Upload magnet_2_0.py

magnet_2_0.py

@@ -0,0 +1,947 @@
+# -*- coding: utf-8 -*-
+"""MagNet 2.0
+
+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
+
+# Simulate wealth distribution (e.g., 100 individuals with a certain wealth amount)
+wealth_distribution = torch.randn(100, 1)  # (100 people, 1 wealth feature)
+
+# Define the target direction (randomly initialized, or learned)
+target_direction = torch.randn(100, 1)
+
+# Define a simple model to transfer wealth in the target direction
+class WealthTransferModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(WealthTransferModel, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, hidden_size)
+        self.fc3 = nn.Linear(hidden_size, output_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate or element-wise)
+        x = torch.cat((x, target), dim=1)
+        # Process wealth signal with dense layers
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Initialize model, loss function, and optimizer
+input_size = wealth_distribution.shape[1] + target_direction.shape[1]  # Input wealth + target direction
+hidden_size = 64  # Hidden layer size (can be adjusted)
+output_size = wealth_distribution.shape[1]  # Output size matches wealth distribution
+
+model = WealthTransferModel(input_size, hidden_size, output_size)
+loss_fn = nn.MSELoss()  # Mean Squared Error loss for simplicity
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Dummy target wealth state (after transfer)
+target_wealth_state = torch.randn(100, 1)  # Random for now; this would be based on business logic
+
+# Training loop (just for illustration; you can adjust the number of epochs)
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Zero gradients
+    optimizer.zero_grad()
+
+    # Forward pass: Compute the wealth transfer
+    output = model(wealth_distribution, target_direction)
+
+    # Compute loss (compare output to the target wealth state)
+    loss = loss_fn(output, target_wealth_state)
+
+    # Backpropagation and optimization step
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+# After training, model should learn how to adjust wealth distribution towards the target direction
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Simulate wealth distribution (e.g., 100 individuals with a certain wealth amount)
+wealth_distribution = torch.randn(100, 1)  # (100 people, 1 wealth feature)
+
+# Define the target direction (randomly initialized, or learned)
+target_direction = torch.randn(100, 1)
+
+# Define a model that includes an LSTM layer for "nerve-like" behavior to store wealth information
+class WealthTransferModelWithNerve(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size):
+        super(WealthTransferModelWithNerve, self).__init__()
+        # First dense layer to process wealth and target information
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer that acts as a "nerve" to store wealth information
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate or element-wise)
+        x = torch.cat((x, target), dim=1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Prepare for LSTM (LSTM requires input of shape (batch_size, seq_length, feature_size))
+        x = x.unsqueeze(1)  # Add a sequence dimension for LSTM (batch_size, 1, hidden_size)
+
+        # Pass through LSTM layer (storing wealth information in "nerves")
+        x, (hn, cn) = self.lstm(x)  # hn: hidden state, cn: cell state
+
+        # Remove sequence dimension for the final dense layer
+        x = x.squeeze(1)
+
+        # Output layer to compute the final wealth transfer
+        x = self.fc2(x)
+        return x
+
+# Initialize model, loss function, and optimizer
+input_size = wealth_distribution.shape[1] + target_direction.shape[1]  # Input wealth + target direction
+hidden_size = 64  # Size for first dense layer
+lstm_hidden_size = 32  # Hidden size of the LSTM layer
+output_size = wealth_distribution.shape[1]  # Output size matches wealth distribution
+
+model = WealthTransferModelWithNerve(input_size, hidden_size, lstm_hidden_size, output_size)
+loss_fn = nn.MSELoss()  # Mean Squared Error loss for simplicity
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Dummy target wealth state (after transfer)
+target_wealth_state = torch.randn(100, 1)  # Random for now; this would be based on business logic
+
+# Training loop (just for illustration; you can adjust the number of epochs)
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Zero gradients
+    optimizer.zero_grad()
+
+    # Forward pass: Compute the wealth transfer with the "nerve" layer
+    output = model(wealth_distribution, target_direction)
+
+    # Compute loss (compare output to the target wealth state)
+    loss = loss_fn(output, target_wealth_state)
+
+    # Backpropagation and optimization step
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+# After training, the model will learn to store and process wealth information in the "nerves" and transfer it towards the target.
