code: def test_variance_explained_ratio():
      pca = PCA()
      pca.fit(X_std)
      assert_almost_equal(np.sum(pca.e_vals_normalized_), 1.0)
      assert np.sum(pca.e_vals_normalized_ < 0.0) == 0
  
file_path: mlxtend/mlxtend/feature_extraction/tests/test_principal_component_analysis.py
imports: import numpy
  from numpy.testing import assert_allclose, assert_almost_equal
  from mlxtend.data import iris_data
  from mlxtend.feature_extraction import PrincipalComponentAnalysis
  from mlxtend.preprocessing import standardize
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 10 for calling a global function with example usage: test_variance_explained_ratio() without return types