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2026-09-16 22:45:45 +08:00 · 2026-06-17 22:02:41 +08:00
parent c7b809c192
commit 7309affb49
3 changed files with 400 additions and 40 deletions
@@ -1,6 +1,8 @@
 from house_operation import *
 from datetime import datetime

+
+# v1
 def get_next_house_id():
    houses = read_houses()
    if not houses:
@@ -8,6 +10,7 @@ def get_next_house_id():
    max_id = max(house["id"] for house in houses)
    return max_id + 1

+
 def add_house(address, price, area, house_type):
    if not address or not house_type:
        print("错误：地址和户型不能为空！")
@@ -32,6 +35,7 @@ def add_house(address, price, area, house_type):
    print(f"房源新增成功！房源ID：{new_house['id']}")
    return True

+
 def delete_house(house_id):
    houses = read_houses()
    for index, house in enumerate(houses):
@@ -43,6 +47,7 @@ def delete_house(house_id):
    print(f"错误：未找到房源ID {house_id}！")
    return False

+
 def update_house(house_id, new_info):
    houses = read_houses()
    for house in houses:
@@ -60,6 +65,7 @@ def update_house(house_id, new_info):
    print(f"错误：未找到房源ID {house_id}！")
    return False

+
 def query_house(condition_type, condition_value):
    houses = read_houses()
    result = []
@@ -81,8 +87,11 @@ def query_house(condition_type, condition_value):
            return []
    return result

+
+# v2
 MAX_STACK_LENGTH = 100

+
 def push_operation_stack(operation_info):
    time_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    full_info = f"[{time_str}] {operation_info}"
@@ -92,6 +101,7 @@ def push_operation_stack(operation_info):
        stack.pop(0)
    write_stack("operation", stack)

+
 def push_browse_stack(house_id):
    stack = read_stack("browse")
    if stack and stack[-1] == house_id:
@@ -101,6 +111,7 @@ def push_browse_stack(house_id):
        stack.pop(0)
    write_stack("browse", stack)

+
 def pop_operation_stack():
    stack = read_stack("operation")
    if not stack:
@@ -109,6 +120,7 @@ def pop_operation_stack():
    write_stack("operation", stack)
    return latest_op

+
 def pop_browse_stack():
    stack = read_stack("browse")
    if not stack:
@@ -117,17 +129,20 @@ def pop_browse_stack():
    write_stack("browse", stack)
    return latest_hid

+
 def get_recent_records(stack_type, limit=10):
    stack = read_stack(stack_type)
    return stack[-limit:][::-1]

+
 def clear_stack(stack_type):
    write_stack(stack_type, [])
    print(f"{stack_type}记录已清空！")

-def parse_address_string(full_address):

-    # 字符串分割（串操作核心）
+#v3
+def parse_address_string(full_address):
+    """解析完整地址字符串：拆分省、市、区、小区"""
    parts = full_address.split("市")
    province_city = parts[0] + "市" if "市" in full_address else parts[0]
    if "省" in province_city:
@@ -138,7 +153,6 @@ def parse_address_string(full_address):
        province = "未知省份"
        city = province_city

-    # 继续拆分区县
    if len(parts) > 1:
        district_part = parts[1]
        if "区" in district_part or "县" in district_part:
@@ -158,32 +172,26 @@ def parse_address_string(full_address):
        "community": community
    }

-def fuzzy_query_by_district(keyword):

+def fuzzy_query_by_district(keyword):
+    """根据区域关键词模糊匹配所有房源"""
    houses = read_houses()
    result = []
    for house in houses:
        addr_info = parse_address_string(house["address"])
-        # 字符串包含匹配
        if keyword in addr_info["district"] or keyword in addr_info["community"]:
            result.append(house)
    return result

+
 def init_community_matrix(community_name_list):
-    """
-    【V3.0新增 二维数组知识点：邻接矩阵初始化】
-    根据小区列表生成距离邻接矩阵，初始化距离为无穷大，自己到自己为0
-    时间复杂度：O(N²)，N为小区数量
-    空间复杂度：O(N²)
-    """
+    """初始化小区距离邻接矩阵"""
    INF = float("inf")
    n = len(community_name_list)
-    # 二维数组初始化（邻接矩阵）
-    distance_matrix = [[INF]*n for _ in range(n)]
+    distance_matrix = [[INF] * n for _ in range(n)]
    for i in range(n):
-        distance_matrix[i][i] = 0   # 自己到自己距离为0
+        distance_matrix[i][i] = 0

-    # 保存到数据文件
    matrix_data = {
        "community_list": community_name_list,
        "distance_matrix": distance_matrix
@@ -191,8 +199,9 @@ def init_community_matrix(community_name_list):
    write_matrix_data(matrix_data)
    print("邻接矩阵初始化完成！")

