398. Random Pick Index

本文介绍了一个使用蓄水池抽样算法解决随机返回数组中给定目标的所有出现索引之一的问题。该算法适用于数组可能非常大的情况,且不会消耗过多额外空间。

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Given an array of integers with possible duplicates, randomly output the index of a given target number. You can assume that the given target number must exist in the array.

Note:
The array size can be very large. Solution that uses too much extra space will not pass the judge.

Example:

int[] nums = new int[] {1,2,3,3,3};
Solution solution = new Solution(nums);

// pick(3) should return either index 2, 3, or 4 randomly. Each index should have equal probability of returning.
solution.pick(3);

// pick(1) should return 0. Since in the array only nums[0] is equal to 1.
solution.pick(1);

理论参考:Reservoir Sampling 蓄水池抽样算法,经典抽样


摘自

https://discuss.leetcode.com/topic/58301/simple-reservoir-sampling-solution

public class Solution {

    int[] nums;
    Random rnd;

    public Solution(int[] nums) {
        this.nums = nums;
        this.rnd = new Random();
    }
    
    public int pick(int target) {
        int result = -1;
        int count = 0;
        for (int i = 0; i < nums.length; i++) {
            if (nums[i] != target)
                continue;
            if (rnd.nextInt(++count) == 0)
                result = i;
        }
        
        return result;
    }
}```

Explanation:

public int pick(int target) {
int result = -1;
int count = 0; // to record how many targets in the array
for (int i = 0; i < nums.length; i++) {
if (nums[i] != target)
continue;
/*
For the nth target, ++count is n. Then the probability that rnd.nextInt(++count)==0 is 1/n. Thus, the probability that return nth target is 1/n.
For (n-1)th target, the probability of returning it is (n-1)/n * 1/(n-1)= 1/n.
*/
if (rnd.nextInt(++count) == 0)
result = i;
}
return result;
}


def spatially_regular_gen(): # Generator loop for i in range(num_per_epoch): # Choose the cloud with the lowest probability cloud_idx = int(np.argmin(self.min_possibility[split])) # choose the point with the minimum of possibility in the cloud as query point point_ind = np.argmin(self.possibility[split][cloud_idx]) # Get all points within the cloud from tree structure points = np.array(self.input_trees[split][cloud_idx].data, copy=False) # Center point of input region center_point = points[point_ind, :].reshape(1, -1) # Add noise to the center point noise = np.random.normal(scale=cfg.noise_init / 10, size=center_point.shape) pick_point = center_point + noise.astype(center_point.dtype) # Check if the number of points in the selected cloud is less than the predefined num_points if len(points) < cfg.num_points: # Query all points within the cloud queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=len(points))[1][0] else: # Query the predefined number of points queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=cfg.num_points)[1][0] # Shuffle index queried_idx = DP.shuffle_idx(queried_idx) # Get corresponding points and colors based on the index queried_pc_xyz = points[queried_idx] queried_pc_xyz = queried_pc_xyz - pick_point queried_pc_colors = self.input_colors[split][cloud_idx][queried_idx] queried_pc_labels = self.input_labels[split][cloud_idx][queried_idx] # Update the possibility of the selected points dists = np.sum(np.square((points[queried_idx] - pick_po
04-04
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