Revert "Revert "Merge branch 'master' into quickselect""

This reverts commit 4d218468cf.
This commit is contained in:
NunoSempere 2023-11-29 23:21:24 +00:00
parent fb123dd14c
commit 58a329bcc3
40 changed files with 298 additions and 278 deletions

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@ -12,10 +12,10 @@ int main()
double p_b = 0.5;
double p_c = p_a * p_b;
double sample_0(uint64_t* seed){ return 0; }
double sample_1(uint64_t* seed) { return 1; }
double sample_few(uint64_t* seed) { return sample_to(1, 3, seed); }
double sample_many(uint64_t* seed) { return sample_to(2, 10, seed); }
double sample_0(uint64_t * seed) { return 0; }
double sample_1(uint64_t * seed) { return 1; }
double sample_few(uint64_t * seed) { return sample_to(1, 3, seed); }
double sample_many(uint64_t * seed) { return sample_to(2, 10, seed); }
int n_dists = 4;
double weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };

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@ -15,8 +15,9 @@ int main()
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with 0
ci beta_1_2_ci_90 = get_90_confidence_interval(beta_1_2_sampler, seed);
ci beta_1_2_ci_90 = sampler_get_90_ci(beta_1_2_sampler, 1000000, seed);
printf("90%% confidence interval of beta(1,2) is [%f, %f]\n", beta_1_2_ci_90.low, beta_1_2_ci_90.high);
printf("You can check this in <https://nunosempere.com/blog/2023/03/15/fit-beta/>\n");
free(seed);
}

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@ -60,7 +60,7 @@ int main()
}
printf("... ]\n");
ci ci_90 = get_90_confidence_interval(mixture, seed);
ci ci_90 = sampler_get_90_ci(mixture, 1000000, seed);
printf("mean: %f\n", array_mean(mixture_result, n));
printf("90%% confidence interval: [%f, %f]\n", ci_90.low, ci_90.high);

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@ -41,7 +41,7 @@ int main()
}
printf("... ]\n");
ci ci_90 = get_90_confidence_interval(sample_minutes_per_day_jumping_rope_needed_to_burn_10kg, seed);
ci ci_90 = sampler_get_90_ci(sample_minutes_per_day_jumping_rope_needed_to_burn_10kg, 1000000, seed);
printf("90%% confidence interval: [%f, %f]\n", ci_90.low, ci_90.high);
free(seed);

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@ -50,7 +50,7 @@ int main()
// Before a first nuclear collapse
printf("## Before the first nuclear collapse\n");
ci ci_90_2023 = get_90_confidence_interval(yearly_probability_nuclear_collapse_2023, seed);
ci ci_90_2023 = sampler_get_90_ci(yearly_probability_nuclear_collapse_2023, 1000000, seed);
printf("90%% confidence interval: [%f, %f]\n", ci_90_2023.low, ci_90_2023.high);
double* yearly_probability_nuclear_collapse_2023_samples = malloc(sizeof(double) * num_samples);
@ -61,7 +61,7 @@ int main()
// After the first nuclear collapse
printf("\n## After the first nuclear collapse\n");
ci ci_90_2070 = get_90_confidence_interval(yearly_probability_nuclear_collapse_after_recovery_example, seed);
ci ci_90_2070 = sampler_get_90_ci(yearly_probability_nuclear_collapse_after_recovery_example, 1000000, seed);
printf("90%% confidence interval: [%f, %f]\n", ci_90_2070.low, ci_90_2070.high);
double* yearly_probability_nuclear_collapse_after_recovery_samples = malloc(sizeof(double) * num_samples);
@ -72,7 +72,7 @@ int main()
// After the first nuclear collapse (antiinductive)
printf("\n## After the first nuclear collapse (antiinductive)\n");
ci ci_90_antiinductive = get_90_confidence_interval(yearly_probability_nuclear_collapse_after_recovery_antiinductive, seed);
ci ci_90_antiinductive = sampler_get_90_ci(yearly_probability_nuclear_collapse_after_recovery_antiinductive, 1000000, seed);
printf("90%% confidence interval: [%f, %f]\n", ci_90_antiinductive.low, ci_90_antiinductive.high);
double* yearly_probability_nuclear_collapse_after_recovery_antiinductive_samples = malloc(sizeof(double) * num_samples);

