造*-Java泛型堆排

个人博客地址：http://kyle.org.cn/2018/03/13/heapsort/

Java实现泛型堆排算法，用于N个对象中选择最大或者最小的前M个，其中M<=N

类似于Mysql中order by + limit的功能，如果有类似场景的需求，可以直接拷贝到项目中使用

Github源码地址：https://github.com/Kyle-Wilson1/Algorithm_Java/tree/master/heapsort

工程目录结构

• BootStrap：启动类，测试入口
• Node：排序的对象
• AbstractHeapObject：对象需要实现的接口
• HeapDirectionEnum：堆方向枚举
• HeapSort：堆排算法实现类
• HomeController：web api测试

启动类

• 作为程序的入口，写了一些测试数据

public class BootStrap {

    public static void main(String[] args) {
        new BootStrap().handle();
    }

    private void handle() {
        HeapSort<Node> heapSort = new HeapSort<>();

        List<Node> arr = new ArrayList<>(Arrays.asList(
                new Node(1L, 1L),
                new Node(2L, 2L),
                new Node(1L, 1L),
                new Node(2L, 2L),
                new Node(3L, 3L),
                new Node(4L, 4L)));
        heapSort.setDirection(HeapDirectionEnum.MAX_ROOT);
        heapSort.setHeapCapability(5);
        heapSort.buildHeap(arr);

        heapSort.getHeap().forEach(System.out::println);

        List<Node> arr1 = new ArrayList<>(Arrays.asList(
                new Node(1L, 1L),
                new Node(2L, 2L),
                new Node(3L, 3L),
                new Node(4L, 4L),
                new Node(5L, 5L)));
        heapSort.buildHeap(arr1);
        System.out.println("insert arr1:");

        heapSort.getHeap().forEach(System.out::println);
        System.out.println("pop: " + heapSort.heapPop());
    }
}

测试结果如下：

Node{key=3, value=3}

Node{key=2, value=2}

Node{key=1, value=1}

Node{key=1, value=1}

Node{key=2, value=2}

insert arr1:

Node{key=2, value=2}

Node{key=2, value=2}

Node{key=1, value=1}

Node{key=1, value=1}

Node{key=1, value=1}

pop: Node{key=2, value=2}

定义对象接口

• 对于要排序的对象，必须先实现该接口
• getKey()返回某一个对象要用于排序的字段

public abstract class AbstractHeapObject {

    public abstract Long getKey();

}

排序对象

• 待排序的类实现，需要实现AbstractHeapObject接口

public class Node extends AbstractHeapObject {

    private Long key;
    private Long value;

    public Node(Long key, Long value) {
        this.key = key;
        this.value = value;
    }

    @Override
    public Long getKey() {
        return key;
    }

    public Long getValue() {
        return value;
    }

    @Override
    public String toString() {
        return "Node{" +
                "key=" + key +
                ", value=" + value +
                '}';
    }
}

定义堆排方向枚举

• 大根堆，适用于N个数中求最小的前M个数
• 小根堆，适用于N个数中求最大的前M个数

public enum HeapDirectionEnum {

    //大根堆，适用于N个数中求最小的前M个数
    MAX_ROOT(0, "MAX_ROOT", "大根堆"),
    //小根堆，适用于N个数中求最大的前M个数
    MIN_ROOT(1, "MIN_ROOT", "小根堆");

    private Integer id;
    private String name;
    private String remark;

    HeapDirectionEnum(Integer id, String name, String remark) {
        this.id = id;
        this.name = name;
        this.remark = remark;
    }

    public Integer getId() {
        return id;
    }

    public String getName() {
        return name;
    }

    public String getRemark() {
        return remark;
    }
}

堆排核心算法

• 堆的向上调整，向下调整，弹出堆等实现

public class HeapSort<T extends AbstractHeapObject> {

    private List<T> sourceData;
    private HeapDirectionEnum direction;
    private List<T> heap;
    private int heapCapability;

    public void setDirection(HeapDirectionEnum direction) {
        this.direction = direction;
    }

    public void setHeapCapability(int heapCapability) {
        this.heapCapability = heapCapability;
    }

    public List<T> getHeap() {
        return heap;
    }

    synchronized public void buildHeap(List<T> sourceData) {

        this.sourceData = sourceData;

        //初始化堆容量
        if (heap == null) {
            heap = new ArrayList<>(heapCapability);
        }

        sourceData.forEach(x -> {
            //如果堆中元素还没有达到最大值，则插入到堆尾并向上调整，否则替换根元素并向下进行调整
            if (heap.size() < heapCapability) {
                heapUp(x);
            } else {
                if (direction == HeapDirectionEnum.MAX_ROOT && x.getKey() < heap.get(0).getKey()
                        || direction == HeapDirectionEnum.MIN_ROOT && x.getKey() > heap.get(0).getKey()) {
                    heap.set(0, x);
                    heapDown(0);
                }
            }
        });

    }

    /**
     * @param current the value to be added at the tail
     */
    private void heapUp(T current) {
        int i, j;
        heap.add(current);
        i = heap.size() - 1;
        j = (i - 1) / 2; //j指向i的父结点

        while (i > 0) {
            if (direction == HeapDirectionEnum.MAX_ROOT && heap.get(j).getKey() >= current.getKey()
                    || direction == HeapDirectionEnum.MIN_ROOT && heap.get(j).getKey() <= current.getKey()) {
                break;
            }
            heap.set(i, heap.get(j));
            i = j;
            j = (i - 1) / 2;
        }
        heap.set(i, current);
    }

    /**
     * @param top the location where the value will be adjusted down
     */
    private void heapDown(int top) {
        int j = 2 * top + 1;
        T x = heap.get(top);
        int heapSize = heap.size() - 1;

        while (j <= heapSize) {
            if (j + 1 <= heapSize && (
                    direction == HeapDirectionEnum.MAX_ROOT && heap.get(j + 1).getKey() > heap.get(j).getKey()
                            || direction == HeapDirectionEnum.MIN_ROOT && heap.get(j + 1).getKey() < heap.get(j).getKey()))
                j++;
            if (direction == HeapDirectionEnum.MAX_ROOT && heap.get(j).getKey() <= x.getKey()
                    || direction == HeapDirectionEnum.MIN_ROOT && heap.get(j).getKey() >= x.getKey()) {
                break;
            }
            heap.set(top, heap.get(j));
            top = j;
            j = 2 * top + 1;
        }
        heap.set(top, x);
    }

    public T heapPop() {
        T ret = heap.get(0);

        heap.set(0, heap.get(heap.size() - 1));
        heap.remove(heap.size() - 1);
        heapDown(0);

        return ret;
    }

}