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Define parameters
+batch_size = 32  # Number of samples in a batch
+seq_length = 10  # Number of timesteps (e.g., 10 timesteps)
+feature_size = 1  # Wealth feature per individual
+
+# Simulate wealth distribution over multiple timesteps for 100 people
+wealth_distribution = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Define the target direction over multiple timesteps
+target_direction = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Define the model with LSTM layer for "nerve-like" processing across timesteps
+class WealthTransferModelWithTimesteps(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size):
+        super(WealthTransferModelWithTimesteps, self).__init__()
+        # First dense layer to process wealth and target information
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer that acts as a "nerve" to store wealth information over timesteps
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along feature dimension)
+        x = torch.cat((x, target), dim=-1)  # Concatenate along the feature axis
+
+        # Process through the first dense layer for each timestep (use .view to flatten)
+        batch_size, seq_length, num_people, _ = x.shape
+        x = x.view(batch_size * seq_length * num_people, -1)  # Flatten for FC layer
+        x = self.relu(self.fc1(x))
+        x = x.view(batch_size, seq_length, num_people, -1)  # Reshape back after FC
+
+        # LSTM expects input of shape (batch_size, seq_length, feature_size)
+        x = x.view(batch_size, seq_length, -1)  # Combine people and features for LSTM
+
+        # Pass through LSTM layer (storing wealth information over timesteps)
+        x, (hn, cn) = self.lstm(x)  # hn: hidden state, cn: cell state
+
+        # Output layer to compute the final wealth transfer for each timestep
+        x = self.fc2(x)
+        x = x.view(batch_size, seq_length, num_people, -1)  # Reshape back to original format
+        return x
+
+# Initialize model, loss function, and optimizer
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Wealth + target info per timestep
+hidden_size = 64  # Hidden size for first dense layer
+lstm_hidden_size = 32  # Hidden size of the LSTM layer
+output_size = wealth_distribution.shape[-1]  # Output size should match wealth feature per person
+
+model = WealthTransferModelWithTimesteps(input_size, hidden_size, lstm_hidden_size, output_size)
+loss_fn = nn.MSELoss()  # Mean Squared Error loss for simplicity
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Dummy target wealth state over multiple timesteps
+target_wealth_state = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Training loop (just for illustration)
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Zero gradients
+    optimizer.zero_grad()
+
+    # Forward pass: Compute the wealth transfer over multiple timesteps
+    output = model(wealth_distribution, target_direction)
+
+    # Compute loss (compare output to the target wealth state)
+    loss = loss_fn(output, target_wealth_state)
+
+    # Backpropagation and optimization step
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+# After training, the model will learn to store and direct wealth information across multiple timesteps.
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Define parameters
+batch_size = 32  # Number of samples in a batch
+seq_length = 10  # Number of timesteps (e.g., 10 timesteps)
+feature_size = 1  # Wealth feature per individual
+
+# Simulate wealth distribution over multiple timesteps for 100 people
+wealth_distribution = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Define the target direction over multiple timesteps
+target_direction = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Define the model with LSTM layer for "nerve-like" processing across timesteps
+class WealthTransferModelWithTimesteps(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size):
+        super(WealthTransferModelWithTimesteps, self).__init__()
+        # First dense layer to process wealth and target information
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer that acts as a "nerve" to store wealth information over timesteps
+        # Changed input_size to hidden_size * 100 to match the output of fc1
+        self.lstm = nn.LSTM(hidden_size * 100, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along feature dimension)
+        x = torch.cat((x, target), dim=-1)  # Concatenate along the feature axis
+
+        # Process through the first dense layer for each timestep (use .view to flatten)
