def test_deep_kl_lag(m, p=.99, trials=100, eps=1e-3, n_samples=300, pre_burnin=100):
net = m_deep_bistable(m+1, p) # using m+1 since leaf node is used for evidence
# compute steady state
model_nodes = net._nodes[:-1]
evidence_node = net._nodes[-1]
ss_distributions = steady_state(net, {evidence_node : 1}, model_nodes, eps)
# accumulate data (KL divergence) in nodes x time_steps x trials
data = np.zeros((m, n_samples, trials))
for t in range(trials):
counts = {node: eps * np.ones(node.size()) for node in model_nodes}
def do_divergence(i, net):
for j,n in enumerate(model_nodes):
counts[n][n.state_index()] += 1
# compute divergence between current estimate and ss_distribution
data[j][i][t] = KL(ss_distributions[n], counts[n] / counts[n].sum())
# prepare net by burning-in to 0-evidence
gibbs_sample(net, {evidence_node : 0}, None, 0, pre_burnin)
# get data on 1-evidence (no burnin this time; demonstrating lag)
gibbs_sample(net, {evidence_node : 1}, do_divergence, n_samples, 0)
return data
def steady_state(net, evidence, nodes, eps=0, M=10000, burnin=100):
"""computes steady state distribution for each node
"""
# eps allows for some small count at each state (to avoid zero-probability states)
counts = {node: eps * np.ones(node.size()) for node in nodes}
def do_count(i, net):
for n in nodes:
counts[n][n.state_index()] += 1
gibbs_sample(net, evidence, do_count, M, burnin)
for _, c in counts.iteritems():
c /= c.sum()
return counts
def steady_state(net, evidence, nodes, eps=0, M=10000, burnin=100):
"""computes steady state distribution for each node
"""
# eps allows for some small count at each state (to avoid zero-probability states)
counts = {node: eps * np.ones(node.size()) for node in nodes}
def do_count(i, net):
for n in nodes:
counts[n][n.state_index()] += 1
gibbs_sample(net, evidence, do_count, M, burnin)
for _, c in counts.iteritems():
c /= c.sum()
return counts
def sample_marginal_states(net, evidence, samples, when=None): """Computes S[i] = vector of marginal probabilities that net is in state id i. If given, when(net) is evaluated to decide whether each sample is included """ n_states = count_states(net) # estimate starting distribution over states by sampling S = np.zeros(n_states) def do_count_state(i, net): if when is None or when(net): S[state_to_id(net, net.state_vector())] += 1 gibbs_sample(net, evidence, do_count_state, samples, 1) return normalized(S, order=1)
def sample_marginal_states(net, evidence, samples, when=None):
"""Computes S[i] = vector of marginal probabilities that net is in state id i.
If given, when(net) is evaluated to decide whether each sample is included
"""
n_states = count_states(net)
# estimate starting distribution over states by sampling
S = np.zeros(n_states)
def do_count_state(i, net):
if when is None or when(net):
S[state_to_id(net, net.state_vector())] += 1
gibbs_sample(net, evidence, do_count_state, samples, 1)
return normalized(S, order=1)
def test_deep_likelihood_lag(m, p=.99, trials=100, eps=1e-3, n_samples=300, pre_burnin=100):
net = m_deep_bistable(m+1, p)
# compute steady state
model_nodes = net._nodes[:-1]
evidence_node = net._nodes[-1]
ss_distributions = steady_state(net, {evidence_node : 1}, model_nodes, eps)
# accumulate data (sample likelihood) in nodes x time_steps x trials
data = np.zeros((m-1, n_samples, trials))
for t in range(trials):
def do_likelihood(i, net):
for j,n in enumerate(model_nodes):
data[j][i][t] = ss_distributions[n][n.state_index()]
# prepare net by burning-in to 0-evidence
gibbs_sample(net, {evidence_node : 0}, None, 0, pre_burnin)
# get data on 1-evidence (no burnin this time; demonstrating lag)
gibbs_sample(net, {evidence_node : 1}, do_likelihood, n_samples, 0)
return data
def compute_distribution():
counter = SwitchedFunction()
gibbs_sample(net, {}, counter, args.samples, args.burnin)
return counter.distribution()
def compute_distribution():
counter = SwitchedFunction()
gibbs_sample(net, {}, counter, args.samples, args.burnin)
return counter.distribution()