Merge pull request #95 from schnell3526/main

factor_analyzer/factor_analyzer.py

@@ -179,7 +179,7 @@ class FactorAnalyzer(BaseEstimator, TransformerMixin):
  If 'randomized', use faster ``randomized_svd``
  function from scikit-learn. The latter should only
  be used if the number of columns is greater than or
- equal to the number of rows in in the dataset. 
+ equal to the number of rows in in the dataset.
  Defaults to 'randomized'
  rotation_kwargs, optional
  Additional key word arguments
@@ -497,7 +497,7 @@ def _fit_principal(self, X):
 
  # perform the randomized singular value decomposition
  if self.svd_method == 'randomized':
- U, S, V = randomized_svd(X, self.n_factors)
+ U, S, V = randomized_svd(X, self.n_factors, random_state=0)
  # otherwise, perform the full SVD
  else:
  U, S, V = np.linalg.svd(X, full_matrices=False)
@@ -687,11 +687,11 @@ def fit(self, X, y=None):
  variance = self._get_factor_variance(loadings)[0]
  new_order = list(reversed(np.argsort(variance)))
  loadings = loadings[:, new_order].copy()
- 
+
  # if the structure matrix exists, reorder
- if structure is not None: 
+ if structure is not None:
  structure = structure[:, new_order].copy()
- 
+
  self.phi_ = phi
  self.structure_ = structure