pflm.fpca.utils.estimate_rho#
- estimate_rho(method_rho: str, flatten_func_data: FlattenFunctionalData, reg_grid: ndarray, mu_obs: ndarray, mu_reg: ndarray, fpca_lambda: ndarray, fpca_phi_obs: ndarray, fpca_phi_reg: ndarray, fitted_cov: ndarray, sigma2: float) float[source][source]#
Estimate the optimal rho parameter for CE scoring.
- Parameters:
- method_rho{“ridge”, “truncated”}
Estimation strategy for rho.
- flatten_func_dataFlattenFunctionalData
Flattened data (y, t, tid, unique_sid, sid_cnt).
- reg_gridnp.ndarray of shape (n_reg_grid,)
Regular grid used to compute variance of reconstructed curves.
- mu_obsnp.ndarray of shape (nt,)
Mean on the observation grid.
- mu_regnp.ndarray of shape (n_reg_grid,)
Mean on the regular grid.
- fpca_lambdanp.ndarray of shape (k,)
FPCA eigenvalues.
- fpca_phi_obsnp.ndarray of shape (nt, k)
Basis on observation grid.
- fpca_phi_regnp.ndarray of shape (n_reg_grid, k)
Basis on regular grid.
- fitted_covnp.ndarray of shape (nt, nt)
Fitted covariance on observation grid.
- sigma2float
Noise variance or starting value (for “truncated” path).
- Returns:
- float
Estimated rho value that best matches target variance.
Notes
Values of method_rho other than “ridge” are treated as “truncated”. When using float32, the results may not be the same with float64 because of the lower precision in calculations.