堆排序
堆是一种完全二叉树结构，完全二叉树就是一种满二叉树或者正在变满的结构。
image-20210225134323949.png
堆分为大根堆和小根堆，大小的意思是指任意子树的头节点都是这棵子树中最大或者最小的值的意思。
堆结构的维护依赖两个重要的算法
heapInsert:将一个节点插入一个已经维护好的堆结构中，使其依然是一个堆结构。
heapify(index):一个堆中除了index位置之外的其他地方都符合堆的定义，index节点向下沉，找到自己的位置使得堆结构形成。
java实现堆结构
public class Heap {
private int size;
private ArrayList elementData = new ArrayList<>();
private Comparator comparator;
public Heap(int initialSize) {
elementData = new ArrayList<>(initialSize);
}
public Heap() {
}
public Heap(Comparator comparator) {
this.comparator = comparator;
}
private void swap(int aIdx, int bIdx) {
T temp = elementData.get(aIdx);
elementData.set(aIdx, elementData.get(bIdx));
elementData.set(bIdx, temp);
}
@SuppressWarnings("unchecked")
private int compare(T a, T b) {
int result;
if (comparator != null) {
result = comparator.compare(a, b);
} else {
result = ((Comparable) a).compareTo(b);
}
return result;
}
private void heapInsert(int index) {
int fatherIdx;
//找到父亲，如果自己优先则交换
while (compare(elementData.get(index), elementData.get(fatherIdx = (index - 1) / 2)) < 0) {
swap(index, fatherIdx);
index = fatherIdx;
}
}
private void heapify(int index) {
if (index < 0) return;
//首先得有左孩子，一个孩子都没有不用往下找了
int leftChild;
while ((leftChild = (index << 1) | 1) < size) {//index*2+1
int largest = leftChild + 1 < size && compare(elementData.get(leftChild + 1), elementData.get(leftChild)) < 0 ? leftChild + 1 : leftChild;
largest = compare(elementData.get(index), elementData.get(largest)) < 0 ? index : largest;
if (largest == index) {
break;
}
swap(index, largest);
index = largest;
}
}
public void add(T value) {
elementData.add(value);
heapInsert(++size - 1);
}
public T poll() {
if (size <= 0) throw new RuntimeException("no more ele...");
final T result = elementData.get(0);
swap(0, size - 1);
elementData.set(size-- - 1, null);//let't GC to work
heapify(0);
return result;
}
public boolean isEmpty() {
return size == 0;
}
}
堆排序的思想就是利用堆的结构特性，它的顶上的值为最小或者是最大值，看你是什么堆。然后将一个数组形成堆结构后不断地弹出堆顶放到最后，堆size减少到0的时候就是有序的了。
java实现HeapSort
public static void heapSort(int[] ary) {
//变成堆
int heapSize = ary.length;
//1.法一：全部heapInsert，时间复杂度O(N*logN)
// for (int i = 0; i < ary.length; i++) {
// heapInsert(ary, i);
// }
//2.法二：从后面开始heapify,时间复杂度O(N)
for(int i = heapSize;i >= 0;i--)
heapify(ary,i,heapSize);
//大根堆，对顶元素最大
while (heapSize>0) {
swap(ary, 0, --heapSize);
heapify(ary, 0, heapSize);
}
}
public static void heapify(int[] ary, int index, int heapSize) {
int leftChild;
while ((leftChild = (index << 1) | 1) < heapSize) {
int largest = leftChild + 1 < heapSize && ary[leftChild + 1] > ary[leftChild] ? leftChild + 1 : leftChild;
largest = ary[largest] > ary[index] ? largest : index;
if (index == largest) break;
swap(ary, largest, index);
index = largest;
}
}
public static void heapInsert(int[] ary, int index) {
int fatherIdx;
while (ary[index] > ary[(fatherIdx = (index - 1) / 2)]) {
swap(ary, index, fatherIdx);
index = fatherIdx;
}
}
public static void swap(int[] ary, int aIdx, int bIdx) {
int temp = ary[aIdx];
ary[aIdx] = ary[bIdx];
ary[bIdx] = temp;
}
public static int[] copyArray(int[] origin) {
if (origin == null) return null;
int[] newArray = new int[origin.length];
for (int i = 0; i < origin.length; i++)
newArray[i] = origin[i];
return newArray;
}
public static boolean isEqlArray(int[] arrA, int[] arrB) {
