move to squiggle.c file, instead of just squiggle.h

examples/01_one_sample/makefile

@@ -9,7 +9,7 @@ CC=gcc
 # CC=tcc # <= faster compilation
 
 # Main file
-SRC=example.c
+SRC=example.c ../../squiggle.c
 OUTPUT=example
 
 ## Dependencies

examples/02_many_samples/makefile

@@ -9,7 +9,7 @@ CC=gcc
 # CC=tcc # <= faster compilation
 
 # Main file
-SRC=example.c
+SRC=example.c ../../squiggle.c
 OUTPUT=example
 
 ## Dependencies

examples/03_gcc_nested_function/makefile

@@ -9,7 +9,7 @@ CC=gcc # required for nested functions
 # CC=tcc # <= faster compilation
 
 # Main file
-SRC=example.c
+SRC=example.c ../../squiggle.c
 OUTPUT=example
 
 ## Dependencies

squiggle.c

@@ -0,0 +1,113 @@
+#include <math.h>
+#include <stdint.h>
+#include <stdlib.h>
+
+// PI constant
+const float PI = M_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 result;
+		int result_set_flag = 0;
+		float p = random_uniform(0, 1, seed);
+		for (int k = 0; k < n_dists; k++) {
+				if (p < cumsummed_normalized_weights[k]) {
+						result = samplers[k](seed);
+						result_set_flag = 1;
+						break;
+				}
+		}
+		if(result_set_flag == 0) result = samplers[n_dists-1](seed);
+
+		free(cumsummed_normalized_weights);
+		return result;
+}

squiggle.h

@@ -1,112 +1,26 @@
-#include <math.h>
-#include <stdint.h>
-#include <stdlib.h>
+#ifndef SQUIGGLEC
+#define SQUIGGLEC
 
-const float PI = 3.14159265358979323846;
+// uint32_t header
+#include <stdint.h>
 
 // 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;
-}
+uint32_t xorshift32(uint32_t* seed);
 
 // 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);
-}
+float rand_0_to_1(uint32_t* seed);
+float rand_float(float max, uint32_t* seed);
+float ur_normal(uint32_t* seed);
+float random_uniform(float from, float to, uint32_t* seed);
+float random_normal(float mean, float sigma, uint32_t* seed);
+float random_lognormal(float logmean, float logsigma, uint32_t* seed);
+float random_to(float low, float high, uint32_t* 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];
-    }
-}
+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)
-{
-    // 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 mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint32_t* seed);
 
-		float result;
-		int result_set_flag = 0;
-		float p = random_uniform(0, 1, seed);
-		for (int k = 0; k < n_dists; k++) {
-				if (p < cumsummed_normalized_weights[k]) {
-						result = samplers[k](seed);
-						result_set_flag = 1;
-						break;
-				}
-		}
-		if(result_set_flag == 0) result = samplers[n_dists-1](seed);
-
-		free(cumsummed_normalized_weights);
-		return result;
-}
+#endif