Changeset 713 for library/bdm

Timestamp:

11/10/09 11:03:09 (15 years ago)

Author:

mido

Message:

_m changed to _mat

emix.cfg prepared, but it is not yet debugged!

Location:

library/bdm

Files:

• base/bdmbase.cpp (modified) (4 diffs)
• base/bdmbase.h (modified) (9 diffs)
• stat/emix.h (modified) (5 diffs)
• stat/exp_family.h (modified) (2 diffs)
• stat/merger.cpp (modified) (1 diff)

library/bdm/base/bdmbase.cpp

@@ -156 +156 @@
 }
 
-mat epdf::sample_m ( int N ) const {
+mat epdf::sample_mat ( int N ) const {
         mat X = zeros ( dim, N );
         for ( int i = 0; i < N; i++ ) X.set_col ( i, this->sample() );
@@ -162 +162 @@
 }
 
-vec epdf::evallog_m ( const mat &Val ) const {
+vec epdf::evallog_mat ( const mat &Val ) const {
         vec x ( Val.cols() );
         for ( int i = 0; i < Val.cols(); i++ ) {
@@ -171 +171 @@
 }
 
-vec epdf::evallog_m ( const Array<vec> &Avec ) const {
+vec epdf::evallog_mat ( const Array<vec> &Avec ) const {
         vec x ( Avec.size() );
         for ( int i = 0; i < Avec.size(); i++ ) {
@@ -180 +180 @@
 }
 
-mat pdf::samplecond_m ( const vec &cond, int N ) {
+mat pdf::samplecond_mat ( const vec &cond, int N ) {
         mat M ( dimension(), N );
         for ( int i = 0; i < N; i++ ) {

library/bdm/base/bdmbase.h

@@ -429 +429 @@
 
         //! Returns \param N samples from the density conditioned on \c cond, \f$x \sim epdf(rv|cond)\f$. \param cond is numeric value of \c rv
-        virtual mat samplecond_m ( const vec &cond, int N );
+        virtual mat samplecond_mat ( const vec &cond, int N );
 
         //! Shortcut for conditioning and evaluation of the internal epdf. In some cases,  this operation can be implemented efficiently.
@@ -438 +438 @@
 
         //! Matrix version of evallogcond
-        virtual vec evallogcond_m ( const mat &Yt, const vec &cond ) {
+        virtual vec evallogcond_mat ( const mat &Yt, const vec &cond ) {
                 vec v ( Yt.cols() );
                 for ( int i = 0; i < Yt.cols(); i++ ) {
@@ -447 +447 @@
 
         //! Array<vec> version of evallogcond
-        virtual vec evallogcond_m ( const Array<vec> &Yt, const vec &cond ) {
+        virtual vec evallogcond_mat ( const Array<vec> &Yt, const vec &cond ) {
                 bdm_error ( "Not implemented" );
                 return vec();
@@ -534 +534 @@
 
         //! Returns N samples, \f$ [x_1 , x_2 , \ldots \ \f$  from density \f$ f_x(rv)\f$
-        virtual mat sample_m ( int N ) const;
+        virtual mat sample_mat ( int N ) const;
 
         //! Compute log-probability of argument \c val
@@ -544 +544 @@
 
         //! Compute log-probability of multiple values argument \c val
-        virtual vec evallog_m ( const mat &Val ) const;
+        virtual vec evallog_mat ( const mat &Val ) const;
 
         //! Compute log-probability of multiple values argument \c val
-        virtual vec evallog_m ( const Array<vec> &Avec ) const;
+        virtual vec evallog_mat ( const Array<vec> &Avec ) const;
 
         //! Return conditional density on the given RV, the remaining rvs will be in conditioning
@@ -669 +669 @@
         double evallogcond ( const vec &val, const vec &cond );
         //! Efficient version of evallogcond for matrices
-        virtual vec evallogcond_m ( const mat &Dt, const vec &cond );
+        virtual vec evallogcond_mat ( const mat &Dt, const vec &cond );
         //! Efficient version of evallogcond for Array<vec>
-        virtual vec evallogcond_m ( const Array<vec> &Dt, const vec &cond );
+        virtual vec evallogcond_mat ( const Array<vec> &Dt, const vec &cond );
         //! Efficient version of samplecond
-        virtual mat samplecond_m ( const vec &cond, int N );
+        virtual mat samplecond_mat ( const vec &cond, int N );
         
         void validate() {
@@ -1117 +1117 @@
 
         //! Matrix version of logpred
-        vec logpred_m ( const mat &Yt ) const {
+        vec logpred_mat ( const mat &Yt ) const {
                 vec tmp ( Yt.cols() );
                 for ( int i = 0; i < Yt.cols(); i++ ) {
@@ -1253 +1253 @@
 
 template<class EPDF>
-mat pdf_internal<EPDF>::samplecond_m ( const vec &cond, int N ) {
+mat pdf_internal<EPDF>::samplecond_mat ( const vec &cond, int N ) {
         condition ( cond );
         mat temp ( dimension(), N );
@@ -1274 +1274 @@
 