+        batch_size, seq_length, num_people, _ = x.shape
+        x = x.view(batch_size * seq_length * num_people, -1)  # Flatten for FC layer
+        x = self.relu(self.fc1(x))
+
+        # Reshape to (batch_size, seq_length, num_people * hidden_size) for LSTM
+        x = x.view(batch_size, seq_length, num_people * hidden_size)  # Reshape for LSTM
+
+        # Pass through LSTM layer (storing wealth information over timesteps)
+        x, (hn, cn) = self.lstm(x)  # hn: hidden state, cn: cell state
+
+        # Output layer to compute the final wealth transfer for each timestep
+        x = self.fc2(x)
+        x = x.view()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Define parameters
+batch_size = 32  # Number of samples in a batch
+seq_length = 10  # Number of timesteps
+feature_size = 1  # Wealth feature per individual
+
+# Simulate wealth distribution over multiple timesteps for 100 people
+wealth_distribution = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Define the target direction over multiple timesteps
+target_direction = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Define the model with LSTM layer and a "VPN" protection layer
+class WealthTransferModelWithVPN(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
+        super(WealthTransferModelWithVPN, self).__init__()
+        # First dense layer to process wealth and target information
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer that acts as a "nerve" to store wealth information over timesteps
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+        # VPN-like encryption layer (simulated with a non-linear transformation)
+        self.vpn_layer = nn.Linear(output_size, vpn_size)  # A layer to "encrypt" the output
+        self.decrypt_layer = nn.Linear(vpn_size, output_size)  # To recover the original output
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along feature dimension)
+        x = torch.cat((x, target), dim=-1)  # Concatenate along the feature axis
+
+        # Process through the first dense layer for each timestep (use .view to flatten)
+        batch_size, seq_length, num_people, _ = x.shape
+        x = x.view(batch_size * seq_length * num_people, -1)  # Flatten for FC layer
+        x = self.relu(self.fc1(x))
+        x = x.view(batch_size, seq_length, num_people, -1)  # Reshape back after FC
+
+        # LSTM expects input of shape (batch_size, seq_length, feature_size)
+        x = x.view(batch_size, seq_length, num_people * hidden_size)  # Combine people and features for LSTM
+
+        # Pass through LSTM layer (storing wealth information over timesteps)
+        x, (hn, cn) = self.lstm(x)  # hn: hidden state, cn: cell state
+
+        # Output layer to compute the final wealth transfer for each timestep
+        x = self.fc2(x)
+        x = x.view(batch_size, seq_length, num_people, -1)  # Reshape back to original format
+
+        # Pass through the VPN encryption layer
+        encrypted_output = torch.sigmoid(self.vpn_layer(x))  # Apply transformation (like encryption)
+
+        # Simulate decryption by passing through another layer
+        decrypted_output = self.decrypt_layer(encrypted_output)
+
+        return decrypted_output  # Return the "secure" output
+
+# Initialize model, loss function, and optimizer
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Wealth + target info per timestep
+hidden_size = 64  # Hidden size for first dense layer
+lstm_hidden_size = 32  # Hidden size of the LSTM layer
+output_size = wealth_distribution.shape[-1]  # Output size should match wealth feature per person
+vpn_size = 128  # Size of the "VPN" layer
+
+model = WealthTransferModelWithVPN(input_size, hidden_size, lstm_hidden_size, output_size, vpn_size)
+loss_fn = nn.MSELoss()  # Mean Squared Error loss for simplicity
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Dummy target wealth state over multiple timesteps
+target_wealth_state = torch.randn(batch_size, seq_length, 100, feature_size)
+
+# Training loop (just for illustration)
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Zero gradients
+    optimizer.zero_grad()
+
+    # Forward pass: Compute the wealth transfer with VPN-like protection
+    output = model(wealth_distribution, target_direction)
+
+    # Compute loss (compare output to the target wealth state)
+    loss = loss_fn(output, target_wealth_state)
+
+    # Backpropagation and optimization step
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+# After training, the model will learn to store and protect wealth information securely while transferring it.
