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Project Description
Copyright 2016 Caleb Evans
Released under the MIT license

Automata is a Python 3 library which implements the structures and algorithms I am learning in my Automata Theory class, particularly finite automata and Turing machines.

Automata requires Python 3.4 or newer.

Installing

You can install the latest version of Automata via pip:

pip install automata-lib

API

class Automaton

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/shared/automaton.py.

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

from automata.shared.automaton import Automaton

class FA

The FA class is an abstract base class from which all finite automata inherit. As such, it cannot be instantiated on its own; you must use the DFA and NFA classes instead (or you may create your own subclass if you’re feeling adventurous). 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

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

DFA properties

Every DFA instance has the following 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

All of these properties must be supplied when the DFA is instantiated (see the examples below).

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'}
)

Please note that the below DFA code examples reference the above dfa object.

DFA.validate_input(self, input_str, step=False)

The validate_input() method checks whether or not the given string is accepted by the DFA.

If the string is accepted, the method returns the state the DFA stopped on (which presumably is a valid final state).

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

If the string is rejected by the DFA, the method will raise a RejectionError.

dfa.validate_input('011')  # raises RejectionError

If you supply the step keyword argument with a value of True, the method will return a generator which yields each state reached as the DFA reads characters from the input string.

list(dfa.validate_input('0111', step=True))
# returns ['q0', 'q0', 'q1', 'q2', 'q1']

Note that the first yielded state is always the DFA’s initial state (before any input has been read) and the last yielded state is always the DFA’s final state (after all input has been read). If the string is rejected by the DFA, the method still raises a RejectionError.

DFA.validate_self(self)

The validate_self() method checks whether the DFA is actually a valid DFA. The method returns True if the DFA 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 DFA is initialized, so it’s only really useful if a DFA object is modified after instantiation.

Copying a DFA

To create a deep copy of a DFA, simply pass an DFA instance into the DFA constructor.

dfa_copy = DFA(dfa)  # returns a deep copy of dfa

class NFA

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

NFA properties

Every NFA contains 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.validate_input(self, input_str, step=False)

The validate_input() method checks whether or not the given string is accepted by the NFA.

If the string is accepted, the method returns a set of states the FA stopped on (which presumably contains at least one valid final state).

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

If the string is rejected by the NFA, the method will raise a RejectionError.

nfa.validate_input('abba')  # raises RejectionError

If you supply the step keyword argument with a value of True, the method will return a generator which yields each set of states reached as the NFA reads characters from the input string.

list(nfa.validate_input('aba', step=True))
# returns [{'q0'}, {'q1', 'q2'}, {'q0'}, {'q1', 'q2'}]

Note that the first yielded set is always the lambda closure of the NFA’s initial state, and the last yielded set always contains the lambda closure of at least one of the NFA’s final states (after all input has been read). If the string is rejected by the NFA, the method still raises a RejectionError.

NFA.validate_self(self)

The validate_self() method checks whether the NFA is actually a valid NFA. The method has the same basic behavior and prescribed use case as the DFA.validate_self() method, despite being less restrictive (since NFAs are naturally less restrictive than DFAs).

Converting an NFA to a DFA

To create a DFA that is equivalent to an existing NFA, simply pass the NFA instance to the DFA constructor.

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

Copying an NFA

To create a deep copy of an NFA, simply pass an NFA instance into the NFA constructor.

nfa_copy = NFA(nfa)  # returns a deep copy of nfa

class PDA

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

class DPDA

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

DPDA properties

Every DPDA instance has the following 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

All of these properties must be supplied when the DPDA is instantiated (see the examples below).

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'}
)

Please note that the below DPDA code examples reference the above dpda object.

DPDA.validate_input(self, input_str, step=False)

The validate_input() method checks whether or not the given string is accepted by the DPDA.

If the string is accepted, the method returns a tuple containing the state the DPDA stopped on (which presumably is a valid final state), as well as a PDAStack object representing the DPDA’s internal stack.

dpda.validate_input('ab')  # returns PDAStack(['0'])

If the string is rejected by the DPDA, the method will raise a RejectionError.

dpda.validate_input('aab')  # raises RejectionError

If you supply the step keyword argument with a value of True, the method will return a generator which yields a tuple containing the current state and the current tape as a PDAStack object.

[(state, stack.copy()) for state, stack in dpda.validate_input('ab', step=True)]
# returns [
#   ('q0', PDAStack(['0'])),
#   ('q1', PDAStack(['0', '1'])),
#   ('q3', PDAStack(['0'])),
# ]

Note that the first yielded state is always the DPDA’s initial state (before any input has been read) and the last yielded state is always the DPDA’s final state (after all input has been read) (or possibly a non-final state if the stack is empty). If the string is rejected by the DPDA, the method still raises a RejectionError.

DPDA.validate_self(self)

The validate_self() method checks whether the DPDA is actually a valid DPDA. The method has the same basic behavior and prescribed use case as the DFA.validate_self() and NFA.validate_self() methods, while (naturally) containing validation checks specific to DPDAs.

Copying a DPDA

To create a deep copy of a DPDA, simply pass an DPDA instance into the DPDA constructor.

dpda_copy = DPDA(dpda)  # returns a deep copy of dpda

class TM

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

class DTM

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

DTM properties

Every DTM instance has the following 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

All of these properties must be supplied when the DTM is instantiated (see the examples below).

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'}
)

Please note that the below DTM code examples reference the above dtm object.

DTM.validate_input(self, input_str, step=False)

The validate_input() method checks whether or not the given string is accepted by the DTM.

If the string is accepted, the method returns a tuple containing the state the machine stopped on (which presumably is a valid final state), as well as a TMTape object representing the DTM’s internal tape.

dtm.validate_input('01')  # returns ('q4', TMTape('xy.'))

If the string is rejected by the DTM, the method will raise a RejectionError.

dtm.validate_input('011')  # raises RejectionError

If you supply the step keyword argument with a value of True, the method will return a generator which yields a tuple containing the current state and the current tape as a TMTape object.

[(state, tape.copy()) for state, tape in dtm.validate_input('01', step=True)]
# returns [
#   ('q0', TMTape('01'))
#   ('q1', TMTape('x1'))
#   ('q2', TMTape('xy'))
#   ('q0', TMTape('xy'))
#   ('q3', TMTape('xy'))
#   ('q3', TMTape('xy.'))
# ]

Please note that each tuple contains a reference to (not a copy of) the current TMTape object. Therefore, if you wish to store the tape at every step, you must copy the tape as you iterate over the machine configurations (as shown above).

Also note that the first yielded state is always the DTM’s initial state (before any input has been read) and the last yielded state is always the DTM’s final state (after all input has been read). If the string is rejected by the DTM, the method still raises a RejectionError.

DTM.validate_self(self)

The validate_self() method checks whether the DTM is actually a valid DTM. The method has the same basic behavior and prescribed use case as the DFA.validate_self() and NFA.validate_self() methods, while (naturally) containing validation checks specific to DTMs.

Copying a DTM

To create a deep copy of a DTM, simply pass a DTM instance into the DTM constructor.

dtm_copy = DTM(dtm)  # returns a deep copy of dtm

Shared exception classes

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

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

import automata.shared.exceptions as exceptions

class AutomatonError

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 FA.

class InvalidSymbolError

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

class MissingStateError

Raised if a state is missing from the machine definition.

class MissingSymbolError

Raised if a symbol is missing from the machine definition.

class InitialStateError

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

class FinalStateError

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

class RejectionError

Raised if the FA 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 tmexceptions

class TMError

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.

Release History

Release History

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