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@ -9,21 +9,22 @@ int main()
// set randomness seed
// uint64_t* seed = malloc(sizeof(uint64_t));
// *seed = 1000; // xorshift can't start with 0
// ^ not necessary, because parallel_sampler takes care of the seed.
// ^ not necessary, because sampler_parallel takes care of the seed.
int n_samples = 1000 * 1000 * 1000;
int n_threads = 16;
double sampler(uint64_t* seed){
double sampler(uint64_t * seed)
{
return sample_lognormal(0, 10, seed);
}
double* results = malloc(n_samples * sizeof(double));
parallel_sampler(sampler, results, n_threads, n_samples);
double avg = array_sum(results, n_samples)/n_samples;
sampler_parallel(sampler, results, n_threads, n_samples);
double avg = array_sum(results, n_samples) / n_samples;
printf("Average of 1B lognormal(0,10): %f", avg);
free(results);
// free(seed);
// ^ not necessary, because parallel_sampler takes care of the seed.
// ^ not necessary, because sampler_parallel takes care of the seed.
}

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@ -9,21 +9,22 @@ int main()
double p_b = 0.5;
double p_c = p_a * p_b;
double sample_0(uint64_t* seed){ return 0; }
double sample_1(uint64_t* seed) { return 1; }
double sample_few(uint64_t* seed) { return sample_to(1, 3, seed); }
double sample_many(uint64_t* seed) { return sample_to(2, 10, seed); }
double sample_0(uint64_t * seed) { return 0; }
double sample_1(uint64_t * seed) { return 1; }
double sample_few(uint64_t * seed) { return sample_to(1, 3, seed); }
double sample_many(uint64_t * seed) { return sample_to(2, 10, seed); }
int n_dists = 4;
double weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
double (*samplers[])(uint64_t*) = { sample_0, sample_1, sample_few, sample_many };
double sampler_result(uint64_t* seed) {
double sampler_result(uint64_t * seed)
{
return sample_mixture(samplers, weights, n_dists, seed);
}
}
int n_samples = 1000 * 1000, n_threads = 16;
double* results = malloc(n_samples * sizeof(double));
parallel_sampler(sampler_result, results, n_threads, n_samples);
printf("Avg: %f\n", array_sum(results, n_samples)/n_samples);
sampler_parallel(sampler_result, results, n_threads, n_samples);
printf("Avg: %f\n", array_sum(results, n_samples) / n_samples);
free(results);
}

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@ -13,57 +13,58 @@ int main()
/* Option 1: parallelize taking from n samples */
// Question being asked: what is the distribution of sampling 1000 times and taking the min?
double sample_min_of_n(uint64_t* seed, int n){
double sample_min_of_n(uint64_t * seed, int n)
{
double min = sample_normal(5, 2, seed);
for(int i=0; i<(n-1); i++){
for (int i = 0; i < (n - 2); i++) {
double sample = sample_normal(5, 2, seed);
if(sample < min){
if (sample < min) {
min = sample;
}
}
return min;
}
double sampler_min_of_1000(uint64_t* seed) {
double sample_min_of_1000(uint64_t * seed)
{
return sample_min_of_n(seed, 1000);
}
}
int n_samples = 10000, n_threads = 16;
int n_samples = 1000000, n_threads = 16;
double* results = malloc(n_samples * sizeof(double));
parallel_sampler(sampler_min_of_1000, results, n_threads, n_samples);
sampler_parallel(sample_min_of_1000, results, n_threads, n_samples);
printf("Mean of the distribution of (taking the min of 1000 samples of a normal(5,2)): %f\n", array_mean(results, n_samples));
free(results);
/* Option 2: take the min from n samples cleverly using parallelism */
// Question being asked: can we take the min of n samples cleverly?
double sample_n_parallel(int n){
double sample_n_parallel(int n)
{
int n_threads = 16;
int quotient = n / 16;
int remainder = n % 16;
uint64_t seed = 100;
uint64_t seed = 1000;
double result_remainder = sample_min_of_n(&seed, remainder);
double sample_min_of_quotient(uint64_t* seed) {
double result = sample_min_of_n(seed, quotient);
// printf("Result: %f\n", result);
return result;
}
double* results = malloc(n_threads * sizeof(double));
parallel_sampler(sample_min_of_quotient, results, n_threads, n_threads);
double min = results[0];
for(int i=1; i<n_threads; i++){
if(min > results[i]){
min = results[i];
double sample_min_of_quotient(uint64_t * seed)
{
return sample_min_of_n(seed, quotient);
}
double* results_quotient = malloc(quotient * sizeof(double));
sampler_parallel(sample_min_of_quotient, results_quotient, n_threads, quotient);
double min = results_quotient[0];
for (int i = 1; i < quotient; i++) {
if (min > results_quotient[i]) {
min = results_quotient[i];
}
}
if(min > result_remainder){
if (min > result_remainder) {
min = result_remainder;
}
free(results);
free(results_quotient);
return min;
}
printf("Minimum of 10M samples of normal(5,2): %f\n", sample_n_parallel(1000 * 1000));
printf("Minimum of 1M samples of normal(5,2): %f\n", sample_n_parallel(1000000));
}

