php版本 使用不除法的array_product函数。 如果我们将i的值临时设为1，那么数组product将完全满足我们的需要

<?php
function product($key, $arr)
{
    $arr[$key] = 1;
    return array_product($arr);
};
$arr = [1, 2, 3, 4, 5];
$newarr = array();


foreach ($arr as $key => $value) {

    $newarr[$key] = product($key, $arr);
}
print_r($newarr);

还有一个O(N^(3/2))非最优解。不过，这很有趣。

首先预处理大小为N^0.5的每个部分乘法(这在O(N)时间复杂度中完成)。然后，计算每个数字的其他值的倍数可以在2*O(N^0.5)时间内完成(为什么?因为您只需要将其他((N^0.5) - 1)数字的最后一个元素相乘，并将结果与属于当前数字组的((N^0.5) - 1)数字相乘。对每一个数都这样做，可以得到O(N^(3/2))时间。

例子:

4, 6, 7, 2, 3, 1, 9, 5, 8

部分结果: 4*6*7 = 168 2*3*1 = 6 9*5*8 = 360

要计算3的值，需要将其他组的值乘以168*360，然后乘以2*1。

试试这个!

import java.util.*;
class arrProduct
{
 public static void main(String args[])
     {
         //getting the size of the array
         Scanner s = new Scanner(System.in);
            int noe = s.nextInt();

        int out[]=new int[noe];
         int arr[] = new int[noe];

         // getting the input array
         for(int k=0;k<noe;k++)
         {
             arr[k]=s.nextInt();
         }

         int val1 = 1,val2=1;
         for(int i=0;i<noe;i++)
         {
             int res=1;

                 for(int j=1;j<noe;j++)
                 {
                if((i+j)>(noe-1))
                {

                    int diff = (i+j)-(noe);

                    if(arr[diff]!=0)
                    {
                    res = res * arr[diff];
                    }
                }

                else
                {
                    if(arr[i+j]!=0)
                    {
                    res= res*arr[i+j];
                    }
                }


             out[i]=res;

         }
         }

         //printing result
         System.out.print("Array of Product: [");
         for(int l=0;l<out.length;l++)
         {
             if(l!=out.length-1)
             {
            System.out.print(out[l]+",");
             }
             else
             {
                 System.out.print(out[l]);
             }
         }
         System.out.print("]");
     }

}

上下两次。在O(N)完成的工作

private static int[] multiply(int[] numbers) {
        int[] multiplied = new int[numbers.length];
        int total = 1;

        multiplied[0] = 1;
        for (int i = 1; i < numbers.length; i++) {
            multiplied[i] = numbers[i - 1] * multiplied[i - 1];
        }

        for (int j = numbers.length - 2; j >= 0; j--) {
            total *= numbers[j + 1];
            multiplied[j] = total * multiplied[j];
        }

        return multiplied;
    }

def products(nums):
    prefix_products = []
    for num in nums:
        if prefix_products:
            prefix_products.append(prefix_products[-1] * num)
        else:
            prefix_products.append(num)

    suffix_products = []
    for num in reversed(nums):
        if suffix_products:
            suffix_products.append(suffix_products[-1] * num)
        else:
            suffix_products.append(num)
        suffix_products = list(reversed(suffix_products))

    result = []
    for i in range(len(nums)):
        if i == 0:
            result.append(suffix_products[i + 1])
        elif i == len(nums) - 1:
            result.append(prefix_products[i-1])
        else:
            result.append(
                prefix_products[i-1] * suffix_products[i+1]
            )
    return result

{-
Recursive solution using sqrt(n) subsets. Runs in O(n).

Recursively computes the solution on sqrt(n) subsets of size sqrt(n). 
Then recurses on the product sum of each subset.
Then for each element in each subset, it computes the product with
the product sum of all other products.
Then flattens all subsets.

Recurrence on the run time is T(n) = sqrt(n)*T(sqrt(n)) + T(sqrt(n)) + n

Suppose that T(n) ≤ cn in O(n).

T(n) = sqrt(n)*T(sqrt(n)) + T(sqrt(n)) + n
    ≤ sqrt(n)*c*sqrt(n) + c*sqrt(n) + n
    ≤ c*n + c*sqrt(n) + n
    ≤ (2c+1)*n
    ∈ O(n)

Note that ceiling(sqrt(n)) can be computed using a binary search 
and O(logn) iterations, if the sqrt instruction is not permitted.
-}

otherProducts [] = []
otherProducts [x] = [1]
otherProducts [x,y] = [y,x]
otherProducts a = foldl' (++) [] $ zipWith (\s p -> map (*p) s) solvedSubsets subsetOtherProducts
    where 
      n = length a

      -- Subset size. Require that 1 < s < n.
      s = ceiling $ sqrt $ fromIntegral n

      solvedSubsets = map otherProducts subsets
      subsetOtherProducts = otherProducts $ map product subsets

      subsets = reverse $ loop a []
          where loop [] acc = acc
                loop a acc = loop (drop s a) ((take s a):acc)