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API

robot.markov

The object available at robot.markov is an instance of ModelPool. It defines the following methods:

createModel(name, options, callback)

Construct a new model. Corresponding backing storage (database tables) will be created if necessary.

Available options include:

  • storage Backing storage implementation name to use for this model. One of memory, redis, or postgres.
  • storageUrl Connection URL appropriate to the storage provider.
  • ply Number of consecutive prior states to use to determine probable next states. The higher this is set, the more accurate to the source material the model becomes, but it requires correspondingly greater storage capacity and training data.
  • min Minimum number of input states to consider when learning new patterns.

All may be omitted; their corresponding environment variable settings will be used if unspecified.

The callback argument will be invoked with an instance of the model once it's been successfully created and connected to its backing store. This is most useful for configuring custom processors.

Raises an Error if:

  • storage is invalid; or
  • a model called name already exists.

modelNamed(name, callback)

Access a MarkovModel instance with a known name. Invoke the provided callback with the model as its argument once the model has completed initialization, or on the next tick if the model is already initialized.

Raises an Error if no model called name exists.

MarkovModel

processWith(processor)

A processor is used to transform raw input objects (usually Strings) into an Array of String states to learn, and to transform generated output state sequences to raw output objects (usually Strings). Processors are used to define what a "state" means to a specific model, or to manipulate or filter presented input in arbitrary ways.

Create a processor by constructing an object with pre and post keys set to functions that perform the corresponding transformation. For example, this is the default processor, which learns sequences of non-empty words:

model.processWith
  pre: (input) -> word for word in input.split /\s+/ when word.length > 0
  post: (output) -> output.join ' '

learn(input, callback)

Present an input exemplar to the model. Apply this model's processor to convert input to an Array of states, learn each transition within the state array, then invoke callback with any errors that occurred once all modifications have been persisted.

generate(seed, max, callback)

Generate output based on the current state of the model. Begin with the states derived by applying the preprocessor to the seed object, then follow transition links within the model with probabilities proportional to the link strengths. Invoke the postprocessor on the generated state sequence and invoke the callback with any error that occurred and the resulting object.

destroy(callback)

Destroy any persistent storage associated with this model. Call callback with any errors that occurred.