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This is generic code for a Markov process.

Every once in a while, I want random but reasonable-looking content. The simplest tool for such a thing is a Markov process. We learn the probability distribution of a text, then generate new text that conforms to that distribution. Very handy.

Generally, I've used this for random names to plug into software tests, but I don't always want a letter-based process. So, this version uses a type parameter. If you want to generate characters, words, heights, instructions, or stacks of processes, this should work, as long as the harness program can specify the data.


The solution includes two projects.

  • Markov itself is just a main program that pushes a text file, character by character, into the process object.

  • MarkovProcess is where the real magic happens. Include this in any project where you want to use it.


Unlike a standard implementation might choose, Markov doesn't have any idea of a "training mode." Separate states are maintained for learning and for generation, so both can happen together, if desired.

The API for the MarkovProcess class includes:

  • AddNext(element) learns the next element in the sequence.

  • ClearTraining() resets the training queue. It does not reset the training data. It only creates a new starting state for the generation.

  • Generate() produces the next element in the output sequence that could plausibly have been part of the input at this point.


Generic Markov process code




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