automata-lib 3.0.0

A Python library for simulating automata and Turing machines

Copyright 2018 Caleb Evans

Released under the MIT license

Automata is a Python 3 library which implements the structures and algorithms for finite automata, pushdown automata, and Turing machines.

Automata requires Python 3.4 or newer.

Migration to v2

If you are using Automata v1, please note that there are some significant changes in v2 to refine the API. If you wish to upgrade, please follow the migration guide.

Installing

You can install the latest version of Automata via pip:

pip install automata-lib

API

• class Automaton

  • class FA

    • class DFA

    • class NFA

  • class PDA

    • class DPDA

    • class NPDA

  • class TM

    • class DTM

    • class NTM

• Exception classes

  • Turing machine exceptions

class Automaton(metaclass=ABCMeta)

The Automaton class is an abstract base class from which all automata (including Turing machines) inherit. As such, it cannot be instantiated on its own; you must use a defined subclasses instead (or you may create your own subclass if you’re feeling adventurous). The Automaton class can be found under automata/base/automaton.py.

If you wish to subclass Automaton, you can import it like so:

from automata.base.automaton import Automaton

The following methods are common to all Automaton subtypes:

Automaton.read_input(self, input_str)

Reads an input string into the automaton, returning the automaton’s final configuration (according to its subtype). If the input is rejected, the method raises a RejectionException.

Automaton.read_input_stepwise(self, input_str)

Reads an input string like read_input(), except instead of returning the final configuration, the method returns a generator. The values yielded by this generator depend on the automaton’s subtype.

If the string is rejected by the automaton, the method still raises a RejectionException.

Automaton.accepts_input(self, input_str)

Reads an input string like read_input(), except it returns a boolean instead of returning the automaton’s final configuration (or raising an exception). That is, the method always returns True if the input is accepted, and it always returns False if the input is rejected.

Automaton.validate(self)

Checks whether the automaton is actually a valid automaton (according to its subtype). It returns True if the automaton is valid; otherwise, it will raise the appropriate exception (e.g. the state transition is missing for a particular symbol).

This method is automatically called when the automaton is initialized, so it’s only really useful if a automaton object is modified after instantiation.

Automaton.copy(self)

Returns a deep copy of the automaton according to its subtype.

class FA(Automaton, metaclass=ABCMeta)

The FA class is an abstract base class from which all finite automata inherit. The FA class can be found under automata/fa/fa.py.

If you wish to subclass FA, you can import it like so:

from automata.fa.fa import FA

class DFA(FA)

The DFA class is a subclass of FA and represents a deterministic finite automaton. It can be found under automata/fa/dfa.py.

Every DFA has the following (required) properties:

1. states: a set of the DFA’s valid states, each of which must be represented as a string

2. input_symbols: a set of the DFA’s valid input symbols, each of which must also be represented as a string

3. transitions: a dict consisting of the transitions for each state. Each key is a state name and each value is a dict which maps a symbol (the key) to a state (the value).

4. initial_state: the name of the initial state for this DFA

5. final_states: a set of final states for this DFA

from automata.fa.dfa import DFA
# DFA which matches all binary strings ending in an odd number of '1's
dfa = DFA(
    states={'q0', 'q1', 'q2'},
    input_symbols={'0', '1'},
    transitions={
        'q0': {'0': 'q0', '1': 'q1'},
        'q1': {'0': 'q0', '1': 'q2'},
        'q2': {'0': 'q2', '1': 'q1'}
    },
    initial_state='q0',
    final_states={'q1'}
)

DFA.read_input(self, input_str)

Returns the final state the DFA stopped on, if the input is accepted.

dfa.read_input('01')  # returns 'q1'

dfa.read_input('011')  # raises RejectionException

DFA.read_input_stepwise(self, input_str)

Yields each state reached as the DFA reads characters from the input string, if the input is accepted.

dfa.read_input_stepwise('0111')
# yields:
# 'q0'
# 'q0'
# 'q1'
# 'q2'
# 'q1'

DFA.accepts_input(self, input_str)

if dfa.accepts_input(my_input_str):
    print('accepted')
else:
    print('rejected')

