mirror of
https://github.com/ceph/ceph-csi.git
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rebase: update kubernetes to 1.28.0 in main
updating kubernetes to 1.28.0 in the main repo. Signed-off-by: Madhu Rajanna <madhupr007@gmail.com>
This commit is contained in:
Madhu Rajanna
committed by
mergify[bot]
mergify[bot]
parent
b2fdc269c3
commit
ff3e84ad67
223
vendor/golang.org/x/exp/slices/slices.go
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vendored
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223
vendor/golang.org/x/exp/slices/slices.go
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@ -0,0 +1,223 @@
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// Copyright 2021 The Go Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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// Package slices defines various functions useful with slices of any type.
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// Unless otherwise specified, these functions all apply to the elements
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// of a slice at index 0 <= i < len(s).
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//
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// Note that the less function in IsSortedFunc, SortFunc, SortStableFunc requires a
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// strict weak ordering (https://en.wikipedia.org/wiki/Weak_ordering#Strict_weak_orderings),
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// or the sorting may fail to sort correctly. A common case is when sorting slices of
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// floating-point numbers containing NaN values.
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package slices
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import "golang.org/x/exp/constraints"
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// Equal reports whether two slices are equal: the same length and all
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// elements equal. If the lengths are different, Equal returns false.
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// Otherwise, the elements are compared in increasing index order, and the
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// comparison stops at the first unequal pair.
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// Floating point NaNs are not considered equal.
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func Equal[E comparable](s1, s2 []E) bool {
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if len(s1) != len(s2) {
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return false
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}
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for i := range s1 {
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if s1[i] != s2[i] {
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return false
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}
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}
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return true
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}
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// EqualFunc reports whether two slices are equal using a comparison
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// function on each pair of elements. If the lengths are different,
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// EqualFunc returns false. Otherwise, the elements are compared in
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// increasing index order, and the comparison stops at the first index
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// for which eq returns false.
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func EqualFunc[E1, E2 any](s1 []E1, s2 []E2, eq func(E1, E2) bool) bool {
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if len(s1) != len(s2) {
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return false
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}
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for i, v1 := range s1 {
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v2 := s2[i]
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if !eq(v1, v2) {
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return false
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}
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}
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return true
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}
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// Compare compares the elements of s1 and s2.
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// The elements are compared sequentially, starting at index 0,
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// until one element is not equal to the other.
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// The result of comparing the first non-matching elements is returned.
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// If both slices are equal until one of them ends, the shorter slice is
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// considered less than the longer one.
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// The result is 0 if s1 == s2, -1 if s1 < s2, and +1 if s1 > s2.
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// Comparisons involving floating point NaNs are ignored.
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func Compare[E constraints.Ordered](s1, s2 []E) int {
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s2len := len(s2)
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for i, v1 := range s1 {
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if i >= s2len {
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return +1
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}
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v2 := s2[i]
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switch {
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case v1 < v2:
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return -1
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case v1 > v2:
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return +1
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}
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}
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if len(s1) < s2len {
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return -1
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}
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return 0
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}
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// CompareFunc is like Compare but uses a comparison function
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// on each pair of elements. The elements are compared in increasing
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// index order, and the comparisons stop after the first time cmp
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// returns non-zero.
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// The result is the first non-zero result of cmp; if cmp always
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// returns 0 the result is 0 if len(s1) == len(s2), -1 if len(s1) < len(s2),
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// and +1 if len(s1) > len(s2).
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func CompareFunc[E1, E2 any](s1 []E1, s2 []E2, cmp func(E1, E2) int) int {
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s2len := len(s2)
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for i, v1 := range s1 {
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if i >= s2len {
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return +1
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}
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v2 := s2[i]
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if c := cmp(v1, v2); c != 0 {
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return c
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}
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}
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if len(s1) < s2len {
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return -1
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}
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return 0
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}
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// Index returns the index of the first occurrence of v in s,
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// or -1 if not present.
