我在一次工作面试中被问到这个问题,我想知道其他人是如何解决这个问题的。我最擅长使用Java,但也欢迎使用其他语言的解决方案。

给定一个数字数组nums,返回一个数字数组products,其中products[i]是所有nums[j]的乘积,j != i。 输入:[1,2,3,4,5] 输出:[(2 * 3 * 4 * 5),(1 * 3 * 4 * 5),(1 * 2 * 4 * 5),(1 * 2 * 3 * 5),(1 * 2 * 3 * 4)] = [120, 60, 40, 30, 24] 你必须在O(N)中不使用除法来做这个。


当前回答

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("]");
     }

}

其他回答

下面是我尝试用Java来解决这个问题。抱歉格式不规范,但代码有很多重复,这是我能做的最好的,使它可读。

import java.util.Arrays;

public class Products {
    static int[] products(int... nums) {
        final int N = nums.length;
        int[] prods = new int[N];
        Arrays.fill(prods, 1);
        for (int
           i = 0, pi = 1    ,  j = N-1, pj = 1  ;
           (i < N)         && (j >= 0)          ;
           pi *= nums[i++]  ,  pj *= nums[j--]  )
        {
           prods[i] *= pi   ;  prods[j] *= pj   ;
        }
        return prods;
    }
    public static void main(String[] args) {
        System.out.println(
            Arrays.toString(products(1, 2, 3, 4, 5))
        ); // prints "[120, 60, 40, 30, 24]"
    }
}

循环不变量为pi = nums[0] * nums[1] *..* nums[N-2] *..num [j + 1]。左边的i部分是“前缀”逻辑,右边的j部分是“后缀”逻辑。


递归一行程序

Jasmeet给出了一个(漂亮的!)递归解;我把它变成了这样(可怕!)Java一行程序。它进行就地修改,堆栈中有O(N)个临时空间。

static int multiply(int[] nums, int p, int n) {
    return (n == nums.length) ? 1
      : nums[n] * (p = multiply(nums, nums[n] * (nums[n] = p), n + 1))
          + 0*(nums[n] *= p);
}

int[] arr = {1,2,3,4,5};
multiply(arr, 1, 0);
System.out.println(Arrays.toString(arr));
// prints "[120, 60, 40, 30, 24]"

下面是一个C实现 O(n)时间复杂度。 输入

#include<stdio.h>
int main()
{
    int x;
    printf("Enter The Size of Array : ");
    scanf("%d",&x);
    int array[x-1],i ;
    printf("Enter The Value of Array : \n");
      for( i = 0 ; i <= x-1 ; i++)
      {
          printf("Array[%d] = ",i);
          scanf("%d",&array[i]);
      }
    int left[x-1] , right[x-1];
    left[0] = 1 ;
    right[x-1] = 1 ;
      for( i = 1 ; i <= x-1 ; i++)
      {
          left[i] = left[i-1] * array[i-1];
      }
    printf("\nThis is Multiplication of array[i-1] and left[i-1]\n");
      for( i = 0 ; i <= x-1 ; i++)
      {
        printf("Array[%d] = %d , Left[%d] = %d\n",i,array[i],i,left[i]);
      }
      for( i = x-2 ; i >= 0 ; i--)
      {
          right[i] = right[i+1] * array[i+1];
      }
   printf("\nThis is Multiplication of array[i+1] and right[i+1]\n");
      for( i = 0 ; i <= x-1 ; i++)
      {
        printf("Array[%d] = %d , Right[%d] = %d\n",i,array[i],i,right[i]);
      }
    printf("\nThis is Multiplication of Right[i] * Left[i]\n");
      for( i = 0 ; i <= x-1 ; i++)
      {
          printf("Right[%d] * left[%d] = %d * %d = %d\n",i,i,right[i],left[i],right[i]*left[i]);
      }
    return 0 ;
}

输出

    Enter The Size of Array : 5
    Enter The Value of Array :
    Array[0] = 1
    Array[1] = 2
    Array[2] = 3
    Array[3] = 4
    Array[4] = 5

    This is Multiplication of array[i-1] and left[i-1]
    Array[0] = 1 , Left[0] = 1
    Array[1] = 2 , Left[1] = 1
    Array[2] = 3 , Left[2] = 2
    Array[3] = 4 , Left[3] = 6
    Array[4] = 5 , Left[4] = 24

    This is Multiplication of array[i+1] and right[i+1]
    Array[0] = 1 , Right[0] = 120
    Array[1] = 2 , Right[1] = 60
    Array[2] = 3 , Right[2] = 20
    Array[3] = 4 , Right[3] = 5
    Array[4] = 5 , Right[4] = 1

    This is Multiplication of Right[i] * Left[i]
    Right[0] * left[0] = 120 * 1 = 120
    Right[1] * left[1] = 60 * 1 = 60
    Right[2] * left[2] = 20 * 2 = 40
    Right[3] * left[3] = 5 * 6 = 30
    Right[4] * left[4] = 1 * 24 = 24

    Process returned 0 (0x0)   execution time : 6.548 s
    Press any key to continue.

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);

这是O(n²)但f#太漂亮了

List.fold (fun seed i -> List.mapi (fun j x -> if i=j+1 then x else x*i) seed) 
          [1;1;1;1;1]
          [1..5]
{-
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)