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 the covariance matrix for the dataset
docstr_tok: ["returns","the","covariance","matrix","for","the","dataset"]
code: def getCovarianceMatrix(self):
      """
      returns the covariance matrix for the dataset
      """
      return np.cov(self.N.T)
  
file_path: statsmodels/statsmodels/sandbox/pca.py
imports: import numpy
label_desc: Use image node_id 3 for calling the Pca obj's underlying member method code with example usage: obj.getCovarianceMatrix() and returns: np