.. _functional_programming:
=========================================
Functional Programming for Mathematicians
=========================================
.. MODULEAUTHOR:: Minh Van Nguyen
This tutorial discusses some techniques of functional programming that
might be of interest to mathematicians or people who use Python for
scientific computation. We start off with a brief overview of
procedural and object-oriented programming, and then discuss
functional programming techniques. Along the way, we briefly review
Python's built-in support for functional programming, including
`filter `_,
`lambda `_,
`map `_ and
`reduce `_.
The tutorial concludes with some resources on detailed information on
functional programming using Python.
Styles of programming
=====================
Python supports several styles of programming. You could program in
the procedural style by writing a program as a list of
instructions. Say you want to implement addition and multiplication
over the integers. A procedural program to do so would be as follows::
sage: def add_ZZ(a, b):
... return a + b
...
sage: def mult_ZZ(a, b):
... return a * b
...
sage: add_ZZ(2, 3)
5
sage: mult_ZZ(2, 3)
6
The Python module
`operator `_
defines several common arithmetic and comparison operators as named
functions. Addition is defined in the built-in function
``operator.add`` and multiplication is defined in
``operator.mul``. The above example can be worked through as
follows::
sage: from operator import add
sage: from operator import mul
sage: add(2, 3)
5
sage: mul(2, 3)
6
Another common style of programming is called object-oriented
programming. Think of an object as code that encapsulates both data
and functionalities. You could encapsulate integer addition and
multiplication as in the following object-oriented implementation::
sage: class MyInteger:
... def __init__(self):
... self.cardinality = "infinite"
... def add(self, a, b):
... return a + b
... def mult(self, a, b):
... return a * b
...
sage: myZZ = MyInteger()
sage: myZZ.cardinality
'infinite'
sage: myZZ.add(2, 3)
5
sage: myZZ.mult(2, 3)
6
Functional programming using map
================================
Functional programming is yet another style of programming in which a
program is decomposed into various functions. The Python built-in
functions ``map``, ``reduce`` and ``filter`` allow you to program in
the functional style. The function ::
map(func, seq1, seq2, ...)
takes a function ``func`` and one or more sequences, and apply
``func`` to elements of those sequences. In particular, you end up
with a list like so::
[func(seq1[0], seq2[0], ...), func(seq1[1], seq2[1], ...), ...]
In many cases, using ``map`` allows you to express the logic of your
program in a concise manner without using list comprehension. For
example, say you have two lists of integers and you want to add them
element-wise. A list comprehension to accomplish this would be as
follows::
sage: A = [1, 2, 3, 4]
sage: B = [2, 3, 5, 7]
sage: [A[i] + B[i] for i in range(len(A))]
[3, 5, 8, 11]
Alternatively, you could use the Python built-in addition function
``operator.add`` together with ``map`` to achieve the same result::
sage: from operator import add
sage: A = [1, 2, 3, 4]
sage: B = [2, 3, 5, 7]
sage: map(add, A, B)
[3, 5, 8, 11]
An advantage of ``map`` is that you do not need to explicitly define
a for loop as was done in the above list comprehension.
Define small functions using lambda
===================================
There are times when you want to write a short, one-liner
function. You could re-write the above addition function as follows::
sage: def add_ZZ(a, b): return a + b
...
Or you could use a ``lambda`` statement to do the same thing in a much
clearer style. The above addition and multiplication functions could
be written using ``lambda`` as follows::
sage: add_ZZ = lambda a, b: a + b
sage: mult_ZZ = lambda a, b: a * b
sage: add_ZZ(2, 3)
5
sage: mult_ZZ(2, 3)
6
Things get more interesting once you combine ``map`` with the
``lambda`` statement. As an exercise, you might try to write a simple
function that implements a constructive algorithm for the
`Chinese Remainder Theorem `_.
