This post includes a standalone (only standard headers, no external libs) ~400 line C++ source file that can analyze text and use an order N Markov chain to randomly generate new text in the same style. The Markov code itself is fairly generic / re-usable and a template parameter to the class lets you specify the order of the chain as well as the type of state data to use. That code is on github at: https://github.com/Atrix256/TextMarkovChain

When I see material on Markov chains, it usually comes in two flavors:

- Very Mathy
- Pretty impressive results light on explanation

It turns out the reason for this is because they CAN be very mathy but they can also be extremely simple.

Without knowing this, I decided it was time to learn about Markov chains. I leveled up my linear algebra knowledge a bit, finally getting a solid grasp on eigen vectors, and learning things like how to put a matrix into an eigen basis form to be able to make matrix exponentiation a trivial operation. There are links at bottom of post if you want to learn this stuff too.

Then, I sat down to learn Markov chains and nearly flipped my table over! Yes, Markov chains can be mathy (and matrix exponentiation is one way to find a Markov chain steady state, but not the best), but that stuff isn’t really required for most uses.

# Markov Chains

A Markov chain is just any situation where you have some number of states, and each state has percentage chances to change to 0 or more other states.

You can get these percentages by looking at actual data, and then you can use these probabilities to GENERATE data of similar types / styles.

# Example

This post uses Markov chains to generate text in the style of provided source text.

The first step it does is analyze source text.

To analyze the source text, it goes through text, and for each word it finds, it keeps track of what words came next, and how many times those words came next.

When analyzing the story “The Tell-Tale Heart” by Edgar Allan Poe for instance (https://poestories.com/read/telltaleheart , also is data/telltale.txt in the code that goes with this post), here are the words that came after “when” and their counts.

- all – 1
- enveloped – 2
- he – 1
- i – 4
- my – 2
- overcharged – 1
- the – 1

Here are the counts for the words that appear after “is”:

- but – 1
- impossible – 1
- merely – 1
- nothing – 1
- only – 1
- the – 2

After all these counts have been gathered up, the next step is to convert them into probabilities. You do this by summing up the words that come after a specific word, and dividing the count of each word by that total sum.

The above examples then turn from counts to probabilities. Here is “when”:

- all – 8%
- enveloped – 16%
- he – 8%
- i – 33%
- my – 16%
- overcharged – 8%
- the – 8%

Here is “is”:

- but – 14%
- impossible – 14%
- merely – 14%
- nothing – 14%
- only – 14%
- the – 28%

Note: The code that goes with this post spits out these counts and percentages in the “out/stats.txt” file if you ever want to see the data.

Once the probabilities are known, you can start generating text. The first thing you do is pick a word purely at random, this is the first word in the text.

Next, you use the probabilities of what words come after that word to randomly choose the next word.

You then use the probabilities of what words come after that word to randomly choose the next word.

This repeats until you’ve generate as much text as you want.

The code with this post generates 1000 words into the “out/generated.txt” file.

That is literally all there is to it. You could do this same process with sheet music to generate more music in the same style, you could do it with weather forecasts to generate realistic weather forecasts (or even try to use it to predict what weather is next). You can do this with any data you can imagine.

# Example Generated Output

Here is 100 words of generated text from various sources.

First is text generated from “The Tell-Tale Heart” by Edgar Allan Poe (https://poestories.com/read/telltaleheart):

…About trifles, and with perfect distinctness — very slowly, my sagacity. I then took me, louder — you cannot imagine how stealthily — with what caution — cautiously — would have told you may think that no longer i knew that no blood – spot. He would not even his room, to do the hour had made up my whole week before him. I knew what dissimulation i showed them causeless, undisturbed. Now a hideous heart, no — wide open — all and the old man, and he would have…

Here is text generated from “The Last Question” by Isaac Asimov (http://hell.pl/szymon/Baen/The%20best%20of%20Jim%20Baens%20Universe/The%20World%20Turned%20Upside%20Down/0743498747__18.htm):

…Glory that. Man said, it into a meaningful answer. Granted, said, might be kept from the entire known to restore the universe for meaningful answer. Mq – talkie robot, ac learned how many stars are dying. The boys appreciated that not. Cosmic ac that, how may be able to reach the small station, said at half the same. He shrugged. We’ll have enough to be alone. And lose itself aloof. When any other kind of universal ac. He consisted of individuals were self – contact…

