class_docstr: Linear Discriminant Analysis Class
  
  Parameters
  ----------
  n_discriminants : int (default: None)
      The number of discrimants for transformation.
      Keeps the original dimensions of the dataset if `None`.
  
  Attributes
  ----------
  w_ : array-like, shape=[n_features, n_discriminants]
      Projection matrix
  e_vals_ : array-like, shape=[n_features]
      Eigenvalues in sorted order.
  e_vecs_ : array-like, shape=[n_features]
     Eigenvectors in sorted order.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/feature_extraction/LinearDiscriminantAnalysis/
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code: def _eigendecom(self, within_scatter, between_scatter):
      e_vals, e_vecs = np.linalg.eig(
          np.linalg.inv(within_scatter).dot(between_scatter)
      )
      sort_idx = np.argsort(e_vals)[::-1]
      e_vals, e_vecs = e_vals[sort_idx], e_vecs[:, sort_idx]
      return e_vals, e_vecs
  
file_path: mlxtend/mlxtend/feature_extraction/linear_discriminant_analysis.py
imports: import numpy
  from .._base import _BaseModel
label_desc: Use image node_id 8 for calling the LinearDiscriminantAnalysis obj's underlying member method code with example usage: obj._eigendecom(within_scatter, between_scatter) and returns: e_vals,  e_vecs