Made rls run a bit faster, especially if the new mode that allows the

regularization to decay away is activated.

dlib/svm/rls.h

@@ -20,7 +20,8 @@ namespace dlib
 
         explicit rls(
             double forget_factor_,
-            double C_ = 1000
+            double C_ = 1000,
+            bool apply_forget_factor_to_C_ = false
         )
         {
             // make sure requires clause is not broken
@@ -36,6 +37,7 @@ namespace dlib
 
             C = C_;
             forget_factor = forget_factor_;
+            apply_forget_factor_to_C = apply_forget_factor_to_C_;
         }
 
         rls(
@@ -43,6 +45,7 @@ namespace dlib
         {
             C = 1000;
             forget_factor = 1;
+            apply_forget_factor_to_C = false;
         }
 
         double get_c(
@@ -57,6 +60,12 @@ namespace dlib
             return forget_factor;
         }
 
+        bool should_apply_forget_factor_to_C (
+        ) const 
+        {
+            return apply_forget_factor_to_C;
+        }
+
         template <typename EXP>
         void train (
             const matrix_exp<EXP>& x,
@@ -84,20 +93,25 @@ namespace dlib
             // multiply by forget factor and incorporate x*trans(x) into R.
             const double l = 1.0/forget_factor;
             const double temp = 1 + l*trans(x)*R*x;
-            matrix<double,0,1> tmp = R*x;
+            tmp = R*x;
             R = l*R - l*l*(tmp*trans(tmp))/temp;
 
             // Since we multiplied by the forget factor, we need to add (1-forget_factor) of the
             // identity matrix back in to keep the regularization alive.  
-            add_eye_to_inv(R, (1-forget_factor)/C);
+            if (forget_factor != 1 && !apply_forget_factor_to_C)
+                add_eye_to_inv(R, (1-forget_factor)/C);
 
             // R should always be symmetric.  This line improves numeric stability of this algorithm.
-            R = 0.5*(R + trans(R));
+            if (cnt%100 == 0)
+                R = 0.5*(R + trans(R));
+            ++cnt;
 
             w = w + R*x*(y - trans(x)*w);
 
         }
 
+
+
         const matrix<double,0,1>& get_w(
         ) const
         {
@@ -145,25 +159,37 @@ namespace dlib
 
         friend inline void serialize(const rls& item, std::ostream& out)
         {
-            int version = 1;
+            int version = 2;
             serialize(version, out);
             serialize(item.w, out);
             serialize(item.R, out);
             serialize(item.C, out);
             serialize(item.forget_factor, out);
+            serialize(item.cnt, out);
+            serialize(item.apply_forget_factor_to_C, out);
         }
 
         friend inline void deserialize(rls& item, std::istream& in)
         {
             int version = 0;
             deserialize(version, in);
-            if (version != 1)
+            if (!(1 <= version && version <= 2))
                 throw dlib::serialization_error("Unknown version number found while deserializing rls object.");
 
-            deserialize(item.w, in);
-            deserialize(item.R, in);
-            deserialize(item.C, in);
-            deserialize(item.forget_factor, in);
+            if (version >= 1)
+            {
+                deserialize(item.w, in);
+                deserialize(item.R, in);
+                deserialize(item.C, in);
+                deserialize(item.forget_factor, in);
+            }
+            item.cnt = 0;
+            item.apply_forget_factor_to_C = false;
+            if (version >= 2)
+            {
+                deserialize(item.cnt, in);
+                deserialize(item.apply_forget_factor_to_C, in);
+            }
         }
 
     private:
@@ -189,6 +215,13 @@ namespace dlib
         matrix<double> R;
         double C;
         double forget_factor;
+        int cnt = 0;
+        bool apply_forget_factor_to_C;
+
+
+        // This object is here only to avoid reallocation during training.  It don't
+        // logically contribute to the state of this object.
+        matrix<double,0,1> tmp;
     };
 
 // ----------------------------------------------------------------------------------------

dlib/svm/rls_abstract.h

@@ -37,7 +37,8 @@ namespace dlib
 
         explicit rls(
             double forget_factor,
-            double C = 1000
+            double C = 1000,
+            bool apply_forget_factor_to_C = false
         );
         /*!
             requires
@@ -47,6 +48,7 @@ namespace dlib
                 - #get_w().size() == 0
                 - #get_c() == C
                 - #get_forget_factor() == forget_factor
+                - #should_apply_forget_factor_to_C() == apply_forget_factor_to_C
         !*/
 
         rls(
@@ -56,6 +58,7 @@ namespace dlib
                 - #get_w().size() == 0
                 - #get_c() == 1000
                 - #get_forget_factor() == 1
+                - #should_apply_forget_factor_to_C() == false
         !*/
 
         double get_c(
@@ -80,6 +83,18 @@ namespace dlib
                   zero the faster old examples are forgotten.
         !*/
 
+        bool should_apply_forget_factor_to_C (
+        ) const;
+        /*!
+            ensures
+                - If this function returns false then it means we are optimizing the
+                  objective function discussed in the WHAT THIS OBJECT REPRESENTS section
+                  above. However, if it returns true then we will allow the forget factor
+                  (get_forget_factor()) to be applied to the C value which causes the
+                  algorithm to slowly increase C and convert into a textbook version of RLS
+                  without regularization.   The main reason you might want to do this is
+                  because it can make the algorithm run significantly faster.
+        !*/
 
         template <typename EXP>
         void train (