Table of Contents
About the Five-Letter Words
In Volume 4, Facsimile 0 of Donald Knuth's Art of Computer Programming, in which Knuth covers graph theory, he introduces a list of five-letter words as part of a data set useful in exploring graph theory and graph algorithms.
See Five Letter Words on the charlesreid1.com wiki for details.
Different by 1, Revisited
This post is revisiting an exercise from the above volume, Exercise 28:
Find pairs of SGB word vectors that differ by +/- 1 in each component.
In a prior blog post (Part 1), we had inerpreted the question as finding word vectors whose Euclidean distance differed by 1 total, which is the same as a Hamming distance of 1.
However, on revisiting the (more interesting) question actually being posed by the author, we find a different and more difficult problem.
As an example of what Knuth is asking for:
rover -> spuds
Each letter of the words are within an edit distance of 1, at each position.
There are 38 such pairs:
$ python diff_by_one_fixed.py abaft babes absit baths adder beefs ambit blahs anger boffs anode boned bider chefs bidet chefs biffs cheer ghost hints hobos inapt holds inker honed inode hoods inner hoofs inner hoots input hoped inode ingot johns needs odder needs offer rider sheds rifer sheds rinds shoer robed spade robot snaps robot spans rover spuds ruffs steer runts stout rusts strut sheer tiffs sheet tiffs shout tints sides theft sneer toffs splat tombs spuds toter stuns tutor Found 38 pairs of words that differ by +/-1 in each component.
The approach we used was as follows:
- Iterate over each word in the wordlist (use the first 1,000 words to keep it shorter for testing)
- For each word:
- Generate all variations that are within +/-1 using recursive backtracking (could also use algorithm to generate all 32 binary codes of length 5, where 0 = -1, 1 = +1)
- For each of the 32 variations,
- Check if the word is in the wordset (O(1) cost if using a hash table/set)
- If so, add ordered pair (word1,word2) to a set of solutions (to avoid dupes)
Different by N, Revisited
We went back and modified the code to take a distance parameter d, but storage and compute cost, as well as the sparsity of the graph of shared bigrams and trigrams among these 5,000 words, means the number of pairs increases exponentially.
| Distance | Number of pairs | Walltime | |-----------|-------------------|----------------| | 1 | 38 | 0.26 s | | 2 | 525 | 5.26 s | | 3 | 4982 | 38.87 s | | 4 | ?? (10^5 est.) | 10 min (est.) |
You can find the
diff_by_n.py script here:
$ python diff_by_n.py abaft babes absit baths adder beefs ambit blahs anger boffs anode boned bider chefs bidet chefs biffs cheer ghost hints .. Found 38 pairs of words that differ by +/-1 in each component. Time: 0.2673 s aback babel aback cabal abaft babes abash cacti abide baked abide caged abide caked abler bands abler bangs abode caned ... Found 525 pairs of words that differ by +/-2 in each component. Time: 5.2617 s abaca ceded abaci babel abaci cabal abaci decaf abaci decal aback babel aback cabal aback decal abaft babes abaft bedew ... Found 4982 pairs of words that differ by +/-3 in each component. Time: 38.8743 s