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  1. import random
  2. import statistics
  3.  
  4. def generate_exact_split():
  5.     # 核心参数（严格锁定）
  6.     TARGET_SUM = 99462       # 总和必须精确等于此值
  7.     MULTIPLIER = 605
  8.     split_parts = 11           # 16份拆分值
  9.     MAX_PRODUCT = 10000        # 最大乘积不超过10000
  10.     MIN_PRODUCT = 6000         # 最小乘积不低于6000
  11.     precision = 1              # 1位小数
  12.  
  13.     # 验证可行性
  14.     if split_parts * MIN_PRODUCT > TARGET_SUM or split_parts * MAX_PRODUCT < TARGET_SUM:
  15.         return f"无法拆分：16份需在[{split_parts*MIN_PRODUCT}, {split_parts*MAX_PRODUCT}]"
  16.  
  17.     # 生成初始值（无规律，低重复）
  18.     split_values = []
  19.     value_counts = {}
  20.  
  21.     while len(split_values) < split_parts:
  22.         max_val = round(MAX_PRODUCT / MULTIPLIER, precision)  # 16.3
  23.         min_val = round(MIN_PRODUCT / MULTIPLIER, precision)  # 9.8
  24.         val = round(random.uniform(min_val, max_val), precision)
  25.  
  26.         # 检查乘积范围
  27.         product = round(val * MULTIPLIER)
  28.         if not (MIN_PRODUCT <= product <= MAX_PRODUCT):
  29.             continue
  30.  
  31.         # 避免连续值（如x和x+0.1）
  32.         has_consecutive = False
  33.         for existing in split_values:
  34.             if 0.1 <= abs(val - existing) <= 0.2:
  35.                 has_consecutive = True
  36.                 break
  37.         if has_consecutive:
  38.             continue
  39.  
  40.         # 控制重复（最多2次）
  41.         if value_counts.get(val, 0) < 2:
  42.             split_values.append(val)
  43.             value_counts[val] = value_counts.get(val, 0) + 1
  44.  
  45.     # 调整总和至精确目标值
  46.     products = [round(v * MULTIPLIER) for v in split_values]
  47.     diff = TARGET_SUM - sum(products)
  48.  
  49.     # 优先调整只出现1次的值
  50.     single_indices = [i for i, v in enumerate(split_values) if value_counts[v] == 1]
  51.     random.shuffle(single_indices)
  52.  
  53.     for i in single_indices:
  54.         if diff == 0:
  55.             break
  56.         step = 0.1 if diff > 0 else -0.1
  57.         new_val = round(split_values[i] + step, precision)
  58.         new_product = round(new_val * MULTIPLIER)
  59.  
  60.         if (MIN_PRODUCT <= new_product <= MAX_PRODUCT and 
  61.             value_counts.get(new_val, 0) < 2):
  62.  
  63.             old_val = split_values[i]
  64.             value_counts[old_val] -= 1
  65.             if value_counts[old_val] == 0:
  66.                 del value_counts[old_val]
  67.  
  68.             split_values[i] = new_val
  69.             products[i] = new_product
  70.             value_counts[new_val] = value_counts.get(new_val, 0) + 1
  71.             diff = TARGET_SUM - sum(products)
  72.  
  73.     # 改进的最终调整逻辑：分散调整多个值以确保总和正确
  74.     if diff != 0:
  75.         # 打乱顺序，使调整更均匀
  76.         indices = list(range(len(products)))
  77.         random.shuffle(indices)
  78.  
  79.         for i in indices:
  80.             if diff == 0:
  81.                 break
  82.  
  83.             # 计算当前值可以调整的最大步长
  84.             current = products[i]
  85.             max_possible = MAX_PRODUCT - current
  86.             min_possible = MIN_PRODUCT - current
  87.  
  88.             if diff > 0:
  89.                 # 需要增加总和，取可能的最大值
  90.                 step = min(diff, max_possible)
  91.             else:
  92.                 # 需要减少总和，取可能的最小值
  93.                 step = max(diff, min_possible)
  94.  
  95.             if step != 0:
  96.                 products[i] += step
  97.                 split_values[i] = round(products[i] / MULTIPLIER, precision)
  98.                 diff -= step
  99.  
  100.     # 验证总和（必须正确）
  101.     final_sum = sum(products)
  102.     assert final_sum == TARGET_SUM, f"总和错误：{final_sum}≠{TARGET_SUM}"
  103.  
  104.     # 统计结果
  105.     value_counts = {v: split_values.count(v) for v in set(split_values)}
  106.     duplicates = [v for v, c in value_counts.items() if c == 2]
  107.     std_dev = round(statistics.stdev(split_values), 2)
  108.  
  109.     # 输出（不排序）
  110.     output = [
  111.         "=== 拆分结果 ===",
  112.         "状态: ✅ 有效（总和精确匹配目标）",
  113.         f"重复情况: 共{len(duplicates)}个值各出现2次: {duplicates}",
  114.         f"分散度（标准差）: {std_dev}",
  115.         f"总和验证: {final_sum} == {TARGET_SUM}",
  116.         " + ".join([f"{v}×{MULTIPLIER}={p}" for v, p in zip(split_values, products)]) + f" = {final_sum}",
  117.         f"拆分值（未排序）: {split_values}"
  118.     ]
  119.  
  120.     return "\n".join(output)
  121.  
  122.  
  123. # 直接运行
  124. print(generate_exact_split())
  125.  
Success #stdin #stdout 0.06s 13108KB
comments (?)
stdin 
Standard input is empty
stdout 
=== 拆分结果 ===
状态: ✅ 有效（总和精确匹配目标）
重复情况: 共1个值各出现2次: [15.2]
分散度（标准差）: 1.93
总和验证: 99462 == 99462
16.5×605=10000 + 15.2×605=9196 + 15.1×605=9136 + 16.5×605=10000 + 10.8×605=6534 + 16.5×605=10000 + 13.3×605=8046 + 16.5×605=10000 + 16.2×605=9801 + 15.2×605=9196 + 12.5×605=7553 = 99462
拆分值（未排序）: [16.5, 15.2, 15.1, 16.5, 10.8, 16.5, 13.3, 16.5, 16.2, 15.2, 12.5]
https://ideone.com/9NV7Oo
language:
Python 3 nbc (python 3.7.3)
created:
18 hours ago
visibility:
public

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