-def update_matrix_distance(community_a, community_b, distance):

+def update_matrix_distance(community_a, community_b, distance):
+    """更新两个小区之间的距离"""
    matrix_data = read_matrix_data()
    community_list = matrix_data["community_list"]
    distance_matrix = matrix_data["distance_matrix"]
@@ -205,4 +214,253 @@ def update_matrix_distance(community_a, community_b, distance):
        write_matrix_data(matrix_data)
        print(f"{community_a} <-> {community_b} 距离更新成功！")
    except ValueError:
-        print("小区名称不存在！")
+        print("小区名称不存在！")
+#v4
+class HouseBSTNode:
+    """
+    二叉查找树节点：按指定字段（租金/面积）作为键值
+    """
+
+    def __init__(self, house_data, key_field="price"):
+        self.key = house_data[key_field]  # 排序键（租金/面积）
+        self.house = house_data  # 房源完整数据
+        self.left = None  # 左子节点（键值更小）
+        self.right = None  # 右子节点（键值更大）
+
+
+#  V4.0 新增：二叉查找树核心操作
+def build_house_bst(houses, key_field="price"):
+    """
+    构建房源二叉查找树（按租金/面积）
+    时间复杂度：O(n log n)（最优/平均），O(n²)（最坏，有序数据）
+    """
+    if not houses:
+        return None
+    # 选第一个元素作为根节点
+    root = HouseBSTNode(houses[0], key_field)
+    # 逐个插入剩余节点
+    for house in houses[1:]:
+        insert_bst_node(root, house, key_field)
+    return root
+
+
+def insert_bst_node(root, house_data, key_field="price"):
+    """
+    向二叉查找树插入节点
+    时间复杂度：O(log n)（平均），O(n)（最坏）
+    """
+    current = root
+    while True:
+        # 键值小于当前节点，走左子树
+        if house_data[key_field] < current.key:
+            if current.left is None:
+                current.left = HouseBSTNode(house_data, key_field)
+                break
+            else:
+                current = current.left
+        # 键值大于等于当前节点，走右子树
+        else:
+            if current.right is None:
+                current.right = HouseBSTNode(house_data, key_field)
+                break
+            else:
+                current = current.right
+
+
+def inorder_bst_traversal(root, result_list):
+    """
+    二叉查找树中序遍历（输出有序列表）
+    时间复杂度：O(n)（遍历所有节点）
+    """
+    if root is not None:
+        inorder_bst_traversal(root.left, result_list)
+        result_list.append(root.house)
+        inorder_bst_traversal(root.right, result_list)
+
+
+def search_bst_range(root, min_val, max_val, key_field="price", result_list=None):
+    """
+    二叉查找树范围查询（如：租金5000-8000）
+    时间复杂度：O(log n + k)（k为符合条件的节点数）
+    """
+    if result_list is None:
+        result_list = []
+    if root is None:
+        return result_list
+
+    # 若当前键值大于最小值，遍历左子树
+    if root.key > min_val:
+        search_bst_range(root.left, min_val, max_val, key_field, result_list)
+    # 若当前键值在范围内，加入结果
+    if min_val <= root.key <= max_val:
+        result_list.append(root.house)
+    # 若当前键值小于最大值，遍历右子树
+    if root.key < max_val:
+        search_bst_range(root.right, min_val, max_val, key_field, result_list)
+    return result_list
+
+def bubble_sort_houses(houses, sort_field="price", reverse=False):
+    """
+    V4.0新增 排序知识点
+    冒泡排序：按指定字段（租金/面积）排序
+    时间复杂度：O(n²)（稳定排序）
+    """
+    n = len(houses)
+    # 深拷贝避免修改原列表
+    sorted_houses = [h.copy() for h in houses]
+    for i in range(n):
+        swapped = False
+        for j in range(0, n - i - 1):
+            if sorted_houses[j][sort_field] > sorted_houses[j + 1][sort_field]:
+                # 交换元素
+                sorted_houses[j], sorted_houses[j + 1] = sorted_houses[j + 1], sorted_houses[j]
+                swapped = True
+        # 无交换则提前退出
+        if not swapped:
+            break
+    # 降序反转
+    if reverse:
+        sorted_houses = sorted_houses[::-1]
+    return sorted_houses
+
+
+def quick_sort_houses(houses, sort_field="price", reverse=False):
+    """
+    V4新增 排序知识点
+    快速排序：按指定字段（租金/面积）排序
+    时间复杂度：O(n log n)（平均），O(n²)（最坏）
+    """
+    if len(houses) <= 1:
+        return houses
+    # 深拷贝避免修改原列表
+    sorted_houses = [h.copy() for h in houses]
