vsm.model.BeagleEnvironment

class vsm.model.BeagleEnvironment(corpus, n_cols=2048, dtype=<type 'numpy.float64'>, context_type='sentence')

BeagleEnvironment is a randomly generated fixed vectors representing the environment.

Methods

__init__(corpus[, n_cols, dtype, context_type])	Initialize BeagleEnvironment.
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()	Sets a m x n environment matrix where m is the number of words in corpus and n is n_cols.

__init__(corpus, n_cols=2048, dtype=<type 'numpy.float64'>, context_type='sentence')

Initialize BeagleEnvironment.

Parameters:

• corpus (Corpus) – Source of observed data.
• n_cols (int, optional) – Number of columns. Default is 2048.
• dtype (np.dtype, optional) – Numpy dtype for matrix attribute. Default is np.float64.
• context_type (string, optional) – Name of tokenization stored in corpus whose tokens will be treated as documents. Default is sentence.

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()

Sets a m x n environment matrix where m is the number of words in corpus and n is n_cols. The matrix consists of randomly generated vectors.