# Common Automata and Transducers (Finite State Machines Generators)¶

Automata and Transducers in Sage can be built through the automata and transducers objects, respectively. It contains generators for common finite state machines. For example,

sage: I = transducers.Identity([0, 1, 2])

generates an identity transducer on the alphabet $$\{0, 1, 2\}$$.

To construct automata and transducers manually, you can use the classes Automaton and Transducer, respectively. See Finite state machines, automata, transducers for more details and a lot of examples.

Automata

 AnyLetter() Return an automaton recognizing any letter. AnyWord() Return an automaton recognizing any word. EmptyWord() Return an automaton recognizing the empty word. Word() Return an automaton recognizing the given word. ContainsWord() Return an automaton recognizing words containing the given word.

Transducers

 Identity() Returns a transducer realizing the identity map. abs() Returns a transducer realizing absolute value. map() Returns a transducer realizing a function. operator() Returns a transducer realizing a binary operation. all() Returns a transducer realizing logical and. any() Returns a transducer realizing logical or. add() Returns a transducer realizing addition. sub() Returns a transducer realizing subtraction. CountSubblockOccurrences() Returns a transducer counting the occurrences of a subblock. Wait() Returns a transducer writing False until first (or k-th) true input is read. weight() Returns a transducer realizing the Hamming weight. GrayCode() Returns a transducer realizing binary Gray code. Recursion() Returns a transducer defined by recursions.

AUTHORS:

• Clemens Heuberger (2014-04-07): initial version

• Sara Kropf (2014-04-10): some changes in TransducerGenerator

• Daniel Krenn (2014-04-15): improved common docstring during review

• Clemens Heuberger, Daniel Krenn, Sara Kropf (2014-04-16–2014-05-02): A couple of improvements. Details see trac ticket #16141, trac ticket #16142, trac ticket #16143, trac ticket #16186.

• Sara Kropf (2014-04-29): weight transducer

• Clemens Heuberger, Daniel Krenn (2014-07-18): transducers Wait, all, any

• Clemens Heuberger (2014-08-10): transducer Recursion

• Clemens Heuberger (2015-07-31): automaton word

• Daniel Krenn (2015-09-14): cleanup trac ticket #18227

ACKNOWLEDGEMENT:

• Clemens Heuberger, Daniel Krenn and Sara Kropf are supported by the Austrian Science Fund (FWF): P 24644-N26.

## Functions and methods¶

class sage.combinat.finite_state_machine_generators.AutomatonGenerators

Bases: object

A collection of constructors for several common automata.

A list of all automata in this database is available via tab completion. Type “automata.” and then hit tab to see which automata are available.

The automata currently in this class include:

AnyLetter(input_alphabet)

Return an automaton recognizing any letter of the given input alphabet.

INPUT:

• input_alphabet – a list, the input alphabet

OUTPUT:

An Automaton.

EXAMPLES:

sage: A = automata.AnyLetter([0, 1])
sage: A([])
False
sage: A([0])
True
sage: A([1])
True
sage: A([0, 0])
False

See also

AnyWord()

AnyWord(input_alphabet)

Return an automaton recognizing any word of the given input alphabet.

INPUT:

• input_alphabet – a list, the input alphabet

OUTPUT:

An Automaton.

EXAMPLES:

sage: A = automata.AnyWord([0, 1])
sage: A([0])
True
sage: A([1])
True
sage: A([0, 1])
True
sage: A([0, 2])
False

This is equivalent to taking the kleene_star() of AnyLetter() and minimizing the result. This method immediately gives a minimized version:

sage: B = automata.AnyLetter([0, 1]).kleene_star().minimization().relabeled()
sage: B == A
True

See also

ContainsWord(word, input_alphabet)

Return an automaton recognizing the words containing the given word as a factor.

INPUT:

• word – a list (or other iterable) of letters, the word we are looking for.

• input_alphabet – a list or other iterable, the input alphabet.

OUTPUT:

An Automaton.

