use new pattern to reduce nested functions extension
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@ -4,12 +4,12 @@
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// Estimate functions
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// Estimate functions
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double sample_model(uint64_t* seed){
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double sample_0(uint64_t* seed) { UNUSED(seed); return 0; }
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double sample_1(uint64_t* seed) { UNUSED(seed); return 1; }
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double sample_few(uint64_t* seed) { return sample_to(1, 3, seed); }
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double sample_many(uint64_t* seed) { return sample_to(2, 10, seed); }
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double sample_0(uint64_t* seed) { UNUSED(seed); return 0; }
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double sample_model(uint64_t* seed){
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double sample_1(uint64_t* seed) { UNUSED(seed); return 1; }
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double sample_few(uint64_t* seed) { return sample_to(1, 3, seed); }
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double sample_many(uint64_t* seed) { return sample_to(2, 10, seed); }
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double p_a = 0.8;
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double p_a = 0.8;
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double p_b = 0.5;
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double p_b = 0.5;
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@ -2,37 +2,35 @@
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#include <stdio.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <stdlib.h>
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double sample_0(uint64_t* seed) { UNUSED(seed); return 0; }
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double sample_1(uint64_t* seed) { UNUSED(seed); return 1; }
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double sample_few(uint64_t* seed) { return sample_to(1, 3, seed); }
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double sample_many(uint64_t* seed) { return sample_to(2, 10, seed); }
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double sample_model(uint64_t* seed){
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double p_a = 0.8;
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double p_b = 0.5;
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double p_c = p_a * p_b;
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int n_dists = 4;
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double weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
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double (*samplers[])(uint64_t*) = { sample_0, sample_1, sample_few, sample_many };
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double result = sample_mixture(samplers, weights, n_dists, seed);
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return result;
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}
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int main()
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int main()
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{
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{
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// set randomness seed
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// set randomness seed
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uint64_t* seed = malloc(sizeof(uint64_t));
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uint64_t* seed = malloc(sizeof(uint64_t));
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*seed = 1000; // xorshift can't start with 0
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*seed = 1000; // xorshift can't start with 0
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double p_a = 0.8;
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double p_b = 0.5;
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double p_c = p_a * p_b;
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double sample_0(uint64_t * seed)
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{
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UNUSED(seed);
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return 0;
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}
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double sample_1(uint64_t * seed)
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{
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UNUSED(seed);
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return 1;
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}
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double sample_few(uint64_t * seed) { return sample_to(1, 3, seed); }
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double sample_many(uint64_t * seed) { return sample_to(2, 10, seed); }
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int n_dists = 4;
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double weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
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double (*samplers[])(uint64_t*) = { sample_0, sample_1, sample_few, sample_many };
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int n_samples = 1000000;
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int n_samples = 1000000;
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double* result_many = (double*)malloc((size_t)n_samples * sizeof(double));
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double* result_many = (double*)malloc((size_t)n_samples * sizeof(double));
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for (int i = 0; i < n_samples; i++) {
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for (int i = 0; i < n_samples; i++) {
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result_many[i] = sample_mixture(samplers, weights, n_dists, seed);
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result_many[i] = sample_model(seed);
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}
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}
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printf("Mean: %f\n", array_mean(result_many, n_samples));
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printf("Mean: %f\n", array_mean(result_many, n_samples));
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@ -2,39 +2,36 @@
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#include <stdio.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <stdlib.h>
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double sample_model(uint64_t* seed){
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double sample_0(uint64_t* seed) { UNUSED(seed); return 0; }
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// Using a gcc extension, you can define a function inside another function
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double sample_1(uint64_t* seed) { UNUSED(seed); return 1; }
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double sample_few(uint64_t* seed) { return sample_to(1, 3, seed); }
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double sample_many(uint64_t* seed) { return sample_to(2, 10, seed); }
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double p_a = 0.8;
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double p_b = 0.5;
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double p_c = p_a * p_b;
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int n_dists = 4;
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double weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
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double (*samplers[])(uint64_t*) = { sample_0, sample_1, sample_few, sample_many };
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double result = sample_mixture(samplers, weights, n_dists, seed);
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return result;
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}
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int main()
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int main()
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{
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{
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// set randomness seed
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// set randomness seed
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uint64_t* seed = malloc(sizeof(uint64_t));
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uint64_t* seed = malloc(sizeof(uint64_t));
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*seed = 1000; // xorshift can't start with 0
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*seed = 1000; // xorshift can't start with 0
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double p_a = 0.8;
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double p_b = 0.5;
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double p_c = p_a * p_b;
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int n_dists = 4;
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// These are nested functions. They will not compile without gcc.
