vsm.model.LdaCgsSeq

class vsm.model.LdaCgsSeq(corpus=None, context_type=None, K=20, V=0, alpha=[], beta=[])

An implementation of LDA using collapsed Gibbs sampling.

Methods

__init__([corpus, context_type, K, V, ...])	Initialize LdaCgsSeq.
load(filename)	A static method for loading a saved LdaCgsMulti model.
save(filename)	Saves the model in an .npz file.
train([n_iterations, verbose, seed])	Takes an optional argument, n_iterations and updates the model n_iterations times.

__init__(corpus=None, context_type=None, K=20, V=0, alpha=[], beta=[])

Initialize LdaCgsSeq.

Parameters:

• corpus (Corpus) – Source of observed data.
• context_type (string, optional) – Name of tokenization stored in corpus whose tokens will be treated as documents.
• K (int, optional) – Number of topics. Default is 20.
• beta (list, optional) – Topic priors. Default is 0.01 for all words.
• alpha (list, optional) – Document priors. Default is a flat prior of 0.01 for all topics.

static load(filename)

A static method for loading a saved LdaCgsMulti model.

Parameters:filename (string) – Name of a saved model to be loaded. Returns:m : LdaCgsMulti object See Also:numpy.load

save(filename)

Saves the model in an .npz file.

Parameters:filename (string) – Name of a saved model to be loaded. See Also:numpy.savez

train(n_iterations=100, verbose=1, seed=None)

Takes an optional argument, n_iterations and updates the model n_iterations times.

Parameters:

• n_iterations (int, optional) – Number of iterations. Default is 100.
• verbose (int, optional) – If 1, current number of iterations are printed out to notify the user. Default is 1.
• seed (int, optional) – Seed for numpy’s RandomState. Default is None.