Five Letter Words: Part 2: More Five-Word Algorithms

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NOTE: The code covered in this post uses Python 3. The scripts can be converted to Python 2 with minimal effort, but the author would encourage any user of Python 2 to "put on your big kid pants" and make the switch to Python 3. Let's all make this painful, drawn-out switch from Python 2 to Python 3 a thing of the past, shall we?

Table of Contents

Introduction

As mentioned in Five Letter Words: Part 1, we covered Donald Knuth's list of five letter words, one of the data sets in the Stanford Graph Base that is covered in greater detail in Knuth's coverage of graph theory in Volume 4, Facsimile 0 of his magnum opus, The Art of Computer Programming.

In the section where Knuth introduces the set of words, he also gives readers several exercises to get to know the list of words. This multi-part series of posts (also see Five Letter Words: Part 1) is covering some of the solutions to these exercises, and expanding on them to illustrate some of the interesting and surprising properties of this data set.

Five-Letter Words with k Distinct Letters

In Exercise 27, Knuth asks the reader to make a list of words composed of a specific number of distinct letters (1, 2, 3, 4, or 5).

In the list of five-letter words, there are 0 words composed of a single letter, 4 words with two distinct letters (0.07%), 163 words with three distinct letters (2.8%), 1756 words with four distinct letters (30.5%), and 3834 words with five distinct letters (66.5%).

Here are a few examples: Two distinct letters: mamma, ahhhh, esses, ohhhh Three distinct letters: added, seems, sense, level, teeth Four distinct letters: which, there, these, where, three Five distinct letters: their, about, would, other, words

To find these, we can design an algorithm that does the following: split each string into characters, add them to a set data type (a set discards any duplicates), and get the size of the set. This will give us the number of unique letters in a given word, and we can use a list of lists to store all words with a specified number of unique letters.

Once again, we're using our get_words function, which we covered in Part 1. See get_words.py for that script.

distinct.py

"""
distinct.py

Donald Knuth, Art of Computer Programming, Volume 4 Facsimile 0
Exercise #27

How many SGB words contain exactly k distinct letters, for 1 <= k <= 5?
"""
from pprint import pprint
from get_words import get_words

if __name__=="__main__":
    words = get_words()

    lengths = [[] for i in range(5+1)]

    for word in words:
        k = len(set(word))
        lengths[k].append(word)

    for i in range(1,5+1):
        print("-"*40)
        print("Number of words with {0:d} letters: {1:d}".format(i, len(lengths[i])))
        print(", ".join(lengths[i][0:5]))

The principal operation here is the statement that gets the length, k:

k = len(set(word))
lengths[k].append(word)

The operation of turning a word into a set is \(O(M)\), where M is the number of letters in the word, and the algorithm performs this operation on each word in sequence, so overall, the algorithm is \(O(N)\), where N is the number of words.

The storage space used by the algorithm is also \(O(N)\), since for each word, the number of distinct letters \(k \in \{ 0 \dots 5 \}\).

If we were dealing with a lot of words, and needed to save some space, we could represent the list of words with \(k\) distinct letters using five bit vectors, where each bit vector represents the words that are composed of \(k\) distinct letters, and has a length of \(N\), the number of words. A 0 would indicate the word is not in the set (is not composed of \(k\) letters), and a 1 would indicate the opposite.

But here, we keep it simple, since we have a small, known set of words.

Examining a Variation

While that's essentially all there is to this algorithm, and it takes all of 10 seconds to come up with the idea, there are some nuances and some bookkeeping details, as there are with the design of any algorithm.

For example, compare the following two approaches; Approach 1 is used in the program, Approach 2 is a less efficient approach:

    # Approach 1:
    for word in words:
        k = len(set(word))
        lengths[k].append(word)


    # Approach 2:
    for k in range(1,5+1):
        if(len(set(word))==k):
            lengths[k].append(word)

While these are both \(O(N)\) runtime, the latter approach is inefficient: we loop over each word five times, and each time we perform the same operation (turning the letters of a word into a set).

