更新代码

X1.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/10/30 15:49
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : X1.py

X2.py

@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/10/30 15:49
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : X2.py
+
+# encoding: utf-8
+# 第一题，讲二进制数转换为10进制数
+def binToDec_int(b):
+    d = 0
+    for i in range(len(b)):
+        # 请在此添加代码，补全函数BinToDec_int()
+        # -----------Begin----------
+        if b[i] == '1':
+            d = d + 2 ** (len(b) - 1 - i)
+    # ------------End-----------
+    return d
+
+
+def binToDec_float(b):
+    # 请在此添加代码，补全函数BinToDec_int()
+    # -----------Begin----------
+    d = 0.0
+    for i in range(len(b)):
+        # 请在此添加代码，补全函数BinToDec_int()
+        # -----------Begin----------
+        if b[i] == '1':
+            d = d + 2 ** (-(i+1))
+    # ------------End-----------
+    return d
+
+
+if __name__ == '__main__':
+
+    tests = []
+    inputlist = input()
+    # print(type(inputlist))
+    # for i in inputlist:
+    #     tests.append(str(i))
+    tests = inputlist.split(',')
+    for num in tests:
+        if '.' in num:
+            num1 = num.split('.')
+            print(binToDec_int(num1[0])+binToDec_float(num1[1]))
+        else:
+            print(binToDec_int(num))

X3.py

@@ -1,205 +1,81 @@
-# -*- encoding: utf-8 -*-
-"""
- @Author: packy945
- @FileName: X3.py
- @DateTime: 2023/7/12 14:07
- @SoftWare: PyCharm
-"""
-# -*- encoding: utf-8 -*-
-from head import *
-
-class Node:
-    # 标记节点按钮
-    def node_mark_display(self):
-        for w in frame_right.winfo_children():
-            w.destroy()
-            # 清除右侧组件
-        curx = -1
-        y0 = 0.1
-        for i in d.Nodes:
-            if i[2] == 1:
-                curx = i[0]
-        lab = tk.Label(frame_right, text="节点操作", bg=RED, fg=BLACK, font=('微软雅黑', 20))
-        lab.place(relx=0.18, rely=0.02, width=200, height=30)
-        lab1 = tk.Label(frame_right, text=f"当前共有{len(d.Nodes)}个节点", bg=RED, fg=BLACK, font=('微软雅黑', 15))
-        lab1.place(relx=0.1, rely=0.0 + y0, width=200, height=30)
-        lab1 = tk.Label(frame_right, text="当前节点:", bg=RED, fg=BLACK, font=('微软雅黑', 12))
-        if curx == -1:
-            text = "未选择"
-        else:
-            text = f"{d.Nodes[curx][1]}"
-
-        lab2 = tk.Label(frame_right, text=text, bg=BLUE, fg=BLACK, font=('微软雅黑', 12))
-        lab1.place(relx=0.1, rely=0.05 + y0, width=80, height=30)
-        lab2.place(relx=0.4, rely=0.05 + y0, width=80, height=30)
-        B = tk.Button(frame_right, text="设置标签", command=lambda: self.node_rename())
-        B.place(relx=0.7, rely=0.05 + y0, width=80, height=30)
-        B = tk.Button(frame_right, text="删除当前节点", command=lambda: self.node_del_exact())
-        B.place(relx=0.1, rely=0.1 + y0, width=150, height=30)
-        if d.Nodes[curx][4] == 1:
-            text = '设为非必经节点'
-        else:
-            text = '设为必经节点'
-        B = tk.Button(frame_right, text=text, command=lambda: self.dominator(curx))
-        B.place(relx=0.1, rely=0.15 + y0, width=150, height=30)
-        B = tk.Button(frame_right, text="增加一个节点", command=lambda: self.node_add())
-        B.place(relx=0.1, rely=0.25 + y0, width=150, height=30)
-        B = tk.Button(frame_right, text="减少一个节点", command=lambda: self.node_del())
-        B.place(relx=0.1, rely=0.3 + y0, width=150, height=30)
-
-    def dominator(self, x):
-        d.Nodes[x][4] ^= 1
-        G.draw()
-        self.node_mark_display()
-
-    def node_mark(self, name):
-        if name >= len(d.Nodes):
-            self.node_404(name)
-            return
-
-        for node in d.Nodes:
-            if node[0] == name:
-                node[2] = 1
+# -*- coding: utf-8 -*-
+# Time : 2023/10/30 15:49
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : X3.py
+
+from collections import Counter
+···
+class Solve ():
+    def node_dfs(self):  # 遍历 深度优先算法
+        self.start = time.time()  # 记录开始时间
+        self.way_sum = 0
+        self.ans.clear()  # 初始化答案列表
+        E.edge_del_all(1)  # 两点间仅保留距离最短路径
+        for edge in d.Edges:  # 清空标记
+            edge[4] = False
+        i = 0  # 起始点
+        flag = [0] * len(d.Nodes)  # 初始化已经过点
+        cur = i  # 初始化起始点
+        flag[cur] = 1  # 将起始点标记为已经过
+        l = len(d.Nodes)
+        distances = [[math.inf for _ in range(l)]
+                     for _ in range(l)]
+        result = d.node_coordinate(l)
+        for j in d.Edges:  # 每一步选择当前最短的链接加到路径中去
+            if j[0] >= len(result):
+                break
             else:
-                node[2] = 0
-        G.draw()
-        self.node_mark_display()
-
-    def node_del_exact(self):
-        if d.nodes_num <= 2:# 若删除节点以后节点过少，则直接返回并警告
-            tk.messagebox.askokcancel(title='错误', message='节点过少')
-            return
-        flag = 0
-        cur = -1
-        for node in d.Nodes:# 寻找当前选定的节点
-            if node[2] == 1:
-                cur = node[0]
-                flag = 1
-        if flag == 0:
-            tk.messagebox.askokcancel(title='错误', message='未选择节点')
-            return
-        d.nodes_num -= 1#节点数目减一
-        d.ang = 360 / d.nodes_num#重新计算圆心角
-        nodes = d.Nodes.copy()#复制节点
-        d.Nodes.clear()#清除Data中的节点
-        num = 0
-        for n in nodes:#逐个复制节点
-            if n[0] == cur:#若是删除的节点，则不复制
-                continue
-            n[0] = num#重新编号
-            rad = math.radians(num * d.ang)#计算当前角度
-            x = int(math.cos(rad) * d.R)#计算当前点x坐标（相对）
-            y = int(math.sin(rad) * d.R)#计算当前点y坐标（相对）
-            n[3] = (d.center[0] + x, d.center[1] + y)#计算绝对坐标
-            d.Nodes.append(n)#将当前点加入点集合中
-            num += 1
-        edges = d.Edges.copy()#将原有边集复制出来
-        d.Edges.clear()#删除Data中的边集
-        num = 0
-        for e in edges:#复制边
-            if e[1] == cur or e[2] == cur:#删除的边不复制
-                continue
-            if e[1] > cur:#修改边对应的点的编号
-                e[1] -= 1
-            if e[2] > cur:#修改边对应的点的编号
-                e[2] -= 1
-            e[0] = num#重新编号
-            d.Edges.append(e)#将边加入边集中
-            num += 1
-        G.draw()#重新绘图
-        self.node_mark_display()
-
-    def node_rename(self):
-        flag = 0
-        for node in d.Nodes:
-            if node[2] == 1:
-                cur = node
-                flag = 1
-        if flag == 1:
-            result = tkinter.simpledialog.askstring(title='修改节点标签', prompt='请输入修改后的标签：',
-                                                    initialvalue=cur[1])
-        else:
-            tk.messagebox.askokcancel(title='错误', message='未选择节点')
-            return
-        if result:
-            cur[1] = result
-        G.draw()
-        self.node_mark_display()
-
-    def node_clear(self):
-        for node in d.Nodes:
-            node[2] = False
-        G.draw()
-
-
-
-    def node_add(self):
-        # 增加一个节点
-        nodes = len(d.Nodes) + 1
-        # 如果节点数目大于五，则将连接详细信息改为不显示
-        if nodes > 5:
-            d.edgeinfo = 0
-        self.node_init(nodes)
-        G.draw()
-        self.node_mark_display()
-
-    def node_set(self):
-        node = tkinter.simpledialog.askinteger(title='修改节点数目', prompt='请输入修改后的节点数目：',
-                                                   initialvalue=str(len(d.Nodes)))
-        if node:
-            self.node_init(node)
-        G.draw()
-
-    def node_del(self):
-        if d.nodes_num <= 2:
-            tk.messagebox.askokcancel(title='错误', message='节点过少')
-            return
-        nodes = len(d.Nodes) - 1
-        if nodes < 6:
-            d.edgeinfo = 1
-        self.node_init(nodes)
-        G.draw()
-        self.node_mark_display()
-
-    def node_init(self, node):
-        if not node:
-            return
-        if d.source == 1:
-            d.data(node)
-        elif d.source == 2:
-            if node > 13:
-                tk.messagebox.askokcancel(title='错误', message='节点过多')
-                return
-            d.HEI_data(node)
-        else:
-            if node > 13:
-                tk.messagebox.askokcancel(title='错误', message='节点过多')
-                return
-            d.LIAO_data(node)
-        if node > 5:
-            d.edgeinfo = 0
-        else:
-            d.edgeinfo = 1
-
-    def graph_del(self):
-        global d
-        del d
-
-
-
-    def node_refresh(self):
-        nodes = len(d.Nodes)
-        self.node_init(nodes)
-        G.draw()
-        self.node_mark_display()
-
-    def node_404(self, name):
-        # 弹出对话框
-        tk.messagebox.askokcancel(title='错误', message='该节点不存在')
-        # print(result)
-
+                if j[6] < 99 and j[7] < 99:
+                    distances[result[j[0]][0]][
+                        result[j[0]][1]] = j[6]
+                    distances[result[j[0]][1]][
+                        result[j[0]][0]] = j[6]
+        cost, ways = self.dfs(distances)
+        print(self.way_sum)
+        self.ans = ways.copy()
+        if len(ways) != l + 1:
+            tk.messagebox.askokcancel(title='结果',
+                                message='该图不存在dfs路径')
+            # print(ways)
+            for way in range(len(ways) - 1):  # 标记一条路径
+                d.Edges[G.way_node_edge(ways[way],
+                                ways[way + 1])][4] = True
+            self.end = time.time()  # 记录结束时间
+            self.ans.clear()
+            return -1
+        for way in range(len(ways) - 1):  # 标记一条路径
+            d.Edges[G.way_node_edge(ways[way],
+                                ways[way + 1])][4] = True
+        tk.messagebox.askokcancel(title='结果',
+                                message='dfs算法路径')
+        self.end = time.time()  # 记录结束时间
+    def greedy(self, distances):
+        num_cities = len(distances)
+        visited = [False] * num_cities
+        path = [0]  # 起始城市为0
+        visited[0] = True
+        for _ in range(num_cities - 1):
+            curr_city = path[-1]
+            min_distance = float('inf')
+            next_city = None
+
+            for city in range(num_cities):
+                if not visited[city] and distances[curr_city][city] \
+                        < min_distance and distances[curr_city][
+                    city] != 0:
+                    min_distance = distances[curr_city][city]
+                    next_city = city
+                    self.way_sum += 1  # 遍历路径的数目加一
+
+            if next_city is not None:
+                path.append(next_city)
+                visited[next_city] = True
+        if distances[path[-1]][0] != np.inf:
+            path.append(0)  # 回到起始城市形成闭合路径
+        # print(path)
+        return path
 if __name__ == '__main__':
-    G.draw()
-    node = Node()
-    node.node_mark_display()
-    mainloop()
+    ···编程[11.2]与编程[11.5]主函数
+    s. node_greedy ()  # 开始遍历搜索合法路径

