go: savepoint before deleting a few comments

C/makefile

@@ -25,6 +25,7 @@ DEBUG= #'-g'
 STANDARD=-std=c99
 WARNINGS=-Wall
 OPTIMIZED=-O3 #-O3 actually gives better performance than -Ofast, at least for this version
+LOCAL=-march=native 
 OPENMP=-fopenmp
 
 ## Formatter
@@ -33,10 +34,10 @@ FORMATTER=clang-format -i -style=$(STYLE_BLUEPRINT)
 
 ## make build
 build: $(SRC)
-	$(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(OPENMP) $(MATH) -o $(OUTPUT)
+	$(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(LOCAL) $(OPENMP) $(MATH) -o $(OUTPUT)
 
 static:
-	$(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(OPENMP) $(MATH) -o $(OUTPUT)
+	$(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(LOCAL) $(OPENMP) $(MATH) -o $(OUTPUT)
 
 format: $(SRC)
 	$(FORMATTER) $(SRC)

README.md

@@ -24,17 +24,17 @@ The name of this repository is a pun on two meanings of "time to": "how much tim
 
 | Language                    | Time      | Lines of code |
 |-----------------------------|-----------|---------------|
-| C                           | 5.6ms     | 252  |
+| C                           | 6.20ms   | 252  |
-| squiggle.c                  | 8.2ms     | 29*  | 
+| squiggle.c                  | 7.20ms   | 29*  | 
-| Nim                         | 40.8ms    | 84   |
+| Nim                         | 41.10ms  | 84   |
-| Lua (LuaJIT)                | 69.9ms    | 82   |
+| Lua (LuaJIT)                | 68.80ms  | 82   |
-| OCaml (flambda)             | 187.9ms   | 123  |
+| OCaml (flambda)             | 185.50ms | 123  |
-| Squiggle (bun)              | 0.387s    | 14*  |
+| Squiggle (bun)              | 384.00ms | 14*  |
-| Javascript (node)           | 0.445s    | 69   |
+| Javascript (node)           | 0.423s   | 69   |
-| SquigglePy (v0.27)          | 1.507s    | 18*  |
+| SquigglePy (v0.27)          | 1.542s   | 18*  |
-| R (3.6.1)                   | 4.508s    | 49   |
+| R (3.6.1)                   | 4.494s   | 49   |
-| Python 3.9                  | 11.879s   | 56   |
+| Python 3.9                  | 11.909s  | 56   |
-| Gavin Howard's bc           | 15.960s   | 101  |
+| Gavin Howard's bc           | 16.170s  | 101  |
 
 Time measurements taken with the [time](https://man7.org/linux/man-pages/man1/time.1.html) tool, using 1M samples. But different implementations use different algorithms and, occasionally, different time measuring methodologies (for the C, Nim and Lua implementations, I run the program 100 times and take the mean). Their speed was also measured under different loads in my machine. So I think that these time estimates are accurate within maybe ~2x or so.
 

go/makefile

@@ -0,0 +1,16 @@
+dev:
+	go run squiggle.go
+
+build:
+	go build squiggle.go  
+
+build-complex: 
+	go build -ldflags="-s -w" squiggle.go  
+	# https://stackoverflow.com/questions/45003259/passing-an-optimization-flag-to-a-go-compiler
+
+run:
+	./squiggle
+
+time-linux: 
+	@echo "Running 100x and taking avg time: ./squiggle"
+	@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {0..100}; do ./squiggle; done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time using 16 threads: |" | sed 's|$$|ms|' && echo

go/notes.md

@@ -0,0 +1,8 @@
+- [x] Hello world program
+- [x] Look into randomness sources in go
+  - rand/v2 api: <https://pkg.go.dev/math/rand/v2>
+- [x] Test with a million samples of a simple lognormal, just to get a sense of speed
+- [x] Add mixture distribution
+- [x] Anonymous functions for nested: https://stackoverflow.com/questions/74523441/nested-functions-in-o
+- [ ] Look into go routines for filling up an array.
+  - Mhh, it's different from threads.

