GS KRR work on comments

shogun-toolbox · Apr 18, 2012 · 7a4b0cc · 7a4b0cc
1 parent 0f16159
commit 7a4b0cc
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Showing 2 changed files with 20 additions and 41 deletions.
diff --git a/src/shogun/regression/KernelRidgeRegression.cpp b/src/shogun/regression/KernelRidgeRegression.cpp
@@ -33,7 +33,7 @@ CKernelRidgeRegression::CKernelRidgeRegression(float64_t tau, CKernel* k, CLabel
 	set_labels(lab);
 	set_kernel(k);
     set_epsilon(0.0001);
-    m_train_func=getTrainFunction(m);
+    m_train_func=m;
 }
 
 void CKernelRidgeRegression::init()
@@ -43,22 +43,6 @@ void CKernelRidgeRegression::init()
 	SG_ADD(&m_tau, "tau", "Regularization parameter", MS_AVAILABLE);
 }
 
-CKernelRidgeRegression::train_method CKernelRidgeRegression::getTrainFunction(ETrainingType id)
-{
-    switch (id)
-    {
-        case PINV:
-            return &CKernelRidgeRegression::train_machine_pinv;
-        break;
-        case GS:
-            return &CKernelRidgeRegression::train_machine_gs;
-        break;
-        default:
-            return &CKernelRidgeRegression::train_machine_pinv;
-        break;
-    }
-}
-
 bool CKernelRidgeRegression::train_machine_pinv(CFeatures* data)
 {
 	if (!m_labels)
@@ -117,14 +101,9 @@ bool CKernelRidgeRegression::train_machine_gs(CFeatures* data)
     }
     ASSERT(kernel && kernel->has_features());
 
-    // Get kernel matrix
-    SGMatrix<float64_t> A = kernel->get_kernel_matrix<float64_t>();
-    int32_t n = A.num_cols;
-    int32_t m = A.num_rows;
-    ASSERT(A.matrix && m>0 && n>0);
-
-    for(int32_t i=0; i < n; i++)
-        A.matrix[i+i*n]+=m_tau;
+    int32_t n = kernel->get_num_vec_rhs();
+    int32_t m = kernel->get_num_vec_lhs();
+    ASSERT(m>0 && n>0);
 
     // re-set alphas of kernel machine
     m_alpha.destroy_vector();
@@ -157,9 +136,9 @@ bool CKernelRidgeRegression::train_machine_gs(CFeatures* data)
             sigma=b[i];
             for(int32_t j=0; j<n; j++)
                 if (i!=j)
-                    sigma-=A.matrix[j+i*n]*m_alpha[j];
+                    sigma-=kernel->kernel(j, i)*m_alpha[j];
             alpha_old=m_alpha[i];
-            m_alpha[i]=sigma/A.matrix[i+i*n];
+            m_alpha[i]=sigma/(kernel->kernel(i, i)+m_tau);
             d=fabs(alpha_old-m_alpha[i]);
             if(d>err)
                 err=d;
@@ -168,14 +147,23 @@ bool CKernelRidgeRegression::train_machine_gs(CFeatures* data)
             flag=false;
     }
 
-    SG_FREE(A.matrix);
-
     return true;
 }
 
 bool CKernelRidgeRegression::train_machine(CFeatures *data)
 {
-    return (this->*m_train_func)(data);
+    switch (m_train_func)
+    {
+        case PINV:
+            return train_machine_pinv(data);
+        break;
+        case GS:
+            return train_machine_gs(data);
+        break;
+        default:
+            return train_machine_pinv(data);
+        break;
+    }
 }
 
 bool CKernelRidgeRegression::load(FILE* srcfile)

diff --git a/src/shogun/regression/KernelRidgeRegression.h b/src/shogun/regression/KernelRidgeRegression.h
@@ -54,11 +54,9 @@ namespace shogun
 class CKernelRidgeRegression : public CKernelMachine
 {
 	public:
-		/** default constructor */
+        /** default constructor */
 		CKernelRidgeRegression();
 
-        typedef bool (CKernelRidgeRegression::*train_method)(CFeatures* data);
-
         enum ETrainingType
         {
             PINV=1,
@@ -74,11 +72,6 @@ class CKernelRidgeRegression : public CKernelMachine
         CKernelRidgeRegression(float64_t tau, CKernel* k, CLabels* lab, ETrainingType m=PINV);
 		virtual ~CKernelRidgeRegression() {}
 
-        /** set training function
-         *
-         */
-        inline void set_train_func(train_method m) { m_train_func = m; };
-
 		/** set regularization constant
 		 *
 		 * @param tau new tau
@@ -159,9 +152,7 @@ class CKernelRidgeRegression : public CKernelMachine
         float64_t m_epsilon;
 
         /** training function */
-        train_method m_train_func;
-
-        static train_method getTrainFunction(ETrainingType id);
+        ETrainingType m_train_func;
 };
 }