Update References

References

@@ -5,4 +5,69 @@ Automatically generated by Colab.
 
 Original file is located at
     https://colab.research.google.com/drive/1n4ADxn-u0nAkYm6mKMzzhiH1vl97qImr
-"""
+"""
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+wealth_distribution = torch.randn(32, 24, 1)
+target_direction = torch.randn(32, 24, 1)
+
+class WealthTransferModelWithVPN(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
+        super(WealthTransferModelWithVPN, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+        self.vpn_layer = nn.Linear(output_size, vpn_size)
+        self.decrypt_layer = nn.Linear(vpn_size, output_size)
+
+    def forward(self, x, target):
+        x = torch.cat((x, target), dim=1)
+
+        x = self.relu(self.fc1(x))
+
+        x, _ = self.lstm(x)
+
+        x = self.fc2(x)
+
+        encrypted_output = torch.sigmoid(self.vpn_layer(x))
+
+        decrypted_output = self.decrypt_layer(encrypted_output)
+
+        return decrypted_output
+
+input_size = wealth_distribution[-1] + target_direction.shape[-1]
+hidden_size = 64
+lstm_hidden_size = 32
+output_size = wealth_distribution.shape[-1]
+vpn_size = 128
+
+model = WealthTransferWithVPN(input_size, hidden_sizse, lstm_hidden_size, vpn_size)
+
+
+with torch.no_grad():
+    output_signal = model(wealth_distribution, target_direction)
+
+wealth_waveform = output_signal[0].squeeze().numpy()
+
+hours = list(range(24))
+
+plt.figure(figsize=(10, 5))
+plt.plot(hours, wealth_waveform, label='Wealth Transfer Signal over 24 hours', marker='o')
+plt.title('Wealth Transfer Signal in 24-Hour Intervals')
+plt.xlabel('Hour of the Day')
+plt.ylabel('Wealth Signal Intensity')
+plt.xticks(hours)
+plt.grid(True)
+plt.legend()
+plt.show()
+
+        
+