#include #include #include const float PI = 3.14159265358979323846; // Pseudo Random number generator uint32_t xorshift32 (uint32_t* seed) { // Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs" // See // https://en.wikipedia.org/wiki/Xorshift // Also some drama: , uint32_t x = *seed; x ^= x << 13; x ^= x >> 17; x ^= x << 5; return *seed = x; } // Distribution & sampling functions float rand_0_to_1(uint32_t* seed){ return ((float) xorshift32(seed)) / ((float) UINT32_MAX); } float rand_float(float max, uint32_t* seed) { return rand_0_to_1(seed) * max; } float ur_normal(uint32_t* seed) { float u1 = rand_0_to_1(seed); float u2 = rand_0_to_1(seed); float z = sqrtf(-2.0 * log(u1)) * sin(2 * PI * u2); return z; } float random_uniform(float from, float to, uint32_t* seed) { return rand_0_to_1(seed) * (to - from) + from; } float random_normal(float mean, float sigma, uint32_t* seed) { return (mean + sigma * ur_normal(seed)); } float random_lognormal(float logmean, float logsigma, uint32_t* seed) { return expf(random_normal(logmean, logsigma, seed)); } float random_to(float low, float high, uint32_t* seed) { const float NORMAL95CONFIDENCE = 1.6448536269514722; float loglow = logf(low); float loghigh = logf(high); float logmean = (loglow + loghigh) / 2; float logsigma = (loghigh - loglow) / (2.0 * NORMAL95CONFIDENCE); return random_lognormal(logmean, logsigma, seed); } // Array helpers float array_sum(float* array, int length) { float output = 0.0; for (int i = 0; i < length; i++) { output += array[i]; } return output; } void array_cumsum(float* array_to_sum, float* array_cumsummed, int length) { array_cumsummed[0] = array_to_sum[0]; for (int i = 1; i < length; i++) { array_cumsummed[i] = array_cumsummed[i - 1] + array_to_sum[i]; } } // Mixture function float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint32_t* seed) { // You can see a simpler version of this function in the git history // or in C-02-better-algorithm-one-thread/ float sum_weights = array_sum(weights, n_dists); float* cumsummed_normalized_weights = (float*) malloc(n_dists * sizeof(float)); cumsummed_normalized_weights[0] = weights[0]/sum_weights; for (int i = 1; i < n_dists; i++) { cumsummed_normalized_weights[i] = cumsummed_normalized_weights[i - 1] + weights[i]/sum_weights; } float result; int result_set_flag = 0; float p = random_uniform(0, 1, seed); for (int k = 0; k < n_dists; k++) { if (p < cumsummed_normalized_weights[k]) { result = samplers[k](seed); result_set_flag = 1; break; } } if(result_set_flag == 0) result = samplers[n_dists-1](seed); free(cumsummed_normalized_weights); return result; }