{"id":1186270,"date":"2025-01-15T19:50:40","date_gmt":"2025-01-15T11:50:40","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1186270.html"},"modified":"2025-01-15T19:50:43","modified_gmt":"2025-01-15T11:50:43","slug":"%e5%a6%82%e4%bd%95%e7%94%9f%e6%88%90%e9%82%bb%e6%8e%a5%e7%9f%a9%e9%98%b5python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1186270.html","title":{"rendered":"\u5982\u4f55\u751f\u6210\u90bb\u63a5\u77e9\u9635python"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25135136\/b6ce080f-860a-4d96-89a2-f28d97a8c1f8.webp\" alt=\"\u5982\u4f55\u751f\u6210\u90bb\u63a5\u77e9\u9635python\" \/><\/p>\n<p><p> \u8981\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u7f51\u7edc\u5e93\uff0c\u5982NetworkX\u3002NetworkX\u662f\u4e00\u4e2a\u7528\u4e8e\u56fe\u8bba\u548c\u590d\u6742\u7f51\u7edc\u5efa\u6a21\u7684\u5f3a\u5927\u5e93\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528\u5b83\u6765\u521b\u5efa\u56fe\uff0c\u7136\u540e\u81ea\u52a8\u751f\u6210\u76f8\u5e94\u7684\u90bb\u63a5\u77e9\u9635\u3002<strong>\u4f7f\u7528NetworkX\u5e93\u3001\u521b\u5efa\u56fe\u3001\u751f\u6210\u90bb\u63a5\u77e9\u9635<\/strong>\u662f\u4e3b\u8981\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><p>\u4e0b\u9762\u662f\u4e00\u4e2a\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u793a\u4f8b\u4ee3\u7801\u6765\u5b9e\u73b0\u8fd9\u4e2a\u76ee\u6807\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u5b89\u88c5NetworkX<\/h3>\n<\/p>\n<p><p>\u9996\u5148\u4f60\u9700\u8981\u5b89\u88c5NetworkX\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\u5b83\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install networkx<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e8c\u3001\u521b\u5efa\u56fe<\/h3>\n<\/p>\n<p><p>\u5728NetworkX\u4e2d\uff0c\u56fe\u7684\u521b\u5efa\u975e\u5e38\u7b80\u5355\u3002\u4f60\u53ef\u4ee5\u521b\u5efa\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u3001\u591a\u91cd\u56fe\u7b49\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u521b\u5efa\u65e0\u5411\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import networkx as nx<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u65e0\u5411\u56fe<\/strong><\/h2>\n<p>G = nx.Graph()<\/p>\n<h2><strong>\u6dfb\u52a0\u8282\u70b9<\/strong><\/h2>\n<p>G.add_node(1)<\/p>\n<p>G.add_node(2)<\/p>\n<p>G.add_node(3)<\/p>\n<h2><strong>\u6dfb\u52a0\u8fb9<\/strong><\/h2>\n<p>G.add_edge(1, 2)<\/p>\n<p>G.add_edge(2, 3)<\/p>\n<p>G.add_edge(3, 1)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u751f\u6210\u90bb\u63a5\u77e9\u9635<\/h3>\n<\/p>\n<p><p>\u4e00\u65e6\u4f60\u521b\u5efa\u4e86\u56fe\uff0c\u5c31\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u751f\u6210\u90bb\u63a5\u77e9\u9635\u3002NetworkX\u63d0\u4f9b\u4e86\u4e00\u4e2a\u51fd\u6570 <code>adjacency_matrix<\/code> \u6765\u751f\u6210\u90bb\u63a5\u77e9\u9635\u3002\u4e0b\u9762\u662f\u5982\u4f55\u4f7f\u7528\u5b83\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u751f\u6210\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>adj_matrix = nx.adjacency_matrix(G)<\/p>\n<h2><strong>\u8f6c\u6362\u4e3a\u5bc6\u96c6\u77e9\u9635\u683c\u5f0f<\/strong><\/h2>\n<p>adj_matrix_dense = adj_matrix.todense()<\/p>\n<h2><strong>\u6253\u5370\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>print(adj_matrix_dense)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u8be6\u7ec6\u63cf\u8ff0\uff1a\u4f7f\u7528NetworkX\u5e93<\/h3>\n<\/p>\n<p><p><strong>\u4f7f\u7528NetworkX\u5e93<\/strong>\u53ef\u4ee5\u7b80\u5316\u751f\u6210\u90bb\u63a5\u77e9\u9635\u7684\u8fc7\u7a0b\u3002\u5b83\u4e0d\u4ec5\u53ef\u4ee5\u65b9\u4fbf\u5730\u521b\u5efa\u548c\u64cd\u4f5c\u56fe\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u5206\u6790\u56fe\u7684\u7ed3\u6784\u548c\u7279\u6027\u3002NetworkX\u652f\u6301\u591a\u79cd\u56fe\u7c7b\u578b\uff0c\u5305\u62ec\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u548c\u591a\u91cd\u56fe\u3002\u901a\u8fc7\u7b80\u5355\u7684API\uff0c\u4f60\u53ef\u4ee5\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\uff0c\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff0c\u8ba1\u7b97\u56fe\u7684\u5404\u79cd\u5c5e\u6027\uff08\u5982\u5ea6\u3001\u4e2d\u5fc3\u6027\u7b49\uff09\uff0c\u5e76\u8fdb\u884c\u56fe\u7684\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<p><p>\u4f7f\u7528NetworkX\u5e93\u7684\u597d\u5904\u5305\u62ec\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u7b80\u5355\u6613\u7528<\/strong>\uff1aNetworkX\u63d0\u4f9b\u4e86\u7b80\u6d01\u7684API\uff0c\u53ef\u4ee5\u5feb\u901f\u4e0a\u624b\u3002<\/li>\n<li><strong>\u529f\u80fd\u4e30\u5bcc<\/strong>\uff1a\u4e0d\u4ec5\u53ef\u4ee5\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u591a\u79cd\u56fe\u8bba\u5206\u6790\u3002<\/li>\n<li><strong>\u7075\u6d3b\u6027\u9ad8<\/strong>\uff1a\u652f\u6301\u591a\u79cd\u56fe\u7c7b\u578b\u548c\u64cd\u4f5c\uff0c\u6ee1\u8db3\u4e0d\u540c\u9700\u6c42\u3002<\/li>\n<\/ol>\n<p><p>\u4e0b\u9762\u662f\u4e00\u4e2a\u66f4\u590d\u6742\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528NetworkX\u521b\u5efa\u6709\u5411\u56fe\uff0c\u5e76\u751f\u6210\u76f8\u5e94\u7684\u90bb\u63a5\u77e9\u9635\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import networkx as nx<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u6709\u5411\u56fe<\/strong><\/h2>\n<p>G = nx.DiGraph()<\/p>\n<h2><strong>\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9<\/strong><\/h2>\n<p>edges = [(1, 2), (2, 3), (3, 1), (1, 3)]<\/p>\n<p>G.add_edges_from(edges)<\/p>\n<h2><strong>\u751f\u6210\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>adj_matrix = nx.adjacency_matrix(G)<\/p>\n<h2><strong>\u8f6c\u6362\u4e3a\u5bc6\u96c6\u77e9\u9635\u683c\u5f0f<\/strong><\/h2>\n<p>adj_matrix_dense = adj_matrix.todense()<\/p>\n<h2><strong>\u6253\u5370\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>print(adj_matrix_dense)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e94\u3001\u90bb\u63a5\u77e9\u9635\u7684\u5176\u4ed6\u751f\u6210\u65b9\u6cd5<\/h2>\n<\/p>\n<p><h3>1\u3001\u624b\u52a8\u751f\u6210\u90bb\u63a5\u77e9\u9635<\/h3>\n<\/p>\n<p><p>\u9664\u4e86\u4f7f\u7528NetworkX\u5e93\uff0c\u4f60\u8fd8\u53ef\u4ee5\u624b\u52a8\u751f\u6210\u90bb\u63a5\u77e9\u9635\u3002\u624b\u52a8\u751f\u6210\u65b9\u6cd5\u9002\u7528\u4e8e\u8f83\u5c0f\u4e14\u7ed3\u6784\u7b80\u5355\u7684\u56fe\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u5b9a\u4e49\u8282\u70b9\u6570<\/strong><\/h2>\n<p>num_nodes = 3<\/p>\n<h2><strong>\u521d\u59cb\u5316\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>adj_matrix = np.zeros((num_nodes, num_nodes), dtype=int)<\/p>\n<h2><strong>\u5b9a\u4e49\u8fb9<\/strong><\/h2>\n<p>edges = [(0, 1), (1, 2), (2, 0)]<\/p>\n<h2><strong>\u586b\u5145\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>for edge in edges:<\/p>\n<p>    adj_matrix[edge[0], edge[1]] = 1<\/p>\n<p>    adj_matrix[edge[1], edge[0]] = 1  # \u5bf9\u4e8e\u65e0\u5411\u56fe\uff0c\u9700\u8981\u5bf9\u79f0\u586b\u5145<\/p>\n<h2><strong>\u6253\u5370\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>print(adj_matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2\u3001\u4f7f\u7528Pandas\u751f\u6210\u90bb\u63a5\u77e9\u9635<\/h3>\n<\/p>\n<p><p>\u4f60\u8fd8\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u751f\u6210\u548c\u5904\u7406\u90bb\u63a5\u77e9\u9635\u3002Pandas\u5e93\u7279\u522b\u9002\u5408\u5904\u7406\u6570\u636e\u8868\u683c\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u5b9a\u4e49\u8282\u70b9\u548c\u8fb9<\/strong><\/h2>\n<p>nodes = [1, 2, 3]<\/p>\n<p>edges = [(1, 2), (2, 3), (3, 1)]<\/p>\n<h2><strong>\u521b\u5efa\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>adj_matrix = pd.DataFrame(0, index=nodes, columns=nodes)<\/p>\n<h2><strong>\u586b\u5145\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>for edge in edges:<\/p>\n<p>    adj_matrix.loc[edge[0], edge[1]] = 1<\/p>\n<p>    adj_matrix.loc[edge[1], edge[0]] = 1  # \u5bf9\u4e8e\u65e0\u5411\u56fe\uff0c\u9700\u8981\u5bf9\u79f0\u586b\u5145<\/p>\n<h2><strong>\u6253\u5370\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>print(adj_matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3\u3001\u4f7f\u7528Numpy\u751f\u6210\u90bb\u63a5\u77e9\u9635<\/h3>\n<\/p>\n<p><p>Numpy\u5e93\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u7684\u5f3a\u5927\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Numpy\u5e93\u521b\u5efa\u548c\u64cd\u4f5c\u90bb\u63a5\u77e9\u9635\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u5b9a\u4e49\u8282\u70b9\u6570<\/strong><\/h2>\n<p>num_nodes = 3<\/p>\n<h2><strong>\u521d\u59cb\u5316\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>adj_matrix = np.zeros((num_nodes, num_nodes), dtype=int)<\/p>\n<h2><strong>\u5b9a\u4e49\u8fb9<\/strong><\/h2>\n<p>edges = [(0, 1), (1, 2), (2, 0)]<\/p>\n<h2><strong>\u586b\u5145\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>for edge in edges:<\/p>\n<p>    adj_matrix[edge[0], edge[1]] = 1<\/p>\n<p>    adj_matrix[edge[1], edge[0]] = 1  # \u5bf9\u4e8e\u65e0\u5411\u56fe\uff0c\u9700\u8981\u5bf9\u79f0\u586b\u5145<\/p>\n<h2><strong>\u6253\u5370\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n<p>print(adj_matrix)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u516d\u3001\u90bb\u63a5\u77e9\u9635\u7684\u5e94\u7528<\/h2>\n<\/p>\n<p><p>\u90bb\u63a5\u77e9\u9635\u5728\u56fe\u8bba\u548c\u7f51\u7edc\u5206\u6790\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u5b83\u53ef\u4ee5\u7528\u6765\u8868\u793a\u56fe\u7684\u7ed3\u6784\uff0c\u8fdb\u884c\u8def\u5f84\u641c\u7d22\uff0c\u8ba1\u7b97\u56fe\u7684\u5404\u79cd\u5c5e\u6027\u7b49\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\uff1a<\/p>\n<\/p>\n<p><h3>1\u3001\u8def\u5f84\u641c\u7d22<\/h3>\n<\/p>\n<p><p>\u90bb\u63a5\u77e9\u9635\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u8def\u5f84\u641c\u7d22\uff0c\u5982\u6df1\u5ea6\u4f18\u5148\u641c\u7d22\uff08DFS\uff09\u548c\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22\uff08BFS\uff09\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u4f7f\u7528\u90bb\u63a5\u77e9\u9635\u8fdb\u884cBFS\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>def bfs(adj_matrix, start_node):<\/p>\n<p>    num_nodes = adj_matrix.shape[0]<\/p>\n<p>    visited = [False] * num_nodes<\/p>\n<p>    queue = [start_node]<\/p>\n<p>    visited[start_node] = True<\/p>\n<p>    while queue:<\/p>\n<p>        node = queue.pop(0)<\/p>\n<p>        print(node, end=&quot; &quot;)<\/p>\n<p>        for neighbor in range(num_nodes):<\/p>\n<p>            if adj_matrix[node, neighbor] == 1 and not visited[neighbor]:<\/p>\n<p>                queue.append(neighbor)<\/p>\n<p>                visited[neighbor] = True<\/p>\n<h2><strong>\u793a\u4f8b<\/strong><\/h2>\n<p>num_nodes = 