Revert "Merge branch 'master' into quickselect"

This reverts commit c77fa34410, reversing
changes made to ffd6e5dcbb.
This commit is contained in:
NunoSempere 2023-11-29 23:17:41 +00:00
parent c77fa34410
commit 4d218468cf
40 changed files with 274 additions and 294 deletions

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

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@ -15,9 +15,8 @@ int main()
uint64_t* seed = malloc(sizeof(uint64_t)); uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with 0 *seed = 1000; // xorshift can't start with 0
ci beta_1_2_ci_90 = sampler_get_90_ci(beta_1_2_sampler, 1000000, seed); ci beta_1_2_ci_90 = get_90_confidence_interval(beta_1_2_sampler, 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("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); free(seed);
} }

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@ -60,7 +60,7 @@ int main()
} }
printf("... ]\n"); printf("... ]\n");
ci ci_90 = sampler_get_90_ci(mixture, 1000000, seed); ci ci_90 = get_90_confidence_interval(mixture, seed);
printf("mean: %f\n", array_mean(mixture_result, n)); printf("mean: %f\n", array_mean(mixture_result, n));
printf("90%% confidence interval: [%f, %f]\n", ci_90.low, ci_90.high); 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"); printf("... ]\n");
ci ci_90 = sampler_get_90_ci(sample_minutes_per_day_jumping_rope_needed_to_burn_10kg, 1000000, seed); ci ci_90 = get_90_confidence_interval(sample_minutes_per_day_jumping_rope_needed_to_burn_10kg, seed);
printf("90%% confidence interval: [%f, %f]\n", ci_90.low, ci_90.high); printf("90%% confidence interval: [%f, %f]\n", ci_90.low, ci_90.high);
free(seed); free(seed);

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

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

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

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

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@ -1,21 +0,0 @@
#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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@ -48,8 +48,7 @@ all:
$(CC) $(OPTIMIZED) $(DEBUG) 10_twitter_thread_example/$(SRC) $(DEPS) -o 10_twitter_thread_example/$(OUTPUT) $(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) $(DEBUG) 11_billion_lognormals_paralell/$(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) $(DEBUG) 12_time_to_botec_parallel/$(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: format-all:
$(FORMATTER) 00_example_template/$(SRC) $(FORMATTER) 00_example_template/$(SRC)
@ -66,7 +65,6 @@ format-all:
$(FORMATTER) 11_billion_lognormals_paralell/$(SRC) $(FORMATTER) 11_billion_lognormals_paralell/$(SRC)
$(FORMATTER) 12_time_to_botec_parallel/$(SRC) $(FORMATTER) 12_time_to_botec_parallel/$(SRC)
$(FORMATTER) 13_parallelize_min/$(SRC) $(FORMATTER) 13_parallelize_min/$(SRC)
$(FORMATTER) 14_check_confidence_interval/$(SRC)
run-all: run-all:
00_example_template/$(OUTPUT) 00_example_template/$(OUTPUT)
@ -83,7 +81,6 @@ run-all:
11_billion_lognormals_paralell/$(OUTPUT) 11_billion_lognormals_paralell/$(OUTPUT)
12_time_to_botec_parallel/$(OUTPUT) 12_time_to_botec_parallel/$(OUTPUT)
13_parallelize_min/$(OUTPUT) 13_parallelize_min/$(OUTPUT)
14_check_confidence_interval/$(OUTPUT)
## make one DIR=06_nuclear_recovery ## make one DIR=06_nuclear_recovery
one: $(DIR)/$(SRC) one: $(DIR)/$(SRC)

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

27
scratchpad/core.c Normal file
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@ -0,0 +1,27 @@
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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@ -10,14 +10,25 @@ int main()
// set randomness seed // set randomness seed
uint64_t* seed = malloc(sizeof(uint64_t)); uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with a seed of 0 *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);
int n = 1000000; }*/
double* xs = malloc(sizeof(double) * n); // Test division
for (int i = 0; i < n; i++) { // printf("\n%d\n", 10 % 3);
xs[i] = sample_to(10, 100, seed); //
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]);
} }
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); free(seed);
} }

