class_docstr: Column centering of vectors and matrices.
  
  Attributes
  -----------
  col_means : numpy.ndarray [n_columns]
      NumPy array storing the mean values for centering after fitting
      the MeanCenterer object.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/preprocessing/MeanCenterer/
docstr: Centers a NumPy array.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Array of data vectors, where n_samples is the number of samples and
      n_features is the number of features.
  
  Returns
  --------
  X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]
      A copy of the input array with the columns centered.
docstr_tok: ["Centers","a","NumPy","array",".","Parameters","--","--","--","--","--","X",":","{","array-like",",","sparse","matrix","}",",","shape","=","[","n_samples",",","n_features","]","Array","of","data","vectors",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","Returns","--","--","--","--","X_tr",":","{","array-like",",","sparse","matrix","}",",","shape","=","[","n_samples",",","n_features","]","A","copy","of","the","input","array","with","the","columns","centered","."]
code: def transform(self, X):
      """Centers a NumPy array.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Array of data vectors, where n_samples is the number of samples and
          n_features is the number of features.
  
      Returns
      --------
      X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]
          A copy of the input array with the columns centered.
  
      """
      if not hasattr(self, "col_means"):
          raise AttributeError("MeanCenterer has not been fitted, yet.")
      X_tr = np.copy(self._get_array(X))
      X_tr = np.apply_along_axis(
          func1d=lambda x: x - self.col_means, axis=1, arr=X_tr
      )
      return X_tr
  
file_path: mlxtend/mlxtend/preprocessing/mean_centering.py
imports: import numpy
label_desc: Use image node_id 2 for calling the MeanCenterer obj's underlying member method code with example usage: obj.transform(X) and returns: X_tr