449 lines
16 KiB
Python
449 lines
16 KiB
Python
import copy
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import heapq
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import re
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import sys
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import time
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from math import isinf, inf
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import networkx as nx
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import matplotlib.pyplot as plt
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from typing import Dict, Generator, List, Optional
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import random
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def main():
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# print("Welcome to Lab1")
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print("功能5中输入@停止功能")
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tu = None
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while True:
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i = input("请输入操作（q:退出, 0:读入文件, 1:展示图形, 2:查找桥接词, 3:生成新文本, 4:计算最短路径, 5:随机游走）：")
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if i == "q":
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sys.exit()
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if i == "0":
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tu = Tu()
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file_name = input("请输入文件名称：")
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tu.generate_directed_dict(tu.read_text_file(file_name))
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tu.generate_directed_graph()
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continue
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if tu is None:
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print("未找到有效的图")
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continue
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if i == "1":
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tu.drawDirectedGraph()
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elif i == "2":
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word1 = input("Enter the word1:")
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word2 = input("Enter the word2:")
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tu.queryBridgeWords(word1, word2)
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elif i == "3":
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text = input("Enter the text:")
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tu.generateNewText(text)
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elif i == "4":
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word1 = input("Enter the word1:")
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word2 = input("Enter the word2,just '' is all")
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tu.calcShortestPath(word1, word2)
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elif i == "5":
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tu.randomWalk()
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else:
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print("Invalid input")
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class Tu:
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def __init__(self):
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self.dict = {}
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self.graph = nx.MultiDiGraph()
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@staticmethod
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def read_text_file(filename):
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try:
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with open(filename, 'r', encoding="utf-8") as file:
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text = file.read()
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# 用正则表达式将非字母字符替换为空格，并将换行符也替换为空格
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text = re.sub(r'[^a-zA-Z\n]+', ' ', text)
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# 将文本转换为小写，并按空格分割成单词列表
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word_sequence = text.lower().split()
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print(word_sequence)
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return word_sequence
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except FileNotFoundError:
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print("File not found.")
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return []
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def generate_directed_dict(self, word_sequence):
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graph = self.dict
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for i in range(len(word_sequence) - 1):
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current_word = word_sequence[i]
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next_word = word_sequence[i + 1]
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# 忽略空字符串
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if current_word == '' or next_word == '':
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continue
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# 将当前单词和下一个单词添加到图中
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if current_word not in graph:
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graph[current_word] = {}
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if next_word not in graph[current_word]:
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graph[current_word][next_word] = 1
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else:
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graph[current_word][next_word] += 1
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# draw_directed_graph(graph)
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return graph
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def generate_directed_graph(self):
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G = self.graph # wufuzhi,yiqigai
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graph = self.dict
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# 添加节点和边
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for node, neighbors in graph.items():
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for neighbor, weight in neighbors.items():
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G.add_edge(node, neighbor, weight=weight)
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# 如果存在相反方向的边，则再画一个箭头
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# if neighbor in graph and node in graph[neighbor]:
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# G.add_edge(neighbor, node, weight=graph[neighbor][node])
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return G
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def draw_directed_graph(self):
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G = self.graph
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pos = nx.spring_layout(G)
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plt.rcParams['figure.figsize'] = (12.8, 7.2)
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nx.draw_networkx(G, pos, with_labels=True, node_size=1000,
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node_color="skyblue", edge_color="black", font_size=10,
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font_weight="bold", arrows=True, connectionstyle='arc3,rad=0.2')
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edge_labels = nx.get_edge_attributes(G, 'weight')
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# 调整箭头位置
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for edge, weight in edge_labels.items():
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if G.has_edge(*edge[::-1]): # 检查反向边是否存在于图中
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dx = pos[edge[0]][0] - pos[edge[1]][0]
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dy = pos[edge[0]][1] - pos[edge[1]][1]
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plt.annotate(weight, (
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(pos[edge[0]][0] + pos[edge[1]][0]) / 2 + dy * 0.1,
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(pos[edge[0]][1] + pos[edge[1]][1]) / 2 - dx * 0.1))
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else: # 反向边不存在
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plt.annotate(weight, ((pos[edge[0]][0] + pos[edge[1]][0]) / 2,
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(pos[edge[0]][1] + pos[edge[1]][1]) / 2))
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plt.show()
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def drawDirectedGraph(self):
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self.draw_directed_graph()
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def find_bridge_words(self, word1, word2):
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graph = self.dict
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if word1 not in graph or word2 not in graph:
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print("No", word1, "or", word2, "in the graph!")
