gensim.similarities.MatrixSimilarity¶

class gensim.similarities.MatrixSimilarity(corpus, num_best=None, dtype=<type 'numpy.float32'>, num_features=None, chunksize=256, corpus_len=None)[source]¶

Compute similarity against a corpus of documents by storing the index matrix in memory. The similarity measure used is cosine between two vectors.

Use this if your input corpus contains dense vectors (such as documents in LSI space) and fits into RAM.

The matrix is internally stored as a dense numpy array. Unless the entire matrix fits into main memory, use Similarity instead.

See also Similarity and SparseMatrixSimilarity in this module.