Given a list of non-overlapping axis-aligned rectangles rects
, write a function pick
which randomly and uniformily picks an integer point in the space covered by the rectangles.
Note:
- An integer point is a point that has integer coordinates.
- A point on the perimeter of a rectangle is included in the space covered by the rectangles.
i
th rectangle =rects[i]
=[x1,y1,x2,y2]
, where[x1, y1]
are the integer coordinates of the bottom-left corner, and[x2, y2]
are the integer coordinates of the top-right corner.- length and width of each rectangle does not exceed
2000
. 1 <= rects.length <= 100
pick
return a point as an array of integer coordinates[p_x, p_y]
pick
is called at most10000
times.
Example 1:
Input:
["Solution","pick","pick","pick"]
[[[[1,1,5,5]]],[],[],[]]
Output:
[null,[4,1],[4,1],[3,3]]
Example 2:
Input:
["Solution","pick","pick","pick","pick","pick"]
[[[[-2,-2,-1,-1],[1,0,3,0]]],[],[],[],[],[]]
Output:
[null,[-1,-2],[2,0],[-2,-1],[3,0],[-2,-2]]
Explanation of Input Syntax:
The input is two lists: the subroutines called and their arguments. Solution
's constructor has one argument, the array of rectangles rects
. pick
has no arguments. Arguments are always wrapped with a list, even if there aren't any.
以开始想到随机挑选一个rec然后随机选里面的点不就行了?但这样是不行的,因为rec面积不同,但你选他们的概率却是相同的,这样不符合random,因为对大面积的rec不公平。上面的链接解释了这一情况
class Solution { TreeMap<Integer, Integer> map; int[][] arrays; int sum; Random rnd= new Random(); public Solution(int[][] rects) { arrays = rects; map = new TreeMap<>(); sum = 0; for(int i = 0; i < rects.length; i++) { int[] rect = rects[i]; // the right part means the number of points can be picked in this rectangle sum += (rect[2] - rect[0] + 1) * (rect[3] - rect[1] + 1); map.put(sum, i); } } public int[] pick() { // nextInt(sum) returns a num in [0, sum -1]. After added by 1, it becomes [1, sum] int c = map.ceilingKey( rnd.nextInt(sum) + 1); return pickInRect(arrays[map.get(c)]); } private int[] pickInRect(int[] rect) { int left = rect[0], right = rect[2], bot = rect[1], top = rect[3]; return new int[]{left + rnd.nextInt(right - left + 1), bot + rnd.nextInt(top - bot + 1) }; } }
正确的方法:
像presum一样,把presum和对应的数组index存到map中,然后rand.nextInt(sum) + 1代表当前取到的随机sum,然后看他在哪个recindex里,随即返回那个rec里的一个点即可。
ceilingKey:return a key larger or equal than parameter.