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return []
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bridge_words = [
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word for word in graph[word1]
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if word2 in graph.get(word, {})
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]
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return bridge_words
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@staticmethod
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def print_bridge_words(bridge_words, word1, word2):
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if not bridge_words:
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print("No bridge words from", word1, "to", word2, "!")
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return []
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else:
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bridge_words_str = ", ".join(bridge_words)
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print("The bridge words from", word1, "to", word2, "are:", bridge_words_str + ".")
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return bridge_words
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def queryBridgeWords(self, word1, word2):
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word1 = self.input_check(word1)
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word2 = self.input_check(word2)
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bridge_words = self.find_bridge_words(word1, word2)
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if bridge_words:
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self.print_bridge_words(bridge_words, word1, word2)
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return bridge_words
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else:
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return bridge_words
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def insert_bridge_words(self, text):
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words = text.split()
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new_text = []
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for i in range(len(words) - 1):
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word1 = words[i].lower()
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word2 = words[i + 1].lower()
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new_text.append(words[i])
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bridge_words = self.find_bridge_words(word1, word2)
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if len(bridge_words) > 0:
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random_bridge_word = random.choice(bridge_words)
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new_text.append(random_bridge_word)
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new_text.append(words[-1])
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return ' '.join(new_text)
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def generateNewText(self, text):
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(text_re, text_ren) = re.subn(r'[^a-zA-Z\n]+', ' ', text)
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if text_ren > 0:
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print("There are illegal signs in your input")
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if not text_re.islower():
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print("There are upper characters in your input")
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print(self.insert_bridge_words(text_re))
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def find_shortest_path_old(self, word1, word2):
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graph = self.graph
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try:
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shortest_path_generator = (
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nx.all_shortest_paths(graph, source=word1, target=word2, weight='weight'))
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shortest_length = (
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nx.shortest_path_length(graph, source=word1, target=word2, weight='weight'))
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return shortest_path_generator, shortest_length
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except nx.NetworkXNoPath:
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return None, None
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def find_shortest_path(self, word1, word2):
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try:
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(shortest_path_list, shortest_length) = self.calc_shortest_path_len(word1, word2)
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return shortest_path_list, shortest_length
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except nx.NetworkXNoPath:
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return None, None
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def find_shortest_path_ex(self, word1, word2):
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try:
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shortest_path_generator = self.all_simple_paths_graph(word1, word2)
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shortest_length = self.calc_shortest_path_len(word1, word2)
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shortest_path_list = []
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for shortest_path in shortest_path_generator:
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if shortest_path.__len__() == shortest_length:
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shortest_path_list.append(shortest_path)
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return shortest_path_list, shortest_length
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except nx.NetworkXNoPath:
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return None, None
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def draw_shortest_path(self, shortest_path_list):
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graph = self.graph
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plt.figure(figsize=(10, 6))
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# !!! HUATU ROLL YANSE
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pos = nx.spring_layout(graph)
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nx.draw_networkx_nodes(graph, pos, node_size=1000, node_color="skyblue")
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nx.draw_networkx_labels(graph, pos, font_size=10, font_weight="bold")
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for shortest_path in shortest_path_list:
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colorslist = '0123456789ABCDEF'
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num = "#"
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for i in range(6):
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num += random.choice(colorslist)
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for edge in graph.edges():
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# print(shortest_path)
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if (edge in zip(shortest_path[:-1], shortest_path[1:]) or
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edge in zip(shortest_path[1:], shortest_path[:-1])):
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nx.draw_networkx_edges(graph, pos, edgelist=[edge], width=2.0, edge_color=num,
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arrows=True, arrowstyle="->", arrowsize=30,
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connectionstyle='arc3,rad=0.2')
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else:
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nx.draw_networkx_edges(graph, pos, edgelist=[edge], width=1.0,
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edge_color="black",
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arrows=True, arrowstyle="->", arrowsize=30,
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connectionstyle='arc3,rad=0.2')
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plt.show()
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time.sleep(1)
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def calcShortestPath(self, word1, word2):
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if word2 == "":
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word1 = self.input_check(word1)
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for wordtmp in self.dict:
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if wordtmp == word1:
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continue
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else:
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self.calc_shortest_path(word1, wordtmp)
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pass
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else:
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word1 = self.input_check(word1)
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word2 = self.input_check(word2)
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self.calc_shortest_path(word1, word2)
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pass
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def calc_shortest_path_old(self, word1, word2):
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shortest_path_generator, shortest_length = self.find_shortest_path_old(word1, word2)
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shortest_path_list = []
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for shortest_path in shortest_path_generator:
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if shortest_path:
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print("Shortest path:", '→'.join(shortest_path))
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print("Length of shortest path:", shortest_length)
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shortest_path_list.append(shortest_path)
