class_docstr: Class for Multivariate Normal Distribution
  
  original full version, kept for testing, new version inherits from
  MVElliptical
  
  uses Cholesky decomposition of covariance matrix for the transformation
  of the data
class_docstr_tok: ["Class","for","Multivariate","Normal","Distribution","original","full","version",",","kept","for","testing",",","new","version","inherits","from","MVElliptical","uses","Cholesky","decomposition","of","covariance","matrix","for","the","transformation","of","the","data"]
docstr: whiten the data by linear transformation
  
  Parameters
  ----------
  X : array_like, 1d or 2d
      Data to be whitened, if 2d then each row contains an independent
      sample of the multivariate random vector
  
  Returns
  -------
  np.dot(x, self.cholcovinv.T)
  
  Notes
  -----
  This only does rescaling, it does not subtract the mean, use standardize
  for this instead
  
  See Also
  --------
  standardize : subtract mean and rescale to standardized random variable.
code: def whiten(self, x):
      """
      whiten the data by linear transformation
  
      Parameters
      ----------
      X : array_like, 1d or 2d
          Data to be whitened, if 2d then each row contains an independent
          sample of the multivariate random vector
  
      Returns
      -------
      np.dot(x, self.cholcovinv.T)
  
      Notes
      -----
      This only does rescaling, it does not subtract the mean, use standardize
      for this instead
  
      See Also
      --------
      standardize : subtract mean and rescale to standardized random variable.
      """
      x = np.asarray(x)
      if np.any(self.cov):
          # return np.dot(self.cholcovinv, x)
          return np.dot(x, self.cholcovinv.T)
      else:
          return x
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 2 for calling the MVNormal0 obj's underlying member method code with example usage: obj.whiten(x) and returns: np, x