4<\/p>\n<p>adj_matrix = np.array([[0, 1, 1, 0],<\/p>\n<p>                       [1, 0, 1, 1],<\/p>\n<p>                       [1, 1, 0, 1],<\/p>\n<p>                       [0, 1, 1, 0]])<\/p>\n<p>bfs(adj_matrix, 0)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2\u3001\u8ba1\u7b97\u56fe\u7684\u5c5e\u6027<\/h3>\n<\/p>\n<p><p>\u90bb\u63a5\u77e9\u9635\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u56fe\u7684\u5404\u79cd\u5c5e\u6027\uff0c\u5982\u5ea6\u3001\u4e2d\u5fc3\u6027\u7b49\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u8ba1\u7b97\u8282\u70b9\u5ea6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>def calculate_degrees(adj_matrix):<\/p>\n<p>    degrees = np.sum(adj_matrix, axis=1)<\/p>\n<p>    return degrees<\/p>\n<h2><strong>\u793a\u4f8b<\/strong><\/h2>\n<p>num_nodes = 4<\/p>\n<p>adj_matrix = np.array([[0, 1, 1, 0],<\/p>\n<p>                       [1, 0, 1, 1],<\/p>\n<p>                       [1, 1, 0, 1],<\/p>\n<p>                       [0, 1, 1, 0]])<\/p>\n<p>degrees = calculate_degrees(adj_matrix)<\/p>\n<p>print(degrees)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3\u3001\u56fe\u7684\u53ef\u89c6\u5316<\/h3>\n<\/p>\n<p><p>\u90bb\u63a5\u77e9\u9635\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u56fe\u7684\u53ef\u89c6\u5316\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528NetworkX\u5e93\u6765\u751f\u6210\u56fe\u7684\u53ef\u89c6\u5316\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import networkx as nx<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u65e0\u5411\u56fe<\/strong><\/h2>\n<p>G = nx.Graph()<\/p>\n<h2><strong>\u5b9a\u4e49\u8282\u70b9\u548c\u8fb9<\/strong><\/h2>\n<p>nodes = [1, 2, 3, 4]<\/p>\n<p>edges = [(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)]<\/p>\n<h2><strong>\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9<\/strong><\/h2>\n<p>G.add_nodes_from(nodes)<\/p>\n<p>G.add_edges_from(edges)<\/p>\n<h2><strong>\u7ed8\u5236\u56fe<\/strong><\/h2>\n<p>nx.draw(G, with_labels=True)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e03\u3001\u603b\u7ed3<\/h2>\n<\/p>\n<p><p>\u751f\u6210\u90bb\u63a5\u77e9\u9635\u662f\u56fe\u8bba\u548c\u7f51\u7edc\u5206\u6790\u4e2d\u7684\u4e00\u4e2a\u57fa\u672c\u64cd\u4f5c\u3002\u4f7f\u7528Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff0c\u5305\u62ec\u4f7f\u7528NetworkX\u5e93\u3001\u624b\u52a8\u751f\u6210\u3001\u4f7f\u7528Pandas\u5e93\u548cNumpy\u5e93\u7b49\u3002<strong>\u4f7f\u7528NetworkX\u5e93<\/strong>\u662f\u6700\u63a8\u8350\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u4e0d\u4ec5\u53ef\u4ee5\u7b80\u5316\u751f\u6210\u90bb\u63a5\u77e9\u9635\u7684\u8fc7\u7a0b\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u5206\u6790\u548c\u53ef\u89c6\u5316\u56fe\u7684\u7ed3\u6784\u3002<\/p>\n<\/p>\n<p><p>\u90bb\u63a5\u77e9\u9635\u5728\u56fe\u8bba\u548c\u7f51\u7edc\u5206\u6790\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5982\u8def\u5f84\u641c\u7d22\u3001\u8ba1\u7b97\u56fe\u7684\u5c5e\u6027\u3001\u56fe\u7684\u53ef\u89c6\u5316\u7b49\u3002\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u76f8\u4fe1\u4f60\u5df2\u7ecf\u638c\u63e1\u4e86\u751f\u6210\u90bb\u63a5\u77e9\u9635\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5e76\u80fd\u5e94\u7528\u5b83\u4eec\u6765\u8fdb\u884c\u56fe\u7684\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n<p><p>\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u4e00\u4e2a\u90bb\u63a5\u77e9\u9635\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efa\u90bb\u63a5\u77e9\u9635\u3002\u9996\u5148\uff0c\u5b89\u88c5NumPy\u5e93\