doc

Author: aadya940

chainopy/markov_switching.py

@@ -48,11 +48,10 @@ def fit(self, ts_data, regime_sequence, lags=1):
 
         Args:
         -----
-        ts_data: ndarray,
+        ts_data: ndarray
             1D Target values at different timepoints
         regime_sequence: list
-                    Training data consisting of Regimes in chronological
-                    Order.
+            Training data consisting of Regimes in chronological Order.
         lags: int,
             Time lags to consider during autoregression
         """
@@ -70,8 +69,7 @@ def _learn_regime_proba(self, regime_sequence: str) -> np.ndarray:
         Args:
         -----
         regime_sequence: list,
-                    Training data consisting of Regimes in chronological
-                    Order.
+            Training data consisting of Regimes in chronological Order.
         """
         __regime_str = ""
         for i, regime in enumerate(regime_sequence):
@@ -119,7 +117,7 @@ def predict(
 
         Returns:
         --------
-        Tuple[np.ndarray, np.ndarray]
+        Tuple[np.ndarray, np.ndarray]: 
             Tuple containing the array of predicted target values and the predicted regime sequence.
         """
         predictions = np.zeros(steps, dtype=np.float32)

chainopy/nn.py

@@ -21,11 +21,9 @@ class MarkovChainNeuralNetwork(nn.Module):
         num_layers : int
             Number of layers in the neural network.
 
-
     Raises:
     -------
         ValueError: If markov_chain is not of type MarkovChain.
-
     """
 
     def __init__(self, markov_chain, num_layers):
@@ -167,7 +165,6 @@ def train_model(
                 Patience parameter for learning rate scheduler (default is 500).
             factor : float, optional
                 Factor by which the learning rate will be reduced (default is 0.5).
-
         """
 
         self.optimizer = optim.SGD(