EXAMPLES:

sage: A = automata.ContainsWord([0, 1, 0, 1, 1],
....:                           input_alphabet=[0, 1])
sage: A([1, 0, 1, 0, 1, 0, 1, 1, 0, 0])
True
sage: A([1, 0, 1, 0, 1, 0, 1, 0])
False

This is equivalent to taking the concatenation of AnyWord(), Word() and AnyWord() and minimizing the result. This method immediately gives a minimized version:

sage: B = (automata.AnyWord([0, 1]) *
....:     automata.Word([0, 1, 0, 1, 1], [0, 1]) *
....:     automata.AnyWord([0, 1])).minimization()
sage: B.is_equivalent(A)
True

See also

EmptyWord(input_alphabet=None)

Return an automaton recognizing the empty word.

INPUT:

• input_alphabet – (default: None) an iterable or None.

OUTPUT:

An Automaton.

EXAMPLES:

sage: A = automata.EmptyWord()
sage: A([])
True
sage: A([0])
False

See also

Word(word, input_alphabet=None)

Return an automaton recognizing the given word.

INPUT:

• word – an iterable.

• input_alphabet – a list or None. If None, then the letters occurring in the word are used.

OUTPUT:

An Automaton.

EXAMPLES:

sage: A = automata.Word([0])
sage: A.transitions()
[Transition from 0 to 1: 0|-]
sage: [A(w) for w in ([], [0], [1])]
[False, True, False]
sage: A = automata.Word([0, 1, 0])
sage: A.transitions()
[Transition from 0 to 1: 0|-,
Transition from 1 to 2: 1|-,
Transition from 2 to 3: 0|-]
sage: [A(w) for w in ([], [0], [0, 1], [0, 1, 1], [0, 1, 0])]
[False, False, False, False, True]

If the input alphabet is not given, it is derived from the given word.

sage: A.input_alphabet
[0, 1]
sage: A = automata.Word([0, 1, 0], input_alphabet=[0, 1, 2])
sage: A.input_alphabet
[0, 1, 2]

See also

class sage.combinat.finite_state_machine_generators.TransducerGenerators

Bases: object

A collection of constructors for several common transducers.

A list of all transducers in this database is available via tab completion. Type “transducers.” and then hit tab to see which transducers are available.

The transducers currently in this class include:

CountSubblockOccurrences(block, input_alphabet)

Returns a transducer counting the number of (possibly overlapping) occurrences of a block in the input.

INPUT:

• block – a list (or other iterable) of letters.

• input_alphabet – a list or other iterable.

OUTPUT:

A transducer counting (in unary) the number of occurrences of the given block in the input. Overlapping occurrences are counted several times.

Denoting the block by $$b_0\ldots b_{k-1}$$, the input word by $$i_0\ldots i_L$$ and the output word by $$o_0\ldots o_L$$, we have $$o_j = 1$$ if and only if $$i_{j-k+1}\ldots i_{j} = b_0\ldots b_{k-1}$$. Otherwise, $$o_j = 0$$.

EXAMPLES:

1. Counting the number of 10 blocks over the alphabet [0, 1]:

sage: T = transducers.CountSubblockOccurrences(
....:     [1, 0],
....:     [0, 1])
sage: sorted(T.transitions())
[Transition from () to (): 0|0,
Transition from () to (1,): 1|0,
Transition from (1,) to (): 0|1,
Transition from (1,) to (1,): 1|0]
sage: T.input_alphabet
[0, 1]
sage: T.output_alphabet
[0, 1]
sage: T.initial_states()
[()]
sage: T.final_states()
[(), (1,)]

Check some sequence:

sage: T([0, 1, 0, 1, 1, 0])
[0, 0, 1, 0, 0, 1]

2. Counting the number of 11 blocks over the alphabet [0, 1]:

sage: T = transducers.CountSubblockOccurrences(
....:     [1, 1],
....:     [0, 1])
sage: sorted(T.transitions())
[Transition from () to (): 0|0,
Transition from () to (1,): 1|0,
Transition from (1,) to (): 0|0,
Transition from (1,) to (1,): 1|1]

Check some sequence:

sage: T([0, 1, 0, 1, 1, 0])
[0, 0, 0, 0, 1, 0]

3. Counting the number of 1010 blocks over the alphabet [0, 1, 2]:

sage: T = transducers.CountSubblockOccurrences(
....:     [1, 0, 1, 0],
....:     [0, 1, 2])
sage: sorted(T.transitions())
[Transition from () to (): 0|0,
Transition from () to (1,): 1|0,
Transition from () to (): 2|0,
Transition from (1,) to (1, 0): 0|0,
Transition from (1,) to (1,): 1|0,
Transition from (1,) to (): 2|0,
Transition from (1, 0) to (): 0|0,
Transition from (1, 0) to (1, 0, 1): 1|0,
Transition from (1, 0) to (): 2|0,
Transition from (1, 0, 1) to (1, 0): 0|1,
Transition from (1, 0, 1) to (1,): 1|0,
Transition from (1, 0, 1) to (): 2|0]
sage: input =  [0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 2]
sage: output = [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0]
sage: T(input) == output
True

See also

ContainsWord()

GrayCode()

Returns a transducer converting the standard binary expansion to Gray code.

