commit 308eecba9844f0194f892b8d88f22f5e2fe51a22
parent b1a58f9b74946c6ede7e47fd5035b1879d2578a1
Author: NunoSempere <nuno.sempere@protonmail.com>
Date: Wed, 27 Sep 2023 15:25:12 +0100
tweaks before twitter thread
Diffstat:
8 files changed, 151 insertions(+), 27 deletions(-)
diff --git a/README.md b/README.md
@@ -2,6 +2,8 @@
squiggle.c is a self-contained C99 library that provides functions for simple Monte Carlo estimation, based on [Squiggle](https://www.squiggle-language.com/).
+
+
## Why C?
- Because it is fast
diff --git a/core.png b/core.png
Binary files differ.
diff --git a/examples/02_many_samples_time_to_botec/example.c b/examples/02_many_samples_time_to_botec/example.c
@@ -1,7 +1,7 @@
+#include "../../squiggle.h"
#include <stdint.h>
-#include <stdlib.h>
#include <stdio.h>
-#include "../../squiggle.h"
+#include <stdlib.h>
// Estimate functions
double sample_0(uint64_t* seed)
@@ -24,10 +24,11 @@ double sample_many(uint64_t* seed)
return sample_to(2, 10, seed);
}
-int main(){
+int main()
+{
// set randomness seed
- uint64_t* seed = malloc(sizeof(uint64_t));
- *seed = 1000; // xorshift can't start with 0
+ uint64_t* seed = malloc(sizeof(uint64_t));
+ *seed = 1000; // xorshift can't start with 0
double p_a = 0.8;
double p_b = 0.5;
@@ -37,18 +38,18 @@ int main(){
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 };
- int n_samples = 1000000;
- double* result_many = (double *) malloc(n_samples * sizeof(double));
- for(int i=0; i<n_samples; i++){
- result_many[i] = sample_mixture(samplers, weights, n_dists, seed);
- }
- printf("Mean: %f\n", array_mean(result_many, n_samples));
-
- // printf("result_many: [");
- // for(int i=0; i<100; i++){
- // printf("%.2f, ", result_many[i]);
- // }
- // printf("]\n");
-
- free(seed);
+ int n_samples = 1000000;
+ double* result_many = (double*)malloc(n_samples * sizeof(double));
+ for (int i = 0; i < n_samples; i++) {
+ result_many[i] = sample_mixture(samplers, weights, n_dists, seed);
+ }
+ printf("Mean: %f\n", array_mean(result_many, n_samples));
+
+ // printf("result_many: [");
+ // for(int i=0; i<100; i++){
+ // printf("%.2f, ", result_many[i]);
+ // }
+ // printf("]\n");
+
+ free(seed);
}
diff --git a/examples/14_twitter_thread_example/example b/examples/14_twitter_thread_example/example
Binary files differ.
diff --git a/examples/14_twitter_thread_example/example.c b/examples/14_twitter_thread_example/example.c
@@ -0,0 +1,43 @@
+#include "../../squiggle.h"
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+double sample_0(uint64_t* seed){
+ return 0;
+}
+
+double sample_1(uint64_t* seed){
+ return 1;
+}
+
+double sample_normal_mean_1_std_2(uint64_t* seed){
+ return sample_normal(1, 2, seed);
+}
+
+double sample_1_to_3(uint64_t* seed){
+ return sample_to(1, 3, seed);
+}
+
+int main()
+{
+ // set randomness seed
+ uint64_t* seed = malloc(sizeof(uint64_t));
+ *seed = 1000; // xorshift can't start with 0
+
+ int n_dists = 4;
+ double weights[] = { 1, 2, 3, 4 };
+ double (*samplers[])(uint64_t*) = {
+ sample_0,
+ sample_1,
+ sample_normal_mean_1_std_2,
+ sample_1_to_3
+ };
+
+ int n_samples = 10;
+ for (int i = 0; i < n_samples; i++) {
+ printf("Sample #%d: %f\n", i, sample_mixture(samplers, weights, n_dists, seed));
+ }
+
+ free(seed);
+}
diff --git a/examples/14_twitter_thread_example/makefile b/examples/14_twitter_thread_example/makefile
@@ -0,0 +1,53 @@
+# Interface:
+# make
+# make build
+# make format
+# make run
+
+# Compiler
+CC=gcc
+# CC=tcc # <= faster compilation
+
+# Main file
+SRC=example.c ../../squiggle.c
+OUTPUT=example
+
+## Dependencies
+MATH=-lm
+
+## Flags
+DEBUG= #'-g'
+STANDARD=-std=c99
+WARNINGS=-Wall
+OPTIMIZED=-O3 #-Ofast
+# OPENMP=-fopenmp
+
+## Formatter
+STYLE_BLUEPRINT=webkit
+FORMATTER=clang-format -i -style=$(STYLE_BLUEPRINT)
+
+## make build
+build: $(SRC)
+ $(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(MATH) -o $(OUTPUT)
+
+format: $(SRC)
+ $(FORMATTER) $(SRC)
+
+run: $(SRC) $(OUTPUT)
+ ./$(OUTPUT) && echo
+
+time-linux:
+ @echo "Requires /bin/time, found on GNU/Linux systems" && echo
+
+ @echo "Running 100x and taking avg time $(OUTPUT)"
+ @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
+
+## Profiling
+
+profile-linux:
+ echo "Requires perf, which depends on the kernel version, and might be in linux-tools package or similar"
+ echo "Must be run as sudo"
+ $(CC) $(SRC) $(MATH) -o $(OUTPUT)
+ sudo perf record ./$(OUTPUT)
+ sudo perf report
+ rm perf.data
diff --git a/scratchpad/core.c b/scratchpad/core.c
@@ -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;
+}
+
diff --git a/squiggle.c b/squiggle.c
@@ -26,8 +26,8 @@ uint64_t xorshift32(uint32_t* seed)
// See:
// <https://en.wikipedia.org/wiki/Xorshift>
// <https://stackoverflow.com/questions/53886131/how-does-xorshift32-works>,
- // Also some drama:
- // <https://www.pcg-random.org/posts/on-vignas-pcg-critique.html>,
+ // Also some drama:
+ // <https://www.pcg-random.org/posts/on-vignas-pcg-critique.html>,
// <https://prng.di.unimi.it/>
uint64_t x = *seed;
x ^= x << 13;
@@ -57,7 +57,7 @@ double sample_unit_uniform(uint64_t* seed)
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 u1 = sample_unit_uniform(seed);
double u2 = sample_unit_uniform(seed);
double z = sqrtf(-2.0 * log(u1)) * sin(2 * PI * u2);
return z;
@@ -109,7 +109,7 @@ double sample_to(double low, double high, uint64_t* seed)
// returns a sample from a lognorma with a matching 90% c.i.
// Key idea: If we want a lognormal with 90% confidence interval [a, b]
// we need but get a normal with 90% confidence interval [log(a), log(b)].
- // Then see code for sample_normal_from_95_confidence_interval
+ // Then see code for sample_normal_from_90_confidence_interval
double loglow = logf(low);
double loghigh = logf(high);
return exp(sample_normal_from_90_confidence_interval(loglow, loghigh, seed));
@@ -511,7 +511,7 @@ lognormal_params convert_ci_to_lognormal_params(ci x)
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};
+ lognormal_params result = { .logmean = logmean, .logstd = logstd };
return result;
}
@@ -520,8 +520,6 @@ 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)};
+ ci result = { .low = exp(loglow), .high = exp(loghigh) };
return result;
-
}
-
