import numpy as np from sklearn.metrics.pairwise import cosine_similarity#创建示例数据data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])#计算相似度矩阵similarity_matrix = cosine_similarity(data)#打印相似度矩阵print(similarity_m
matrix2.T)norm_matrix1=np.linalg.norm(matrix1,axis=1)norm_matrix2=np.linalg.norm(matrix2,axis=1)similarity=dot_product/(norm_matrix1[:,np.newaxis]*norm_matrix2)returnsimilarity# 示例矩阵matrix1=np.array([[1,2,3],[4,5,6]])matrix2=np.array([[2,3,4],[5,6,7]])similarity=cosi...
matrix = np.array([[1, 2], [3, 4]]) 计算矩阵的Frobenius范数 frobenius_norm = np.linalg.norm(matrix, 'fro') print("矩阵的Frobenius范数为:", frobenius_norm) 输出结果为: 矩阵的Frobenius范数为: 5.477225575051661 在这个示例中,我们定义了一个2×2矩阵,然后使用numpy.linalg.norm函数计算其Frobeniu...
#Create Matrix count_matrix = count_vect.fit_transform(df['ensemble']) # Compute the cosine similarity matrix cosine_sim =cosine_similarity(count_matrix, count_matrix) 顾名思义,命令cosine_similarity计算count_matrix中每一行的余弦相似度。count_matrix上的每一行都是一个向量,其中包含集合列中出现的每...
corpus_norm_df= pd.DataFrame(corpus_array, columns=vocs)print(corpus_norm_df.head())fromsklearn.metrics.pairwiseimportcosine_similarity similarity_matrix=cosine_similarity(corpus_array) similarity_matrix_df=pd.DataFrame(similarity_matrix)print(similarity_matrix_df)...
1from sklearn.metrics.pairwise import cosine_similarity 2 3# 计算电影之间的相似度 4similarity_matrix = cosine_similarity(movie_features) 5 6# 推荐电影 7defrecommend_movies(user_movie_id, similarity_matrix, movies_data): 8 similar_movies = similarity_matrix[movies_data[movies_data['movie_id...
similarity_matrix = cosine_similarity(tfidf_matrix) 推荐相似商品 def recommend_similar(product_index, top_n=3): similar_indices = similarity_matrix[product_index].argsort()[-top_n-1:-1][::-1] return similar_indices print(recommend_similar(0)) ...
similarity_matrix = np.dot(X, X.T) # Compute the point matrix using CF X_train = np.array(X_train.fillna(0)) for i in range(X_train.shape[0]): indexs = np.argsort(similarity_matrix[i, :])[::-1] for j in range(X_train.shape[1]): ...
A = csr_matrix(A) B = csr_matrix(B) ``` 接下来,我们可以使用scipy库中的cosine_similarity函数来计算稀疏矩阵的余弦相似度: ```python from sklearn.metrics.pairwise import cosine_similarity similarity_matrix = cosine_similarity(A, B) ``` 这样,我们就可以得到稀疏矩阵A和B之间的余弦相似度矩阵了...
#Create Matrixcount_matrix = count_vect.fit_transform(df['ensemble']) # Compute the cosine similarity matrixcosine_sim = cosine_similarity(count_matrix, count_matrix) 顾名思义,命令cosine_similarity计算count_matrix中每一行的余弦相似度。count_matrix上的每一行都是一个向量,其中包含集合列中出现的每个...