INPUT:

Nothing.

OUTPUT:

A transducer.

Cf. the Wikipedia article Gray_code for a description of the Gray code.

EXAMPLES:

sage: G = transducers.GrayCode()
sage: G
Transducer with 3 states
sage: for v in srange(10):
....:     print("{} {}".format(v, G(v.digits(base=2))))
0 []
1 [1]
2 [1, 1]
3 [0, 1]
4 [0, 1, 1]
5 [1, 1, 1]
6 [1, 0, 1]
7 [0, 0, 1]
8 [0, 0, 1, 1]
9 [1, 0, 1, 1]

In the example Gray Code in the documentation of the Finite state machines, automata, transducers module, the Gray code transducer is derived from the algorithm converting the binary expansion to the Gray code. The result is the same as the one given here.

Identity(input_alphabet)

Returns the identity transducer realizing the identity map.

INPUT:

• input_alphabet – a list or other iterable.

OUTPUT:

A transducer mapping each word over input_alphabet to itself.

EXAMPLES:

sage: T = transducers.Identity([0, 1])
sage: sorted(T.transitions())
[Transition from 0 to 0: 0|0,
Transition from 0 to 0: 1|1]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T.input_alphabet
[0, 1]
sage: T.output_alphabet
[0, 1]
sage: T([0, 1, 0, 1, 1])
[0, 1, 0, 1, 1]
Recursion(recursions, base, function=None, var=None, input_alphabet=None, word_function=None, is_zero=None, output_rings=[Integer Ring, Rational Field])

Return a transducer realizing the given recursion when reading the digit expansion with base base.

INPUT:

• recursions – list or iterable of equations. Each equation has either the form

• f(base^K * n + r) == f(base^k * n + s) + t for some integers 0 <= k < K, r and some t—valid for all n such that the arguments on both sides are non-negative—

or the form

• f(r) == t for some integer r and some t.

Alternatively, an equation may be replaced by a transducers.RecursionRule with the attributes K, r, k, s, t as above or a tuple (r, t). Note that t must be a list in this case.

• base – base of the digit expansion.

• function – symbolic function f occurring in the recursions.

• var – symbolic variable.

• input_alphabet – (default: None) a list of digits to be used as the input alphabet. If None and the base is an integer, input_alphabet is chosen to be srange(base.abs()).

• word_function – (default: None) a symbolic function. If not None, word_function(arg1, ..., argn) in a symbolic recurrence relation is interpreted as a transition with output [arg1, ..., argn]. This could not be entered in a symbolic recurrence relation because lists do not coerce into the SymbolicRing.

• is_zero – (default: None) a callable. The recursion relations are only well-posed if there is no cycle with non-zero output and input consisting of zeros. This parameter is used to determine whether the output of such a cycle is non-zero. By default, the output must evaluate to False as a boolean.

• output_rings – (default: [ZZ, QQ]) a list of rings. The output labels are converted into the first ring of the list in which they are contained. If they are not contained in any ring, they remain in whatever ring they are after parsing the recursions, typically the symbolic ring.

OUTPUT:

A transducer T.

The transducer is constructed such that T(expansion) == f(n) if expansion is the digit expansion of n to the base base with the given input alphabet as set of digits. Here, the + on the right hand side of the recurrence relation is interpreted as the concatenation of words.

The formal equations and initial conditions in the recursion have to be selected such that f is uniquely defined.

