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Gonglja committed Jan 18, 2023
2 parents 6f9d7ef + d3caf81 commit 44df72e
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2 changes: 1 addition & 1 deletion codes/go/chapter_heap/my_heap.go
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Expand Up @@ -24,7 +24,7 @@ func newHeap() *maxHeap {

/* 构造函数,根据切片建堆 */
func newMaxHeap(nums []any) *maxHeap {
// 所有元素入堆
// 将列表元素原封不动添加进堆
h := &maxHeap{data: nums}
for i := len(h.data) - 1; i >= 0; i-- {
// 堆化除叶结点以外的其他所有结点
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2 changes: 1 addition & 1 deletion codes/java/chapter_heap/my_heap.java
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Expand Up @@ -20,7 +20,7 @@ public MaxHeap() {

/* 构造函数,根据输入列表建堆 */
public MaxHeap(List<Integer> nums) {
// 所有元素入堆
// 将列表元素原封不动添加进堆
maxHeap = new ArrayList<>(nums);
// 堆化除叶结点以外的其他所有结点
for (int i = parent(size() - 1); i >= 0; i--) {
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15 changes: 14 additions & 1 deletion codes/zig/build.zig
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Expand Up @@ -201,7 +201,20 @@ pub fn build(b: *std.build.Builder) void {
if (b.args) |args| run_cmd_heap.addArgs(args);
const run_step_heap = b.step("run_heap", "Run heap");
run_step_heap.dependOn(&run_cmd_heap.step);


// Source File: "chapter_heap/my_heap.zig"
// Run Command: zig build run_my_heap
const exe_my_heap = b.addExecutable("my_heap", "chapter_heap/my_heap.zig");
exe_my_heap.addPackagePath("include", "include/include.zig");
exe_my_heap.setTarget(target);
exe_my_heap.setBuildMode(mode);
exe_my_heap.install();
const run_cmd_my_heap = exe_my_heap.run();
run_cmd_my_heap.step.dependOn(b.getInstallStep());
if (b.args) |args| run_cmd_my_heap.addArgs(args);
const run_step_my_heap = b.step("run_my_heap", "Run my_heap");
run_step_my_heap.dependOn(&run_cmd_my_heap.step);

// Section: "Linear Search"
// Source File: "chapter_searching/linear_search.zig"
// Run Command: zig build run_linear_search
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190 changes: 190 additions & 0 deletions codes/zig/chapter_heap/my_heap.zig
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@@ -0,0 +1,190 @@
// File: my_heap.zig
// Created Time: 2023-01-14
// Author: sjinzh ([email protected])

const std = @import("std");
const inc = @import("include");

// 堆类简易实现
// 编译期泛型
pub fn MaxHeap(comptime T: type) type {
return struct {
const Self = @This();

maxHeap: ?std.ArrayList(T) = null, // 使用列表而非数组,这样无需考虑扩容问题

// 构造函数,根据输入列表建堆
pub fn init(self: *Self, allocator: std.mem.Allocator, nums: []const T) !void {
if (self.maxHeap != null) return;
self.maxHeap = std.ArrayList(T).init(allocator);
// 将列表元素原封不动添加进堆
try self.maxHeap.?.appendSlice(nums);
// 堆化除叶结点以外的其他所有结点
var i: usize = parent(self.size() - 1) + 1;
while (i > 0) : (i -= 1) {
try self.siftDown(i - 1);
}
}

// 析构函数,释放内存
pub fn deinit(self: *Self) void {
if (self.maxHeap != null) self.maxHeap.?.deinit();
}

// 获取左子结点索引
fn left(i: usize) usize {
return 2 * i + 1;
}

// 获取右子结点索引
fn right(i: usize) usize {
return 2 * i + 2;
}

// 获取父结点索引
fn parent(i: usize) usize {
// return (i - 1) / 2; // 向下整除
return @divFloor(i - 1, 2);
}

// 交换元素
fn swap(self: *Self, i: usize, j: usize) !void {
var a = self.maxHeap.?.items[i];
var b = self.maxHeap.?.items[j];
var tmp = a;
try self.maxHeap.?.replaceRange(i, 1, &[_]T{b});
try self.maxHeap.?.replaceRange(j, 1, &[_]T{tmp});
}

// 获取堆大小
pub fn size(self: *Self) usize {
return self.maxHeap.?.items.len;
}

// 判断堆是否为空
pub fn isEmpty(self: *Self) bool {
return self.size() == 0;
}

// 访问堆顶元素
pub fn peek(self: *Self) T {
return self.maxHeap.?.items[0];
}

// 元素入堆
pub fn push(self: *Self, val: T) !void {
// 添加结点
try self.maxHeap.?.append(val);
// 从底至顶堆化
try self.siftUp(self.size() - 1);
}

