Jensen Shannon distance

Jensen_Shannon_distance.py

def jensen_shannon(query, matrix):
    """
    This function implements a Jensen-Shannon similarity
    between the input query (an LDA topic distribution for a document)
    and the entire corpus of topic distributions.
    It returns an array of length M where M is the number of documents in the corpus
    """
    # lets keep with the p,q notation above
    p = query[None,:].T # take transpose
    q = matrix.T # transpose matrix
    m = 0.5*(p + q)
    return np.sqrt(0.5*(entropy(p,m) + entropy(q,m)))

def get_most_similar_documents(query,matrix,k=10):
    """
    This function implements the Jensen-Shannon distance above
    and retruns the top k indices of the smallest jensen shannon distances
    """
    sims = jensen_shannon(query,matrix) # list of jensen shannon distances
    return sims.argsort()[:k] # the top k positional index of the smallest Jensen Shannon distances