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Simulate wealth distribution for 100 people
+wealth_distribution = torch.randn(100, 1)  # (100 people, 1 wealth feature)
+
+# Define the target direction (randomly initialized or learned)
+target_direction = torch.randn(100, 1)
+
+# Define a simple dense model to process wealth and target direction
+class WealthTransferModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(WealthTransferModel, self).__init__()
+        # First dense layer
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # Second dense layer
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate or element-wise)
+        x = torch.cat((x, target), dim=1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Output layer to compute the final wealth transfer signal
+        x = self.fc2(x)
+        return x
+
+# Initialize the model
+input_size = wealth_distribution.shape[1] + target_direction.shape[1]  # Input wealth + target direction
+hidden_size = 64  # Hidden layer size
+output_size = wealth_distribution.shape[1]  # Output size matches wealth distribution
+
+model = WealthTransferModel(input_size, hidden_size, output_size)
+
+# Define loss function and optimizer
+loss_fn = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Dummy target wealth state (after transfer)
+target_wealth_state = torch.randn(100, 1)  # Random for now; this would be based on business logic
+
+# Training loop (just for illustration)
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Zero gradients
+    optimizer.zero_grad()
+
+    # Forward pass: compute the wealth transfer
+    output = model(wealth_distribution, target_direction)
+
+    # Compute loss (compare output to the target wealth state)
+    loss = loss_fn(output, target_wealth_state)
+
+    # Backpropagation and optimization step
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Simulate wealth distribution for 100 people
+wealth_distribution = torch.randn(32, 100, 1)  # (batch_size, 100 people, 1 wealth feature)
+
+# Define the target direction (randomly initialized or learned)
+target_direction = torch.randn(32, 100, 1)  # (batch_size, 100 people, 1 feature for direction)
+
+# Define a model with LSTM to store wealth signal in the "nerves"
+class WealthTransferModelWithNerves(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size):
+        super(WealthTransferModelWithNerves, self).__init__()
+        # First dense layer
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer to store wealth signal in the "nerves"
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along the feature dimension)
+        x = torch.cat((x, target), dim=-1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Pass through the LSTM layer (to store the wealth signal in the nerves)
+        x, _ = self.lstm(x)
+
+        # Output layer to compute the final wealth transfer signal
+        x = self.fc2(x)
+        return x
+
+# Initialize the model
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Input: wealth + target direction
+hidden_size = 64  # Hidden layer size
+lstm_hidden_size = 32  # LSTM hidden size (for storing wealth signal in the nerves)
+output_size = wealth_distribution.shape[-1]  # Output size matches wealth distribution
+
+model = WealthTransferModelWithNerves(input_size, hidden_size, lstm_hidden_size, output_size)
+
+# Define loss function and optimizer
+loss_fn = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Dummy target wealth state (after transfer)
+target_wealth_state = torch.randn(32, 100, 1)  # Random for now
+
+# Training loop (just for illustration)
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Zero gradients
+    optimizer.zero_grad()
+
+    # Forward pass: compute the wealth transfer
+    output = model(wealth_distribution, target_direction)
+
+    # Compute loss (compare output to the target wealth state)
+    loss = loss_fn(output, target_wealth_state)
+
+    # Backpropagation and optimization step
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Simulate wealth distribution for 100 people
+wealth_distribution = torch.randn(32, 100, 1)  # (batch_size, 100 people, 1 wealth feature)
+
+# Define the target direction (randomly initialized or learned)
+target_direction = torch.randn(32, 100, 1)  # (batch_size, 100 people, 1 feature for direction)
+
+# Define the model with LSTM and VPN-like layer for protection
+class WealthTransferModelWithVPN(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
+        super(WealthTransferModelWithVPN, self).__init__()
+        # First dense layer
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer to store wealth signal in the "nerves"
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+        # VPN-like encryption layer (simulated with a non-linear transformation)
+        self.vpn_layer = nn.Linear(output_size, vpn_size)  # A layer to "encrypt" the output
+        self.decrypt_layer = nn.Linear(vpn_size, output_size)  # To recover the original output
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along the feature dimension)