+    # 选第一个元素作为基准
+    pivot = sorted_houses[0][sort_field]
+    left = [h for h in sorted_houses[1:] if h[sort_field] <= pivot]
+    right = [h for h in sorted_houses[1:] if h[sort_field] > pivot]
+    # 递归排序
+    result = quick_sort_houses(left, sort_field) + [sorted_houses[0]] + quick_sort_houses(right, sort_field)
+    # 降序反转
+    if reverse:
+        result = result[::-1]
+    return result
+
+#V5
+def print_community_graph():
+    """
+    文本形式可视化小区图（邻接矩阵）
+    时间复杂度：O(n²)，n为小区数量
+    """
+    matrix_data = read_matrix_data()
+    community_list = matrix_data["community_list"]
+    distance_matrix = matrix_data["distance_matrix"]
+
+    if not community_list:
+        print("暂无小区图数据！请先初始化邻接矩阵。")
+        return
+
+    # 打印表头
+    print("\n===== 小区距离图（邻接矩阵） =====")
+    print("    " + "    ".join([c[:4].ljust(4) for c in community_list]))
+    # 打印每行数据
+    for i, community in enumerate(community_list):
+        row_str = community[:4].ljust(4)
+        for j in range(len(community_list)):
+            val = distance_matrix[i][j]
+            if val == float("inf"):
+                row_str += "  ∞  "
+            else:
+                row_str += f"{val:5.1f}"
+        print(row_str)
+
+
+def dijkstra_shortest_path(start_community, end_community):
+    """
+    Dijkstra算法：计算两个小区间的最短路径
+    时间复杂度：O(n²)（邻接矩阵实现），n为小区数量
+    return: (最短距离, 路径列表)
+    """
+    matrix_data = read_matrix_data()
+    community_list = matrix_data["community_list"]
+    distance_matrix = matrix_data["distance_matrix"]
+
+    # 校验小区是否存在
+    if start_community not in community_list or end_community not in community_list:
+        print("起始/目标小区不存在！")
+        return (None, None)
+
+    n = len(community_list)
+    start_idx = community_list.index(start_community)
+    end_idx = community_list.index(end_community)
+
+    # 初始化距离数组和前驱节点数组
+    INF = float("inf")
+    dist = [INF] * n  # 各节点到起点的距离
+    visited = [False] * n  # 节点是否已访问
+    prev = [-1] * n  # 前驱节点索引
+
+    dist[start_idx] = 0  # 起点到自己的距离为0
+
+    # 核心Dijkstra循环
+    for _ in range(n):
+        # 找到未访问的距离最小节点
+        min_dist = INF
+        u = -1
+        for i in range(n):
+            if not visited[i] and dist[i] < min_dist:
+                min_dist = dist[i]
+                u = i
+
+        if u == -1 or dist[u] == INF:
+            break  # 无可达路径
+        visited[u] = True
+
+        # 松弛操作：更新邻接节点的距离
+        for v in range(n):
+            if not visited[v] and distance_matrix[u][v] != INF:
+                if dist[v] > dist[u] + distance_matrix[u][v]:
+                    dist[v] = dist[u] + distance_matrix[u][v]
+                    prev[v] = u
+
+    # 回溯路径
+    if dist[end_idx] == INF:
+        print(f"{start_community} 到 {end_community} 无可达路径！")
+        return (None, None)
+
+    # 从终点回溯到起点
+    path = []
+    current = end_idx
+    while current != -1:
+        path.append(community_list[current])
+        current = prev[current]
+    path.reverse()  # 反转得到正序路径
+
+    return (dist[end_idx], path)
+
+
+def recommend_houses_by_path(target_community, max_distance):
+    """
+    推荐目标小区最短距离≤max_distance的周边房源
+    """
+    matrix_data = read_matrix_data()
+    community_list = matrix_data["community_list"]
+    if target_community not in community_list:
+        print("目标小区不存在！")
+        return []
+
+    # 计算目标小区到所有小区的最短路径
+    recommend_communities = []
+    for comm in community_list:
+        dist, _ = dijkstra_shortest_path(target_community, comm)
+        if dist is not None and dist <= max_distance:
+            recommend_communities.append(comm)
+
+    # 查询这些小区的房源
+    all_houses = read_houses()
+    recommend_houses = []
+    for house in all_houses:
+        addr_info = parse_address_string(house["address"])
+        if addr_info["community"] in recommend_communities:
+            recommend_houses.append(house)
+
+    return recommend_houses