X5.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/10/30 15:49
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : X5.py

demo.py

@@ -0,0 +1,36 @@
+class Ways():
+    def way_change(self):
+        global entry3, entry4
+        value1 = entry3.get()  # 获取第一个输入框的值
+        value2 = entry4.get()  # 获取第二个输入框的值
+        try:
+            value1 = int(value1)  # 将值转换为整数
+            value2 = int(value2)  # 将值转换为整数
+            print("值1:", value1)
+            print("值2:", value2)
+            ways_list = [int(node[1]) for node in d.Nodes]
+            # 判断两点是否连接
+            if abs(value2) > 10 and abs(value1) > 10:
+                tk.messagebox.askokcancel(title='错误', message='请重新输入正确的编号！')
+                return
+            else:
+                if value1 < value2:
+                    str_route = f'{value1}-{value2}'
+                else:
+                    str_route = f'{value2}-{value1}'
+            no = 0
+            for item in d.Edges:
+                if str_route == item[5]:
+                    no = G.node_edge(value1 - 1, value2 - 1)
+                    if d.Edges[no][3] == 0:
+                        tk.messagebox.askokcancel(title='提醒', message='当前路线不存在！')
+                        return
+            top = creat_window(str_route + '路径参数修改')  # 创建弹出窗口
+            distance_entry = create_input_box(top, "路程/公里：（1-99）", d.Edges[no][6])  # 创建一个输入框，获取卷积核大小
+            time_entry = create_input_box(top, "时间/h：（1-99）", d.Edges[no][7])  # 创建一个输入框，获取卷积步长
+            def get_input():  # 创建
+                ···与前面way_add里get_input相似
+            button = tk.Button(top, text="获取信息", command=get_input)
+            button.pack()
+        except ValueError:
+            tk.messagebox.askokcancel(title='错误', message='请输入正确的城市编号！')

