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update squiggle version

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This commit is contained in:
NunoSempere 2024-02-11 19:43:28 +01:00
parent 54bd358f7e
commit 3fb6eb0c0e
5 changed files with 266 additions and 258 deletions
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2
squiggle.c/makefile
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@ -1,7 +1,7 @@
OUTPUT=./samples
build:
gcc -O3 samples.c ./squiggle_c/squiggle.c ./squiggle_c/squiggle_more.c -lm -fopenmp -o $(OUTPUT)
gcc -O3 -march=native samples.c ./squiggle_c/squiggle.c ./squiggle_c/squiggle_more.c -lm -fopenmp -o $(OUTPUT)
install:
rm -r squiggle_c

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squiggle.c/samples
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9
squiggle.c/samples.c
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@ -3,7 +3,7 @@
#include <stdio.h>
#include <stdlib.h>
int main()
double sampler_result(uint64_t * seed)
{
double p_a = 0.8;
double p_b = 0.5;
@ -17,10 +17,11 @@ int main()
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)
{
return sample_mixture(samplers, weights, n_dists, seed);
}
}
int main()
{
int n_samples = 1000 * 1000, n_threads = 16;
double* results = malloc((size_t)n_samples * sizeof(double));

481
squiggle.c/squiggle_c/squiggle_more.c
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@ -43,15 +43,14 @@ void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_
int divisor_multiple = quotient * n_threads;
// uint64_t** seeds = malloc((size_t)n_threads * sizeof(uint64_t*));
seed_cache_box* cache_box = (seed_cache_box*) malloc(sizeof(seed_cache_box) * (size_t)n_threads);
// seed_cache_box cache_box[n_threads];
seed_cache_box* cache_box = (seed_cache_box*)malloc(sizeof(seed_cache_box) * (size_t)n_threads);
// seed_cache_box cache_box[n_threads]; // we could use the C stack. On normal linux machines, it's 8MB ($ ulimit -s). However, it doesn't quite feel right.
srand(1);
for (int i = 0; i < n_threads; i++) {
// Constraints:
// - xorshift can't start with 0
// - the seeds should be reasonably separated and not correlated
cache_box[i].seed = (uint64_t)rand() * (UINT64_MAX / RAND_MAX);
// printf("#%ld: %lu\n",i, *seeds[i]);
// Other initializations tried:
// *seeds[i] = 1 + i;
@ -60,22 +59,51 @@ void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_
}
int i;
#pragma omp parallel private(i, quotient)
#pragma omp parallel private(i)
{
#pragma omp for
for (i = 0; i < n_threads; i++) {
int quotient = n_samples / n_threads;
// It's possible I don't need the for, and could instead call omp
// in some different way and get the thread number with omp_get_thread_num()
int lower_bound_inclusive = i * quotient;
int upper_bound_not_inclusive = ((i + 1) * quotient); // note the < in the for loop below,
for (int j = lower_bound_inclusive; j < upper_bound_not_inclusive; j++) {
results[j] = sampler(&(cache_box[i].seed));
// Could also result in inefficient cache stuff, but hopefully not too often
/*
t starts at 0 and ends at T
at t=0,
thread i accesses: results[i*quotient +0],
thread i+1 acccesses: results[(i+1)*quotient +0]
at t=T
thread i accesses: results[(i+1)*quotient -1]
thread i+1 acccesses: results[(i+2)*quotient -1]
The results[j] that are directly adjacent are
results[(i+1)*quotient -1] (accessed by thread i at time T)
results[(i+1)*quotient +0] (accessed by thread i+1 at time 0)
and these are themselves adjacent to
results[(i+1)*quotient -2] (accessed by thread i at time T-1)
results[(i+1)*quotient +1] (accessed by thread i+1 at time 2)
If T is large enough, which it is, two threads won't access the same
cache line at the same time.
Pictorially:
at t=0 ....i.........I.........
at t=T .............i.........I
and the two never overlap
Note that results[j] is a double, a double has 8 bytes (64 bits)
8 doubles fill a cache line of 64 bytes.
