continue with big refactor

- delete cdf functions from header file
- delete docs
- rework examples so as to not use sample_90_ci function
- add a few items to README
This commit is contained in:
NunoSempere 2024-02-01 20:54:52 +01:00
parent 0975512412
commit 4113c87e4d
26 changed files with 63 additions and 389 deletions

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@ -195,10 +195,6 @@ The bailey:
- I think the core interface is not likely to change much, though I've recently changed the interface for parallelism and for getting confidence intervals.
- I am using this code for a few important consulting projects, and I trust myself to operate it correctly.
## Licensing
This project is released under the MIT license, a permissive open-source license. You can see it in the LICENSE.txt file.
## squiggle extra functions
### Division between core functions and squiggle_more expansions
@ -250,32 +246,7 @@ That said, I found adding parallelism to be an interesting an engaging task. Mos
`squiggle_more.c` has some functions to do some simple algebra manipulations: sums of normals and products of lognormals. You can see some example usage [here](examples/more/07_algebra/example.c) and [here](examples/more/08_algebra_and_conversion/example.c).
#### Extra: cdf auxiliary functions
## Licensing
I provide some auxiliary functions that take a cdf, and return a sample from the distribution produced by that cdf. This might make it easier to program models, at the cost of a 20x to 60x slowdown in the parts of the code that use it.
I don't have any immediate plans or reason to delete these functions, but I probably will, since they are too slow/inefficient for my taste.
#### Extra: Error propagation vs exiting on error
The process of taking a cdf and returning a sample might fail, e.g., it's a Newton method which might fail to converge because of cdf artifacts. The cdf itself might also fail, e.g., if a distribution only accepts a range of parameters, but is fed parameters outside that range.
This library provides two approaches:
1. Print the line and function in which the error occurred, then exit on error
2. In situations where there might be an error, return a struct containing either the correct value or an error message:
```C
struct box {
int empty;
double content;
char* error_msg;
};
```
The first approach produces terser programs but might not scale. The second approach seems like it could lead to more robust programs, but is more verbose.
Behaviour on error can be toggled by the `EXIT_ON_ERROR` variable. This library also provides a convenient macro, `PROCESS_ERROR`, to make error handling in either case much terser—see the usage in example 4 in the examples/ folder.
Overall, I'd describe the error handling capabilities of this library as pretty rudimentary. For example, this program might fail in surprising ways if you ask for a lognormal with negative standard deviation, because I haven't added error checking for that case yet.
This project is released under the MIT license, a permissive open-source license. You can see it in the LICENSE.txt file.

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@ -2,15 +2,17 @@
## To do
- [ ] Big refactor
- [x] Big refactor
- [ ] Come up with a better headline example; fermi paradox paper is too complicated
- [ ] Make README.md less messy
- [x] Make README.md less messy
- [ ] Give examples of new functions
- [ ] Reference commit with cdf functions, even though deleted
- [ ] Post on suckless subreddit
- [ ] Drive in a few more real-life applications
- [ ] US election modelling?
- [ ] Look into using size_t instead of int for sample numbers
- [ ] Reorganize code a little bit to reduce usage of gcc's nested functions
- [ ] Rename examples
## Done