if (arrA == arrB) return true;
if (arrA == null || arrB == null) return false;
if (arrA.length != arrB.length) return false;
for (int i = 0; i < arrA.length; i++)
if (arrA[i] != arrB[i]) return false;
return true;
}
public static void main(String[] args) {
int maxSize = 10, maxValue = 100, times = 1000000;
int i = 0;
for (; i < times; i++) {
int[] nums = MathUtil.generalRandomArray(maxSize, maxValue);
int[] cpyNums = copyArray(nums);
heapSort(nums);
Arrays.sort(cpyNums);
if (!isEqlArray(nums, cpyNums)) {
Logger.getGlobal().info(Arrays.toString(nums));
Logger.getGlobal().info(Arrays.toString(cpyNums));
break;
}
}
Logger.getGlobal().info(i == times ? "finish" : "fucking");
Heap heap = new Heap<>(new Comparator() {
@Override
public int compare(Integer o1, Integer o2) {
return o2-o1;
}
});
heap.add(3);
heap.add(4);
heap.add(2);
heap.add(1);
System.out.println(heap.poll());
System.out.println(heap.poll());
System.out.println(heap.poll());
System.out.println(heap.poll());
}
数组形成堆有两种方法，一个是从最后一个元素开始做heapify，一个是从第一个元素开始做heapInsert，为什么heapify的时间复杂度为O(N)比起heapInsert要好呢？
因为一个树中大量的元素是聚集在底部的，所以若是大量的元素进行heapify后移动的少那么时间复杂度就会低。这个会减少常数项时间，两种方式最终堆排序的时间复杂度都是O(N*logN)，因为后面弹出堆顶每次弹完后进行一个heapify，这个时间复杂度无法优化肯定是O(N*logN)
java代码实现
public static void heapSort(int[] ary) {
//变成堆
int heapSize = ary.length;
//1.法一：全部heapInsert，时间复杂度O(N*logN)
for (int i = 0; i < ary.length; i++) {
heapInsert(ary, i);
}
//2.法二：从后面开始heapify,时间复杂度O(N)
// for(int i = heapSize;i >= 0;i--)
// heapify(ary,i,heapSize);
//大根堆，对顶元素最大
while (heapSize>0) {
swap(ary, 0, --heapSize);
heapify(ary, 0, heapSize);
}
}
public static void heapify(int[] ary, int index, int heapSize) {
int leftChild;
while ((leftChild = (index << 1) | 1) < heapSize) {
int largest = leftChild + 1 < heapSize && ary[leftChild + 1] > ary[leftChild] ? leftChild + 1 : leftChild;
largest = ary[largest] > ary[index] ? largest : index;
if (index == largest) break;
swap(ary, largest, index);
index = largest;
}
}
public static void heapInsert(int[] ary, int index) {
int fatherIdx;
while (ary[index] > ary[(fatherIdx = (index - 1) / 2)]) {
swap(ary, index, fatherIdx);
index = fatherIdx;
}
}
public static void swap(int[] ary, int aIdx, int bIdx) {
int temp = ary[aIdx];
ary[aIdx] = ary[bIdx];
ary[bIdx] = temp;
}
public static int[] copyArray(int[] origin) {
if (origin == null) return null;
int[] newArray = new int[origin.length];
for (int i = 0; i < origin.length; i++)
newArray[i] = origin[i];
return newArray;
}
public static boolean isEqlArray(int[] arrA, int[] arrB) {
if (arrA == arrB) return true;
if (arrA == null || arrB == null) return false;
if (arrA.length != arrB.length) return false;
for (int i = 0; i < arrA.length; i++)
if (arrA[i] != arrB[i]) return false;
return true;
}
public static void main(String[] args) {
int maxSize = 10000000, maxValue = 100, times = 1;
int i = 0;
for (; i < times; i++) {
int[] nums = MathUtil.generalRandomArray(maxSize, maxValue);
int[] cpyNums = copyArray(nums);
heapSort(nums);
Arrays.sort(cpyNums);
if (!isEqlArray(nums, cpyNums)) {
Logger.getGlobal().info(Arrays.toString(nums));
Logger.getGlobal().info(Arrays.toString(cpyNums));
break;
}
}
Logger.getGlobal().info(i == times ? "finish" : "fucking");
Heap heap = new Heap<>(new Comparator() {
@Override
public int compare(Integer o1, Integer o2) {
return o2-o1;
}
});
}