+        x = torch.cat((x, target), dim=-1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Pass through the LSTM layer (to store the wealth signal in the nerves)
+        x, _ = self.lstm(x)
+
+        # Output layer to compute the final wealth transfer signal
+        x = self.fc2(x)
+
+        # Pass through the VPN encryption layer
+        encrypted_output = torch.sigmoid(self.vpn_layer(x))  # Apply transformation (like encryption)
+
+        # Simulate decryption by passing through another layer
+        decrypted_output = self.decrypt_layer(encrypted_output)
+
+        return decrypted_output  # Return the "secure" output
+
+# Initialize the model
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Input: wealth + target direction
+hidden_size = 64  # Hidden layer size
+lstm_hidden_size = 32  # LSTM hidden size (for storing wealth signal in the nerves)
+output_size = wealth_distribution.shape[-1]  # Output size matches wealth distribution
+vpn_size = 128  # Size of the "VPN" encryption layer
+
+model = WealthTransferModelWithVPN(input_size, hidden_size, lstm_hidden_size, output_size, vpn_size)
+
+# Define loss function and optimizer
+loss_fn = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Dummy target wealth state (after transfer)
+target_wealth_state = torch.randn(32, 100, 1)  # Random for now
+
+# Training loop (just for illustration)
+num_epochs = 100
+for epoch in range(num_epochs):
+    # Zero gradients
+    optimizer.zero_grad()
+
+    # Forward pass: compute the wealth transfer with VPN-like protection
+    output = model(wealth_distribution, target_direction)
+
+    # Compute loss (compare output to the target wealth state)
+    loss = loss_fn(output, target_wealth_state)
+
+    # Backpropagation and optimization step
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+# Simulate wealth distribution for 100 people
+wealth_distribution = torch.randn(32, 100, 1)  # (batch_size, 100 people, 1 wealth feature)
+
+# Define the target direction (randomly initialized or learned)
+target_direction = torch.randn(32, 100, 1)  # (batch_size, 100 people, 1 feature for direction)
+
+# Define the model with LSTM and VPN-like layer for protection
+class WealthTransferModelWithVPN(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
+        super(WealthTransferModelWithVPN, self).__init__()
+        # First dense layer
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer to store wealth signal in the "nerves"
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+        # VPN-like encryption layer (simulated with a non-linear transformation)
+        self.vpn_layer = nn.Linear(output_size, vpn_size)  # A layer to "encrypt" the output
+        self.decrypt_layer = nn.Linear(vpn_size, output_size)  # To recover the original output
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along the feature dimension)
+        x = torch.cat((x, target), dim=-1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Pass through the LSTM layer (to store the wealth signal in the nerves)
+        x, _ = self.lstm(x)
+
+        # Output layer to compute the final wealth transfer signal
+        x = self.fc2(x)
+
+        # Pass through the VPN encryption layer
+        encrypted_output = torch.sigmoid(self.vpn_layer(x))  # Apply transformation (like encryption)
+
+        # Simulate decryption by passing through another layer
+        decrypted_output = self.decrypt_layer(encrypted_output)
+
+        return decrypted_output  # Return the "secure" output
+
+# Initialize the model
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Input: wealth + target direction
+hidden_size = 64  # Hidden layer size
+lstm_hidden_size = 32  # LSTM hidden size (for storing wealth signal in the nerves)
+output_size = wealth_distribution.shape[-1]  # Output size matches wealth distribution
+vpn_size = 128  # Size of the "VPN" encryption layer
+
+model = WealthTransferModelWithVPN(input_size, hidden_size, lstm_hidden_size, output_size, vpn_size)
+
+# Forward pass: compute the wealth transfer signal (without training for simplicity)
+with torch.no_grad():
+    output_signal = model(wealth_distribution, target_direction)
+
+# Select one example (first sample from batch) for plotting
+wealth_waveform = output_signal[0].squeeze().numpy()  # Remove extra dimensions (100,)
+
+# Plot the wealth signal as a waveform
+plt.figure(figsize=(10, 5))
+plt.plot(wealth_waveform, label='Wealth Transfer Signal')
+plt.title('Wealth Transfer Signal Waveform')
+plt.xlabel('Individual (or Time Step)')
+plt.ylabel('Wealth Signal Intensity')
+plt.legend()
+plt.grid(True)
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+# Simulate wealth distribution for 100 people across 24 hours
+# Let's assume each sample corresponds to a different time step (hour)
+wealth_distribution = torch.randn(32, 24, 1)  # (batch_size, 24 hours, 1 wealth feature)
+
+# Define the target direction (randomly initialized or learned) for 24 hours
+target_direction = torch.randn(32, 24, 1)  # (batch_size, 24 hours, 1 feature for direction)
+
+# Define the model with LSTM and VPN-like layer for protection
