我的问题是参考this链接的方法2。这里给出了两个相等长度的排序数组,我们必须找到合并的两个数组的中位数。
Algorithm:
1) Calculate the medians m1 and m2 of the input arrays ar1[]
and ar2[] respectively.
2) If m1 and m2 both are equal then we are done.
return m1 (or m2)
3) If m1 is greater than m2, then median is present in one
of the below two subarrays.
a) From first element of ar1 to m1 (ar1[0...|_n/2_|])
b) From m2 to last element of ar2 (ar2[|_n/2_|...n-1])
4) If m2 is greater than m1, then median is present in one
of the below two subarrays.
a) From m1 to last element of ar1 (ar1[|_n/2_|...n-1])
b) From first element of ar2 to m2 (ar2[0...|_n/2_|])
5) Repeat the above process until size of both the subarrays
becomes 2.
6) If size of the two arrays is 2 then use below formula to get
the median.
Median = (max(ar1[0], ar2[0]) + min(ar1[1], ar2[1]))/2
Example:
ar1[] = {1, 12, 15, 26, 38}
ar2[] = {2, 13, 17, 30, 45}
For above two arrays m1 = 15 and m2 = 17
For the above ar1[] and ar2[], m1 is smaller than m2. So median is present in one of the following two subarrays.
[15, 26, 38] and [2, 13, 17]
Let us repeat the process for above two subarrays:
m1 = 26 m2 = 13.
m1 is greater than m2. So the subarrays become
[15, 26] and [13, 17]
Now size is 2, so median = (max(ar1[0], ar2[0]) + min(ar1[1], ar2[1]))/2
= (max(15, 13) + min(26, 17))/2
= (15 + 17)/2
= 16
我理解它们如何排除数组的一半,并说中间元素将特别是数组的一半,即步骤1,2,3,4,5 。
但我无法理解,他们怎么说合并后的数组的中位数将是修剪数组的一半后得到的合并数组的中位数,即 {1,12,15,26,38}和{2,13,17,30,45}的合并数组的中值将是{2,13,17}和{15,26}的合并数组的中值。 38}。
请解释一下。提前谢谢。
答案 0 :(得分:10)
让我帮你想象一下。让我们说它是案例3,其他案例也是如此。这意味着我们已经确定中位数存在于ar1的上半部分或ar2的后半部分。现在问题是为什么这两半的中位数与原始数组的中位数相同,正确。
因此可视化将这些相关的一半按排序顺序排列并找到其中位数。现在把另一半留在这张图片中,他们会去哪里。 ar2的前半部分,所有n / 2个元素都必须到达这个新中位数的顶部,而arr1的后半部分所有n / 2个元素都必须低于这个中位数(确切的位置对于中位数来说并不重要)。这意味着它仍然是一个中位数,因为在它上面和下面添加了相同数量的元素。因此,两个新一半的中位数与原始集合的中位数相同。
更准确地说,让我们看看为什么ar2的前半部分(剩下的一半)必须超过新的中位数。情况就是这样,因为当我们将所有元素放在一起时m2必须超过新的中位数(因为m2 仔细观察你的算法。虽然方法是正确的,但算法中可能会有轻微错误,同时处理奇数和偶数情况,因此在实施时要小心。
答案 1 :(得分:3)
......他们怎么说中位数呢 合并后的数组将是之后得到的合并数组的中位数 修剪数组的一半,即{1的合并数组的中值 12,15,26,38}和{2,13,17,30,45}将是中位数 合并{2,13,17}和{15,26,38}的数组。
这是因为你用来修剪一半的不平等以及中位数的定义。中位数将一组有序数字分成两半。
您知道15 <= 17
(第一组的中位数小于或等于第二组的中位数),因此中位数必须介于这两个值之间。任何小于15的东西都会被修剪,任何大于17的东西都会被修剪掉,因为它们不能包含中值(因为它们不会将这两个分割成两半)。然后你现在将相同的步骤应用于更窄的集合;修剪后,你的搜索量减半。
我尝试将其可视化为该示例。各个中位数用*标记,但基本情况除外,其中*标记用于计算该示例中的中位数的数字。
1 12 *15* 26 38 2 13 *17* 30 45
15 *26* 38 2 *13* 17
*15* 26 13 *17* <-- base case
16
还有其他基本案例,但只有少数。如果考虑到所有基本情况,您可以确保算法终止并返回正确的中位数。
我假设中位数是一个计算出的数字,它将一组分成两半 当总集合具有奇数个元素时,中值是该集合的数量。但是在偶数的情况下,有时你会发现它按照我在那个例子中显示的方式计算(但是如果你必须确保中位数来自集合,那么有时会选择较小的元素,在这种情况下它将是15)。
答案 2 :(得分:1)
对于可变长度,您只需检查特殊情况,即任何一个数组在每个递归级别中只有1个元素。如果其中一个这样,不要进一步划分,只是将它原样传递给另一个也变成长度为2.当给出最终答案处理时,其中一个只有1个元素。