Here is text generated from a research paper “Projective Blue-Noise Sampling” (http://resources.mpi-inf.mpg.de/ProjectiveBlueNoise/ProjectiveBlueNoise.pdf):

…Numerical integration. Mj patterns to vector multiplication to achieve a way that the above question whether there exist distributions have addressed anisotropic classic lloyd relaxation green and rotated pattern significantly worse than the j 1, where each site: our projective blue – noise point distributions along both axes. Previous work sampling when undergoing one after a certain number of common blue noise patterns, but at the publisher s ., cohen – left constructs a quality of latinizing the non – sample counts however, as a set only in a theory 28, this shrinkage…

Here is text generated from an example (not real, but representative) psych report from my wife who is a school psychologist:

…Brother had to mildly impaired body movement, the school and placement after a 90 probability that student: adapting to struggle as video games. Student’s planning and he request, spelling subtest scores. This time. The student: this time and accurately with both, including morphology, 2013. Administrators should consider participation in the following are student as intellectually disabled specific auditory comprehension of reading: mr. Mrs. The two subtest is designed to use of or economic disadvantages, gestures, vitality or economic disadvantages, picking at approximately 5th grade prior…

Here we generate a markov chain using ALL the above source texts, to get a mash up of all of them.

…Restore the sphere packing radius is likely an adaptive skills. Please see inset in the conner s problems, we’ll just have well and visualization and he is computed on 1 2 was contacted by things, and restricted number of his abilities. We can simply like them, as well as a s difficulty interacting with a closer to cry, the process based on the standards – appropriate to spurious aliasing artefacts mit87, making a meaningful answer. Finally, 11 months through hyperspace to try his eye contact. Jerrodine’s eyes were going out if…

Lastly, here is only Poe and Asimov combined:

…Could not forever, and continually increased. And stood for a sudden springing to get back and the eighth night i to that man, 2061, but the original star and made trips. A very, and fell full youthfulness even to feel — i then stop someday in five words on a while i heard all the noise steadily for us, calling him to pluto and now a galaxy alone pours out, quick sound would think of individuals. He stirred his hideous veil over the ceiling. Twenty billion years ago, man, …

# Nth Order Markov Chains

Using one word to generate the next word works somewhat well – the generated Poe text definitely seemed like Poe for instance – but there are plenty of times when things don’t make much sense.

A markov chain can become higher order when you don’t just look at the current state to transition to the next state, but you look at the last N states to transition to the next state.

In the text generation case, it means that a 2nd order Markov chain would look at the previous 2 words to make the next word. An order 3 markov chain would look at the previous 3 words to make the next word.

Interestingly, an order 0 Markov chain looks at NO WORDS to generate the next word, so is purely random word generation, with similar word counts (by percentage) as the original text.

The code that goes along with this post lets you specify the order on the Markov chain.

Here is “The Tell-Tale Heart” with an order two markov chain.

…Dark as midnight. As the bell sounded the hour, there came to my ears: but he had been too wary for that. A tub had caught all — ha ha when i describe the wise precautions i took for the concealment of the old man sprang up in bed, crying out — no blood – spot whatever. I removed the bed and examined the corpse. Yes, he was stone, stone dead. I knew that he had been lodged at the police. A watch’s minute hand moves more quickly than did…

If you compare that to the actual story, you can find fairly large sections of that are taken verbatim from the source text, but the arrangement of those larger chunks are different.

The reason for this is that when you have two words mapping to the next word, the number of these go up, which makes it so on average, there are going to be fewer choices for “next words”, which make the results less random, and more deterministic.

If you gave it more text (like, maybe, all of Edgar Allan Poe’s work), there would be more options for the next word after specific 2 word pairs, but with a single short story, it doesn’t have very many choices. If you look at the out/stats.txt file and compare order 1 vs order 2, you can see that order 2 has a lot more situations where a current state maps to a single next state.

