解释这段代码：similarity_matrix_1 = pdist(data1);

这段代码使用了Python中的scipy库中的pdist函数，用于计算一个矩阵中每两个向量之间的距离。具体来说，这里的data1是一个矩阵，其中每一行表示一个向量，pdist函数会计算出data1中每两个向量之间的距离，并将这些距离存储在一个向量中，即similarity_matrix_1。这里的距离指的是向量之间的欧几里得距离，也可以根据需要使用其他距离度量方式。

相关问题

代码：# 随机生成数据集 X, y = make_classification(n_samples=100, n_features=10, n_classes=3, n_clusters_per_class=1, random_state=42) # 构建图 G = nx.complete_graph(len(X)) # 计算相似度 similarity_matrix = np.zeros((len(X), len(X))) for i in range(len(X)): for j in range(len(X)): if i != j: similarity_matrix[i][j] = np.dot(X[i], X[j]) / (np.linalg.norm(X[i]) * np.linalg.norm(X[j])) # 图坍缩 for i in range(len(X)): neighbors = sorted(G.neighbors(i), key=lambda x: similarity_matrix[i][x], reverse=True) for j in neighbors: if i != j: G = nx.contracted_edge(G, (i, j)) 报错：KeyError: 1 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "E:/403/myworld/GraphNet.py", line 23, in <module> neighbors = sorted(G.neighbors(i), key=lambda x: similarity_matrix[i][x], reverse=True) File "D:\code\myworld\lib\site-packages\networkx\classes\graph.py", line 1356, in neighbors raise NetworkXError(f"The node {n} is not in the graph.") from err networkx.exception.NetworkXError: The node 1 is not in the graph. 进程已结束,退出代码1 如何修改

这个错误可能是因为你的图 `G` 中没有节点 `1`。建议在计算相似度矩阵时，将对角线上的元素设置为0，以避免出现自环。另外，你也可以尝试在使用 `sorted` 函数前，先检查一下节点 `i` 是否存在于图中，例如：

for i in range(len(X)):
    if not G.has_node(i):
        continue
    neighbors = sorted(G.neighbors(i), key=lambda x: similarity_matrix[i][x], reverse=True)
    for j in neighbors:
        if i != j and G.has_node(j):
            G = nx.contracted_edge(G, (i, j))

这里我们增加了 `if not G.has_node(i): continue` 和 `if i != j and G.has_node(j):` 条件语句，以确保只有存在于图中的节点才会被处理。

逐行分析下面的代码：import random import numpy as np import pandas as pd import math from operator import itemgetter data_path = './ml-latest-small/' data = pd.read_csv(data_path+'ratings.csv') data.head() data.pivot(index='userId', columns='newId', values='rating') trainSet, testSet = {}, {} trainSet_len, testSet_len = 0, 0 pivot = 0.75 for ele in data.itertuples(): user, new, rating = getattr(ele, 'userId'), getattr(ele, 'newId'), getattr(ele, 'rating') if random.random() < pivot: trainSet.setdefault(user, {}) trainSet[user][new] = rating trainSet_len += 1 else: testSet.setdefault(user, {}) testSet[user][new] = rating testSet_len += 1 print('Split trainingSet and testSet success!') print('TrainSet = %s' % trainSet_len) print('TestSet = %s' % testSet_len) new_popular = {} for user, news in trainSet.items(): for new in news: if new not in new_popular: new_popular[new] = 0 new_popular[new] += 1 new_count = len(new_popular) print('Total movie number = %d' % new_count) print('Build user co-rated news matrix ...') new_sim_matrix = {} for user, news in trainSet.items(): for m1 in news: for m2 in news: if m1 == m2: continue new_sim_matrix.setdefault(m1, {}) new_sim_matrix[m1].setdefault(m2, 0) new_sim_matrix[m1][m2] += 1 print('Build user co-rated movies matrix success!') print('Calculating news similarity matrix ...') for m1, related_news in new_sim_matrix.items(): for m2, count in related_news.items(): if new_popular[m1] == 0 or new_popular[m2] == 0: new_sim_matrix[m1][m2] = 0 else: new_sim_matrix[m1][m2] = count / math.sqrt(new_popular[m1] * new_popular[m2]) print('Calculate news similarity matrix success!') k = 20 n = 10 aim_user = 20 rank ={} watched_news = trainSet[aim_user] for new, rating in watched_news.items(): for related_new, w in sorted(new_sim_matrix[new].items(), key=itemgetter(1), reverse=True)[:k]: if related_new in watched_news: continue rank.setdefault(related_new, 0) rank[related_new] += w * float(rating) rec_news = sorted(rank.items(), key=itemgetter(1), reverse=True)[:n] rec_news

