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#include "structure/wavelet/wavelet-matrix-rectangle-sum.hpp"静的配列に対して、値範囲での重み総和を処理するデータ構造です。
WaveletMatrixRectangleSum< T, MAXLOG, D > は値配列 v と重み配列 d を受け取り、区間 [l, r) かつ値域 [lower, upper) の重み総和を返します。
(1) WaveletMatrixRectangleSum()
(2) WaveletMatrixRectangleSum(const vector<T>& v, const vector<D>& d)
(2) は v[i] に重み d[i] を対応づけて構築します。
v.size() == d.size()(1) D rect_sum(int l, int r, T upper)
(2) D rect_sum(int l, int r, T lower, T upper)
(1) 区間 [l, r) で v[i] < upper を満たす要素の重み総和を返します。
(2) 区間 [l, r) で lower <= v[i] < upper を満たす要素の重み総和を返します。
座標圧縮を内部で行うラッパです。値が疎な場合はこちらを使うと扱いやすくなります。
CompressedWaveletMatrixRectangleSum(const vector<T>& v, const vector<D>& d)
配列 v を座標圧縮して構築します。
v.size() == d.size()(1) D rect_sum(int l, int r, T upper)
(2) D rect_sum(int l, int r, T lower, T upper)
(1) 区間 [l, r) で v[i] < upper を満たす要素の重み総和を返します。
(2) 区間 [l, r) で lower <= v[i] < upper を満たす要素の重み総和を返します。
#pragma once
#include <algorithm>
#include <cassert>
#include <cstddef>
#include <iterator>
#include <numeric>
#include <tuple>
#include <utility>
#include <vector>
#include "succinct-indexable-dictionary.hpp"
/*
* @brief Wavelet Matrix Rectangle Sum
*
*/
template <typename T, int MAXLOG, typename D>
struct WaveletMatrixRectangleSum {
std::size_t length;
SuccinctIndexableDictionary matrix[MAXLOG];
std::vector<D> ds[MAXLOG];
int mid[MAXLOG];
WaveletMatrixRectangleSum() = default;
WaveletMatrixRectangleSum(const std::vector<T>& v, const std::vector<D>& d)
: length(v.size()) {
assert(v.size() == d.size());
std::vector<int> l(length), r(length), ord(length);
std::iota(std::begin(ord), std::end(ord), 0);
for (int level = MAXLOG - 1; level >= 0; level--) {
matrix[level] = SuccinctIndexableDictionary(length + 1);
int left = 0, right = 0;
for (int i = 0; i < length; i++) {
if (((v[ord[i]] >> level) & 1)) {
matrix[level].set(i);
r[right++] = ord[i];
} else {
l[left++] = ord[i];
}
}
mid[level] = left;
matrix[level].build();
std::swap(ord, l);
for (int i = 0; i < right; i++) {
ord[left + i] = r[i];
}
ds[level].resize(length + 1);
ds[level][0] = D();
for (int i = 0; i < length; i++) {
ds[level][i + 1] = ds[level][i] + d[ord[i]];
}
}
}
std::pair<int, int> succ(bool f, int l, int r, int level) {
return {matrix[level].rank(f, l) + mid[level] * f,
matrix[level].rank(f, r) + mid[level] * f};
}
// count d[i] s.t. (l <= i < r) && (v[i] < upper)
D rect_sum(int l, int r, T upper) {
D ret = 0;
for (int level = MAXLOG - 1; level >= 0; level--) {
bool f = ((upper >> level) & 1);
if (f)
ret += ds[level][matrix[level].rank(false, r)] -
ds[level][matrix[level].rank(false, l)];
std::tie(l, r) = succ(f, l, r, level);
}
return ret;
}
D rect_sum(int l, int r, T lower, T upper) {
return rect_sum(l, r, upper) - rect_sum(l, r, lower);
}
};
template <typename T, int MAXLOG, typename D>
struct CompressedWaveletMatrixRectangleSum {
WaveletMatrixRectangleSum<int, MAXLOG, D> mat;
std::vector<T> ys;
CompressedWaveletMatrixRectangleSum(const std::vector<T>& v,
const std::vector<D>& d)
: ys(v) {
std::sort(std::begin(ys), std::end(ys));
ys.erase(std::unique(std::begin(ys), std::end(ys)), std::end(ys));
std::vector<int> t(v.size());
for (int i = 0; i < v.size(); i++) t[i] = get(v[i]);
mat = WaveletMatrixRectangleSum<int, MAXLOG, D>(t, d);
}
inline int get(const T& x) {
return std::lower_bound(std::begin(ys), std::end(ys), x) - std::begin(ys);
}
D rect_sum(int l, int r, T upper) { return mat.rect_sum(l, r, get(upper)); }
D rect_sum(int l, int r, T lower, T upper) {
return mat.rect_sum(l, r, get(lower), get(upper));
}
};
