class_docstr: Representation of a positive semidefinite matrix in factored form.
  
  The representation is constructed based on a vector `diag` and
  rectangular matrix `root`, such that the PSD matrix represented by
  the class instance is Diag + root * root', where Diag is the
  square diagonal matrix with `diag` on its main diagonal.
  
  Parameters
  ----------
  diag : 1d array_like
      See above
  root : 2d array_like
      See above
  
  Notes
  -----
  The matrix is represented internally in the form Diag^{1/2}(I +
  factor * scales * factor')Diag^{1/2}, where `Diag` and `scales`
  are diagonal matrices, and `factor` is an orthogonal matrix.
docstr: Returns the PSD matrix represented by this instance as a full
  (square) matrix.
docstr_tok: ["Returns","the","PSD","matrix","represented","by","this","instance","as","a","full","(","square",")","matrix","."]
code: def to_matrix(self):
      """
      Returns the PSD matrix represented by this instance as a full
      (square) matrix.
      """
      return np.diag(self.diag) + np.dot(self.root, self.root.T)
  
imports: import numpy
  import scipy.sparse
  from scipy.sparse.linalg import svds
  from scipy.optimize import fminbound
  import warnings
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.sm_exceptions import IterationLimitWarning, iteration_limit_doc
label_desc: Use image node_id 2 for calling the FactoredPSDMatrix obj's underlying member method code with example usage: obj.to_matrix() and returns: unknown