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if shortest_path_list.__len__() > 0:
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self.draw_shortest_path(shortest_path_list)
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else:
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print("No path exists between", word1, "and", word2)
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def calc_shortest_path(self, word1, word2):
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shortest_path_list, shortest_length = self.find_shortest_path_ex(word1, word2)
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for shortest_path in shortest_path_list:
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if shortest_path:
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print("Shortest path:", '→'.join(shortest_path))
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print("Length of shortest path:", shortest_length)
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if shortest_path_list.__len__() > 0:
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self.draw_shortest_path(shortest_path_list)
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else:
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print("No path exists between", word1, "and", word2)
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def random_traversal(self):
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# 用户输入文件名
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filename = input("Enter the filename to save traversal results: ")
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filename += ".txt"
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graph = copy.deepcopy(self.dict)
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start_node = random.choice(list(graph.keys()))
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visited_nodes = set()
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visited_edges = []
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current_node = start_node
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with open(filename, 'w') as file:
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while True:
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visited_nodes.add(current_node)
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file.write(current_node + '\n')
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print(current_node)
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if input() == "@":
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file.close()
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break
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if graph.get(current_node) is not None:
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pass
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else:
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break
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neighbors = list(graph[current_node].keys())
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if not neighbors:
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break
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next_node = random.choice(neighbors)
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visited_edges.append((current_node, next_node))
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graph[current_node].pop(next_node)
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current_node = next_node
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file.close()
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print("Visited nodes:", visited_nodes)
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print("Visited edges:", visited_edges)
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return visited_nodes, visited_edges
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def randomWalk(self):
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self.random_traversal()
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@staticmethod
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def input_check(input_word):
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(input_word_re, input_word_ren) = re.subn(r'[^a-zA-Z\n]', ' ', input_word)
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if input_word_ren > 0:
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print("There are illegal signs in your input,the amount is " + str(input_word_ren))
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if not input_word_re.islower():
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print("There are upper characters in your input")
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tmp = input_word_re.lower().split()
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if len(tmp) > 1:
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print("There are more than one word in your input")
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return tmp[0]
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def all_simple_paths_graph(self, source: str, targets: str) -> Generator[List[str], None, None]:
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G = self.graph
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cutoff = len(G) - 1 # 设置路径的最大深度，防止无限循环。
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visited = dict.fromkeys([source]) # 使用字典跟踪访问过的节点
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stack = [iter(G[source])] # 使用栈保存当前路径中的节点迭代器
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while stack:
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children = stack[-1] # 获取当前栈顶节点的迭代器
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child = next(children, None)
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if child is None:
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stack.pop()
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visited.popitem() # 移除最近访问的节点
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elif len(visited) < cutoff:
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if child in visited:
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continue
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if child == targets:
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yield list(visited) + [child]
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visited[child] = None
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if {targets} - set(visited.keys()): # 扩展栈直到找到目标
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stack.append(iter(G[child]))
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else:
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visited.popitem() # 可能有到达子节点的其他路径
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else: # 达到最大深度:
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for target in ({targets} & (set(children) | {child})) - set(visited.keys()):
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yield list(visited) + [target]
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stack.pop()
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visited.popitem()
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def calc_shortest_path_len(self, word1: str, word2: str) -> int:
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if word1 not in self.graph.nodes or word2 not in self.graph.nodes:
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return 0
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distances = {node: inf for node in self.graph.nodes}
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# 存储word1到所有结点的距离，初始化为无穷大
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previous_nodes: Dict[str, Optional[str]] = {node: None for node in
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self.graph.nodes} # 存储每个节点最短路径中的前一个节点，初始化为None
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distances[word1] = 0 # 距离初始化为0
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priority_queue = [(0, word1)] # 优先级队列，用于按照从小到大获取节点
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while priority_queue:
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current_distance, current_node = heapq.heappop(priority_queue) # 优先弹出距离最小的节点
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if current_node == word2:
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break
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if current_distance > distances[current_node]:
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continue
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for neighbor, attributes in self.graph[current_node].items(): # 遍历所有邻居节点及属性
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weight = attributes.get('weight', 1)
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distance = current_distance + weight # 更新距离，当前距离加入权重
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if distance < distances[neighbor]:
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distances[neighbor] = distance
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previous_nodes[neighbor] = current_node
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heapq.heappush(priority_queue, (distance, neighbor))
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path = []
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current_node = word2
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while prev := previous_nodes[current_node]: # 从目标节点回溯直到起始节点
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path.insert(0, current_node)
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current_node = prev
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if path:
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path.insert(0, current_node)
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if isinf(distances[word2]): # 目标节点不可达
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return 0
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return path.__len__()
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# return (' '.join(path),path.__len__())
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if __name__ == '__main__':
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main()
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