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@ -1,102 +0,0 @@
#include "../../../squiggle.h"
#include "../../../squiggle_more.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define NUM_SAMPLES 1000000
// Example cdf
double cdf_uniform_0_1(double x)
{
if (x < 0) {
return 0;
} else if (x > 1) {
return 1;
} else {
return x;
}
}
double cdf_squared_0_1(double x)
{
if (x < 0) {
return 0;
} else if (x > 1) {
return 1;
} else {
return x * x;
}
}
double cdf_normal_0_1(double x)
{
double mean = 0;
double std = 1;
return 0.5 * (1 + erf((x - mean) / (std * sqrt(2)))); // erf from math.h
}
// Some testers
void test_inverse_cdf_double(char* cdf_name, double cdf_double(double))
{
box result = inverse_cdf_double(cdf_double, 0.5);
if (result.empty) {
printf("Inverse for %s not calculated\n", cdf_name);
exit(1);
} else {
printf("Inverse of %s at %f is: %f\n", cdf_name, 0.5, result.content);
}
}
void test_and_time_sampler_double(char* cdf_name, double cdf_double(double), uint64_t* seed)
{
printf("\nGetting some samples from %s:\n", cdf_name);
clock_t begin = clock();
for (int i = 0; i < NUM_SAMPLES; i++) {
box sample = sampler_cdf_double(cdf_double, seed);
if (sample.empty) {
printf("Error in sampler function for %s", cdf_name);
} else {
// printf("%f\n", sample.content);
}
}
clock_t end = clock();
double time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
printf("Time spent: %f\n", time_spent);
}
int main()
{
// Test inverse cdf double
test_inverse_cdf_double("cdf_uniform_0_1", cdf_uniform_0_1);
test_inverse_cdf_double("cdf_squared_0_1", cdf_squared_0_1);
test_inverse_cdf_double("cdf_normal_0_1", cdf_normal_0_1);
// Testing samplers
// set randomness seed
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with 0
// Test double sampler
test_and_time_sampler_double("cdf_uniform_0_1", cdf_uniform_0_1, seed);
test_and_time_sampler_double("cdf_squared_0_1", cdf_squared_0_1, seed);
test_and_time_sampler_double("cdf_normal_0_1", cdf_normal_0_1, seed);
// Get some normal samples using a previous approach
printf("\nGetting some samples from sample_unit_normal\n");
clock_t begin_2 = clock();
double* normal_samples = malloc(NUM_SAMPLES * sizeof(double));
for (int i = 0; i < NUM_SAMPLES; i++) {
normal_samples[i] = sample_unit_normal(seed);
// printf("%f\n", normal_sample);
}
clock_t end_2 = clock();
double time_spent_2 = (double)(end_2 - begin_2) / CLOCKS_PER_SEC;
printf("Time spent: %f\n", time_spent_2);
free(seed);
return 0;
}