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@ -0,0 +1,21 @@
#include "../../../squiggle.h"
#include "../../../squiggle_more.h"
#include <stdio.h>
#include <stdlib.h>
int main()
{
// set randomness seed
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with a seed of 0
int n = 1000000;
double* xs = malloc(sizeof(double) * n);
for (int i = 0; i < n; i++) {
xs[i] = sample_to(10, 100, seed);
}
ci ci_90 = array_get_90_ci(xs, n);
printf("Recovering confidence interval of sample_to(10, 100):\n low: %f, high: %f\n", ci_90.low, ci_90.high);
free(seed);
}

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@ -48,7 +48,8 @@ all:
$(CC) $(OPTIMIZED) $(DEBUG) 10_twitter_thread_example/$(SRC) $(DEPS) -o 10_twitter_thread_example/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) 11_billion_lognormals_paralell/$(SRC) $(DEPS) -o 11_billion_lognormals_paralell/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) 12_time_to_botec_parallel/$(SRC) $(DEPS) -o 12_time_to_botec_parallel/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) 13_parallelize_min/$(SRC) $(DEPS) -o 13_parallelize_min/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) 13_parallelize_min/$(SRC) $(DEPS) -o 13_parallelize_min/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) 14_check_confidence_interval/$(SRC) $(DEPS) -o 14_check_confidence_interval/$(OUTPUT)
format-all:
$(FORMATTER) 00_example_template/$(SRC)
@ -65,6 +66,7 @@ format-all:
$(FORMATTER) 11_billion_lognormals_paralell/$(SRC)
$(FORMATTER) 12_time_to_botec_parallel/$(SRC)
$(FORMATTER) 13_parallelize_min/$(SRC)
$(FORMATTER) 14_check_confidence_interval/$(SRC)
run-all:
00_example_template/$(OUTPUT)
@ -81,6 +83,7 @@ run-all:
11_billion_lognormals_paralell/$(OUTPUT)
12_time_to_botec_parallel/$(OUTPUT)
13_parallelize_min/$(OUTPUT)
14_check_confidence_interval/$(OUTPUT)
## make one DIR=06_nuclear_recovery
one: $(DIR)/$(SRC)

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@ -13,8 +13,8 @@ format-examples:
cd examples/more && make format-all
format: squiggle.c squiggle.h
$(FORMATTER) squiggle.c
$(FORMATTER) squiggle.h
$(FORMATTER) squiggle.c squiggle.h
$(FORMATTER) squiggle_more.c squiggle_more.h
lint:
clang-tidy squiggle.c -- -lm

