add squiggle_c beginnings.

C/squiggle_c/example_simple/example.c

@@ -0,0 +1,62 @@
+#include "../squiggle.h"
+#include <stdint.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+// Estimate functions
+float sample_0(uint32_t* seed)
+{
+    return 0;
+}
+
+float sample_1(uint32_t* seed)
+{
+    return 1;
+}
+
+float sample_few(uint32_t* seed)
+{
+    return random_to(1, 3, seed);
+}
+
+float sample_many(uint32_t* seed)
+{
+    return random_to(2, 10, seed);
+}
+
+int main(){
+    // set randomness seed
+		uint32_t* seed = malloc(sizeof(uint32_t));
+		*seed = 1000; // xorshift can't start with 0
+
+    float p_a = 0.8;
+    float p_b = 0.5;
+    float p_c = p_a * p_b;
+
+    int n_dists = 4;
+    float weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
+    float (*samplers[])(uint32_t*) = { sample_0, sample_1, sample_few, sample_many };
+
+    float result_one = mixture(samplers, weights, n_dists, seed);
+		printf("result_one: %f\n", result_one);
+		
+		int n_samples = 1000000;
+		float* result_many = (float *) malloc(n_samples * sizeof(float));
+		for(int i=0; i<n_samples; i++){
+      result_many[i] = mixture(samplers, weights, n_dists, seed);
+		}
+		
+		printf("result_many: [");
+		for(int i=0; i<100; i++){
+		  printf("%.2f, ", result_many[i]);
+		}
+		printf("]\n");
+}
+
+/* 
+Aggregation mechanisms:
+- Quantiles (requires a sort)
+- Sum 
+- Average
+- Std
+*/

C/squiggle_c/example_simple/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
+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) $(OUTPUT)
+	OMP_NUM_THREADS=1 ./$(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

C/squiggle_c/squiggle.h

@@ -0,0 +1,109 @@
+#include <math.h>
+#include <stdint.h>
+#include <stdlib.h>
+
+const float PI = 3.14159265358979323846;
+
+// Pseudo Random number generator
+
+uint32_t xorshift32
+(uint32_t* seed)
+{
+	// Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
+  // See <https://stackoverflow.com/questions/53886131/how-does-xorshift32-works>
+  // https://en.wikipedia.org/wiki/Xorshift
+	// Also some drama: <https://www.pcg-random.org/posts/on-vignas-pcg-critique.html>, <https://prng.di.unimi.it/>
+
+	uint32_t x = *seed;
+	x ^= x << 13;
+	x ^= x >> 17;
+	x ^= x << 5;
+	return *seed = x;
+}
+
+// Distribution & sampling functions
+
+float rand_0_to_1(uint32_t* seed){
+	return ((float) xorshift32(seed)) / ((float) UINT32_MAX);
+}
+
+float rand_float(float max, uint32_t* seed)
+{
+    return rand_0_to_1(seed) * max;
+}
+
+float ur_normal(uint32_t* seed)
+{
+    float u1 = rand_0_to_1(seed);
+    float u2 = rand_0_to_1(seed);
+    float z = sqrtf(-2.0 * log(u1)) * sin(2 * PI * u2);
+    return z;
+}
+
+float random_uniform(float from, float to, uint32_t* seed)
+{
+    return rand_0_to_1(seed) * (to - from) + from;
+}
+
+float random_normal(float mean, float sigma, uint32_t* seed)
+{
+    return (mean + sigma * ur_normal(seed));
+}
+
+float random_lognormal(float logmean, float logsigma, uint32_t* seed)
+{
+    return expf(random_normal(logmean, logsigma, seed));
+}
+
+float random_to(float low, float high, uint32_t* seed)
+{
+    const float NORMAL95CONFIDENCE = 1.6448536269514722;
+    float loglow = logf(low);
+    float loghigh = logf(high);
+    float logmean = (loglow + loghigh) / 2;
+    float logsigma = (loghigh - loglow) / (2.0 * NORMAL95CONFIDENCE);
+    return random_lognormal(logmean, logsigma, seed);
+}
+
+// Array helpers
+float array_sum(float* array, int length)
+{
+    float output = 0.0;
+    for (int i = 0; i < length; i++) {
+        output += array[i];
+    }
+    return output;
+}
+
+void array_cumsum(float* array_to_sum, float* array_cumsummed, int length)
+{
+    array_cumsummed[0] = array_to_sum[0];
+    for (int i = 1; i < length; i++) {
+        array_cumsummed[i] = array_cumsummed[i - 1] + array_to_sum[i];
+    }
+}
+
+// Mixture function
+float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint32_t* seed)
+{
+    // You can see a simpler version of this function in the git history
+    // or in C-02-better-algorithm-one-thread/
+    float sum_weights = array_sum(weights, n_dists);
+		float* cumsummed_normalized_weights = (float*) malloc(n_dists * sizeof(float));
+		cumsummed_normalized_weights[0] = weights[0]/sum_weights;
+    for (int i = 1; i < n_dists; i++) {
+        cumsummed_normalized_weights[i] = cumsummed_normalized_weights[i - 1] + weights[i]/sum_weights;
+    }
+
+		float p = random_uniform(0, 1, seed);
+		float result;
+		for (int k = 0; k < n_dists; k++) {
+				if (p < cumsummed_normalized_weights[k]) {
+						result = samplers[k](seed);
+						break;
+				}
+		}
+
+		free(cumsummed_normalized_weights);
+		return result;
+}

C/squiggle_c/to-do.md

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+
+- [ ] Add example for only one sample
+- [ ] Add example for many samples
+- [ ] Use gcc extension to define functions nested inside main.
+- [ ] Use OpenMP for acceleration
+- [ ] Chain various mixture functions
+- [ ] Have some more complicated & realistic example
+- [ ] Add summarization functions, like mean, std, 90% ci (or all c.i.?)
+- [ ] Add beta distribution