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java實現(xiàn)的各種排序算法代碼示例

 更新時間:2017年10月19日 11:58:38   作者:黃小魚ZZZ  
這篇文章主要介紹了java實現(xiàn)的各種排序算法代碼示例,比較全面,代碼親測可用,如有不足之處,歡迎留言指出。

折半插入排序

折半插入排序是對直接插入排序的簡單改進。此處介紹的折半插入,其實就是通過不斷地折半來快速確定第i個元素的
插入位置,這實際上是一種查找算法:折半查找。Java的Arrays類里的binarySearch()方法,就是折半查找的實現(xiàn),用
于從指定數(shù)組中查找指定元素,前提是該數(shù)組已經(jīng)處于有序狀態(tài)。與直接插入排序的效果相同,只是更快了一些,因
為折半插入排序可以更快地確定第i個元素的插入位置

代碼:

package interview; 
/** 
 * @author Administrator 
 * 折半插入排序 
 */ 
public class BinaryInsertSort { 
  public static void binaryInsertSort(DataWrap[] data) { 
    System.out.println("開始排序"); 
    int arrayLength = data.length; 
    for (int i = 1; i < arrayLength; i++) { 
      DataWrap temp = data[i]; 
      int low = 0; 
      int high = i - 1; 
      while (low <= high) { 
        int mid = (low + high) / 2; 
        if (temp.compareTo(data[mid]) > 0) { 
          low = mid + 1; 
        } else { 
          high = mid - 1; 
        } 
      } 
      for (int j = i; j > low; j--) { 
        data[j] = data[j - 1]; 
      } 
      data[low] = temp; 
      System.out.println(java.util.Arrays.toString(data)); 
    } 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = { new DataWrap(9, ""), new DataWrap(-16, ""), 
        new DataWrap(21, "*"), new DataWrap(23, ""), 
        new DataWrap(-30, ""), new DataWrap(-49, ""), 
        new DataWrap(21, ""), new DataWrap(30, "*"), 
        new DataWrap(30, "")}; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    binaryInsertSort(data); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
}

結(jié)果:

排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
開始排序
[-16, 9, 21*, 23, -30, -49, 21, 30*, 30]
[-16, 9, 21*, 23, -30, -49, 21, 30*, 30]
[-16, 9, 21*, 23, -30, -49, 21, 30*, 30]
[-30, -16, 9, 21*, 23, -49, 21, 30*, 30]
[-49, -30, -16, 9, 21*, 23, 21, 30*, 30]
[-49, -30, -16, 9, 21, 21*, 23, 30*, 30]
[-49, -30, -16, 9, 21, 21*, 23, 30*, 30]
[-49, -30, -16, 9, 21, 21*, 23, 30, 30*]
排序之后:
[-49, -30, -16, 9, 21, 21*, 23, 30, 30*]

冒泡排序

代碼:

package interview; 
/** 
 * @author Administrator 
 * 冒泡排序 
 */ 
public class BubbleSort { 
  public static void bubbleSort(DataWrap[] data) { 
    System.out.println("開始排序"); 
    int arrayLength = data.length; 
    for (int i = 0; i < arrayLength - 1; i++) { 
      boolean flag = false; 
      for (int j = 0; j < arrayLength - 1 - i; j++) { 
        if (data[j].compareTo(data[j + 1]) > 0) { 
          DataWrap temp = data[j + 1]; 
          data[j + 1] = data[j]; 
          data[j] = temp; 
          flag = true; 
        } 
      } 
      System.out.println(java.util.Arrays.toString(data)); 
      if (!flag) 
        break; 
    } 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = { new DataWrap(9, ""), new DataWrap(-16, ""), 
        new DataWrap(21, "*"), new DataWrap(23, ""), 
        new DataWrap(-30, ""), new DataWrap(-49, ""), 
        new DataWrap(21, ""), new DataWrap(30, "*"), 
        new DataWrap(30, "")}; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    bubbleSort(data); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 