DFA.validate(self)

dfa.validate()  # returns True

DFA.copy(self)

dfa.copy()  # returns deep copy of dfa

DFA.minify(self)

Creates a minimal DFA which accepts the same inputs as the old one. Unreachable states are removed and equivalent states are merged.

minimal_dfa = dfa.minify()

DFA.from_nfa(cls, nfa)

Creates a DFA that is equivalent to the given NFA.

from automata.fa.dfa import DFA
from automata.fa.nfa import NFA
dfa = DFA.from_nfa(nfa)  # returns an equivalent DFA

class NFA(FA)

The NFA class is a subclass of FA and represents a nondeterministic finite automaton. It can be found under automata/fa/nfa.py.

Every NFA has the same five DFA properties: state, input_symbols, transitions, initial_state, and final_states. However, the structure of the transitions object has been modified slightly to accommodate the fact that a single state can have more than one transition for the same symbol. Therefore, instead of mapping a symbol to one end state in each sub-dict, each symbol is mapped to a set of end states.

from automata.fa.nfa import NFA
# NFA which matches strings beginning with 'a', ending with 'a', and containing
# no consecutive 'b's
nfa = NFA(
    states={'q0', 'q1', 'q2'},
    input_symbols={'a', 'b'},
    transitions={
        'q0': {'a': {'q1'}},
        # Use '' as the key name for empty string (lambda/epsilon) transitions
        'q1': {'a': {'q1'}, '': {'q2'}},
        'q2': {'b': {'q0'}}
    },
    initial_state='q0',
    final_states={'q1'}
)

NFA.read_input(self, input_str)

Returns a set of final states the FA stopped on, if the input is accepted.

nfa.read_input('aba')  # returns {'q1', 'q2'}

nfa.read_input('abba')  # raises RejectionException

NFA.read_input_stepwise(self, input_str)

Yields each set of states reached as the NFA reads characters from the input string, if the input is accepted.

nfa.read_input_stepwise('aba')
# yields:
# {'q0'}
# {'q1', 'q2'}
# {'q0'}
# {'q1', 'q2'}

NFA.accepts_input(self, input_str)

if nfa.accepts_input(my_input_str):
    print('accepted')
else:
    print('rejected')

NFA.validate(self)

nfa.validate()  # returns True

NFA.copy(self)

nfa.copy()  # returns deep copy of nfa

NFA.from_dfa(cls, dfa)

Creates an NFA that is equivalent to the given DFA.

from automata.fa.nfa import NFA
from automata.fa.dfa import DFA
nfa = NFA.from_dfa(dfa)  # returns an equivalent NFA

class PDA(Automaton, metaclass=ABCMeta)

The PDA class is an abstract base class from which all pushdown automata inherit. It can be found under automata/pda/pda.py.

class DPDA(PDA)

The DPDA class is a subclass of PDA and represents a deterministic finite automaton. It can be found under automata/pda/dpda.py.

Every DPDA has the following (required) properties:

1. states: a set of the DPDA’s valid states, each of which must be represented as a string

2. input_symbols: a set of the DPDA’s valid input symbols, each of which must also be represented as a string

3. stack_symbols: a set of the DPDA’s valid stack symbols

4. transitions: a dict consisting of the transitions for each state; see the example below for the exact syntax

5. initial_state: the name of the initial state for this DPDA

6. initial_stack_symbol: the name of the initial symbol on the stack for this DPDA

7. final_states: a set of final states for this DPDA

from automata.pda.dpda import DPDA
# DPDA which which matches zero or more 'a's, followed by the same
# number of 'b's (accepting by final state)
dpda = DPDA(
    states={'q0', 'q1', 'q2', 'q3'},
    input_symbols={'a', 'b'},
    stack_symbols={'0', '1'},
    transitions={
        'q0': {
            'a': {'0': ('q1', ('1', '0'))}  # transition pushes '1' to stack
        },
        'q1': {
            'a': {'1': ('q1', ('1', '1'))},
            'b': {'1': ('q2', '')}  # transition pops from stack
        },
        'q2': {
            'b': {'1': ('q2', '')},
            '': {'0': ('q3', ('0',))}  # transition does not change stack
        }
    },
    initial_state='q0',
    initial_stack_symbol='0',
    final_states={'q3'}
)

