add serious model, add template.
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README.md
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README.md
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@ -20,12 +20,14 @@ A self-contained C99 library that provides a subset of [Squiggle](https://www.sq
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You can follow some example usage in the examples/ folder
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1. In the first example, we define a small model, and draw one sample from it
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2. In the second example, we define a small model, and return many samples
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3. In the third example, we use a gcc extension—nested functions—to rewrite the code from point 2. in a more linear way.
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4. In the fourth example, we define some simple cdfs, and we draw samples from those cdfs. We see that this approach is slower than using the built-in samplers, e.g., the normal sampler.
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5. In the fifth example, we define the cdf for the beta distribution, and we draw samples from it.
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6. In the sixth example, we take samples from simple gamma and beta distributions, using the samplers provided by this library.
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1. In the 1st example, we define a small model, and draw one sample from it
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2. In the 2nd example, we define a small model, and return many samples
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3. In the 3rd example, we use a gcc extension—nested functions—to rewrite the code from point 2. in a more linear way.
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4. In the 4th example, we define some simple cdfs, and we draw samples from those cdfs. We see that this approach is slower than using the built-in samplers, e.g., the normal sampler.
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5. In the 5th example, we define the cdf for the beta distribution, and we draw samples from it.
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6. In the 6th example, we take samples from simple gamma and beta distributions, using the samplers provided by this library.
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7. In the 7th example, we get the 90% confidence interval of a beta distribution
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8. The 8th example translates the models from Eli and Nuño from [Samotsvety Nuclear Risk Forecasts — March 2022](https://forum.nunosempere.com/posts/KRFXjCqqfGQAYirm5/samotsvety-nuclear-risk-forecasts-march-2022#Nu_o_Sempere) into squiggle.c, then creates a mixture from both, and returns the mean probability of death per month and the 90% confidence interval.
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## Commentary
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14
examples/00_example_template/example.c
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examples/00_example_template/example.c
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@ -0,0 +1,14 @@
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#include "../../squiggle.h"
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#include <stdint.h>
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#include <stdio.h>
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#include <stdlib.h>
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int main()
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{
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// set randomness seed
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uint64_t* seed = malloc(sizeof(uint64_t));
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*seed = 1000; // xorshift can't start with 0
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free(seed);
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}
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53
examples/00_example_template/makefile
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53
examples/00_example_template/makefile
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@ -0,0 +1,53 @@
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# Interface:
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# make
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# make build
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# make format
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# make run
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# Compiler
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CC=gcc
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# CC=tcc # <= faster compilation
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# Main file
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SRC=example.c ../../squiggle.c
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OUTPUT=example
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## Dependencies
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MATH=-lm
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## Flags
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DEBUG= #'-g'
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STANDARD=-std=c99
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WARNINGS=-Wall
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OPTIMIZED=-O3 #-Ofast
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# OPENMP=-fopenmp
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## Formatter
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STYLE_BLUEPRINT=webkit
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FORMATTER=clang-format -i -style=$(STYLE_BLUEPRINT)
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## make build
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build: $(SRC)
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$(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(MATH) -o $(OUTPUT)
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format: $(SRC)
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$(FORMATTER) $(SRC)
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run: $(SRC) $(OUTPUT)
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OMP_NUM_THREADS=1 ./$(OUTPUT) && echo
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time-linux:
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@echo "Requires /bin/time, found on GNU/Linux systems" && echo
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@echo "Running 100x and taking avg time $(OUTPUT)"
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@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {1..100}; do $(OUTPUT); done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time using 1 thread: |" | sed 's|$$|ms|' && echo
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## Profiling
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profile-linux:
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echo "Requires perf, which depends on the kernel version, and might be in linux-tools package or similar"
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echo "Must be run as sudo"
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$(CC) $(SRC) $(MATH) -o $(OUTPUT)
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sudo perf record ./$(OUTPUT)
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sudo perf report
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rm perf.data
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56
squiggle.c
56
squiggle.c
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@ -94,12 +94,12 @@ double sample_gamma(double alpha, uint64_t* seed)
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// A Simple Method for Generating Gamma Variables, Marsaglia and Wan Tsang, 2001
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// https://dl.acm.org/doi/pdf/10.1145/358407.358414
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// see also the references/ folder
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// Note that the Wikipedia page for the gamma distribution includes a scaling parameter
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// k or beta
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// https://en.wikipedia.org/wiki/Gamma_distribution
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// such that gamma_k(alpha, k) = k * gamma(alpha)
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// or gamma_beta(alpha, beta) = gamma(alpha) / beta
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// So far I have not needed to use this, and thus the second parameter is by default 1.
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// Note that the Wikipedia page for the gamma distribution includes a scaling parameter
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// k or beta
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// https://en.wikipedia.org/wiki/Gamma_distribution
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// such that gamma_k(alpha, k) = k * gamma(alpha)
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// or gamma_beta(alpha, beta) = gamma(alpha) / beta
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// So far I have not needed to use this, and thus the second parameter is by default 1.
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if (alpha >= 1) {
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double d, c, x, v, u;
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d = alpha - 1.0 / 3.0;
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@ -386,38 +386,40 @@ struct box sampler_cdf_double(double cdf(double), uint64_t* seed)
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// Get confidence intervals, given a sampler
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struct c_i {
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float low;
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float high;
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float low;
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float high;
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};
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int compare_doubles(const void *p, const void *q) {
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// https://wikiless.esmailelbob.xyz/wiki/Qsort?lang=en
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double x = *(const double *)p;
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double y = *(const double *)q;
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int compare_doubles(const void* p, const void* q)
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{
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// https://wikiless.esmailelbob.xyz/wiki/Qsort?lang=en
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double x = *(const double*)p;
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double y = *(const double*)q;
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/* Avoid return x - y, which can cause undefined behaviour
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because of signed integer overflow. */
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if (x < y)
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return -1; // Return -1 if you want ascending, 1 if you want descending order.
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return -1; // Return -1 if you want ascending, 1 if you want descending order.
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else if (x > y)
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return 1; // Return 1 if you want ascending, -1 if you want descending order.
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return 1; // Return 1 if you want ascending, -1 if you want descending order.
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return 0;
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}
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struct c_i get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* seed){
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int n = 100 * 1000;
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double* samples_array = malloc(n * sizeof(double));
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for(int i=0; i<n; i++){
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samples_array[i] = sampler(seed);
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}
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qsort(samples_array, n, sizeof(double), compare_doubles);
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struct c_i get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* seed)
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{
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int n = 100 * 1000;
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double* samples_array = malloc(n * sizeof(double));
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for (int i = 0; i < n; i++) {
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samples_array[i] = sampler(seed);
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}
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qsort(samples_array, n, sizeof(double), compare_doubles);
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struct c_i result = {
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.low = samples_array[5000],
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.high =samples_array[94999],
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};
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free(samples_array);
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struct c_i result = {
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.low = samples_array[5000],
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.high = samples_array[94999],
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};
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free(samples_array);
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return result;
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return result;
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}
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/* Could also define other variations, e.g.,
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