Changing least-squares argument 'rcond=None' also for result extraction
@@ -468,7 +468,7 @@ class VectorFitting:
logging.info('A_matrix: condition number = {}'.format(np.linalg.cond(A_matrix)))
# solve least squares and obtain results as stack of real part vector and imaginary part vector
- x, residuals, rank, singular_vals = np.linalg.lstsq(A_matrix, b_vector, rcond=-1)
+ x, residuals, rank, singular_vals = np.linalg.lstsq(A_matrix, b_vector, rcond=None)
i = 0
zeros_response = []
