any2sparse(vec[, eps]) |
Convert a numpy/scipy vector into gensim document format (=list of 2-tuples). |
argsort(x[, topn, reverse]) |
Return indices of the topn smallest elements in array x, in ascending order. |
blas(name, ndarray) |
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corpus2csc(corpus[, num_terms, dtype, ...]) |
Convert a streamed corpus into a sparse matrix, in scipy.sparse.csc_matrix format, with documents as columns. |
corpus2dense(corpus, num_terms[, num_docs, ...]) |
Convert corpus into a dense numpy array (documents will be columns). |
cossim(vec1, vec2) |
Return cosine similarity between two sparse vectors. |
dense2vec(vec[, eps]) |
Convert a dense numpy array into the sparse document format (sequence of 2-tuples). |
entropy(pk[, qk, base]) |
Calculate the entropy of a distribution for given probability values. |
full2sparse(vec[, eps]) |
Convert a dense numpy array into the sparse document format (sequence of 2-tuples). |
full2sparse_clipped(vec, topn[, eps]) |
Like full2sparse, but only return the topn elements of the greatest magnitude (abs). |
get_lapack_funcs(names[, arrays, dtype]) |
Return available LAPACK function objects from names. |
hellinger(vec1, vec2) |
Hellinger distance is a distance metric to quantify the similarity between two probability distributions. |
isbow(vec) |
Checks if vector passed is in bag of words representation or not. |
ismatrix(m) |
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iteritems(d, **kw) |
Return an iterator over the (key, value) pairs of a dictionary. |
itervalues(d, **kw) |
Return an iterator over the values of a dictionary. |
jaccard(vec1, vec2) |
A distance metric between bags of words representation. |
kullback_leibler(vec1, vec2[, num_features]) |
A distance metric between two probability distributions. |
pad(mat, padrow, padcol) |
Add additional rows/columns to a numpy.matrix mat. |
qr_destroy(la) |
Return QR decomposition of la[0]. |
ret_normalized_vec(vec, length) |
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scipy2sparse(vec[, eps]) |
Convert a scipy.sparse vector into gensim document format (=list of 2-tuples). |
sparse2full(doc, length) |
Convert a document in sparse document format (=sequence of 2-tuples) into a dense numpy array (of size length). |
triu(m[, k]) |
Make a copy of a matrix with elements below the k-th diagonal zeroed. |
triu_indices(n[, k, m]) |
Return the indices for the upper-triangle of an (n, m) array. |
unitvec(vec[, norm]) |
Scale a vector to unit length. |
veclen(vec) |
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zeros_aligned(shape, dtype[, order, align]) |
Like numpy.zeros(), but the array will be aligned at align byte boundary. |