demo11.py

@@ -0,0 +1,21 @@
+# 导入aip模块，这是一个来自百度AI平台的Python SDK，可以用来调用百度NLP（自然语言处理）服务
+from aip import AipNlp
+# 定义一个名为address的函数，它接受一个字符串参数addr，默认为空字符串
+def address(addr=''):
+    # 在这个函数中定义了APP_ID、API_KEY和SECRET_KEY三个变量，这些变量是你在百度AI平台上申请的凭证
+    APP_ID = '41768293'
+    API_KEY = 'CAnsdV8e40GQtjhK4qHkucUO'
+    SECRET_KEY = 'jU1NbbnwrhTahngr2p3cz8p2EIAa8zWj'
+
+    # 使用凭证创建一个AipNlp的客户端对象，这个对象会用来发送请求到百度AI平台
+    client = AipNlp(APP_ID, API_KEY, SECRET_KEY)
+    """ 调用词法分析 """
+    # 调用客户端对象的address方法，将参数addr发送到百度AI平台进行地址识别，返回的结果保存在变量re中
+    re = client.address(addr)
+    # 返回识别的结果
+
+    return re
+
+    # 调用address函数，参数为你要识别的具体地址，并将返回的结果打印出来
+
+print(address('海南省三亚市吉阳区迎宾大道学院路三亚学院'))

main.py

@@ -1,248 +1,61 @@
-# -*- encoding: utf-8 -*-
-from NODE import *
-from SOLVE import *
-from EDGE import *
-from WAYS import *
-
-N = Node()
-E = Edge()
-W = Ways()
-
-mode = tk.IntVar(value=1)
-# 目前的操作状态：
-# 1为节点操作
-# 2为链接操作
-# 3为路径操作
-# 默认为1
-
-
-RIGHT = -1
-B = tk.Button(frame, text="修改", command=lambda: N.node_set())
-B.place(x=180, y=10, width=50, height=30)
-
-
-def left1(event):
-    # 查找鼠标左键按下时位置是否在某个节点内
-    n = -1
-    for node in d.Nodes:
-        if dir2([event.x, event.y], [node[3][0], node[3][1]]) < 20 * 20:
-            n = node[0]
-
-    # n为点击的节点，若没有则为-1
-    m = mode.get()  # 查看现在操作类型
-    if m == 1 and not n == -1:
-        # 节点操作
-
-        N.node_mark(n)
-    elif m == 2 and not n == -1:
-        # 链接操作
-        if len(E.mark) == 0:
-            E.mark.append(n)
-            E.node_mark1(E.mark[0])
-        elif len(E.mark) == 1:
-            E.mark.append(n)
-            E.node_mark1(E.mark[0])
-            E.node_mark2(E.mark[1])
-        elif not n == E.mark[1]:
-            n1 = E.mark[1]
-            E.mark.clear()
-            E.mark.append(n1)
-            E.mark.append(n)
-            E.node_mark1(E.mark[0])
-            E.node_mark2(E.mark[1])
-    # 路径操作
-    elif m == 3 and not n == -1:
-        W.user_way_add(n)
-    # 自动判断操作
-    elif m == 4 and not n == -1:
-        if len(solve.mark) == 0:
-            solve.mark.append(n)
-            solve.node_mark1(solve.mark[0])
-        elif len(solve.mark) == 1:
-            solve.mark.append(n)
-            solve.node_mark1(solve.mark[0])
-            solve.node_mark2(solve.mark[1])
-        elif not n == solve.mark[1]:
-            n1 = solve.mark[1]
-            solve.mark.clear()
-            solve.mark.append(n1)
-            solve.mark.append(n)
-            solve.node_mark1(solve.mark[0])
-            solve.node_mark2(solve.mark[1])
-
-
-def onRightButtonUp(event):
-    global RIGHT
-    # 查找鼠标右键按下时位置是否在某个节点内
-    n = -1
-    for node in d.Nodes:
-        if dir2([event.x, event.y], [node[3][0], node[3][1]]) < 20 * 20:
-            n = node[0]
-    if n == -1:
-        menu.post(event.x_root, event.y_root)
-    else:
-        RIGHT = n
-        menu_node.post(event.x_root, event.y_root)
-
-
-# 右键菜单
-def m_node():
-    mode.set(1)
-    W.clear()
-    N.node_mark_display()
-
-
-def m_edge():
-    mode.set(2)
-    W.clear()
-    E.edge_mark_display()
-
-
-def m_ways():
-    mode.set(3)
-    W.user_way_clear()
-    W.way_mark_display()
-
-
-def m_info():
-    d.edgeinfo ^= 1
-    G.draw()
-
-
-def m_solve():
-    W.clear()
-    mode.set(4)
-    solve.Solve_display()
-
-
-def graph_init():
-    nodes = random.randint(4, 10)
-    N.node_init(nodes)
-    G.draw()
-    N.node_mark_display()
-
-
-def sourc_set(num):
-    W.clear()
-    d.source = num
-    N.node_init(nodes)
-    G.draw()
-
-
-def node_del(num):
-    N.node_mark(num)
-    N.node_del_exact()
-
-
-def node_name(num):
-    N.node_mark(num)
-    N.node_rename()
-
-
-def node_condition(num):
-    d.Nodes[num][4] ^= 1
-    G.draw()
-
-
-menu = tk.Menu(root, tearoff=0)
-menu.add_command(label='节点操作', command=m_node)
-menu.add_command(label='连接操作', command=m_edge)
-menu.add_command(label='路径操作', command=m_ways)
-menu.add_command(label='显示/隐藏链接信息', command=m_info)
-
-menu.add_command(label='自动判断相关', command=m_solve)
-fmenu1 = Menu(menu, tearoff=False)
-fmenu1.add_command(label='使用模拟数据', command=lambda: sourc_set(1))
-fmenu1.add_command(label='使用黑龙江省数据', command=lambda: sourc_set(2))
-fmenu1.add_command(label='使用辽宁省数据', command=lambda: sourc_set(3))
-menu.add_cascade(label="切换数据源", menu=fmenu1)
-
-menu_node = tk.Menu(root, tearoff=0)
-menu_node.add_command(label='删除节点', command=lambda: node_del(RIGHT))
-menu_node.add_command(label='切换必经/非必经', command=lambda: node_condition(RIGHT))
-menu_node.add_command(label='重命名节点', command=lambda: node_name(RIGHT))
-# menu_node.add_command(label='显示/隐藏链接信息', command=m_info)
-
-cv.bind('<Button-1>', left1)
-cv.bind('<ButtonRelease-3>', onRightButtonUp)
-
-
-# 释放右键时打开菜单
-
-def auto_opera_mu(menu: tk.Menu):
-    """TSP问题自动生成与自动求解相关"""
-    menu_node = Menu(menu, tearoff=False)
-    menu_node.add_command(label="自动随机产生一个TSP问题", command=lambda: graph_init())
-    menu_node.add_command(label="重新生成", command=lambda: N.node_refresh())
-    menu_node.add_command(label="自动求解最优路径-遍历", command=lambda: solve.dfs_panel())
-    menu_node.add_command(label="自动求解优化路径-贪心", command=lambda: solve.greedy_panel())
-    menu_node.add_command(label="检查是否存在路径", command=lambda: solve.check())
-
-    # 在主目录菜单上新增"文件"选项，并通过menu参数与下拉菜单绑定
-    menu.add_cascade(label="TSP问题自动生成与自动求解相关", menu=menu_node)
-
-
-def node_opera_mu(menu: tk.Menu):
-    """节点操作"""
-
-    # fmenu1 = Menu(menu, tearoff=False)
-    # for i in range(10):
-    #    fmenu1.add_command(label='选择节点 ' + str(i + 1), command=lambda num=i: node_mark(num))
-
-    menu_node = Menu(menu, tearoff=False)
-    menu_node.add_command(label="添加一个节点", command=lambda: N.node_add())
-    menu_node.add_command(label="删除一个节点", command=lambda: N.node_del())
-    # menu_node.add_cascade(label="选择一个节点", menu=fmenu1)
-    menu_node.add_command(label="选择一个节点", command=lambda: N.node_mark_display())
-    # menu_node.add_command(label="停止标记一个节点", command=lambda: node_mark_display1())
-    # menu_node.add_command(label="设置节点标签", command=lambda:node_rename())
-
-    # 在主目录菜单上新增"文件"选项，并通过menu参数与下拉菜单绑定
-    menu.add_cascade(label="节点操作", menu=menu_node)
-
-
-def edge_opera_mu(menu: tk.Menu):
-    """连接操作"""
-
-    # fmenu1 = Menu(menu, tearoff=False)
-    # for i in range(10):
-    #     fmenu1.add_command(label='选择另一个节点 ' + str(i + 1), command=lambda num=i: node_mark2(num))
-    #
-    menu_node = Menu(menu, tearoff=False)
-    # menu_node.add_cascade(label="选择另一个节点", menu=fmenu1)
-
-    menu_node.add_command(label="选择一个连接", command=lambda: E.edge_mark_display())
-
-    # menu_node.add_command(label="建立一个连接",command=lambda :edge_add())
-    menu_node.add_command(label="删除所有连接", command=lambda: E.edge_delall())
-    menu_node.add_command(label="删除所有多余连接", command=lambda: E.newedge_del_all(1))
-    # menu_node.add_command(label="设置连接标签", command=lambda: edge_rename())
-    # 在主目录菜单上新增"文件"选项，并通过menu参数与下拉菜单绑定
-    menu.add_cascade(label="连接操作", menu=menu_node)
-
-
-def path_opera_mu(menu: tk.Menu):
-    """路径操作"""
-    menu_node = Menu(menu, tearoff=False)
-    menu_node.add_command(label="开始标记路径", command=lambda: W.way_mark_display())
-    menu_node.add_command(label="撤销一步路径", command=lambda: W.user_way_del())
-    menu_node.add_command(label="取消所有路径标记", command=lambda: W.user_way_clear())
-    # 在主目录菜单上新增"文件"选项，并通过menu参数与下拉菜单绑定
-    menu.add_cascade(label="路径操作", menu=menu_node, )
-
-
-auto_opera_mu(main_menu)
-node_opera_mu(main_menu)
-edge_opera_mu(main_menu)
-path_opera_mu(main_menu)
-
-
-def main():
-    G = Graph()
-    G.draw()
-    sourc_set(2)
-    mainloop()
-
-
-if __name__ == '__main__':
-    main()
+import sys
+
+import numpy as np
+
+
+def tsp_nearest_neighbor1(distances):
+    num_cities = len(distances)
+    visited = [False] * num_cities
+    path = [0] # 起始城市为0
+    visited[0] = True
+
+    for _ in range(num_cities - 1):
+        curr_city = path[-1]
+        min_distance = sys.maxsize
+        nearest_city = None
+
+        for city in range(num_cities):
+            if not visited[city] and distances[curr_city][city] < min_distance and distances[curr_city][city] != 0:
+                min_distance = distances[curr_city][city]
+                nearest_city = city
+
+        if nearest_city is not None:
+            path.append(nearest_city)
+            visited[nearest_city] = True
+
+    path.append(0)  # 回到起始城市形成闭合路径
+    return path
+def tsp_nearest_neighbor(distances):
+    num_cities = len(distances)
+    visited = [False] * num_cities
+    path = [0] # 起始城市为0
+    visited[0] = True
+
+    for _ in range(num_cities - 1):
+        curr_city = path[-1]
+        min_distance = sys.maxsize
+        nearest_city = None
+
+        for city in range(num_cities):
+            if not visited[city] and distances[curr_city][city] < min_distance and distances[curr_city][city] != 0:
+                min_distance = distances[curr_city][city]
+                nearest_city = city
+
+        if nearest_city is not None:
+            path.append(nearest_city)
+            visited[nearest_city] = True
+
+    path.append(0)  # 回到起始城市形成闭合路径
+    return path
+# 示例用法
+distances = np.array([
+    [np.inf, 50, 99, np.inf, 64, np.inf],
+    [50, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+
+path = tsp_nearest_neighbor1(distances)
+print(path)

