Python matutils.corpus2dense方法代碼示例

# 需要導入模塊: from gensim import matutils [as 別名]
# 或者: from gensim.matutils import corpus2dense [as 別名]
def testTransform(self):
        """Test lsi[vector] transformation."""
        # create the transformation model
        model = lsimodel.LsiModel(self.corpus, num_topics=2)

        # make sure the decomposition is enough accurate
        u, s, vt = scipy.linalg.svd(matutils.corpus2dense(self.corpus, self.corpus.num_terms), full_matrices=False)
        self.assertTrue(numpy.allclose(s[:2], model.projection.s)) # singular values must match

        # transform one document
        doc = list(self.corpus)[0]
        transformed = model[doc]
        vec = matutils.sparse2full(transformed, 2) # convert to dense vector, for easier equality tests
        expected = numpy.array([-0.6594664, 0.142115444]) # scaled LSI version
        # expected = numpy.array([-0.1973928, 0.05591352]) # non-scaled LSI version
        self.assertTrue(numpy.allclose(abs(vec), abs(expected))) # transformed entries must be equal up to sign 

# 需要導入模塊: from gensim import matutils [as 別名]
# 或者: from gensim.matutils import corpus2dense [as 別名]
def to_features(dictionary, words):
    tmp = dictionary.doc2bow(words)
    dense = list(matutils.corpus2dense([tmp], num_terms=len(dictionary)).T[0])

    return dense 

# 需要導入模塊: from gensim import matutils [as 別名]
# 或者: from gensim.matutils import corpus2dense [as 別名]
def __getitem__(self, bow, scaled=False, chunksize=512):
        """
        Return latent representation, as a list of (topic_id, topic_value) 2-tuples.

        This is done by folding input document into the latent topic space.
        """
        assert self.projection.u is not None, "decomposition not initialized yet"

        # if the input vector is in fact a corpus, return a transformed corpus as a result
        is_corpus, bow = utils.is_corpus(bow)
        if is_corpus and chunksize:
            # by default, transform `chunksize` documents at once, when called as `lsi[corpus]`.
            # this chunking is completely transparent to the user, but it speeds
            # up internal computations (one mat * mat multiplication, instead of
            # `chunksize` smaller mat * vec multiplications).
            return self._apply(bow, chunksize=chunksize)

        if not is_corpus:
            bow = [bow]

        # convert input to scipy.sparse CSC, then do "sparse * dense = dense" multiplication
        vec = matutils.corpus2csc(bow, num_terms=self.num_terms, dtype=self.projection.u.dtype)
        topic_dist = (vec.T * self.projection.u[:, :self.num_topics]).T # (x^T * u).T = u^-1 * x

        # # convert input to dense, then do dense * dense multiplication
        # # ± same performance as above (BLAS dense * dense is better optimized than scipy.sparse), but consumes more memory
        # vec = matutils.corpus2dense(bow, num_terms=self.num_terms, num_docs=len(bow))
        # topic_dist = numpy.dot(self.projection.u[:, :self.num_topics].T, vec)

        # # use numpy's advanced indexing to simulate sparse * dense
        # # ± same speed again
        # u = self.projection.u[:, :self.num_topics]
        # topic_dist = numpy.empty((u.shape[1], len(bow)), dtype=u.dtype)
        # for vecno, vec in enumerate(bow):
        #     indices, data = zip(*vec) if vec else ([], [])
        #     topic_dist[:, vecno] = numpy.dot(u.take(indices, axis=0).T, numpy.array(data, dtype=u.dtype))

        if scaled:
            topic_dist = (1.0 / self.projection.s[:self.num_topics]) * topic_dist # s^-1 * u^-1 * x

        # convert a numpy array to gensim sparse vector = tuples of (feature_id, feature_weight),
        # with no zero weights.
        if not is_corpus:
            # lsi[single_document]
            result = matutils.full2sparse(topic_dist.flat)
        else:
            # lsi[chunk of documents]
            result = matutils.Dense2Corpus(topic_dist)
        return result