$post = $this->_app->request->post();
$button = $post['delete']; //value of clicked button
$input= $post['delete1']; //value of first user
答案 3 :(得分:0)
由于长度约束相等,当我们比较两个中位数时,我们可以安全地丢弃值。
如果m2大于m1,我们知道数组2必须包含比数组1更大数量的值,因此只要我们丢弃相同数量的大值,所有m1以下的小值都不会有意义。从数组2开始。结果将是一个较短的数组,但我们正在寻找的中位数没有改变,因为我们从两边平均修剪。
它让我想起找到一个物体的质心,用双手远离它来支撑物体,然后将它们慢慢地放在一起,保持物体平衡。
答案 4 :(得分:0)
PHP解决方案:
function Solve( $aArrayOne, $aArrayTwo )
{
// Base case
if( empty( $aArrayOne ) || empty( $aArrayTwo ) )
{
return false;
}
$iCountOne = count( $aArrayOne );
$iCountTwo = count( $aArrayTwo );
// Single value arrays base case
if( $iCountOne === 1 && $iCountOne === $iCountTwo )
{
return ( $aArrayOne[ 0 ] + $aArrayTwo[ 0 ] ) / 2;
}
$iTotalElements = $iCountOne + $iCountTwo;
$iHalfElements = floor( $iTotalElements / 2 );
$aPartial = [];
$n = 0;
// Append elements to new combined array until midway point
while( $n <= $iHalfElements )
{
// Compared both of the first elements to get the
// smallest one into the partial array
if( $aArrayOne[ 0 ] <= $aArrayTwo[ 0 ] )
{
$aPartial[] = array_shift( $aArrayOne );
}
else
{
$aPartial[] = array_shift( $aArrayTwo );
}
++$n;
}
// Check to see if we have an odd or an even array for final element math.
$bIsOddAndPrecise = $iTotalElements % 2;
$iMedian = ( $bIsOddAndPrecise )
? $aPartial[ $n - 1 ]
: ( $aPartial[ $n - 1 ] + $aPartial[ $n - 2 ] ) / 2;
return $iMedian;
}
测试用例:
// $aArrayOne = [1, 3, 4 ];
// $aArrayTwo = [1, 2, 3 ];
// EXPECTED 1,1,2,3,3,4 -> (2+3)/2 2.5
// $aArrayOne = [1, 3, 4, 7, 8, 11, 44, 55, 62];
// $aArrayTwo = [2, 4, 5, 7, 33, 56, 77];
// Expected: 1,2,3,4,4,5,7,7,8,11,33,44,55,56,62,77 -> (7+8)/2 7.5
// $aArrayOne = [1, 3, 4 ];
// $aArrayTwo = [ 100, 100];
// EXPECTED 1,3,4,100,100 -> 4
// $aArrayOne = [1,5,8,10];
// $aArrayTwo = [7,9,14,];
// EXPECTED 1,2,7,8,9,10,14 - > 8
// $aArrayOne = [1,5,8,10];
// $aArrayTwo = [7];
// EXPECTED 1,5,7,8,10 - > 7
// $aArrayOne = [1,5,10];
// $aArrayTwo = [50, 50];
// EXPECTED 1,5,10,50,50 - > 10
// $aArrayOne = [50, 50];
// $aArrayTwo = [1,5,10];
// EXPECTED 1,5,10,50,50 - > 10
// $aArrayOne = [1];
// $aArrayTwo = [1];
// EXPECTED-> 1
// $aArrayOne = [100, 100];
// $aArrayTwo = [100];
// EXPECTED -> 100
答案 5 :(得分:0)
这是我的C#解决方案:
public double FindMedianSortedArrays(int [] nums1,int [] nums2){
List<int> sorted = new List<int>();
if(nums1.Length>nums2.Length){
for(int i=0; i<nums1.Length; i++){
sorted.Add(nums1[i]);
if(i<nums2.Length)
sorted.Add(nums2[i]);
}
}
else{
for(int i=0; i<nums2.Length; i++){
sorted.Add(nums2[i]);
if(i<nums1.Length)
sorted.Add(nums1[i]);
}
}
sorted.Sort();
if(sorted.Count % 2 !=0)
return (double)sorted[sorted.Count/2];
