add many more comments, start adding to header file.

README.md

@@ -344,7 +344,9 @@ It emits one warning about something I already took care of, so by default I've
 - [x] Add a few functions for doing simple algebra on normals, and lognormals
   - [x] Add prototypes
   - [x] Use named structs
+  - [x] Add to header file
   - [ ] Test results
   - [ ] Provide example
-  - [ ] Add to headers
+  - [ ] Add conversion between 90%ci and parameters.
-  - [ ] Move to own file
+  - [ ] Move to own file? Or signpost in file?
+- [ ] Disambiguate sample_laplace--successes vs failures || successes vs total trials as two distinct and differently named functions

squiggle.c

@@ -7,15 +7,22 @@
 #include <sys/types.h>
 #include <time.h>
 
+// Some error niceties; these won't be used until later
 #define MAX_ERROR_LENGTH 500
 #define EXIT_ON_ERROR 0
 #define PROCESS_ERROR(error_msg) process_error(error_msg, EXIT_ON_ERROR, __FILE__, __LINE__)
 
 const double PI = 3.14159265358979323846; // M_PI in gcc gnu99
 
+// # Key functionality
+// Define the minimum number of functions needed to do simple estimation
+// Starts here, ends until the end of the mixture function
+
 // Pseudo Random number generator
 uint64_t xorshift32(uint32_t* seed)
 {
+    // The reader isn't expected to understand this code immediately;
+    // read the linked Wikipedia page!
     // Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
     // See <https://stackoverflow.com/questions/53886131/how-does-xorshift64-works>
     // https://en.wikipedia.org/wiki/Xorshift
@@ -71,17 +78,40 @@ double sample_lognormal(double logmean, double logstd, uint64_t* seed)
     return exp(sample_normal(logmean, logstd, seed));
 }
 
+inline double sample_normal_from_95_confidence_interval(double low, double high, uint64_t* seed){
+    // Explanation of key idea:
+    // 1. We know that the 90% confidence interval of the unit normal is 
+    // [-1.6448536269514722, 1.6448536269514722]
+    // see e.g.: https://stackoverflow.com/questions/20626994/how-to-calculate-the-inverse-of-the-normal-cumulative-distribution-function-in-p
+    // 2. So if we take a unit normal and multiply it by
+    // L / 1.6448536269514722, its new 90% confidence interval will be 
+    // [-L, L], i.e., length 2 * L
+    // 3. Instead, if we want to get a confidence interval of length L, 
+    // we should multiply the unit normal by 
+    // L / (2 * 1.6448536269514722)
+    // Meaning that its standard deviation should be multiplied by that amount
+    // see: https://en.wikipedia.org/wiki/Normal_distribution?lang=en#Operations_on_a_single_normal_variable
+    // 4. So we have learnt that Normal(0, L / (2 * 1.6448536269514722))
+    // has a 90% confidence interval of length L
+    // 5. If we want a 90% confidence interval from high to low, 
+    // we can set mean = (high + low)/2; the midpoint, and L = high-low, 
+    // Normal([high + low]/2, [high - low]/(2 * 1.6448536269514722))
+    const double NORMAL95CONFIDENCE = 1.6448536269514722;
+    double mean = (high + low) / 2.0;
+    double std = (high - low) / (2.0 * NORMAL95CONFIDENCE );
+    return sample_normal(mean, std);
+}
+
 double sample_to(double low, double high, uint64_t* seed)
 {
     // Given a (positive) 90% confidence interval,
-    // returns a sample from a lognormal
+    // returns a sample from a lognorma with a matching 90% c.i.
-    // with a matching 90% c.i.
+    // Key idea: If we want a lognormal with 90% confidence interval [a, b]
-    const double NORMAL95CONFIDENCE = 1.6448536269514722;
+    // we need but get a normal with 90% confidence interval [log(a), log(b)].
+    // Then see code for sample_normal_from_95_confidence_interval
     double loglow = logf(low);
     double loghigh = logf(high);
-    double logmean = (loglow + loghigh) / 2;
+    return exp(sample_normal_from_95_confidence_interval(loglow, loghigh));
-    double logstd = (loghigh - loglow) / (2.0 * NORMAL95CONFIDENCE);
-    return sample_lognormal(logmean, logstd, seed);
 }
 
 double sample_gamma(double alpha, uint64_t* seed)
@@ -129,13 +159,14 @@ double sample_gamma(double alpha, uint64_t* seed)
 
