Consider the digraph G whose nodes are the integers from 1 to 12 inclusive and such that (i,j) is an arc if and only if i is a proper divisor of j . Write down the adjacency matrix representation of G and of the reversed graph Gr.

时间: 2024-02-23 17:02:39 浏览: 90
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To write down the adjacency matrix of G, we need to determine whether there is a directed edge from each node i to each node j. In this case, there is a directed edge from i to j if and only if i is a proper divisor of j. The adjacency matrix A for G is therefore: ``` 1 2 3 4 5 6 7 8 9 10 11 12 1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 1 0 1 0 0 0 0 0 0 3 1 1 0 0 0 1 0 0 0 0 0 0 4 1 0 1 0 0 1 1 0 0 0 0 0 5 1 0 0 0 0 1 0 0 0 0 0 0 6 1 1 1 1 0 1 0 1 0 0 0 0 7 1 0 0 0 0 1 0 0 0 0 0 0 8 1 1 0 1 0 1 1 0 1 0 0 0 9 1 0 1 0 0 1 0 0 0 1 0 0 10 1 1 0 1 0 1 1 0 0 0 1 0 11 1 0 0 0 0 1 0 0 0 0 0 1 12 1 1 1 1 1 1 1 1 1 1 1 0 ``` To write down the adjacency matrix of the reversed graph Gr, we need to determine whether there is a directed edge from each node i to each node j in Gr. In this case, there is a directed edge from i to j in Gr if and only if there is a directed edge from j to i in G. The adjacency matrix A' for Gr is therefore: ``` 1 2 3 4 5 6 7 8 9 10 11 12 1 0 1 1 1 1 1 1 1 1 1 1 1 2 0 0 1 0 0 1 0 1 0 1 0 1 3 0 0 0 1 0 1 0 0 1 0 0 1 4 0 0 0 0 0 1 0 1 0 1 0 1 5 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 1 0 1 0 1 7 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 1 0 ```
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The Digraph Analysis Project-开源

一组用于分析可以表示为有向图的信息的工具。

Given the adjacency matrix of a digraph G, and a pair of starting node s and destination node d, determine if the destination d is reachable from s. Implement the following function is_reachable(adj_matrix, s, d), where adj_matrix is the adjacency matrix with 1 denoting an arc exists, and 0 otherwise. The function return True if d is reachable from s, and False otherwise. Note: You can use any Python built-in package to implement stack/queue/priority queue if needed. For example: Test Result adj_matrix = [[0,1,1,0],[0,0,1,0],[1,0,0,0],[0,1,0,0]] print(is_reachable(adj_matrix, 0, 3)) print(is_reachable(adj_matrix, 3, 0)) False True

It then enters a loop where it repeatedly dequeues a node from the queue, and checks if it is equal to the destination node d. If it is, the function returns True, indicating that d is reachable from...

Classify the arcs of a digraph while running the depth-first search algorithm. Implement the depth-first search function dfs(adj_matrix), which takes the adjacency matrix of the digraph as input and returns four lists - the lists of tree arcs, forward arcs, back arcs and cross arcs. Each arc (i, j) is denoted by a Python tuple (i, j). Note: The arcs in each list returned should be ranked in the ascending order of the starting node, then the destination node. For example, if the tree arcs are (1, 2), (1, 3), (0, 1), you should return the tree arcs list as [(0, 1), (1, 2), (1, 3)]. For example: Test Result adj_matrix = [[0,1,1,1],[0,0,0,1],[0,0,0,1],[0,0,0,0]] tree, forward, back, cross = dfs(adj_matrix) print('Tree arcs: {}'.format(tree)) print('Forward arcs: {}'.format(forward)) print('Back arcs: {}'.format(back)) print('Cross arcs: {}'.format(cross)) Tree arcs: [(0, 1), (0, 2), (1, 3)] Forward arcs: [(0, 3)] Back arcs: [] Cross arcs: [(2, 3)]

If (u, v) is an arc and v has not been visited yet, then (u, v) is a tree arc and we add it to the tree list and recursively call dfs_visit on v. If v has already been visited and is ...

