dtype(self):
p = asarray([0.5])
b = Bernoulli(p)
self.assertEqual(b.sample(1).samples.dtype, numpy.bool8)
def test_sample_type(self):
p = asarray([0.5])
b = Bernoulli(p)
self.assertTrue(isinstance(b.sample(1), Sample))