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func Index[E comparable](s []E, v E) int {
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for i, vs := range s {
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if v == vs {
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return i
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}
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}
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return -1
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}
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// IndexFunc returns the first index i satisfying f(s[i]),
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// or -1 if none do.
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func IndexFunc[E any](s []E, f func(E) bool) int {
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for i, v := range s {
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if f(v) {
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return i
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}
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}
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return -1
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}
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// Contains reports whether v is present in s.
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func Contains[E comparable](s []E, v E) bool {
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return Index(s, v) >= 0
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}
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// Insert inserts the values v... into s at index i,
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// returning the modified slice.
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// In the returned slice r, r[i] == v[0].
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// Insert panics if i is out of range.
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// This function is O(len(s) + len(v)).
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func Insert[S ~[]E, E any](s S, i int, v ...E) S {
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tot := len(s) + len(v)
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if tot <= cap(s) {
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s2 := s[:tot]
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copy(s2[i+len(v):], s[i:])
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copy(s2[i:], v)
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return s2
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}
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s2 := make(S, tot)
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copy(s2, s[:i])
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copy(s2[i:], v)
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copy(s2[i+len(v):], s[i:])
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return s2
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}
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// Delete removes the elements s[i:j] from s, returning the modified slice.
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// Delete panics if s[i:j] is not a valid slice of s.
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// Delete modifies the contents of the slice s; it does not create a new slice.
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// Delete is O(len(s)-j), so if many items must be deleted, it is better to
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// make a single call deleting them all together than to delete one at a time.
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// Delete might not modify the elements s[len(s)-(j-i):len(s)]. If those
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// elements contain pointers you might consider zeroing those elements so that
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// objects they reference can be garbage collected.
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func Delete[S ~[]E, E any](s S, i, j int) S {
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_ = s[i:j] // bounds check
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return append(s[:i], s[j:]...)
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}
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// Clone returns a copy of the slice.
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// The elements are copied using assignment, so this is a shallow clone.
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func Clone[S ~[]E, E any](s S) S {
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// Preserve nil in case it matters.
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if s == nil {
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return nil
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}
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return append(S([]E{}), s...)
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}
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// Compact replaces consecutive runs of equal elements with a single copy.
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// This is like the uniq command found on Unix.
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// Compact modifies the contents of the slice s; it does not create a new slice.
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func Compact[S ~[]E, E comparable](s S) S {
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if len(s) == 0 {
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return s
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}
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i := 1
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last := s[0]
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for _, v := range s[1:] {
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if v != last {
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s[i] = v
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i++
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last = v
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}
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}
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return s[:i]
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}
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// CompactFunc is like Compact but uses a comparison function.
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func CompactFunc[S ~[]E, E any](s S, eq func(E, E) bool) S {
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if len(s) == 0 {
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return s
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}
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i := 1
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last := s[0]
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for _, v := range s[1:] {
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if !eq(v, last) {
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s[i] = v
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i++
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last = v
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}
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}
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return s[:i]
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}
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// Grow increases the slice's capacity, if necessary, to guarantee space for
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// another n elements. After Grow(n), at least n elements can be appended
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// to the slice without another allocation. Grow may modify elements of the
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// slice between the length and the capacity. If n is negative or too large to
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// allocate the memory, Grow panics.
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func Grow[S ~[]E, E any](s S, n int) S {
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return append(s, make(S, n)...)[:len(s)]
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}
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// Clip removes unused capacity from the slice, returning s[:len(s):len(s)].
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func Clip[S ~[]E, E any](s S) S {
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return s[:len(s):len(s)]
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}
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127
vendor/golang.org/x/exp/slices/sort.go
generated
vendored
Normal file
127
vendor/golang.org/x/exp/slices/sort.go
generated
vendored
Normal file
@ -0,0 +1,127 @@
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// Copyright 2022 The Go Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package slices
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import (
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"math/bits"
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"golang.org/x/exp/constraints"
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)
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// Sort sorts a slice of any ordered type in ascending order.
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// Sort may fail to sort correctly when sorting slices of floating-point
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// numbers containing Not-a-number (NaN) values.
