Referencia de la Clase vsm.model.lsa.Lsa

Diagrama de herencias de vsm.model.lsa.Lsa

Diagrama de colaboración para vsm.model.lsa.Lsa:

Métodos públicos
def	__init__
def	train
def	save

Métodos públicos estáticos
def	load
def	from_tf
def	from_tfidf

Atributos públicos
	word_matrix
	doc_matrix
	eigenvalues
	context_type
	corpus
	td_matrix

Descripción detallada

Documentación del constructor y destructor

def vsm.model.lsa.Lsa.__init__	(		self,
			corpus = None,
			context_type = None,
			td_matrix = None
	)

Initialize Lsa.

:param corpus: A Corpus object containing the training data.
:type corpus: Corpus, optional

:param context_type: Name of tokenization whose tokens will be
    treated as documents. Default is `None`.
:type context_type: string, optional

:param td_matrix: Term-Document matrix. Default is `None`.
:type td_matrix: np.array, optional

Documentación de las funciones miembro

def vsm.model.lsa.Lsa.from_tf (   tf_model )

static

Takes a `Tf` model object and generates a `TfIdf` model.

def vsm.model.lsa.Lsa.from_tfidf (   tfidf_model )

static

Takes a `Tf` model object and generates a `TfIdf` model.

def vsm.model.lsa.Lsa.load (   f )

static

Loads LSA model data from a numpy archive file with extension
`npz`. The expected keys for the component matrices are
`word_matrix`, `eigenvalues` and `doc_matrix`.

:param f: Designates the file from which to load data. See
    `numpy.load` for further details.
:type f: str-like or file-like object
    
:returns: a saved Lsa model.

:See Also: :meth:`numpy.load`

def vsm.model.lsa.Lsa.save	(		self,
			f
	)

Saves model data as a numpy archive file with extension `npz`.
The keys for the component matrices are `word_matrix`,
`eigenvalues` and `doc_matrix`.

:param f: Designates the file to which to save data. See
    `numpy.savez` for further details.
:type f: str-like or file-like object
    
:See Also: :meth:`numpy.savez`

def vsm.model.lsa.Lsa.train	(		self,
			k_factors = 300
	)

Trains the model.

:param k_factors: Default is 300.
:type k_factors: int, optional

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La documentación para esta clase fue generada a partir del siguiente fichero:

• vsm/vsm/model/lsa.py