Is there ever a case where we would want an approach like #2?

The short answer is, never.

To give a longer answer, let's consider a case where approach #2 might provide an advantage. Suppose we were considering a case where \(k\) could be larger - a list of 15-letter words, for example, so k could be up to 15 - and we were only interested in a particular value, or small set of values, of \(k\), like 3 and 4.
Approach 1 would store unnecessary intermediate results (the values of k for all words) and therefore use extra space, compared with approach #2 where we could change the for loop to for k in [3,4]:.

Even here, though, approach #2 results in unnecessary work, because approach #1 is still computationally more efficient by looping over the list of words only once, compared with approach #2, which would loop over the list of words twice.

We may further consider a case where approach #2 would give us an advantage, and that is the case where we are copying data into the list lengths, instead of just storing a reference to a string. Because we only deal with references in Python, we aren't making copies in the code given above. But because strings are immutable, we could conceivably be making copies if we stored word.upper() instead of word. Approach #2 would use less space, because it only considers the values of k that are of interest.

But even here, approach #1 requires only a small modification to wipe out the space advantage of approach #2: add an if statement before calling the append function: if k in [3,4]. Now the calculation of turning a word into a set of characters is performed only once for approach #1, and we don't end up storing unnecessary intermediate results.

The take-home lesson: even if the core idea behind an algorithm is straightforward, there are still many ways to do it better or worse.

Lexicographic Ordering of Letters

Knuth points out that the word "first" contains letters that occur in lexicograhpic order. Exercise #30 of AOCP Volume 4 Facsimile 0 asks us to find the first and last such word that occurs in Knuth's set of five letter words.

To do this, we'll take each word and turn it into a list of characters. We'll then sort the characters, and turn the sorted list of characters back into a string. If the string constructed from sorted characters equals the original string, we have our word, formed from lexicographically ordered letters.

We can also perform the reverse - search for words whose letters are in reverse lexicographic order. One such word is "spied". Implementing this task requires a bit more care, because of the fact that Python 3 returns generators where Python 2 would return lists, but we can get around this with the list() function, as we shall see shortly.

Five-Letter Words with Lexicographically Ordered Letters

Exercise 30 asks us to find the first and last word in the set of five letter words whose letters occur in sorted lexicographic order. We begin by sorting all of the words, and we find the first such word is "abbey", while the last such word is "pssst".

There are 105 total words that fit this description. As we might expect, a majority of them begin with letters at the beginning of the alphabet:

  • abbey
  • abbot
  • abhor
  • abort
  • abuzz
  • achoo
  • adder
  • adept
  • adios
  • adopt
  • aegis
  • affix
  • afoot
  • aglow
  • ahhhh
  • allot
  • allow
  • alloy
  • ammos
  • annoy
  • beefs
  • beefy
  • beeps
  • beers
  • beery
  • befit
  • begin
  • begot
  • bells
  • belly
  • below
  • berry
  • bills
  • billy
  • bitty
  • blowy
  • boors
  • boost
  • booty
  • bossy
  • ceils
  • cello
  • cells

The full output is here:

lexico output

The code to find these words is given below:

lexico.py

"""
lexico.py

Donald Knuth, Art of Computer Programming, Volume 4 Facsimile 0
Exercise #30

Each letter of the word "first" appears in correct lexicographic order.
Find the first and last such words in the SGB words.
"""
from get_words import get_words

def in_sorted_order(word):
    chars = list(word)
    if(str(chars)==str(sorted(chars))):
        return True
    else:
        return False

if __name__=="__main__":

    words = get_words()
    words = sorted(words)

    count = 0
    print("-"*40)
    print("ALL lexicographically sorted words:")
    for word in words:
        if(in_sorted_order(word)):
            print(word)
            count += 1
    print("{0:d} total.".format(count))

    print("-"*40)
    for word in words:
        if(in_sorted_order(word)):
            print("First lexicographically sorted word:")
            print(word)
            break

    words.reverse()

    print("-"*40)
    for word in words:
        if(in_sorted_order(word)):
            print("Last lexicographically sorted word:")
            print(word)
            break

The heart of the method here is the in_sorted_order() method: this performs the task, as described above. We take the word passed to the function (a string), and turn it into a list using the list() function. We then turn this list back into a string (which is the same as the variable word), and compare it to the sorted list of characters, turned back into a string, using the call str(sorted(chars)).