 double sample_beta(double a, double b, uint64_t* seed)
 {
+    // See: https://en.wikipedia.org/wiki/Gamma_distribution#Related_distributions
     double gamma_a = sample_gamma(a, seed);
     double gamma_b = sample_gamma(b, seed);
     return gamma_a / (gamma_a + gamma_b);
 }
 
 double sample_laplace(double successes, double failures, uint64_t* seed){
-	// see <https://wikiless.esmailelbob.xyz/wiki/Beta_distribution?lang=en#Rule_of_succession>
+	// see <https://en.wikipedia.org/wiki/Beta_distribution?lang=en#Rule_of_succession>
 	return sample_beta(successes + 1, failures + 1, seed);
 }
 
@@ -177,8 +208,7 @@ double array_std(double* array, int length)
 // Mixture function
 double sample_mixture(double (*samplers[])(uint64_t*), double* weights, int n_dists, uint64_t* seed)
 {
-    // You can see a simpler version of this function in the git history
+    // Sample from samples with frequency proportional to their weights.
-    // or in C-02-better-algorithm-one-thread/
     double sum_weights = array_sum(weights, n_dists);
     double* cumsummed_normalized_weights = (double*)malloc(n_dists * sizeof(double));
     cumsummed_normalized_weights[0] = weights[0] / sum_weights;
@@ -203,7 +233,11 @@ double sample_mixture(double (*samplers[])(uint64_t*), double* weights, int n_di
     return result;
 }
 
-// Sample from an arbitrary cdf
+// # More cool stuff
+// This is no longer necessary to do basic estimation,
+// but is still cool
+
+// ## Sample from an arbitrary cdf
 struct box {
     int empty;
     double content;
@@ -436,45 +470,52 @@ struct c_i get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* se
     return result;
 }
 
+// # Small algebra system
+
 // Do algebra over lognormals and normals
-typedef struct {
+// here I discover named structs, 
+// which mean that I don't have to be typing
+// struct blah all the time.
+typedef struct normal_params_t {
     double mean;
     double std;
-} normal_parameters;
+} normal_params;
 
-struct {
+typedef struct lognormal_params_t {
     double logmean;
     double logstd;
-} lognormal_parameters;
+} lognormal_params;
 
-normal_parameters algebra_sum_normals(normal_parameters a, normal_parameters b)
+normal_params algebra_sum_normals(normal_params a, normal_params b)
 {
-    normal_parameters result = {
+    normal_params result = {
         .mean = a.mean + b.mean,
         .std = sqrt((a.std * a.std) + (b.std * b.std)),
     };
     return result;
 }
-normal_parameters algebra_shift_normal(normal_parameters a, double shift)
+normal_params algebra_shift_normal(normal_params a, double shift)
 {
-    normal_parameters result = {
+    normal_params result = {
         .mean = a.mean + shift,
         .std = a.std,
     };
     return result;
 }
 
-lognormal_parameters algebra_product_lognormals(lognormal_parameters a, lognormal_parameters b)
+// Also add stretching
+
+lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params b)
 {
-    lognormal_parameters result = {
+    lognormal_params result = {
         .logmean = a.logmean + b.logmean,
         .logstd = sqrt((a.logstd * a.logstd) + (b.logstd * b.logstd)),
     };
     return result;
 }
-lognormal_parameters algebra_scale_lognormal(lognormal_parameters a, double k)
+lognormal_params algebra_scale_lognormal(lognormal_params a, double k)
 {
-    lognormal_parameters result = {
+    lognormal_params result = {
         .logmean = a.logmean + k,
         .logstd = a.logstd,
     };

squiggle.h

@@ -58,4 +58,24 @@ struct c_i {
 };
 struct c_i get_90_confidence_interval(double (*sampler)(uint64_t*), uint64_t* seed);
 
+// small algebra system
+
+typedef struct normal_params_t {
+    double mean;
+    double std;
+} normal_params;
+
+typedef struct lognormal_params_t {
+    double logmean;
+    double logstd;
+} lognormal_params;
+
+
+normal_params algebra_sum_normals(normal_params a, normal_params b);
+normal_params algebra_shift_normal(normal_params a, double shift);
+
+
+lognormal_params algebra_product_lognormals(lognormal_params a, lognormal_params b);
+lognormal_params algebra_scale_lognormal(lognormal_params a, double k);
+
 #endif