28 November 2015
Fun with Trigrams
Inspired by a recent talk about the benefits of code katas, I decided, this afternoon, to give one a go. Before setting out on a search katas to complete, I knew that I wanted to do something that was completely new to me so that I could document the process I was learning.
It turns out htttp://codekata.com has some pretty interesting challenges. I chose kata 14: Tom Swift Under the Milkwood as trigrams are something I haven’t had much experience with and wanted to learn more about.
To mutate an existing set of text into a new form using trigrams.
Before I even started sketching out code, I wanted to jot down the steps I thought I would need to take as a rough framework and so I could reflect on them after finishing the task:
First, read in the source text (I took Dave Thomas’ advice and picked a book from Project Gutenberg and chose “The Hound of the Baskervilles”).
Next, parse it for sentences so I could split those up into chunks of 3 and form my trigrams.
Parsing the text into chunks is something I considered to be out of scope for this exercise, so I called on
pragmatic_segmenter to help split the text at proper sentence boundaries.
Finally, arrange a new body of text from the trigrams by picking a key at random, printing it and one of its values before picking a new key based on the last two words and repeating, stopping and picking a new key at random if I run out of matches based on the last two words of my text.
Now to step into some code.
First things first, set up some variables and require
Next, read in the text and initialise
gsub here is used to remove DOS file endings which were causing
pragmatic_segmenter to think a sentence had terminated.
At this point, I had also assumed that I would need to strip all non-alphanumeric characters and lowercase the text. Turns out while this makes for cleaner output and standardises trigram keys, the output is not all that interesting.
Next I created my trigram map:
What this does is split each sentence up using spaces as the pattern to match and then strips any extra spaces before chunking each sentence into groups of 3 or less.
With each of those groups, we check their size so we know we can use the first 2 words as the key and the last word as the value. Then we either create a new key in the hash and set its value to an empty array, or if a key already exists, we push another value into the value array.
This part still seems rudimentary and not all that great, but it does the job for now and it is something I will revisit when I make my next pass at the code.
Now we can assemble our new text!
First we pick a random key to start with and add it to our output:
I needed some sort of end condition, so I just chose to print out 1000 characters before stopping.
The conditional checks if
next_key is nil, or if the
value we try and find with
next_key is nil and then finds a new random key to start with.
If we are able to find a value based on
next_key, we add it to our output and then set the
next_key based on the last two words of our output.
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