uff08\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff09\uff0c\u7136\u540e\u53ef\u4ee5\u901a\u8fc7\u5b9a\u4e49\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\u6765\u8868\u793a\u56fe\u7684\u90bb\u63a5\u77e9\u9635\u3002\u6bcf\u4e2a\u5143\u7d20\u7684\u4f4d\u7f6e\u8868\u793a\u4e24\u4e2a\u8282\u70b9\u4e4b\u95f4\u7684\u8fde\u63a5\u5173\u7cfb\uff0c1\u4ee3\u8868\u8fde\u63a5\uff0c0\u4ee3\u8868\u6ca1\u6709\u8fde\u63a5\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u751f\u6210\u4e00\u4e2a\u7b80\u5355\u7684\u90bb\u63a5\u77e9\u9635\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\n\n# \u5b9a\u4e49\u90bb\u63a5\u77e9\u9635\nadjacency_matrix = np.array([[0, 1, 0],\n                              [1, 0, 1],\n                              [0, 1, 0]])\nprint(adjacency_matrix)\n<\/code><\/pre>\n<p><strong>\u5982\u4f55\u4ece\u8fb9\u5217\u8868\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff1f<\/strong><br \/>\u5982\u679c\u4f60\u6709\u4e00\u4e2a\u8fb9\u5217\u8868\uff0c\u53ef\u4ee5\u901a\u8fc7\u8fed\u4ee3\u8fb9\u7684\u65b9\u5f0f\u751f\u6210\u90bb\u63a5\u77e9\u9635\u3002\u5047\u8bbe\u4f60\u6709\u4e00\u4e2a\u5305\u542b\u8282\u70b9\u5bf9\u7684\u5217\u8868\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u8f6c\u6362\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\n\nedges = [(0, 1), (1, 2)]  # \u8fb9\u5217\u8868\nnum_nodes = 3  # \u8282\u70b9\u6570\u91cf\nadjacency_matrix = np.zeros((num_nodes, num_nodes), dtype=int)\n\nfor edge in edges:\n    adjacency_matrix[edge[0]][edge[1]] = 1\n    adjacency_matrix[edge[1]][edge[0]] = 1  # \u5982\u679c\u662f\u65e0\u5411\u56fe\n\nprint(adjacency_matrix)\n<\/code><\/pre>\n<p><strong>\u5982\u4f55\u5904\u7406\u5e26\u6743\u56fe\u7684\u90bb\u63a5\u77e9\u9635\uff1f<\/strong><br \/>\u5728\u5904\u7406\u5e26\u6743\u56fe\u65f6\uff0c\u90bb\u63a5\u77e9\u9635\u7684\u5143\u7d20\u53ef\u4ee5\u8868\u793a\u8fb9\u7684\u6743\u91cd\uff0c\u800c\u4e0d\u4ec5\u4ec5\u662f\u8fde\u63a5\u5173\u7cfb\u3002\u53ef\u4ee5\u4f7f\u7528\u7c7b\u4f3c\u7684\u65b9\u6cd5\u6765\u751f\u6210\u5e26\u6743\u7684\u90bb\u63a5\u77e9\u9635\uff0c\u53ea\u9700\u5c06\u6743\u91cd\u503c\u586b\u5165\u77e9\u9635\u4e2d\u3002\u4f8b\u5982\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\n\n# \u5b9a\u4e49\u5e26\u6743\u90bb\u63a5\u77e9\u9635\nweighted_adjacency_matrix = np.array([[0, 5, 0],\n                                       [5, 0, 3],\n                                       [0, 3, 0]])\nprint(weighted_adjacency_matrix)\n<\/code><\/pre>\n<p>\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u8282\u70b90\u548c\u8282\u70b91\u4e4b\u95f4\u7684\u6743\u91cd\u4e3a5\uff0c\u8282\u70b91\u548c\u8282\u70b92\u4e4b\u95f4\u7684\u6743\u91cd\u4e3a3\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u7f51\u7edc\u5e93\uff0c\u5982NetworkX\u3002NetworkX\u662f\u4e00\u4e2a\u7528\u4e8e\u56fe\u8bba\u548c\u590d\u6742\u7f51\u7edc\u5efa [&hellip;]","protected":false},"author":3,"featured_media":1186283,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186270"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1186270"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186270\/revisions"}],"predecessor-version":[{"id":1186286,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186270\/revisions\/1186286"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1186283"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1186270"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1186270"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1186270"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}