At order 3 there are even fewer choices, and it hits a pattern loop:

…Had been lodged at the police office, and they the officers had been deputed to search the premises. I smiled, — for what had i now to fear there entered three men, who introduced themselves, with perfect suavity, as officers of the police. A shriek had been heard by a neighbor during the night; suspicion of foul play had been aroused; information had been lodged at the police office, and they the officers had been deputed to search the premises. I smiled, — for what had i now to…

Here is an order 2 mashup of Poe and Asimov:

…Crossing the floor, and still chatted. The universal ac interrupted zee prime’s own. It had to be contrary, and jerrodette i. Ask multivac. As the passage through hyperspace was completed in its place, each cared for by perfect automatons, equally incorruptible, each with its dreadful echo, the real essence of men was to be contrary now, now, honeys. I’ll ask microvac. Don’t shout. When the sun, and their only concern at the visiplate change as the frightened technicians felt they could hold their breath no…

Lastly, here’s an order 2 mashup of all 4 source texts:

…Mathematics: student does not require special education and related services, the radius of each other, indistinguishable. Man said, ac organized the program. The purpose of this report provides information about the child s educational performance. Other pertinent future work includes the extension of our projective lloyd patterns against other patterns on a role not based on his scores on this scale is different for the sake of visual clarity, we specify all spaces via a set x. In a way, man, i undid it just so much that a single…

# Other Implementation Details

When combining the texts, it might make sense to “normalize” the percentages for each source text. How it works now with raw counts makes it so longer documents have more of their style preserved in the final output document.

You may also want to give weightings to different text so you can have a sliding scale between Poe and Asimov for instance, by basically scaling the counts from their files higher or lower to give more or less representation in the results.

When analyzing the text, I had to think about what to do with punctuation. I chose to treat punctuation as words in themselves, but ignored some punctuation that was giving weird results – like double quotes. I’ve only just now realized that I incorrectly ignore question marks. Oops.

When generating text, i made it so some words don’t put a space before themselves (like, a period!), and i also made it so words would have their first letter capitalized after a period or similar. There seems to need ad hoc, domain specific massaging to get reasonable results.

It’s possible (especially with higher order markov chains) that you can get into a situation where your current state has nothing to transition to. You’d have to figure out what to do in this case. One idea would be to choose a next word at random. Another idea would be to fall back to a lower order markov chain maybe?

I feel like once you understand the algorithm, it’s an art form to teach and tune the Markov chain to get good results. I bet there are some interesting techniques beyond the simple things I’ve done here.

# Links

**Mathy Markov Chain Info**

If you want to dive into the mathy side of markov chains, here are some great resources you can follow to get there…

A great linear algebra online “text book”, that is very easy to read and understand: http://immersivemath.com/ila/index.html

Some great videos on linear algebra: https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

A 9 part series on markov chains. It’s this long because it’s very explicit and works through the details by hand. I watched it at like 1.5x speed and was fine ๐

Some “mathy” notes about Markov chains, including higher order ones:

http://personal.psu.edu/jol2/course/stat416/notes/chap4.pdf

**Q Learning**

Related to markov chains, Q learning is essentially is a way to learn a Markov chain from data – for instance learning how to play tic tac toe, or how to traverse a maze.

I would like to learn Q learning better and make a post (and code!) at some point.

Q Learning Explained With HTML5

https://blockulator.github.io/Q-Learning-Explained-With-HTML5/

An introduction to Q-Learning: reinforcement learning

https://medium.freecodecamp.org/an-introduction-to-q-learning-reinforcement-learning-14ac0b4493cc

Reinforcement Learning Tutorial Part 1: Q-Learning

https://blog.valohai.com/reinforcement-learning-tutorial-part-1-q-learning

Reinforcement Learning Tutorial Part 2: Cloud Q-learning

https://blog.valohai.com/reinforcement-learning-tutorial-cloud-q-learning

Reinforcement Learning Tutorial Part 3: Basic Deep Q-Learning

https://towardsdatascience.com/reinforcement-learning-tutorial-part-3-basic-deep-q-learning-186164c3bf4

**Other**

Here is a twitter conversation about some compelling uses of Markov chains

Here’s a video “Markov Chain Monte Carlo and the Metropolis Algorithm” which uses Markov chains to help calculate integrals numerically.

# Code

Again, the code for this post is up on github at https://github.com/Atrix256/TextMarkovChain

The code is written for readability and runs plenty fast for this demo (nearly instant in release, a couple seconds in debug) but There are lots of string copies etc that you would want to fix up if using this code seriously.

Thanks for reading!