test/DP.py

@@ -0,0 +1,49 @@
+import numpy as np
+
+def tsp_dp(distances):
+    n = len(distances)
+    dp = np.full((1 << n, n), np.inf)
+    path = np.zeros((1 << n, n), dtype=int)
+
+    for i in range(n):
+        dp[1 << i][i] = 0
+
+    for S in range(1, 1 << n):
+        for j in range(n):
+            if not (S & (1 << j)):
+                continue
+            for k in range(n):
+                if k == j or not (S & (1 << k)):
+                    continue
+                if dp[S][j] > dp[S - (1 << j)][k] + distances[k][j]:
+                    dp[S][j] = dp[S - (1 << j)][k] + distances[k][j]
+                    path[S][j] = k
+
+    final_path = []
+    S = (1 << n) - 1
+    j = 0
+    while S:
+        final_path.append(j)
+        k = path[S][j]
+        S -= 1 << j
+        j = k
+    final_path.append(0)
+
+    ans = float('inf')
+    for j in range(n):  # 枚举所有结束点
+        if distances[j][0] != np.inf:  # 如果结束点j与起点0相连
+            ans = min(ans, dp[(1 << n) - 1][j] + distances[j][0])
+
+    return ans, final_path
+# 示例使用
+distances = np.array([
+    [np.inf, 50, 99, np.inf, 64, np.inf],
+    [50, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+min_cost, min_path = tsp_dp(distances)
+print("Minimum cost:", min_cost)
+print("Minimum path:", min_path)

