Method inner_product

gensim/similarities/termsim.py:518–629

Get the inner product(s) between real vectors / corpora X and Y. Return the inner product(s) between real vectors / corpora vec1 and vec2 expressed in a non-orthogonal normalized basis, where the dot product between the basis vectors is given by the sparse term similarity ma

(self, X, Y, normalized=(False, False))

        self.matrix = source.tocsc()

    def inner_product(self, X, Y, normalized=(False, False)):
        """Get the inner product(s) between real vectors / corpora X and Y.

        Return the inner product(s) between real vectors / corpora vec1 and vec2 expressed in a
        non-orthogonal normalized basis, where the dot product between the basis vectors is given by
        the sparse term similarity matrix.

        Parameters
        ----------
        vec1 : list of (int, float) or iterable of list of (int, float)
            A query vector / corpus in the sparse bag-of-words format.
        vec2 : list of (int, float) or iterable of list of (int, float)
            A document vector / corpus in the sparse bag-of-words format.
        normalized : tuple of {True, False, 'maintain'}, optional
            First/second value specifies whether the query/document vectors in the inner product
            will be L2-normalized (True; corresponds to the soft cosine measure), maintain their
            L2-norm during change of basis ('maintain'; corresponds to query expansion with partial
            membership), or kept as-is (False; corresponds to query expansion; default).

        Returns
        -------
        `self.matrix.dtype`,  `scipy.sparse.csr_matrix`, or :class:`numpy.matrix`
            The inner product(s) between `X` and `Y`.

        References
        ----------
        The soft cosine measure was perhaps first described by [sidorovetal14]_.
        Further notes on the efficient implementation of the soft cosine measure are described by
        [novotny18]_.

        .. [sidorovetal14] Grigori Sidorov et al., "Soft Similarity and Soft Cosine Measure: Similarity
           of Features in Vector Space Model", 2014, http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2043/1921.

        .. [novotny18] Vít Novotný, "Implementation Notes for the Soft Cosine Measure", 2018,
           http://dx.doi.org/10.1145/3269206.3269317.

        """
        if not X or not Y:
            return self.matrix.dtype.type(0.0)

        normalized_X, normalized_Y = normalized
        valid_normalized_values = (True, False, 'maintain')

        if normalized_X not in valid_normalized_values:
            raise ValueError('{} is not a valid value of normalize'.format(normalized_X))
        if normalized_Y not in valid_normalized_values:
            raise ValueError('{} is not a valid value of normalize'.format(normalized_Y))

        is_corpus_X, X = is_corpus(X)
        is_corpus_Y, Y = is_corpus(Y)

        if not is_corpus_X and not is_corpus_Y:
            X = dict(X)
            Y = dict(Y)
            word_indices = np.array(sorted(set(chain(X, Y))))
            dtype = self.matrix.dtype
            X = np.array([X[i] if i in X else 0 for i in word_indices], dtype=dtype)
            Y = np.array([Y[i] if i in Y else 0 for i in word_indices], dtype=dtype)