DPDA.read_input(self, input_str)

Returns a PDAConfiguration object representing the DPDA’s config. This is basically a tuple containing the final state the DPDA stopped on, the remaining input (an empty string) as well as a PDAStack object representing the DPDA’s stack (if the input is accepted).

dpda.read_input('ab')  # returns PDAConfiguration('q3', '', PDAStack(('0')))

dpda.read_input('aab')  # raises RejectionException

DPDA.read_input_stepwise(self, input_str)

Yields PDAConfiguration objects. These are basically tuples containing the current state, the remaining input and the current stack as a PDAStack object, if the input is accepted.

dpda.read_input_stepwise('ab')
# yields:
# PDAConfiguration('q0', 'ab', PDAStack(('0')))
# PDAConfiguration('q1', 'a', PDAStack(('0', '1')))
# PDAConfiguration('q3', '', PDAStack(('0')))

DPDA.accepts_input(self, input_str)

if dpda.accepts_input(my_input_str):
    print('accepted')
else:
    print('rejected')

DPDA.validate(self)

dpda.validate()  # returns True

DPDA.copy(self)

dpda.copy()  # returns deep copy of dpda

class NPDA(PDA)

The NPDA class is a subclass of PDA and represents a nondeterministic pushdown automaton. It can be found under automata/pda/npda.py.

Every NPDA has the following (required) properties:

1. states: a set of the NPDA’s valid states, each of which must be represented as a string

2. input_symbols: a set of the NPDA’s valid input symbols, each of which must also be represented as a string

3. stack_symbols: a set of the NPDA’s valid stack symbols

4. transitions: a dict consisting of the transitions for each state; see the example below for the exact syntax

5. initial_state: the name of the initial state for this NPDA

6. initial_stack_symbol: the name of the initial symbol on the stack for this NPDA

7. final_states: a set of final states for this NPDA

from automata.pda.npda import NPDA
# NPDA which matches palindromes consisting of 'a's and 'b's
# (accepting by final state)
# q0 reads the first half of the word, q1 the other half, q2 accepts.
# But we have to guess when to switch.
npda = NPDA(
    states={'q0', 'q1', 'q2'},
    input_symbols={'a', 'b'},
    stack_symbols={'A', 'B', '#'},
    transitions={
        'q0': {
            '': {
                '#': {('q2', '#')},
            },
            'a': {
                '#': {('q0', ('A', '#'))},
                'A': {
                    ('q0', ('A', 'A')),
                    ('q1', ''),
                },
                'B': {('q0', ('A', 'B'))},
            },
            'b': {
                '#': {('q0', ('B', '#'))},
                'A': {('q0', ('B', 'A'))},
                'B': {
                    ('q0', ('B', 'B')),
                    ('q1', ''),
                },
            },
        },
        'q1': {
            '': {'#': {('q2', '#')}},
            'a': {'A': {('q1', '')}},
            'b': {'B': {('q1', '')}},
        },
    },
    initial_state='q0',
    initial_stack_symbol='#',
    final_states={'q2'}
)

NPDA.read_input(self, input_str)

Returns a set of PDAConfigurations representing all of the NPDA’s configurations. Each of these is basically a tuple containing the final state the NPDA stopped on, the remaining input (an empty string) as well as a PDAStack object representing the NPDA’s stack (if the input is accepted).

npda.read_input("aaaa") # returns {PDAConfiguration('q2', '', PDAStack('#',))}

npda.read_input('ab')  # raises RejectionException

NPDA.read_input_stepwise(self, input_str)

Yields sets of PDAConfiguration object. Each of these is basically a tuple containing the current state, the remaining input and the current stack as a PDAStack object, if the input is accepted.

