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Diffstat (limited to 'dependencies/pkg/mod/golang.org/x/exp@v0.0.0-20220613132600-b0d781184e0d/slices/zsortordered.go')
| -rw-r--r-- | dependencies/pkg/mod/golang.org/x/exp@v0.0.0-20220613132600-b0d781184e0d/slices/zsortordered.go | 481 |
1 files changed, 481 insertions, 0 deletions
diff --git a/dependencies/pkg/mod/golang.org/x/exp@v0.0.0-20220613132600-b0d781184e0d/slices/zsortordered.go b/dependencies/pkg/mod/golang.org/x/exp@v0.0.0-20220613132600-b0d781184e0d/slices/zsortordered.go new file mode 100644 index 0000000..efaa1c8 --- /dev/null +++ b/dependencies/pkg/mod/golang.org/x/exp@v0.0.0-20220613132600-b0d781184e0d/slices/zsortordered.go @@ -0,0 +1,481 @@ +// Code generated by gen_sort_variants.go; DO NOT EDIT. + +// Copyright 2022 The Go Authors. All rights reserved. +// Use of this source code is governed by a BSD-style +// license that can be found in the LICENSE file. + +package slices + +import "golang.org/x/exp/constraints" + +// insertionSortOrdered sorts data[a:b] using insertion sort. +func insertionSortOrdered[E constraints.Ordered](data []E, a, b int) { + for i := a + 1; i < b; i++ { + for j := i; j > a && (data[j] < data[j-1]); j-- { + data[j], data[j-1] = data[j-1], data[j] + } + } +} + +// siftDownOrdered implements the heap property on data[lo:hi]. +// first is an offset into the array where the root of the heap lies. +func siftDownOrdered[E constraints.Ordered](data []E, lo, hi, first int) { + root := lo + for { + child := 2*root + 1 + if child >= hi { + break + } + if child+1 < hi && (data[first+child] < data[first+child+1]) { + child++ + } + if !(data[first+root] < data[first+child]) { + return + } + data[first+root], data[first+child] = data[first+child], data[first+root] + root = child + } +} + +func heapSortOrdered[E constraints.Ordered](data []E, a, b int) { + first := a + lo := 0 + hi := b - a + + // Build heap with greatest element at top. + for i := (hi - 1) / 2; i >= 0; i-- { + siftDownOrdered(data, i, hi, first) + } + + // Pop elements, largest first, into end of data. + for i := hi - 1; i >= 0; i-- { + data[first], data[first+i] = data[first+i], data[first] + siftDownOrdered(data, lo, i, first) + } +} + +// pdqsortOrdered sorts data[a:b]. +// The algorithm based on pattern-defeating quicksort(pdqsort), but without the optimizations from BlockQuicksort. +// pdqsort paper: https://arxiv.org/pdf/2106.05123.pdf +// C++ implementation: https://github.com/orlp/pdqsort +// Rust implementation: https://docs.rs/pdqsort/latest/pdqsort/ +// limit is the number of allowed bad (very unbalanced) pivots before falling back to heapsort. +func pdqsortOrdered[E constraints.Ordered](data []E, a, b, limit int) { + const maxInsertion = 12 + + var ( + wasBalanced = true // whether the last partitioning was reasonably balanced + wasPartitioned = true // whether the slice was already partitioned + ) + + for { + length := b - a + + if length <= maxInsertion { + insertionSortOrdered(data, a, b) + return + } + + // Fall back to heapsort if too many bad choices were made. + if limit == 0 { + heapSortOrdered(data, a, b) + return + } + + // If the last partitioning was imbalanced, we need to breaking patterns. + if !wasBalanced { + breakPatternsOrdered(data, a, b) + limit-- + } + + pivot, hint := choosePivotOrdered(data, a, b) + if hint == decreasingHint { + reverseRangeOrdered(data, a, b) + // The chosen pivot was pivot-a elements after the start of the array. + // After reversing it is pivot-a elements before the end of the array. + // The idea came from Rust's implementation. + pivot = (b - 1) - (pivot - a) + hint = increasingHint + } + + // The slice is likely already sorted. + if wasBalanced && wasPartitioned && hint == increasingHint { + if partialInsertionSortOrdered(data, a, b) { + return + } + } + + // Probably the slice contains many duplicate elements, partition the slice into + // elements equal to and elements greater than the pivot. + if a > 0 && !(data[a-1] < data[pivot]) { + mid := partitionEqualOrdered(data, a, b, pivot) + a = mid + continue + } + + mid, alreadyPartitioned := partitionOrdered(data, a, b, pivot) + wasPartitioned = alreadyPartitioned + + leftLen, rightLen := mid-a, b-mid + balanceThreshold := length / 8 + if leftLen < rightLen { + wasBalanced = leftLen >= balanceThreshold + pdqsortOrdered(data, a, mid, limit) + a = mid + 1 + } else { + wasBalanced = rightLen >= balanceThreshold + pdqsortOrdered(data, mid+1, b, limit) + b = mid + } + } +} + +// partitionOrdered does one quicksort partition. +// Let p = data[pivot] +// Moves elements in data[a:b] around, so that data[i]<p and data[j]>=p for i<newpivot and j>newpivot. +// On return, data[newpivot] = p +func partitionOrdered[E constraints.Ordered](data []E, a, b, pivot int) (newpivot int, alreadyPartitioned bool) { + data[a], data[pivot] = data[pivot], data[a] + i, j := a+1, b-1 // i and j are inclusive of the elements remaining to be partitioned + + for i <= j && (data[i] < data[a]) { + i++ + } + for i <= j && !