go/squiggle.go

@@ -0,0 +1,147 @@
+package main
+
+import "fmt"
+import "math"
+import "sync"
+import rand "math/rand/v2"
+
+type src = *rand.Rand
+type func64 = func(src) float64
+
+// https://pkg.go.dev/math/rand/v2
+
+func sample_unit_uniform(r src) float64 {
+	return r.Float64()
+}
+
+func sample_unit_normal(r src) float64 {
+	return r.NormFloat64()
+}
+
+func sample_uniform(start float64, end float64, r src) float64 {
+	return sample_unit_uniform(r)*(end-start) + start
+}
+
+func sample_normal(mean float64, sigma float64, r src) float64 {
+	return mean + sample_unit_normal(r)*sigma
+}
+
+func sample_lognormal(logmean float64, logstd float64, r src) float64 {
+	return (math.Exp(sample_normal(logmean, logstd, r)))
+}
+
+func sample_normal_from_90_ci(low float64, high float64, r src) float64 {
+	var normal90 float64 = 1.6448536269514727
+	var mean float64 = (high + low) / 2.0
+	var std float64 = (high - low) / (2.0 * normal90)
+	return sample_normal(mean, std, r)
+
+}
+
+func sample_to(low float64, high float64, r src) float64 {
+	// Given a (positive) 90% confidence interval,
+	// 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_90_ci
+	var loglow float64 = math.Log(low)
+	var loghigh float64 = math.Log(high)
+	return math.Exp(sample_normal_from_90_ci(loglow, loghigh, r))
+}
+
+func sample_mixture(fs []func64, weights []float64, r src) float64 {
+
+	// fmt.Println("weights initially: ", weights)
+	var sum_weights float64 = 0
+	for _, weight := range weights {
+		sum_weights += weight
+	}
+
+	var total float64 = 0
+	var cumsummed_normalized_weights = append([]float64(nil), weights...)
+	for i, weight := range weights {
+		total += weight / sum_weights
+		cumsummed_normalized_weights[i] = total
+	}
+
+	var result float64
+	var flag int = 0
+	var p float64 = r.Float64()
+
+	for i, cnw := range cumsummed_normalized_weights {
+		if p < cnw {
+			result = fs[i](r)
+			flag = 1
+			break
+		}
+	}
+	// fmt.Println(cumsummed_normalized_weights)
+
+	if flag == 0 {
+		result = fs[len(fs)-1](r)
+	}
+	return result
+
+}
+
+func slice_fill(xs []float64, fs func64, r src) {
+	for i := range xs {
+		xs[i] = fs(r)
+	}
+}
+
+func sample_parallel(f func64, n_samples int) []float64 {
+	var num_threads = 16
+	var xs = make([]float64, n_samples)
+	var wg sync.WaitGroup
+	var h = n_samples / num_threads
+	wg.Add(num_threads)
+	for i := range num_threads {
+		var xs_i = xs[i*h : (i+1)*h]
+		go func(f func64) {
+			defer wg.Done()
+			var r = rand.New(rand.NewPCG(uint64(i), uint64(i+1)))
+			for i := range xs_i {
+				xs_i[i] = f(r)
+			}
+		}(f)
+	}
+
+	wg.Wait()
+	return xs
+
+}
+
+func main() {
+
+	var p_a float64 = 0.8
+	var p_b float64 = 0.5
+	var p_c float64 = p_a * p_b
+	ws := [4](float64){1 - p_c, p_c / 2, p_c / 4, p_c / 4}
+
+	sample_0 := func(r src) float64 { return 0 }
+	sample_1 := func(r src) float64 { return 1 }
+	sample_few := func(r src) float64 { return sample_to(1, 3, r) }
+	sample_many := func(r src) float64 { return sample_to(2, 10, r) }
+	fs := [4](func64){sample_0, sample_1, sample_few, sample_many}
+
+	model := func(r src) float64 { return sample_mixture(fs[0:], ws[0:], r) }
+	n_samples := 1_000_000
+	xs := sample_parallel(model, n_samples)
+	var avg float64 = 0
+	for _, x := range xs {
+		avg += x
+	}
+	avg = avg / float64(n_samples)
+	fmt.Printf("Average: %v\n", avg)
+	/*
+		n_samples := 1_000_000
+		var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
+		var avg float64 = 0
+		for i := 0; i < n_samples; i++ {
+			avg += sample_mixture(fs[0:], ws[0:], r)
+		}
+		avg = avg / float64(n_samples)
+		fmt.Printf("Average: %v\n", avg)
+	*/
+}

squiggle.c/makefile

@@ -1,7 +1,8 @@
 OUTPUT=./samples
+CC=gcc
 
 build:
-	gcc -O3 samples.c ./squiggle_c/squiggle.c  ./squiggle_c/squiggle_more.c -lm -fopenmp -o $(OUTPUT)
+	$(CC) -O3 -march=native samples.c ./squiggle_c/squiggle.c  ./squiggle_c/squiggle_more.c -lm -fopenmp -o $(OUTPUT)
 
 install: 
 	rm -r squiggle_c

squiggle.c/samples.c

@@ -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);
-    {
+}
-        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));

squiggle.c/squiggle_c/squiggle_more.c

@@ -8,17 +8,20 @@
 #include <stdlib.h>
 #include <string.h> // memcpy
 
-/* Parallel sampler */
+/* Cache optimizations */
 #define CACHE_LINE_SIZE 64
+// getconf LEVEL1_DCACHE_LINESIZE
+// <https://stackoverflow.com/questions/794632/programmatically-get-the-cache-line-size>
 typedef struct seed_cache_box_t {
     uint64_t seed;
-    char padding[CACHE_LINE_SIZE - sizeof(uint64_t*)];
+    char padding[CACHE_LINE_SIZE - sizeof(uint64_t)];
+    // Cache line size is 64 *bytes*, uint64_t is 64 *bits* (8 bytes). Different units!
 } seed_cache_box;
 // This avoids "false sharing", i.e., different threads competing for the same cache line
-// It's possible dealing with this shaves ~2ms
+// Dealing with this shaves 4ms from a 12ms process, or a third of runtime
-// However, it's possible it doesn't, since pointers aren't changed, just their contents (and the location of their contents doesn't necessarily have to be close, since they are malloc'ed sepately)
+// <http://www.nic.uoregon.edu/~khuck/ts/acumem-report/manual_html/ch06s07.html>
-// Still, I thought it was interesting
 