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double sample_0(uint64_t * seed)
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{
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UNUSED(seed);
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return 0;
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}
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double sample_1(uint64_t * seed)
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{
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UNUSED(seed);
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return 1;
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}
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double sample_few(uint64_t * seed) { return sample_to(1, 3, seed); }
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double sample_many(uint64_t * seed) { return sample_to(2, 10, seed); }
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double (*samplers[])(uint64_t*) = { sample_0, sample_1, sample_few, sample_many };
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double weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
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int n_samples = 1000000;
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int n_samples = 1000000;
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double* result_many = (double*)malloc((size_t)n_samples * sizeof(double));
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double* result_many = (double*)malloc((size_t)n_samples * sizeof(double));
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for (int i = 0; i < n_samples; i++) {
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for (int i = 0; i < n_samples; i++) {
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result_many[i] = sample_mixture(samplers, weights, n_dists, seed);
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result_many[i] = sample_model(seed);
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}
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}
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printf("result_many: [");
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printf("result_many: [");
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printf("%.2f, ", result_many[i]);
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printf("%.2f, ", result_many[i]);
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}
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}
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printf("]\n");
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printf("]\n");
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free(seed);
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free(seed);
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}
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}
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#include <stdio.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <stdlib.h>
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// Estimate functions
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int main()
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int main()
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{
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{
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// set randomness seed
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// set randomness seed
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@ -11,33 +9,21 @@ int main()
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*seed = 1000; // xorshift can't start with 0
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*seed = 1000; // xorshift can't start with 0
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int n = 1000 * 1000;
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int n = 1000 * 1000;
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/*
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double* gamma_array = malloc(sizeof(double) * (size_t)n);
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for (int i = 0; i < n; i++) {
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double gamma_0 = sample_gamma(0.0, seed);
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// printf("sample_gamma(0.0): %f\n", gamma_0);
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}
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printf("\n");
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*/
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double* gamma_1_array = malloc(sizeof(double) * (size_t)n);
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for (int i = 0; i < n; i++) {
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for (int i = 0; i < n; i++) {
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double gamma_1 = sample_gamma(1.0, seed);
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gamma_array[i] = sample_gamma(1.0, seed);
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// printf("sample_gamma(1.0): %f\n", gamma_1);
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gamma_1_array[i] = gamma_1;
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}
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}
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printf("gamma(1) summary statistics = mean: %f, std: %f\n", array_mean(gamma_1_array, n), array_std(gamma_1_array, n));
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printf("gamma(1) summary statistics = mean: %f, std: %f\n", array_mean(gamma_array, n), array_std(gamma_array, n));
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free(gamma_1_array);
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printf("\n");
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printf("\n");
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double* beta_1_2_array = malloc(sizeof(double) * (size_t)n);
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double* beta_array = malloc(sizeof(double) * (size_t)n);
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for (int i = 0; i < n; i++) {
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for (int i = 0; i < n; i++) {
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double beta_1_2 = sample_beta(1, 2.0, seed);
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beta_array[i] = sample_beta(1, 2.0, seed);
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// printf("sample_beta(1.0, 2.0): %f\n", beta_1_2);
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beta_1_2_array[i] = beta_1_2;
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}
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}
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printf("beta(1,2) summary statistics: mean: %f, std: %f\n", array_mean(beta_1_2_array, n), array_std(beta_1_2_array, n));
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printf("beta(1,2) summary statistics: mean: %f, std: %f\n", array_mean(beta_array, n), array_std(beta_array, n));
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free(beta_1_2_array);
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printf("\n");
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printf("\n");
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free(gamma_array);
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free(beta_array);
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free(seed);
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free(seed);
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}
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}
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#include <stdio.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <stdlib.h>
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int main()
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double sample_fermi_logspace(uint64_t * seed)
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{
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{
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// Replicate <https://arxiv.org/pdf/1806.02404.pdf>, and in particular the red line in page 11.
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// Replicate <https://arxiv.org/pdf/1806.02404.pdf>, and in particular the red line in page 11.
