code: def corr_nearest(corr, threshold=1e-15, n_fact=100):
      """
      Find the nearest correlation matrix that is positive semi-definite.
  
      The function iteratively adjust the correlation matrix by clipping the
      eigenvalues of a difference matrix. The diagonal elements are set to one.
  
      Parameters
      ----------
      corr : ndarray, (k, k)
          initial correlation matrix
      threshold : float
          clipping threshold for smallest eigenvalue, see Notes
      n_fact : int or float
          factor to determine the maximum number of iterations. The maximum
          number of iterations is the integer part of the number of columns in
          the correlation matrix times n_fact.
  
      Returns
      -------
      corr_new : ndarray, (optional)
          corrected correlation matrix
  
      Notes
      -----
      The smallest eigenvalue of the corrected correlation matrix is
      approximately equal to the ``threshold``.
      If the threshold=0, then the smallest eigenvalue of the correlation matrix
      might be negative, but zero within a numerical error, for example in the
      range of -1e-16.
  
      Assumes input correlation matrix is symmetric.
  
      Stops after the first step if correlation matrix is already positive
      semi-definite or positive definite, so that smallest eigenvalue is above
      threshold. In this case, the returned array is not the original, but
      is equal to it within numerical precision.
  
      See Also
      --------
      corr_clipped
      cov_nearest
  
      """
      k_vars = corr.shape[0]
      if k_vars != corr.shape[1]:
          raise ValueError("matrix is not square")
  
      diff = np.zeros(corr.shape)
      x_new = corr.copy()
      diag_idx = np.arange(k_vars)
  
      for ii in range(int(len(corr) * n_fact)):
          x_adj = x_new - diff
          x_psd, clipped = clip_evals(x_adj, value=threshold)
          if not clipped:
              x_new = x_psd
              break
          diff = x_psd - x_adj
          x_new = x_psd.copy()
          x_new[diag_idx, diag_idx] = 1
      else:
          warnings.warn(iteration_limit_doc, IterationLimitWarning)
  
      return x_new
  
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 a global function with example usage: corr_nearest(corr, threshold, n_fact) and returns: x_new