test/Prin.py

@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/11/3 17:42
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : Prin.py
+
+import numpy as np
+
+
+def prim_exists_path(distances):
+    num_cities = distances.shape[0]
+    visited = set()
+    pq = []
+    start_node = 0
+
+    visited.add(start_node)
+    for neighbor in range(num_cities):
+        if neighbor != start_node and np.isfinite(distances[start_node, neighbor]):
+            pq.append((distances[start_node, neighbor], start_node, neighbor))
+
+    pq.sort(key=lambda x: x[0])
+
+    while pq:
+        weight, node1, node2 = pq.pop(0)
+        if node2 not in visited:
+            visited.add(node2)
+            for neighbor in range(num_cities):
+                if neighbor != node2 and np.isfinite(distances[node2, neighbor]):
+                    pq.append((distances[node2, neighbor], node2, neighbor))
+
+            pq.sort(key=lambda x: x[0])
+
+    return len(visited) == num_cities
+
+
+distances = np.array([
+    [np.inf, 50, 99, np.inf, 64, np.inf],
+    [50, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+
+print(prim_exists_path(distances))

test/demo1.py

@@ -0,0 +1,76 @@
+import random
+
+def initialize_population(num_points, pop_size):
+    population = []
+    for _ in range(pop_size):
+        individual = list(range(num_points))
+        random.shuffle(individual)
+        population.append(individual)
+    return population
+
+def evaluate_fitness(individual, distances):
+    fitness = 0
+    num_points = len(individual)
+    for i in range(num_points):
+        city1 = individual[i]
+        city2 = individual[(i + 1) % num_points]
+        fitness += distances[city1][city2]
+    return fitness
+
+def crossover(parent1, parent2):
+    child = [-1] * len(parent1)
+    start_idx = random.randint(0, len(parent1)-1)
+    end_idx = random.randint(start_idx+1, len(parent1))
+    child[start_idx:end_idx] = parent1[start_idx:end_idx]
+    for city in parent2:
+        if city not in child:
+            for i in range(len(child)):
+                if child[i] == -1:
+                    child[i] = city
+                    break
+    return child
+
+def mutate(individual):
+    idx1, idx2 = random.sample(range(len(individual)), 2)
+    individual[idx1], individual[idx2] = individual[idx2], individual[idx1]
+
+def genetic_algorithm(distances, pop_size, num_generations):
+    num_points = len(distances)
+    population = initialize_population(num_points, pop_size)
+
+    for generation in range(num_generations):
+        population_fitness = []
+        for individual in population:
+            fitness = evaluate_fitness(individual, distances)
+            population_fitness.append((individual, fitness))
+
+        population_fitness.sort(key=lambda x: x[1])  # 按适应度排序
+        elite_individual = population_fitness[0][0]
+        new_population = [elite_individual]
+
+        while len(new_population) < pop_size:
+            parent1, parent2 = random.choices(population_fitness, k=2)
+            child = crossover(parent1[0], parent2[0])
+            mutate(child)
+            new_population.append(child)
+
+        population = new_population
+
+    best_individual = population_fitness[0][0]
+    return best_individual
+
+# 示例使用
+# distances为距离矩阵
+distances = [
+    [0, 50, 99, 0, 64, 0],
+    [50, 0, 12, 45, 74, 13],
+    [99, 12, 0, 25, 0, 61],
+    [0, 45, 25, 0, 45, 47],
+    [64, 74, 0, 45, 0, 0],
+    [0, 13, 61, 47, 0, 0]
+]
+population_size = 100
+num_generations = 1000
+
+best_solution = genetic_algorithm(distances, population_size, num_generations)
+print(best_solution)

test/demo10.py

@@ -0,0 +1,46 @@
+import numpy as np
+
+inf = float('inf')
+
+distances = np.array([
+    [inf, 72, inf, inf, 81, 67],
+    [72, inf, 26, inf, 61, inf],
+    [inf, 26, inf, 53, 97, inf],
+    [inf, inf, 53, inf, inf, inf],
+    [81, 61, 97, inf, inf, 38],
+    [67, inf, inf, inf, 38, inf]
+])
+
+def find_cycles(start, distances):
+    n = len(distances)
+    path = [start]
+    cycles = []
+
+    def is_valid(node, position):
+        if distances[path[position - 1]][node] == inf:
+            return False
+        if node in path:
+            return False
+        return True
+
+    def find_paths(node):
+        for next_node in range(n):
+            if is_valid(next_node, len(path)):
+                path.append(next_node)
+                if len(path) < n:
+                    find_paths(next_node)
+                elif len(path) == n and distances[path[-1]][start] != inf:
+                    cycles.append(path.copy())
+                path.pop()
+
+    find_paths(start)
+    return cycles
+
+all_cycles = find_cycles(0, distances)
+
+if all_cycles:
+    print("经过全部节点能返回起点的回路的全部路径为：")
+    for cycle in all_cycles:
+        print(cycle + [cycle[0]])  # 添加起点以形成完整的回路
+else:
+    print("不存在经过全部节点能返回起点的回路")

test/demo11.py

@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/11/9 11:26
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : demo11.py
+
+import os
+
+# 遍历指定文件夹
+def rename_files(folder_path):
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            # 检查文件类型是否为图片类型（这里使用了简单的判断方式）
+            if file.endswith('.jpg') or file.endswith('.png'):
+                # 获取当前文件所在的子文件夹名称
+                subdir_name = os.path.basename(root)
+                # 组合新的文件名，添加子文件夹名称前缀
+                new_file_name = subdir_name + '_' + file
+                # 使用 os.rename() 方法来重命名文件
+                os.rename(os.path.join(root, file), os.path.join(root, new_file_name))
+
+# 调用函数来重命名文件
+rename_files('D:/weixin/WeChat Files/wxid_f2yvfdxbyuag22/FileStorage/File/2023-11/球/')

test/demo12.py

@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/11/9 11:28
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : demo12.py
+
+import os
+import shutil
+
+# 遍历指定文件夹并复制图片文件到新的文件夹
+def copy_images(folder_path, target_folder_path):
+    # 创建目标文件夹（如果它不存在）
+    if not os.path.exists(target_folder_path):
+        os.makedirs(target_folder_path)
+
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            # 检查文件类型是否为图片类型（这里使用了简单的判断方式）
+            if file.endswith('.jpg') or file.endswith('.png'):
+                # 构造源文件的完整路径
+                source_file_path = os.path.join(root, file)
+                # 构造目标文件的完整路径
+                target_file_path = os.path.join(target_folder_path, file)
+                # 使用 shutil 模块的 copy2() 函数复制文件（保留源文件的元数据）
+                shutil.copy2(source_file_path, target_file_path)
+
+# 调用函数来复制图片文件
+copy_images('D:/weixin/WeChat Files/wxid_f2yvfdxbyuag22/FileStorage/File/2023-11/球/', 'K:/工作/traveler/background/')

