forked from personal/squiggle.c
fix dumb beta sampling bug
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squiggle.c
86
squiggle.c
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@ -74,46 +74,48 @@ float sample_to(float low, float high, uint32_t* seed)
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return sample_lognormal(logmean, logsigma, seed);
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}
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float sample_gamma(float alpha, uint32_t* seed){
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float sample_gamma(float alpha, uint32_t* seed)
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{
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// A Simple Method for Generating Gamma Variables, Marsaglia and Wan Tsang, 2001
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// https://dl.acm.org/doi/pdf/10.1145/358407.358414
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// see also the references/ folder
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if(alpha >=1){
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float d, c, x, v, u;
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d = alpha - 1.0/3.0;
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c = 1.0/sqrt(9.0 * d);
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while(1){
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// A Simple Method for Generating Gamma Variables, Marsaglia and Wan Tsang, 2001
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// https://dl.acm.org/doi/pdf/10.1145/358407.358414
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// see also the references/ folder
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if (alpha >= 1) {
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float d, c, x, v, u;
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d = alpha - 1.0 / 3.0;
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c = 1.0 / sqrt(9.0 * d);
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while (1) {
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do {
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x = sample_unit_normal(seed);
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v = 1.0 + c * x;
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} while(v <= 0.0);
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do {
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x = sample_unit_normal(seed);
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v = 1.0 + c * x;
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} while (v <= 0.0);
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v = pow(v, 3);
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u = sample_unit_uniform(seed);
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if( u < 1.0 - 0.0331 * pow(x, 4)){ // Condition 1
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// the 0.0331 doesn't inspire much confidence
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// however, this isn't the whole story
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// by knowing that Condition 1 implies condition 2
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// we realize that this is just a way of making the algorithm faster
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// i.e., of not using the logarithms
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return d*v;
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}
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if(log(u) < 0.5*pow(x,2) + d*(1.0 - v + log(v))){ // Condition 2
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return d*v;
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}
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}
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}else{
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return sample_gamma(1 + alpha, seed) * pow(sample_unit_uniform(seed), 1/alpha);
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// see note in p. 371 of https://dl.acm.org/doi/pdf/10.1145/358407.358414
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}
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v = pow(v, 3);
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u = sample_unit_uniform(seed);
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if (u < 1.0 - 0.0331 * pow(x, 4)) { // Condition 1
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// the 0.0331 doesn't inspire much confidence
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// however, this isn't the whole story
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// by knowing that Condition 1 implies condition 2
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// we realize that this is just a way of making the algorithm faster
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// i.e., of not using the logarithms
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return d * v;
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}
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if (log(u) < 0.5 * pow(x, 2) + d * (1.0 - v + log(v))) { // Condition 2
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return d * v;
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}
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}
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} else {
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return sample_gamma(1 + alpha, seed) * pow(sample_unit_uniform(seed), 1 / alpha);
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// see note in p. 371 of https://dl.acm.org/doi/pdf/10.1145/358407.358414
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}
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}
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float sample_beta(float a, float b, uint32_t* seed){
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float gamma_a = sample_gamma(a, seed);
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float gamma_b = sample_gamma(b, seed);
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return a / (a + b);
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float sample_beta(float a, float b, uint32_t* seed)
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{
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float gamma_a = sample_gamma(a, seed);
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float gamma_b = sample_gamma(b, seed);
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return gamma_a / (gamma_a + gamma_b);
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}
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// Array helpers
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@ -134,18 +136,20 @@ void array_cumsum(float* array_to_sum, float* array_cumsummed, int length)
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}
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}
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float array_mean(float* array, int length){
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float sum = array_sum(array, length);
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return sum / length;
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float array_mean(float* array, int length)
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{
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float sum = array_sum(array, length);
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return sum / length;
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}
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float array_std(float* array, int length){
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float mean = array_mean(array, length);
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float array_std(float* array, int length)
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{
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float mean = array_mean(array, length);
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float std = 0.0;
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for (int i = 0; i < length; i++) {
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std += pow(array[i] - mean, 2.0);
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}
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std=sqrt(std/length);
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std = sqrt(std / length);
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return std;
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}
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