+class WealthTransferModelWithVPN(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
+        super(WealthTransferModelWithVPN, self).__init__()
+        # First dense layer
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer to store wealth signal in the "nerves"
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+        # VPN-like encryption layer (simulated with a non-linear transformation)
+        self.vpn_layer = nn.Linear(output_size, vpn_size)  # A layer to "encrypt" the output
+        self.decrypt_layer = nn.Linear(vpn_size, output_size)  # To recover the original output
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along the feature dimension)
+        x = torch.cat((x, target), dim=-1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Pass through the LSTM layer (to store the wealth signal in the nerves)
+        x, _ = self.lstm(x)
+
+        # Output layer to compute the final wealth transfer signal
+        x = self.fc2(x)
+
+        # Pass through the VPN encryption layer
+        encrypted_output = torch.sigmoid(self.vpn_layer(x))  # Apply transformation (like encryption)
+
+        # Simulate decryption by passing through another layer
+        decrypted_output = self.decrypt_layer(encrypted_output)
+
+        return decrypted_output  # Return the "secure" output
+
+# Initialize the model
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Input: wealth + target direction
+hidden_size = 64  # Hidden layer size
+lstm_hidden_size = 32  # LSTM hidden size (for storing wealth signal in the nerves)
+output_size = wealth_distribution.shape[-1]  # Output size matches wealth distribution
+vpn_size = 128  # Size of the "VPN" encryption layer
+
+model = WealthTransferModelWithVPN(input_size, hidden_size, lstm_hidden_size, output_size, vpn_size)
+
+# Forward pass: compute the wealth transfer signal (without training for simplicity)
+with torch.no_grad():
+    output_signal = model(wealth_distribution, target_direction)
+
+# Select one example (first sample from batch) for plotting
+wealth_waveform = output_signal[0].squeeze().numpy()  # Remove extra dimensions (24 hours,)
+
+# Create an x-axis for 24 hours (from 0 to 23 hours)
+hours = list(range(24))
+
+# Plot the wealth signal as a waveform over 24 hours
+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)  # Show each hour as a tick on the x-axis
+plt.grid(True)
+plt.legend()
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Simulate wealth distribution for 100 people across 24 hours
+wealth_distribution = torch.randn(32, 24, 1)  # (batch_size, 24 hours, 1 wealth feature)
+
+# Define the target direction (randomly initialized or learned) for 24 hours
+target_direction = torch.randn(32, 24, 1)  # (batch_size, 24 hours, 1 feature for direction)
+
+# Define the model with LSTM and VPN-like layer for protection
+class WealthTransferModelWithVPN(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
+        super(WealthTransferModelWithVPN, self).__init__()
+        # First dense layer
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer to store wealth signal in the "nerves"
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+        # VPN-like encryption layer (simulated with a non-linear transformation)
+        self.vpn_layer = nn.Linear(output_size, vpn_size)  # A layer to "encrypt" the output
+        self.decrypt_layer = nn.Linear(vpn_size, output_size)  # To recover the original output
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along the feature dimension)
+        x = torch.cat((x, target), dim=-1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Pass through the LSTM layer (to store the wealth signal in the nerves)
+        x, _ = self.lstm(x)
+
+        # Output layer to compute the final wealth transfer signal
+        x = self.fc2(x)
+
+        # Pass through the VPN encryption layer
+        encrypted_output = torch.sigmoid(self.vpn_layer(x))  # Apply transformation (like encryption)
+
+        # Simulate decryption by passing through another layer
+        decrypted_output = self.decrypt_layer(encrypted_output)
+
+        return decrypted_output  # Return the "secure" output
+
+# Initialize the model
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Input: wealth + target direction
+hidden_size = 64  # Hidden layer size
+lstm_hidden_size = 32  # LSTM hidden size (for storing wealth signal in the nerves)
+output_size = wealth_distribution.shape[-1]  # Output size matches wealth distribution
+vpn_size = 128  # Size of the "VPN" encryption layer
+
+model = WealthTransferModelWithVPN(input_size, hidden_size, lstm_hidden_size, output_size, vpn_size)
+
+# Forward pass: compute the wealth transfer signal (without training for simplicity)
+with torch.no_grad():
+    output_signal = model(wealth_distribution, target_direction)
+
+# Select one example (first sample from batch) for plotting
+wealth_waveform = output_signal[0].squeeze().numpy()  # Remove extra dimensions (24 hours,)
+
+# Create a mask (example: mask where signal < 0.5)
+mask = wealth_waveform > 0.5  # Only display parts of the signal that exceed 0.5 in intensity
+
