magnet_2_0.py

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()