scorer.py 文件源码

def similarity_function(vec1,vec2, similarity):

    #compute cosine similarity or other similarities

    v1 = np.array(vec1)

    v2 = np.array(vec2)

    if len(v1)*len(v2) == 0: #any of the two is 0
        global count
        count +=1

        return 0

    else:

        if similarity == 'cosine':

            return cosine_similarity([v1],[v2])[0][0] #returns a double array [[sim]]

        elif similarity == 'softmax':

            return np.exp(np.dot(v1,v2)) #normalization is useless for relative comparisons

        elif similarity == 'linear_kernel':
            return linear_kernel(v1,v2)[0][0]

        elif similarity == 'euclidean':
            return euclidean_distances(v1,v2)[0][0]
        else:
            raise NameError('Choose a valid similarity function')