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@ -1,27 +0,0 @@
uint64_t xorshift64(uint64_t* seed)
{
// Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
// <https://en.wikipedia.org/wiki/Xorshift>
uint64_t x = *seed;
x ^= x << 13;
x ^= x >> 7;
x ^= x << 17;
return *seed = x;
}
double sample_unit_uniform(uint64_t* seed)
{
// samples uniform from [0,1] interval.
return ((double)xorshift64(seed)) / ((double)UINT64_MAX);
}
double sample_unit_normal(uint64_t* seed)
{
// // See: <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform>
double u1 = sample_unit_uniform(seed);
double u2 = sample_unit_uniform(seed);
double z = sqrtf(-2.0 * log(u1)) * sin(2 * PI * u2);
return z;
}

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@ -10,25 +10,14 @@ int main()
// set randomness seed
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with a seed of 0
/*
for (int i = 0; i < 100; i++) {
double draw = sample_unit_uniform(seed);
printf("%f\n", draw);
}*/
// Test division
// printf("\n%d\n", 10 % 3);
//
int n_samples = 100, n_threads = 16;
double* results = malloc(n_samples * sizeof(double));
double sampler_scratchpad(uint64_t* seed){
return 1;
}
parallel_sampler(sampler_scratchpad, results, n_threads, n_samples);
for(int i=0; i<n_samples; i++){
printf("Sample %d: %f\n", i, results[i]);
int n = 1000000;
double* xs = malloc(sizeof(double) * n);
for (int i = 0; i < n; i++) {
xs[i] = sample_to(10, 100, seed);
}
ci ci_90 = array_get_90_ci(xs, n);
printf("Recovering confidence interval of sample_to(10, 100):\n low: %f, high: %f\n", ci_90.low, ci_90.high);
free(seed);
}

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@ -8,7 +8,7 @@
#define NORMAL90CONFIDENCE 1.6448536269514727
// Pseudo Random number generator
uint64_t xorshift32(uint32_t* seed)
static uint64_t xorshift32(uint32_t* seed)
{
// Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
// See:
@ -24,7 +24,7 @@ uint64_t xorshift32(uint32_t* seed)
return *seed = x;
}
uint64_t xorshift64(uint64_t* seed)
static uint64_t xorshift64(uint64_t* seed)
{
// same as above, but for generating doubles instead of floats
uint64_t x = *seed;