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

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@ -1,195 +1,67 @@
#include "squiggle.h"
#include <float.h> #include <float.h>
#include <limits.h>
#include <math.h> #include <math.h>
#include <limits.h>
#include <omp.h> #include <omp.h>
#include <stdint.h> #include <stdint.h>
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include "squiggle.h"
/* Parallel sampler */ /* Math constants */
void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples) #define PI 3.14159265358979323846 // M_PI in gcc gnu99
{ #define NORMAL90CONFIDENCE 1.6448536269514727
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
}
int i; /* Some error niceties */
#pragma omp parallel private(i) // These won't be used until later
{ #define MAX_ERROR_LENGTH 500
#pragma omp for #define EXIT_ON_ERROR 0
for (i = 0; i < n_threads; i++) { #define PROCESS_ERROR(error_msg) process_error(error_msg, EXIT_ON_ERROR, __FILE__, __LINE__)
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 */ /* Get confidence intervals, given a sampler */
// Not in core yet because I'm not sure how much I like the struct // Not in core yet because I'm not sure how much I like the struct
// and the built-in 100k samples // and the built-in 100k samples
// to do: add n to function parameters and document // to do: add n to function parameters and document
typedef struct ci_t { typedef struct ci_t {
double low; float low;
double high; float high;
} ci; } ci;
int compare_doubles(const void* p, const void* q)
static void swp(int i, int j, double xs[])
{ {
double tmp = xs[i]; // https://wikiless.esmailelbob.xyz/wiki/Qsort?lang=en
xs[i] = xs[j]; double x = *(const double*)p;
xs[j] = tmp; 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;
} }
ci get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* seed)
static int partition(int low, int high, double xs[], int length)
{ {
// To understand this function: int n = 100 * 1000;
// - see the note after gt variable definition double* samples_array = malloc(n * sizeof(double));
// - 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++;
}
}
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 = quickselect(low_k, xs, n),
.high = quickselect(high_k, xs, n),
};
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++) { for (int i = 0; i < n; i++) {
xs[i] = sampler(seed); samples_array[i] = sampler(seed);
} }
ci result = array_get_ci(interval, xs, n); qsort(samples_array, n, sizeof(double), compare_doubles);
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 */ ci result = {
// here I discover named structs, .low = samples_array[5000],
// which mean that I don't have to be typing .high = samples_array[94999],
// 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; free(samples_array);
}
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; return result;
} }
/* Scaffolding to handle errors */ /* Scaffolding to handle errors */
// We will sample from an arbitrary cdf // We are building towards sample from an arbitrary cdf
// and that operation might fail // and that operation might fail
// so we build some scaffolding here // 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 { struct box {
int empty; int empty;
double content; double content;
@ -374,21 +246,122 @@ double sampler_cdf_danger(struct box cdf(double), uint64_t* seed)
{ {
double p = sample_unit_uniform(seed); double p = sample_unit_uniform(seed);
struct box result = inverse_cdf_box(cdf, p); struct box result = inverse_cdf_box(cdf, p);
if (result.empty) { if(result.empty){
exit(1); exit(1);
} else { }else{
return result.content; return result.content;
} }
} }
/* array print: potentially useful for debugging */ /* 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;
void array_print(double xs[], int n) normal_params algebra_sum_normals(normal_params a, normal_params b)
{ {
printf("["); normal_params result = {
for (int i = 0; i < n - 1; i++) { .mean = a.mean + b.mean,
printf("%f, ", xs[i]); .std = sqrt((a.std * a.std) + (b.std * b.std)),
} };
printf("%f", xs[n - 1]); return result;
printf("]\n"); }
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;
}
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);
} }

View File

@ -1,20 +1,35 @@
#ifndef SQUIGGLE_C_EXTRA #ifndef SQUIGGLE_C_EXTRA
#define SQUIGGLE_C_EXTRA #define SQUIGGLE_C_EXTRA
/* Parallel sampling */ // Box
void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples); struct box {
int empty;
double content;
char* error_msg;
};
/* Get 90% confidence interval */ // 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
typedef struct ci_t { typedef struct ci_t {
double low; float low;
double high; float high;
} ci; } ci;
ci array_get_ci(ci interval, double* xs, int n); ci get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* seed);
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);
/* Algebra manipulations */ // small algebra manipulations
typedef struct normal_params_t { typedef struct normal_params_t {
double mean; double mean;
@ -29,26 +44,8 @@ typedef struct lognormal_params_t {
lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params b); lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params b);
lognormal_params convert_ci_to_lognormal_params(ci x); 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);
/* Error handling */ void parallel_sampler(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples);
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 #endif