return (double)(sorted[sorted.Count/2-1]+ sorted[sorted.Count/2])/2;
}
答案 6 :(得分:0)
const findMedian = (arr1, arr2) => {
const len = arr1.length + arr2.length;
return len % 2 ? oddMedian(Math.floor(len/2), arr1, arr2) : evenMedian((len/2)-1, len/2, arr1, arr2);
}
const oddMedian = (medianIndex, arr1, arr2) => {
if (arr1[arr1.length-1] < arr2[0]) {
if (arr1.length > medianIndex) {
return arr1[medianIndex];
} else if (arr1.length <= medianIndex) {
return arr2[medianIndex - arr1.length];
}
} else if (arr2[arr2.length-1] < arr1[0]) {
if (arr2.length > medianIndex) {
return arr2[medianIndex];
} else if (arr2.length <= medianIndex) {
return arr1[medianIndex - arr2.length];
}
} else {
const [shorterArr, largerArr] = arr1.length < arr2.length ? [arr1, arr2] : [arr2, arr1];
let j = 0;
let k = 0;
const sortedArr = [];
for (let i = 0; i <= medianIndex; i++) {
if (shorterArr[j] <= largerArr[k]) {
sortedArr[i] = shorterArr[j];
j++;
} else {
sortedArr[i] = largerArr[k];
k++;
}
}
return sortedArr[medianIndex];
}
}
const evenMedian = (medianIndex1, medianIndex2, arr1, arr2) => {
if (arr1[arr1.length-1] < arr2[0]) {
if (arr1.length-1 >= medianIndex2) {
return (arr1[medianIndex1]+arr1[medianIndex2])/2;
} else if (arr1.length-1 < medianIndex1) {
const firstMedianIndex = medianIndex1 - arr1.length;
return (arr2[firstMedianIndex]+arr2[firstMedianIndex+1])/2;
} else {
return (arr1[arr1.length-1] + arr2[0])/2;
}
} else if (arr2[arr2.length-1] < arr1[0]) {
if (arr2.length-1 >= medianIndex2) {
return (arr2[medianIndex1]+arr2[medianIndex2])/2;
} else if (arr2.length-1 < medianIndex1) {
const firstMedianIndex = medianIndex1 - arr2.length;
return (arr1[firstMedianIndex]+arr1[firstMedianIndex+1])/2;
} else {
return (arr2[arr2.length-1] + arr1[0])/2;
}
} else {
const [shorterArr, largerArr] = arr1.length < arr2.length ? [arr1, arr2] : [arr2, arr1];
let i = 0;
let j = 0;
let k = 0;
const sortedArr = [];
for (let i = 0; i <= medianIndex2; i++) {
if (shorterArr[j] <= largerArr[k]) {
sortedArr.push(shorterArr[j]);
j++;
} else {
sortedArr.push(largerArr[k]);
k++;
}
}
return (sortedArr[medianIndex1] + sortedArr[medianIndex2])/2;
}
}
console.log("Result:", findMedian([1,3,5], [2,4,6,8]));
console.log("Result:", findMedian([1,3,5,7,10], [2,4,6,8]));
console.log("Result:", findMedian([1,3,5,7,10], [2,4,6,8,9]));
console.log("Result:", findMedian([1,3,5], [2,4,6,8,9]));
console.log("Result:", findMedian([1,3,5,7], [2,4,6,8,9,10]));
console.log("Result:", findMedian([1,3,5,7,10], [2,4,6]));
console.log("Result:", findMedian([1,3,5,7], [2,4]));
console.log("Result:", findMedian([1,2,4], [3,5,6,7,8,9,10,11]));