Master the visual output of a simple digraph. Master to design arrow arcs of the digraph with JavaFX. Import the adjacency matrix of a simple digraph. Output the graph with JavaFX and the graphic user interface. Draw the arrows of the digraph base on the line object of JavaFX. (1) open ECLIPSE IDE （2）build Java project in the workspace （3）import the JavaFX toolkit

You also want to import the adjacency matrix of the digraph and output the graph with JavaFX and a graphic user interface. Finally, you want to draw the arrows of the digraph based on the line object...

Import the adjacency matrix of the digraph and output the graph with JavaFX and a graphic user interface. Draw the arrows of the digraph based on the line object of JavaFX. Show me the code by using java language.

Sure, here is an example code that imports the adjacency matrix of a digraph, outputs the graph with JavaFX, and draws the arrows of the digraph based on the line object: java import javafx....

how to input the adjacency matrix of a digraph to judge whether it is a simple digraph.

If there is an edge from i to j, the entry is 1, otherwise it is 0. 2. Check if there are any loops in the digraph. A loop is an edge that starts and ends at the same vertex. If there are any ...

import networkx as nx import matplotlib.pyplot as plt # 输入数据 locations = [ [125.330802,125.401931,125.326444,125.332284,125.322837,125.32563,125.334942,125.378548,125.386251,125.426883,125.42665,125.437111,125.453763,125.431396,125.430705,125.41968,125.437906,125.404171,125.385772,125.341942,125.341535,125.300812,125.307316,125.345642,125.331492,125.330322,125.284474,125.334851,125.30606,125.377211,125.381077,125.417041,125.41427,125.416371,125.432283,125.401676,125.403855,125.38582,125.426733,125.291], [43.917542,43.919075,43.905821,43.90266,43.900238,43.89703,43.888187,43.904508,43.892574,43.907904,43.896354,43.894605,43.889122,43.88774,43.882928,43.887149,43.8789,43.879647,43.883112,43.873763,43.861505,43.854652,43.876513,43.850479,43.833745,43.825044,43.812019,43.803154,43.793054,43.788869,43.824152,43.816805,43.801673,43.82893,43.83235,43.843713,43.854322,43.868372,43.871792,43.8306] ] num_flights = 4 flight_capacity = [10, 10, 10, 10] # 将坐标转化为图 G = nx.Graph() for i in range(len(locations[0])): G.add_node(i+1, pos=(locations[0][i], locations[1][i])) for i in range(len(locations[0])): for j in range(i+1, len(locations[0])): dist = ((locations[0][i]-locations[0][j])**2 + (locations[1][i]-locations[1][j])**2)**0.5 G.add_edge(i+1, j+1, weight=dist) # 添加起点和终点 start_node = len(locations[0])+1 end_node = len(locations[0])+2 G.add_node(start_node, pos=(0, 0)) G.add_node(end_node, pos=(0, 0)) # 添加边和边权 for i in range(len(locations[0])): G.add_edge(start_node, i+1, weight=0) G.add_edge(i+1, end_node, weight=0) for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(i+1, len(locations[0])+flen(locations[0])+i+1, weight=0) G.add_edge(len(locations[0])+flen(locations[0])+i+1, end_node, weight=0) # 添加航班容量的限制 for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(len(locations[0])+flen(locations[0])+i+1, len(locations[0])+flen(locations[0])+len(locations[0])+1, weight=-flight_capacity[f]) # 创造路径规划模型 path_model = nx.DiGraph() for i in range(len(locations[0])): for f in range(num_flights): for j in range(len(locations[0])): if i != j: path_model.add_edge(len(locations[0])+flen(locations[0])+i+1, len(locations[0])+flen(locations[0])+j+1, weight=G[i+1][j+1]['weight']+G[len(locations[0])+flen(locations[0])+i+1][len(locations[0])+flen(locations[0])+j+1]['weight']) # 添加航班时间的限制 for f in range(num_flights): for i in range(len(locations[0])): for j in range(len(locations[0])): if i != j: path_model.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+((f+1)%num_flights)len(locations[0])+j+1, weight=G[i+1][j+1]['weight']) # 求解最短路径 path = nx.bellman_ford_path(path_model, source=start_node, target=end_node, weight='weight') # 绘制路径图 pos = nx.get_node_attributes(G, 'pos') nx.draw_networkx_nodes(G, pos, node_size=50, node_color='w') nx.draw_networkx_labels(G, pos) nx.draw_networkx_edges(G, pos, edgelist=G.edges(), width=0.5) for f in range(num_flights): start = len(locations[0])+flen(locations[0])+1 end = len(locations[0])+(f+1)*len(locations[0])+1 nx.draw_networkx_edges(G, pos, edgelist=path[start:end], edge_color='r', width=2.0, alpha=0.7) plt.axis('off') plt.show()找出错误并改正

