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Location: AENC/switchchain/cpp/powerlaw.hpp - annotation
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Add scatter plot of GCM success rate vs mixing time
bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 bfca8e3039c5 | #pragma once
#include <cassert>
#include <cmath>
#include <random>
// Discrete powerlaw distribution
// obtained by rounding a continuous powerlaw distribution
class powerlaw_distribution {
public:
// xmin and xmax are inclusive
powerlaw_distribution(float a, unsigned int _xmin, unsigned int _xmax)
: alpha(a), xmin(_xmin), xmax(_xmax) {
assert(xmin > 0);
assert(xmin <= xmax);
assert(alpha > 1.0f);
_exponent = -1.0f / (alpha - 1.0f);
}
~powerlaw_distribution() {}
template <class Generator>
unsigned int operator()(Generator& rng) const {
unsigned int x = xmax + 1;
while (x > xmax) {
// Generate uniform value in [0,1)
double r = std::generate_canonical<
double, std::numeric_limits<double>::digits>(rng);
x = std::round(std::pow(r, _exponent) * xmin);
}
return x;
}
private:
float alpha;
double _exponent;
unsigned int xmin, xmax;
};
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