Adds normalized variance explained and mean centering for svd in PCA

[rasbt]

Description

• Adds an additional attribute for storing normalized eivenvalues in PCA.
• Also, mean centers the data when SVD solver is used to guarantee that the results from the SVD solver are always the same as with the covariance matrix approach

Related issues or pull requests

Pull Request Checklist

• Added a note about the modification or contribution to the ./docs/sources/CHANGELOG.md file (if applicable)
• Added appropriate unit test functions in the ./mlxtend/*/tests directories (if applicable)
• Modify documentation in the corresponding Jupyter Notebook under mlxtend/docs/sources/ (if applicable)
• Ran nosetests ./mlxtend -sv and make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g., nosetests ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)
• Checked for style issues by running flake8 ./mlxtend

[pep8speaks]

Hello @rasbt! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2019-05-29 02:37:25 UTC

[coveralls]

Coverage increased (+0.01%) to 92.1% when pulling e9be9eb on norm_evecs into 2787aa4 on master.