这段代码是一个电影推荐系统的实现。下面是对代码的逐行分析：

1. `import random import numpy as np import pandas as pd import math from operator import itemgetter`：导入需要使用的库

2. `data_path = './ml-latest-small/' data = pd.read_csv(data_path+'ratings.csv') data.head()`：读取电影评分数据，将其存储在一个DataFrame中，并输出前5行数据

3. `data.pivot(index='userId', columns='newId', values='rating')`：将数据按照用户ID和电影ID进行重塑，创建一个用户-电影评分的矩阵

4. `trainSet, testSet = {}, {} trainSet_len, testSet_len = 0, 0 pivot = 0.75`：初始化训练集和测试集，并设置训练集占比为0.75

5. `for ele in data.itertuples():`：遍历数据中的每一行

6. `user, new, rating = getattr(ele, 'userId'), getattr(ele, 'newId'), getattr(ele, 'rating')`：获取每一行数据中的用户ID、电影ID和评分

7. `if random.random() < pivot: trainSet.setdefault(user, {}) trainSet[user][new] = rating trainSet_len += 1 else: testSet.setdefault(user, {}) testSet[user][new] = rating testSet_len += 1`：根据训练集占比将数据划分为训练集和测试集，并统计训练集和测试集中的电影数量

8. `print('Split trainingSet and testSet success!') print('TrainSet = %s' % trainSet_len) print('TestSet = %s' % testSet_len)`：输出训练集和测试集的电影数量

9. `new_popular = {} for user, news in trainSet.items(): for new in news: if new not in new_popular: new_popular[new] = 0 new_popular[new] += 1`：统计每部电影的流行度（出现次数）

10. `new_count = len(new_popular) print('Total movie number = %d' % new_count)`：输出电影总数

11. `new_sim_matrix = {} for user, news in trainSet.items(): for m1 in news: for m2 in news: if m1 == m2: continue new_sim_matrix.setdefault(m1, {}) new_sim_matrix[m1].setdefault(m2, 0) new_sim_matrix[m1][m2] += 1`：构建用户-电影协同过滤矩阵，统计每对电影被多少个用户共同观看过

12. `print('Build user co-rated movies matrix success!')`：输出构建协同过滤矩阵成功信息

13. `for m1, related_news in new_sim_matrix.items(): for m2, count in related_news.items(): if new_popular[m1] == 0 or new_popular[m2] == 0: new_sim_matrix[m1][m2] = 0 else: new_sim_matrix[m1][m2] = count / math.sqrt(new_popular[m1] * new_popular[m2])`：计算电影之间的相似度，使用余弦相似度度量

14. `print('Calculate news similarity matrix success!')`：输出计算电影相似度成功信息

15. `k = 20 n = 10 aim_user = 20`：定义参数，包括推荐电影的数量和目标用户ID

16. `rank ={} watched_news = trainSet[aim_user] for new, rating in watched_news.items(): for related_new, w in sorted(new_sim_matrix[new].items(), key=itemgetter(1), reverse=True)[:k]: if related_new in watched_news: continue rank.setdefault(related_new, 0) rank[related_new] += w * float(rating) rec_news = sorted(rank.items(), key=itemgetter(1), reverse=True)[:n]`：为目标用户推荐电影，根据用户观看历史和电影相似度计算推荐度，并将推荐度排序输出前n个推荐电影。