EXAMPLES:

• The following example computes the Hamming weight of the ternary expansion of integers.

sage: function('f')
f
sage: var('n')
n
sage: T = transducers.Recursion([
....:     f(3*n + 1) == f(n) + 1,
....:     f(3*n + 2) == f(n) + 1,
....:     f(3*n) == f(n),
....:     f(0) == 0],
....:     3, f, n)
sage: T.transitions()
[Transition from (0, 0) to (0, 0): 0|-,
Transition from (0, 0) to (0, 0): 1|1,
Transition from (0, 0) to (0, 0): 2|1]

To illustrate what this transducer does, we consider the example of $$n=601$$:

sage: ternary_expansion = 601.digits(base=3)
sage: ternary_expansion
[1, 2, 0, 1, 1, 2]
sage: weight_sequence = T(ternary_expansion)
sage: weight_sequence
[1, 1, 1, 1, 1]
sage: sum(weight_sequence)
5

Note that the digit zero does not show up in the output because the equation f(3*n) == f(n) means that no output is added to f(n).

• The following example computes the Hamming weight of the non-adjacent form, cf. the Wikipedia article Non-adjacent_form.

sage: function('f')
f
sage: var('n')
n
sage: T = transducers.Recursion([
....:     f(4*n + 1) == f(n) + 1,
....:     f(4*n - 1) == f(n) + 1,
....:     f(2*n) == f(n),
....:     f(0) == 0],
....:     2, f, n)
sage: T.transitions()
[Transition from (0, 0) to (0, 0): 0|-,
Transition from (0, 0) to (1, 1): 1|-,
Transition from (1, 1) to (0, 0): 0|1,
Transition from (1, 1) to (1, 0): 1|1,
Transition from (1, 0) to (1, 1): 0|-,
Transition from (1, 0) to (1, 0): 1|-]
sage: [(s.label(), s.final_word_out)
....:  for s in T.iter_final_states()]
[((0, 0), []),
((1, 1), [1]),
((1, 0), [1])]

As we are interested in the weight only, we also output $$1$$ for numbers congruent to $$3$$ mod $$4$$. The actual expansion is computed in the next example.

Consider the example of $$29=(100\bar 101)_2$$ (as usual, the digit $$-1$$ is denoted by $$\bar 1$$ and digits are written from the most significant digit at the left to the least significant digit at the right; for the transducer, we have to give the digits in the reverse order):

sage: NAF = [1, 0, -1, 0, 0, 1]
sage: ZZ(NAF, base=2)
29
sage: binary_expansion = 29.digits(base=2)
sage: binary_expansion
[1, 0, 1, 1, 1]
sage: T(binary_expansion)
[1, 1, 1]
sage: sum(T(binary_expansion))
3

Indeed, the given non-adjacent form has three non-zero digits.

• The following example computes the non-adjacent form from the binary expansion, cf. the Wikipedia article Non-adjacent_form. In contrast to the previous example, we actually compute the expansion, not only the weight.

We have to write the output $$0$$ when converting an even number. This cannot be encoded directly by an equation in the symbolic ring, because f(2*n) == f(n) + 0 would be equivalent to f(2*n) == f(n) and an empty output would be written. Therefore, we wrap the output in the symbolic function w and use the parameter word_function to announce this.

Similarly, we use w(-1, 0) to write an output word of length $$2$$ in one iteration. Finally, we write f(0) == w() to write an empty word upon completion.

Moreover, there is a cycle with output [0] which—from the point of view of this method—is a contradicting recursion. We override this by the parameter is_zero.

sage: var('n')
n
sage: function('f w')
(f, w)
sage: T = transducers.Recursion([
....:      f(2*n) == f(n) + w(0),
....:      f(4*n + 1) == f(n) + w(1, 0),
....:      f(4*n - 1) == f(n) + w(-1, 0),
....:      f(0) == w()],
....:      2, f, n,
....:      word_function=w,
....:      is_zero=lambda x: sum(x).is_zero())
sage: T.transitions()
[Transition from (0, 0) to (0, 0): 0|0,
Transition from (0, 0) to (1, 1): 1|-,
Transition from (1, 1) to (0, 0): 0|1,0,
Transition from (1, 1) to (1, 0): 1|-1,0,
Transition from (1, 0) to (1, 1): 0|-,
Transition from (1, 0) to (1, 0): 1|0]
sage: for s in T.iter_states():
....:     print("{} {}".format(s, s.final_word_out))
(0, 0) []
(1, 1) [1, 0]
(1, 0) [1, 0]

We again consider the example of $$n=29$$:

sage: T(29.digits(base=2))
[1, 0, -1, 0, 0, 1, 0]

