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 _between_scatter(self, X, y, mean_vectors):
      overall_mean = np.mean(X, axis=0)
      S_B = np.zeros((X.shape[1], X.shape[1]))
      for i, mean_vec in enumerate(mean_vectors):
          n = X[y == i + 1, :].shape[0]
          mean_vec = mean_vec.reshape(X.shape[1], 1)
          overall_mean = overall_mean.reshape(X.shape[1], 1)
          S_B += n * (mean_vec - overall_mean).dot(
              (mean_vec - overall_mean).T
          )
      return S_B
  
file_path: mlxtend/mlxtend/feature_extraction/linear_discriminant_analysis.py
imports: import numpy
  from .._base import _BaseModel
label_desc: Use image node_id 7 for calling the LinearDiscriminantAnalysis obj's underlying member method code with example usage: obj._between_scatter(X, y, mean_vectors) and returns: S_B