vsm.model.BeagleOrderMulti

class vsm.model.BeagleOrderMulti(corpus, env_matrix, context_type='sentence', psi=None, rand_perm=None, lmda=7)

BeagleOrderSeq stores word order information in the context.

Methods

__init__(corpus, env_matrix[, context_type, ...])	Initialize BeagleOrderMulti.
load(f)	Takes a filename or file object and loads it as an npz archive
save(f)	Takes a filename or file object and saves self.matrix in an npz archive.
train([n_procs])	Trains the model using n_procs processors.

__init__(corpus, env_matrix, context_type='sentence', psi=None, rand_perm=None, lmda=7)

Initialize BeagleOrderMulti.

Parameters:

• corpus (Corpus) – Source of observed data.
• env_matrix (2-D array) – BEAGLE environement matrix.
• context_type (string, optional) – Name of tokenization stored in corpus whose tokens will be treated as documents. Default is sentence.
• psi (int, optional) –
• rand_perm (boolean, optional) –
• lmda (int, optional) –

static load(f)

Takes a filename or file object and loads it as an npz archive into a BaseModel object.

Parameters:file (str-like or file-like object) – Designates the file to read. If file is a string ending in .gz, the file is first gunzipped. See numpy.load for further details. Returns:A dictionary storing the data found in file. See Also:numpy.load()

save(f)

Takes a filename or file object and saves self.matrix in an npz archive.

Parameters:file (str-like or file-like object) – Designates the file to which to save data. See numpy.savez for further details. Returns:None See Also:numpy.savez()

train(n_procs=2)

Trains the model using n_procs processors.

Parameters:n_procs (int, optional) – Number of processors. Default is 2.