path_model.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+((f+1)%num_flights)*len(locations[0])+j+1, weight=G[i+1][j+1]['weight']+G[len(locations[0])+f*len(locations[0])+i+1]...

import pandas as pd import numpy as np import networkx as nx import matplotlib.pyplot as plt # 读取Excel文件中的邻接矩阵 adjacency_matrix = pd.read_excel('output.xlsx', index_col=0) # 将邻接矩阵转换为numpy数组 adjacency_matrix = adjacency_matrix.to_numpy() # 创建有向图对象 G = nx.DiGraph(adjacency_matrix) def preprocess(G): p = 0 directedGraph = nx.DiGraph() for u in G.nodes(): for v in G.neighbors(u): if (v != u): propProb = G.number_of_edges(u, v) / G.degree(v) directedGraph.add_edge(u, v, pp=propProb) return directedGraph def simulate(G, seedNode, propProbability): newActive = True currentActiveNodes = seedNode.copy() newActiveNodes = set() activatedNodes = seedNode.copy() influenceSpread = len(seedNode) while newActive: for node in currentActiveNodes: for neighbor in G.neighbors(node): if neighbor not in activatedNodes: if G[node][neighbor]['pp'] > propProbability: newActiveNodes.add(neighbor) activatedNodes.append(neighbor) influenceSpread += len(newActiveNodes) if newActiveNodes: currentActiveNodes = list(newActiveNodes) newActiveNodes = set() else: newActive = False return influenceSpread def flipCoin(probability): return np.random.random() < probability # 可视化传播过程 def visualizePropagation(G, seedNode, propProbability): pos = nx.spring_layout(G) # 选择布局算法 labels = {node: node for node in G.nodes()} # 节点标签为节点名 colors = ['r' if node in seedNode else 'b' for node in G.nodes()] # 种子节点为红色，其他节点为蓝色 plt.figure(figsize=(10,6)) nx.draw_networkx_nodes(G, pos, node_color=colors) nx.draw_networkx_edges(G, pos) nx.draw_networkx_labels(G, pos, labels) plt.title('Propagation Visualization') plt.show() # 示例用法 seedNode = [7,36,17] propProbability = 0.7 directedGraph = preprocess(G) influenceSpread = simulate(directedGraph, seedNode, propProbability) print("Influence Spread:", influenceSpread) visualizePropagation(directedGraph, seedNode, propProbability)修改这个代码使得输出图形节点之间间隔合理能够看清

colors = ['r' if node in seedNode else 'b' for node in G.nodes()] # 种子节点为红色，其他节点为蓝色 plt.figure(figsize=(10,6)) nx.draw_networkx_nodes(G, pos, node_color=colors) nx.draw_networkx_...