運行結(jié)果:

排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
開始排序
[-16, 9, 21*, -30, -49, 21, 23, 30*, 30]
[-16, 9, -30, -49, 21*, 21, 23, 30*, 30]
[-16, -30, -49, 9, 21*, 21, 23, 30*, 30]
[-30, -49, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
排序之后:
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]

桶式排序

算法的時間效率:時間效率極高,只需經(jīng)過兩輪遍歷即可算法的空間效率:空間開銷較大,需要兩個數(shù)組來完成,算
法的穩(wěn)定性:穩(wěn)定
代碼:

package interview; 
import java.util.Arrays; 
/** 
 * @author Administrator 
 * 桶式排序 
 */ 
public class BucketSort { 
  public static void bucketSort(DataWrap[] data, int min, int max) { 
    System.out.println("開始排序"); 
    int arrayLength = data.length; 
    DataWrap[] temp = new DataWrap[arrayLength]; 
    int[] buckets = new int[max - min]; 
    for (int i = 0; i < arrayLength; i++) { 
      buckets[data[i].data - min]++; 
    } 
    System.out.println(Arrays.toString(buckets)); 
    for (int i = 1; i < max - min; i++) { 
      buckets[i] = buckets[i] + buckets[i - 1]; 
    } 
    System.out.println(Arrays.toString(buckets)); 
    System.arraycopy(data, 0, temp, 0, arrayLength); 
    for (int k = arrayLength - 1; k >= 0; k--) { 
      data[--buckets[temp[k].data - min]] = temp[k]; 
    } 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = { new DataWrap(9, ""), new DataWrap(5, ""), 
        new DataWrap(-1, ""), new DataWrap(8, ""), 
        new DataWrap(5, "*"), new DataWrap(7, ""), 
        new DataWrap(3, ""), new DataWrap(-3, ""), 
        new DataWrap(1, ""),new DataWrap(3, "*")}; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    bucketSort(data, -3, 10); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 

結(jié)果

排序之前:
[9, 5, -1, 8, 5*, 7, 3, -3, 1, 3*]
開始排序
[1, 0, 1, 0, 1, 0, 2, 0, 2, 0, 1, 1, 1]
[1, 1, 2, 2, 3, 3, 5, 5, 7, 7, 8, 9, 10]
排序之后:
[-3, -1, 1, 3, 3*, 5, 5*, 7, 8, 9]

堆排序

代碼:

package interview; 
/** 
 * @author Administrator 
 * 堆排序 
 */ 
public class HeapSort { 
  public static void heapSort(DataWrap[] data) { 
    System.out.println("開始排序"); 
    int arrayLength = data.length; 
    // 循環(huán)建堆 
    for (int i = 0; i < arrayLength - 1; i++) { 
      // 建堆 
      builMaxdHeap(data, arrayLength - 1 - i); 
      // 交換堆頂和最后一個元素 
      swap(data, 0, arrayLength - 1 - i); 
      System.out.println(java.util.Arrays.toString(data)); 
    } 
  } 
  // 對data數(shù)組從0到lastIndex建大頂堆 
  private static void builMaxdHeap(DataWrap[] data, int lastIndex) { 
    // 從lastIndex處節(jié)點(最后一個節(jié)點)的父節(jié)點開始 
    for (int i = (lastIndex - 1) / 2; i >= 0; i--) { 
      // k保存當(dāng)前正在判斷的節(jié)點 
      int k = i; 
      // 如果當(dāng)前k節(jié)點的子節(jié)點存在 
      while (k * 2 + 1 <= lastIndex) { 
        // k節(jié)點的左子節(jié)點的索引 
        int biggerIndex = 2 * k + 1; 
        // 如果biggerIndex小于lastIndex,即biggerIndex +1 
        // 代表k節(jié)點的右子節(jié)點存在 
        if (biggerIndex < lastIndex) { 
          // 如果右子節(jié)點的值較大 
          if (data[biggerIndex].compareTo(data[biggerIndex + 1]) < 0) { 
            // biggerIndex總是記錄較大子節(jié)點的索引 
            biggerIndex++; 
          } 
        } 
        // 如果k節(jié)點的值小于其較大子節(jié)點的值 
        if (data[k].compareTo(data[biggerIndex]) < 0) { 
          // 交換它們 
          swap(data, k, biggerIndex); 
          // 將biggerIndex賦給k,開始while循環(huán)的下一次循環(huán) 
          // 重新保證k節(jié)點的值大于其左、右節(jié)點的值 
          k = biggerIndex; 
        } else { 
          break; 
        } 
      } 
    } 
  } 
  // 交換data數(shù)組中i、j兩個索引處的元素 
  private static void swap(DataWrap[] data, int i, int j) { 
    DataWrap temp = data[i]; 
    data[i] = data[j]; 
    data[j] = temp; 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = { new DataWrap(9, ""), new DataWrap(-16, ""), 
        new DataWrap(21, "*"), new DataWrap(23, ""), 
        new DataWrap(-30, ""), new DataWrap(-49, ""), 
        new DataWrap(21, ""), new DataWrap(30, "*"), 
        new DataWrap(30, "")}; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    heapSort(data); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 