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@ -1,67 +1,195 @@
#include "squiggle.h"
#include <float.h>
#include <math.h>
#include <limits.h>
#include <math.h>
#include <omp.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include "squiggle.h"
/* Math constants */
#define PI 3.14159265358979323846 // M_PI in gcc gnu99
#define NORMAL90CONFIDENCE 1.6448536269514727
/* Parallel sampler */
void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples)
{
if ((n_samples % n_threads) != 0) {
fprintf(stderr, "Number of samples isn't divisible by number of threads, aborting\n");
exit(1);
}
uint64_t** seeds = malloc(n_threads * sizeof(uint64_t*));
for (uint64_t i = 0; i < n_threads; i++) {
seeds[i] = malloc(sizeof(uint64_t));
*seeds[i] = i + 1; // xorshift can't start with 0
}
/* Some error niceties */
// These won't be used until later
#define MAX_ERROR_LENGTH 500
#define EXIT_ON_ERROR 0
#define PROCESS_ERROR(error_msg) process_error(error_msg, EXIT_ON_ERROR, __FILE__, __LINE__)
int i;
#pragma omp parallel private(i)
{
#pragma omp for
for (i = 0; i < n_threads; i++) {
int lower_bound = i * (n_samples / n_threads);
int upper_bound = ((i + 1) * (n_samples / n_threads)) - 1;
// printf("Lower bound: %d, upper bound: %d\n", lower_bound, upper_bound);
for (int j = lower_bound; j < upper_bound; j++) {
results[j] = sampler(seeds[i]);
}
}
}
for (uint64_t i = 0; i < n_threads; i++) {
free(seeds[i]);
}
free(seeds);
}
/* Get confidence intervals, given a sampler */
// Not in core yet because I'm not sure how much I like the struct
// and the built-in 100k samples
// to do: add n to function parameters and document
typedef struct ci_t {
float low;
float high;
double low;
double high;
} ci;
int compare_doubles(const void* p, const void* q)
static void swp(int i, int j, double xs[])
{
// https://wikiless.esmailelbob.xyz/wiki/Qsort?lang=en
double x = *(const double*)p;
double y = *(const double*)q;
/* Avoid returning x - y, which can cause undefined behaviour
because of signed integer overflow. */
if (x < y)
return -1; // Return -1 if you want ascending, 1 if you want descending order.
else if (x > y)
return 1; // Return 1 if you want ascending, -1 if you want descending order.
return 0;
double tmp = xs[i];
xs[i] = xs[j];
xs[j] = tmp;
}
ci get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* seed)
static int partition(int low, int high, double xs[], int length)
{
int n = 100 * 1000;
double* samples_array = malloc(n * sizeof(double));
for (int i = 0; i < n; i++) {
samples_array[i] = sampler(seed);
// To understand this function:
// - see the note after gt variable definition
// - go to commit 578bfa27 and the scratchpad/ folder in it, which has printfs sprinkled throughout
int pivot = low + floor((high - low) / 2);
double pivot_value = xs[pivot];
swp(pivot, high, xs);
int gt = low; /* This pointer will iterate until finding an element which is greater than the pivot. Then it will move elements that are smaller before it--more specifically, it will move elements to its position and then increment. As a result all elements between gt and i will be greater than the pivot. */
for (int i = low; i < high; i++) {
if (xs[i] < pivot_value) {
swp(gt, i, xs);
gt++;
}
}
qsort(samples_array, n, sizeof(double), compare_doubles);
swp(high, gt, xs);
return gt;
}
static double quickselect(int k, double xs[], int n)
{
// https://en.wikipedia.org/wiki/Quickselect
int low = 0;
int high = n - 1;
for (;;) {
if (low == high) {
return xs[low];
}
int pivot = partition(low, high, xs, n);
if (pivot == k) {
return xs[pivot];
} else if (k < pivot) {
high = pivot - 1;
} else {
low = pivot + 1;
}
}
}
ci array_get_ci(ci interval, double* xs, int n)
{
int low_k = floor(interval.low * n);
int high_k = ceil(interval.high * n);
ci result = {
.low = samples_array[5000],
.high = samples_array[94999],
.low = quickselect(low_k, xs, n),
.high = quickselect(high_k, xs, n),
};
free(samples_array);
return result;
}
ci array_get_90_ci(double xs[], int n)
{
return array_get_ci((ci) { .low = 0.05, .high = 0.95 }, xs, n);
}
ci sampler_get_ci(ci interval, double (*sampler)(uint64_t*), int n, uint64_t* seed)
{
double* xs = malloc(n * sizeof(double));
for (int i = 0; i < n; i++) {
xs[i] = sampler(seed);
}
ci result = array_get_ci(interval, xs, n);
free(xs);
return result;
}
ci sampler_get_90_ci(double (*sampler)(uint64_t*), int n, uint64_t* seed)
{
return sampler_get_ci((ci) { .low = 0.05, .high = 0.95 }, sampler, n, seed);
}
/* Algebra manipulations */
// here I discover named structs,
// which mean that I don't have to be typing
// struct blah all the time.
#define NORMAL90CONFIDENCE 1.6448536269514727
typedef struct normal_params_t {
double mean;
double std;
} normal_params;
normal_params algebra_sum_normals(normal_params a, normal_params b)
{
normal_params result = {
.mean = a.mean + b.mean,
.std = sqrt((a.std * a.std) + (b.std * b.std)),
};
return result;
}
typedef struct lognormal_params_t {
double logmean;
double logstd;
} lognormal_params;
lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params b)
