Bits of Learning

Learning sometimes happens in big jumps, but mostly in little tiny steps. I share my baby steps of learning here, mostly on topics around programming, programming languages, software engineering, and computing in general. But occasionally, even on other disciplines of engineering or even science. I mostly learn through examples and doing. And this place is a logbook of my experiences in learning something. You may find several things interesting here: little cute snippets of (hopefully useful) code, a bit of backing theory, and a lot of gyan on how learning can be so much fun.

Friday, March 29, 2013

Counter in Python

Presenting here two implementations of a counter.

What does the counter do?

Given a base n, this counter will allow you to do base n counting. What's base-n counting? There are n digits: 0, 1, 2, ..., n - 1. At each place, the digit changes from i to i + 1 if i < n - 1, and to 0 if i = n - 1. This change happens when the change at that place is triggered. And when does the trigger happen. For the unit's place, the trigger happens whenever the function is called. For all other places, the trigger happens when the previous place's digit rolls over from n - 1 to 0. That's all. That's a bit technical description for the simple act of counting we learn in our KG. But if we want to implement the counter, it needs to be written in this algorithmic language.

A 3-bit binary (base-2) counter goes like:
000, 001, 010, 011, 100, 101, 110, 111, 000, ...

A 2-digit base-4 counter would count as:
00, 01, 02, 03, 10, 11, 12, 13, 20, 21, 22, 23, 30, 31, 32, 33, 00, ...

What do we usually do in day-to-day counting? decimal (base-10) counting.

How do we Use the Counter Program?

Below is a piece of code that could be used to implement a binary counter on a 3-bit number.

base = 2
inc = makeCounter_iter(base)
n = [0, 0, 0]
print n
for i in range(base ** len(n)):
    n = inc(n)
    print n

 The inc(n) gives n + 1 in the base n number system which is pre-decided while instantiating the counter:
base = 2

def makeCounter_iter(base):
    def isZero(lst): return lst == [0] * len(lst)

    def inc(num):
        if(len(num) > 1):
            rem = inc(num[:-1])
                if(num[-1] == base - 1):    rem.extend([0]) 
                else:                rem.extend([num[-1] + 1])
            else:    rem.extend([num[-1]])
            return rem
            new = [0]
            if(not num[0] == base - 1):    new[0] = num[0] + 1
            return new
    return inc

Now follows the recursive version.

def makeCounter_rec(base):
    def incDigit(num, pos):
        new = num[:]
        if(num[pos] == base - 1):
            new[pos] = 0
            if(pos < len(num) - 1):    return incDigit(new, pos + 1)
            new[pos] = new[pos] + 1
        return new

    def inc(num): return incDigit(num, 0)
    return inc

Any day, the recursive implementation trumps over the iterative version. The recursive implementation is smaller and more readable because it's very like the way the process of counting is formally defined in the beginning of the article.

Functional Programming Flavour

One thing to notice is that we use functional language aspect of python. The function makeCounter (makeCounter_iter and makeCounter_rec) is a higher order function that returns another function inc, the increment function. Also, the value of base is fixed in the creation context of inc inside the makeCounter function. The inc function uses the value of base at a place in the code which is typically outside the lexical scope where base is defined. This is known as the closure feature of functional programming. In a typical object-oriented scenario, this effect is achieved using the builder design pattern.

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