If the two match, we have not affected the order of characters by sorting them in lexicographic (alphabetic) order, and therefore the original string was in sorted order, and we return True. Otherwise, we return False.

Here's that method one more time:

def in_sorted_order(word):
    chars = list(word)
    if(str(chars)==str(sorted(chars))):
        return True
    else:
        return False

Five-Letter Words with Lexicographically Reversed Letters

There are significantly fewer five-letter words whose letters are in reverse lexicographic order - 37, compared to the 105 in sorted order. Here is the full list:

  • mecca
  • offed
  • ohhhh
  • plied
  • poked
  • poled
  • polka
  • skied
  • skiff
  • sniff
  • soled
  • solid
  • sonic
  • speed
  • spied
  • spiff
  • spoke
  • spoof
  • spook
  • spool
  • spoon
  • toked
  • toned
  • tonic
  • treed
  • tried
  • troll
  • unfed
  • upped
  • urged
  • vroom
  • wheee
  • wooed
  • wrong
  • yoked
  • yucca
  • zoned

The code to do this requires only minor modifications to the original, sorted order code.

To reverse the procedure, we just need to modify the in_sorted_order() function to reverse the sorted list of characters before we reassemble it into a string. We can feed the output of the call to sorted() to the reversed() function. However, in Python 3, this returns a generator object, which is lazy - it does not automatically enumerate every character. Unless, of course, we force it to.

That's where the call to list() comes in handy - by passing a generator to list(), we force Python to enumerate the output of the reversed, sorted list generator. Then we turn the reversed, sorted list into a reversed, sorted string:

def in_reverse_sorted_order(word):
    chars = list(word)
    # Note: reversed returns a generator,
    # so we have to pass it to list()
    # to explicitly enumerate the reversed results.
    if(str(chars)==str(list(reversed(sorted(chars))))):
        return True
    else:
        return False

Meanwhile, the rest of the script can stay virtually the same.

reverse_lexico.py

"""
reverse_lexico.py

Donald Knuth, Art of Computer Programming, Volume 4 Facsimile 0
Variation on Exercise #30

Each letter of the word "spied" appears in reversed lexicographic order.
Find more words whose letters appear in reverse lexicographic order.
"""
from get_words import get_words

def in_reverse_sorted_order(word):
    chars = list(word)
    # Note: reversed returns a generator, 
    # so we have to pass it to list() 
    # to explicitly enumerate the reversed results.
    if(str(chars)==str(list(reversed(sorted(chars))))):
        return True
    else:
        return False

if __name__=="__main__":

    words = get_words()
    words = sorted(words)

    count = 0
    print("-"*40)
    print("ALL lexicographically reversed words:")
    for word in words:
        if(in_reverse_sorted_order(word)):
            print(word)
            count += 1
    print("{0:d} total.".format(count))

    print("-"*40)
    for word in words:
        if(in_reverse_sorted_order(word)):
            print("First reverse lexicographically sorted word:")
            print(word)
            break

    words.reverse()

    print("-"*40)
    for word in words:
        if(in_reverse_sorted_order(word)):
            print("Last lexicographically sorted word:")
            print(word)
            break

Finding Palindromes

Palindromes are words or sets of words that have a reflective property, namely, they spell the same thing forward and reverse (e.g., "race car", or "Ere I was able, I saw Malta", or "Doc, note I dissent - a fast never prevents a fatness. I diet on cod.").

