MarkovModel

A Markov model records which symbols tend to follow which, then uses those odds to produce fresh sequences. The order sets how much history each step looks back on. A first-order model (order 1) looks at one symbol to pick the next (bigram probabilities). A second-order model (order 2) looks at the previous pair (trigram probabilities), and so on.

Train the model with learn(), then make new sequences with generate(). A symbol can be anything. For music it is usually a pitch.

Creating a MarkovModel

You can create a MarkovModel using the following functions:

MarkovModel()

MarkovModel(order)

Parameter	Type	Default	Description
order	int	1	How many previous symbols each step looks back on.

For example,

model = MarkovModel(3)

Functions

Once a MarkovModel model has been created, the following functions are available:

Function	Description
clear()	Empty the model, forgetting everything it has learned.
learn(listOfSymbols)	Learn the patterns in a sequence of symbols.
put(tupleOfSymbols)	Add a single transition to the model by hand.
get(tupleOfSymbols)	Pick a random symbol to follow a context, weighted by how often each was seen.
getTransitions(tupleOfSymbols)	Return every symbol that has followed a context, with how often each did.
generate()	Generate a new sequence in the style the model learned.
getNumberOfSymbols()	Return how many different symbols the model has learned.
getNumberOfTransitions()	Return how many transitions the model has learned.