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// Use slices.SortFunc(x, func(a, b float64) bool {return a < b || (math.IsNaN(a) && !math.IsNaN(b))})
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// instead if the input may contain NaNs.
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func Sort[E constraints.Ordered](x []E) {
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n := len(x)
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pdqsortOrdered(x, 0, n, bits.Len(uint(n)))
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}
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// SortFunc sorts the slice x in ascending order as determined by the less function.
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// This sort is not guaranteed to be stable.
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//
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// SortFunc requires that less is a strict weak ordering.
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// See https://en.wikipedia.org/wiki/Weak_ordering#Strict_weak_orderings.
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func SortFunc[E any](x []E, less func(a, b E) bool) {
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n := len(x)
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pdqsortLessFunc(x, 0, n, bits.Len(uint(n)), less)
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}
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// SortStable sorts the slice x while keeping the original order of equal
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// elements, using less to compare elements.
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func SortStableFunc[E any](x []E, less func(a, b E) bool) {
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stableLessFunc(x, len(x), less)
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}
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// IsSorted reports whether x is sorted in ascending order.
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func IsSorted[E constraints.Ordered](x []E) bool {
|
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for i := len(x) - 1; i > 0; i-- {
|
||||
if x[i] < x[i-1] {
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||||
return false
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||||
}
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||||
}
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||||
return true
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||||
}
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||||
|
||||
// IsSortedFunc reports whether x is sorted in ascending order, with less as the
|
||||
// comparison function.
|
||||
func IsSortedFunc[E any](x []E, less func(a, b E) bool) bool {
|
||||
for i := len(x) - 1; i > 0; i-- {
|
||||
if less(x[i], x[i-1]) {
|
||||
return false
|
||||
}
|
||||
}
|
||||
return true
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||||
}
|
||||
|
||||
// BinarySearch searches for target in a sorted slice and returns the position
|
||||
// where target is found, or the position where target would appear in the
|
||||
// sort order; it also returns a bool saying whether the target is really found
|
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// in the slice. The slice must be sorted in increasing order.
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||||
func BinarySearch[E constraints.Ordered](x []E, target E) (int, bool) {
|
||||
// search returns the leftmost position where f returns true, or len(x) if f
|
||||
// returns false for all x. This is the insertion position for target in x,
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// and could point to an element that's either == target or not.
|
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pos := search(len(x), func(i int) bool { return x[i] >= target })
|
||||
if pos >= len(x) || x[pos] != target {
|
||||
return pos, false
|
||||
} else {
|
||||
return pos, true
|
||||
}
|
||||
}
|
||||
|
||||
// BinarySearchFunc works like BinarySearch, but uses a custom comparison
|
||||
// function. The slice must be sorted in increasing order, where "increasing" is
|
||||
// defined by cmp. cmp(a, b) is expected to return an integer comparing the two
|
||||
// parameters: 0 if a == b, a negative number if a < b and a positive number if
|
||||
// a > b.
|
||||
func BinarySearchFunc[E any](x []E, target E, cmp func(E, E) int) (int, bool) {
|
||||
pos := search(len(x), func(i int) bool { return cmp(x[i], target) >= 0 })
|
||||
if pos >= len(x) || cmp(x[pos], target) != 0 {
|
||||
return pos, false
|
||||
} else {
|
||||
return pos, true
|
||||
}
|
||||
}
|
||||
|
||||
func search(n int, f func(int) bool) int {
|
||||
// Define f(-1) == false and f(n) == true.
|
||||
// Invariant: f(i-1) == false, f(j) == true.
|
||||
i, j := 0, n
|
||||
for i < j {
|
||||
h := int(uint(i+j) >> 1) // avoid overflow when computing h
|
||||
// i ≤ h < j
|
||||
if !f(h) {
|
||||
i = h + 1 // preserves f(i-1) == false
|
||||
} else {
|
||||
j = h // preserves f(j) == true
|
||||
}
|
||||
}
|
||||
// i == j, f(i-1) == false, and f(j) (= f(i)) == true => answer is i.
