Trains a term-frequency model.
In a term-frequency model, the number of occurrences of a word type in a context is counted for all word types and contexts. Word types correspond to matrix rows and contexts correspond to matrix columns.
See Also: | vsm.model.base, vsm.corpus.Corpus, scipy.sparse.coo_matrix |
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Methods
__init__([corpus, context_type]) | Initialize TfSeq. |
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() | Counts word-type occurrences per context and stores the results in |
Initialize TfSeq.
Parameters: |
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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. |
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Returns: | A dictionary storing the data found in file. |
See Also: | numpy.load() |
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. |
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Returns: | None |
See Also: | numpy.savez() |
Counts word-type occurrences per context and stores the results in self.matrix.