+# Apply the mask to the wealth waveform
+masked_signal = wealth_waveform * mask  # Set masked elements to 0
+
+# Create an x-axis for 24 hours (from 0 to 23 hours)
+hours = list(range(24))
+
+# Plot the masked wealth signal as a colorful waveform
+plt.figure(figsize=(10, 5))
+
+# Use a colormap to display the intensity of the signal
+scatter = plt.scatter(hours, masked_signal, c=masked_signal, cmap='viridis', s=100, edgecolor='k', marker='o')
+
+# Add a color bar to show intensity mapping
+plt.colorbar(scatter, label="Wealth Signal Intensity")
+
+plt.title('Masked Wealth Transfer Signal in 24-Hour Intervals (Colorful Waveform)')
+plt.xlabel('Hour of the Day')
+plt.ylabel('Wealth Signal Intensity')
+plt.xticks(hours)  # Show each hour as a tick on the x-axis
+plt.grid(True)
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Simulate wealth distribution for 100 people across 24 hours
+wealth_distribution = torch.randn(32, 24, 1)  # (batch_size, 24 hours, 1 wealth feature)
+
+# Define the target direction (randomly initialized or learned) for 24 hours
+target_direction = torch.randn(32, 24, 1)  # (batch_size, 24 hours, 1 feature for direction)
+
+# Define the model with LSTM and VPN-like layer for protection
+class WealthTransferModelWithVPN(nn.Module):
+    def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
+        super(WealthTransferModelWithVPN, self).__init__()
+        # First dense layer
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.relu = nn.ReLU()
+
+        # LSTM layer to store wealth signal in the "nerves"
+        self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
+
+        # Final dense layer to transfer wealth in the target direction
+        self.fc2 = nn.Linear(lstm_hidden_size, output_size)
+
+        # VPN-like encryption layer (simulated with a non-linear transformation)
+        self.vpn_layer = nn.Linear(output_size, vpn_size)  # A layer to "encrypt" the output
+        self.decrypt_layer = nn.Linear(vpn_size, output_size)  # To recover the original output
+
+    def forward(self, x, target):
+        # Combine wealth signal with target information (concatenate along the feature dimension)
+        x = torch.cat((x, target), dim=-1)
+
+        # Process through the first dense layer
+        x = self.relu(self.fc1(x))
+
+        # Pass through the LSTM layer (to store the wealth signal in the nerves)
+        x, _ = self.lstm(x)
+
+        # Output layer to compute the final wealth transfer signal
+        x = self.fc2(x)
+
+        # Pass through the VPN encryption layer
+        encrypted_output = torch.sigmoid(self.vpn_layer(x))  # Apply transformation (like encryption)
+
+        # Simulate decryption by passing through another layer
+        decrypted_output = self.decrypt_layer(encrypted_output)
+
+        return decrypted_output  # Return the "secure" output
+
+# Initialize the model
+input_size = wealth_distribution.shape[-1] + target_direction.shape[-1]  # Input: wealth + target direction
+hidden_size = 64  # Hidden layer size
+lstm_hidden_size = 32  # LSTM hidden size (for storing wealth signal in the nerves)
+output_size = wealth_distribution.shape[-1]  # Output size matches wealth distribution
+vpn_size = 128  # Size of the "VPN" encryption layer
+
+model = WealthTransferModelWithVPN(input_size, hidden_size, lstm_hidden_size, output_size, vpn_size)
+
+# Forward pass: compute the wealth transfer signal (without training for simplicity)
+with torch.no_grad():
+    output_signal = model(wealth_distribution, target_direction)
+
+# Select one example (first sample from batch) for plotting
+wealth_waveform = output_signal[0].squeeze().numpy()  # Remove extra dimensions (24 hours,)
+
+# Create the first mask (example: mask where signal < 0.5)
+mask1 = wealth_waveform > 0.5  # First mask: Only display parts of the signal that exceed 0.5 in intensity
+
+# Apply the first mask to the wealth waveform
+masked_signal1 = wealth_waveform * mask1  # Set masked elements to 0
+
+# Create the second mask (example: mask where signal > 0.2)
+mask2 = wealth_waveform < 0.2  # Second mask: Only display parts of the signal below 0.2 in intensity
+
+# Apply the second mask to the wealth waveform
+masked_signal2 = wealth_waveform * mask2  # Set masked elements to 0
+
+# Combine both masked signals (for visualization purposes)
+combined_masked_signal = masked_signal1 + masked_signal2
+
+# Create an x-axis for 24 hours (from 0 to 23 hours)
+hours = list(range(24))
+
+# Plot the combined masked wealth signal as a colorful waveform
+plt.figure(figsize=(10, 5))
+
+# Use a colormap to display the intensity of the signal
+scatter = plt.scatter(hours, combined_masked_signal, c=combined_masked_signal, cmap='plasma', s=100, edgecolor='k', marker='o')
+
+# Add a color bar to show intensity mapping
+plt.colorbar(scatter, label="Wealth Signal Intensity")
+
+plt.title('Combined Masked Wealth Transfer Signal in 24-Hour Intervals (Colorful Waveform)')
+plt.xlabel('Hour of the Day')
+plt.ylabel('Wealth Signal Intensity')
+plt.xticks(hours)  # Show each hour as a tick on the x-axis
+plt.grid(True)
+plt.show()