mirror of
https://codeberg.org/anoncontributorxmr/monero.git
synced 2024-11-25 08:52:26 +00:00
250 lines
6.3 KiB
C++
250 lines
6.3 KiB
C++
// Copyright (c) 2019-2022, The Monero Project
|
|
//
|
|
// All rights reserved.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification, are
|
|
// permitted provided that the following conditions are met:
|
|
//
|
|
// 1. Redistributions of source code must retain the above copyright notice, this list of
|
|
// conditions and the following disclaimer.
|
|
//
|
|
// 2. Redistributions in binary form must reproduce the above copyright notice, this list
|
|
// of conditions and the following disclaimer in the documentation and/or other
|
|
// materials provided with the distribution.
|
|
//
|
|
// 3. Neither the name of the copyright holder nor the names of its contributors may be
|
|
// used to endorse or promote products derived from this software without specific
|
|
// prior written permission.
|
|
//
|
|
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
|
|
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
|
|
// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
|
|
// THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
|
|
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
|
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
|
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
|
// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
|
|
// THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
//
|
|
// Adapted from source by AShelly:
|
|
// Copyright (c) 2011 ashelly.myopenid.com, licenced under the MIT licence
|
|
// https://stackoverflow.com/questions/5527437/rolling-median-in-c-turlach-implementation
|
|
// https://stackoverflow.com/questions/1309263/rolling-median-algorithm-in-c
|
|
// https://ideone.com/XPbl6
|
|
|
|
#pragma once
|
|
|
|
#include "misc_language.h"
|
|
|
|
#include <stdlib.h>
|
|
#include <stdint.h>
|
|
|
|
namespace epee
|
|
{
|
|
namespace misc_utils
|
|
{
|
|
|
|
template<typename Item>
|
|
struct rolling_median_t
|
|
{
|
|
private:
|
|
Item* data; //circular queue of values
|
|
int* pos; //index into `heap` for each value
|
|
int* heap; //max/median/min heap holding indexes into `data`.
|
|
int N; //allocated size.
|
|
int idx; //position in circular queue
|
|
int minCt; //count of items in min heap
|
|
int maxCt; //count of items in max heap
|
|
int sz; //count of items in heap
|
|
|
|
private:
|
|
|
|
//returns true if heap[i] < heap[j]
|
|
bool mmless(int i, int j) const
|
|
{
|
|
return data[heap[i]] < data[heap[j]];
|
|
}
|
|
|
|
//swaps items i&j in heap, maintains indexes
|
|
bool mmexchange(int i, int j)
|
|
{
|
|
const int t = heap[i];
|
|
heap[i] = heap[j];
|
|
heap[j] = t;
|
|
pos[heap[i]] = i;
|
|
pos[heap[j]] = j;
|
|
return 1;
|
|
}
|
|
|
|
//swaps items i&j if i<j; returns true if swapped
|
|
bool mmCmpExch(int i, int j)
|
|
{
|
|
return mmless(i, j) && mmexchange(i, j);
|
|
}
|
|
|
|
//maintains minheap property for all items below i.
|
|
void minSortDown(int i)
|
|
{
|
|
for (i *= 2; i <= minCt; i *= 2)
|
|
{
|
|
if (i < minCt && mmless(i + 1, i))
|
|
++i;
|
|
if (!mmCmpExch(i, i / 2))
|
|
break;
|
|
}
|
|
}
|
|
|
|
//maintains maxheap property for all items below i. (negative indexes)
|
|
void maxSortDown(int i)
|
|
{
|
|
for (i *= 2; i >= -maxCt; i *= 2)
|
|
{
|
|
if (i > -maxCt && mmless(i, i - 1))
|
|
--i;
|
|
if (!mmCmpExch(i / 2, i))
|
|
break;
|
|
}
|
|
}
|
|
|
|
//maintains minheap property for all items above i, including median
|
|
//returns true if median changed
|
|
bool minSortUp(int i)
|
|
{
|
|
while (i > 0 && mmCmpExch(i, i / 2))
|
|
i /= 2;
|
|
return i == 0;
|
|
}
|
|
|
|
//maintains maxheap property for all items above i, including median
|
|
//returns true if median changed
|
|
bool maxSortUp(int i)
|
|
{
|
|
while (i < 0 && mmCmpExch(i / 2, i))
|
|
i /= 2;
|
|
return i == 0;
|
|
}
|
|
|
|
protected:
|
|
rolling_median_t &operator=(const rolling_median_t&) = delete;
|
|
|
|
public:
|
|
//creates new rolling_median_t: to calculate `nItems` running median.