The same transducer can also be entered bypassing the symbolic equations:

sage: R = transducers.RecursionRule
sage: TR = transducers.Recursion([
....:       R(K=1, r=0, k=0, s=0, t=[0]),
....:       R(K=2, r=1, k=0, s=0, t=[1, 0]),
....:       R(K=2, r=-1, k=0, s=0, t=[-1, 0]),
....:       (0, [])],
....:       2,
....:       is_zero=lambda x: sum(x).is_zero())
sage: TR == T
True

• Here is an artificial example where some of the $$s$$ are negative:

sage: function('f')
f
sage: var('n')
n
sage: T = transducers.Recursion([
....:     f(2*n + 1) == f(n-1) + 1,
....:     f(2*n) == f(n),
....:     f(1) == 1,
....:     f(0) == 0], 2, f, n)
sage: T.transitions()
[Transition from (0, 0) to (0, 0): 0|-,
Transition from (0, 0) to (1, 1): 1|-,
Transition from (1, 1) to (-1, 1): 0|1,
Transition from (1, 1) to (0, 0): 1|1,
Transition from (-1, 1) to (-1, 2): 0|-,
Transition from (-1, 1) to (1, 2): 1|-,
Transition from (-1, 2) to (-1, 1): 0|1,
Transition from (-1, 2) to (0, 0): 1|1,
Transition from (1, 2) to (-1, 2): 0|1,
Transition from (1, 2) to (1, 2): 1|1]
sage: [(s.label(), s.final_word_out)
....:  for s in T.iter_final_states()]
[((0, 0), []),
((1, 1), [1]),
((-1, 1), [0]),
((-1, 2), [0]),
((1, 2), [1])]

• Abelian complexity of the paperfolding sequence (cf. [HKP2015], Example 2.8):

sage: T = transducers.Recursion([
....:     f(4*n) == f(2*n),
....:     f(4*n+2) == f(2*n+1)+1,
....:     f(16*n+1) == f(8*n+1),
....:     f(16*n+5) == f(4*n+1)+2,
....:     f(16*n+11) == f(4*n+3)+2,
....:     f(16*n+15) == f(2*n+2)+1,
....:     f(1) == 2, f(0) == 0]
....:     + [f(16*n+jj) == f(2*n+1)+2 for jj in [3,7,9,13]],
....:     2, f, n)
sage: T.transitions()
[Transition from (0, 0) to (0, 1): 0|-,
Transition from (0, 0) to (1, 1): 1|-,
Transition from (0, 1) to (0, 1): 0|-,
Transition from (0, 1) to (1, 1): 1|1,
Transition from (1, 1) to (1, 2): 0|-,
Transition from (1, 1) to (3, 2): 1|-,
Transition from (1, 2) to (1, 3): 0|-,
Transition from (1, 2) to (5, 3): 1|-,
Transition from (3, 2) to (3, 3): 0|-,
Transition from (3, 2) to (7, 3): 1|-,
Transition from (1, 3) to (1, 3): 0|-,
Transition from (1, 3) to (1, 1): 1|2,
Transition from (5, 3) to (1, 2): 0|2,
Transition from (5, 3) to (1, 1): 1|2,
Transition from (3, 3) to (1, 1): 0|2,
Transition from (3, 3) to (3, 2): 1|2,
Transition from (7, 3) to (1, 1): 0|2,
Transition from (7, 3) to (2, 1): 1|1,
Transition from (2, 1) to (1, 1): 0|1,
Transition from (2, 1) to (2, 1): 1|-]
sage: for s in T.iter_states():
....:     print("{} {}".format(s, s.final_word_out))
(0, 0) []
(0, 1) []
(1, 1) [2]
(1, 2) [2]
(3, 2) [2, 2]
(1, 3) [2]
(5, 3) [2, 2]
(3, 3) [2, 2]
(7, 3) [2, 2]
(2, 1) [1, 2]
sage: list(sum(T(n.bits())) for n in srange(1, 21))
[2, 3, 4, 3, 4, 5, 4, 3, 4, 5, 6, 5, 4, 5, 4, 3, 4, 5, 6, 5]

• We now demonstrate the use of the output_rings parameter. If no output_rings are specified, the output labels are converted into ZZ:

sage: function('f')
f
sage: var('n')
n
sage: T = transducers.Recursion([
....:     f(2*n + 1) == f(n) + 1,
....:     f(2*n) == f(n),
....:     f(0) == 2],
....:     2, f, n)
sage: for t in T.transitions():
....:     print([x.parent() for x in t.word_out])
[]
[Integer Ring]
sage: [x.parent() for x in T.states()[0].final_word_out]
[Integer Ring]

