Introduction


While browsing the golang documentation and packages I stumbled upon something entirely new, a “codewalk” for a markov chain algorithm that generates text based on a “trained” markov chain from user input [1]. This reminded me of the time I studied Markov Chains for a University course but never got to see a software implementation of what it might look. To my surprise it was pretty simple and straightforward.

Brushing up my knowledge of markov chains and checking out a visual explanations [2] and another more complex implementation of the same algorithm [3], I decided to try and make my own and then connect it to my API.

Now if you’re not interested in how I did it and just want to dump some obscene amounts of text for my poor little free Heroku hosted API to handle, then skip to here. If you wish to see the GitHub repo go here.

What are Markov Chains


I wouldn’t call myself a mathematician so if you want a more mathematical explanation check out Wikipedia, or any the referenced posts.

I will do my best and explain it with an example. If we provide the following block of text to a markov chain:

I am an engineer
I am not a mathematician

We can split this text into sequences of different sizes

// size 1
["I" "am" "an" "engineer" "I" "am" "not" "a" "mathematician" ...]

// size 2
["I am" "am an" "an engineer" "engineer I" "I am" "am not" ...]

// size 3
"I am an" "am an engineer" "an engineer I" "engineer I am" ...]

Now for each sequence we must also store the following token, thus creating a pair. Not that in our example the size 2 sequence "I am" can be followed by multiple words: an not so our transition mapping has to keep both relations and their occurrence count (as a pair may occur multiple times this increasing its frequency)

Pair {
    Sequence: "I am"
    Next: {
        "an":  1
        "not": 1
    }
}

After the transition map is created on a sequence of input data, it’s actually up to you how you decide to generate the next token, the way I did it is by giving the Builder a seed which is actually just a sequence of word, if non is provided the Builder will pick at random a starting sequence and go from there.

Implementation


It was easy to create a simple markov chain using the following structs:

// represents a grouping of individual words
// eg: []string{“I”, “am”, “Alex”}, this can be extracted
// from an original string of any form or shape:
// “I am Alex”, “I   am  Alex”, “I:am:Alex”
// and it’s all up to the caller to split their strings into sequences
type Sequence []string
// a pairs represents a possible transition between a sequence of n words
// and the next (single) word
// the Current sequence must be of an equal lenght to the chain pair size
// meaning you can’t have some transitions for 2-grouped words and 1-grouped words
type Pair struct {
	Current Sequence
	Next    string
}
// by having a
type transitionMap map[string]int
// and then nested inside
frequencyMatrix map[string]transitionMap
// we generate our mapping of all encountered
// sequences to their respective next word
// and the number of times this occurs

And then by creating the Builder on top of a chain, we can continually generate new words, append them to a sequence and then use that sequence to further poll the Chain for new words.

Improvements


There are plenty of improvements that can be made but this was intended as a fun little project to kill off some of the time I don’t have. Maybe another day I’ll pick this up and make it generate even more accurate and funny text, until then please feel free to enjoy an “interactive” version below.

Try it


You can submit any sort of text here (it’s not stored anywhere) with a character limit of 50.000 (and yes it’s both frontend and backend validated). You will then be redirected to my api, where you’ll get a generated text of 200 words max.

References