So we specifically won't get problems as long as n_samples/n_threads > 8
n_threads is normally 16, so n_samples > 128
Note also that this is only a problem in terms of speed, if n_samples<128
the results are still computed, it'll just be slower
*/
}
}
}
for (int j = divisor_multiple; j < n_samples; j++) {
results[j] = sampler(&(cache_box[0].seed));
// we can just reuse a seed, this isn't problematic because we are not doing multithreading
// we can just reuse a seed,
// this isn't problematic because we;ve now stopped doing multithreading
}
free(cache_box);
@ -88,7 +116,7 @@ typedef struct ci_t {
double high;
} ci;
static void swp(int i, int j, double xs[])
inline static void swp(int i, int j, double xs[])
{
double tmp = xs[i];
xs[i] = xs[j];
@ -120,7 +148,7 @@ static double quickselect(int k, double xs[], int n)
{
// https://en.wikipedia.org/wiki/Quickselect
double *ys = malloc((size_t)n * sizeof(double));
double* ys = malloc((size_t)n * sizeof(double));
memcpy(ys, xs, (size_t)n * sizeof(double));
// ^: don't rearrange item order in the original array
@ -161,18 +189,222 @@ 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 array_get_median(double xs[], int n)
{
UNUSED(seed); // don't want to use it right now, but want to preserve ability to do so (e.g., remove parallelism from internals). Also nicer for consistency.
double* xs = malloc((size_t)n * sizeof(double));
sampler_parallel(sampler, xs, 16, n);
ci result = array_get_ci(interval, xs, n);
free(xs);
return result;
int median_k = (int)floor(0.5 * n);
return quickselect(median_k, xs, n);
}
ci sampler_get_90_ci(double (*sampler)(uint64_t*), int n, uint64_t* seed)
/* array print: potentially useful for debugging */
void array_print(double xs[], int n)
{
return sampler_get_ci((ci) { .low = 0.05, .high = 0.95 }, sampler, n, seed);
printf("[");
for (int i = 0; i < n - 1; i++) {
printf("%f, ", xs[i]);
}
printf("%f", xs[n - 1]);
printf("]\n");
}
void array_print_stats(double xs[], int n)
{
ci ci_90 = array_get_ci((ci) { .low = 0.05, .high = 0.95 }, xs, n);
ci ci_80 = array_get_ci((ci) { .low = 0.1, .high = 0.9 }, xs, n);
ci ci_50 = array_get_ci((ci) { .low = 0.25, .high = 0.75 }, xs, n);
double median = array_get_median(xs, n);
double mean = array_mean(xs, n);
double std = array_std(xs, n);
printf("| Statistic | Value |\n"
"| --- | --- |\n"
"| Mean | %lf |\n"
"| Median | %lf |\n"
"| Std | %lf |\n"
"| 90%% confidence interval | %lf to %lf |\n"
"| 80%% confidence interval | %lf to %lf |\n"
"| 50%% confidence interval | %lf to %lf |\n",
mean, median, std, ci_90.low, ci_90.high, ci_80.low, ci_80.high, ci_50.low, ci_50.high);
}
void array_print_histogram(double* xs, int n_samples, int n_bins)
{
// Interface inspired by <https://github.com/red-data-tools/YouPlot>
if (n_bins <= 1) {
fprintf(stderr, "Number of bins must be greater than 1.\n");
return;
} else if (n_samples <= 1) {
fprintf(stderr, "Number of samples must be higher than 1.\n");
return;
}
int* bins = (int*)calloc((size_t)n_bins, sizeof(int));
if (bins == NULL) {
fprintf(stderr, "Memory allocation for bins failed.\n");
return;
}
// Find the minimum and maximum values from the samples
double min_value = xs[0], max_value = xs[0];
for (int i = 0; i < n_samples; i++) {
if (xs[i] < min_value) {
min_value = xs[i];
}
if (xs[i] > max_value) {
max_value = xs[i];
}
}
// Avoid division by zero for a single unique value
if (min_value == max_value) {
max_value++;
}
// Calculate bin width
double bin_width = (max_value - min_value) / n_bins;
// Fill the bins with sample counts
for (int i = 0; i < n_samples; i++) {
