msa*_*yag 247
在java-8中,您可以使用流:
int[] a = {10,20,30,40,50};
int sum = IntStream.of(a).sum();
System.out.println("The sum is " + sum);
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输出:
总和是150.
它在包中 java.util.stream
import java.util.stream.*;
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Ale*_* C. 40
如果您使用的是Java 8,则Arrays该类提供了一个stream(int[] array)返回IntStream指定int数组的顺序的方法.它也被重载double和long阵列.
int [] arr = {1,2,3,4};
int sum = Arrays.stream(arr).sum(); //prints 10
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它还提供了一种方法
stream(int[] array, int startInclusive, int endExclusive),允许您获取指定范围的数组(这可能很有用):
int sum = Arrays.stream(new int []{1,2,3,4}, 0, 2).sum(); //prints 3
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最后,它可以采用一种类型的数组T.因此,您可以在每个示例中包含一个String包含数字作为输入的内容,如果您想要对它们求和,则只需:
int sum = Arrays.stream("1 2 3 4".split("\\s+")).mapToInt(Integer::parseInt).sum();
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wkl*_*wkl 33
这是标准Java API中不存在(AFAIK)的简单事物之一.写你自己很容易.
其他答案都很好,但是这里有一些含有一些每个语法糖.
int someArray[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
int sum = 0;
for (int i : someArray)
sum += i;
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此外,Java 7语言规范中甚至还显示了数组求和的示例.该示例来自第10.4节 - 阵列访问.
class Gauss {
public static void main(String[] args) {
int[] ia = new int[101];
for (int i = 0; i < ia.length; i++) ia[i] = i;
int sum = 0;
for (int e : ia) sum += e;
System.out.println(sum);
}
}
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use*_*665 18
你不能.其他语言有一些方法,如PHP中的array_sum(),但Java没有.
只是..
int[] numbers = {1,2,3,4};
int sum = 0;
for( int i : numbers) {
sum += i;
}
System.out.println(sum);
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Pet*_*rey 13
我将添加到以前的解决方案的唯一一点是,我将使用long来累积总数以避免任何溢出值.
int[] someArray = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, Integer.MAX_VALUE};
long sum = 0;
for (int i : someArray)
sum += i;
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int sum = 0;
for (int i = 0; i < yourArray.length; i++)
{
sum = sum + yourArray[i];
}
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小智 6
int sum = 0;
for (int i = 0; i < myArray.length; i++)
sum += myArray[i];
}
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在 Java 8
代码:
int[] array = new int[]{1,2,3,4,5};
int sum = IntStream.of(array).reduce( 0,(a, b) -> a + b);
System.out.println("The summation of array is " + sum);
System.out.println("Another way to find summation :" + IntStream.of(array).sum());
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输出:
The summation of array is 15
Another way to find summation :15
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说明:
在Java 8,你可以使用简化概念来做你的补充.
有点惊讶的是,上面的答案都没有认为使用线程池可以快很多倍。在这里,parallel使用 fork-join 线程池并自动将流分成多个部分并并行运行它们,然后合并。如果您只记住下面这行代码,您可以在很多地方使用它。
因此,最快的简短而甜蜜的代码奖颁给了 -
int[] nums = {1,2,3};
int sum = Arrays.stream(nums).parallel().reduce(0, (a,b)-> a+b);
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假设您想要执行sum of squaresArrays.stream(nums).parallel().map(x->x*x).reduce(0, (a,b)-> a+b) 操作。想法是你仍然可以执行reduce,而不需要map。
这取决于。您要添加多少个数字?测试上述许多建议:
import java.text.NumberFormat;
import java.util.Arrays;
import java.util.Locale;
public class Main {
public static final NumberFormat FORMAT = NumberFormat.getInstance(Locale.US);
public static long sumParallel(int[] array) {
final long start = System.nanoTime();
int sum = Arrays.stream(array).parallel().reduce(0,(a,b)-> a + b);
final long end = System.nanoTime();
System.out.println(sum);
return end - start;
}
public static long sumStream(int[] array) {
final long start = System.nanoTime();
int sum = Arrays.stream(array).reduce(0,(a,b)-> a + b);
final long end = System.nanoTime();
System.out.println(sum);
return end - start;
}
public static long sumLoop(int[] array) {
final long start = System.nanoTime();
int sum = 0;
for (int v: array) {
sum += v;
}
final long end = System.nanoTime();
System.out.println(sum);
return end - start;
}
public static long sumArray(int[] array) {
final long start = System.nanoTime();
int sum = Arrays.stream(array) .sum();
final long end = System.nanoTime();
System.out.println(sum);
return end - start;
}
public static long sumStat(int[] array) {
final long start = System.nanoTime();
int sum = 0;
final long end = System.nanoTime();
System.out.println(sum);
return end - start;
}
public static void test(int[] nums) {
System.out.println("------");
System.out.println(FORMAT.format(nums.length) + " numbers");
long p = sumParallel(nums);
System.out.println("parallel " + FORMAT.format(p));
long s = sumStream(nums);
System.out.println("stream " + FORMAT.format(s));
long ar = sumArray(nums);
System.out.println("arrays " + FORMAT.format(ar));
long lp = sumLoop(nums);
System.out.println("loop " + FORMAT.format(lp));
}
public static void testNumbers(int howmany) {
int[] nums = new int[howmany];
for (int i =0; i < nums.length;i++) {
nums[i] = (i + 1)%100;
}
test(nums);
}
public static void main(String[] args) {
testNumbers(3);
testNumbers(300);
testNumbers(3000);
testNumbers(30000);
testNumbers(300000);
testNumbers(3000000);
testNumbers(30000000);
testNumbers(300000000);
}
}
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我发现,使用 8 核、16 G Ubuntu18 机器,对于较小的值,循环速度最快,对于较大的值,并行速度最快。但当然,这取决于您正在运行的硬件:
------
3 numbers
6
parallel 4,575,234
6
stream 209,849
6
arrays 251,173
6
loop 576
------
300 numbers
14850
parallel 671,428
14850
stream 73,469
14850
arrays 71,207
14850
loop 4,958
------
3,000 numbers
148500
parallel 393,112
148500
stream 306,240
148500
arrays 335,795
148500
loop 47,804
------
30,000 numbers
1485000
parallel 794,223
1485000
stream 1,046,927
1485000
arrays 366,400
1485000
loop 459,456
------
300,000 numbers
14850000
parallel 4,715,590
14850000
stream 1,369,509
14850000
arrays 1,296,287
14850000
loop 1,327,592
------
3,000,000 numbers
148500000
parallel 3,996,803
148500000
stream 13,426,933
148500000
arrays 13,228,364
148500000
loop 1,137,424
------
30,000,000 numbers
1485000000
parallel 32,894,414
1485000000
stream 131,924,691
1485000000
arrays 131,689,921
1485000000
loop 9,607,527
------
300,000,000 numbers
1965098112
parallel 338,552,816
1965098112
stream 1,318,649,742
1965098112
arrays 1,308,043,340
1965098112
loop 98,986,436
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