|
||||
return i
|
||||
}
|
||||
|
||||
type sortedHint int // hint for pdqsort when choosing the pivot
|
||||
|
||||
const (
|
||||
unknownHint sortedHint = iota
|
||||
increasingHint
|
||||
decreasingHint
|
||||
)
|
||||
|
||||
// xorshift paper: https://www.jstatsoft.org/article/view/v008i14/xorshift.pdf
|
||||
type xorshift uint64
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||||
|
||||
func (r *xorshift) Next() uint64 {
|
||||
*r ^= *r << 13
|
||||
*r ^= *r >> 17
|
||||
*r ^= *r << 5
|
||||
return uint64(*r)
|
||||
}
|
||||
|
||||
func nextPowerOfTwo(length int) uint {
|
||||
return 1 << bits.Len(uint(length))
|
||||
}
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||||
479
vendor/golang.org/x/exp/slices/zsortfunc.go
generated
vendored
Normal file
479
vendor/golang.org/x/exp/slices/zsortfunc.go
generated
vendored
Normal file
@ -0,0 +1,479 @@
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||||
// 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
|
||||
|
||||
// insertionSortLessFunc sorts data[a:b] using insertion sort.
|
||||
func insertionSortLessFunc[E any](data []E, a, b int, less func(a, b E) bool) {
|
||||
for i := a + 1; i < b; i++ {
|
||||
for j := i; j > a && less(data[j], data[j-1]); j-- {
|
||||
data[j], data[j-1] = data[j-1], data[j]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// siftDownLessFunc implements the heap property on data[lo:hi].
|
||||
// first is an offset into the array where the root of the heap lies.
|
||||
func siftDownLessFunc[E any](data []E, lo, hi, first int, less func(a, b E) bool) {
|
||||
root := lo
|
||||
for {
|
||||
child := 2*root + 1
|
||||
if child >= hi {
|
||||
break
|
||||
}
|
||||
if child+1 < hi && less(data[first+child], data[first+child+1]) {
|
||||
child++
|
||||
}
|
||||
if !less(data[first+root], data[first+child]) {
|
||||
return
|
||||
}
|
||||
data[first+root], data[first+child] = data[first+child], data[first+root]
|
||||
root = child
|
||||
}
|
||||
}
|
||||
|
||||
func heapSortLessFunc[E any](data []E, a, b int, less func(a, b E) bool) {
|
||||
first := a
|
||||
lo := 0
|
||||
hi := b - a
|
||||
|
||||
// Build heap with greatest element at top.
|
||||
for i := (hi - 1) / 2; i >= 0; i-- {
|
||||
siftDownLessFunc(data, i, hi, first, less)
|
||||
}
|
||||
|
||||
// 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]
|
||||
siftDownLessFunc(data, lo, i, first, less)
|
||||
}
|
||||
}
|
||||
|
||||
// pdqsortLessFunc 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 pdqsortLessFunc[E any](data []E, a, b, limit int, less func(a, b E) bool) {
|
||||
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 {
|
||||
insertionSortLessFunc(data, a, b, less)
|
||||
return
|
||||
}
|
||||
|
||||
// Fall back to heapsort if too many bad choices were made.
|
||||
if limit == 0 {
|
||||
heapSortLessFunc(data, a, b, less)
|
||||
return
|
||||
}
|
||||
|
||||
// If the last partitioning was imbalanced, we need to breaking patterns.
|
||||
if !wasBalanced {
|
||||
breakPatternsLessFunc(data, a, b, less)
|
||||
limit--
|
||||
}
|
||||
|
||||
pivot, hint := choosePivotLessFunc(data, a, b, less)
|
||||
if hint == decreasingHint {
|
||||
reverseRangeLessFunc(data, a, b, less)
|
||||
// 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 partialInsertionSortLessFunc(data, a, b, less) {
|
||||
return
|
||||
}
|
||||
}
|
||||
|
||||
// Probably the slice contains many duplicate elements, partition the slice into
|
||||
// elements equal to and elements greater than the pivot.