In Exercise 29, Knuth asks the reader to perform a straightforward task - find the palindromes in the list of five letter words. (An example of one such word is "kayak".) But Knuth goes further, and points out that palindromes can also be formed from pairs of words. He gives the example "regal lager". He asks the reader to find all palindrome pairs as well.

When working on these exercises, we became curious about palindromic near-misses. How many words are almost palindromes? (Example: "going" is very close to a palindrome, if we just changed the n to an o or vice-versa.) In fact, we already have all the tools we need at our disposal, as we already covered a script to perform a Euclidean distance calculation.

We will cover Python code to find words that fit into each of these categories, and provide some interesting examples. (One of the most surprising things to us was just how many words meet these criteria!)

Palindromes

The first task is finding palindromes in the set of five letter words. There are 18 such words. They are given below:

  • level
  • refer
  • radar
  • madam
  • rotor
  • civic
  • sexes
  • solos
  • sagas
  • kayak
  • minim
  • tenet
  • shahs
  • stats
  • stets
  • kaiak
  • finif
  • dewed

The code to check if a word is a palindrome consists of two simple logical test: Is the character at position 0 equal to the character at position 4? Is the character at position 1 equal to the character at position 3? If both of these are true, the word is a palindrome. Here's the Python function to check if a word is a palindrome:

palindromes.py

def is_palindrome(word):
    test1 = word[0]==word[4]
    test2 = word[1]==word[3]
    if(test1 and test2):
        return True
    return False

and the main driver method, which actually runs the function on each word:

if __name__=="__main__":
    words = get_words()

    kp = 0
    palindromes = []

    # Check for palindromes
    for i in range(len(words)):
        if(is_palindrome(words[i])):
            kp += 1
            palindromes.append(words[i])

    print("-"*40)
    print("Palindromes: \n")
    print(", ".join(palindromes))
    print("There are {0:d} palindromes.".format(kp))

Palindrome Pairs

There are 34 palindromic pairs, if we disallow palindromes from being considered palindromic pairs with themselves. These are:

  • parts, strap
  • lived, devil
  • speed, deeps
  • sleep, peels
  • straw, warts
  • faced, decaf
  • spots, stops
  • fires, serif
  • lever, revel
  • smart, trams
  • ports, strop
  • pools, sloop
  • stool, loots
  • draws, sward
  • mined, denim
  • spins, snips
  • alley, yella
  • loops, spool
  • sleek, keels
  • repel, leper
  • snaps, spans
  • depot, toped
  • timed, demit
  • debut, tubed
  • laced, decal
  • stink, knits
  • regal, lager
  • tuber, rebut
  • remit, timer
  • pacer, recap
  • snoot, toons
  • namer, reman
  • hales, selah
  • tarps, sprat

The code to check for palindrome pairs is a little more involved, but also consists of a few logical tests to see if letters in one position of the first word match letters in another position of the second word:

palindromes.py

def is_palindrome_pair(word1,word2):
    test0 = word1[0]==word2[4]
    test1 = word1[1]==word2[3]
    test2 = word1[2]==word2[2]
    test3 = word1[3]==word2[1]
    test4 = word1[4]==word2[0]
    if(test0 and test1 and test2 and test3 and test4):
        return True
    return False

and the main driver method:

if __name__=="__main__":
    words = get_words()

    kpp = 0
    palindrome_pairs = []

    # Check for palindrome pairs
    for i in range(len(words)):
        for j in range(i,len(words)):
            if(is_palindrome_pair(words[i],words[j])):
                # Palindromes shouldn't count as palindrome pairs
                if(words[i] is not words[j]):
                    kpp += 1
                    palindrome_pairs.append((words[i],words[j]))

    print("-"*40)
    print("Palindrome Pairs: \n")
    for pair in palindrome_pairs:
        print(", ".join(pair))
    print("There are {0:d} palindrome pairs.".format(kpp))

Near Palindromes

A near-palindrome is a word that would be a palindrome, if one of its letters were slightly modified. We use a "tolerance" parameter to specify how much modification we are willing to live with to consider a word a near-palindrome.

