give more expressive names to main functions

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function replace(){
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}

README.md

@@ -77,7 +77,7 @@ Behaviour on error can be toggled by the `EXIT_ON_ERROR` variable. This library
 
 ## To do list
 
-- [ ] Rename functions to something more self-explanatory, e.g,. sample_unit_normal.
+- [ ] Rename functions to something more self-explanatory, e.g,. sample_sample_sample_unit_normal.
 - [ ] Have some more complicated & realistic example
 - [ ] Add summarization functions, like mean, std, 90% ci (or all c.i.?)
 - [ ] Publish online
@@ -90,7 +90,7 @@ Behaviour on error can be toggled by the `EXIT_ON_ERROR` variable. This library
 - [x] Add example for many samples
 - ~~[ ] Add a custom preprocessor to allow simple nested functions that don't rely on local scope?~~
 - [x] Use gcc extension to define functions nested inside main.
-- [x] Chain various mixture functions
+- [x] Chain various sample_mixture functions
 - [x] Add beta distribution
   - See <https://stats.stackexchange.com/questions/502146/how-does-numpy-generate-samples-from-a-beta-distribution> for a faster method.
 - ~~[-] Use OpenMP for acceleration~~

examples/01_one_sample/example.c

@@ -16,12 +16,12 @@ float sample_1(uint32_t* seed)
 
 float sample_few(uint32_t* seed)
 {
-    return random_to(1, 3, seed);
+    return sample_to(1, 3, seed);
 }
 
 float sample_many(uint32_t* seed)
 {
-    return random_to(2, 10, seed);
+    return sample_to(2, 10, seed);
 }
 
 int main(){
@@ -37,7 +37,7 @@ int main(){
     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);
+    float result_one = sample_mixture(samplers, weights, n_dists, seed);
 		printf("result_one: %f\n", result_one);
 }
 

examples/02_many_samples/example.c

@@ -16,12 +16,12 @@ float sample_1(uint32_t* seed)
 
 float sample_few(uint32_t* seed)
 {
-    return random_to(1, 3, seed);
+    return sample_to(1, 3, seed);
 }
 
 float sample_many(uint32_t* seed)
 {
-    return random_to(2, 10, seed);
+    return sample_to(2, 10, seed);
 }
 
 int main(){
@@ -40,7 +40,7 @@ int main(){
 		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);
+      result_many[i] = sample_mixture(samplers, weights, n_dists, seed);
 		}
 		
 		printf("result_many: [");

examples/03_gcc_nested_function/example.c

@@ -16,8 +16,8 @@ int main(){
 		
 		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_few(uint32_t* seed){ return sample_to(1, 3, seed); }
-		float sample_many(uint32_t* seed){ return random_to(2, 10, seed); } 
+		float sample_many(uint32_t* seed){ return sample_to(2, 10, seed); } 
 		
     float (*samplers[])(uint32_t*) = { sample_0, sample_1, sample_few, sample_many };
 		float weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
@@ -25,7 +25,7 @@ int main(){
 		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);
+      result_many[i] = sample_mixture(samplers, weights, n_dists, seed);
 		}
 		
 		printf("result_many: [");

examples/04_sample_from_cdf_simple/example.c

@@ -84,12 +84,12 @@ int main()
     test_and_time_sampler_float("cdf_normal_0_1", cdf_normal_0_1, seed);
 
     // Get some normal samples using a previous approach
-    printf("\nGetting some samples from unit_normal\n");
+    printf("\nGetting some samples from sample_unit_normal\n");
 
     clock_t begin_2 = clock();
 
     for (int i = 0; i < NUM_SAMPLES; i++) {
-        float normal_sample = unit_normal(seed);
+        float normal_sample = sample_unit_normal(seed);
         // printf("%f\n", normal_sample);
     }
 

squiggle.c

@@ -28,48 +28,44 @@ uint32_t xorshift32(uint32_t* seed)
 }
 
 // Distribution & sampling functions
-float rand_0_to_1(uint32_t* seed)
+float sample_unit_uniform(uint32_t* seed)
 {
+	  // samples uniform from [0,1] interval.
     return ((float)xorshift32(seed)) / ((float)UINT32_MAX);
 }
 