npda.read_input_stepwise('aa')
# yields:
# {PDAConfiguration('q0', 'aa', PDAStack('#',))}
# {PDAConfiguration('q0', 'a', PDAStack('#', 'A')), PDAConfiguration('q2', 'aa', PDAStack('#',))}
# {PDAConfiguration('q0', '', PDAStack('#', 'A', 'A')), PDAConfiguration('q1', '', PDAStack('#',))}
# {PDAConfiguration('q2', '', PDAStack('#',))}

NPDA.accepts_input(self, input_str)

if npda.accepts_input(my_input_str):
    print('accepted')
else:
    print('rejected')

NPDA.validate(self)

npda.validate()  # returns True

NPDA.copy(self)

npda.copy()  # returns deep copy of npda

class TM(Automaton, metaclass=ABCMeta)

The TM class is an abstract base class from which all Turing machines inherit. It can be found under automata/tm/tm.py.

class DTM(TM)

The DTM class is a subclass of TM and represents a deterministic Turing machine. It can be found under automata/tm/dtm.py.

Every DTM has the following (required) properties:

1. states: a set of the DTM’s valid states, each of which must be represented as a string

2. input_symbols: a set of the DTM’s valid input symbols represented as strings

3. tape_symbols: a set of the DTM’s valid tape symbols represented as strings

4. transitions: a dict consisting of the transitions for each state; each key is a state name and each value is a dict which maps a symbol (the key) to a state (the value)

5. initial_state: the name of the initial state for this DTM

6. blank_symbol: a symbol from tape_symbols to be used as the blank symbol for this DTM

7. final_states: a set of final states for this DTM

from automata.tm.dtm import DTM
# DTM which matches all strings beginning with '0's, and followed by
# the same number of '1's
dtm = DTM(
    states={'q0', 'q1', 'q2', 'q3', 'q4'},
    input_symbols={'0', '1'},
    tape_symbols={'0', '1', 'x', 'y', '.'},
    transitions={
        'q0': {
            '0': ('q1', 'x', 'R'),
            'y': ('q3', 'y', 'R')
        },
        'q1': {
            '0': ('q1', '0', 'R'),
            '1': ('q2', 'y', 'L'),
            'y': ('q1', 'y', 'R')
        },
        'q2': {
            '0': ('q2', '0', 'L'),
            'x': ('q0', 'x', 'R'),
            'y': ('q2', 'y', 'L')
        },
        'q3': {
            'y': ('q3', 'y', 'R'),
            '.': ('q4', '.', 'R')
        }
    },
    initial_state='q0',
    blank_symbol='.',
    final_states={'q4'}
)

The direction N (for no movement) is also supported.

DTM.read_input(self, input_str)

Returns a TMConfiguration. This is basically a tuple containing the final state the machine stopped on, as well as a TMTape object representing the DTM’s internal tape (if the input is accepted).

dtm.read_input('01')  # returns TMConfiguration('q4', TMTape('xy..', 3))

Calling config.print() will produce a more readable output:

dtm.read_input('01').print()
# q4: xy..
#        ^

dtm.read_input('011')  # raises RejectionException

DTM.read_input_stepwise(self, input_str)

Yields TMConfigurations. Those are basically tuples containing the current state and the current tape as a TMTape object.

dtm.read_input_stepwise('01')
# yields:
# TMConfiguration('q0', TMTape('01', 0))
# TMConfiguration('q1', TMTape('x1', 1))
# TMConfiguration('q2', TMTape('xy', 0))
# TMConfiguration('q0', TMTape('xy', 1))
# TMConfiguration('q3', TMTape('xy.', 2))
# TMConfiguration('q4', TMTape('xy..', 3))

DTM.accepts_input(self, input_str)

if dtm.accepts_input(my_input_str):
    print('accepted')
else:
    print('rejected')

DTM.validate(self)

dtm.validate()  # returns True

DTM.copy(self)

dtm.copy()  # returns deep copy of dtm

class NTM(TM)

The NTM class is a subclass of TM and represents a nondeterministic Turing machine. It can be found under automata/tm/ntm.py.

