Changeset 60 for bdm/stat

Timestamp:

03/31/08 13:53:36 (16 years ago)

Author:

smidl

Message:

nove rozlozeni Q

Location:

bdm/stat

Files:

• libEF.cpp (modified) (1 diff)
• libEF.h (modified) (9 diffs)

bdm/stat/libEF.cpp

@@ -60 +60 @@
 
 vec mgamma::samplecond ( vec &cond, double &ll ) {
-        *_beta=k / cond ;
+        this->condition(cond );
         vec smp = epdf.sample();
         ll = epdf.evalpdflog ( smp );

bdm/stat/libEF.h

@@ -96 +96 @@
         double eval ( const vec &val ) const ;
         double evalpdflog ( const vec &val ) const;
-        vec mean()const {return mu;}
+        vec mean() const {return mu;}
 
 //Access methods
@@ -140 +140 @@
         //! Returns poiter to alpha and beta. Potentially dengerous: use with care!
         void _param ( vec* &a, vec* &b ) {a=αb=β};
-        vec mean()const {vec pom(alpha); pom/=beta; return pom;}
+        vec mean() const {vec pom ( alpha ); pom/=beta; return pom;}
 };
 /*
@@ -228 +228 @@
 */
 class mgamma : public mEF {
+protected:
         //! Internal epdf that arise by conditioning on \c rvc
         egamma epdf;
@@ -247 +248 @@
 };
 
+/*!
+\brief  Gamma random walk around a fixed point
+
+Mean value, \f$\mu\f$, of this density is given by a geometric combination of \c rvc and given fixed point, $p$. $k$ is the coefficient of the geometric combimation
+\f[ \mu = \mu_{t-1} ^{l} p^{1-l}\f]
+
+Standard deviation of the random walk is proportional to one $k$-th the mean.
+This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$.
+
+The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$.
+*/
+class mgamma_fix : public mgamma {
+protected:
+        double l;
+        vec refl;
+public:
+        //! Constructor
+        mgamma_fix ( const RV &rv,const RV &rvc ) : mgamma ( rv,rvc ),refl ( rv.count() ) {};
+        //! Set value of \c k
+        void set_parameters ( double k0 , vec ref0, double l0 ) {
+                mgamma::set_parameters ( k0 );
+                refl=pow ( ref0,1.0-l0 );l=l0;
+        };
+
+        void condition ( const vec &val ) {vec mean=elem_mult ( refl,pow ( val,l ) ); *_beta=k/mean;};
+};
+
 //! Switch between various resampling methods.
 enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
@@ -264 +292 @@
 public:
         //! Default constructor
-        eEmp ( const RV &rv0 ,int n0) :epdf ( rv0 ),n(n0),w(n),samples(n) {};
+        eEmp ( const RV &rv0 ,int n0 ) :epdf ( rv0 ),n ( n0 ),w ( n ),samples ( n ) {};
         //! Set sample
         void set_parameters ( const vec &w0, epdf* pdf0 );
@@ -276 +304 @@
         vec sample() const {it_error ( "Not implemented" );return 0;}
         //! inherited operation : NOT implemneted
-        double evalpdflog(const vec &val) const {it_error ( "Not implemented" );return 0.0;}
-        vec mean()const {vec pom=zeros(rv.count()); 
-                for (int i=0;i<n;i++){pom+=samples(i)*w(i);}
+        double evalpdflog ( const vec &val ) const {it_error ( "Not implemented" );return 0.0;}
+        vec mean() const {
+                vec pom=zeros ( rv.count() );
+                for ( int i=0;i<n;i++ ) {pom+=samples ( i ) *w ( i );}
                 return pom;
         }
@@ -287 +316 @@
 
 template<class sq_T>
-enorm<sq_T>::enorm ( RV &rv ) :eEF(rv), mu ( rv.count() ),R ( rv.count() ),_iR ( rv.count() ),cached ( false ),dim ( rv.count() ) {};
+enorm<sq_T>::enorm ( RV &rv ) :eEF ( rv ), mu ( rv.count() ),R ( rv.count() ),_iR ( rv.count() ),cached ( false ),dim ( rv.count() ) {};
 
 template<class sq_T>
@@ -320 +349 @@
 
 template<class sq_T>
-mat enorm<sq_T>::sample ( int N )const {
+mat enorm<sq_T>::sample ( int N ) const {
         mat X ( dim,N );
         vec x ( dim );
@@ -346 +375 @@
 template<class sq_T>
 double enorm<sq_T>::evalpdflog ( const vec &val ) const {
-        if ( !cached ) {it_error("this should not happen, see cached");}
+        if ( !cached ) {it_error ( "this should not happen, see cached" );}
 
         // 1.83787706640935 = log(2pi)