How Can I Generate Weighted Random Numbers Using the Boost Library?
Dec 20, 2024 pm 08:14 PM
Weighted Random Numbers in Boost
The Boost library provides extensive random number generation functionality. While it offers simple random number generation, it lacks built-in support for weighted random numbers. This article explores techniques for implementing weighted random number picking using raw Boost functions.
Weighted Randomization Algorithm
To generate a weighted random number, we can employ the following algorithm:
- Calculate the sum of all weights.
- Generate a random number between 0 and the sum of weights (inclusive).
- Iteratively subtract weights from the random number until it becomes negative. The index of the current item is the selected weighted number.
Adaptation to Boost
This algorithm can be adapted to Boost as follows:
using namespace boost;
int weightedRandom(const std::vector<int>& weights) {
// Calculate the sum of weights
int sum = accumulate(weights.begin(), weights.end(), 0);
// Generate a random number between 0 and the sum
variate_generator<mt19937, uniform_int_distribution<int>> rand(generator());
int rnd = rand(sum);
// Traverse weights and subtract until random number becomes negative
for (size_t i = 0; i < weights.size(); ++i) {
if (rnd - weights[i] < 0) {
return i;
}
rnd -= weights[i];
}
// Should never reach here
assert(false);
}
Optimization for Unchanging Weights
If weights are rarely changed and the list is reasonably long, we can optimize by precomputing cumulative weight sums and using binary search.
Reservoir Sampling for Unknown List Size
For lists with unknown size, reservoir sampling can be employed with weighted adaptations.
Conclusion
While Boost lacks a dedicated weighted random number function, the presented techniques enable efficient and accurate generation using raw Boost functions.
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