There are several ways to do this, but we'll keep it simple: we consider the totla number of changes to all characters in the word required to make a word a palindrome, and test whether the changes required to make the word a palindrome are less than or equal to a specified parameter, tolerance.

For example, if our tolerance were 1, we would consider the words "going" and "moron" to be near-palindromes; if our tolerance were 2, we would consider the words "tsars" and "jewel" to be near-palindromes.

Here is the list of 37 off-by-one palindromes:

  • going
  • seeds
  • tight
  • trust
  • suits
  • sends
  • plump
  • slums
  • sighs
  • erase
  • serfs
  • soaps
  • sewer
  • soups
  • sever
  • slams
  • scabs
  • moron
  • ceded
  • scads
  • suets
  • fugue
  • seder
  • tryst
  • educe
  • twixt
  • tutus
  • shags
  • slims
  • abaca
  • anima
  • celeb
  • selfs
  • scuds
  • tikis
  • topos
  • rajas

and the list of off-by-two palindromes:

  • often
  • stars
  • sight
  • visit
  • towns
  • climb
  • flame
  • reads
  • sings
  • hatch
  • tends
  • naval
  • robot
  • reeds
  • cocoa
  • stout
  • spins
  • onion
  • sinks
  • edged
  • spurs
  • jewel
  • snaps
  • silks
  • nasal
  • theft
  • pagan
  • reefs
  • stirs
  • snips
  • tufts
  • truss
  • strut
  • spans
  • smelt
  • spars
  • flake
  • rusts
  • skims
  • sways
  • runts
  • tsars
  • tress
  • feted
  • rends
  • romps
  • cilia
  • ephod
  • fluke
  • reset
  • farad
  • peter
  • natal
  • thugs
  • newel
  • paean
  • emend
  • snoot
  • fiche
  • porno
  • flume
  • toons
  • roans
  • offen
  • klunk
  • feued
  • nihil
  • pavan
  • relet
  • heigh
  • revet
  • sicks
  • spoor

The check for near-palindromes follows the palindrome test fairly closely, except instead of checking if letters in two positions are equal, we check of those two letters are a certain specified distance from one another.

Here is the code for finding near-palindromes:

"""
near_palindromes.py

Donald Knuth, Art of Computer Programming, Volume 4 Facsimile 0
Variation on Exercise #29

Find SGB words that are near-palindromes
(edit distance of one or two letters away from a palindrome).
"""
from get_words import get_words
from euclidean_distance import euclidean_distance
from pprint import pprint

def is_near_palindrome(word,lo,hi):
    d1 = euclidean_distance(word[0],word[4])
    d2 = euclidean_distance(word[1],word[3])

    if( (d1+d2) > lo and (d1+d2) <= hi ):
        return True

    return False

if __name__=="__main__":
    words = get_words()

    knp = 0
    near_palindromes = []

    # Euclidean distance tolerance
    lo = 0.0
    hi = 1.0

    for i in range(len(words)):
        if(is_near_palindrome(words[i],lo,hi)):
            knp += 1
            near_palindromes.append(words[i])

    print("-"*40)
    print("Near Palindromes: \n")
    print(", ".join(near_palindromes))
    print("The number of near-palindromes is {0:d}".format(len(near_palindromes)))

References

  1. Knuth, Donald. The Art of Computer Programming. Upper Saddle River, NJ: Addison-Wesley, 2008.

  2. Knuth, Donald. The Stanford GraphBase: A Platform for Combinatorial Computing. New York: ACM Press, 1994. <http://www-cs-faculty.stanford.edu/~knuth/sgb.html>

  3. "Five Letter Words." Git repository, git.charlesreid1.com. Charles Reid. Updated 1 September 2017. <http://git.charlesreid1.com/cs/five-letter-words>

Tags:    python    computer science    graphs    algorithms    art of computer programming    knuth    language