You could use list comprehension together with ``map`` and
``lambda`` as shown below. Here, the parameter ``A`` is a list of
integers and ``M`` is a list of moduli. ::
sage: def crt(A, M):
... Mprod = prod(M)
... Mdiv = map(lambda x: Integer(Mprod / x), M)
... X = map(inverse_mod, Mdiv, M)
... x = sum([A[i]*X[i]*Mdiv[i] for i in range(len(A))])
... return mod(x, Mprod).lift()
...
sage: A = [2, 3, 1]
sage: M = [3, 4, 5]
sage: x = crt(A, M); x
11
sage: mod(x, 3)
2
sage: mod(x, 4)
3
sage: mod(x, 5)
1
To produce a random matrix over a ring, say `\ZZ`, you could start by
defining a matrix space and then obtain a random element of that
matrix space::
sage: MS = MatrixSpace(ZZ, nrows=5, ncols=3)
sage: MS.random_element() # random
[ 6 1 0]
[-1 5 0]
[-1 0 0]
[-5 0 1]
[ 1 -1 -3]
Or you could use the function ``random_matrix``::
sage: random_matrix(ZZ, nrows=5, ncols=3) # random
[ 2 -50 0]
[ -1 0 -6]
[ -4 -1 -1]
[ 1 1 3]
[ 2 -1 -1]
The next example uses ``map`` to construct a list of random integer
matrices::
sage: rows = [randint(1, 10) for i in range(10)]
sage: cols = [randint(1, 10) for i in range(10)]
sage: rings = [ZZ]*10
sage: M = map(random_matrix, rings, rows, cols)
sage: M[0] # random
[ -1 -3 -1 -37 1 -1 -4 5]
[ 2 1 1 5 2 1 -2 1]
[ -1 0 -4 0 -2 1 -2 1]
If you want more control over the entries of your matrices than the
``random_matrix`` function permits, you could use ``lambda``
together with ``map`` as follows::
sage: rand_row = lambda n: [randint(1, 10) for i in range(n)]
sage: rand_mat = lambda nrows, ncols: [rand_row(ncols) for i in range(nrows)]
sage: matrix(rand_mat(5, 3)) # random
[ 2 9 10]
[ 8 8 9]
[ 6 7 6]
[ 9 2 10]
[ 2 6 2]
sage: rows = [randint(1, 10) for i in range(10)]
sage: cols = [randint(1, 10) for i in range(10)]
sage: M = map(rand_mat, rows, cols)
sage: M = map(matrix, M)
sage: M[0] # random
[ 9 1 5 2 10 10 1]
[ 3 4 3 7 4 3 7]
[ 4 8 7 6 4 2 10]
[ 1 6 3 3 6 2 1]
[ 5 5 2 6 4 3 4]
[ 6 6 2 9 4 5 1]
[10 2 5 5 7 10 4]
[ 2 7 3 5 10 8 1]
[ 1 5 1 7 8 8 6]
Reducing a sequence to a value
==============================
The function ``reduce`` takes a function of two arguments and apply
it to a given sequence to reduce that sequence to a single value. The
function
`sum `_
is an example of a ``reduce`` function. The following sample code
uses ``reduce`` and the built-in function ``operator.add`` to add
together all integers in a given list. This is followed by using
``sum`` to accomplish the same task::
sage: from operator import add
sage: L = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sage: reduce(add, L)
55
sage: sum(L)
55
In the following sample code, we consider a vector as a list of real
numbers. The
`dot product `_
is then implemented using the functions ``operator.add`` and
``operator.mul``, in conjunction with the built-in Python functions
``reduce`` and ``map``. We then show how ``sum`` and ``map`` could be
combined to produce the same result. ::
sage: from operator import add
sage: from operator import mul
sage: U = [1, 2, 3]
sage: V = [2, 3, 5]
sage: reduce(add, map(mul, U, V))
23
sage: sum(map(mul, U, V))
23
Or you could use Sage's built-in support for the dot product::
sage: u = vector([1, 2, 3])
sage: v = vector([2, 3, 5])
sage: u.dot_product(v)
23
Here is an implementation of the Chinese Remainder Theorem without
using ``sum`` as was done previously. The version below uses
``operator.add`` and defines ``mul3`` to multiply three numbers
instead of two. ::
sage: def crt(A, M):
... from operator import add
... Mprod = prod(M)
... Mdiv = map(lambda x: Integer(Mprod / x), M)
... X = map(inverse_mod, Mdiv, M)
... mul3 = lambda a, b, c: a * b * c
... x = reduce(add, map(mul3, A, X, Mdiv))
... return mod(x, Mprod).lift()
...