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// You can see a simple version of this function in naive.c in this same folder
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double log_rate_of_star_formation = sample_uniform(log(1), log(100), seed);
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double log_fraction_of_stars_with_planets = sample_uniform(log(0.1), log(1), seed);
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double log_number_of_habitable_planets_per_star_system = sample_uniform(log(0.1), log(1), seed);
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double log_rate_of_life_formation_in_habitable_planets = sample_normal(1, 50, seed);
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double log_fraction_of_habitable_planets_in_which_any_life_appears;
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/*
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Consider:
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a = underlying normal
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b = rate_of_life_formation_in_habitable_planets = exp(underlying normal) = exp(a)
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c = 1 - exp(-b) = fraction_of_habitable_planets_in_which_any_life_appears
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d = log(c)
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Looking at the Taylor expansion for c = 1 - exp(-b), it's
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b - b^2/2 + b^3/6 - x^b/24, etc.
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<https://www.wolframalpha.com/input?i=1-exp%28-x%29>
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When b ~ 0 (as is often the case), this is close to b.
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But now, if b ~ 0, c ~ b
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and d = log(c) ~ log(b) = log(exp(a)) = a
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Now, we could play around with estimating errors,
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and indeed if we want b^2/2 = exp(a)^2/2 < 10^(-n), i.e., to have n decimal digits of precision,
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we could compute this as e.g., a < (nlog(10) + log(2))/2
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so for example if we want ten digits of precision, that's a < -11
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Empirically, the two numbers as calculated in C do become really close around 11 or so,
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and at 38 that calculation results in a -inf (so probably a floating point error or similar.)
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So we should be using that formula for somewhere between -38 << a < -11
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I chose -16 as a happy medium after playing around with
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double invert(double x){
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return log(1-exp(-exp(-x)));
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}
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for(int i=0; i<64; i++){
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double j = i;
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printf("for %lf, log(1-exp(-exp(-x))) is calculated as... %lf\n", j, invert(j));
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}
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and <https://www.wolframalpha.com/input?i=log%281-exp%28-exp%28-16%29%29%29>
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*/
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if (log_rate_of_life_formation_in_habitable_planets < -16) {
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log_fraction_of_habitable_planets_in_which_any_life_appears = log_rate_of_life_formation_in_habitable_planets;
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} else {
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double rate_of_life_formation_in_habitable_planets = exp(log_rate_of_life_formation_in_habitable_planets);
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double fraction_of_habitable_planets_in_which_any_life_appears = -expm1(-rate_of_life_formation_in_habitable_planets);
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log_fraction_of_habitable_planets_in_which_any_life_appears = log(fraction_of_habitable_planets_in_which_any_life_appears);
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}
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double log_fraction_of_planets_with_life_in_which_intelligent_life_appears = sample_uniform(log(0.001), log(1), seed);
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double log_fraction_of_intelligent_planets_which_are_detectable_as_such = sample_uniform(log(0.01), log(1), seed);
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double log_longevity_of_detectable_civilizations = sample_uniform(log(100), log(10000000000), seed);
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double log_n =
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log_rate_of_star_formation +
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log_fraction_of_stars_with_planets +
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log_number_of_habitable_planets_per_star_system +
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log_fraction_of_habitable_planets_in_which_any_life_appears +
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log_fraction_of_planets_with_life_in_which_intelligent_life_appears +
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log_fraction_of_intelligent_planets_which_are_detectable_as_such +
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log_longevity_of_detectable_civilizations;
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return log_n;
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}
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double sample_are_we_alone_logspace(uint64_t * seed)
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{
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double log_n = sample_fermi_logspace(seed);
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return ((log_n > 0) ? 1 : 0);
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// log_n > 0 => n > 1
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}
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int main()
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{
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// set randomness seed
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// set randomness seed
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uint64_t* seed = malloc(sizeof(uint64_t));
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uint64_t* seed = malloc(sizeof(uint64_t));
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*seed = 1001; // xorshift can't start with a seed of 0
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*seed = 1001; // xorshift can't start with a seed of 0
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double sample_fermi_logspace(uint64_t * seed)
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{
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// You can see a simple version of this function in naive.c in this same folder
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double log_rate_of_star_formation = sample_uniform(log(1), log(100), seed);
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double log_fraction_of_stars_with_planets = sample_uniform(log(0.1), log(1), seed);
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double log_number_of_habitable_planets_per_star_system = sample_uniform(log(0.1), log(1), seed);
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double log_rate_of_life_formation_in_habitable_planets = sample_normal(1, 50, seed);
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double log_fraction_of_habitable_planets_in_which_any_life_appears;
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/*
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Consider:
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a = underlying normal
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b = rate_of_life_formation_in_habitable_planets = exp(underlying normal) = exp(a)
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c = 1 - exp(-b) = fraction_of_habitable_planets_in_which_any_life_appears
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d = log(c)
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Looking at the Taylor expansion for c = 1 - exp(-b), it's
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b - b^2/2 + b^3/6 - x^b/24, etc.