test/demo13.py

@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/11/9 11:54
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : demo13.py
+
+import os
+
+# 读取文件夹中的图像文件名到列表
+def get_image_filenames(folder_path):
+    image_filenames = []
+
+    for file_name in os.listdir(folder_path):
+        # 检查文件类型是否为图像类型（这里使用了简单的判断方式）
+        if file_name.endswith('.jpg') or file_name.endswith('.png'):
+            # 将文件名添加到列表中
+            image_filenames.append(file_name)
+
+    return image_filenames
+
+# 调用函数来获取图像文件列表
+image_files = get_image_filenames('K:/工作/traveler/background/')
+print(image_files)

test/demo2.py

@@ -0,0 +1,106 @@
+import numpy as np
+
+class AntColonyOptimization:
+    def __init__(self, distances, num_ants=10, num_iterations=100, alpha=1, beta=1, evaporation=0.5, q0=0.9):
+        self.distances = distances
+        self.num_ants = num_ants
+        self.num_iterations = num_iterations
+        self.alpha = alpha
+        self.beta = beta
+        self.evaporation = evaporation
+        self.q0 = q0
+
+    def optimize(self):
+        num_cities = len(self.distances)
+        pheromone = np.ones((num_cities, num_cities))
+        best_path = None
+        best_length = float('inf')
+
+        for _ in range(self.num_iterations):
+            paths = []
+            lengths = []
+
+            for _ in range(self.num_ants):
+                path = self.construct_path(pheromone)
+                length = self.calculate_length(path)
+                paths.append(path)
+                lengths.append(length)
+
+                if length < best_length:
+                    best_length = length
+                    best_path = path
+
+            self.update_pheromone(pheromone, paths, lengths)
+
+        return best_path
+
+    def construct_path(self, pheromone):
+        num_cities = len(pheromone)
+        path = [0]  # 起始城市为0
+        visited = np.zeros(num_cities, dtype=bool)
+        visited[0] = True
+
+        for _ in range(num_cities - 1):
+            probs = self.calculate_probs(pheromone, path[-1], visited)
+            next_city = self.choose_next_city(probs)
+
+            path.append(next_city)
+            visited[next_city] = True
+
+        return path
+
+    def calculate_probs(self, pheromone, curr_city, visited):
+        num_cities = len(pheromone)
+        probs = np.zeros(num_cities)
+
+        denominator = 0
+        for city in range(num_cities):
+            if not visited[city]:
+                denominator += pheromone[curr_city, city] ** self.alpha + (1.0 / self.distances[curr_city, city]) ** self.beta
+
+        for city in range(num_cities):
+            if not visited[city]:
+                numerator = pheromone[curr_city, city] ** self.alpha + (1.0 / self.distances[curr_city, city]) ** self.beta
+                probs[city] = numerator / denominator
+
+        return probs
+
+    def choose_next_city(self, probs):
+        q = np.random.rand()
+
+        if q < self.q0:
+            next_city = np.argmax(probs)
+        else:
+            next_city = np.random.choice(len(probs), p=probs)
+
+        return next_city
+
+    def calculate_length(self, path):
+        length = 0
+        for i in range(len(path) - 1):
+            length += self.distances[path[i], path[i+1]]
+
+        return length
+
+    def update_pheromone(self, pheromone, paths, lengths):
+        pheromone *= self.evaporation
+
+        for i, path in enumerate(paths):
+            for j in range(len(path) - 1):
+                city1 = path[j]
+                city2 = path[j+1]
+                pheromone[city1, city2] += 1.0 / lengths[i]
+                pheromone[city2, city1] += 1.0 / lengths[i]
+
+distances = np.array([
+    [np.inf, 50, 99, np.inf, 64, np.inf],
+    [50, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+
+aco = AntColonyOptimization(distances)
+best_path = aco.optimize()
+print(best_path)

test/demo3.py

@@ -0,0 +1,66 @@
+import numpy as np
+
+def tsp_backtracking(distances):
+    num_cities = len(distances)
+    path = [0]  # 起始城市为0
+    visited = [False] * num_cities
+    visited[0] = True
+    min_distance = float('inf')
+    shortest_path = None
+
+    def backtrack(curr_city, curr_distance, visited, path):
+        nonlocal min_distance, shortest_path
+        if len(path) == num_cities:
+            # 到达所有城市，更新最短路径
+            if curr_distance + distances[curr_city][0] < min_distance:
+                min_distance = curr_distance + distances[curr_city][0]
+                shortest_path = path + [0]
+        else:
+            for next_city in range(num_cities):
+                if not visited[next_city]:
+                    # 选择下一个未访问的城市
+                    visited[next_city] = True
+                    path.append(next_city)
+                    new_distance = curr_distance + distances[curr_city][next_city]
+                    # 剪枝条件：当前路径已经大于最短路径，不继续搜索
+                    if new_distance < min_distance:
+                        backtrack(next_city, new_distance, visited, path)
+                    # 回溯
+                    visited[next_city] = False
+                    path.pop()
+    backtrack(0, 0, visited, path)
+    return shortest_path
+# 示例用法
+# distances = np.array([
+#     [np.inf, 50, 99, np.inf, 64, np.inf],
+#     [50, np.inf, 12, 45, 74, 13],
+#     [99, 12, np.inf, 25, np.inf, 61],
+#     [np.inf, 45, 25, np.inf, 45, 47],
+#     [64, 74, np.inf, 45, np.inf, np.inf],
+#     [np.inf, 13, 61, 47, np.inf, np.inf]
+# ])
+# inf = np.inf
+# distances = np.array(
+#     [[inf, 69, 48, inf, 41, 78],
+#      [69, inf, 48, 53, 91, inf],
+#      [48, 48, inf, 19, 86, inf],
+#      [inf, 53, 19, inf, 96, 10],
+#      [41, 91, 86, 96, inf, inf],
+#      [78, inf, inf, 10, inf, inf]]
+# )
+
+inf = float('inf')
+
+distances = np.array([
+    [inf, 72, inf, inf, 81, 67],
+    [72, inf, 26, inf, 61, inf],
+    [inf, 26, inf, 53, 97, inf],
+    [inf, inf, 53, inf, inf, inf],
+    [81, 61, 97, inf, inf, 38],
+    [67, inf, inf, inf, 38, inf]
+])
+path = tsp_backtracking(distances)
+if path:
+    print(path)
+else:
+    print('不存在')