(data[j] < data[a]) { + j-- + } + if i > j { + data[j], data[a] = data[a], data[j] + return j, true + } + data[i], data[j] = data[j], data[i] + i++ + j-- + + for { + for i <= j && (data[i] < data[a]) { + i++ + } + for i <= j && !(data[j] < data[a]) { + j-- + } + if i > j { + break + } + data[i], data[j] = data[j], data[i] + i++ + j-- + } + data[j], data[a] = data[a], data[j] + return j, false +} + +// partitionEqualOrdered partitions data[a:b] into elements equal to data[pivot] followed by elements greater than data[pivot]. +// It assumed that data[a:b] does not contain elements smaller than the data[pivot]. +func partitionEqualOrdered[E constraints.Ordered](data []E, a, b, pivot int) (newpivot int) { + data[a], data[pivot] = data[pivot], data[a] + i, j := a+1, b-1 // i and j are inclusive of the elements remaining to be partitioned + + for { + for i <= j && !(data[a] < data[i]) { + i++ + } + for i <= j && (data[a] < data[j]) { + j-- + } + if i > j { + break + } + data[i], data[j] = data[j], data[i] + i++ + j-- + } + return i +} + +// partialInsertionSortOrdered partially sorts a slice, returns true if the slice is sorted at the end. +func partialInsertionSortOrdered[E constraints.Ordered](data []E, a, b int) bool { + const ( + maxSteps = 5 // maximum number of adjacent out-of-order pairs that will get shifted + shortestShifting = 50 // don't shift any elements on short arrays + ) + i := a + 1 + for j := 0; j < maxSteps; j++ { + for i < b && !(data[i] < data[i-1]) { + i++ + } + + if i == b { + return true + } + + if b-a < shortestShifting { + return false + } + + data[i], data[i-1] = data[i-1], data[i] + + // Shift the smaller one to the left. + if i-a >= 2 { + for j := i - 1; j >= 1; j-- { + if !(data[j] < data[j-1]) { + break + } + data[j], data[j-1] = data[j-1], data[j] + } + } + // Shift the greater one to the right. + if b-i >= 2 { + for j := i + 1; j < b; j++ { + if !(data[j] < data[j-1]) { + break + } + data[j], data[j-1] = data[j-1], data[j] + } + } + } + return false +} + +// breakPatternsOrdered scatters some elements around in an attempt to break some patterns +// that might cause imbalanced partitions in quicksort. +func breakPatternsOrdered[E constraints.Ordered](data []E, a, b int) { + length := b - a + if length >= 8 { + random := xorshift(length) + modulus := nextPowerOfTwo(length) + + for idx := a + (length/4)*2 - 1; idx <= a+(length/4)*2+1; idx++ { + other := int(uint(random.Next()) & (modulus - 1)) + if other >= length { + other -= length + } + data[idx], data[a+other] = data[a+other], data[idx] + } + } +} + +// choosePivotOrdered chooses a pivot in data[a:b]. +// +// [0,8): chooses a static pivot. +// [8,shortestNinther): uses the simple median-of-three method. +// [shortestNinther,∞): uses the Tukey ninther method. +func choosePivotOrdered[E constraints.Ordered](data []E, a, b int) (pivot int, hint sortedHint) { + const ( + shortestNinther = 50 + maxSwaps = 4 * 3 + ) + + l := b - a + + var ( + swaps int + i = a + l/4*1 + j = a + l/4*2 + k = a + l/4*3 + ) + + if l >= 8 { + if l >= shortestNinther { + // Tukey ninther method, the idea came from Rust's implementation. + i = medianAdjacentOrdered(data, i, &swaps) + j = medianAdjacentOrdered(data, j, &swaps) + k = medianAdjacentOrdered(data, k, &swaps) + } + // Find the median among i, j, k and stores it into j. + j = medianOrdered(data, i, j, k, &swaps) + } + + switch swaps { + case 0: + return j, increasingHint + case maxSwaps: + return j, decreasingHint + default: + return j, unknownHint + } +} + +// order2Ordered returns x,y where data[x] <= data[y], where x,y=a,b or x,y=b,a. +func order2Ordered[E constraints.Ordered](data []E, a, b int, swaps *int) (int, int) { + if data[b] < data[a] { + *swaps++ + return b, a + } + return a, b +} + +// medianOrdered returns x where data[x] is the median of data[a],data[b],data[c], where x is a, b, or c. +func medianOrdered[E constraints.Ordered](data []E, a, b, c int, swaps *int) int { + a, b = order2Ordered(data, a, b, swaps) + b, c = order2Ordered(data, b, c, swaps) + a, b = order2Ordered(data, a, b, swaps) + return b +} + +// medianAdjacentOrdered finds the median of data[a - 1], data[a], data[a + 1] and stores the index into a. +func medianAdjacentOrdered[E constraints.Ordered](data []E, a int, swaps *int) int { + return medianOrdered(data, a-1, a, a+1, swaps) +} + +func reverseRangeOrdered[E constraints.Ordered](data []E, a, b int) { + i := a + j := b - 1 + for i < j { + data[i], data[j] = data[j], data[i] + i++ + j-- + } +} + +func swapRangeOrdered[E constraints.Ordered](data []E, a, b, n int) { + for i := 0; i < n; i++ { + data[a+i], data[b+i] = data[b+i], data[a+i] + } +} + +func stableOrdered[E constraints.Ordered](data []E, n int) { + blockSize := 20 // must be > 0 + a, b := 0, blockSize + for b <= n { + insertionSortOrdered(data, a, b) + a = b + b += blockSize + } + insertionSortOrdered(data, a, n) + + for blockSize < n { + a, b = 0, 2*blockSize + for b <= n { + symMergeOrdered(data, a, a+blockSize, b) + a = b + b += 2 * blockSize + } + if m := a + blockSize; m < n { + symMergeOrdered(data, a, m, n) + } + blockSize *= 2 + } +} + +// symMergeOrdered merges the two sorted subsequences data[a:m] and data[m:b] using +// the SymMerge algorithm from Pok-Son Kim and Arne Kutzner, "Stable Minimum +// Storage Merging by Symmetric Comparisons", in Susanne Albers and Tomasz +// Radzik, editors, Algorithms - ESA 2004, volume 3221 of Lecture Notes in +// Computer Science, pages 714-723. Springer, 2004. +// +// Let M = m-a and N = b-n. Wolog M < N. +// The recursion depth is bound by ceil(log(N+M)). +// The algorithm needs O(M*log(N/M + 1)) calls to data.Less. +// The algorithm needs O((M+N)*log(M)) calls to data.Swap. +// +// The paper gives O((M+N)*log(M)) as the number of assignments assuming a +// rotation algorithm which uses O(M+N+gcd(M+N)) assignments. The argumentation +// in the paper carries through for Swap operations, especially as the block +// swapping rotate uses only O(M+N) Swaps. +// +// symMerge assumes non-degenerate arguments: a < m && m < b. +// Having the caller check this condition eliminates many leaf recursion calls, +// which improves performance. +func symMergeOrdered[E constraints.Ordered](data []E, a, m, b int) { + // Avoid unnecessary recursions of symMerge + // by direct insertion of data[a] into data[m:b] + // if data[a:m] only contains one element. + if m-a == 1 { + // Use binary search to find the lowest index i + // such that data[i] >= data[a] for m <= i < b. + // Exit the search loop with i == b in case no such index exists. + i := m + j := b + for i < j { + h := int(uint(i+j) >> 1) + if data[h] < data[a] { + i = h + 1 + } else { + j = h + } + } + // Swap values until data[a] reaches the position before i. + for k := a; k < i-1; k++ { + data[k], data[k+1] = data[k+1], data[k] + } + return + } + + // Avoid unnecessary recursions of symMerge + // by direct insertion of data[m] into data[a:m] + // if data[m:b] only contains one element. + if b-m == 1 { + // Use binary search to find the lowest index i + // such that data[i] > data[m] for a <= i < m. + // Exit the search loop with i == m in case no such index exists. + i := a + j := m + for i < j { + h := int(uint(i+j) >> 1) + if !(data[m] < data[h]) { + i = h + 1 + } else { + j = h + } + } + // Swap values until data[m] reaches the position i. + for k := m; k > i; k-- { + data[k], data[k-1] = data[k-1], data[k] + } + return + } + + mid := int(uint(a+b) >> 1) + n := mid + m + var start, r int + if m > mid { + start = n - b + r = mid + } else { + start = a + r = m + } + p := n - 1 + + for start < r { + c := int(uint(start+r) >> 1) + if !(data[p-c] < data[c]) { + start = c + 1 + } else { + r = c + } + } + + end := n - start + if start < m && m < end { + rotateOrdered(data, start, m, end) + } + if a < start && start < mid { + symMergeOrdered(data, a, start, mid) + } + if mid < end && end < b { + symMergeOrdered(data, mid, end, b) + } +} + +// rotateOrdered rotates two consecutive blocks u = data[a:m] and v = data[m:b] in data: +// Data of the form 'x u v y' is changed to 'x v u y'. +// rotate performs at most b-a many calls to data.Swap, +// and it assumes non-degenerate arguments: a < m && m < b. +func rotateOrdered[E constraints.Ordered](data []E, a, m, b int) { + i := m - a + j := b - m + + for i != j { + if i > j { + swapRangeOrdered(data, m-i, m, j) + i -= j + } else { + swapRangeOrdered(data, m-i, m+j-i, i) + j -= i + } + } + // i == j + swapRangeOrdered(data, m-i, m, i) +} |