// 从结点 i 开始,从底至顶堆化
fn siftUp(self: *Self, i_: usize) !void {
var i = i_;
while (true) {
// 获取结点 i 的父结点
var p = parent(i);
// 当“越过根结点”或“结点无需修复”时,结束堆化
if (p < 0 or self.maxHeap.?.items[i] <= self.maxHeap.?.items[p]) break;
// 交换两结点
try self.swap(i, p);
// 循环向上堆化
i = p;
}
}

// 元素出堆
pub fn poll(self: *Self) !T {
// 判断处理
if (self.isEmpty()) unreachable;
// 交换根结点与最右叶结点(即交换首元素与尾元素)
try self.swap(0, self.size() - 1);
// 删除结点
var val = self.maxHeap.?.pop();
// 从顶至底堆化
try self.siftDown(0);
// 返回堆顶元素
return val;
}

// 从结点 i 开始,从顶至底堆化
fn siftDown(self: *Self, i_: usize) !void {
var i = i_;
while (true) {
// 判断结点 i, l, r 中值最大的结点,记为 ma
var l = left(i);
var r = right(i);
var ma = i;
if (l < self.size() and self.maxHeap.?.items[l] > self.maxHeap.?.items[ma]) ma = l;
if (r < self.size() and self.maxHeap.?.items[r] > self.maxHeap.?.items[ma]) ma = r;
// 若结点 i 最大或索引 l, r 越界,则无需继续堆化,跳出
if (ma == i) break;
// 交换两结点
try self.swap(i, ma);
// 循环向下堆化
i = ma;
}
}

fn lessThan(context: void, a: T, b: T) std.math.Order {
_ = context;
return std.math.order(a, b);
}

fn greaterThan(context: void, a: T, b: T) std.math.Order {
return lessThan(context, a, b).invert();
}

// 打印堆(二叉树)
pub fn print(self: *Self, mem_allocator: std.mem.Allocator) !void {
const PQgt = std.PriorityQueue(T, void, greaterThan);
var queue = PQgt.init(std.heap.page_allocator, {});
defer queue.deinit();
try queue.addSlice(self.maxHeap.?.items);
try inc.PrintUtil.printHeap(T, mem_allocator, queue);
}
};
}

// Driver Code
pub fn main() !void {
// 初始化内存分配器
var mem_arena = std.heap.ArenaAllocator.init(std.heap.page_allocator);
defer mem_arena.deinit();
const mem_allocator = mem_arena.allocator();

// 初始化大顶堆
var maxHeap = MaxHeap(i32){};
try maxHeap.init(std.heap.page_allocator, &[_]i32{ 9, 8, 6, 6, 7, 5, 2, 1, 4, 3, 6, 2 });
defer maxHeap.deinit();
std.debug.print("\n输入列表并建堆后\n", .{});
try maxHeap.print(mem_allocator);

// 获取堆顶元素
var peek = maxHeap.peek();
std.debug.print("\n堆顶元素为 {}\n", .{peek});

// 元素入堆
const val = 7;
try maxHeap.push(val);
std.debug.print("\n元素 {} 入堆后\n", .{val});
try maxHeap.print(mem_allocator);

// 堆顶元素出堆
peek = try maxHeap.poll();
std.debug.print("\n堆顶元素 {} 出堆后\n", .{peek});
try maxHeap.print(mem_allocator);

// 获取堆的大小
var size = maxHeap.size();
std.debug.print("\n堆元素数量为 {}", .{size});

// 判断堆是否为空
var isEmpty = maxHeap.isEmpty();
std.debug.print("\n堆是否为空 {}\n", .{isEmpty});

const getchar = try std.io.getStdIn().reader().readByte();
_ = getchar;
}

4 changes: 2 additions & 2 deletions docs/chapter_heap/heap.md
Original file line number Diff line number Diff line change
Expand Up @@ -710,10 +710,10 @@ comments: true
```go title="my_heap.go"
/* 构造函数,根据切片建堆 */
func newMaxHeap(nums []any) *maxHeap {
// 所有元素入堆
// 将列表元素原封不动添加进堆
h := &maxHeap{data: nums}
// 堆化除叶结点以外的其他所有结点
for i := len(h.data) - 1; i >= 0; i-- {
// 堆化除叶结点以外的其他所有结点
h.siftDown(i)
}
return h
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2 changes: 1 addition & 1 deletion docs/chapter_sorting/bubble_sort.md
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Expand Up @@ -228,7 +228,7 @@ comments: true

**稳定排序**:不交换相等元素。

**自适排序**:引入 `flag` 优化后(见下文),最佳时间复杂度为 $O(N)$ 。
**自适应排序**:引入 `flag` 优化后(见下文),最佳时间复杂度为 $O(N)$ 。

## 效率优化

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Binary file modified docs/chapter_tree/binary_search_tree.assets/bst_degradation.png
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