README.md

Finite State Machine

This example illustrates the construction of a Finite State Machine. Although simplistic, it demonstrates how Finite State Machines can be thought of as Network Automata. A Finite State Machine can be seen as a Network Automaton with a single node.

The "Turnstile" FSM in this case contains two states: Locked (0) and Unlocked (1). It can undergo two kinds of transitions: Push ("p") and Coin ("c"). The machine starts off in the Locked state. If a push is given in the Locked state, it remains Locked. If a coin is given in the Locked state, it transitions to Unlocked. If a push is given in the Unlocked state, it transitions to Locked. If a coin is given in the Unlocked state, it remains Unlocked.

In the example below, the machine is subjected to a sequence of five events: Coin, Push, Coin, Push, Push.

import netomaton as ntm

states = {'locked': 0, 'unlocked': 1}
transitions = {'PUSH': 'p', 'COIN': 'c'}

# a FSM can be thought of as a Network Automaton with a single node
network = ntm.topology.from_adjacency_matrix([[1]])

# the FSM starts off in the Locked state
initial_conditions = [states['locked']]

events = "cpcpp"

def fsm_rule(ctx):
    if ctx.input == transitions['PUSH']:
        return states['locked']
    else:
        # COIN event
        return states['unlocked']

trajectory = ntm.evolve(initial_conditions=initial_conditions, network=network,
                        input=events, activity_rule=fsm_rule)

activities = ntm.get_activities_over_time_as_list(trajectory)
print("final state: %s" % activities[-1][0])

ntm.plot_activities(trajectory)

The full source code for this example can be found here.