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250 lines
6.3 KiB
C++
250 lines
6.3 KiB
C++
// Copyright (c) 2019-2022, The Monero Project
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//
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// All rights reserved.
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//
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// Redistribution and use in source and binary forms, with or without modification, are
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// permitted provided that the following conditions are met:
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//
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// 1. Redistributions of source code must retain the above copyright notice, this list of
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// conditions and the following disclaimer.
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//
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// 2. Redistributions in binary form must reproduce the above copyright notice, this list
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// of conditions and the following disclaimer in the documentation and/or other
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// materials provided with the distribution.
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//
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// 3. Neither the name of the copyright holder nor the names of its contributors may be
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// used to endorse or promote products derived from this software without specific
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// prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
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// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
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// THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
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// THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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//
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// Adapted from source by AShelly:
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// Copyright (c) 2011 ashelly.myopenid.com, licenced under the MIT licence
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// https://stackoverflow.com/questions/5527437/rolling-median-in-c-turlach-implementation
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// https://stackoverflow.com/questions/1309263/rolling-median-algorithm-in-c
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// https://ideone.com/XPbl6
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#pragma once
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#include "misc_language.h"
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#include <stdlib.h>
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#include <stdint.h>
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namespace epee
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{
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namespace misc_utils
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{
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template<typename Item>
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struct rolling_median_t
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{
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private:
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Item* data; //circular queue of values
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int* pos; //index into `heap` for each value
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int* heap; //max/median/min heap holding indexes into `data`.
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int N; //allocated size.
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int idx; //position in circular queue
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int minCt; //count of items in min heap
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int maxCt; //count of items in max heap
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int sz; //count of items in heap
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private:
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//returns true if heap[i] < heap[j]
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bool mmless(int i, int j) const
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{
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return data[heap[i]] < data[heap[j]];
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}
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//swaps items i&j in heap, maintains indexes
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bool mmexchange(int i, int j)
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{
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const int t = heap[i];
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heap[i] = heap[j];
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heap[j] = t;
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pos[heap[i]] = i;
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pos[heap[j]] = j;
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return 1;
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}
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//swaps items i&j if i<j; returns true if swapped
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bool mmCmpExch(int i, int j)
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{
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return mmless(i, j) && mmexchange(i, j);
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}
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//maintains minheap property for all items below i.
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void minSortDown(int i)
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{
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for (i *= 2; i <= minCt; i *= 2)
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{
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if (i < minCt && mmless(i + 1, i))
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++i;
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if (!mmCmpExch(i, i / 2))
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break;
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}
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}
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//maintains maxheap property for all items below i. (negative indexes)
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void maxSortDown(int i)
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{
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for (i *= 2; i >= -maxCt; i *= 2)
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{
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if (i > -maxCt && mmless(i, i - 1))
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--i;
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if (!mmCmpExch(i / 2, i))
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break;
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}
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}
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//maintains minheap property for all items above i, including median
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//returns true if median changed
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bool minSortUp(int i)
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{
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while (i > 0 && mmCmpExch(i, i / 2))
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i /= 2;
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return i == 0;
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}
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//maintains maxheap property for all items above i, including median
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//returns true if median changed
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bool maxSortUp(int i)
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{
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while (i < 0 && mmCmpExch(i / 2, i))
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i /= 2;
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return i == 0;
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}
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protected:
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rolling_median_t &operator=(const rolling_median_t&) = delete;
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public:
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//creates new rolling_median_t: to calculate `nItems` running median.
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rolling_median_t(size_t N): N(N)
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{
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int size = N * (sizeof(Item) + sizeof(int) * 2);
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data = (Item*)malloc(size);
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pos = (int*) (data + N);
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heap = pos + N + (N / 2); //points to middle of storage.
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clear();
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}
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rolling_median_t(const rolling_median_t &other)
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{
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N = other.N;
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int size = N * (sizeof(Item) + sizeof(int) * 2);
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data = (Item*)malloc(size);
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memcpy(data, other.data, size);
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pos = (int*) (data + N);
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heap = pos + N + (N / 2); //points to middle of storage.
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idx = other.idx;
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minCt = other.minCt;
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maxCt = other.maxCt;
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sz = other.sz;
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}
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rolling_median_t(rolling_median_t &&m)
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{
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memcpy(this, &m, sizeof(rolling_median_t));
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m.data = NULL;
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}
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rolling_median_t &operator=(rolling_median_t &&m)
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{
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free(data);
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memcpy(this, &m, sizeof(rolling_median_t));
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m.data = NULL;
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return *this;
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}
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~rolling_median_t()
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{
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free(data);
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}
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void clear()
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{
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idx = 0;
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minCt = 0;
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maxCt = 0;
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sz = 0;
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int nItems = N;
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while (nItems--) //set up initial heap fill pattern: median,max,min,max,...
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{
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pos[nItems] = ((nItems + 1) / 2) * ((nItems & 1) ? -1 : 1);
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heap[pos[nItems]] = nItems;
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}
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}
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int size() const
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{
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return sz;
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}
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//Inserts item, maintains median in O(lg nItems)
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void insert(Item v)
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{
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int p = pos[idx];
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Item old = data[idx];
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data[idx] = v;
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idx = (idx + 1) % N;
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sz = std::min<int>(sz + 1, N);
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if (p > 0) //new item is in minHeap
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{
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if (minCt < (N - 1) / 2)
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{
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++minCt;
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}
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else if (v > old)
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{
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minSortDown(p);
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return;
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}
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if (minSortUp(p) && mmCmpExch(0, -1))
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maxSortDown(-1);
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}
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else if (p < 0) //new item is in maxheap
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{
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if (maxCt < N / 2)
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{
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++maxCt;
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}
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else if (v < old)
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{
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maxSortDown(p);
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return;
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}
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if (maxSortUp(p) && minCt && mmCmpExch(1, 0))
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minSortDown(1);
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}
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else //new item is at median
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{
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if (maxCt && maxSortUp(-1))
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maxSortDown(-1);
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if (minCt && minSortUp(1))
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minSortDown(1);
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}
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}
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//returns median item (or average of 2 when item count is even)
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Item median() const
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{
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Item v = data[heap[0]];
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if (minCt < maxCt)
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{
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v = get_mid<Item>(v, data[heap[-1]]);
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}
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return v;
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}
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};
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}
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}
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