code: def test_corr_nearest_factor_arrpack(self):
      # regression results for svds call
      u2 = np.array(
          [
              [
                  6.39407581e-19,
                  9.15225947e-03,
                  1.82631698e-02,
                  2.72917181e-02,
                  3.61975557e-02,
                  4.49413101e-02,
                  5.34848732e-02,
                  6.17916613e-02,
                  6.98268388e-02,
                  7.75575058e-02,
                  8.49528448e-02,
                  9.19842264e-02,
                  9.86252769e-02,
                  1.04851906e-01,
                  1.10642305e-01,
                  1.15976906e-01,
                  1.20838331e-01,
                  1.25211306e-01,
                  1.29082570e-01,
                  1.32440778e-01,
                  1.35276397e-01,
                  1.37581605e-01,
                  1.39350201e-01,
                  1.40577526e-01,
                  1.41260396e-01,
                  1.41397057e-01,
                  1.40987160e-01,
                  1.40031756e-01,
                  1.38533306e-01,
                  1.36495727e-01,
                  1.33924439e-01,
                  1.30826443e-01,
                  1.27210404e-01,
                  1.23086750e-01,
                  1.18467769e-01,
                  1.13367717e-01,
                  1.07802909e-01,
                  1.01791811e-01,
                  9.53551023e-02,
                  8.85157320e-02,
                  8.12989329e-02,
                  7.37322125e-02,
                  6.58453049e-02,
                  5.76700847e-02,
                  4.92404406e-02,
                  4.05921079e-02,
                  3.17624629e-02,
                  2.27902803e-02,
                  1.37154584e-02,
                  4.57871801e-03,
                  -4.57871801e-03,
                  -1.37154584e-02,
                  -2.27902803e-02,
                  -3.17624629e-02,
                  -4.05921079e-02,
                  -4.92404406e-02,
                  -5.76700847e-02,
                  -6.58453049e-02,
                  -7.37322125e-02,
                  -8.12989329e-02,
                  -8.85157320e-02,
                  -9.53551023e-02,
                  -1.01791811e-01,
                  -1.07802909e-01,
                  -1.13367717e-01,
                  -1.18467769e-01,
                  -1.23086750e-01,
                  -1.27210404e-01,
                  -1.30826443e-01,
                  -1.33924439e-01,
                  -1.36495727e-01,
                  -1.38533306e-01,
                  -1.40031756e-01,
                  -1.40987160e-01,
                  -1.41397057e-01,
                  -1.41260396e-01,
                  -1.40577526e-01,
                  -1.39350201e-01,
                  -1.37581605e-01,
                  -1.35276397e-01,
                  -1.32440778e-01,
                  -1.29082570e-01,
                  -1.25211306e-01,
                  -1.20838331e-01,
                  -1.15976906e-01,
                  -1.10642305e-01,
                  -1.04851906e-01,
                  -9.86252769e-02,
                  -9.19842264e-02,
                  -8.49528448e-02,
                  -7.75575058e-02,
                  -6.98268388e-02,
                  -6.17916613e-02,
                  -5.34848732e-02,
                  -4.49413101e-02,
                  -3.61975557e-02,
                  -2.72917181e-02,
                  -1.82631698e-02,
                  -9.15225947e-03,
                  -3.51829569e-17,
              ]
          ]
      ).T
      s2 = np.array([24.88812183])
  
      d = 100
      dm = 1
  
      # Construct a test matrix with exact factor structure
      X = np.zeros((d, dm), dtype=np.float64)
      x = np.linspace(0, 2 * np.pi, d)
      for j in range(dm):
          X[:, j] = np.sin(x * (j + 1))
      _project_correlation_factors(X)
      X *= 0.7
      mat = np.dot(X, X.T)
      np.fill_diagonal(mat, 1.0)
  
      from scipy.sparse.linalg import svds
  
      u, s, vt = svds(mat, dm)
  
      # difference in sign
      dsign = np.sign(u[1]) * np.sign(u2[1])
  
      assert_allclose(u, dsign * u2, rtol=1e-6, atol=1e-14)
      assert_allclose(s, s2, rtol=1e-6)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_almost_equal, assert_allclose
  import scipy.sparse
  import pytest
  from statsmodels.stats.correlation_tools import corr_nearest, corr_clipped, cov_nearest, _project_correlation_factors, corr_nearest_factor, _spg_optim, corr_thresholded, cov_nearest_factor_homog, FactoredPSDMatrix
  from statsmodels.tools.testing import Holder
label_desc: Use image node_id 1 for calling the Test_Factor obj's underlying member method code with example usage: obj.test_corr_nearest_factor_arrpack() without return types