In contrast, if output_rings is set to the empty list, the results are not converted:

sage: T = transducers.Recursion([
....:     f(2*n + 1) == f(n) + 1,
....:     f(2*n) == f(n),
....:     f(0) == 2],
....:     2, f, n, output_rings=[])
sage: for t in T.transitions():
....:     print([x.parent() for x in t.word_out])
[]
[Symbolic Ring]
sage: [x.parent() for x in T.states()[0].final_word_out]
[Symbolic Ring]

Finally, we use a somewhat questionable conversion:

sage: T = transducers.Recursion([
....:     f(2*n + 1) == f(n) + 1,
....:     f(2*n) == f(n),
....:     f(0) == 0],
....:     2, f, n, output_rings=[GF(5)])
sage: for t in T.transitions():
....:     print([x.parent() for x in t.word_out])
[]
[Finite Field of size 5]

Todo

Extend the method to

• non-integral bases,

• higher dimensions.

ALGORITHM:

See [HKP2015], Section 6. However, there are also recursion transitions for states of level $$<\kappa$$ if the recursion rules allow such a transition. Furthermore, the intermediate step of a non-deterministic transducer is left out by implicitly using recursion transitions. The well-posedness is checked in a truncated version of the recursion digraph.

class RecursionRule

Bases: tuple

K

Alias for field number 0

k

Alias for field number 2

r

Alias for field number 1

s

Alias for field number 3

t

Alias for field number 4

Wait(input_alphabet, threshold=1)

Writes False until reading the threshold-th occurrence of a true input letter; then writes True.

INPUT:

• input_alphabet – a list or other iterable.

• threshold – a positive integer specifying how many occurrences of True inputs are waited for.

OUTPUT:

A transducer writing False until the threshold-th true (Python’s standard conversion to boolean is used to convert the actual input to boolean) input is read. Subsequently, the transducer writes True.

EXAMPLES:

sage: T = transducers.Wait([0, 1])
sage: T([0, 0, 1, 0, 1, 0])
[False, False, True, True, True, True]
sage: T2 = transducers.Wait([0, 1], threshold=2)
sage: T2([0, 0, 1, 0, 1, 0])
[False, False, False, False, True, True]
abs(input_alphabet)

Returns a transducer which realizes the letter-wise absolute value of an input word over the given input alphabet.

INPUT:

• input_alphabet – a list or other iterable.

OUTPUT:

A transducer mapping $$i_0\ldots i_k$$ to $$|i_0|\ldots |i_k|$$.

EXAMPLES:

The following transducer realizes letter-wise absolute value:

sage: T = transducers.abs([-1, 0, 1])
sage: T.transitions()
[Transition from 0 to 0: -1|1,
Transition from 0 to 0: 0|0,
Transition from 0 to 0: 1|1]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T([-1, -1, 0, 1])
[1, 1, 0, 1]
add(input_alphabet, number_of_operands=2)

Returns a transducer which realizes addition on pairs over the given input alphabet.

INPUT:

• input_alphabet – a list or other iterable.

• number_of_operands – (default: $$2$$) it specifies the number of input arguments the operator takes.

OUTPUT:

A transducer mapping an input word $$(i_{01}, \ldots, i_{0d})\ldots (i_{k1}, \ldots, i_{kd})$$ to the word $$(i_{01} + \cdots + i_{0d})\ldots (i_{k1} + \cdots + i_{kd})$$.

The input alphabet of the generated transducer is the Cartesian product of number_of_operands copies of input_alphabet.

EXAMPLES:

The following transducer realizes letter-wise addition:

sage: T = transducers.add([0, 1])
sage: T.transitions()
[Transition from 0 to 0: (0, 0)|0,
Transition from 0 to 0: (0, 1)|1,
Transition from 0 to 0: (1, 0)|1,
Transition from 0 to 0: (1, 1)|2]
sage: T.input_alphabet
[(0, 0), (0, 1), (1, 0), (1, 1)]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T([(0, 0), (0, 1), (1, 0), (1, 1)])
[0, 1, 1, 2]

More than two operands can also be handled:

sage: T3 = transducers.add([0, 1], number_of_operands=3)
sage: T3.input_alphabet
[(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1),
(1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]
sage: T3([(0, 0, 0), (0, 1, 0), (0, 1, 1), (1, 1, 1)])
[0, 1, 2, 3]
all(input_alphabet, number_of_operands=2)

Returns a transducer which realizes logical and over the given input alphabet.