結(jié)果:

排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
開始排序
[-16, 30, 21*, 23, -30, -49, 21, 9, 30*]
[-16, 23, 21*, 9, -30, -49, 21, 30, 30*]
[21, 9, 21*, -16, -30, -49, 23, 30, 30*]
[-49, 9, 21*, -16, -30, 21, 23, 30, 30*]
[-30, 9, -49, -16, 21*, 21, 23, 30, 30*]
[-30, -16, -49, 9, 21*, 21, 23, 30, 30*]
[-49, -30, -16, 9, 21*, 21, 23, 30, 30*]
[-49, -30, -16, 9, 21*, 21, 23, 30, 30*]
排序之后:
[-49, -30, -16, 9, 21*, 21, 23, 30, 30*]

直接插入排序

package interview;
public class InsertSort {
	public static void insertSort(DataWrap[] data){ 
	  System.out.println("開始排序"); 
	  int arrayLength = data.length; 
	  for(int i = 1;i < arrayLength;i++){ 
	    DataWrap temp = data[i]; 
	    if(data[i].compareTo(data[i-1]) < 0){ 
	      int j = i -1; 
	      for(;j >= 0 && data[j].compareTo(temp) > 0;j--){ 
	        data[j +1] = data[j]; 
	      } 
	      data[j + 1] = temp; 
	    } 
	    System.out.println(java.util.Arrays.toString(data)); 
	  } 
	} 
	public static void main(String[] args) { 
	  DataWrap[] data = { new DataWrap(9, ""), new DataWrap(-16, ""), 
	      new DataWrap(21, "*"), new DataWrap(23, ""), 
	      new DataWrap(-30, ""), new DataWrap(-49, ""), 
	      new DataWrap(21, ""), new DataWrap(30, "*"), 
	      new DataWrap(30, "")}; 
	  System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
	  insertSort(data); 
	  System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
	} 
}

結(jié)果

排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
開始排序
[-16, 9, 21*, 23, -30, -49, 21, 30*, 30]
[-16, 9, 21*, 23, -30, -49, 21, 30*, 30]
[-16, 9, 21*, 23, -30, -49, 21, 30*, 30]
[-30, -16, 9, 21*, 23, -49, 21, 30*, 30]
[-49, -30, -16, 9, 21*, 23, 21, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
排序之后:
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]