Every NTM has the following (required) properties:

1. states: a set of the NTM’s valid states, each of which must be represented as a string

2. input_symbols: a set of the NTM’s valid input symbols represented as strings

3. tape_symbols: a set of the NTM’s valid tape symbols represented as strings

4. transitions: a dict consisting of the transitions for each state; each key is a state name and each value is a dict which maps a symbol (the key) to a set of states (the values)

5. initial_state: the name of the initial state for this NTM

6. blank_symbol: a symbol from tape_symbols to be used as the blank symbol for this NTM

7. final_states: a set of final states for this NTM

from automata.tm.ntm import NTM
# NTM which matches all strings beginning with '0's, and followed by
# the same number of '1's
# Note that the nondeterminism is not really used here.
ntm = NTM(
    states={'q0', 'q1', 'q2', 'q3', 'q4'},
    input_symbols={'0', '1'},
    tape_symbols={'0', '1', 'x', 'y', '.'},
    transitions={
        'q0': {
            '0': {('q1', 'x', 'R')},
            'y': {('q3', 'y', 'R')},
        },
        'q1': {
            '0': {('q1', '0', 'R')},
            '1': {('q2', 'y', 'L')},
            'y': {('q1', 'y', 'R')},
        },
        'q2': {
            '0': {('q2', '0', 'L')},
            'x': {('q0', 'x', 'R')},
            'y': {('q2', 'y', 'L')},
        },
        'q3': {
            'y': {('q3', 'y', 'R')},
            '.': {('q4', '.', 'R')},
        }
    },
    initial_state='q0',
    blank_symbol='.',
    final_states={'q4'}
)

The direction N (for no movement) is also supported.

NTM.read_input(self, input_str)

Returns a set of TMConfigurations. These are basically tuples containing the final state the machine stopped on, as well as a TMTape object representing the DTM’s internal tape (if the input is accepted).

ntm.read_input('01')  # returns {TMConfiguration('q4', TMTape('xy..', 3))}

Calling config.print() will produce a more readable output:

ntm.read_input('01').pop().print()
# q4: xy..
#        ^

ntm.read_input('011')  # raises RejectionException

NTM.read_input_stepwise(self, input_str)

Yields sets of TMConfigurations. Those are basically tuples containing the current state and the current tape as a TMTape object.

ntm.read_input_stepwise('01')
# yields:
# {TMConfiguration('q0', TMTape('01', 0))}
# {TMConfiguration('q1', TMTape('x1', 1))}
# {TMConfiguration('q2', TMTape('xy', 0))}
# {TMConfiguration('q0', TMTape('xy', 1))}
# {TMConfiguration('q3', TMTape('xy.', 2))}
# {TMConfiguration('q4', TMTape('xy..', 3))}

NTM.accepts_input(self, input_str)

if ntm.accepts_input(my_input_str):
    print('accepted')
else:
    print('rejected')

NTM.validate(self)

ntm.validate()  # returns True

NTM.copy(self)

ntm.copy()  # returns deep copy of ntm

Base exception classes

The library also includes a number of exception classes to ensure that errors never pass silently (unless explicitly silenced). See automata/base/exceptions.py for these class definitions.

To reference these exceptions (so as to catch them in a try..except block or whatnot), simply import automata.base.exceptions however you’d like:

import automata.base.exceptions as exceptions

class AutomatonException

A base class from which all other automata exceptions inherit (including finite automata and Turing machines).

class InvalidStateError

Raised if a state is not a valid state for this automaton.

class InvalidSymbolError

Raised if a symbol is not a valid symbol for this automaton.

class MissingStateError

Raised if a state is missing from the automaton definition.

class MissingSymbolError

Raised if a symbol is missing from the automaton definition.

class InitialStateError

Raised if the initial state fails to meet some required condition for this type of automaton.

class FinalStateError

Raised if a final state fails to meet some required condition for this type of automaton.

class RejectionException

Raised if the automaton stopped on a non-final state after validating input.

Turing machine exception classes

The automata.tm package also includes a module for exceptions specific to Turing machines. You can reference these exception classes like so:

import automata.tm.exceptions as tm_exceptions

class TMException

A base class from which all other Turing machine exceptions inherit.

class InvalidDirectionError

Raised if a direction specified in this machine’s transition map is not a valid direction.