int bin_index = (int)((xs[i] - min_value) / bin_width);
if (bin_index == n_bins) {
bin_index--; // Last bin includes max_value
}
bins[bin_index]++;
}
// Calculate the scaling factor based on the maximum bin count
int max_bin_count = 0;
for (int i = 0; i < n_bins; i++) {
if (bins[i] > max_bin_count) {
max_bin_count = bins[i];
}
}
const int MAX_WIDTH = 50; // Adjust this to your terminal width
double scale = max_bin_count > MAX_WIDTH ? (double)MAX_WIDTH / max_bin_count : 1.0;
// Print the histogram
for (int i = 0; i < n_bins; i++) {
double bin_start = min_value + i * bin_width;
double bin_end = bin_start + bin_width;
int decimalPlaces = 1;
if ((0 < bin_width) && (bin_width < 1)) {
int magnitude = (int)floor(log10(bin_width));
decimalPlaces = -magnitude;
decimalPlaces = decimalPlaces > 10 ? 10 : decimalPlaces;
}
printf("[%*.*f, %*.*f", 4 + decimalPlaces, decimalPlaces, bin_start, 4 + decimalPlaces, decimalPlaces, bin_end);
char interval_delimiter = ')';
if (i == (n_bins - 1)) {
interval_delimiter = ']'; // last bucket is inclusive
}
printf("%c: ", interval_delimiter);
int marks = (int)(bins[i] * scale);
for (int j = 0; j < marks; j++) {
printf("█");
}
printf(" %d\n", bins[i]);
}
// Free the allocated memory for bins
free(bins);
}
void array_print_90_ci_histogram(double* xs, int n_samples, int n_bins)
{
// Code duplicated from previous function
// I'll consider simplifying it at some future point
// Possible ideas:
// - having only one function that takes any confidence interval?
// - having a utility function that is called by both functions?
ci ci_90 = array_get_90_ci(xs, n_samples);
if (n_bins <= 1) {
fprintf(stderr, "Number of bins must be greater than 1.\n");
return;
} else if (n_samples <= 10) {
fprintf(stderr, "Number of samples must be higher than 10.\n");
return;
}
int* bins = (int*)calloc((size_t)n_bins, sizeof(int));
if (bins == NULL) {
fprintf(stderr, "Memory allocation for bins failed.\n");
return;
}
double min_value = ci_90.low, max_value = ci_90.high;
// Avoid division by zero for a single unique value
if (min_value == max_value) {
max_value++;
}
double bin_width = (max_value - min_value) / n_bins;
// Fill the bins with sample counts
int below_min = 0, above_max = 0;
for (int i = 0; i < n_samples; i++) {
if (xs[i] < min_value) {
below_min++;
} else if (xs[i] > max_value) {
above_max++;
} else {
int bin_index = (int)((xs[i] - min_value) / bin_width);
if (bin_index == n_bins) {
bin_index--; // Last bin includes max_value
}
bins[bin_index]++;
}
}
// Calculate the scaling factor based on the maximum bin count
int max_bin_count = 0;
for (int i = 0; i < n_bins; i++) {
if (bins[i] > max_bin_count) {
max_bin_count = bins[i];
}
}
const int MAX_WIDTH = 40; // Adjust this to your terminal width
double scale = max_bin_count > MAX_WIDTH ? (double)MAX_WIDTH / max_bin_count : 1.0;
// Print the histogram
int decimalPlaces = 1;
if ((0 < bin_width) && (bin_width < 1)) {
int magnitude = (int)floor(log10(bin_width));
decimalPlaces = -magnitude;
decimalPlaces = decimalPlaces > 10 ? 10 : decimalPlaces;
}
printf("(%*s, %*.*f): ", 6 + decimalPlaces, "-∞", 4 + decimalPlaces, decimalPlaces, min_value);
int marks_below_min = (int)(below_min * scale);
for (int j = 0; j < marks_below_min; j++) {
printf("█");
}
printf(" %d\n", below_min);
for (int i = 0; i < n_bins; i++) {
double bin_start = min_value + i * bin_width;
double bin_end = bin_start + bin_width;
printf("[%*.*f, %*.*f", 4 + decimalPlaces, decimalPlaces, bin_start, 4 + decimalPlaces, decimalPlaces, bin_end);
char interval_delimiter = ')';
if (i == (n_bins - 1)) {