import networkx as nx import matplotlib.pyplot as plt # 输入数据 locations = [[125.330802,125.401931,125.326444,125.332284,125.322837,125.32563,125.334942,125.378548,125.386251,125.426883,125.42665,125.437111,125.453763,125.431396,125.430705,125.41968,125.437906,125.404171,125.385772,125.341942,125.341535,125.300812,125.307316,125.345642,125.331492,125.330322,125.284474,125.334851,125.30606,125.377211,125.381077,125.417041,125.41427,125.416371,125.432283,125.401676,125.403855,125.38582,125.426733,125.291], [43.917542,43.919075,43.905821,43.90266,43.900238,43.89703,43.888187,43.904508,43.892574,43.907904,43.896354,43.894605,43.889122,43.88774,43.882928,43.887149,43.8789,43.879647,43.883112,43.873763,43.861505,43.854652,43.876513,43.850479,43.833745,43.825044,43.812019,43.803154,43.793054,43.788869,43.824152,43.816805,43.801673,43.82893,43.83235,43.843713,43.854322,43.868372,43.871792,43.8306]] num_flights = 4 flight_capacity = [10, 10, 10, 10] # 将坐标转化为图 G = nx.Graph() for i in range(len(locations[0])): G.add_node(i+1, pos=(locations[0][i], locations[1][i])) for i in range(len(locations[0])): for j in range(i+1, len(locations[0])): dist = ((locations[0][i]-locations[0][j])**2 + (locations[1][i]-locations[1][j])**2)**0.5 G.add_edge(i+1, j+1, weight=dist) # 添加起点和终点 start_node = len(locations[0])+1 end_node = len(locations[0])+2 G.add_node(start_node, pos=(0, 0)) G.add_node(end_node, pos=(0, 0)) # 添加边和边权 for i in range(len(locations[0])): G.add_edge(start_node, i+1, weight=0) G.add_edge(i+1, end_node, weight=0) for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(i+1, len(locations[0])+f*len(locations[0])+i+1, weight=0) G.add_edge(len(locations[0])+f*len(locations[0])+i+1, end_node, weight=0) # 添加航班容量的限制 for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+f*len(locations[0])+len(locations[0])+1, weight=-flight_capacity[f]) #创造路径规划模型 path_model = nx.DiGraph() for i in range(len(locations[0])): for f in range(num_flights): for j in range(len(locations[0])): if i != j: path_model.add_edge(len(locations[0])+flen(locations[0])+i+1, len(locations[0])+flen(locations[0])+j+1, weight=G[i+1][j+1]['weight']+G[len(locations[0])+flen(locations[0])+i+1][len(locations[0])+f*len(locations[0])+j+1]['weight']) 添加航班时间的限制 for f in range(num_flights): for i in range(len(locations[0])): for j in range(len(locations[0])): if i != j: path_model.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+((f+1)%num_flights)*len(locations[0])+j+1, weight=G[i+1][j+1]['weight']) 求解最短路径 path = nx.bellman_ford_path(path_model, source=start_node, target=end_node, weight='weight') 绘制路径图 pos = nx.get_node_attributes(G, 'pos') nx.draw_networkx_nodes(G, pos, node_size=50, node_color='w') nx.draw_networkx_labels(G, pos) nx.draw_networkx_edges(G, pos, edgelist=G.edges(), width=0.5) for f in range(num_flights): start = len(locations[0])+f*len(locations[0])+1 end = len(locations[0])+(f+1)*len(locations[0])+1 nx.draw_networkx_edges(G, pos, edgelist=path[start:end], edge_color='r', width=2.0, alpha=0.7) plt.axis('off') plt.show()找出错误并修改

weight=G[i+1][j+1]['weight']+G[len(locations[0])+f*len(locations[0])+i+1][len(locations[0])+f*len(locations[0])+j+1]['weight']) # 添加航班时间的限制 for f in range(num_flights): for i in range(len...

import pandas as pd import networkx as nx # 读取数据 data = pd.read_excel('附件1.xlsx') # 构建有向图 G = nx.DiGraph() for _, row in data.iterrows(): source = row['发货城市'] target = row['收货城市'] weight = row['快递数量'] if not G.has_edge(source, target): G.add_edge(source, target, weight=weight) else: G[source][target]['weight'] += weight # 计算节点的入度和出度 in_degree = dict(G.in_degree(weight='weight')) out_degree = dict(G.out_degree(weight='weight')) # 归一化处理 total_in = sum(in_degree.values()) total_out = sum(out_degree.values()) for node in G.nodes(): if total_in > 0: in_degree[node] /= total_in if total_out > 0: out_degree[node] /= total_out # 计算 PageRank 值 pr = nx.pagerank(G, weight='weight') # 排序输出结果 result = sorted(pr.items(), key=lambda x: x[1], reverse=True)[:5] for i, (node, score) in enumerate(result): print(f'{i+1}. {node} ({score:.4f})')

2. 构建有向图，使用 DiGraph() 函数创建一个有向图对象 G。 3. 遍历 DataFrame 中的每一行数据，将发货城市和收货城市作为有向图中的节点，将快递数量作为边的权重。 4. 如果有向图中不存在该边，则添加该边并...

