余弦相似度

from sklearn.metrics.pairwise import cosine_similarity
a = [[1, 3, 2,5,6,7], [2, 2, 1,3,4,3],[2,2,2,2,2,2]]
b = cosine_similarity(a)
print(b)

from sklearn.metrics.pairwise import cosine_similarity

a = [[1, 3, 2,5,6,7], [2, 2, 1,3,4,3]]
b = cosine_similarity(a)
print(b)

补充

转自
1、欧式距离

given two data points, calculate the euclidean distance between them
def get_distance(data1, data2):
points = zip(data1, data2)
diffs_squared_distance = [pow(a - b, 2) for (a, b) in points]
return math.sqrt(sum(diffs_squared_distance))

2、余弦相似度
def cosin_distance(vector1, vector2):
dot_product = 0.0
normA = 0.0
normB = 0.0
for a, b in zip(vector1, vector2):
dot_product += a * b
normA += a ** 2
normB += b ** 2
if normA == 0.0 or normB == 0.0:
return None
else:
return dot_product / ((normA * normB) ** 0.5)

3、用Numpy进行余弦相似度计算

sim = user_item_matric.dot(user_item_matric.T)
norms = np.array([np.sqrt(np.diagonal(sim))])
user_similarity=(sim / norms / norms.T)

4、用scikit cosine_similarity计算相似度

from sklearn.metrics.pairwise import cosine_similarity
user_similarity=cosine_similarity(user_tag_matric)

5、用scikit pairwise_distances计算相似度

from sklearn.metrics.pairwise import pairwise_distances
user_similarity = pairwise_distances(user_tag_matric, metric=‘cosine’)

需要注意的一点是，用pairwise_distances计算的Cosine distance是1-（cosine similarity）结果