test/demo5.py

@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/10/31 11:01
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : demo5.py
+
+import numpy as np
+
+inf = float('inf')
+
+distances = np.array([[inf, 71, 88, 28, 67, inf], [71, inf, inf, inf, 51, 98], [88, inf, inf, 70, inf, inf], [28, inf, 70, inf, 90, 75], [67, 51, inf, 90, inf, inf], [inf, 98, inf, 75, inf, inf]])
+
+def find_hamiltonian_cycles(start, distances):
+    n = len(distances)
+    path = [start]
+    cycles = []
+
+    def is_valid(node, position):
+        if distances[path[position - 1]][node] == inf:
+            return False
+        if node in path:
+            return False
+        return True
+
+    def find_paths(node):
+        for next_node in range(n):
+            if is_valid(next_node, len(path)):
+                path.append(next_node)
+                if len(path) < n:
+                    find_paths(next_node)
+                elif len(path) == n and distances[path[-1]][start] != inf:
+                    cycles.append(path + [start])
+                path.pop()
+
+    find_paths(start)
+    return cycles
+
+all_cycles = find_hamiltonian_cycles(0, distances)
+
+if all_cycles:
+    print("经过全部节点能返回起点的回路的全部路径为：")
+    for cycle in all_cycles:
+        print(cycle)  # 输出所有回路路径
+else:
+    print("不存在经过全部节点能返回起点的回路")
+

test/demo6.py

@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/11/3 17:21
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : demo6.py
+
+import sys
+import itertools
+
+import numpy as np
+
+
+def tsp_dp(distances):
+    n = len(distances)
+    dp = [[sys.maxsize] * n for _ in range(1 << n)]
+    dp[1][0] = 0
+
+    for mask in range(1, 1 << n):
+        for current in range(n):
+            if mask & (1 << current):
+                for previous in range(n):
+                    if previous != current and mask & (1 << previous):
+                        dp[mask][current] = min(dp[mask][current], dp[mask ^ (1 << current)][previous] + distances[previous][current])
+
+    shortest_path = sys.maxsize
+    end_mask = (1 << n) - 1
+    last_city = -1
+
+    for i in range(1, n):
+        if shortest_path > dp[end_mask][i] + distances[i][0]:
+            shortest_path = dp[end_mask][i] + distances[i][0]
+            last_city = i
+
+    path = []
+    mask = end_mask
+    while last_city >= 0:
+        path.append(last_city)
+        new_mask = mask ^ (1 << last_city)
+        last_city = -1
+        for i in range(n):
+            if new_mask & (1 << i):
+                if last_city == -1 or dp[mask][last_city] + distances[last_city][i] == dp[mask][i]:
+                    last_city = i
+        mask = new_mask
+
+    path.append(0)
+    path.reverse()
+
+    return shortest_path, path
+#
+# # 测试代码
+# distances = [
+#     [0, 2, 9, 10],
+#     [1, 0, 6, 4],
+#     [15, 7, 0, 8],
+#     [6, 3, 12, 0]
+# ]
+
+
+
+# 测试代码
+distances = np.array([
+    [np.inf, 80, 99, np.inf, 64, np.inf],
+    [80, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+
+shortest_path, path = tsp_dp(distances)
+print("最短路径长度为:", shortest_path)
+print("最短路径为:", path)

test/demo7.py

@@ -0,0 +1,119 @@
+from docx import Document
+from docx.oxml import OxmlElement
+from docx.oxml.ns import qn
+from operator import eq
+
+flag = True
+
+def get_paragraph_shading(p):
+    """
+    Get paragraph`s shading
+    Usage:
+    get_paragraph_shading(paragraph)
+    """
+    p_pr = p._p.get_or_add_pPr()        # 获取段落的pPr
+    # print(p_pr)
+    p_shd = p_pr.first_child_found_in("w:shd")      # 找到 'w:shd' 标签
+    # 如果不存在，就创建
+    if p_shd is None:
+        p_shd = OxmlElement('w:shd')
+        p_pr.append(p_shd)
+    # 获取各属性的值
+    val = p_shd.get(qn('w:val'))
+    # color = p_shd.get(qn('w:color'))
+    fill = p_shd.get(qn('w:fill'))
+    print([val, fill])
+    return [val, fill]
+
+def get_paragraph_boders(p):
+    """
+    Get paragraph`s boders
+    Usage:
+    get_paragraph_boders(paragraph)
+    """
+    p_pr = p._p.get_or_add_pPr()        # 获取段落的pPr
+    p_borders = p_pr.first_child_found_in("w:pBdr")      # 找到 'p_borders' 标签
+    # 如果不存在，就创建
+    if p_borders is None:
+        p_borders = OxmlElement('w:pBdr')
+        p_pr.append(p_borders)
+    # 获取各属性的值
+    borders = []
+    for edge in ('left', 'top', 'right', 'bottom'):
+        element = p_borders.find(qn('w:{}'.format(edge)))
+        if element is None:
+            element = OxmlElement('w:{}'.format(edge))
+            p_borders.append(element)
+        border = []
+        for key in ["sz", "val", "color"]:
+            temp = str(element.get(qn('w:{}'.format(key))))
+            if temp == 'none' or temp == '0' or temp == 'auto' or temp == 'NONE':
+                temp = 'None'
+            border.append(temp)
+        borders.append(border)
+    return borders
+
+def get_text_borders(p):
+    t_pr = p.runs[0].element.rPr
+
+    t_bor = t_pr.first_child_found_in("w:bdr")  # 找到 'w:bdr' 标签
+    # 如果不存在，就创建
+    if t_bor is None:
+        t_bor = OxmlElement('w:bdr')
+        t_pr.append(t_bor)
+    # # 获取各属性的值
+    val = t_bor.get(qn('w:val'))
+    color = t_bor.get(qn('w:color'))
+    sz = t_bor.get(qn('w:sz'))
+    return [val, sz, color]
+
+def Comp(name1, name2):
+    """
+    Compare the diferences of two files
+    Usage:
+    Comp(file_name1, file_name2)
+    """
+    global flag
+    ff = 'K:/工作/traveler/test/'
+    # s = Document(name1)
+    # t = Document(name2)
+    s = Document(ff + name1)
+    t = Document(ff + name2)
+
+    # 获取段落
+    x = s.paragraphs
+
+    y = t.paragraphs
+
+    l = len(x)
+    for i in range(l-1):
+        s_shd = get_paragraph_shading(x[i])
+        t_shd = get_paragraph_shading(y[i])
+        s_border = get_paragraph_boders(x[i])
+        t_border = get_paragraph_boders(y[i])
+        if not eq(s_shd, t_shd):
+            print('效果对比出错，问题：',s_shd,'应该修改为：',t_shd)
+            print(1)
+            flag = False
+            break
+        if not eq(s_border, t_border):
+            print(1)
+            print('效果对比出错，问题：',s_border,'应该修改为：',t_border)
+            flag = False
+            break
+
+    # 判断标题的文字边框
+    if not eq(get_text_borders(x[0]), get_text_borders(y[0])):
+        print('文字边框设置出错，问题：',get_text_borders(x[0]),'应该修改为：',get_text_borders(y[0]))
+        flag = False
+
+try:
+    Comp('中国共产党党史.docx', '美化文档.docx')
+
+    if flag:
+        print('Accepted!')
+    else:
+        print('文件内容错误，请调整后重试！')
+except Exception as e:
+    print(e)
+    print('请按步骤创建、操作文本后重试！')