歸并排序

算法的時間效率:歸并算法需要遞歸地進行分解、合并,每進行一趟歸并排序,需要merge()方法一次,每次執(zhí)行
merge()需要比較n次,較差,需要一個與原始序列同樣大小的輔助序列。算法的穩(wěn)定性:穩(wěn)定
代碼:

package interview; 
/** 
 * @author Administrator 
 * 歸并排序 
 */ 
public class MergeSort { 
  public static void mergeSort(DataWrap[] data) { 
    // 歸并排序 
    sort(data, 0, data.length - 1); 
  } 
  // 將索引從left到right范圍的數(shù)組元素進行歸并排序 
  private static void sort(DataWrap[] data, int left, int right) { 
    if(left < right){ 
      //找出中間索引 
      int center = (left + right)/2; 
      sort(data,left,center); 
      sort(data,center+1,right); 
      //合并 
      merge(data,left,center,right); 
    } 
  } 
  // 將兩個數(shù)組進行歸并,歸并前兩個數(shù)組已經(jīng)有序,歸并后依然有序 
  private static void merge(DataWrap[] data, int left, int center, int right) { 
    DataWrap[] tempArr = new DataWrap[data.length]; 
    int mid = center + 1; 
    int third = left; 
    int temp = left; 
    while (left <= center && mid <= right) { 
      if (data[left].compareTo(data[mid]) <= 0) { 
        tempArr[third++] = data[left++]; 
      } else { 
        tempArr[third++] = data[mid++]; 
      } 
    } 
    while (mid <= right) { 
      tempArr[third++] = data[mid++]; 
    } 
    while (left <= center) { 
      tempArr[third++] = data[left++]; 
    } 
    while (temp <= right) { 
      data[temp] = tempArr[temp++]; 
    } 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = { new DataWrap(9, ""), new DataWrap(-16, ""), 
        new DataWrap(21, "*"), new DataWrap(23, ""), 
        new DataWrap(-30, ""), new DataWrap(-49, ""), 
        new DataWrap(21, ""), new DataWrap(30, "*"), 
        new DataWrap(30, "") }; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    mergeSort(data); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 

結(jié)果:

排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
排序之后:
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]

基數(shù)排序

基數(shù)排序已經(jīng)不再是一種常規(guī)的排序方法,它更多地像是一種排序方法的應(yīng)用,基數(shù)排序必須依賴于另外的排序方法。
基數(shù)排序的總體思路就是將待排數(shù)據(jù)拆分成多個關(guān)鍵字進行排序,也就是說,基數(shù)排序的實質(zhì)是多關(guān)鍵字排序。
多關(guān)鍵字排序的思路是將待排數(shù)據(jù)里的排序關(guān)鍵字拆分成多個排序關(guān)鍵字:第1個子關(guān)鍵字、第2個子關(guān)鍵字、第3個子
關(guān)鍵字。。。然后,根據(jù)子關(guān)鍵字對待排數(shù)據(jù)進行排序。在進行多關(guān)鍵字排序時有兩種解決方案:

最高位優(yōu)先法MSD
最低位優(yōu)先法LSD

比較MSD法和LSD法,一般來講,LSD法要比MSD法來得簡單,因為LSD法是從頭到尾進行若干次分配和收集,執(zhí)行
的次數(shù)取決于構(gòu)成關(guān)鍵字值的成分為多少;而MSD法則要處理各序列與子序列的獨立排序問題,就可能復(fù)雜一些。

代碼:

package interview; 
 
import java.util.Arrays; 
 