-float rand_float(float max, uint32_t* seed)
+float sample_unit_normal(uint32_t* seed)
-{
-    return rand_0_to_1(seed) * max;
-}
-
-float unit_normal(uint32_t* seed)
 {
     // See: <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform>
-    float u1 = rand_0_to_1(seed);
+    float u1 = sample_unit_uniform(seed);
-    float u2 = rand_0_to_1(seed);
+    float u2 = sample_unit_uniform(seed);
     float z = sqrtf(-2.0 * log(u1)) * sin(2 * PI * u2);
     return z;
 }
 
-float random_uniform(float from, float to, uint32_t* seed)
+float sample_uniform(float from, float to, uint32_t* seed)
 {
-    return rand_0_to_1(seed) * (to - from) + from;
+    return sample_unit_uniform(seed) * (to - from) + from;
 }
 
-float random_normal(float mean, float sigma, uint32_t* seed)
+float sample_normal(float mean, float sigma, uint32_t* seed)
 {
-    return (mean + sigma * unit_normal(seed));
+    return (mean + sigma * sample_unit_normal(seed));
 }
 
-float random_lognormal(float logmean, float logsigma, uint32_t* seed)
+float sample_lognormal(float logmean, float logsigma, uint32_t* seed)
 {
-    return expf(random_normal(logmean, logsigma, seed));
+    return expf(sample_normal(logmean, logsigma, seed));
 }
 
-float random_to(float low, float high, uint32_t* seed)
+float sample_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);
+    return sample_lognormal(logmean, logsigma, seed);
 }
 
 // Array helpers
@@ -91,7 +87,7 @@ void array_cumsum(float* array_to_sum, float* array_cumsummed, int length)
 }
 
 // Mixture function
-float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint32_t* seed)
+float sample_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/
@@ -104,7 +100,7 @@ float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint3
 
     float result;
     int result_set_flag = 0;
-    float p = random_uniform(0, 1, seed);
+    float p = sample_uniform(0, 1, seed);
     for (int k = 0; k < n_dists; k++) {
         if (p < cumsummed_normalized_weights[k]) {
             result = samplers[k](seed);
@@ -289,13 +285,13 @@ struct box inverse_cdf_box(struct box cdf_box(float), float p)
 // Sampler based on inverse cdf and randomness function
 struct box sampler_cdf_box(struct box cdf(float), uint32_t* seed)
 {
-    float p = rand_0_to_1(seed);
+    float p = sample_unit_uniform(seed);
     struct box result = inverse_cdf_box(cdf, p);
     return result;
 }
 struct box sampler_cdf_float(float cdf(float), uint32_t* seed)
 {
-    float p = rand_0_to_1(seed);
+    float p = sample_unit_uniform(seed);
     struct box result = inverse_cdf_float(cdf, p);
     return result;
 }
@@ -303,7 +299,7 @@ struct box sampler_cdf_float(float cdf(float), uint32_t* seed)
 /* Could also define other variations, e.g.,
 float sampler_danger(struct box cdf(float), uint32_t* seed)
 {
-    float p = rand_0_to_1(seed);
+    float p = sample_unit_uniform(seed);
     struct box result = inverse_cdf_box(cdf, p);
 		if(result.empty){
 			exit(1);

squiggle.h

@@ -8,22 +8,21 @@
 uint32_t xorshift32(uint32_t* seed);
 
 // Basic distribution sampling functions
-float rand_0_to_1(uint32_t* seed);
+float sample_unit_uniform(uint32_t* seed);
-float rand_float(float max, uint32_t* seed);
+float sample_unit_normal(uint32_t* seed);
-float unit_normal(uint32_t* seed);
 
 // Composite distribution sampling functions
-float random_uniform(float from, float to, uint32_t* seed);
+float sample_uniform(float from, float to, uint32_t* seed);
-float random_normal(float mean, float sigma, uint32_t* seed);
+float sample_normal(float mean, float sigma, uint32_t* seed);
-float random_lognormal(float logmean, float logsigma, uint32_t* seed);
+float sample_lognormal(float logmean, float logsigma, uint32_t* seed);
-float random_to(float low, float high, uint32_t* seed);
+float sample_to(float low, float high, uint32_t* seed);
 
 // Array helpers
 float array_sum(float* array, int length);
 void array_cumsum(float* array_to_sum, float* array_cumsummed, int length);
 
 // Mixture function
-float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint32_t* seed);
+float sample_mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint32_t* seed);
 
 // Box
 struct box {