分析一下下面的代码from scipy.sparse import csgraph import subprocess from scipy import sparse if __name__ == '__main__': print("====================================") row = [2, 3, 3, 2] col = [3, 4, 2, 3] data = [1, 2, 3, 10] c = sparse.coo_matrix((data, (row, col)), shape=(5, 6)) print(c.col, c.row, c.data) print(c.toarray()) print("====================================") # Find the shortest path # Define the graph in the DOT language code = """ digraph graph1{ rankdir=LR; size="8,5" node [shape = circle]; A -> B [ label = "10" ]; B -> C [ label = "5" ]; A -> C [ label = "3" ]; C -> D [ label = "7" ]; D -> A [ label = "4" ]; D -> C [ label = "6" ]; } """ # Define the command and its arguments to run Graphviz dot_args = ["dot", "-T", "svg"] # Run Graphviz and pass the DOT code as input p = subprocess.Popen(dot_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate(code.encode('utf-8')) # Save the SVG image to a file with open("output.svg", "w") as f: f.write(stdout.decode("utf-8")) w = sparse.dok_matrix((4,4)) edges = [(0, 1, 10), (1, 2, 5), (0, 2, 3), (2, 3, 7), (3, 0, 4), (3, 2, 6)] for i, j, v in edges: w[i, j] = v d, p = csgraph.dijkstra(csgraph=w, directed=True, indices=0, return_predecessors=True) print(d) print(p)

1. 利用scipy库构建一个稀疏矩阵，并对其进行一些基本操作，比如输出矩阵的列、行、非零元素等信息，以及将其转换为稠密矩阵。 2. 利用Graphviz工具绘制一个有向图，并利用scipy库中的函数求解该有向图的最短路径和...

1:f = open(path, 'r', encoding='utf-8') 2:reader = csv.reader(f) 3:data = [row for row in reader] 4:dg = nx.DiGraph() 5:for i, row in enumerate(data): 6: if i==0: 7: dg.add_node(root) 8: dg.nodes[][] 9: else: 10: dg.add_node(a) 11: dg.add_node(b) 12: dg.add_edge(a,b) 13: dg.nodes[][] 14: end if 15:end for这段代码的时间复杂度和空间复杂度是多少

1. 打开文件并读取数据，时间复杂度为O(n)。 2. csv.reader() 函数的时间复杂度为O(1)。 3. 列表推导式的时间复杂度为O(n)。 4. 创建一个空的有向图，时间复杂度为O(1)。 5. 循环遍历数据，时间复杂度为O(n)。 6. ...

对以下代码进行讲解：df1 = pd.read_excel('附件2(Attachment 2)2023-51MCM-Problem B.xlsx', index_col=None) df2 = pd.read_excel('附件3(Attachment 3)2023-51MCM-Problem B.xlsx', index_col=None) df1_grouped = df1.groupby(['日期(年/月/日) (Date Y/M/D)','发货城市 (Delivering city)', '收货城市 (Receiving city)'])['快递运输数量(件) (Express delivery quantity (PCS))'].sum().reset_index() df2_grouped = df2.groupby(['起点 (Start)'])['终点 (End)'] cities = set(df2['起点 (Start)'].tolist() + df2['终点 (End)'].tolist()) workbook = load_workbook(filename="附件3(Attachment 3)2023-51MCM-Problem B.xlsx") sheet = workbook.active data = [[cell.value for cell in row[:3]] for row in sheet.iter_rows(min_row=2)] date = '2023-04-23' df1_date = df1[df1['日期(年/月/日) (Date Y/M/D)'] == date] data1 = list(zip(df1_date['发货城市 (Delivering city)'], df1_date['收货城市 (Receiving city)'], df1_date['快递运输数量(件) (Express delivery quantity (PCS))'])) G=nx.DiGraph() G.add_nodes_from(cities) G.add_weighted_edges_from(data) cost=0 for i in data1: start_city=i[0] end_city=i[1] weight_huo=i[2] print(dijkstra_path(G,start_city,end_city,weight="weight")) print(dijkstra_path_length(G,start_city,end_city,weight="weight")*(1+ (weight_huo/200)*(weight_huo/200)*(weight_huo/200))) cost=cost+dijkstra_path_length(G,start_city,end_city,weight="weight")*(1+ (weight_huo/200)*(weight_huo/200)*(weight_huo/200)) print(cost)

