Major bugfix for nonlinear GenSVM

Nonlinear GenSVM depends on the eigendecomposition of the
kernel matrix. Mathematically, the Sigma vector in the code
should contain the square root of the eigenvalues. Taking
the square root was however neglected, which resulted in
poor performance of nonlinear GenSVM. This is now fixed,
which means that the performance of nonlinear GenSVM will
be much better.

1 files changed, 15 insertions, 15 deletions

diff --git a/tests/aux/test_train_kernel.m b/tests/aux/test_train_kernel.m
index 7bf14b8..b561bd4 100644
--- a/tests/aux/test_train_kernel.m
+++ b/tests/aux/test_train_kernel.m
@@ -1,21 +1,21 @@
 function [V] = test_train_kernel()
-  
+
   clear;
   more off;
   rand('state', 654321);
-  
+
   n = 10;
   m = 5;
   classes = 4;
   cutoff = 5e-3;
-  
+
   X = rand(n, m);
   Z = [ones(n, 1), X];
   set_matrix(Z, "data->Z", "data->m+1");
-  
+
   y = [2 1 3 2 3 2 4 1 3 4];
   set_matrix(y, "data->y", "1");
-  
+
   p = 1.2143;
   kappa = 0.90298;
   lambda = 0.00219038;
@@ -36,24 +36,24 @@ function [V] = test_train_kernel()
 
   eigenvalues = diag(Sigma);
   ratios = eigenvalues ./ eigenvalues(end, end);
- 
+
   realP = fliplr(P(:, ratios > cutoff));
-  realSigma = flipud(eigenvalues(ratios > cutoff));
-  
+  realSigma = sqrt(flipud(eigenvalues(ratios > cutoff)));
+
   assert_matrix(realSigma, "data->Sigma", "1");
-  
+
   r = sum(ratios > cutoff);
   fprintf("mu_assert(data->r == %i);\n", r);
-  
+
   M = realP * diag(realSigma);
   size(M)
-  
+
   assert_matrix(Z, "data->RAW", "data->m+1");
-  
-  seedV = zeros(size(M, 2) + 1, classes - 1);  
+
+  seedV = zeros(size(M, 2) + 1, classes - 1);
   [W, t] = msvmmaj(M, y, rho, p, kappa, lambda, epsilon, 'show', 0, seedV);
   V = [t'; W];
-  
+
   fprintf('\n');
   assert_matrix_abs(V, "model->V", "model->K-1");
 
@@ -89,4 +89,4 @@ function assert_matrix_abs(A, name, cols)
     end
   end
   fprintf("\n");
-end
\ No newline at end of file
+end