 template<class EPDF>
-vec pdf_internal<EPDF>::evallogcond_m ( const mat &Yt, const vec &cond ) {
+vec pdf_internal<EPDF>::evallogcond_mat ( const mat &Yt, const vec &cond ) {
         condition ( cond );
-        return iepdf.evallog_m ( Yt );
+        return iepdf.evallog_mat ( Yt );
 }
 
 template<class EPDF>
-vec pdf_internal<EPDF>::evallogcond_m ( const Array<vec> &Yt, const vec &cond ) {
+vec pdf_internal<EPDF>::evallogcond_mat ( const Array<vec> &Yt, const vec &cond ) {
         condition ( cond );
-        return iepdf.evallog_m ( Yt );
+        return iepdf.evallog_mat ( Yt );
 }
 

library/bdm/stat/emix.h

@@ -159 +159 @@
                 return tmp;
         };
-        vec evallog_m ( const mat &Val ) const {
+        vec evallog_mat ( const mat &Val ) const {
                 vec x = zeros ( Val.cols() );
                 for ( int i = 0; i < w.length(); i++ ) {
-                        x += w ( i ) * exp ( Coms ( i )->evallog_m ( Val ) );
+                        x += w ( i ) * exp ( Coms ( i )->evallog_mat ( Val ) );
                 }
                 return log ( x );
         };
+        /*
         //! Auxiliary function that returns pdflog for each component
-        mat evallog_M ( const mat &Val ) const {
+        mat evallog_mat ( const mat &Val ) const {
                 mat X ( w.length(), Val.cols() );
                 for ( int i = 0; i < w.length(); i++ ) {
-                        X.set_row ( i, w ( i ) *exp ( Coms ( i )->evallog_m ( Val ) ) );
+                        X.set_row ( i, w ( i ) *exp ( Coms ( i )->evallog_mat ( Val ) ) );
                 }
                 return X;
         };
+        */
 
         shared_ptr<epdf> marginal ( const RV &rv ) const;
@@ -180 +182 @@
         shared_ptr<pdf> condition ( const RV &rv ) const;
 
-//Access methods
+        //Access methods
         //! returns a pointer to the internal mean value. Use with Care!
         vec& _w() {
@@ -197 +199 @@
                 }
         }
+
+        //! Load from structure with elements:
+        //!  \code
+        //! { class='emix';
+        //!   pdfs = (..., ...);     // list of pdfs in the mixture
+        //!   weights = ( 0.5, 0.5 ); // weights of pdfs in the mixture
+        //! }
+        //! \endcode
+        //!@}
+        void from_setting ( const Setting &set ) {
+                UI::get ( Coms, set, "pdfs", UI::compulsory );
+
+                if( !UI::get( w, set, "weights", UI::optional ) )
+                {
+                        int len = Coms.length();
+                        w.set_length( len );
+                        double default_weight = 1.0 / len;
+                        for( int i=0; i < len; i++)
+                                w(i) = default_weight;
+                }
+        }
 };
 SHAREDPTR( emix );
+UIREGISTER ( emix );
 
 /*!
@@ -322 +346 @@
                 return res;
         }
-        vec evallogcond_m ( const mat &Dt, const vec &cond ) {
+        vec evallogcond_mat ( const mat &Dt, const vec &cond ) {
                 vec tmp ( Dt.cols() );
                 for ( int i = 0; i < Dt.cols(); i++ ) {
@@ -329 +353 @@
                 return tmp;
         };
-        vec evallogcond_m ( const Array<vec> &Dt, const vec &cond ) {
+        vec evallogcond_mat ( const Array<vec> &Dt, const vec &cond ) {
                 vec tmp ( Dt.length() );
                 for ( int i = 0; i < Dt.length(); i++ ) {

library/bdm/stat/exp_family.h

@@ -58 +58 @@
                 }
                 //!Evaluate normalized log-probability for many samples
-                virtual vec evallog_m (const mat &Val) const {
+                virtual vec evallog_mat (const mat &Val) const {
                         vec x (Val.cols());
                         for (int i = 0;i < Val.cols();i++) {x (i) = evallog_nn (Val.get_col (i)) ;}
@@ -64 +64 @@
                 }
                 //!Evaluate normalized log-probability for many samples
-                virtual vec evallog_m (const Array<vec> &Val) const {
+                virtual vec evallog_mat (const Array<vec> &Val) const {
                         vec x (Val.length());
                         for (int i = 0;i < Val.length();i++) {x (i) = evallog_nn (Val (i)) ;}

library/bdm/stat/merger.cpp

@@ -151 +151 @@
                         if ( pdfs ( i )->dimension() == dim ) {
                                 // no need for conditioning or marginalization
-                                lw_src = pdfs ( i )->evallogcond_m ( Smp , vec(0));
+                                lw_src = pdfs ( i )->evallogcond_mat ( Smp , vec(0));
                         } else {
                                 // compute likelihood of marginal on the conditional variable