|
|
rolling_median_t(size_t N): N(N)
|
|
{
|
|
int size = N * (sizeof(Item) + sizeof(int) * 2);
|
|
data = (Item*)malloc(size);
|
|
pos = (int*) (data + N);
|
|
heap = pos + N + (N / 2); //points to middle of storage.
|
|
clear();
|
|
}
|
|
|
|
rolling_median_t(const rolling_median_t &other)
|
|
{
|
|
N = other.N;
|
|
int size = N * (sizeof(Item) + sizeof(int) * 2);
|
|
data = (Item*)malloc(size);
|
|
memcpy(data, other.data, size);
|
|
pos = (int*) (data + N);
|
|
heap = pos + N + (N / 2); //points to middle of storage.
|
|
idx = other.idx;
|
|
minCt = other.minCt;
|
|
maxCt = other.maxCt;
|
|
sz = other.sz;
|
|
}
|
|
|
|
rolling_median_t(rolling_median_t &&m)
|
|
{
|
|
memcpy(this, &m, sizeof(rolling_median_t));
|
|
m.data = NULL;
|
|
}
|
|
rolling_median_t &operator=(rolling_median_t &&m)
|
|
{
|
|
free(data);
|
|
memcpy(this, &m, sizeof(rolling_median_t));
|
|
m.data = NULL;
|
|
return *this;
|
|
}
|
|
|
|
~rolling_median_t()
|
|
{
|
|
free(data);
|
|
}
|
|
|
|
void clear()
|
|
{
|
|
idx = 0;
|
|
minCt = 0;
|
|
maxCt = 0;
|
|
sz = 0;
|
|
int nItems = N;
|
|
while (nItems--) //set up initial heap fill pattern: median,max,min,max,...
|
|
{
|
|
pos[nItems] = ((nItems + 1) / 2) * ((nItems & 1) ? -1 : 1);
|
|
heap[pos[nItems]] = nItems;
|
|
}
|
|
}
|
|
|
|
int size() const
|
|
{
|
|
return sz;
|
|
}
|
|
|
|
//Inserts item, maintains median in O(lg nItems)
|
|
void insert(Item v)
|
|
{
|
|
int p = pos[idx];
|
|
Item old = data[idx];
|
|
data[idx] = v;
|
|
idx = (idx + 1) % N;
|
|
sz = std::min<int>(sz + 1, N);
|
|
if (p > 0) //new item is in minHeap
|
|
{
|
|
if (minCt < (N - 1) / 2)
|
|
{
|
|
++minCt;
|
|
}
|
|
else if (v > old)
|
|
{
|
|
minSortDown(p);
|
|
return;
|
|
}
|
|
if (minSortUp(p) && mmCmpExch(0, -1))
|
|
maxSortDown(-1);
|
|
}
|
|
else if (p < 0) //new item is in maxheap
|
|
{
|
|
if (maxCt < N / 2)
|
|
{
|
|
++maxCt;
|
|
}
|
|
else if (v < old)
|
|
{
|
|
maxSortDown(p);
|
|
return;
|
|
}
|
|
if (maxSortUp(p) && minCt && mmCmpExch(1, 0))
|
|
minSortDown(1);
|
|
}
|
|
else //new item is at median
|
|
{
|
|
if (maxCt && maxSortUp(-1))
|
|
maxSortDown(-1);
|
|
if (minCt && minSortUp(1))
|
|
minSortDown(1);
|
|
}
|
|
}
|
|
|
|
//returns median item (or average of 2 when item count is even)
|
|
Item median() const
|
|
{
|
|
Item v = data[heap[0]];
|
|
if (minCt < maxCt)
|
|
{
|
|
v = get_mid<Item>(v, data[heap[-1]]);
|
|
}
|
|
return v;
|
|
}
|
|
};
|
|
|
|
}
|
|
}
|