INPUT:

• input_alphabet – a list or other iterable.

• number_of_operands – (default: $$2$$) specifies the number of input arguments for the and operation.

OUTPUT:

A transducer mapping an input word $$(i_{01}, \ldots, i_{0d})\ldots (i_{k1}, \ldots, i_{kd})$$ to the word $$(i_{01} \land \cdots \land i_{0d})\ldots (i_{k1} \land \cdots \land i_{kd})$$.

The input alphabet of the generated transducer is the Cartesian product of number_of_operands copies of input_alphabet.

EXAMPLES:

The following transducer realizes letter-wise logical and:

sage: T = transducers.all([False, True])
sage: T.transitions()
[Transition from 0 to 0: (False, False)|False,
Transition from 0 to 0: (False, True)|False,
Transition from 0 to 0: (True, False)|False,
Transition from 0 to 0: (True, True)|True]
sage: T.input_alphabet
[(False, False), (False, True), (True, False), (True, True)]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T([(False, False), (False, True), (True, False), (True, True)])
[False, False, False, True]

More than two operands and other input alphabets (with conversion to boolean) are also possible:

sage: T3 = transducers.all([0, 1], number_of_operands=3)
sage: T3([(0, 0, 0), (1, 0, 0), (1, 1, 1)])
[False, False, True]
any(input_alphabet, number_of_operands=2)

Returns a transducer which realizes logical or over the given input alphabet.

INPUT:

• input_alphabet – a list or other iterable.

• number_of_operands – (default: $$2$$) specifies the number of input arguments for the or operation.

OUTPUT:

A transducer mapping an input word $$(i_{01}, \ldots, i_{0d})\ldots (i_{k1}, \ldots, i_{kd})$$ to the word $$(i_{01} \lor \cdots \lor i_{0d})\ldots (i_{k1} \lor \cdots \lor i_{kd})$$.

The input alphabet of the generated transducer is the Cartesian product of number_of_operands copies of input_alphabet.

EXAMPLES:

The following transducer realizes letter-wise logical or:

sage: T = transducers.any([False, True])
sage: T.transitions()
[Transition from 0 to 0: (False, False)|False,
Transition from 0 to 0: (False, True)|True,
Transition from 0 to 0: (True, False)|True,
Transition from 0 to 0: (True, True)|True]
sage: T.input_alphabet
[(False, False), (False, True), (True, False), (True, True)]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T([(False, False), (False, True), (True, False), (True, True)])
[False, True, True, True]

More than two operands and other input alphabets (with conversion to boolean) are also possible:

sage: T3 = transducers.any([0, 1], number_of_operands=3)
sage: T3([(0, 0, 0), (1, 0, 0), (1, 1, 1)])
[False, True, True]
map(f, input_alphabet)

Return a transducer which realizes a function on the alphabet.

INPUT:

• f – function to realize.

• input_alphabet – a list or other iterable.

OUTPUT:

A transducer mapping an input letter $$x$$ to $$f(x)$$.

EXAMPLES:

The following binary transducer realizes component-wise absolute value (this transducer is also available as abs()):

sage: T = transducers.map(abs, [-1, 0, 1])
sage: T.transitions()
[Transition from 0 to 0: -1|1,
Transition from 0 to 0: 0|0,
Transition from 0 to 0: 1|1]
sage: T.input_alphabet
[-1, 0, 1]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T([-1, 1, 0, 1])
[1, 1, 0, 1]

See also

operator(operator, input_alphabet, number_of_operands=2)

Returns a transducer which realizes an operation on tuples over the given input alphabet.

INPUT:

• operator – operator to realize. It is a function which takes number_of_operands input arguments (each out of input_alphabet).

• input_alphabet – a list or other iterable.

• number_of_operands – (default: $$2$$) it specifies the number of input arguments the operator takes.

OUTPUT:

A transducer mapping an input letter $$(i_1, \dots, i_n)$$ to $$\mathrm{operator}(i_1, \dots, i_n)$$. Here, $$n$$ equals number_of_operands.

