Sunday, November 20, 2016

LeetCode - 462 Minimum Moves to Equal Array Elements II


LeetCode 453 - Minimum Moves to Equal Array Elements
http://bookshadow.com/weblog/2016/11/20/leetcode-minimum-moves-to-equal-array-elements-ii/
Given a non-empty integer array, find the minimum number of moves required to make all array elements equal, where a move is incrementing a selected element by 1 or decrementing a selected element by 1.
You may assume the array's length is at most 10,000.
Example:
Input:
[1,2,3]

Output:
2

Explanation:
Only two moves are needed (remember each move increments or decrements one element):

[1,2,3]  =>  [2,2,3]  =>  [2,2,2]
https://discuss.leetcode.com/topic/68736/java-just-like-meeting-point-problem
Need not to sort the entire array. Linear time QuickSelect is preferred here to compute median and solve this problem in O(n).
Suppose you have two endpoints A and B, when you want to find a point C that has minimum sum of distance between AC and BC, the point C will always between A and B. Draw a graph and you will understand it. Lets keep moving forward. After we locating the point C between A and B, we can define that
dis(AC) = c - a; dis(CB) = b - c;
sum = dis(AC) + dis(CB) = b - a.
b - a will be a constant value, given specific b and a. Thus there will be no difference between points among A and B.
In this problem, we set two boundaries, saying i and j, and we move the i and j to do the computation.
you can think the number in the array is the position in 1d array. so, we need to find a position that all positions can reach and the sum is minimum.
https://discuss.leetcode.com/topic/68762/java-solution-with-thinking-process/5
    public int minMoves2(int[] nums) {
        Arrays.sort(nums);
        int i = 0, j = nums.length-1;
        int count = 0;
        while(i < j){
            count += nums[j]-nums[i];
            i++;
            j--;
        }
        return count;
    }
    public int minMoves2(int[] nums) {
        Arrays.sort(nums);
        int res = 0, mid = nums.length/2;
        for(int i = 0; i < nums.length; i++){
            res += i > mid ? nums[i] - nums[mid] : nums[mid] - nums[i];
        }
        return res;
    }

求数组各元素与中位数差的绝对值之和
def minMoves2(self, nums): """ :type nums: List[int] :rtype: int """ nums.sort() median = nums[len(nums) / 2] return sum(abs(num - median) for num in nums)

参考《编程之美》 小飞的电梯调度算法 解析
def minMoves2(self, nums): """ :type nums: List[int] :rtype: int """ cnt = collections.Counter(nums) last, size = min(nums), len(nums) ans = mov = sum(nums) - last * size lo, hi = 0, size for k in sorted(cnt): mov += (lo - hi) * (k - last) hi -= cnt[k] lo += cnt[k] ans = min(ans, mov) last = k return ans

X. Quick Select
https://discuss.leetcode.com/topic/69236/o-n-solution-with-detailed-explanation
// Imagine the nums are sorted, and the final value is k, we start find k from the first element.
// If we increase k, the elements <= k will need move one step more, and the elements > k will need to move one step less.
// If there are more elements > k than elements <= k, we should increase k to minimize the moves.
// So we just increase k, until k reach the median of of the nums array. By then, the number of elements <= k equals to that of elements > k.
// (There is a slight different when the number of array is odd, but it's similar).
// If we keep increasing k after k reach the median of the array, more numbers >k than <= k, and more moves needed, so we should stop.
//
// The sort is not needed since we find the k is the median of the array, there is an average O(n) algorithm to find such k.

https://discuss.leetcode.com/topic/68758/java-o-n-time-using-quickselect
This solution relies on the fact that if we increment/decrement each element to the median of all the elements, the optimal number of moves is necessary. The median of all elements can be found in expected O(n) time using QuickSelect (or deterministic O(n) time using Median of Medians).
public int minMoves2(int[] nums) {
    int sum = 0;
    int median = findMedian(nums);
    for (int i=0;i<nums.length;i++) {
        sum += Math.abs(nums[i] - median);
    }
    return sum;
}

public int findMedian(int[] nums) {
    return getKth(nums.length/2+1, nums, 0, nums.length - 1);
}

public int getKth(int k, int[] nums, int start, int end) {
    int pivot = nums[end];
    int left = start;
    int right = end;

    while (true) {
        while (nums[left] < pivot && left < right) left++;
        while (nums[right] >= pivot && right > left) right--;
        if (left == right) break;
        swap(nums, left, right);
    }

    swap(nums, left, end);
    if (k == left + 1)  return pivot;
    else if (k < left + 1) return getKth(k, nums, start, left - 1);
    else return getKth(k, nums, left + 1, end);
}

public void swap(int[] nums, int n1, int n2) {
 int tmp = nums[n1];
 nums[n1] = nums[n2];
 nums[n2] = tmp;
}



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