interval_delimiter = ']'; // last bucket is inclusive
}
printf("%c: ", interval_delimiter);
int marks = (int)(bins[i] * scale);
for (int j = 0; j < marks; j++) {
printf("█");
}
printf(" %d\n", bins[i]);
}
printf("(%*.*f, %*s): ", 4 + decimalPlaces, decimalPlaces, max_value, 6 + decimalPlaces, "+∞");
int marks_above_max = (int)(above_max * scale);
for (int j = 0; j < marks_above_max; j++) {
printf("█");
}
printf(" %d\n", above_max);
// Free the allocated memory for bins
free(bins);
}
/* Algebra manipulations */
@ -225,216 +457,3 @@ ci convert_lognormal_params_to_ci(lognormal_params y)
ci result = { .low = exp(loglow), .high = exp(loghigh) };
return result;
}
/* Scaffolding to handle errors */
// 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__)
typedef struct box_t {
int empty;
double content;
char* error_msg;
} box;
box process_error(const char* error_msg, int should_exit, char* file, int line)
{
if (should_exit) {
printf("%s, @, in %s (%d)", error_msg, file, line);
exit(1);
} else {
char error_msg[MAX_ERROR_LENGTH];
snprintf(error_msg, MAX_ERROR_LENGTH, "@, in %s (%d)", file, line); // NOLINT: We are being carefull here by considering MAX_ERROR_LENGTH explicitly.
box error = { .empty = 1, .error_msg = error_msg };
return error;
}
}
/* Invert an arbitrary cdf at a point */
// Version #1:
// - input: (cdf: double => double, p)
// - output: Box(number|error)
box inverse_cdf_double(double cdf(double), double p)
{
// given a cdf: [-Inf, Inf] => [0,1]
// returns a box with either
// x such that cdf(x) = p
// or an error
// if EXIT_ON_ERROR is set to 1, it exits instead of providing an error
double low = -1.0;
double high = 1.0;
// 1. Make sure that cdf(low) < p < cdf(high)
int interval_found = 0;
while ((!interval_found) && (low > -DBL_MAX / 4) && (high < DBL_MAX / 4)) {
// for floats, use FLT_MAX instead
// Note that this approach is overkill
// but it's also the *correct* thing to do.
int low_condition = (cdf(low) < p);
int high_condition = (p < cdf(high));
if (low_condition && high_condition) {
interval_found = 1;
} else if (!low_condition) {
low = low * 2;
} else if (!high_condition) {
high = high * 2;
}
}
if (!interval_found) {
return PROCESS_ERROR("Interval containing the target value not found, in function inverse_cdf");
} else {
int convergence_condition = 0;
int count = 0;
while (!convergence_condition && (count < (INT_MAX / 2))) {
double mid = (high + low) / 2;
int mid_not_new = (mid == low) || (mid == high);
// double width = high - low;
// if ((width < 1e-8) || mid_not_new){
if (mid_not_new) {
convergence_condition = 1;
} else {
double mid_sign = cdf(mid) - p;
if (mid_sign < 0) {
low = mid;
} else if (mid_sign > 0) {
high = mid;
} else if (mid_sign == 0) {
low = mid;
high = mid;
}
}
}
if (convergence_condition) {
box result = { .empty = 0, .content = low };
return result;
} else {
return PROCESS_ERROR("Search process did not converge, in function inverse_cdf");
}
}
}
// Version #2:
// - input: (cdf: double => Box(number|error), p)
// - output: Box(number|error)
box inverse_cdf_box(box cdf_box(double), double p)
{
// given a cdf: [-Inf, Inf] => Box([0,1])
// returns a box with either
// x such that cdf(x) = p
// or an error
// if EXIT_ON_ERROR is set to 1, it exits instead of providing an error
double low = -1.0;
double high = 1.0;
// 1. Make sure that cdf(low) < p < cdf(high)
int interval_found = 0;
while ((!interval_found) && (low > -DBL_MAX / 4) && (high < DBL_MAX / 4)) {
// for floats, use FLT_MAX instead
// Note that this approach is overkill
// but it's also the *correct* thing to do.
