Qwen2.5-Coder-1.5B效果展示：自动生成数据结构与算法实现

Fitz Hoo

244人浏览 · 2026-02-21 00:29:14

Fitz Hoo · 2026-02-21 00:29:14 发布

Qwen2.5-Coder-1.5B效果展示：自动生成数据结构与算法实现

1. 开篇：当AI遇上算法学习

算法学习一直是程序员成长路上的必经之路，但面对复杂的逻辑和抽象概念，很多人都会感到头疼。最近我试用了Qwen2.5-Coder-1.5B这个专门为代码生成优化的模型，发现它在数据结构与算法方面的表现相当惊艳。

这个模型虽然只有1.5B参数，但在代码生成任务上展现出了超出预期的能力。它能理解自然语言描述，然后生成对应的算法实现，而且代码质量相当不错。对于正在学习算法或者需要快速实现某些数据结构的开发者来说，这确实是个很实用的工具。

2. 基础数据结构生成效果

2.1 链表实现展示

让我先从一个简单的例子开始。我让模型生成一个双向链表的Python实现：

class DoublyLinkedListNode:
    def __init__(self, data):
        self.data = data
        self.prev = None
        self.next = None

class DoublyLinkedList:
    def __init__(self):
        self.head = None
        self.tail = None
        self.size = 0
    
    def append(self, data):
        new_node = DoublyLinkedListNode(data)
        if not self.head:
            self.head = new_node
            self.tail = new_node
        else:
            new_node.prev = self.tail
            self.tail.next = new_node
            self.tail = new_node
        self.size += 1
    
    def prepend(self, data):
        new_node = DoublyLinkedListNode(data)
        if not self.head:
            self.head = new_node
            self.tail = new_node
        else:
            new_node.next = self.head
            self.head.prev = new_node
            self.head = new_node
        self.size += 1
    
    def display_forward(self):
        current = self.head
        while current:
            print(current.data, end=" <-> " if current.next else "\n")
            current = current.next
    
    def display_backward(self):
        current = self.tail
        while current:
            print(current.data, end=" <-> " if current.prev else "\n")
            current = current.prev

生成的代码结构清晰，包含了双向链表的核心操作：追加、前插、正向遍历和反向遍历。每个方法都有适当的边界条件处理，代码风格也很规范。

2.2 二叉树实现效果

再来看看二叉搜索树的生成效果：

class TreeNode:
    def __init__(self, value):
        self.value = value
        self.left = None
        self.right = None

class BinarySearchTree:
    def __init__(self):
        self.root = None
    
    def insert(self, value):
        if not self.root:
            self.root = TreeNode(value)
        else:
            self._insert_recursive(self.root, value)
    
    def _insert_recursive(self, node, value):
        if value < node.value:
            if node.left is None:
                node.left = TreeNode(value)
            else:
                self._insert_recursive(node.left, value)
        else:
            if node.right is None:
                node.right = TreeNode(value)
            else:
                self._insert_recursive(node.right, value)
    
    def search(self, value):
        return self._search_recursive(self.root, value)
    
    def _search_recursive(self, node, value):
        if node is None or node.value == value:
            return node
        if value < node.value:
            return self._search_recursive(node.left, value)
        return self._search_recursive(node.right, value)
    
    def inorder_traversal(self):
        result = []
        self._inorder_recursive(self.root, result)
        return result
    
    def _inorder_recursive(self, node, result):
        if node:
            self._inorder_recursive(node.left, result)
            result.append(node.value)
            self._inorder_recursive(node.right, result)

这个实现包含了二叉搜索树的基本操作：插入、搜索和中序遍历。递归实现的方式很标准，代码可读性也很好。

3. 经典算法实现展示

3.1 排序算法生成

排序算法是算法学习的基础，看看模型的表现：

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    
    return quick_sort(left) + middle + quick_sort(right)

def merge_sort(arr):
    if len(arr) <= 1:
        return arr
    
    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])
    
    return merge(left, right)

def merge(left, right):
    result = []
    i = j = 0
    
    while i < len(left) and j < len(right):
        if left[i] <= right[j]:
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1
    
    result.extend(left[i:])
    result.extend(right[j:])
    return result

快速排序和归并排序的实现都很优雅。快速排序使用了列表推导式，代码简洁；归并排序的合并函数处理边界条件也很到位。

3.2 图算法实现

图算法通常比较复杂，但模型的表现依然不错：

from collections import deque

class Graph:
    def __init__(self):
        self.adjacency_list = {}
    
    def add_vertex(self, vertex):
        if vertex not in self.adjacency_list:
            self.adjacency_list[vertex] = []
    
    def add_edge(self, vertex1, vertex2):
        if vertex1 in self.adjacency_list and vertex2 in self.adjacency_list:
            self.adjacency_list[vertex1].append(vertex2)
            self.adjacency_list[vertex2].append(vertex1)
    
    def bfs(self, start_vertex):
        visited = set()
        queue = deque([start_vertex])
        visited.add(start_vertex)
        result = []
        