/** 
 * @author Administrator 
 * 基數(shù)排序 
 */ 
public class MultiKeyRadixSort { 
  public static void radixSort(int[] data, int radix, int d) { 
    System.out.println("開始排序:"); 
    int arrayLength = data.length; 
    int[] temp = new int[arrayLength]; 
    int[] buckets = new int[radix]; 
    for (int i = 0, rate = 1; i < d; i++) { 
      // 重置count數(shù)組,開始統(tǒng)計第二個關(guān)鍵字 
      Arrays.fill(buckets, 0); 
      // 當(dāng)data數(shù)組的元素復(fù)制到temp數(shù)組中進行緩存 
      System.arraycopy(data, 0, temp, 0, arrayLength); 
      for (int j = 0; j < arrayLength; j++) { 
        int subKey = (temp[j] / rate) % radix; 
        buckets[subKey]++; 
      } 
      for (int j = 1; j < radix; j++) { 
        buckets[j] = buckets[j] + buckets[j - 1]; 
      } 
      for (int m = arrayLength - 1; m >= 0; m--) { 
        int subKey = (temp[m] / rate) % radix; 
        data[--buckets[subKey]] = temp[m]; 
      } 
      System.out.println("對" + rate + "位上子關(guān)鍵字排序:" 
          + java.util.Arrays.toString(data)); 
      rate *= radix; 
    } 
  } 
 
  public static void main(String[] args) { 
    int[] data = { 1100, 192, 221, 12, 13 }; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    radixSort(data, 10, 4); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 

結(jié)果

排序之前:
[1100, 192, 221, 12, 13]
開始排序:
對1位上子關(guān)鍵字排序:[1100, 221, 192, 12, 13]
對10位上子關(guān)鍵字排序:[1100, 12, 13, 221, 192]
對100位上子關(guān)鍵字排序:[12, 13, 1100, 192, 221]
對1000位上子關(guān)鍵字排序:[12, 13, 192, 221, 1100]
排序之后:
[12, 13, 192, 221, 1100]

快速排序

代碼:

package interview; 
/** 
 * @author Administrator 
 * 快速排序 
 */ 
public class QuickSort { 
  private static void swap(DataWrap[] data, int i, int j) { 
    DataWrap temp = data[i]; 
    data[i] = data[j]; 
    data[j] = temp; 
  } 
  private static void subSort(DataWrap[] data, int start, int end) { 
    if (start < end) { 
      DataWrap base = data[start]; 
      int i = start; 
      int j = end + 1; 
      while (true) { 
        while (i < end && data[++i].compareTo(base) <= 0) 
          ; 
        while (j > start && data[--j].compareTo(base) >= 0) 
          ; 
        if (i < j) { 
          swap(data, i, j); 
        } else { 
          break; 
        } 
      } 
      swap(data, start, j); 
      subSort(data, start, j - 1); 
      subSort(data, j + 1, end); 
    } 
  } 
  public static void quickSort(DataWrap[] data){ 
    subSort(data,0,data.length-1); 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = { new DataWrap(9, ""), new DataWrap(-16, ""), 
        new DataWrap(21, "*"), new DataWrap(23, ""), 
        new DataWrap(-30, ""), new DataWrap(-49, ""), 
        new DataWrap(21, ""), new DataWrap(30, "*"), 
        new DataWrap(30, "") }; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    quickSort(data); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 

結(jié)果

排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
排序之后:
[-49, -30, -16, 9, 21, 21*, 23, 30*, 30]

直接選擇排序

代碼:

package interview; 
/** 
 * @author Administrator 
 * 直接選擇排序 
 */ 
public class SelectSort { 
  public static void selectSort(DataWrap[] data) { 
    System.out.println("開始排序"); 
    int arrayLength = data.length; 
    for (int i = 0; i < arrayLength - 1; i++) { 
      for (int j = i + 1; j < arrayLength; j++) { 
        if (data[i].compareTo(data[j]) > 0) { 
          DataWrap temp = data[i]; 
          data[i] = data[j]; 
          data[j] = temp; 
        } 
      } 
      System.out.println(java.util.Arrays.toString(data)); 
    } 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = { new DataWrap(9, ""), new DataWrap(-16, ""), 
        new DataWrap(21, "*"), new DataWrap(23, ""), 
        new DataWrap(-30, ""), new DataWrap(-49, ""), 
        new DataWrap(21, ""), new DataWrap(30, "*"), 
        new DataWrap(30, "") }; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    selectSort(data); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 
排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
開始排序
[-49, 9, 21*, 23, -16, -30, 21, 30*, 30]
[-49, -30, 21*, 23, 9, -16, 21, 30*, 30]
[-49, -30, -16, 23, 21*, 9, 21, 30*, 30]
[-49, -30, -16, 9, 23, 21*, 21, 30*, 30]
[-49, -30, -16, 9, 21*, 23, 21, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
排序之后:
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]