box cdf_low = cdf_box(low);
if (cdf_low.empty) {
return PROCESS_ERROR(cdf_low.error_msg);
}
box cdf_high = cdf_box(high);
if (cdf_high.empty) {
return PROCESS_ERROR(cdf_low.error_msg);
}
int low_condition = (cdf_low.content < p);
int high_condition = (p < cdf_high.content);
if (low_condition && high_condition) {
interval_found = 1;
} else if (!low_condition) {
low = low * 2;
} else if (!high_condition) {
high = high * 2;
}
}
if (!interval_found) {
return PROCESS_ERROR("Interval containing the target value not found, in function inverse_cdf");
} else {
int convergence_condition = 0;
int count = 0;
while (!convergence_condition && (count < (INT_MAX / 2))) {
double mid = (high + low) / 2;
int mid_not_new = (mid == low) || (mid == high);
// double width = high - low;
if (mid_not_new) {
// if ((width < 1e-8) || mid_not_new){
convergence_condition = 1;
} else {
box cdf_mid = cdf_box(mid);
if (cdf_mid.empty) {
return PROCESS_ERROR(cdf_mid.error_msg);
}
double mid_sign = cdf_mid.content - p;
if (mid_sign < 0) {
low = mid;
} else if (mid_sign > 0) {
high = mid;
} else if (mid_sign == 0) {
low = mid;
high = mid;
}
}
}
if (convergence_condition) {
box result = { .empty = 0, .content = low };
return result;
} else {
return PROCESS_ERROR("Search process did not converge, in function inverse_cdf");
}
}
}
/* Sample from an arbitrary cdf */
// Before: invert an arbitrary cdf at a point
// Now: from an arbitrary cdf, get a sample
box sampler_cdf_box(box cdf(double), uint64_t* seed)
{
double p = sample_unit_uniform(seed);
box result = inverse_cdf_box(cdf, p);
return result;
}
box sampler_cdf_double(double cdf(double), uint64_t* seed)
{
double p = sample_unit_uniform(seed);
box result = inverse_cdf_double(cdf, p);
return result;
}
double sampler_cdf_danger(box cdf(double), uint64_t* seed)
{
double p = sample_unit_uniform(seed);
box result = inverse_cdf_box(cdf, p);
if (result.empty) {
exit(1);
} else {
return result.content;
}
}
/* 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");
}

30
squiggle.c/squiggle_c/squiggle_more.h
View File

@ -4,15 +4,18 @@
/* Parallel sampling */
void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples);
/* Get 90% confidence interval */
/* Stats */
double array_get_median(double xs[], int n);
typedef struct ci_t {
double low;
double high;
} ci;
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);
void array_print_stats(double xs[], int n);
void array_print_histogram(double* xs, int n_samples, int n_bins);
void array_print_90_ci_histogram(double* xs, int n, int n_bins);
/* Algebra manipulations */
@ -31,24 +34,9 @@ lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params
lognormal_params convert_ci_to_lognormal_params(ci x);
ci convert_lognormal_params_to_ci(lognormal_params y);
/* Error handling */
typedef struct box_t {
int empty;
double content;
char* error_msg;
} box;
#define MAX_ERROR_LENGTH 500
#define EXIT_ON_ERROR 0
#define PROCESS_ERROR(error_msg) process_error(error_msg, EXIT_ON_ERROR, __FILE__, __LINE__)
box process_error(const char* error_msg, int should_exit, char* file, int line);
void array_print(double* array, int length);
/* Utilities */
/* Inverse cdf */
box inverse_cdf_double(double cdf(double), double p);
box inverse_cdf_box(box cdf_box(double), double p);
/* Samplers from cdf */
box sampler_cdf_double(double cdf(double), uint64_t* seed);
box sampler_cdf_box(box cdf(double), uint64_t* seed);
#define THOUSAND 1000
#define MILLION 1000000
#endif