v0.1.1: Minor updates, reduced printing.

README.md

@@ -5,7 +5,7 @@ Python implementation of the 2L-VKOGA algorithm, which uses a kernel optimizatio
 
 ## Installation
 
-pip install git+https://gitlab.mathematik.uni-stuttgart.de/pub/ians-anm/2l-vkoga@v0.1.0
+pip install git+https://gitlab.mathematik.uni-stuttgart.de/pub/ians-anm/2l-vkoga@v0.1.1
 
 
 ## Usage

setup.py

@@ -8,7 +8,7 @@ setup(
     author='Tizian Wenzel',
     author_email='tizian.wenzel@mathematik.uni-stuttgart.de',
     license='GNU v3.ß',
-    packages=['vkoga_2l'],
+    packages=['vkoga_2L'],
     install_requires=['numpy==1.24.1', 'torch==2.1.1', 'matplotlib==3.7.2', 'scipy==1.11.4', 'scikit-learn==1.3.2'],
     classifiers=[
         'Development Status :: 1 - Planning',

vkoga_2l/vkoga_2L.py → vkoga_2L/vkoga_2L.py

@@ -142,7 +142,7 @@ class VKOGA_2L(BaseEstimator):
                                                 n_folds=None, flag_initialize_diagonal=True,
                                                 flag_symmetric_A=False)
             model_OptimKernel.optimize(torch.from_numpy(X).float(), torch.from_numpy(y).float(),
-                                       flag_optim_verbose=True)
+                                       flag_optim_verbose=self.verbose)
 
             self.A = model_OptimKernel.A.detach().numpy()