##[ Heap queue algorithm (a.k.a. priority queue). Ported from Python heapq. Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for all k, counting elements from 0. For the sake of comparison, non-existing elements are considered to be infinite. The interesting property of a heap is that a[0] is always its smallest element. ]## type HeapQueue*[T] = distinct seq[T] proc newHeapQueue*[T](): HeapQueue[T] {.inline.} = HeapQueue[T](newSeq[T]()) proc newHeapQueue*[T](h: var HeapQueue[T]) {.inline.} = h = HeapQueue[T](newSeq[T]()) proc len*[T](h: HeapQueue[T]): int {.inline.} = seq[T](h).len proc `[]`*[T](h: HeapQueue[T], i: int): T {.inline.} = seq[T](h)[i] proc `[]=`[T](h: var HeapQueue[T], i: int, v: T) {.inline.} = seq[T](h)[i] = v proc add[T](h: var HeapQueue[T], v: T) {.inline.} = seq[T](h).add(v) proc heapCmp[T](x, y: T): bool {.inline.} = return (x < y) # 'heap' is a heap at all indices >= startpos, except possibly for pos. pos # is the index of a leaf with a possibly out-of-order value. Restore the # heap invariant. proc siftdown[T](heap: var HeapQueue[T], startpos, p: int) = var pos = p var newitem = heap[pos] # Follow the path to the root, moving parents down until finding a place # newitem fits. while pos > startpos: let parentpos = (pos - 1) shr 1 let parent = heap[parentpos] if heapCmp(newitem, parent): heap[pos] = parent pos = parentpos else: break heap[pos] = newitem proc siftup[T](heap: var HeapQueue[T], p: int) = let endpos = len(heap) var pos = p let startpos = pos let newitem = heap[pos] # Bubble up the smaller child until hitting a leaf. var childpos = 2*pos + 1 # leftmost child position while childpos < endpos: # Set childpos to index of smaller child. let rightpos = childpos + 1 if rightpos < endpos and not heapCmp(heap[childpos], heap[rightpos]): childpos = rightpos # Move the smaller child up. heap[pos] = heap[childpos] pos = childpos childpos = 2*pos + 1 # The leaf at pos is empty now. Put newitem there, and bubble it up # to its final resting place (by sifting its parents down). heap[pos] = newitem siftdown(heap, startpos, pos) proc push*[T](heap: var HeapQueue[T], item: T) = ## Push item onto heap, maintaining the heap invariant. (seq[T](heap)).add(item) siftdown(heap, 0, len(heap)-1) proc pop*[T](heap: var HeapQueue[T]): T = ## Pop the smallest item off the heap, maintaining the heap invariant. let lastelt = seq[T](heap).pop() if heap.len > 0: result = heap[0] heap[0] = lastelt siftup(heap, 0) else: result = lastelt proc del*[T](heap: var HeapQueue[T], index: int) = ## Removes element at `index`, maintaining the heap invariant. swap(seq[T](heap)[^1], seq[T](heap)[index]) seq[T](heap).setLen(heap.len - 1) heap.siftup(index) proc replace*[T](heap: var HeapQueue[T], item: T): T = ## Pop and return the current smallest value, and add the new item. ## This is more efficient than pop() followed by push(), and can be ## more appropriate when using a fixed-size heap. Note that the value ## returned may be larger than item! That constrains reasonable uses of ## this routine unless written as part of a conditional replacement: ## if item > heap[0]: ## item = replace(heap, item) result = heap[0] heap[0] = item siftup(heap, 0) proc pushpop*[T](heap: var HeapQueue[T], item: T): T = ## Fast version of a push followed by a pop. if heap.len > 0 and heapCmp(heap[0], item): swap(item, heap[0]) siftup(heap, 0) return item when isMainModule: block: # Simple sanity test var heap = newHeapQueue[int]() let data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] for item in data: push(heap, item) doAssert(heap[0] == 0) var sort = newSeq[int]() while heap.len > 0: sort.add(pop(heap)) doAssert(sort == @[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) block: # Test del var heap = newHeapQueue[int]() let data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] for item in data: push(heap, item) heap.del(0) doAssert(heap[0] == 1) heap.del(seq[int](heap).find(7)) heap.del(seq[int](heap).find(5)) heap.del(seq[int](heap).find(6)) heap.del(seq[int](heap).find(2)) var sort = newSeq[int]() while heap.len > 0: sort.add(pop(heap)) doAssert(sort == @[1, 3, 4, 8, 9])