One of the most interesting algorithms is the Fibonacci numbers. It’s pretty tricky ’cause it might leads to a binary tree recursion if coded carelessly.

There’s a lot of ways to go around that issue, and I’d like to approach two of them using Cython.

Accumulators and tail-call optimisation

Prolog is a declarative logic programming language, consisting of describing the factual domain and then querying it.

The simpliest (and wrong) way to code Fibonacci in Prolog is:

% vim: filetype=prolog
:- module(fib, [fib/2]).

fib(N, R) :- % step
  N > 0,
  N1 is N - 1,
  N2 is N - 2,
  fib(N1, R1),
  fib(N2, R2),
  R is R1 + R2.

fib(0, 1). % stop

This describes Fibonacci number precisely, but don’t do it. It dives into a binary tree, doubling the stack every step.

The way to fix it is using two accumulators, A and B:

% vim: filetype=prolog
:- module(fib, [fib/2]).

fib(N, R) :- N >= 0, fib(N, 0, 1, R).

fib(N, A, B, R) :- % step
  N > 0,
  N1 is N - 1,
  AB is A + B,
  fib(N1, B, AB, R).

fib(0, A, R, R). % stop

Now it accumulates the values lineraly until the stop condition, when the last B is bound to R.

Try it:

?- [fib].
true.

?- findall(X, (between(0, 5, I), fib(I, X)), R).
R = [1, 1, 2, 3, 5, 8].

?-

Prolog was used as basis for another programming language called Datalog, focused on database query.

The whole thing becomes simplier when Datalog comes into play. Let’s see the same domain coded in Datalog:

fib(0, A, B, R) :- B = R.
fib(N, A, B, R) :- N > 0, fib(N-1, B, A+B, R).
fib(N, R) :- N >= 0, fib(N, 0, 1, R).

And then:

> between(0, 5, I), fib(I, X)?
fib(0, 1).
fib(1, 1).
fib(2, 2).
fib(3, 3).
fib(4, 5).
fib(5, 8).
>

Enter pyDatalog

Python has a Datalog bind egg called pyDatalog, installed by a simple pip:

python3.8 -mpip install pyDatalog

You can use a virtual environment, or install directly into your system as root – your choice.

We’re gonna need Cython too:

python3.8 -mpip install cython

In order to do some Datalog inside Python/Cython code, we need to declare the Datalog terms we’re using.

This below is the very same Datalog code, using a cpdef to expose the fib/2:

#cython: language_level=3
from libc.stdint cimport uint64_t
from pyDatalog.pyParser import Term

cdef:
    object _fib = Term()
    object A = Term()
    object B = Term()
    object R = Term()
    object N = Term()
    object X = Term()

_fib(0, A, B, R) <= (R == B)
_fib(N, A, B, R) <= (N > 0) & _fib(N-1, B, A+B, R)

cpdef uint64_t fib(size_t n) except -1:
    _fib(n, 0, 1, X)
    return X.v()

Now we need to compile it. Save it as fib.pyx and run:

cythonize fib.pyx
clang `python3.8-config --cflags` -fPIC -c fib.c
clang -o fib.so `python3.8-config --libs` -shared fib.o

(Or use gcc.)

It’s time to see it working. Open the bpython:

>>> from fib import fib
>>> [fib(i) for i in range(5)]
[1, 1, 2, 3, 5, 8]
>>>

Using matrices

The Fibonacci numbers can also be represented as a matrix power:

│1 1│ⁿ
│1 0│

That’s a very elegant approach. We can do it by using NumPy. First let’s install the egg just like before:

python3.8 -mpip install numpy

Now let’s recode Fibonacci using matrices:

#cython: language_level=3
from libc.stdint cimport uint64_t
from numpy cimport ndarray
from numpy import matrix, uint64

cdef:
    ndarray m = matrix('1, 1; 1, 0', dtype=uint64)

cpdef uint64_t fib(size_t n) except -1:
    return (m ** n)[0, 0]

NumPy represents the Fibonacci matrix as '1, 1; 1, 0'. You can compile the code exactly the same way you did before, with the same results.

Adapted from the Portuguese post.

Also in DEV Community 👩‍💻👨‍💻.