My high school Latin teachers are now writhing in horror because of my blog post title for the Perl Weekly Challenge 010
Class action against Roman grammar
One of the super strong parts of the Perl 6 language is the ability to structure your regular expressions into expressive and easy to understand grammars, and pair them with a class that operates on each matching sub-expression in a sort of love letter to hierarchical abstract syntax trees.
This is great for turning a string of specific letters into a number using a
vaguely defined set of rules – many people would (almost correctly) say that
CMCMXLXLIIIIII is not a valid Roman numeral, but that shouldn’t stop us from
So I put together a grammar where each part of the input is parsed as:
prefix must be the smaller decimal prefix ((X, V) ⇒ I, (C, L) ⇒ X, (M,
D) ⇒ C) of
suffix is the sub-expression parsing the next
I then parse the input string using this grammar and a class with equivalent named methods. Each method:
- Determines the value matching numerals,
- Adds the already calculated suffix value, then
- subtracts the prefix value(s).
The result parses numerals that are strictly correct according to rules invented long after the fall of the Roman empire, and also a whole lot of stuff that looks weird, in the spirit of minimal but effective validation.
And the other way
The solution to this is almost self documenting.
- Map each component from its value to its numeral (or numeral pair in the case of subtractive prefixes),
- then loop through these, assigning the required number of each component.
Conveniently, only one of any half-decimal or subtractive prefix will fit into the remainder of its decimal predecessor, so nothing extra has to be done for strictly conforming output.
Bags of distance between Jaro-Winkler
The Jaro-Winkler distance of two strings is just one minus their similarity, which is a fairly inoffensive algorithm that takes the Jaro similarity and adds a weight based on up to the first four letters of the word being the same (which produces some differences like for example vettel will match vent more closely than event does).
The Jaro similarity algorithm seemed pretty simple to implement at first glance – just take all the characters that are in both strings, and average these between the length of both strings and the number of transposed / out of sequence characters, and in fact I had an implementation of this working to my satisfaction before I realised that I’d missed something important.
The matching characters cannot be more than half the length of the longer of the two strings away from each other.
I had put together an elegant solution that would intersect the combed arrays of the two strings together, and then loop through the matching characters in sequence according to the first string, adding transpositions for any character that was not in the right place in the second string, and now I had to replace one line with an ∩ operator with a highly inelegant looking loop through the first string that marks off any matching character in the second string within the specified distance limits, unless it’s already been marked off.
Due to being unable to find more than two pieces of test data (both of which work the same with or without the distance restriction), I’m not sure if it’s even correct.