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Commit bf93043

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add type hint to AOLS methods
1 parent 17c64cb commit bf93043

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1 file changed

+54
-11
lines changed

1 file changed

+54
-11
lines changed

‎sysidentpy/model_structure_selection/accelerated_orthogonal_least_squares.py

Lines changed: 54 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
# Authors:
44
# Wilson Rocha Lacerda Junior <wilsonrljr@outlook.com>
55
# License: BSD 3 clause
6-
from typing import Tuple, Union
6+
from typing import Tuple, Union, Optional
77

88
import numpy as np
99
from numpy import linalg as LA
@@ -262,7 +262,7 @@ def aols(
262262
theta = theta[theta != 0]
263263
return theta.reshape(-1, 1), pivv, residual_norm
264264

265-
def fit(self, *, X=None, y=None):
265+
def fit(self, *, X: Optional[np.ndarray] =None, y: Optional[np.ndarray] =None):
266266
"""Fit polynomial NARMAX model using AOLS algorithm.
267267
268268
The 'fit' function allows a friendly usage by the user.
@@ -343,7 +343,14 @@ def fit(self, *, X=None, y=None):
343343
] # just to use the `results` method. Will be changed in next update.
344344
return self
345345

346-
def predict(self, *, X=None, y=None, steps_ahead=None, forecast_horizon=None):
346+
def predict(
347+
self,
348+
*,
349+
X: Optional[np.ndarray] = None,
350+
y: Optional[np.ndarray] = None,
351+
steps_ahead: Optional[int] = None,
352+
forecast_horizon: int = 0,
353+
) -> np.ndarray:
347354
"""Return the predicted values given an input.
348355
349356
The predict function allows a friendly usage by the user.
@@ -401,7 +408,9 @@ def predict(self, *, X=None, y=None, steps_ahead=None, forecast_horizon=None):
401408
yhat = np.concatenate([y[: self.max_lag], yhat], axis=0)
402409
return yhat
403410

404-
def _one_step_ahead_prediction(self, X, y):
411+
def _one_step_ahead_prediction(
412+
self, X: Optional[np.ndarray], y: Optional[np.ndarray]
413+
) -> np.ndarray:
405414
"""Perform the 1-step-ahead prediction of a model.
406415
407416
Parameters
@@ -435,7 +444,12 @@ def _one_step_ahead_prediction(self, X, y):
435444
yhat = super()._one_step_ahead_prediction(X_base)
436445
return yhat.reshape(-1, 1)
437446

438-
def _n_step_ahead_prediction(self, X, y, steps_ahead):
447+
def _n_step_ahead_prediction(
448+
self,
449+
X: Optional[np.ndarray],
450+
y: Optional[np.ndarray],
451+
steps_ahead: Optional[int],
452+
) -> np.ndarray:
439453
"""Perform the n-steps-ahead prediction of a model.
440454
441455
Parameters
@@ -455,7 +469,12 @@ def _n_step_ahead_prediction(self, X, y, steps_ahead):
455469
yhat = super()._n_step_ahead_prediction(X, y, steps_ahead)
456470
return yhat
457471

458-
def _model_prediction(self, X, y_initial, forecast_horizon=None):
472+
def _model_prediction(
473+
self,
474+
X: Optional[np.ndarray],
475+
y_initial: Optional[np.ndarray],
476+
forecast_horizon: int = 1,
477+
) -> np.ndarray:
459478
"""Perform the infinity steps-ahead simulation of a model.
460479
461480
Parameters
@@ -481,7 +500,12 @@ def _model_prediction(self, X, y_initial, forecast_horizon=None):
481500
f"model_type must be NARMAX, NAR or NFIR. Got {self.model_type}"
482501
)
483502

484-
def _narmax_predict(self, X, y_initial, forecast_horizon):
503+
def _narmax_predict(
504+
self,
505+
X: Optional[np.ndarray],
506+
y_initial: Optional[np.ndarray],
507+
forecast_horizon: int = 1,
508+
) -> np.ndarray:
485509
if len(y_initial) < self.max_lag:
486510
raise ValueError(
487511
"Insufficient initial condition elements! Expected at least"
@@ -499,11 +523,18 @@ def _narmax_predict(self, X, y_initial, forecast_horizon):
499523
y_output = super()._narmax_predict(X, y_initial, forecast_horizon)
500524
return y_output
501525

502-
def _nfir_predict(self, X, y_initial):
526+
def _nfir_predict(
527+
self, X: Optional[np.ndarray], y_initial: Optional[np.ndarray]
528+
) -> np.ndarray:
503529
y_output = super()._nfir_predict(X, y_initial)
504530
return y_output
505531

506-
def _basis_function_predict(self, X, y_initial, forecast_horizon=None):
532+
def _basis_function_predict(
533+
self,
534+
X: Optional[np.ndarray],
535+
y_initial: Optional[np.ndarray],
536+
forecast_horizon: int = 1,
537+
) -> np.ndarray:
507538
if X is not None:
508539
forecast_horizon = X.shape[0]
509540
else:
@@ -515,7 +546,13 @@ def _basis_function_predict(self, X, y_initial, forecast_horizon=None):
515546
yhat = super()._basis_function_predict(X, y_initial, forecast_horizon)
516547
return yhat.reshape(-1, 1)
517548

518-
def _basis_function_n_step_prediction(self, X, y, steps_ahead, forecast_horizon):
549+
def _basis_function_n_step_prediction(
550+
self,
551+
X: Optional[np.ndarray],
552+
y: Optional[np.ndarray],
553+
steps_ahead: Optional[int],
554+
forecast_horizon: int,
555+
) -> np.ndarray:
519556
"""Perform the n-steps-ahead prediction of a model.
520557
521558
Parameters
@@ -548,7 +585,13 @@ def _basis_function_n_step_prediction(self, X, y, steps_ahead, forecast_horizon)
548585
)
549586
return yhat.reshape(-1, 1)
550587

551-
def _basis_function_n_steps_horizon(self, X, y, steps_ahead, forecast_horizon):
588+
def _basis_function_n_steps_horizon(
589+
self,
590+
X: Optional[np.ndarray],
591+
y: Optional[np.ndarray],
592+
steps_ahead: Optional[int],
593+
forecast_horizon: int,
594+
) -> np.ndarray:
552595
yhat = super()._basis_function_n_steps_horizon(
553596
X, y, steps_ahead, forecast_horizon
554597
)

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