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@ -1,168 +0,0 @@
#include "../../../squiggle.h"
#include "../../../squiggle_more.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define NUM_SAMPLES 10000
#define STOP_BETA 1.0e-8
#define TINY_BETA 1.0e-30
// Incomplete beta function
box incbeta(double a, double b, double x)
{
// Descended from <https://github.com/codeplea/incbeta/blob/master/incbeta.c>,
// <https://codeplea.com/incomplete-beta-function-c>
// but modified to return a box struct and doubles instead of doubles.
// [ ] to do: add attribution in README
// Original code under this license:
/*
* zlib License
*
* Regularized Incomplete Beta Function
*
* Copyright (c) 2016, 2017 Lewis Van Winkle
* http://CodePlea.com
*
* This software is provided 'as-is', without any express or implied
* warranty. In no event will the authors be held liable for any damages
* arising from the use of this software.
*
* Permission is granted to anyone to use this software for any purpose,
* including commercial applications, and to alter it and redistribute it
* freely, subject to the following restrictions:
*
* 1. The origin of this software must not be misrepresented; you must not
* claim that you wrote the original software. If you use this software
* in a product, an acknowledgement in the product documentation would be
* appreciated but is not required.
* 2. Altered source versions must be plainly marked as such, and must not be
* misrepresented as being the original software.
* 3. This notice may not be removed or altered from any source distribution.
*/
if (x < 0.0 || x > 1.0) {
return PROCESS_ERROR("x out of bounds [0, 1], in function incbeta");
}
/*The continued fraction converges nicely for x < (a+1)/(a+b+2)*/
if (x > (a + 1.0) / (a + b + 2.0)) {
box symmetric_incbeta = incbeta(b, a, 1.0 - x);
if (symmetric_incbeta.empty) {
return symmetric_incbeta; // propagate error
} else {
box result = {
.empty = 0,
.content = 1 - symmetric_incbeta.content
};
return result;
}
}
/*Find the first part before the continued fraction.*/
const double lbeta_ab = lgamma(a) + lgamma(b) - lgamma(a + b);
const double front = exp(log(x) * a + log(1.0 - x) * b - lbeta_ab) / a;
/*Use Lentz's algorithm to evaluate the continued fraction.*/
double f = 1.0, c = 1.0, d = 0.0;
int i, m;
for (i = 0; i <= 200; ++i) {
m = i / 2;
double numerator;
if (i == 0) {
numerator = 1.0; /*First numerator is 1.0.*/
} else if (i % 2 == 0) {
numerator = (m * (b - m) * x) / ((a + 2.0 * m - 1.0) * (a + 2.0 * m)); /*Even term.*/
} else {
numerator = -((a + m) * (a + b + m) * x) / ((a + 2.0 * m) * (a + 2.0 * m + 1)); /*Odd term.*/
}
/*Do an iteration of Lentz's algorithm.*/
d = 1.0 + numerator * d;
if (fabs(d) < TINY_BETA)
d = TINY_BETA;
d = 1.0 / d;
c = 1.0 + numerator / c;
if (fabs(c) < TINY_BETA)
c = TINY_BETA;
const double cd = c * d;
f *= cd;
/*Check for stop.*/
if (fabs(1.0 - cd) < STOP_BETA) {
box result = {
.empty = 0,
.content = front * (f - 1.0)
};
return result;
}
}
return PROCESS_ERROR("More loops needed, did not converge, in function incbeta");
}
box cdf_beta(double x)
{
if (x < 0) {
box result = { .empty = 0, .content = 0 };
return result;
} else if (x > 1) {
box result = { .empty = 0, .content = 1 };
return result;
} else {
double successes = 1, failures = (2023 - 1945);
return incbeta(successes, failures, x);
}
}
// Some testers
void test_inverse_cdf_box(char* cdf_name, box cdf_box(double))
{
box result = inverse_cdf_box(cdf_box, 0.5);
if (result.empty) {
printf("Inverse for %s not calculated\n", cdf_name);
exit(1);
} else {
printf("Inverse of %s at %f is: %f\n", cdf_name, 0.5, result.content);
}
}
void test_and_time_sampler_box(char* cdf_name, box cdf_box(double), uint64_t* seed)
{
printf("\nGetting some samples from %s:\n", cdf_name);
clock_t begin = clock();
for (int i = 0; i < NUM_SAMPLES; i++) {
box sample = sampler_cdf_box(cdf_box, seed);
if (sample.empty) {
printf("Error in sampler function for %s", cdf_name);
} else {
// printf("%f\n", sample.content);
}
}
clock_t end = clock();
double time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
printf("Time spent: %f\n", time_spent);
}
int main()
{
// Test inverse cdf box
test_inverse_cdf_box("cdf_beta", cdf_beta);
// Test box sampler
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with 0
test_and_time_sampler_box("cdf_beta", cdf_beta, seed);
// Ok, this is slower than python!!
// Partly this is because I am using a more general algorithm,
// which applies to any cdf
// But I am also using absurdly precise convergence conditions.
// This could be optimized.
free(seed);
return 0;
}

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@ -4,9 +4,9 @@
#include <stdlib.h>
// Estimate functions
double beta_1_2_sampler(uint64_t* seed)
double sample_beta_3_2(uint64_t* seed)
{
return sample_beta(1, 2.0, seed);
return sample_beta(3.0, 2.0, seed);
}
int main()
@ -15,9 +15,16 @@ int main()
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with 0
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");
int n_samples = 1 * MILLION;
double* xs = malloc(sizeof(double) * (size_t)n_samples);
for (int i = 0; i < n_samples; i++) {
xs[i] = sample_beta_3_2(seed);
}
printf("\n# Stats\n");
array_print_stats(xs, n_samples);
printf("\n# Histogram\n");
array_print_histogram(xs, n_samples, 23);
free(seed);
}

