我已经在C中实现了滚动中位数计算器(Gist)。它使用max-median-min堆结构:中位数位于heap [0](位于K项数组的中心)。从heap [1]开始有一个minheap,在heap [-1]有一个maxheap(使用负索引)。 它与R源中的Turlach实现不完全相同:它支持即时插入值,而R版本一次作用于整个缓冲区。但是我相信时间复杂度是相同的。而且它可以轻松地用于实现整个缓冲区版本(可能添加一些代码来处理R的“ endrules”) 。
接口:
//Customize for your data Item type
typedef int Item;
#define ItemLess(a,b) ((a)<(b))
#define ItemMean(a,b) (((a)+(b))/2)
typedef struct Mediator_t Mediator;
//creates new Mediator: to calculate `nItems` running median.
//mallocs single block of memory, caller must free.
Mediator* MediatorNew(int nItems);
//returns median item (or average of 2 when item count is even)
Item MediatorMedian(Mediator* m);
//Inserts item, maintains median in O(lg nItems)
void MediatorInsert(Mediator* m, Item v)
{
int isNew = (m->ct < m->N);
int p = m->pos[m->idx];
Item old = m->data[m->idx];
m->data[m->idx] = v;
m->idx = (m->idx+1) % m->N;
m->ct += isNew;
if (p > 0) //new item is in minHeap
{ if (!isNew && ItemLess(old, v)) { minSortDown(m, P*2); }
else if (minSortUp(m, p)) { maxSortDown(m,-1); }
}
else if (p < 0) //new item is in maxheap
{ if (!isNew && ItemLess(v, old)) { maxSortDown(m, P*2); }
else if (maxSortUp(m, p)) { minSortDown(m, 1); }
}
else //new item is at median
{ if (maxCt(m)) { maxSortDown(m,-1); }
if (minCt(m)) { minSortDown(m, 1); }
}
}