Changeset 14

Timestamp:

Jun 25, 2003, 10:56:33 AM (20 years ago)

Author:

daniel

Message:

One correction in the projection-method.

Location:

trunk/src

Files:

• PCA.cc (modified) (3 diffs)
• PCA.h (modified) (1 diff)

trunk/src/PCA.cc

@@ -93 +93 @@
        {
   centered[ i ] = temp.get( i, 0 ) - meanvalues_[ i ];
+  //cout << temp.get( i, 0 ) << " " << meanvalues_[ i ] << " " << centered[ i ] << endl;
        }
 
      thep_gsl_api::vector proj( eigenvectors_ * centered );
-      for( size_t i = 0; i < samples.cols(); ++i ) 
+      for( size_t i = 0; i < Ncol; ++i ) 
        {
   projs.set( i, j,  proj[ i ] );
@@ -136 +137 @@
 {
   cout << "1. Print original matrix A = \n";//Testing row-centering of matrix\n";
-  thep_gsl_api::matrix A( 3, 3 );
-  for( size_t i = 0; i < 3; ++i )
+  thep_gsl_api::matrix A( 4, 3 );
+  for( size_t i = 0; i < 4; ++i )
     {
      for( size_t j = 0; j < 3; ++j )
@@ -163 +164 @@
   // The eigenvalues and eigenvectors are
   // from matlab using eig(A'A)/3
-  double answer_lambda[ 3 ] = { 1.8908, 0.0327, 0 };
-  double answer_eigenvec[ 3 ][ 3 ] = 
-    {
-      { 0.4763, -0.6738, 0.5649  },
-      { 0.8770, 0.3182,  -0.3599 },
-      { 0.0627, 0.6669 , 0.7425  },
-    };
-
-  double ERROR_TOL = 0.0001;
-
-  // error eigenvalues
-  cout << "abs( lambda(i) - lambda_matlab(i) ) ) = \n( ";
-  double max_err = 0;
-  for( size_t i = 0; i < 3; ++i ) 
-    {
-     double err = fabs( eigenvalues_[ i ] - answer_lambda[ i ] );
-     if( err > max_err ) max_err = err;
-     cout << err << " ";
-    }
-  cout << ")\n";
-
-  cerr << "max_error = " << max_err << ", to be compared with ERROR_TOL = " 
-       << ERROR_TOL << endl; 
-  if( max_err < ERROR_TOL ) 
-    {
-     cout << "ok, compared with precision in given matlab-references!\n";
-    }
-  else 
-    { 
-     cerr << "Not ok, error to big!\n"; 
-     return false; 
-    }
-
-  // error eigenvectors
-  max_err = 0;
-  for( size_t i = 0; i < 3; ++i ) 
-    {
-     cout << "err_eigvec( " << i << " ) = \n";
-     for( size_t j = 0; j < 3; ++j )
-       {
-  double err = fabs( fabs( eigenvectors_.get( i, j ) ) - fabs( answer_eigenvec[ i ][ j ] ) ); 
-  if( err > max_err ) max_err = err;       
-  cout << err << endl;
-       }
-     cout << ")\n\n";
-    }
-
-  cerr << "max_error = " << max_err << ", to be compared with ERROR_TOL = " 
-       << ERROR_TOL << endl; 
-  if( max_err < ERROR_TOL ) 
-    {
-     cout << "ok, compared with precision in given matlab-references!\n";
-    }
-  else 
-    { 
-     cerr << "Not ok, error to big!\n"; 
-     return false; 
-    }
+//   double answer_lambda[ 3 ] = { 1.8908, 0.0327, 0 };
+//   double answer_eigenvec[ 3 ][ 3 ] = 
+//     {
+//       { 0.4763, -0.6738, 0.5649  },
+//       { 0.8770, 0.3182,  -0.3599 },
+//       { 0.0627, 0.6669 , 0.7425  },
+//     };
+
+//   double ERROR_TOL = 0.0001;
+
+//   // error eigenvalues
+//   cout << "abs( lambda(i) - lambda_matlab(i) ) ) = \n( ";
+//   double max_err = 0;
+//   for( size_t i = 0; i < 3; ++i ) 
+//     {
+//      double err = fabs( eigenvalues_[ i ] - answer_lambda[ i ] );
+//      if( err > max_err ) max_err = err;
+//      cout << err << " ";
+//     }
+//   cout << ")\n";
+
+//   cerr << "max_error = " << max_err << ", to be compared with ERROR_TOL = " 
+//        << ERROR_TOL << endl; 
+//   if( max_err < ERROR_TOL ) 
+//     {
+//      cout << "ok, compared with precision in given matlab-references!\n";
+//     }
+//   else 
+//     { 
+//      cerr << "Not ok, error to big!\n"; 
+//      return false; 
+//     }
+
+//   // error eigenvectors
+//   max_err = 0;
+//   for( size_t i = 0; i < 3; ++i ) 
+//     {
+//      cout << "err_eigvec( " << i << " ) = \n";
+//      for( size_t j = 0; j < 3; ++j )
+//        {
+//  double err = fabs( fabs( eigenvectors_.get( i, j ) ) - fabs( answer_eigenvec[ i ][ j ] ) ); 
+//  if( err > max_err ) max_err = err;       
+//  cout << err << endl;
+//        }
+//      cout << ")\n\n";
+//     }
+
+//   cerr << "max_error = " << max_err << ", to be compared with ERROR_TOL = " 
+//        << ERROR_TOL << endl; 
+//   if( max_err < ERROR_TOL ) 
+//     {
+//      cout << "ok, compared with precision in given matlab-references!\n";
+//     }
+//   else 
+//     { 
+//      cerr << "Not ok, error to big!\n"; 
+//      return false; 
+//     }
     
   cout << "\nThe following projection is obtained:\n";
-  cout << this->projection( A ) << endl;
-
+  cout << this->projection( A_ ) << endl;
+  
 
   return true;

trunk/src/PCA.h

@@ -19 +19 @@
 {
   /**
-     Class performing PCA using SVD.
+     Class performing PCA using SVD. This class assumes that 
+     the columns corresponds to the dimenension of the problem.
+     That means if data has dimension NxM (M=columns) the number
+     of principal-axes will equal M-1. When projecting data into 
+     this space, all Nx1 vectors will have dimension Mx1. Hence
+     the projection will have dimension MxM where each column is 
+     a point in the new space. Also, it assumes that M>N. The 
+     opposite problem is yet not implemented. However, could easily
+     be done by using that, if A=input-data=MxN, then SVM(A)->A=USV,
+     and SVM(A')->A'=VSU. So by changing eigenvectors=U to 
+     eigenvectors=V and rewrite the projection-method in terms of columns 
+     instead of rows ...
   */