[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

neural_network/lstm.py

@@ -42,7 +42,7 @@ class LSTM:
     def __init__(self, data: str, hidden_dim: int = 25, epochs: int = 1000, lr: float = 0.05) -> None:
         """
         Initialize the LSTM network with the given data and hyperparameters.
-        
+
         :param data: The input data as a string.
         :param hidden_dim: The number of hidden units in the LSTM layer.
         :param epochs: The number of training epochs.
@@ -69,7 +69,7 @@ def __init__(self, data: str, hidden_dim: int = 25, epochs: int = 1000, lr: floa
     def one_hot_encode(self, char: str) -> np.ndarray:
         """
         One-hot encode a character.
-        
+
         :param char: The character to encode.
         :return: A one-hot encoded vector.
         """
@@ -99,7 +99,7 @@ def initialize_weights(self) -> None:
     def init_weights(self, input_dim: int, output_dim: int) -> np.ndarray:
         """
         Initialize weights with random values.
-        
+
         :param input_dim: The input dimension.
         :param output_dim: The output dimension.
         :return: A matrix of initialized weights.
@@ -110,7 +110,7 @@ def init_weights(self, input_dim: int, output_dim: int) -> np.ndarray:
     def sigmoid(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
         """
         Sigmoid activation function.
-        
+
         :param x: The input array.
         :param derivative: Whether to compute the derivative.
         :return: The sigmoid activation or its derivative.
@@ -122,7 +122,7 @@ def sigmoid(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
     def tanh(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
         """
         Tanh activation function.
-        
+
         :param x: The input array.
         :param derivative: Whether to compute the derivative.
         :return: The tanh activation or its derivative.
@@ -134,7 +134,7 @@ def tanh(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
     def softmax(self, x: np.ndarray) -> np.ndarray:
         """
         Softmax activation function.
-        
+
         :param x: The input array.
         :return: The softmax activation.
         """
@@ -161,7 +161,7 @@ def reset(self) -> None:
     def forward(self, inputs: list) -> list:
         """
         Perform forward propagation through the LSTM network.
-        
+
         :param inputs: The input data as a list of one-hot encoded vectors.
         :return: The outputs of the network.
         """
@@ -186,7 +186,7 @@ def forward(self, inputs: list) -> list:
     def backward(self, errors: list, inputs: list) -> None:
         """
         Perform backpropagation through time to compute gradients and update weights.
-        
+
         :param errors: The errors at each time step.
         :param inputs: The input data as a list of one-hot encoded vectors.
         """
@@ -224,7 +224,7 @@ def backward(self, errors: list, inputs: list) -> None:
             d_i = d_cs * self.candidate_gates[t] * self.sigmoid(self.input_gates[t], derivative=True)
             d_wi += np.dot(d_i, inputs[t].T)
             d_bi += d_i
-            
+
             # Candidate Gate Weights and Biases Errors
             d_c = d_cs * self.input_gates[t] * self.tanh(self.candidate_gates[t], derivative=True)
             d_wc += np.dot(d_c, inputs[t].T)
@@ -270,7 +270,7 @@ def train(self) -> None:
                 errors[-1][self.char_to_idx[self.train_y[t]]] += 1
 
             self.backward(errors, self.concat_inputs)
-    
+
     def test(self) -> None:
         """
         Test the trained LSTM network on the input data and print the accuracy.
@@ -289,7 +289,7 @@ def test(self) -> None:
 
         print(f'Ground Truth:\n{self.train_y}\n')
         print(f'Predictions:\n{output}\n')
-        
+
         print(f'Accuracy: {round(accuracy * 100 / len(self.train_X), 2)}%')
 
 ##### Data #####
@@ -314,4 +314,4 @@ def test(self) -> None:
     ##### Testing #####
     # lstm.test()
 
-# testing can be done by uncommenting the above lines of code.
+# testing can be done by uncommenting the above lines of code.