我知道这可能是一个愚蠢的问题,也许今天最愚蠢的问题,但我不得不问：我是否发明了这种排序算法？
昨天我对基于交换的排序算法有一点灵感.今天,我实现了它,它的工作.
它可能已经存在,因为有许多不太受欢迎的排序算法在这里几乎没有或没有信息,几乎没有实现它们.
说明：基本上,这个算法需要一个项目,一个对,再一个项目,直到列表的末尾.对于每个项目/对,将距离配对空间或项目的半径距离相同的两个项目进行比较,直到达到阵列的边界,然后根据需要交换这些项目.对列表的每一对/项重复此操作.
一个英文伪代码：
FOR i index to last index of Array (starting from 0)
L index is i - 1
R index is i + 1
//Odd case,where i is the center
WHILE (L is in array range and R is in array range)
IF item Array[L] is greater than Array[R]
EXCHANGE item Array[L] with Array[R]
END-IF
ADD 1 to R
REST 1 to L
END-WHILE
//Even case,where i is not the center
L index is now i
R index in now i + 1
WHILE (L is in array range and R is in array range)
IF item Array[L] is greater than Array[R]
EXCHANGE Array[L] with Array[R]
END-IF
ADD 1 to R
REST 1 to L
END-WHILE
END FOR
这是Java中的实现：
//package sorting;
public class OrbitSort {
public static void main(String[] args) {
int[] numbers ={ 15,8,6,3,11,1,2,14,13,7,9,4,10,5,12 };
System.out.println("Original list:");
display(numbers);
sort(numbers);
System.out.println("\nSorted list:");
display(numbers);
}
//Sorting algorithm
public static void sort(int[] array) {
for(int i = 0; i < array.length; i++){
int L = i - 1;
int R = i + 1;
//Odd case (with a central item)
while(L >= 0 && R < array.length){
if(array[L] > array[R])
swap(array,L,R);
L--;
R++;
}
//Even case (with no central item)
L = i;
R = i + 1;
while(L >= 0 && R < array.length) {
if(array[L] > array[R])
swap(array,R);
L--;
R++;
}
}
}
//Swap two items in array.
public static void swap(int[] array,int x,int y) {
int temp = array[x];
array[x] = array[y];
array[y] = temp;
}
//Display items
public static void display(int[] numbers){
for(int i: numbers)
System.out.print(" " + i);
System.out.println();
}
}
我知道可以更短,但这只是一个早期的实现.
它可能运行在O(n ^ 2),但我不确定.
所以你怎么看？它已经存在吗？
解决方法
对我来说,它看起来像是一个改进的气泡排序算法,对于输入元素的某些排列可能会更好.
不一定是公平的,我使用你的输入数组做了一个基准测试,用于比较：
不一定是公平的,我使用你的输入数组做了一个基准测试,用于比较：
> java.util.Arrays.sort(),这是一个合并快速排序实现
> BubbleSort.sort(),一个Java实现的气泡排序算法
> OrbitSort.sort(),你的算法
结果：
input size: 8192
warmup iterations: 32
Arrays.sort()
iterations : 10000
total time : 4940.0ms
avg time : 0.494ms
BubbleSort.sort()
iterations : 100
total time : 8360.0ms
avg time : 83.6ms
OrbitSort.sort()
iterations : 100
total time : 8820.0ms
avg time : 88.2ms
当然,性能取决于输入的大小和布置
直接代码：
package com.sam.tests;
import java.math.BigDecimal;
import java.math.RoundingMode;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Random;
import java.util.concurrent.Callable;
public class SortBenchmark {
public static class OrbitSort {
// Sorting algorithm
public static void sort(int[] array) {
for (int i = 0; i < array.length; i++) {
int L = i - 1;
int R = i + 1;
// Odd case (with a central item)
while (L >= 0 && R < array.length) {
if (array[L] > array[R])
swap(array,R);
L--;
R++;
}
// Even case (with no central item)
L = i;
R = i + 1;
while (L >= 0 && R < array.length) {
if (array[L] > array[R])
swap(array,R);
L--;
R++;
}
}
}
// Swap two items in array.