+/* Parallel sampler */
 void sampler_parallel(double (*sampler)(uint64_t* seed), double* results, int n_threads, int n_samples)
 {
 
@@ -40,14 +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 = (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;
@@ -56,28 +59,53 @@ 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
     }
-    /*
+
-    for (int i = 0; i < n_threads; i++) {
-        free(cache_box[i].seed);
-    }
-    */
     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.
+    int median_k = (int)floor(0.5 * n);
-    double* xs = malloc((size_t)n * sizeof(double));
+    return quickselect(median_k, xs, n);
-    sampler_parallel(sampler, xs, 16, n);
-    ci result = array_get_ci(interval, xs, n);
-    free(xs);
-    return result;
 }
-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");
-}

squiggle.c/squiggle_c/squiggle_more.h

@@ -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 */
+/* Utilities */
-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);
 
-/* Inverse cdf */
+#define THOUSAND 1000
-box inverse_cdf_double(double cdf(double), double p);
+#define MILLION 1000000
-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);
 
 #endif

time.txt

@@ -1,39 +1,39 @@
 # bc
 time ghbc -l squiggle.bc estimate.bc
-.8907201178102747
+.8872657001481914
 
-real	0m15.960s
+real    0m16.170s
-user	0m15.948s
+user    0m16.115s
-sys	0m0.000s
+sys     0m0.008s
 
 
 # C
 Running 100x and taking avg time: OMP_NUM_THREADS=16 out/samples
-Time using 16 threads: 5.60ms
+Time using 16 threads: 6.20ms
 
 
 
 # js (bun)
-0.8867426270252042
+0.8861715640546732
 
-real	0m0.551s
+real    0m0.562s
-user	0m0.527s
+user    0m0.540s
-sys	0m0.055s
+sys     0m0.074s
 
 
 # js (node)
-0.8878977218582866
+0.8863245179136781
 
-real	0m0.445s
+real    0m0.423s
-user	0m0.523s
+user    0m0.509s
-sys	0m0.060s
+sys     0m0.077s
 
 
 # lua (luajit)
 Requires /bin/time, found on GNU/Linux systems
 
 Running 100x and taking avg time of: luajit samples.lua
-Time: 69.90ms
+Time: 68.80ms
 
 
 
@@ -41,7 +41,7 @@ Time: 69.90ms
 Requires /bin/time, found on GNU/Linux systems
 
 Running 100x and taking avg time of:
-Time: 40.80ms
+Time: 41.10ms
 
 
 
@@ -49,48 +49,47 @@ Time: 40.80ms
 Requires /bin/time, found on GNU/Linux systems
 
 Running 100x and taking avg time of:
-Time: 187.90ms
+Time: 185.50ms
 
 
 
 # Python (3.9)
 0.8887373869178242
 
-real	0m11.879s
+real    0m11.909s
-user	0m12.129s
+user    0m12.149s
-sys	0m1.055s
+sys     0m1.145s
 
 
 # R (3.6.1)
-[1] 0.8899922
+[1] 0.8862725
 
-real	0m4.508s
+real    0m4.494s
-user	0m4.476s
+user    0m4.465s
-sys	0m0.028s
+sys     0m0.025s
 
 
 # Squiggle (0.8.6)
 Requires /bin/time, found on GNU/Linux systems
 
 Running 100x and taking avg time of:
-Time: 386.80ms
+Time: 384.00ms
 
 
 
 # SquigglePy (0.27)
 time python3.9 samples.py
-
  0%|          | 0/4 [00:00<?, ?it/s]
 75%|███████▌  | 3/4 [00:00<00:00, 27.07it/s]
100%|██████████| 4/4 [00:00<00:00, 23.38it/s]
+100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 22.58it/s]
-
  0%|          | 0/1000000 [00:00<?, ?it/s]
 10%|█         | 104035/1000000 [00:00<00:00, 1040346.03it/s]
 24%|██▍       | 238684/1000000 [00:00<00:00, 1220429.41it/s]
 38%|███▊      | 376402/1000000 [00:00<00:00, 1292004.08it/s]
 51%|█████▏    | 514235/1000000 [00:00<00:00, 1326083.80it/s]
 65%|██████▌   | 654235/1000000 [00:00<00:00, 1352735.46it/s]
 80%|███████▉  | 795746/1000000 [00:00<00:00, 1373942.14it/s]
 93%|█████████▎| 934912/1000000 [00:00<00:00, 1379731.72it/s]

it/s]
+

it/s]
-0.8879525229675179
+0.8876134007583529
 
-real	0m1.507s
+real    0m1.542s
-user	0m1.969s
+user    0m1.989s
-sys	0m2.201s
+sys     0m2.226s
 
 
 # squiggle.c
 Running 100x and taking avg time: OMP_NUM_THREADS=16 ./samples
-Time using 16 threads: 12.70ms
+Time using 16 threads: 7.20ms
-