Added LSTM model

src/models/lstm_model.py

@@ -0,0 +1,45 @@
+import torch
+
+class LSTMModel(torch.nn.Module):
+    def __init__(self, inputSize, output_size, num_layers: int, hidden_size: int, dropout: float = 0.2):
+        super(LSTMModel, self).__init__()
+        self.inputSize = inputSize
+        self.output_size = output_size
+
+        self.num_layers = num_layers
+        self.hidden_size = hidden_size
+        self.dropout = dropout
+
+        self.lstm = torch.nn.LSTM(input_size=inputSize[-1], hidden_size=hidden_size, num_layers=num_layers, dropout=dropout, batch_first=True)
+        self.linear = torch.nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        # Forward pass through the LSTM layers
+        _, (hidden_state, _) = self.lstm(x)
+        
+        # Use the hidden state from the last time step for the output
+        output = self.linear(hidden_state[-1])
+        
+        return output
+
+class GRUModel(torch.nn.Module):
+    def __init__(self, inputSize, output_size, num_layers: int, hidden_size: int, dropout: float = 0.2):
+        super(GRUModel, self).__init__()
+        self.inputSize = inputSize
+        self.output_size = output_size
+
+        self.num_layers = num_layers
+        self.hidden_size = hidden_size
+        self.dropout = dropout
+
+        self.gru = torch.nn.GRU(input_size=inputSize[-1], hidden_size=hidden_size, num_layers=num_layers, dropout=dropout, batch_first=True)
+        self.linear = torch.nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        # Forward pass through the GRU layers
+        _, hidden_state = self.gru(x)
+        
+        # Use the hidden state from the last time step for the output
+        output = self.linear(hidden_state[-1])
+        
+        return output