{
lognormal_params result = {
.logmean = a.logmean + b.logmean,
.logstd = sqrt((a.logstd * a.logstd) + (b.logstd * b.logstd)),
};
return result;
}
lognormal_params convert_ci_to_lognormal_params(ci x)
{
double loghigh = logf(x.high);
double loglow = logf(x.low);
double logmean = (loghigh + loglow) / 2.0;
double logstd = (loghigh - loglow) / (2.0 * NORMAL90CONFIDENCE);
lognormal_params result = { .logmean = logmean, .logstd = logstd };
return result;
}
ci convert_lognormal_params_to_ci(lognormal_params y)
{
double h = y.logstd * NORMAL90CONFIDENCE;
double loghigh = y.logmean + h;
double loglow = y.logmean - h;
ci result = { .low = exp(loglow), .high = exp(loghigh) };
return result;
}
/* Scaffolding to handle errors */
// We are building towards sample from an arbitrary cdf
// We will sample from an arbitrary cdf
// and that operation might fail
// so we build some scaffolding here
#define MAX_ERROR_LENGTH 500
#define EXIT_ON_ERROR 0
#define PROCESS_ERROR(error_msg) process_error(error_msg, EXIT_ON_ERROR, __FILE__, __LINE__)
struct box {
int empty;
double content;
@ -148,7 +276,7 @@ struct box inverse_cdf_double(double cdf(double), double p)
}
}
// Version #2:
// Version #2:
// - input: (cdf: double => Box(number|error), p)
// - output: Box(number|error)
struct box inverse_cdf_box(struct box cdf_box(double), double p)
@ -246,122 +374,21 @@ double sampler_cdf_danger(struct box cdf(double), uint64_t* seed)
{
double p = sample_unit_uniform(seed);
struct box result = inverse_cdf_box(cdf, p);
if(result.empty){
exit(1);
}else{
return result.content;
}
}
/* Algebra manipulations */
// here I discover named structs,
// which mean that I don't have to be typing
// struct blah all the time.
typedef struct normal_params_t {
double mean;
double std;
} normal_params;
normal_params algebra_sum_normals(normal_params a, normal_params b)
{
normal_params result = {
.mean = a.mean + b.mean,
.std = sqrt((a.std * a.std) + (b.std * b.std)),
};
return result;
}
typedef struct lognormal_params_t {
double logmean;
double logstd;
} lognormal_params;
lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params b)
{
lognormal_params result = {
.logmean = a.logmean + b.logmean,
.logstd = sqrt((a.logstd * a.logstd) + (b.logstd * b.logstd)),
};
return result;
}
lognormal_params convert_ci_to_lognormal_params(ci x)
{
double loghigh = logf(x.high);
double loglow = logf(x.low);
double logmean = (loghigh + loglow) / 2.0;
double logstd = (loghigh - loglow) / (2.0 * NORMAL90CONFIDENCE);
lognormal_params result = { .logmean = logmean, .logstd = logstd };
return result;
}
ci convert_lognormal_params_to_ci(lognormal_params y)
{
double h = y.logstd * NORMAL90CONFIDENCE;
double loghigh = y.logmean + h;
double loglow = y.logmean - h;
ci result = { .low = exp(loglow), .high = exp(loghigh) };
return result;
}
/* Parallel sampler */
void parallel_sampler(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples){
// Division terminology:
// a = b * quotient + reminder
// a = (a/b)*b + (a%b)
// dividend: a
// divisor: b
// quotient = a / b
// reminder = a % b
// "divisor's multiple" := (a/b)*b
// now, we have n_samples and n_threads
// to make our life easy, each thread will have a number of samples of: a/b (quotient)
// and we'll compute the remainder of samples separately
// to possibly do by Jorge: improve so that the remainder is included in the threads
int quotient = n_samples / n_threads;
int remainder = n_samples % n_threads;
int divisor_multiple = quotient * n_threads;
uint64_t** seeds = malloc(n_threads * sizeof(uint64_t*));
// printf("UINT64_MAX: %lu\n", UINT64_MAX);
srand(1);
for (uint64_t i = 0; i < n_threads; i++) {
seeds[i] = malloc(sizeof(uint64_t));
// Constraints:
// - xorshift can't start with 0
// - the seeds should be reasonably separated and not correlated
*seeds[i] = (uint64_t) rand() * (UINT64_MAX / RAND_MAX);
// printf("#%ld: %lu\n",i, *seeds[i]);
// Other initializations tried:
// *seeds[i] = 1 + i;
// *seeds[i] = (i + 0.5)*(UINT64_MAX/n_threads);
// *seeds[i] = (i + 0.5)*(UINT64_MAX/n_threads) + constant * i;
if (result.empty) {
exit(1);
} else {
return result.content;
}
int i;
#pragma omp parallel private(i)
{
#pragma omp for
for (i = 0; i < n_threads; i++) {
int lower_bound_inclusive = i * quotient;
int upper_bound_not_inclusive = ((i+1) * quotient); // note the < in the for loop below,
// printf("Lower bound: %d, upper bound: %d\n", lower_bound, upper_bound);
for (int j = lower_bound_inclusive; j < upper_bound_not_inclusive; j++) {
results[j] = sampler(seeds[i]);
}
}
}
for(int j=divisor_multiple; j<n_samples; j++){
results[j] = sampler(seeds[0]);
// we can just reuse a seed, this isn't problematic because we are not doing multithreading
}
for (uint64_t i = 0; i < n_threads; i++) {
free(seeds[i]);
}
free(seeds);
}
/* array print: potentially useful for debugging */
void array_print(double xs[], int n)
{
printf("[");
for (int i = 0; i < n - 1; i++) {
printf("%f, ", xs[i]);
}
printf("%f", xs[n - 1]);
printf("]\n");
}