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@ -34,7 +34,7 @@ double probability_of_dying_eli(uint64_t* seed)
return probability_of_dying;
}
double mixture(uint64_t* seed)
double sample_nuclear_model(uint64_t* seed)
{
double (*samplers[])(uint64_t*) = { probability_of_dying_nuno, probability_of_dying_eli };
double weights[] = { 0.5, 0.5 };
@ -47,23 +47,17 @@ int main()
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with 0
int n = 1000 * 1000;
double* mixture_result = malloc(sizeof(double) * (size_t)n);
int n = 1 * MILLION;
double* xs = malloc(sizeof(double) * (size_t)n);
for (int i = 0; i < n; i++) {
mixture_result[i] = mixture(seed);
xs[i] = sample_nuclear_model(seed);
}
printf("mixture_result: [ ");
for (int i = 0; i < 9; i++) {
printf("%.6f, ", mixture_result[i]);
}
printf("... ]\n");
printf("\n# Stats\n");
array_print_stats(xs, n);
printf("\n# Histogram\n");
array_print_90_ci_histogram(xs, n, 20);
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);
free(mixture_result);
free(xs);
free(seed);
}

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@ -27,22 +27,17 @@ int main()
*seed = 1000; // xorshift can't start with 0
int n = 1000 * 1000;
double* result = malloc(sizeof(double) * (size_t)n);
double* xs = malloc(sizeof(double) * (size_t)n);
for (int i = 0; i < n; i++) {
result[i] = sample_minutes_per_day_jumping_rope_needed_to_burn_10kg(seed);
xs[i] = sample_minutes_per_day_jumping_rope_needed_to_burn_10kg(seed);
}
printf("## How many minutes per day do I have to jump rope to lose 10kg of fat by the end of the year?\n");
printf("Mean: %f\n", array_mean(result, n));
printf("A few samples: [ ");
for (int i = 0; i < 9; i++) {
printf("%.6f, ", result[i]);
}
printf("... ]\n");
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);
printf("\n# Stats\n");
array_print_stats(xs, n);
printf("\n# Histogram\n");
array_print_histogram(xs, n, 23);
free(seed);
}

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@ -46,41 +46,37 @@ int main()
uint64_t* seed = malloc(sizeof(uint64_t));
*seed = 1000; // xorshift can't start with 0
int num_samples = 1000000;
int n_samples = 1000000;
// Before a first nuclear collapse
printf("## Before the first nuclear collapse\n");
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) * (size_t)num_samples);
for (int i = 0; i < num_samples; i++) {
double* yearly_probability_nuclear_collapse_2023_samples = malloc(sizeof(double) * (size_t)n_samples);
for (int i = 0; i < n_samples; i++) {
yearly_probability_nuclear_collapse_2023_samples[i] = yearly_probability_nuclear_collapse_2023(seed);
}
printf("mean: %f\n", array_mean(yearly_probability_nuclear_collapse_2023_samples, num_samples));
ci ci_90_2023 = array_get_90_ci(yearly_probability_nuclear_collapse_2023_samples, n_samples);
printf("90%% confidence interval: [%f, %f]\n", ci_90_2023.low, ci_90_2023.high);
// After the first nuclear collapse
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);
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) * (size_t)num_samples);
for (int i = 0; i < num_samples; i++) {
double* yearly_probability_nuclear_collapse_after_recovery_samples = malloc(sizeof(double) * (size_t)n_samples);
for (int i = 0; i < n_samples; i++) {
yearly_probability_nuclear_collapse_after_recovery_samples[i] = yearly_probability_nuclear_collapse_after_recovery_example(seed);
}
printf("mean: %f\n", array_mean(yearly_probability_nuclear_collapse_after_recovery_samples, num_samples));
ci ci_90_2070 = array_get_90_ci(yearly_probability_nuclear_collapse_after_recovery_samples, 1000000);
printf("90%% confidence interval: [%f, %f]\n", ci_90_2070.low, ci_90_2070.high);
// After the first nuclear collapse (antiinductive)
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);
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) * (size_t)num_samples);
for (int i = 0; i < num_samples; i++) {
double* yearly_probability_nuclear_collapse_after_recovery_antiinductive_samples = malloc(sizeof(double) * (size_t)n_samples);
for (int i = 0; i < n_samples; i++) {
yearly_probability_nuclear_collapse_after_recovery_antiinductive_samples[i] = yearly_probability_nuclear_collapse_after_recovery_antiinductive(seed);
}
printf("mean: %f\n", array_mean(yearly_probability_nuclear_collapse_after_recovery_antiinductive_samples, num_samples));
ci ci_90_antiinductive = array_get_90_ci(yearly_probability_nuclear_collapse_after_recovery_antiinductive_samples, 1000000);
printf("90%% confidence interval: [%f, %f]\n", ci_90_antiinductive.low, ci_90_antiinductive.high);
// free seeds
free(yearly_probability_nuclear_collapse_2023_samples);
free(yearly_probability_nuclear_collapse_after_recovery_samples);
free(yearly_probability_nuclear_collapse_after_recovery_antiinductive_samples);

