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拓展到桶排序
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<a href="https://github.com/krahets/hello-algo/tree/main/docs/chapter_sorting/bucket_sort.md" title="编辑此页" class="md-content__button md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4v-2m10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1 2.1 2.1Z"/></svg>
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<h1>Bucket sort</h1>
<h2 id="_1">拓展到桶排序<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h2>
<p>如果我们把上述 <code>bucket</code> 中的每个索引想象成一个桶,那么可以将计数排序理解为把 <span class="arithmatex">\(n\)</span> 个元素分配到对应的桶中,再根据桶与桶之间天然的有序性来实现排序。</p>
<p>以上解读便是「桶排序 Bucket Sort」的核心思想。具体地桶排序考虑将 <span class="arithmatex">\(n\)</span> 个元素根据大小范围均匀地分配到 <span class="arithmatex">\(k\)</span> 个桶中,由于桶之间是有序的,<strong>因此仅需在每个桶内部执行排序</strong>,最终按照桶之间的大小关系将元素依次排列,即可得到排序结果。</p>
<p>假设使用「快速排序」来排序各个桶内的元素,每个桶内元素数量为 <span class="arithmatex">\(\frac{n}{k}\)</span> ,则排序单个桶使用 <span class="arithmatex">\(O(\frac{n}{k} \log\frac{n}{k})\)</span> 时间,排序所有桶使用 <span class="arithmatex">\(O(n \log\frac{n}{k})\)</span> 时间。<strong>当桶数量 <span class="arithmatex">\(k\)</span> 接近 <span class="arithmatex">\(n\)</span> 时,时间复杂度则趋向于 <span class="arithmatex">\(O(n)\)</span></strong></p>
<p>(图)</p>
<p>理论上桶排序的时间复杂度是 <span class="arithmatex">\(O(n)\)</span> <strong>但前提是需要将元素均匀分配到各个桶中</strong>,而这是不太容易做到的。假设我们要把淘宝中的 <span class="arithmatex">\(100\)</span> 万件商品根据价格范围平均分配到 <span class="arithmatex">\(100\)</span> 个桶中,由于商品价格不是均匀分布的,比如 <span class="arithmatex">\(1\)</span> ~ <span class="arithmatex">\(100\)</span> 元的商品非常多、<span class="arithmatex">\(1\)</span> 万元以上的商品非常少等,因此难以简单地设定各个桶的价格分界线。解决方案有:</p>
<ul>
<li>先初步设置一个分界线,将元素分配完后,<strong>把元素较多的桶继续划分为多个桶</strong>,直至每个桶内元素数量合理为止;该做法一般使用递归实现;</li>
<li>如果我们提前知道商品价格的概率分布,<strong>则可以根据已知分布来设置每个桶的价格分界线</strong>;值得说明的是,数据分布不一定需要 case-by-case 地统计,有时可以采用一些常见分布来近似,例如自然界的正态分布;</li>
</ul>
<p>(图)</p>
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