|
||||
if a > 0 && !less(data[a-1], data[pivot]) {
|
||||
mid := partitionEqualLessFunc(data, a, b, pivot, less)
|
||||
a = mid
|
||||
continue
|
||||
}
|
||||
|
||||
mid, alreadyPartitioned := partitionLessFunc(data, a, b, pivot, less)
|
||||
wasPartitioned = alreadyPartitioned
|
||||
|
||||
leftLen, rightLen := mid-a, b-mid
|
||||
balanceThreshold := length / 8
|
||||
if leftLen < rightLen {
|
||||
wasBalanced = leftLen >= balanceThreshold
|
||||
pdqsortLessFunc(data, a, mid, limit, less)
|
||||
a = mid + 1
|
||||
} else {
|
||||
wasBalanced = rightLen >= balanceThreshold
|
||||
pdqsortLessFunc(data, mid+1, b, limit, less)
|
||||
b = mid
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// partitionLessFunc 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 partitionLessFunc[E any](data []E, a, b, pivot int, less func(a, b E) bool) (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 && less(data[i], data[a]) {
|
||||
i++
|
||||
}
|
||||
for i <= j && !less(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 && less(data[i], data[a]) {
|
||||
i++
|
||||
}
|
||||
for i <= j && !less(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
|
||||
}
|
||||
|
||||
// partitionEqualLessFunc 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 partitionEqualLessFunc[E any](data []E, a, b, pivot int, less func(a, b E) bool) (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 && !less(data[a], data[i]) {
|
||||
i++
|
||||
}
|
||||
for i <= j && less(data[a], data[j]) {
|
||||
j--
|
||||
}
|
||||
if i > j {
|
||||
break
|
||||
}
|
||||
data[i], data[j] = data[j], data[i]
|
||||
i++
|
||||
j--
|
||||
}
|
||||
return i
|
||||
}
|
||||
|
||||
// partialInsertionSortLessFunc partially sorts a slice, returns true if the slice is sorted at the end.
|
||||
func partialInsertionSortLessFunc[E any](data []E, a, b int, less func(a, b E) bool) 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 && !less(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 !less(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 !less(data[j], data[j-1]) {
|
||||
break
|
||||
}
|
||||
data[j], data[j-1] = data[j-1], data[j]
|
||||
}
|
||||
}
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
// breakPatternsLessFunc scatters some elements around in an attempt to break some patterns
|
||||
// that might cause imbalanced partitions in quicksort.
|
||||
func breakPatternsLessFunc[E any](data []E, a, b int, less func(a, b E) bool) {
|
||||
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]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// choosePivotLessFunc 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 choosePivotLessFunc[E any](data []E, a, b int, less func(a, b E) bool) (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 = medianAdjacentLessFunc(data, i, &swaps, less)
|
||||
j = medianAdjacentLessFunc(data, j, &swaps, less)
|
||||
k = medianAdjacentLessFunc(data, k, &swaps, less)
|
||||
}
|
||||
// Find the median among i, j, k and stores it into j.
|
||||
j = medianLessFunc(data, i, j, k, &swaps, less)
|
||||
}
|
||||
|
||||
switch swaps {
|
||||
case 0:
|
||||
return j, increasingHint
|
||||
case maxSwaps:
|
||||
return j, decreasingHint
|
||||
default:
|
||||
return j, unknownHint
|
||||
}
|
||||
}
|
||||
|
||||
// order2LessFunc returns x,y where data[x] <= data[y], where x,y=a,b or x,y=b,a.
|
||||
func order2LessFunc[E any](data []E, a, b int, swaps *int, less func(a, b E) bool) (int, int) {
|
||||
if less(data[b], data[a]) {
|
||||
*swaps++
|
||||
return b, a
|
||||
}
|
||||
return a, b
|
||||
}
|
||||
|
||||
// medianLessFunc returns x where data[x] is the median of data[a],data[b],data[c], where x is a, b, or c.