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@ -39,8 +39,6 @@ FORMATTER=clang-format -i -style=$(STYLE_BLUEPRINT)
## make all
all:
$(CC) $(OPTIMIZED) $(DEBUG) $(WARN) 00_example_template/$(SRC) $(DEPS) -o 00_example_template/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) $(WARN) 01_sample_from_cdf/$(SRC) $(DEPS) -o 01_sample_from_cdf/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) $(WARN) 02_sample_from_cdf_beta/$(SRC) $(DEPS) -o 02_sample_from_cdf_beta/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) $(WARN) 03_ci_beta/$(SRC) $(DEPS) -o 03_ci_beta/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) $(WARN) 04_nuclear_war/$(SRC) $(DEPS) -o 04_nuclear_war/$(OUTPUT)
$(CC) $(OPTIMIZED) $(DEBUG) $(WARN) 05_burn_10kg_fat/$(SRC) $(DEPS) -o 05_burn_10kg_fat/$(OUTPUT)

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@ -212,15 +212,15 @@ void array_print_stats(double xs[], int n){
double median = array_get_median(xs, n);
double mean = array_mean(xs, n);
double std = array_std(xs, n);
printf("Mean: %lf\n"
" Std: %lf\n"
" 5%%: %lf\n"
" 10%%: %lf\n"
" 25%%: %lf\n"
" 50%%: %lf\n"
" 75%%: %lf\n"
" 90%%: %lf\n"
" 95%%: %lf\n",
printf("Avg: %lf\n"
"Std: %lf\n"
" 5%%: %lf\n"
"10%%: %lf\n"
"25%%: %lf\n"
"50%%: %lf\n"
"75%%: %lf\n"
"90%%: %lf\n"
"95%%: %lf\n",
mean, std, ci_90.low, ci_80.low, ci_50.low, median, ci_50.high, ci_80.high, ci_90.high);
}
@ -293,7 +293,7 @@ void array_print_histogram(double* xs, int n_samples, int n_bins) {
decimalPlaces = -magnitude;
decimalPlaces = decimalPlaces > 10 ? 10 : decimalPlaces;
}
printf(" [%*.*f, %*.*f", 4+decimalPlaces, decimalPlaces, bin_start, 4+decimalPlaces, decimalPlaces, bin_end);
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
@ -377,12 +377,12 @@ void array_print_90_ci_histogram(double* xs, int n_samples, int n_bins){
decimalPlaces = -magnitude;
decimalPlaces = decimalPlaces > 10 ? 10 : decimalPlaces;
}
printf( " (-∞, %*.*f): %d\n", 4+decimalPlaces, decimalPlaces, min_value, below_min);
printf( "(-∞, %*.*f): %d\n", 4+decimalPlaces, decimalPlaces, min_value, 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);
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
@ -395,7 +395,7 @@ void array_print_90_ci_histogram(double* xs, int n_samples, int n_bins){
}
printf(" %d\n", bins[i]);
}
printf( " (%*.*f, +∞): %d\n", 4+decimalPlaces, decimalPlaces, max_value, above_max);
printf( "(%*.*f, +∞): %d\n", 4+decimalPlaces, decimalPlaces, max_value, above_max);
// Free the allocated memory for bins
free(bins);

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@ -17,10 +17,6 @@ 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);
// Deprecated: get confidence intervals directly from samplers
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 */
typedef struct normal_params_t {
@ -38,24 +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