希爾排序

代碼:

package interview; 
/** 
 * @author Administrator 
 * Shell排序 
 */ 
public class ShellSort { 
  public static void ShellSort(DataWrap[] data) { 
    System.out.println("開始排序"); 
    int arrayLength = data.length; 
    int h = 1; 
    /** 
     * 將數(shù)組分割成若干個子序列 
     */ 
    while (h <= arrayLength / 3) { 
      h = h * 3 + 1; 
      System.out.println("h的結(jié)果:" + h); 
    } 
    while (h > 0) { 
      System.out.println("===h的值:" + h + "==="); 
      /** 
       * 將分成的若干子序列進行直接插入排序 
       */ 
      for (int i = h; i < arrayLength; i++) { 
        DataWrap temp = data[i]; 
        if (data[i].compareTo(data[i - h]) < 0) { 
          int j = i - h; 
          for (; j >= 0 && data[j].compareTo(temp) > 0; j -= h) { 
            data[j + h] = data[j]; 
          } 
          data[j + h] = temp; 
        } 
        System.out.println(java.util.Arrays.toString(data)); 
      } 
      h = (h - 1) / 3; 
    } 
  } 
  public static void main(String[] args) { 
    DataWrap[] data = {  
        new DataWrap(9, ""), new DataWrap(-16, ""), 
        new DataWrap(21, "*"), new DataWrap(23, ""), 
        new DataWrap(-30, ""), new DataWrap(-49, ""), 
        new DataWrap(21, ""), new DataWrap(30, "*"), 
        new DataWrap(30, "")}; 
    System.out.println("排序之前:\n" + java.util.Arrays.toString(data)); 
    ShellSort(data); 
    System.out.println("排序之后:\n" + java.util.Arrays.toString(data)); 
  } 
} 

結(jié)果:

排序之前:
[9, -16, 21*, 23, -30, -49, 21, 30*, 30]
開始排序
h的結(jié)果:4
===h的值:4===
[-30, -16, 21*, 23, 9, -49, 21, 30*, 30]
[-30, -49, 21*, 23, 9, -16, 21, 30*, 30]
[-30, -49, 21*, 23, 9, -16, 21, 30*, 30]
[-30, -49, 21*, 23, 9, -16, 21, 30*, 30]
[-30, -49, 21*, 23, 9, -16, 21, 30*, 30]
===h的值:1===
[-49, -30, 21*, 23, 9, -16, 21, 30*, 30]
[-49, -30, 21*, 23, 9, -16, 21, 30*, 30]
[-49, -30, 21*, 23, 9, -16, 21, 30*, 30]
[-49, -30, 9, 21*, 23, -16, 21, 30*, 30]
[-49, -30, -16, 9, 21*, 23, 21, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]
排序之后:
[-49, -30, -16, 9, 21*, 21, 23, 30*, 30]

所需要的工具類:

package interview;
public class DataWrap implements Comparable<DataWrap>{  
	 int data;  
	 String flag;  
	 public DataWrap(int data, String flag) {  
	   this.data = data;  
	   this.flag = flag;  
	 }  
	 public String toString(){  
	   return data + flag;  
	 }  
	 @Override  
	 public int compareTo(DataWrap dw) {  
	   return this.data > dw.data ?  
	       1 : (this.data == dw.data ? 0 : -1);  
	 }  
	}  

以上代碼親測可用,供大家參考。

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