console.log("Result:", findMedian([1], [2, 3, 4]));
console.log("Result:", findMedian([1, 2], [3, 4]));
console.log("Result:", findMedian([1], [2, 3]));
Result: 4
Result: 5
Result: 5.5
Result: 4.5
Result: 5.5
Result: 4.5
Result: 3.5
Result: 6
Result: 2.5
Result: 2.5
Result: 2
答案 7 :(得分:0)
Java中两个数组的中位数
class Solution {
public double findMedianSortedArrays(int[] nums1, int[] nums2) {
int n1 = nums1.length;
int n2 = nums2.length;
double find =0;
ArrayList list = new ArrayList();
for(int i =0;i<n1;i++)
list.add(nums1[i]);
for(int j =0;j<n2;j++)
list.add(nums2[j]);
Collections.sort(list);
int n = list.size();
if(n%2 != 0)
{
find = (Integer)list.get(n/2);
}
else if(n%2==0){
find = (Integer)list.get(n/2-1)+(Integer)list.get(n/2);
find = find/2;
}
return find;
}
}
答案 8 :(得分:0)
Swift 解决方案 100% 有效,经过测试
//Given 2 sorted arrays Ar1 and Ar2 of size N each. Merge the given arrays and
find the sum of the two middle elements of the merged array.
func sumOfSortedArray(Ar1:[Int], Ar2:[Int],N:Int)->Int{
var newArray = [Int]()
newArray.append(contentsOf: Ar1)
newArray.append(contentsOf: Ar2)
newArray = newArray.sorted()
//this is how we can get middle index by total of both array divided by 2 and need to minus 1 because array always start with 0 not 1.
let middleElementIndex = ((N+N)/2) - 1
let sum = newArray[middleElementIndex] + newArray[middleElementIndex + 1]
print(sum)
return sum
}
答案 9 :(得分:-1)
@jayadev: 我不同意你的回答 “ ar2的前半部分,所有n / 2个元素都必须到达这个新中位数的顶部,而arr1的后半部分所有n / 2个元素都必须低于这个中位数”
考虑这个测试用例: a1 = {1,2,15,16,17} a2 = {4,5,10,18,20}
答案 10 :(得分:-1)
Here is a very simple solution.
Actually it need to merger two sorted array and then find the middle.
import java.util.Arrays;
public class MedianofTwoArray {
/**
* @param args
*/
public static void main(String[] args) {
int []array1= {1,2,3,4,5};
int []array2= {6,7,8,9,10};
int median;
median=findMedian(array1,array2);
System.out.println(median);
}
public static int findMedian(int []arr1,int []arr2) {
int [] tempArr=new int[arr1.length+arr2.length]; //creating an array of the length ,equals to sum of arr1 and arr2
int i=0;
int j=0;
int k=0;
while(i<arr1.length&&j<arr2.length) { /*comparing elements of the two arrays and copying the smaller one into tempArr and
incrementing the index of the array from which value is copied */
if(arr1[i]<=arr2[j]) {
tempArr[k]=arr1[i];
i++;
}else {
tempArr[k]=arr2[j];
j++;
}
k++;
}
//copying the left over elements from both arrays
if(i==arr1.length) {
while(j<arr2.length) {
tempArr[k]=arr2[j];
j++;
k++;
}
}else {
while(i<arr1.length) {
tempArr[k]=arr2[j];
j++;
k++;
}
}
System.out.println(Arrays.toString(tempArr));
return tempArr[tempArr.length/2];
}
}