test/demo8.py

@@ -0,0 +1,30 @@
+import numpy as np
+import networkx as nx
+
+inf = float('inf')
+
+
+def find_hamiltonian_cycles(distances):
+    num_nodes = distances.shape[0]
+    graph = nx.DiGraph()
+    for i in range(num_nodes):
+        for j in range(num_nodes):
+            if distances[i][j] != inf:
+                graph.add_edge(i, j, weight=distances[i][j])
+
+    hamiltonian_cycles = list(nx.simple_cycles(graph))
+    hamiltonian_cycles = [cycle + [cycle[0]] for cycle in hamiltonian_cycles if len(cycle) == num_nodes - 1]
+
+    return hamiltonian_cycles
+
+
+# distances = np.array([[inf, 69, 48, inf, 41, 78], [69, inf, 48, 53, 91, inf], [48, 48, inf, 19, 86, inf], [inf, 53, 19, inf, 96, 10], [41, 91, 86, 96, inf, inf], [78, inf, inf, 10, inf, inf]])
+distances = np.array([[inf, 94, 67, 65, inf], [94, inf, inf, 41, inf], [67, inf, inf, inf, 65], [65, 41, inf, inf, 54], [inf, inf, 65, 54, inf]])
+all_hamiltonian_cycles = find_hamiltonian_cycles(distances)
+
+if all_hamiltonian_cycles:
+    print("存在的哈密尔顿回路路径为：")
+    for cycle in all_hamiltonian_cycles:
+        print(cycle)
+else:
+    print("不存在哈密尔顿回路")

test/demo9.py

@@ -0,0 +1,42 @@
+import numpy as np
+
+inf = float('inf')
+
+
+def find_all_cycles(distances):
+    num_nodes = distances.shape[0]
+    visited = [False] * num_nodes
+    cycles = []
+
+    def dfs(node, start, path):
+        visited[node] = True
+        for neighbor in range(num_nodes):
+            if distances[node, neighbor] != inf:
+                if neighbor == start and len(path) > 1:
+                    cycles.append(path + [start])
+                elif not visited[neighbor]:
+                    dfs(neighbor, start, path + [neighbor])
+        visited[node] = False
+
+    for start_node in range(num_nodes):
+        dfs(start_node, start_node, [start_node])
+
+    return cycles
+
+
+distances = np.array([
+    [inf, inf, 21, inf, 53],
+    [inf, inf, 77, 84, 93],
+    [21, 77, inf, inf, 53],
+    [inf, 84, inf, inf, inf],
+    [53, 93, 53, inf, inf]
+])
+
+all_cycles = find_all_cycles(distances)
+
+if all_cycles:
+    print("存在的回路路径为：")
+    for cycle in all_cycles:
+        print(cycle)
+else:
+    print("不存在回路")

test/dfs.py

@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/11/3 17:43
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : dfs.py
+
+import numpy as np
+def dfs_exists_path(distances):
+    num_cities = distances.shape[0]
+    visited = set()
+    start_node = 0
+
+    def dfs(node):
+        visited.add(node)
+        for neighbor in range(num_cities):
+            if neighbor != node and neighbor not in visited and np.isfinite(distances[node, neighbor]):
+                dfs(neighbor)
+
+    dfs(start_node)
+
+    return len(visited) == num_cities
+
+
+def exists_path_without_inf(distances):
+    # Check if there are any infinite distances
+    if np.any(np.isinf(distances)):
+        return False
+
+    # Set the infinite distances to a large value
+    max_distance = np.max(distances) + 1
+    distances[np.isinf(distances)] = max_distance
+
+    # Use DFS to check if there exists a path
+    return dfs_exists_path(distances)
+
+
+distances = np.array([
+    [np.inf, 50, 99, np.inf, 64, np.inf],
+    [50, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+
+print(exists_path_without_inf(distances))

test/dfs_1.py

@@ -0,0 +1,53 @@
+import numpy as np
+
+
+def dfs_tsp(graph, start, current, path, visited, cost, min_cost, min_path):
+    if len(path) == len(graph) and graph[current][start] != np.inf:
+        path.append(start)
+        cost += graph[current][start]
+        if cost < min_cost[0]:
+            min_cost[0] = cost
+            min_path[0] = path.copy()
+        path.pop()
+        cost -= graph[current][start]
+        return
+
+    for next_node in range(len(graph)):
+        if graph[current][next_node] != np.inf and not visited[next_node]:
+            visited[next_node] = True
+            path.append(next_node)
+            cost += graph[current][next_node]
+            dfs_tsp(graph, start, next_node, path, visited, cost, min_cost, min_path)
+            visited[next_node] = False
+            path.pop()
+            cost -= graph[current][next_node]
+
+
+def tsp_dfs(graph):
+    n = len(graph)
+    min_cost = [float('inf')]
+    min_path = [[]]
+
+    for start_node in range(n):
+        visited = [False] * n
+        path = [start_node]
+        cost = 0
+        visited[start_node] = True
+        dfs_tsp(graph, start_node, start_node, path, visited, cost, min_cost, min_path)
+
+    return min_cost[0], min_path[0]
+
+
+# 示例使用
+distances = np.array([
+    [np.inf, 50, 99, np.inf, 64, np.inf],
+    [50, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+
+min_cost, min_path = tsp_dfs(distances)
+print("Minimum cost:", min_cost)
+print("Minimum path:", min_path)

test/greedy.py

@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+# Time : 2023/10/31 10:22
+# Author : lirunsheng
+# User : l'r's
+# Software: PyCharm
+# File : greedy.py
+
+import numpy as np
+
+def tsp_greedy(distances):
+    num_cities = len(distances)
+    visited = [False] * num_cities
+    path = [0]  # 起始城市为0
+    visited[0] = True
+
+    for _ in range(num_cities - 1):
+        curr_city = path[-1]
+        min_distance = float('inf')
+        next_city = None
+
+        for city in range(num_cities):
+            if not visited[city] and distances[curr_city][city] < min_distance and distances[curr_city][city] != 0:
+                min_distance = distances[curr_city][city]
+                next_city = city
+
+        if next_city is not None:
+            path.append(next_city)
+            visited[next_city] = True
+    if distances[path[-1]][0] != np.inf:
+        path.append(0)  # 回到起始城市形成闭合路径
+    return path
+
+distances = np.array([
+    [np.inf, 80, 99, np.inf, 64, np.inf],
+    [80, np.inf, 12, 45, 74, 13],
+    [99, 12, np.inf, 25, np.inf, 61],
+    [np.inf, 45, 25, np.inf, 45, 47],
+    [64, 74, np.inf, 45, np.inf, np.inf],
+    [np.inf, 13, 61, 47, np.inf, np.inf]
+])
+
+path = tsp_greedy(distances)
+if len(path) == 6:
+    print('贪心算法路径：')
+    print(path)
+else:
+    print('未找到！')