Changeset 1342 for trunk

Timestamp:

Jun 18, 2008, 6:09:29 PM (15 years ago)

Author:

Peter

Message:

fixing doxygen problems

File:

• trunk/yat/random/random.h (modified) (5 diffs)

trunk/yat/random/random.h

@@ -275 +275 @@
   };
 
-  ///
-  /// @brief Discrete uniform distribution
-  ///
-  /// Discrete uniform distribution also known as the "equally likely
-  /// outcomes" distribution. Each outcome, in this case an integer
-  /// from [0,n-1] , have equal probability to occur.
-  ///
-  /// Distribution function \f$ p(k) = \frac{1}{n+1} \f$ for \f$ 0 \le
-  /// k < n \f$ \n 
-  /// Expectation value: \f$ \frac{n-1}{2} \f$ \n 
-  /// Variance: \f$ \frac{1}{12}(n-1)(n+1) \f$
-  ///
+  /**
+     @brief Discrete uniform distribution
+  
+     Discrete uniform distribution also known as the "equally likely
+     outcomes" distribution. Each outcome, in this case an integer
+     from [0,n-1] , have equal probability to occur.
+     
+     Distribution function \f$ p(k) = \frac{1}{n+1} \f$ for \f$ 0 \le
+     k < n \f$ \n 
+     Expectation value: \f$ \frac{n-1}{2} \f$ \n 
+     Variance: \f$ \frac{1}{12}(n-1)(n+1) \f$
+  */
   class DiscreteUniform : public Discrete 
   {
@@ -332 +332 @@
   };
 
-  ///
-  /// @brief Poisson Distribution
-  ///
-  /// Having a Poisson process (i.e. no memory), number of occurences
-  /// within a given time window is Poisson distributed. This
-  /// distribution is the limit of a Binomial distribution when number
-  /// of attempts is large, and the probability for one attempt to be
-  /// succesful is small (in such a way that the expected number of
-  /// succesful attempts is \f$ m \f$.
-  ///
-  /// Probability function \f$ p(k) = e^{-m}\frac{m^k}{k!} \f$ for \f$ 0 \le
-  /// k  \f$ \n 
-  /// Expectation value: \f$ m \f$ \n 
-  /// Variance: \f$ m \f$
-  ///
+  /**
+     @brief Poisson Distribution
+  
+     Having a Poisson process (i.e. no memory), number of occurences
+     within a given time window is Poisson distributed. This
+     distribution is the limit of a Binomial distribution when number
+     of attempts is large, and the probability for one attempt to be
+     succesful is small (in such a way that the expected number of
+     succesful attempts is \f$ m \f$.
+     
+     Probability function \f$ p(k) = e^{-m}\frac{m^k}{k!} \f$ for \f$ 0 \le
+     k  \f$ \n 
+     Expectation value: \f$ m \f$ \n 
+     Variance: \f$ m \f$
+  */
   class Poisson : public Discrete
   {
@@ -413 +413 @@
     /// @see seed, RNG::seed_from_devurandom, RNG::seed
     ///
-    unsigned long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); }
+    unsigned long seed_from_devurandom(void) 
+    { return rng_->seed_from_devurandom(); }
 
     ///
@@ -472 +473 @@
   };
 
-  ///
-  /// @brief Generator of random numbers from an exponential
-  /// distribution.
-  ///
-  /// The distribution function is \f$ f(x) = \frac{1}{m}\exp(-x/a)
-  /// \f$ for \f$ x \f$ with the expectation value \f$ m \f$ and
-  /// variance \f$ m^2 \f$
-  ///
+  /**
+     \brief Generator of random numbers from an exponential
+     distribution.
+     
+     The distribution function is \f$ f(x) = \frac{1}{m}\exp(-x/a)
+     \f$ for \f$ x \f$ with the expectation value \f$ m \f$ and
+     variance \f$ m^2 \f$
+  */
   class Exponential : public Continuous
   {
@@ -507 +508 @@
   };
 
-  ///
-  /// @brief Gaussian distribution
-  ///
-  /// Class for generating a random number from a Gaussian
-  /// distribution between zero and unity. Utilizes the Box-Muller
-  /// algorithm, which needs two calls to random generator.
-  ///
-  /// Distribution function \f$ f(x) =
-  /// \frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{(x-\mu)^2}{2\sigma^2})
-  /// \f$ \n
-  /// Expectation value: \f$ \mu \f$ \n
-  /// Variance: \f$ \sigma^2 \f$
-  ///
+  /**
+     @brief Gaussian distribution
+     
+     Class for generating a random number from a Gaussian distribution
+     between zero and unity. Utilizes the Box-Muller algorithm, which
+     needs two calls to random generator.
+     
+     Distribution function \f$ f(x) =
+     \frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{(x-\mu)^2}{2\sigma^2})
+     \f$ \n
+     Expectation value: \f$ \mu \f$ \n
+     Variance: \f$ \sigma^2 \f$
+  */
   class Gaussian : public Continuous
   {