        while queue:
            current_vertex = queue.popleft()
            result.append(current_vertex)
            
            for neighbor in self.adjacency_list[current_vertex]:
                if neighbor not in visited:
                    visited.add(neighbor)
                    queue.append(neighbor)
        
        return result
    
    def dfs(self, start_vertex):
        visited = set()
        result = []
        
        def dfs_recursive(vertex):
            visited.add(vertex)
            result.append(vertex)
            
            for neighbor in self.adjacency_list[vertex]:
                if neighbor not in visited:
                    dfs_recursive(neighbor)
        
        dfs_recursive(start_vertex)
        return result

这个图实现包含了邻接表表示、BFS和DFS遍历。代码结构清晰，使用了Python的deque来优化队列操作，递归实现的DFS也很标准。

4. 高级算法挑战

4.1 动态规划问题

动态规划是算法中的难点，看看模型如何处理：

def knapsack(weights, values, capacity):
    n = len(weights)
    dp = [[0] * (capacity + 1) for _ in range(n + 1)]
    
    for i in range(1, n + 1):
        for w in range(1, capacity + 1):
            if weights[i-1] <= w:
                dp[i][w] = max(values[i-1] + dp[i-1][w-weights[i-1]], dp[i-1][w])
            else:
                dp[i][w] = dp[i-1][w]
    
    return dp[n][capacity]

def longest_common_subsequence(text1, text2):
    m, n = len(text1), len(text2)
    dp = [[0] * (n + 1) for _ in range(m + 1)]
    
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if text1[i-1] == text2[j-1]:
                dp[i][j] = dp[i-1][j-1] + 1
            else:
                dp[i][j] = max(dp[i-1][j], dp[i][j-1])
    
    return dp[m][n]

这两个动态规划问题的实现都很标准。背包问题使用了二维DP数组，LCS问题也是经典的实现方式。代码逻辑清晰，变量命名也很合理。

4.2 贪心算法实现

def activity_selection(start, finish):
    n = len(start)
    activities = list(zip(start, finish, range(n)))
    activities.sort(key=lambda x: x[1])
    
    selected = []
    last_finish = 0
    
    for start_time, finish_time, index in activities:
        if start_time >= last_finish:
            selected.append(index)
            last_finish = finish_time
    
    return selected

def huffman_coding(freq):
    import heapq
    
    class Node:
        def __init__(self, char, freq):
            self.char = char
            self.freq = freq
            self.left = None
            self.right = None
        
        def __lt__(self, other):
            return self.freq < other.freq
    
    heap = [Node(char, f) for char, f in freq.items()]
    heapq.heapify(heap)
    
    while len(heap) > 1:
        left = heapq.heappop(heap)
        right = heapq.heappop(heap)
        
        merged = Node(None, left.freq + right.freq)
        merged.left = left
        merged.right = right
        heapq.heappush(heap, merged)
    
    codes = {}
    
    def generate_codes(node, current_code):
        if node is None:
            return
        if node.char is not None:
            codes[node.char] = current_code
            return
        generate_codes(node.left, current_code + "0")
        generate_codes(node.right, current_code + "1")
    
    generate_codes(heap[0], "")
    return codes

活动选择问题的贪心策略实现得很正确，哈夫曼编码的实现也很完整，包含了节点类和递归生成编码的过程。

5. 实际应用场景

5.1 算法竞赛常见题目

def two_sum(nums, target):
    num_map = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in num_map:
            return [num_map[complement], i]
        num_map[num] = i
    return []

def reverse_linked_list(head):
    prev = None
    current = head
    
    while current:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    
    return prev

def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    
    return -1

这些算法竞赛常见题目的实现都很优化。两数之和使用了哈希表来达到O(n)时间复杂度，链表反转和二分查找的实现也都是最优解。

5.2 实际工程应用

class LRUCache:
    class Node:
        def __init__(self, key, value):
            self.key = key
            self.value = value
            self.prev = None
            self.next = None
    
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}
        self.head = self.Node(0, 0)
        self.tail = self.Node(0, 0)
        self.head.next = self.tail
        self.tail.prev = self.head
    
    def _remove(self, node):
        prev = node.prev
        next = node.next
        prev.next = next
        next.prev = prev
    
    def _add_to_head(self, node):
        node.next = self.head.next
        node.prev = self.head
        self.head.next.prev = node
        self.head.next = node
    
    def get(self, key):
        if key in self.cache:
            node = self.cache[key]
            self._remove(node)
            self._add_to_head(node)
            return node.value
        return -1
    
    def put(self, key, value):
        if key in self.cache:
            node = self.cache[key]
            node.value = value
            self._remove(node)
            self._add_to_head(node)
        else:
            if len(self.cache) >= self.capacity:
                lru = self.tail.prev
                self._remove(lru)
                del self.cache[lru.key]
            
            new_node = self.Node(key, value)
            self.cache[key] = new_node
            self._add_to_head(new_node)

这个LRU缓存的实现相当完整，使用了双向链表和哈希表的组合，处理了所有的边界情况，代码质量很高。