|
||||
func medianLessFunc[E any](data []E, a, b, c int, swaps *int, less func(a, b E) bool) int {
|
||||
a, b = order2LessFunc(data, a, b, swaps, less)
|
||||
b, c = order2LessFunc(data, b, c, swaps, less)
|
||||
a, b = order2LessFunc(data, a, b, swaps, less)
|
||||
return b
|
||||
}
|
||||
|
||||
// medianAdjacentLessFunc finds the median of data[a - 1], data[a], data[a + 1] and stores the index into a.
|
||||
func medianAdjacentLessFunc[E any](data []E, a int, swaps *int, less func(a, b E) bool) int {
|
||||
return medianLessFunc(data, a-1, a, a+1, swaps, less)
|
||||
}
|
||||
|
||||
func reverseRangeLessFunc[E any](data []E, a, b int, less func(a, b E) bool) {
|
||||
i := a
|
||||
j := b - 1
|
||||
for i < j {
|
||||
data[i], data[j] = data[j], data[i]
|
||||
i++
|
||||
j--
|
||||
}
|
||||
}
|
||||
|
||||
func swapRangeLessFunc[E any](data []E, a, b, n int, less func(a, b E) bool) {
|
||||
for i := 0; i < n; i++ {
|
||||
data[a+i], data[b+i] = data[b+i], data[a+i]
|
||||
}
|
||||
}
|
||||
|
||||
func stableLessFunc[E any](data []E, n int, less func(a, b E) bool) {
|
||||
blockSize := 20 // must be > 0
|
||||
a, b := 0, blockSize
|
||||
for b <= n {
|
||||
insertionSortLessFunc(data, a, b, less)
|
||||
a = b
|
||||
b += blockSize
|
||||
}
|
||||
insertionSortLessFunc(data, a, n, less)
|
||||
|
||||
for blockSize < n {
|
||||
a, b = 0, 2*blockSize
|
||||
for b <= n {
|
||||
symMergeLessFunc(data, a, a+blockSize, b, less)
|
||||
a = b
|
||||
b += 2 * blockSize
|
||||
}
|
||||
if m := a + blockSize; m < n {
|
||||
symMergeLessFunc(data, a, m, n, less)
|
||||
}
|
||||
blockSize *= 2
|
||||
}
|
||||
}
|
||||
|
||||
// symMergeLessFunc 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 symMergeLessFunc[E any](data []E, a, m, b int, less func(a, b E) bool) {
|
||||
// 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 less(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 !less(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 !less(data[p-c], data[c]) {
|
||||
start = c + 1
|
||||
} else {
|
||||
r = c
|
||||
}
|
||||
}
|
||||
|
||||
end := n - start
|
||||
if start < m && m < end {
|
||||
rotateLessFunc(data, start, m, end, less)
|
||||
}
|
||||
if a < start && start < mid {
|
||||
symMergeLessFunc(data, a, start, mid, less)
|
||||
}
|
||||
if mid < end && end < b {
|
||||
symMergeLessFunc(data, mid, end, b, less)
|
||||
}
|
||||
}
|
||||
|
||||
// rotateLessFunc 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 rotateLessFunc[E any](data []E, a, m, b int, less func(a, b E) bool) {
|
||||
i := m - a
|
||||
j := b - m
|
||||
|
||||
for i != j {
|
||||
if i > j {
|
||||
swapRangeLessFunc(data, m-i, m, j, less)
|
||||
i -= j
|
||||
} else {
|
||||
swapRangeLessFunc(data, m-i, m+j-i, i, less)
|
||||
j -= i
|
||||
}
|
||||
}
|
||||
// i == j
|
||||
swapRangeLessFunc(data, m-i, m, i, less)
|
||||
}
|
||||
481
vendor/golang.org/x/exp/slices/zsortordered.go
generated
vendored
Normal file
481
vendor/golang.org/x/exp/slices/zsortordered.go
generated
vendored
Normal file
@ -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)
|
||||
}
|
||||
Reference in New Issue
Block a user