Changeset 565 for library/bdm/stat/exp_family.h

Timestamp:

08/19/09 16:54:24 (15 years ago)

Author:

vbarta

Message:

using own error macros (basically copied from IT++, but never aborting)

Files:

• library/bdm/stat/exp_family.h (modified) (15 diffs)

library/bdm/stat/exp_family.h

@@ -44 +44 @@
                 //! logarithm of the normalizing constant, \f$\mathcal{I}\f$
                 virtual double lognc() const = 0;
+
                 //!Evaluate normalized log-probability
-                virtual double evallog_nn (const vec &val) const{it_error ("Not implemented");return 0.0;};
+                virtual double evallog_nn (const vec &val) const {
+                        bdm_error ("Not implemented");
+                        return 0.0;
+                }
+
                 //!Evaluate normalized log-probability
                 virtual double evallog (const vec &val) const {
                         double tmp;
                         tmp = evallog_nn (val) - lognc();
-//                              it_assert_debug ( std::isfinite ( tmp ),"Infinite value" );
                         return tmp;
                 }
@@ -65 +69 @@
                         return x -lognc();
                 }
+
                 //!Power of the density, used e.g. to flatten the density
-                virtual void pow (double p) {it_error ("Not implemented");};
+                virtual void pow (double p) {
+                        bdm_error ("Not implemented");
+                }
 };
 
@@ -84 +91 @@
                 BMEF (const BMEF &B) : BM (B), frg (B.frg), last_lognc (B.last_lognc) {}
                 //!get statistics from another model
-                virtual void set_statistics (const BMEF* BM0) {it_error ("Not implemented");};
+                virtual void set_statistics (const BMEF* BM0) {
+                        bdm_error ("Not implemented");
+                }
+
                 //! Weighted update of sufficient statistics (Bayes rule)
                 virtual void bayes (const vec &data, const double w) {};
                 //original Bayes
                 void bayes (const vec &dt);
+
                 //!Flatten the posterior according to the given BMEF (of the same type!)
-                virtual void flatten (const BMEF * B) {it_error ("Not implemented");}
-                //!Flatten the posterior as if to keep nu0 data
-//      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}
-
-                BMEF* _copy_ () const {it_error ("function _copy_ not implemented for this BM"); return NULL;};
+                virtual void flatten (const BMEF * B) {
+                        bdm_error ("Not implemented");
+                }
+
+                BMEF* _copy_ () const {
+                        bdm_error ("function _copy_ not implemented for this BM");
+                        return NULL;
+                }
 };
 
@@ -122 +136 @@
                 void from_setting (const Setting &root);
                 void validate() {
-                        it_assert (mu.length() == R.rows(), "parameters mismatch");
+                        bdm_assert_debug (mu.length() == R.rows(), "parameters mismatch");
                         dim = mu.length();
                 }
@@ -272 +286 @@
                 //!@}
 