The input alphabet of the generated transducer is the Cartesian product of number_of_operands copies of input_alphabet.

EXAMPLES:

The following binary transducer realizes component-wise addition (this transducer is also available as add()):

sage: import operator
sage: T = transducers.operator(operator.add, [0, 1])
sage: T.transitions()
[Transition from 0 to 0: (0, 0)|0,
Transition from 0 to 0: (0, 1)|1,
Transition from 0 to 0: (1, 0)|1,
Transition from 0 to 0: (1, 1)|2]
sage: T.input_alphabet
[(0, 0), (0, 1), (1, 0), (1, 1)]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T([(0, 0), (0, 1), (1, 0), (1, 1)])
[0, 1, 1, 2]

Note that for a unary operator the input letters of the new transducer are tuples of length $$1$$:

sage: T = transducers.operator(abs,
....:                          [-1, 0, 1],
....:                          number_of_operands=1)
sage: T([-1, 1, 0])
Traceback (most recent call last):
...
ValueError: Invalid input sequence.
sage: T([(-1,), (1,), (0,)])
[1, 1, 0]

Compare this with the transducer generated by map():

sage: T = transducers.map(abs,
....:                     [-1, 0, 1])
sage: T([-1, 1, 0])
[1, 1, 0]

In fact, this transducer is also available as abs():

sage: T = transducers.abs([-1, 0, 1])
sage: T([-1, 1, 0])
[1, 1, 0]
sub(input_alphabet)

Returns a transducer which realizes subtraction on pairs over the given input alphabet.

INPUT:

• input_alphabet – a list or other iterable.

OUTPUT:

A transducer mapping an input word $$(i_0, i'_0)\ldots (i_k, i'_k)$$ to the word $$(i_0 - i'_0)\ldots (i_k - i'_k)$$.

The input alphabet of the generated transducer is the Cartesian product of two copies of input_alphabet.

EXAMPLES:

The following transducer realizes letter-wise subtraction:

sage: T = transducers.sub([0, 1])
sage: T.transitions()
[Transition from 0 to 0: (0, 0)|0,
Transition from 0 to 0: (0, 1)|-1,
Transition from 0 to 0: (1, 0)|1,
Transition from 0 to 0: (1, 1)|0]
sage: T.input_alphabet
[(0, 0), (0, 1), (1, 0), (1, 1)]
sage: T.initial_states()
[0]
sage: T.final_states()
[0]
sage: T([(0, 0), (0, 1), (1, 0), (1, 1)])
[0, -1, 1, 0]
weight(input_alphabet, zero=0)

Returns a transducer which realizes the Hamming weight of the input over the given input alphabet.

INPUT:

• input_alphabet – a list or other iterable.

• zero – the zero symbol in the alphabet used

OUTPUT:

A transducer mapping $$i_0\ldots i_k$$ to $$(i_0\neq 0)\ldots(i_k\neq 0)$$.

The Hamming weight is defined as the number of non-zero digits in the input sequence over the alphabet input_alphabet (see Wikipedia article Hamming_weight). The output sequence of the transducer is a unary encoding of the Hamming weight. Thus the sum of the output sequence is the Hamming weight of the input.

EXAMPLES:

sage: W = transducers.weight([-1, 0, 2])
sage: W.transitions()
[Transition from 0 to 0: -1|1,
Transition from 0 to 0: 0|0,
Transition from 0 to 0: 2|1]
sage: unary_weight = W([-1, 0, 0, 2, -1])
sage: unary_weight
[1, 0, 0, 1, 1]
sage: weight = add(unary_weight)
sage: weight
3

Also the joint Hamming weight can be computed:

sage: v1 = vector([-1, 0])
sage: v0 = vector([0, 0])
sage: W = transducers.weight([v1, v0])
sage: unary_weight = W([v1, v0, v1, v0])
sage: add(unary_weight)
2

For the input alphabet [-1, 0, 1] the weight transducer is the same as the absolute value transducer abs():

sage: W = transducers.weight([-1, 0, 1])
sage: A = transducers.abs([-1, 0, 1])
sage: W == A
True

For other input alphabets, we can specify the zero symbol:

sage: W = transducers.weight(['a', 'b'], zero='a')
sage: add(W(['a', 'b', 'b']))
2