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The rejection sampling method involves generating a larger range of values than needed using multiple calls to rand7(), then rejecting any values that fall outside the desired range. The goal is to convert an output range span (like 1 to 7) to another (like 1 to 10). You can do this by mapping combinations of rand7() outputs together.
The idea is to create a smaller uniform distribution using a larger one and then scale it down to the desired range.
The expected time complexity is O(1), but the actual time depends on the success rate of generating a number that falls within the desired range. The space complexity is O(1) since we only use a limited amount of storage regardless of input size.
1int rand7();
2int rand10() {
3 int result;
4 do {
5 int row = rand7();
6 int col = rand7();
7 result = (row - 1) * 7 + col;
8 } while (result > 40);
9 return 1 + (result - 1) % 10;
10}This C++ solution uses a similar rejection sampling technique as in C language. By combining calls to rand7(), it achieves a 49-wide number range and then scales it down to produce numbers between 1 and 10.
This approach explores random walks over a 2D grid formed by two calls to rand7(). By defining a grid dimension and using rerolls for values that fall outside of a specific boundary, it effectively resamples portions of the distribution space.
We consider a grid of 7x7 points and use this combined space to extract valid outcomes for our 1-10 range.
The average time complexity is O(1) due to expected constant writes, but can vary based on rerolls. The space used is fixed so the space complexity is O(1).
Python implements this approach using a two-layer grid where each cell generated ultimately leads to a suitable number range through rejections/re-rolls directed at errors outside 1-40.