def go(routing_params, variant_weights, start_states, explicit_conv=True):
tmat = builder.build_transition_matrix(routing_params, enc_graph,
enc_meta)
return automaton_builder.all_nodes_absorbing_solve(
builder,
tmat,
variant_weights,
start_states,
steps=1000,
backtrack_fails_prob=0.01,
explicit_conv=explicit_conv)
def solve_one(one_edge_routing_params, one_edge_variant_weights):
"""Run one of the edge-specific automata."""
# Build the automaton on the provided graph.
transition_matrix = builder.build_transition_matrix(
one_edge_routing_params, encoded_graph, static_metadata)
# Start state is always state 0.
start_machine_states = jnp.broadcast_to(
(jnp.arange(num_fsm_states) == 0)[None, :],
(num_nodes, num_fsm_states))
return automaton_builder.all_nodes_absorbing_solve(
builder=builder,
transition_matrix=transition_matrix,
variant_weights=one_edge_variant_weights,
start_machine_states=start_machine_states,
steps=steps,
backtrack_fails_prob=backtrack_fails_prob)
def test_all_nodes_absorbing_solve(self):
schema, graph = self.build_doubly_linked_list_graph(4)
builder = automaton_builder.AutomatonBuilder(schema)
enc_graph, enc_meta = builder.encode_graph(graph)
# We set up the automaton with 5 variants and 2 states, but only use the
# first state, to make sure that variants and states are interleaved
# correctly.
# Variant 0: move forward
# Variant 1: move backward
# Variant 2: finish
# Variant 3: restart
# Variant 4: fail
variant_weights = jnp.array([
# From node 0, go forward.
[[1, 0, 0, 0, 0], [1, 0, 0, 0, 0], [.7, 0, .3, 0, 0],
[0, 0, 1, 0, 0]],
# From node 1, go backward with small failure probabilities.
[[0, 0.9, 0, 0, 0.1], [0, 0.9, 0, 0, 0.1], [.7, 0, .3, 0, 0],
[0, 0, 1, 0, 0]],
# Node 2 bounces around and ultimately accepts on node 0.
[[0.9, 0, 0.1, 0, 0], [0, 1, 0, 0, 0], [0.5, 0.5, 0, 0, 0],
[0, 1, 0, 0, 0]],
# Node 3 immediately accepts, or restarts after 0 or 1 steps.
[[0, 0, 1, 0, 0], [1, 0, 0, 0, 0], [0, 0, 0, 1, 0],
[0, 0.1, 0.8, 0.1, 0]],
])
routing_params = automaton_builder.RoutingParams(
move=jnp.pad(
jnp.array([
[1., 0., 1., 0., 1., 0.],
[0., 1., 0., 1., 0., 1.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
]).reshape([5, 6, 1, 1]), [(0, 0), (0, 0), (0, 1), (0, 1)]),
special=jnp.pad(
jnp.array([
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
[[1., 0., 0.], [1., 0., 0.], [1., 0., 0.]],
[[0., 1., 0.], [0., 1., 0.], [0., 1., 0.]],
[[0., 0., 1.], [0., 0., 1.], [0., 0., 1.]],
]).reshape([5, 3, 1, 3]), [(0, 0), (0, 0), (0, 1), (0, 0)]))
tmat = builder.build_transition_matrix(routing_params, enc_graph,
enc_meta)
# Absorbing probs follow the paths described above.
# Note that when starting at node 3, with probability 0.2 the automaton
# tries to backtrack, but with probability 0.2 * 0.01 backtracking fails
# (as specified by backtrack_fails_prob) and thus the total absorbing
# probability is 0.8 / (0.8 + 0.2 * 0.01) = 0.997506
expected_absorbing_probs = jnp.array([
[0, 0, 0.3, 0.7],
[0, 0, 0, 0.81],
[1, 0, 0, 0],
[0, 0, 0, 0.997506],
])
absorbing_probs = automaton_builder.all_nodes_absorbing_solve(
builder,
tmat,
variant_weights,
jnp.pad(jnp.ones([4, 1]), [(0, 0), (0, 1)]),
steps=1000,
backtrack_fails_prob=0.01)
jax.test_util.check_close(absorbing_probs, expected_absorbing_probs)