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<https://www.wolframalpha.com/input?i=1-exp%28-x%29>
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When b ~ 0 (as is often the case), this is close to b.
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But now, if b ~ 0, c ~ b
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and d = log(c) ~ log(b) = log(exp(a)) = a
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Now, we could play around with estimating errors,
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and indeed if we want b^2/2 = exp(a)^2/2 < 10^(-n), i.e., to have n decimal digits of precision,
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we could compute this as e.g., a < (nlog(10) + log(2))/2
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so for example if we want ten digits of precision, that's a < -11
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Empirically, the two numbers as calculated in C do become really close around 11 or so,
|
|
||||||
and at 38 that calculation results in a -inf (so probably a floating point error or similar.)
|
|
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So we should be using that formula for somewhere between -38 << a < -11
|
|
||||||
|
|
||||||
I chose -16 as a happy medium after playing around with
|
|
||||||
double invert(double x){
|
|
||||||
return log(1-exp(-exp(-x)));
|
|
||||||
}
|
|
||||||
for(int i=0; i<64; i++){
|
|
||||||
double j = i;
|
|
||||||
printf("for %lf, log(1-exp(-exp(-x))) is calculated as... %lf\n", j, invert(j));
|
|
||||||
}
|
|
||||||
and <https://www.wolframalpha.com/input?i=log%281-exp%28-exp%28-16%29%29%29>
|
|
||||||
*/
|
|
||||||
if (log_rate_of_life_formation_in_habitable_planets < -16) {
|
|
||||||
log_fraction_of_habitable_planets_in_which_any_life_appears = log_rate_of_life_formation_in_habitable_planets;
|
|
||||||
} else {
|
|
||||||
double rate_of_life_formation_in_habitable_planets = exp(log_rate_of_life_formation_in_habitable_planets);
|
|
||||||
double fraction_of_habitable_planets_in_which_any_life_appears = -expm1(-rate_of_life_formation_in_habitable_planets);
|
|
||||||
log_fraction_of_habitable_planets_in_which_any_life_appears = log(fraction_of_habitable_planets_in_which_any_life_appears);
|
|
||||||
}
|
|
||||||
|
|
||||||
double log_fraction_of_planets_with_life_in_which_intelligent_life_appears = sample_uniform(log(0.001), log(1), seed);
|
|
||||||
double log_fraction_of_intelligent_planets_which_are_detectable_as_such = sample_uniform(log(0.01), log(1), seed);
|
|
||||||
double log_longevity_of_detectable_civilizations = sample_uniform(log(100), log(10000000000), seed);
|
|
||||||
|
|
||||||
double log_n =
|
|
||||||
log_rate_of_star_formation +
|
|
||||||
log_fraction_of_stars_with_planets +
|
|
||||||
log_number_of_habitable_planets_per_star_system +
|
|
||||||
log_fraction_of_habitable_planets_in_which_any_life_appears +
|
|
||||||
log_fraction_of_planets_with_life_in_which_intelligent_life_appears +
|
|
||||||
log_fraction_of_intelligent_planets_which_are_detectable_as_such +
|
|
||||||
log_longevity_of_detectable_civilizations;
|
|
||||||
return log_n;
|
|
||||||
}
|
|
||||||
|
|
||||||
double sample_are_we_alone_logspace(uint64_t * seed)
|
|
||||||
{
|
|
||||||
double log_n = sample_fermi_logspace(seed);
|
|
||||||
return ((log_n > 0) ? 1 : 0);
|
|
||||||
// log_n > 0 => n > 1
|
|
||||||
}
|
|
||||||
|
|
||||||
double logspace_fermi_proportion = 0;
|
double logspace_fermi_proportion = 0;
|
||||||
int n_samples = 1000 * 1000;
|
int n_samples = 1000 * 1000;
|
||||||
for (int i = 0; i < n_samples; i++) {
|
for (int i = 0; i < n_samples; i++) {
|
||||||
|
|
Loading…
Reference in New Issue
Block a user