使用add_nodes_from方法将城市添加为节点，使用add_weighted_edges_from方法将起点、终点和权重添加为边。 然后，定义了一个变量cost用于存储成本，并通过遍历data1中的每个元组进行计算。对于每个元组，...

import pandas as pd import numpy as np import networkx as nx # 读取Excel文件中的邻接矩阵 adjacency_matrix = pd.read_excel('output.xlsx', index_col=0) # 将邻接矩阵转换为numpy数组 adjacency_matrix = adjacency_matrix.to_numpy() # 创建有向图对象 G = nx.DiGraph(adjacency_matrix) def preprocess(G): p = 0 directedGraph = nx.DiGraph() for u in G.nodes(): for v in G.neighbors(u): if (v != u): # propProb = G.number_of_edges(u, v) / G.in_degree(v) propProb = G.number_of_edges(u, v) / G.degree(v) directedGraph.add_edge(u, v, pp=propProb) # p += propProb # print(propProb) # print('平均阈值：', p/2939) return directedGraph def simulate(G, seedNode, propProbability): newActive = True currentActiveNodes = copy.deepcopy(seedNode) newActiveNodes = set() activatedNodes = copy.deepcopy(seedNode) # Biar ga keaktivasi 2 kali influenceSpread = len(seedNode) while (newActive): for node in currentActiveNodes: for neighbor in G.neighbors( node): # Harus dicek udah aktif apa belom, jangan sampe ngaktifin yang udah aktif if (neighbor not in activatedNodes): if (G[node][neighbor]['pp'] > propProbability): # flipCoin(propProbability) newActiveNodes.add(neighbor) activatedNodes.append(neighbor) influenceSpread += len(newActiveNodes) if newActiveNodes: currentActiveNodes = list(newActiveNodes) newActiveNodes = set() else: newActive = False # print("activatedNodes",len(activatedNodes),activatedNodes) return influenceSpread def flipCoin(probability): return random.random() < probability解释一下这个代码

这代码是一个传播模型的实现，用于模拟信息在一个有向图中的传播过程。首先，它读取一个Excel文件，其中包含了一个邻接矩阵，表示图中节点之间的连接关系。然后，将邻接矩阵转换为numpy数组，并创建一个有向图对象。...

How to check the features in Digraph object in networkx?

1. To check the number of nodes and edges in the graph, use the number_of_nodes() and number_of_edges() methods respectively: python num_nodes = G.number_of_nodes() num_edges = G.number_of_...

graph = nx.DiGraph() graph.add_nodes_from(example.dominoModules[i].nodes()) graph.add_edges_from(example.dominoModules[i].edges()) pos = nx.spring_layout(graph) plt.title(f'Bayesian Network Based On {example.dominoModules[i].get_roots()[0]}') nx.draw(graph, pos, with_labels=True, node_color='lightblue', node_size=2000, edge_color='gray', arrows=True) plt.show()改成使用multipartite_layout绘制

G = nx.DiGraph() # 添加节点和层次信息 nodes = model.nodes() levels = {} for node in nodes: level = model.get_independencies(node).level() levels[node] = level G.add_node(node, level=level) # 添加...

graph = nx.DiGraph() graph.add_nodes_from(example.dominoModules[i].nodes()) graph.add_edges_from(example.dominoModules[i].edges()) pos = nx.spring_layout(graph) plt.title(f'Bayesian Network Based On {example.dominoModules[i].get_roots()[0]}') nx.draw(graph, pos, with_labels=True, node_color='lightblue', node_size=2000, edge_color='gray', arrows=True) plt.show()这样绘制的贝叶斯网络图形很乱怎么调整

1. 更换布局算法：networkx库提供了多种布局算法可供选择。除了spring_layout，还可以尝试使用其他布局算法，如circular_layout、shell_layout、kamada_kawai_layout等。不同的布局算法适用于不同类型的...
zip

白色大气风格的旅游酒店企业网站模板.zip

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