public static void swap(int[] array,int y) {
int temp = array[x];
array[x] = array[y];
array[y] = temp;
}
}
public static class BubbleSort {
public static void sort(int[] numbers) {
boolean swapped = true;
for (int i = numbers.length - 1; i > 0 && swapped; i--) {
swapped = false;
for (int j = 0; j < i; j++) {
if (numbers[j] > numbers[j + 1]) {
int temp = numbers[j];
numbers[j] = numbers[j + 1];
numbers[j + 1] = temp;
swapped = true;
}
}
}
}
}
public static class TestDataFactory {
public static enum ElementOrder {
Ascending,Descending,Random
}
public static int[] createIntArray(final int size,final ElementOrder elementOrder) {
int[] array = new int[size];
switch (elementOrder) {
case Ascending:
for (int i = 0; i < size; ++i)
array[i] = i;
break;
case Descending:
for (int i = 0; i < size; ++i)
array[i] = size - i - 1;
break;
case Random:
default:
Random rg = new Random(System.nanoTime());
for (int i = 0; i < size; ++i)
array[i] = rg.nextInt(size);
break;
}
return array;
}
}
public static class Benchmark {
// misc constants
public static final int NANOS_PER_MSEC = 1000000;
// config constants
public static final int BIGDECIMAL_PRECISION = 6;
// constant defaults
public static final long AUTOTUNING_MIN_ITERATIONS_DEFAULT = 1;
public static final long AUTOTUNING_MIN_DURATION_DEFAULT = 125;
public static final long BENCHMARK_MIN_ITERATIONS_DEFAULT = 1;
public static final long BENCHMARK_MAX_ITERATIONS_DEFAULT = Integer.MAX_VALUE;
public static final long BENCHMARK_TARGET_DURATION_DEFAULT = 125;
// private static final ThreadMXBean threadBean =
// ManagementFactory.getThreadMXBean();
public static final long getNanoTime() {
// return threadBean.getCurrentThreadcpuTime();// not good,runs at
// some time slice resolution
return System.nanoTime();
}
public static class Result {
public String name;
public long iterations;
public long totalTime; // nanoseconds
public Result(String name,long iterations,long startTime,long endTime) {
this.name = name;
this.iterations = iterations;
this.totalTime = endTime - startTime;
}
@Override
public String toString() {
final double totalTimeMSecs = ((double) totalTime) / NANOS_PER_MSEC;
final BigDecimal avgTimeMsecs = new BigDecimal(this.totalTime).divide(new BigDecimal(this.iterations).multiply(new BigDecimal(NANOS_PER_MSEC)),BIGDECIMAL_PRECISION,RoundingMode.HALF_UP);
final String newLine = System.getProperty("line.separator");
StringBuilder sb = new StringBuilder();
sb.append(name).append(newLine);
sb.append(" ").append("iterations : ").append(iterations).append(newLine);
sb.append(" ").append("total time : ").append(totalTimeMSecs).append(" ms").append(newLine);
sb.append(" ").append("avg time : ").append(avgTimeMsecs).append(" ms").append(newLine);
return sb.toString();
}
}
public static <T> Result executionTime(final String name,final long iterations,final long warmupIterations,final Callable<T> test) throws Exception {
// vars
@SuppressWarnings("unused")
T ret;
long startTime;
long endTime;
// warmup
for (long i = 0; i < warmupIterations; ++i)
ret = test.call();
// actual benchmark iterations
{
startTime = getNanoTime();
for (long i = 0; i < iterations; ++i)
ret = test.call();
endTime = getNanoTime();
}
// return result
return new Result(name,iterations,startTime,endTime);
}
/**
* Auto tuned execution time measurement for test callbacks with steady
* execution time
*
* @param name
* @param test
* @return
* @throws Exception
*/
public static <T> Result executionTimeAutotuned(final String name,final Callable<T> test) throws Exception {
final long autoTuningMinIterations = AUTOTUNING_MIN_ITERATIONS_DEFAULT;
