我知道这可能是一个愚蠢的问题，也许是今天最愚蠢的问题，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了一些启发。今天，我实现了它，并且它有效。
它可能已经存在，因为有许多不太流行的排序算法，它们几乎没有信息，几乎没有实现它们。
描述：基本上，这个算法采用一个项目，它们是一对，然后是一个项目......直到列表的末尾。对于每个项目/对，比较距离对空间或项目相同半径距离的每两个项目，直到到达阵列的边界，然后根据需要交换这些项目。对列表中的每对/项重复此操作。
基于英语的伪代码：
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, 0, 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, L, 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施。
它可能在O（n ^ 2）中运行，但我不确定。
那么，您怎么看？它已经存在吗？
答案 0 :(得分：7)
对我来说，它看起来像一个修改过的冒泡排序算法，对于输入元素的某些排列可能表现得更好。 尽管不一定公平，我使用输入数组进行了预热循环的基准测试，以便比较：
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，性能取决于输入大小和排列
直截了当的代码：
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, 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, L, 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;
}
}
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, iterations, startTime, endTime);
}
}
public static void executeBenchmark(int inputSize, ArrayList<Benchmark.Result> results) {
// final int[] inputArray = { 15, 8, 6, 3, 11, 1, 2, 0, 14, 13, 7, 9, 4,
// 10, 5, 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()", new Callable<Void>() {
@Override
public Void call() throws Exception {
int[] temp = Arrays.copyOf(inputArray, inputArray.length);
BubbleSort.sort(temp);
return null;
}
}));
results.add(Benchmark.executionTimeAutotuned("OrbitSort.sort()", new Callable<Void>() {
@Override
public Void call() throws Exception {
int[] temp = Arrays.copyOf(inputArray, 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("----------------------------------------------------");
}
}
}
答案 1 :(得分：2)
它是O（n ^ 2）（假设它有效，我不确定），至于已经存在 - 也许 - 它不是真正的原创，因为它可以被认为是一个简单的排序实现的变体，但是我怀疑是否有任何公布的算法与这个算法完全相同，特别是有两个连续内循环的算法。
我并不是说它没有价值，可能有一个用例，其行为是唯一有效的（也许在哪里读取比写入快得多，并且缓存行为有利于其访问模式）。
要知道为什么它是O（n ^ 2），考虑第一个n / 6外循环迭代，内循环在O（n）长度上运行O（n）次。