-                vec sample() const {it_error ("Not implemented");return vec_1 (0.0);};
+                vec sample() const {
+                        bdm_error ("Not implemented");
+                        return vec();
+                }
+
                 vec mean() const {return beta / sum (beta);};
                 vec variance() const {double gamma = sum (beta); return elem_mult (beta, (beta + 1)) / (gamma* (gamma + 1));}
@@ -278 +296 @@
                 double evallog_nn (const vec &val) const {
                         double tmp; tmp = (beta - 1) * log (val);
-//                              it_assert_debug ( std::isfinite ( tmp ),"Infinite value" );
                         return tmp;
-                };
+                }
+
                 double lognc () const {
                         double tmp;
@@ -287 +305 @@
                         for (int i = 0;i < beta.length();i++) {lgb += lgamma (beta (i));}
                         tmp = lgb - lgamma (gam);
-//                              it_assert_debug ( std::isfinite ( tmp ),"Infinite value" );
                         return tmp;
-                };
+                }
+
                 //!access function
                 vec& _beta()  {return beta;}
@@ -397 +415 @@
                 }
                 void validate() {
-                        it_assert (alpha.length() == beta.length(), "parameters do not match");
+                        bdm_assert_debug (alpha.length() == beta.length(), "parameters do not match");
                         dim = alpha.length();
                 }
@@ -446 +464 @@
         void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}
         vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};
-        vec sample() {it_error ( "Not implemented" );return 0;}
 };
 */
@@ -472 +489 @@
                 void set_parameters (const vec &low0, const vec &high0) {
                         distance = high0 - low0;
-                        it_assert_debug (min (distance) > 0.0, "bad support");
+                        bdm_assert_debug (min (distance) > 0.0, "bad support");
                         low = low0;
                         high = high0;
@@ -535 +552 @@
                 //! Set \c A and \c R
                 void set_parameters (const  mat &A0, const vec &mu0, const sq_T &R0) {
-                        it_assert_debug (A0.rows() == mu0.length(), "");
-                        it_assert_debug (A0.rows() == R0.rows(), "");
+                        bdm_assert_debug (A0.rows() == mu0.length(), "mlnorm: A vs. mu mismatch");
+                        bdm_assert_debug (A0.rows() == R0.rows(), "mlnorm: A vs. R mismatch");
 
                         this->iepdf.set_parameters (zeros (A0.rows()), R0);
@@ -660 +677 @@
                 //! constructor function
                 void set_parameters (const mat &A0, const vec &mu0, const ldmat &R0, const ldmat& Lambda0) {
-                        it_assert_debug (A0.rows() == mu0.length(), "");
-                        it_assert_debug (R0.rows() == A0.rows(), "");
+                        bdm_assert_debug (A0.rows() == mu0.length(), "mlstudent: A vs. mu mismatch");
+                        bdm_assert_debug (R0.rows() == A0.rows(), "mlstudent: A vs. R mismatch");
 
                         iepdf.set_parameters (mu0, Lambda);  //
@@ -1138 +1155 @@
                 //! Function performs resampling, i.e. removal of low-weight samples and duplication of high-weight samples such that the new samples represent the same density.
                 ivec resample (RESAMPLING_METHOD method = SYSTEMATIC);
-                //! inherited operation : NOT implemneted
-                vec sample() const {it_error ("Not implemented");return 0;}
-                //! inherited operation : NOT implemneted
-                double evallog (const vec &val) const {it_error ("Not implemented");return 0.0;}
+
+                //! inherited operation : NOT implemented
+                vec sample() const {
+                        bdm_error ("Not implemented");
+                        return vec();
+                }
+
+                //! inherited operation : NOT implemented
+                double evallog (const vec &val) const {
+                        bdm_error ("Not implemented");
+                        return 0.0;
+                }
+
                 vec mean() const {
                         vec pom = zeros (dim);
@@ -1279 +1305 @@
 void enorm<sq_T>::marginal ( const RV &rvn, enorm<sq_T> &target ) const
 {
-        it_assert_debug (isnamed(), "rv description is not assigned");
+        bdm_assert_debug (isnamed(), "rv description is not assigned");
         ivec irvn = rvn.dataind (rv);
 
@@ -1302 +1328 @@
         typedef mlnorm<sq_T> TMlnorm;
 
-        it_assert_debug (isnamed(), "rvs are not assigned");
+        bdm_assert_debug (isnamed(), "rvs are not assigned");
         TMlnorm &uptarget = dynamic_cast<TMlnorm &>(target);
 
         RV rvc = rv.subt (rvn);
-        it_assert_debug ( (rvc._dsize() + rvn._dsize() == rv._dsize()), "wrong rvn");
+        bdm_assert_debug ( (rvc._dsize() + rvn._dsize() == rv._dsize()), "wrong rvn");
         //Permutation vector of the new R
         ivec irvn = rvn.dataind (rv);