class_docstr: A basic class for Principal Component Analysis (PCA).
  
  p is the number of dimensions, while N is the number of data points
docstr: returns a tuple of (eigenvalues,eigenvectors) for the data set.
docstr_tok: ["returns","a","tuple","of","(","eigenvalues",",","eigenvectors",")","for","the","data","set","."]
code: def getEigensystem(self):
      """
      returns a tuple of (eigenvalues,eigenvectors) for the data set.
      """
      if self._eig is None:
          res = np.linalg.eig(self.getCovarianceMatrix())
          sorti = np.argsort(res[0])[::-1]
          res = (res[0][sorti], res[1][:, sorti])
          self._eig = res
      return self._eig
  
file_path: statsmodels/statsmodels/sandbox/pca.py
imports: import numpy
label_desc: Use image node_id 4 for calling the Pca obj's underlying member method code with example usage: obj.getEigensystem() and returns: self