Adding exponential and weibull distributions (#2247)

* Added exponential distribution

* tab problem removed?

* forgot std::

* Also added Weibull distribution. Very useful indeed.

* Simple Weibull distribution unit test

* don't forget std::

* sorry, typo

* [RAND] 	- seed the random number generators for consistency and no nasty surprises
		- added parameter for tolerance
		- added unit test for exponential distribution

* [RAND] print the spinner more often

* [RAND] up the tolerance for kurtosis a bit

* [RAND] refactored parameters to reflect documentation on wikipedia.

* [RAND] added documentation to _abstract

* [RAND] i switched the order of the arguments to get_random_weibull and didn't update the unit tests. oops
Co-authored-by: pf <pf@pf-ubuntu-dev>

dlib/rand/rand_kernel_1.h

@@ -265,6 +265,31 @@ namespace dlib
                 return r.real();
             }
 
+            double get_random_exponential (
+                double lambda
+            )
+            {
+                DLIB_ASSERT(lambda > 0, "lambda must be greater than zero");
+                double u = 0.0;
+                while (u == 0.0)
+                    u = get_random_double();
+                return -std::log( u ) / lambda;
+            }
+
+            double get_random_weibull (
+                double lambda,
+                double k,
+                double gamma
+            )
+            {
+                DLIB_ASSERT(k > 0, "k must be greater than zero");
+                DLIB_ASSERT(lambda > 0, "lambda must be greater than zero");
+                double u = 0.0;
+                while (u == 0.0)
+                    u = get_random_double();
+                return gamma + lambda*std::pow(-std::log(u), 1.0 / k);
+            }
+            
             void swap (
                 rand& item
             )

dlib/rand/rand_kernel_abstract.h

@@ -188,7 +188,28 @@ namespace dlib
                     - returns a random complex number sampled from a Gaussian distribution 
                       with mean 0 and standard deviation 1. 
             !*/
+            
+            double get_random_exponential (
+                double lambda
+            );
+            /*!
+                ensures
+                    - returns a random number sampled from an exponential distribution
+                      with rate parameter lambda
+            !*/
 
+            double get_random_weibull (
+                double lambda,
+                double k,
+                double gamma
+            );
+            /*!
+                ensures
+                    - returns a random number sampled from a Weibull distribution
+                      with shape parameter k, scale parameter lambda and 
+                      threshold parameter gamma.
+            !*/
+            
             void swap (
                 rand& item
             );

dlib/test/rand.cpp

@@ -10,6 +10,7 @@
 #include <dlib/rand.h>
 #include <dlib/compress_stream.h>
 #include <dlib/hash.h>
+#include <dlib/statistics.h>
 
 #include "tester.h"
 
@@ -407,6 +408,47 @@ namespace
 
     }
 
+    void test_weibull_distribution()
+    {
+        print_spinner();
+        dlib::rand rnd(0);
+        
+        const size_t N = 1024*1024*4;
+        const double tol = 0.01;
+        double k=1.0, lambda=2.0, g=6.0;
+
+        dlib::running_stats<double> stats;
+        for (size_t i = 0; i < N; i++) 
+            stats.add(rnd.get_random_weibull(lambda, k, g));
+
+        double expected_mean = g + lambda*std::tgamma(1 + 1.0 / k);
+        double expected_var  = lambda*lambda*(std::tgamma(1 + 2.0 / k) - std::pow(std::tgamma(1 + 1.0 / k),2));
+        DLIB_TEST(std::abs(stats.mean() - expected_mean) < tol);
+        DLIB_TEST(std::abs(stats.variance() - expected_var) < tol);
+    }
+    
+    void test_exponential_distribution()
+    {
+        print_spinner();
+        dlib::rand rnd(0);
+        
+        const size_t N = 1024*1024*128;
+        const double tol = 0.01;
+        
+        for (double lambda = 6 ; lambda < 20 ; lambda += 0.1)
+        {
+            print_spinner();
+            dlib::running_stats<double> stats;
+            for (size_t i = 0; i < N; i++) 
+                stats.add(rnd.get_random_exponential(lambda));
+            
+            DLIB_TEST(std::abs(stats.mean() - 1.0 / lambda) < tol);
+            DLIB_TEST(std::abs(stats.variance() - 1.0 / (lambda*lambda)) < tol);
+            DLIB_TEST(std::abs(stats.skewness() - 2.0) < tol);
+            DLIB_TEST(std::abs(stats.ex_kurtosis() - 6.0) < 0.1);
+        }
+    }
+    
     class rand_tester : public tester
     {
     public:
@@ -428,6 +470,8 @@ namespace
             test_gaussian_random_hash();
             test_uniform_random_hash();
             test_get_integer();
+            test_weibull_distribution();
+            test_exponential_distribution();
         }
     } a;