sage: A = [2, 3, 1]
sage: M = [3, 4, 5]
sage: x = crt(A, M); x
11
Filtering with filter
=====================
The Python built-in function ``filter`` takes a function of one
argument and a sequence. It then returns a list of all those items
from the given sequence such that any item in the new list results in
the given function returning ``True``. In a sense, you are filtering
out all items that satisfy some condition(s) defined in the given
function. For example, you could use ``filter`` to filter out all
primes between 1 and 50, inclusive. ::
sage: filter(is_prime, [1..50])
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
For a given positive integer `n`, the
`Euler phi function `_
counts the number of integers `a`, with `1 \leq a \leq n`, such that
`\gcd(a, n) = 1`. You could use list comprehension to obtain all such
`a`'s when `n = 20`::
sage: [k for k in range(1, 21) if gcd(k, 20) == 1]
[1, 3, 7, 9, 11, 13, 17, 19]
A functional approach is to use ``lambda`` to define a function that
determines whether or not a given integer is relatively prime
to 20. Then you could use ``filter`` instead of list comprehension
to obtain all the required `a`'s. ::
sage: is_coprime = lambda k: gcd(k, 20) == 1
sage: filter(is_coprime, range(1, 21))
[1, 3, 7, 9, 11, 13, 17, 19]
The function ``primroots`` defined below returns all primitive roots
modulo a given positive prime integer `p`. It uses ``filter`` to
obtain a list of integers between `1` and `p - 1`, inclusive, each
integer in the list being relatively prime to the order of the
multiplicative group `(\ZZ/p\ZZ)^{\ast}`. ::
sage: def primroots(p):
... g = primitive_root(p)
... znorder = p - 1
... is_coprime = lambda x: gcd(x, znorder) == 1
... good_odd_integers = filter(is_coprime, [1..p-1, step=2])
... all_primroots = [power_mod(g, k, p) for k in good_odd_integers]
... all_primroots.sort()
... return all_primroots
...
sage: primroots(3)
[2]
sage: primroots(5)
[2, 3]
sage: primroots(7)
[3, 5]
sage: primroots(11)
[2, 6, 7, 8]
sage: primroots(13)
[2, 6, 7, 11]
sage: primroots(17)
[3, 5, 6, 7, 10, 11, 12, 14]
sage: primroots(23)
[5, 7, 10, 11, 14, 15, 17, 19, 20, 21]
sage: primroots(29)
[2, 3, 8, 10, 11, 14, 15, 18, 19, 21, 26, 27]
sage: primroots(31)
[3, 11, 12, 13, 17, 21, 22, 24]
Further resources
=================
This has been a rather short tutorial to functional programming
with Python. The Python standard documentation has a list of built-in
functions, many of which are useful in functional programming. For
example, you might want to read up on
`all `_,
`any `_,
`max `_,
`min `_, and
`zip `_. The
Python module
`operator `_
has numerous built-in arithmetic and comparison operators, each
operator being implemented as a function whose name reflects its
intended purpose. For arithmetic and comparison operations, it is
recommended that you consult the ``operator`` module to determine if
there is a built-in function that satisfies your requirement. The
module
`itertools `_
has numerous built-in functions to efficiently process sequences of
items. The functions ``filter``, ``map`` and ``zip`` have their
counterparts in ``itertools`` as
`itertools.ifilter `_,
`itertools.imap `_
and
`itertools.izip `_.
Another useful resource for functional programming in Python is the
`Functional Programming HOWTO `_
by A. M. Kuchling. Steven F. Lott's book
`Building Skills in Python `_
has a chapter on
`Functional Programming using Collections `_.
See also the chapter
`Functional Programming `_
from Mark Pilgrim's book
`Dive Into Python `_.
You might also want to consider experimenting with
`Haskell `_
for expressing mathematical concepts. For an example of Haskell in
expressing mathematical algorithms, see J. Gibbons' article
`Unbounded Spigot Algorithms for the Digits of Pi `_
in the American Mathematical Monthly.