def test_adapt_does_nothing(self):
dimension = 3
ps = rand(dimension)
distribution = Bernoulli(ps)
kernel = HypercubeKernel(1.)
Z = zeros((2, distribution.dimension))
threshold = 0.5
spread = .5
sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
# serialise, call adapt, load, compare
f = NamedTemporaryFile()
dump(sampler, f)
f.seek(0)
sampler_copy = load(f)
f.close()
sampler.adapt(None, None)
# rough check for equality, dont do a proper one here
self.assertEqual(type(sampler_copy.kernel), type(sampler.kernel))
self.assertEqual(sampler_copy.kernel.gamma, sampler.kernel.gamma)
self.assertEqual(type(sampler_copy.distribution), type(sampler.distribution))
self.assertEqual(sampler_copy.distribution.dimension, sampler.distribution.dimension)
self.assertEqual(type(sampler_copy.Z), type(sampler.Z))
self.assertEqual(sampler_copy.Z.shape, sampler.Z.shape)
self.assertAlmostEqual(norm(sampler_copy.Z - sampler.Z), 0)
self.assertEqual(sampler_copy.spread, sampler.spread)
# this is none, so just compare
self.assertEqual(sampler.Q, sampler_copy.Q)
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_construct_proposal(self):
dimension = 3
num_history = 4
ps = rand(dimension)
distribution = Bernoulli(ps)
kernel = HypercubeKernel(1.)
Z = randint(0, 2, (num_history, dimension)).astype(numpy.bool8)
threshold = 0.5
spread = 0.5
sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
y = randint(0, 2, dimension).astype(dtype=numpy.bool8)
p = sampler.construct_proposal(y)
self.assertTrue(isinstance(p, DiscreteRandomWalkProposal))
self.assertEqual(p.spread, spread)
def test_construct_proposal(self):
dimension = 3
num_history = 4
ps = rand(dimension)
distribution = Bernoulli(ps)
kernel = HypercubeKernel(1.)
Z = randint(0, 2, (num_history, dimension)).astype(numpy.bool8)
threshold = 0.5
spread = 0.5
sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
y = randint(0, 2, dimension).astype(dtype=numpy.bool8)
p = sampler.construct_proposal(y)
self.assertTrue(isinstance(p, DiscreteRandomWalkProposal))
self.assertEqual(p.spread, spread)
def main():
d = 5
b = randn(d)
V = randn(d, d)
W = V + V.T
fill_diagonal(W, zeros(d))
hopfield = Hopfield(W, b)
current_state = [rand() < 0.5 for _ in range(d)]
distribution = HopfieldFullConditionals(full_target=hopfield,
current_state=current_state)
print("Running Gibbs to produce chain history")
Z = sample_gibbs(distribution, num_samples=2000)[1500:].astype(numpy.bool8)
inds = permutation(len(Z))
Z = Z[inds[:500]]
print("done")
threshold = 0.8
spread = 0.2
gamma = 0.2
kernel = HypercubeKernel(gamma)
mcmc_sampler = DiscreteKameleon(hopfield, kernel, Z, threshold, spread)
start = zeros(distribution.dimension, dtype=numpy.bool8)
mcmc_params = MCMCParams(start=start, num_iterations=10000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(plot_times=True))
chain.append_mcmc_output(DiscretePlottingOutput(plot_from=0, lag=500))
chain.run()
def run_kameleon_chain(Z, hopfield, start, num_iterations):
threshold = 0.8
spread = 0.03
gamma = 0.2
kernel = HypercubeKernel(gamma)
sampler = DiscreteKameleon(hopfield, kernel, Z, threshold, spread)
params = MCMCParams(start=start, num_iterations=num_iterations)
chain = MCMCChain(sampler, params)
chain.run()
return chain
def test_contructor(self):
dimension = 3
ps = rand(dimension)
distribution = Bernoulli(ps)
kernel = HypercubeKernel(1.)
Z = zeros((2, distribution.dimension))
threshold = 0.5
spread = 0.5
sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
self.assertEqual(sampler.distribution, distribution)
self.assertEqual(sampler.kernel, kernel)
self.assertTrue(sampler.Z is Z)
self.assertEqual(sampler.threshold, threshold)
self.assertEqual(sampler.spread, spread)
def test_adapt_does_nothing(self):
dimension = 3
ps = rand(dimension)
distribution = Bernoulli(ps)
kernel = HypercubeKernel(1.)
Z = zeros((2, distribution.dimension))
threshold = 0.5
spread = .5
sampler = DiscreteKameleon(distribution, kernel, Z, threshold, spread)
# serialise, call adapt, load, compare
f = NamedTemporaryFile()
dump(sampler, f)
f.seek(0)
sampler_copy = load(f)
f.close()
sampler.adapt(None, None)
# rough check for equality, dont do a proper one here
self.assertEqual(type(sampler_copy.kernel), type(sampler.kernel))
self.assertEqual(sampler_copy.kernel.gamma, sampler.kernel.gamma)
self.assertEqual(type(sampler_copy.distribution),
type(sampler.distribution))
self.assertEqual(sampler_copy.distribution.dimension,
sampler.distribution.dimension)
self.assertEqual(type(sampler_copy.Z), type(sampler.Z))
self.assertEqual(sampler_copy.Z.shape, sampler.Z.shape)
self.assertAlmostEqual(norm(sampler_copy.Z - sampler.Z), 0)
self.assertEqual(sampler_copy.spread, sampler.spread)
# this is none, so just compare
self.assertEqual(sampler.Q, sampler_copy.Q)
