// // FILE: Prandom.cpp // AUTHOR: Rob dot Tillaart at gmail dot com // VERSION: 0.1.5 // PURPOSE: Arduino library for random number generation with Python random interface // URL: https://github.com/RobTillaart/Prandom // // code based upon Python implementation although some small // optimizations and tweaks were needed to get it working. #include "Prandom.h" Prandom::Prandom() { seed(); } Prandom::Prandom(uint32_t s) { seed(s); } void Prandom::seed() { // no argument ==> time based. seed(_rndTime()); } void Prandom::seed(uint32_t s, uint32_t t) { // set Marsaglia constants, prevent 0 as value if (s == 0) s = 1; if (t == 0) t = 2; _m_w = s; _m_z = t; } uint32_t Prandom::getrandbits(uint8_t n) { uint8_t shift = min(31, n - 1); return _rnd(1UL << shift); } uint32_t Prandom::randrange(uint32_t stop) { return _rnd(stop); } uint32_t Prandom::randrange(uint32_t start, uint32_t stop, uint32_t step) { if (step == 1) return start + _rnd(stop - start); return start + step * _rnd((stop - start + step - 1) / step); } // returns value between 0 and top which defaults to 1.0 // the parameter does not exist in Python // note: not all possible (0xFFFFFFFF) values are used // function has an uniform distribution. float Prandom::random(const float top) { if (top == 0) return 0; float f = (top * __random()) / 0xFFFFFFFF; return f; } float Prandom::uniform(float lo, float hi) { if (lo == hi) return lo; return lo + random(hi - lo); } float Prandom::triangular(float lo, float hi, float mid) { if (lo == hi) return lo; float val = random(); if (val > mid) { val = 1 - val; mid = 1 - mid; float t = hi; hi = lo; lo = t; } return lo + (hi - lo) * sqrt(val * mid); } // minor optimization. float Prandom::normalvariate(float mu, float sigma) { // const float NV_MAGICCONST = 4 * exp(-0.5)/sqrt(2.0); const float NV_MAGICCONST = 2 * exp(-0.5) / sqrt(2.0); float u1, u2, z; while (true) { u1 = random(); u2 = 1 - random(); z = NV_MAGICCONST * (u1 - 0.5) / u2 ; // if ((z * z / 4) <= -log(u2)) break; if ((z * z) <= -log(u2)) break; } return z * sigma + mu; } float Prandom::lognormvariate(float mu, float sigma) { return exp(normalvariate(mu, sigma)); } // implemented slightly differently float Prandom::gauss(float mu, float sigma) { static bool generate = false; static float next = 0; float z = 0; generate = !generate; if (generate == false) { z = next; } else { float x2pi = random(TWO_PI); float g2rad = sqrt( -2.0 * log(1.0 - random())); z = cos(x2pi) * g2rad; next = sin(x2pi) * g2rad; } return z * sigma + mu; }; float Prandom::expovariate(float lambda) { return -log(1.0 - random()) / lambda; } // alpha & beta > 0 float Prandom::gammavariate(float alpha, float beta) { const float LOG4 = log(4); const float SG_MAGICCONST = 1.0 + log(4.5); if (alpha > 1.0) { // # Uses R.C.H. Cheng, "The generation of Gamma // # variables with non-integral shape parameters", // # Applied Statistics, (1977), 26, No. 1, p71-74 float ainv = sqrt(2.0 * alpha - 1.0); float bbb = alpha - LOG4; float ccc = alpha + ainv; float u1, u2, v, x, z, r; while (true) { u1 = random(); if (u1 < 1e-7) continue; if (u1 > 0.9999999) continue; // needed? u2 = 1.0 - random(); v = log(u1 / (1.0 - u1)) / ainv; x = alpha * exp(v); z = u1 * u1 * u2; r = bbb + ccc * v - x; if ( ( (r + SG_MAGICCONST - 4.5 * z) >= 0.0) || (r >= log(z)) ) { return x * beta; } } } else if (alpha == 1.0) { return -log(1.0 - random()) * beta; } else // alpha in 0..1 { // # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle float u, b, p, x, u1; while (true) { u = random(); b = (EULER + alpha) / EULER; p = b * u; if ( p <= 1.0) x = pow(p, (1.0 / alpha)); else x = -log((b - p) / alpha); u1 = random(); if (p > 1.0) { if (u1 <= pow(x, (alpha - 1.0))) break; } else { if (u1 <= exp(-x)) break; } } return x * beta; } } float Prandom::betavariate(float alpha, float beta) { float y = gammavariate(alpha, 1.0); if (y == 0) return 0.0; return y / (y + gammavariate(beta, 1.0)); }; float Prandom::paretovariate(float alpha) { float u = 1 - random(); return pow(u, (-1.0 / alpha)); } float Prandom::weibullvariate(float alpha, float beta) { float u = 1 - random(); return alpha * pow(-log(u), 1.0 / beta); } float Prandom::vonmisesvariate(float mu, float kappa) { if (kappa <= 1e-6) return TWO_PI * random(); float s = 0.5 / kappa; float r = s + sqrt(1.0 + s * s); float u1, u2, u3, z, d, q, f, theta; do { u1 = random(); z = cos(PI * u1); d = z / (r + z); u2 = random(); } while ( ( u2 >= 1.0 - d * d ) && (u2 > (1.0 - d) * exp(d)) ); q = 1.0 / r; f = (q + z) / (1.0 + q * z); u3 = random(); if (u3 > 0.5) theta = mu + acos(f); else theta = mu - acos(f); while (theta < 0) theta += TWO_PI; while (theta > TWO_PI) theta -= TWO_PI; return theta; } //////////////////////////////////////////////////////////////////////////// // // PRIVATE // uint32_t Prandom::_rndTime() { return (micros() + (micros() >> 2) ) ^ (millis()); } // TODO how to guarantee it uniform between 0 .. n-1 uint32_t Prandom::_rnd(uint32_t n) { // float formula works fastest but it looses precision for large values of n // as floats have only 23 bit mantissa uint32_t val = __random(); if (n > 0x003FFFFF) return val % n; // distribution will fail here return (n * 1.0 * val) / 0xFFFFFFFF; } // An example of a simple pseudo-random number generator is the // Multiply-with-carry method invented by George Marsaglia. // two initializers (not null) uint32_t Prandom::__random() { _m_z = 36969L * (_m_z & 65535L) + (_m_z >> 16); _m_w = 18000L * (_m_w & 65535L) + (_m_w >> 16); return (_m_z << 16) + _m_w; /* 32-bit result */ } // -- END OF FILE --