final long autoTuningMinDuration = AUTOTUNING_MIN_DURATION_DEFAULT;
final long benchmarkTargetDuration = BENCHMARK_TARGET_DURATION_DEFAULT;
final long benchmarkMinIterations = BENCHMARK_MIN_ITERATIONS_DEFAULT;
final long benchmarkMaxIterations = BENCHMARK_MAX_ITERATIONS_DEFAULT;
// vars
@SuppressWarnings("unused")
T ret;
final int prevThreadPriority;
long warmupIterations = 0;
long autoTuningDuration = 0;
long iterations = benchmarkMinIterations;
long startTime;
long endTime;
// store current thread priority and set it to max
prevThreadPriority = Thread.currentThread().getPriority();
Thread.currentThread().setPriority(Thread.MAX_PRIORITY);
// warmup and iteration count tuning
{
final long autoTuningMinTimeNanos = autoTuningMinDuration * NANOS_PER_MSEC;
long autoTuningConsecutiveLoops = 1;
double avgExecutionTime = 0;
do {
{
startTime = getNanoTime();
for (long i = 0; i < autoTuningConsecutiveLoops; ++i,++warmupIterations) {
ret = test.call();
}
endTime = getNanoTime();
autoTuningDuration += (endTime - startTime);
}
avgExecutionTime = ((double) autoTuningDuration) / ((double) (warmupIterations));
if ((autoTuningDuration >= autoTuningMinTimeNanos) && (warmupIterations >= autoTuningMinIterations)) {
break;
} else {
final double remainingAutotuningIterations = ((double) (autoTuningMinTimeNanos - autoTuningDuration)) / avgExecutionTime;
autoTuningConsecutiveLoops = Math.max(1,Math.min(Integer.MAX_VALUE,(long) Math.ceil(remainingAutotuningIterations)));
}
} while (warmupIterations < Integer.MAX_VALUE);
final double requiredIterations = ((double) benchmarkTargetDuration * NANOS_PER_MSEC) / avgExecutionTime;
iterations = Math.max(1,Math.min(benchmarkMaxIterations,(long) Math.ceil(requiredIterations)));
}
// actual benchmark iterations
{
startTime = getNanoTime();
for (long i = 0; i < iterations; ++i)
ret = test.call();
endTime = getNanoTime();
}
// restore prevIoUs thread priority
Thread.currentThread().setPriority(prevThreadPriority);
// return result
return new Result(name,endTime);
}
}
public static void executeBenchmark(int inputSize,ArrayList<Benchmark.Result> results) {
// final int[] inputArray = { 15,// 10,12 };
final int[] inputArray = TestDataFactory.createIntArray(inputSize,TestDataFactory.ElementOrder.Random);
try {
// compare against Arrays.sort()
{
final int[] ref = inputArray.clone();
Arrays.sort(ref);
{
int[] temp = inputArray.clone();
BubbleSort.sort(temp);
if (!Arrays.equals(temp,ref))
throw new Exception("BubbleSort.sort() Failed");
}
{
int[] temp = inputArray.clone();
OrbitSort.sort(temp);
if (!Arrays.equals(temp,ref))
throw new Exception("OrbitSort.sort() Failed");
}
}
results.add(Benchmark.executionTimeAutotuned("Arrays.sort()",new Callable<Void>() {
@Override
public Void call() throws Exception {
int[] temp = Arrays.copyOf(inputArray,inputArray.length);
Arrays.sort(temp);
return null;
}
}));
results.add(Benchmark.executionTimeAutotuned("BubbleSort.sort()",inputArray.length);
BubbleSort.sort(temp);
return null;
}
}));
results.add(Benchmark.executionTimeAutotuned("OrbitSort.sort()",inputArray.length);
OrbitSort.sort(temp);
return null;
}
}));
} catch (Exception e) {
e.printStackTrace();
}
}
public static void main(String[] args) {
ArrayList<Benchmark.Result> results = new ArrayList<Benchmark.Result>();
for (int i = 16; i <= 16384; i <<= 1) {
results.clear();
executeBenchmark(i,results);
System.out.println("input size : " + i);
System.out.println("");
for (Benchmark.Result result : results) {
System.out.print(result.toString());
}
System.out.println("----------------------------------------------------");
}
}
}