#line 2 "structure/wavelet/wavelet-matrix-rectangle-sum.hpp"
#include <algorithm>
#include <cassert>
#include <cstddef>
#include <iterator>
#include <numeric>
#include <tuple>
#include <utility>
#include <vector>
#line 2 "structure/wavelet/succinct-indexable-dictionary.hpp"
#line 5 "structure/wavelet/succinct-indexable-dictionary.hpp"
/**
* @brief Succinct Indexable Dictionary(完備辞書)
*/
struct SuccinctIndexableDictionary {
std::size_t length;
std::size_t blocks;
std::vector<unsigned> bit, sum;
SuccinctIndexableDictionary() = default;
SuccinctIndexableDictionary(std::size_t length)
: length(length), blocks((length + 31) >> 5) {
bit.assign(blocks, 0U);
sum.assign(blocks, 0U);
}
void set(int k) { bit[k >> 5] |= 1U << (k & 31); }
void build() {
sum[0] = 0U;
for (int i = 1; i < blocks; i++) {
sum[i] = sum[i - 1] + __builtin_popcount(bit[i - 1]);
}
}
bool operator[](int k) { return (bool((bit[k >> 5] >> (k & 31)) & 1)); }
int rank(int k) {
return (sum[k >> 5] +
__builtin_popcount(bit[k >> 5] & ((1U << (k & 31)) - 1)));
}
int rank(bool val, int k) { return (val ? rank(k) : k - rank(k)); }
};
#line 13 "structure/wavelet/wavelet-matrix-rectangle-sum.hpp"
/*
* @brief Wavelet Matrix Rectangle Sum
*
*/
template <typename T, int MAXLOG, typename D>
struct WaveletMatrixRectangleSum {
std::size_t length;
SuccinctIndexableDictionary matrix[MAXLOG];
std::vector<D> ds[MAXLOG];
int mid[MAXLOG];
WaveletMatrixRectangleSum() = default;
WaveletMatrixRectangleSum(const std::vector<T>& v, const std::vector<D>& d)
: length(v.size()) {
assert(v.size() == d.size());
std::vector<int> l(length), r(length), ord(length);
std::iota(std::begin(ord), std::end(ord), 0);
for (int level = MAXLOG - 1; level >= 0; level--) {
matrix[level] = SuccinctIndexableDictionary(length + 1);
int left = 0, right = 0;
for (int i = 0; i < length; i++) {
if (((v[ord[i]] >> level) & 1)) {
matrix[level].set(i);
r[right++] = ord[i];
} else {
l[left++] = ord[i];
}
}
mid[level] = left;
matrix[level].build();
std::swap(ord, l);
for (int i = 0; i < right; i++) {
ord[left + i] = r[i];
}
ds[level].resize(length + 1);
ds[level][0] = D();
for (int i = 0; i < length; i++) {
ds[level][i + 1] = ds[level][i] + d[ord[i]];
}
}
}
std::pair<int, int> succ(bool f, int l, int r, int level) {
return {matrix[level].rank(f, l) + mid[level] * f,
matrix[level].rank(f, r) + mid[level] * f};
}
// count d[i] s.t. (l <= i < r) && (v[i] < upper)
D rect_sum(int l, int r, T upper) {
D ret = 0;
for (int level = MAXLOG - 1; level >= 0; level--) {
bool f = ((upper >> level) & 1);
if (f)
ret += ds[level][matrix[level].rank(false, r)] -
ds[level][matrix[level].rank(false, l)];
std::tie(l, r) = succ(f, l, r, level);
}
return ret;
}
D rect_sum(int l, int r, T lower, T upper) {
return rect_sum(l, r, upper) - rect_sum(l, r, lower);
}
};
template <typename T, int MAXLOG, typename D>
struct CompressedWaveletMatrixRectangleSum {
WaveletMatrixRectangleSum<int, MAXLOG, D> mat;
std::vector<T> ys;
CompressedWaveletMatrixRectangleSum(const std::vector<T>& v,
const std::vector<D>& d)
: ys(v) {
std::sort(std::begin(ys), std::end(ys));
ys.erase(std::unique(std::begin(ys), std::end(ys)), std::end(ys));
std::vector<int> t(v.size());
for (int i = 0; i < v.size(); i++) t[i] = get(v[i]);
mat = WaveletMatrixRectangleSum<int, MAXLOG, D>(t, d);
}
inline int get(const T& x) {
return std::lower_bound(std::begin(ys), std::end(ys), x) - std::begin(ys);
}
D rect_sum(int l, int r, T upper) { return mat.rect_sum(l, r, get(upper)); }
D rect_sum(int l, int r, T lower, T upper) {
return mat.rect_sum(l, r, get(lower), get(upper));
}
};