View File

@ -1,35 +1,20 @@
#ifndef SQUIGGLE_C_EXTRA
#define SQUIGGLE_C_EXTRA
#define SQUIGGLE_C_EXTRA
// Box
struct box {
int empty;
double content;
char* error_msg;
};
/* Parallel sampling */
void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples);
// Macros to handle errors
#define MAX_ERROR_LENGTH 500
#define EXIT_ON_ERROR 0
#define PROCESS_ERROR(error_msg) process_error(error_msg, EXIT_ON_ERROR, __FILE__, __LINE__)
struct box process_error(const char* error_msg, int should_exit, char* file, int line);
// Inverse cdf
struct box inverse_cdf_double(double cdf(double), double p);
struct box inverse_cdf_box(struct box cdf_box(double), double p);
// Samplers from cdf
struct box sampler_cdf_double(double cdf(double), uint64_t* seed);
struct box sampler_cdf_box(struct box cdf(double), uint64_t* seed);
// Get 90% confidence interval
/* Get 90% confidence interval */
typedef struct ci_t {
float low;
float high;
double low;
double high;
} ci;
ci get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* seed);
ci array_get_ci(ci interval, double* xs, int n);
ci array_get_90_ci(double xs[], int n);
ci sampler_get_ci(ci interval, double (*sampler)(uint64_t*), int n, uint64_t* seed);
ci sampler_get_90_ci(double (*sampler)(uint64_t*), int n, uint64_t* seed);
// small algebra manipulations
/* Algebra manipulations */
typedef struct normal_params_t {
double mean;
@ -44,8 +29,26 @@ typedef struct lognormal_params_t {
lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params b);
lognormal_params convert_ci_to_lognormal_params(ci x);
ci convert_lognormal_params_to_ci(lognormal_params y);
ci convert_lognormal_params_to_ci(lognormal_params y);
void parallel_sampler(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples);
/* Error handling */
struct box {
int empty;
double content;
char* error_msg;
};
#define MAX_ERROR_LENGTH 500
#define EXIT_ON_ERROR 0
#define PROCESS_ERROR(error_msg) process_error(error_msg, EXIT_ON_ERROR, __FILE__, __LINE__)
struct box process_error(const char* error_msg, int should_exit, char* file, int line);
void array_print(double* array, int length);
/* Inverse cdf */
struct box inverse_cdf_double(double cdf(double), double p);
struct box inverse_cdf_box(struct box cdf_box(double), double p);
/* Samplers from cdf */
struct box sampler_cdf_double(double cdf(double), uint64_t* seed);
struct box sampler_cdf_box(struct box cdf(double), uint64_t* seed);
#endif