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magnet_3_0.py

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+# -*- coding: utf-8 -*-
+"""magnet 3.0
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1OEi6S1t2F49Lh-JfMGJr3aUBsYhjHtJC
+"""
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+# Define grid size
+grid_size = 20
+
+# Create a grid with random initial wealth data
+wealth_data = torch.rand((grid_size, grid_size))
+
+# Define a simple neural network that will adjust the wealth data
+class WealthNet(nn.Module):
+    def __init__(self):
+        super(WealthNet, self).__init__()
+        self.fc1 = nn.Linear(grid_size * grid_size, 128)
+        self.fc2 = nn.Linear(128, grid_size * grid_size)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# Instantiate the network, loss function, and optimizer
+net = WealthNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Target direction to direct wealth (e.g., bottom right corner)
+target_wealth = torch.zeros((grid_size, grid_size))
+target_wealth[-5:, -5:] = 1  # Direct wealth towards the bottom right corner
+
+# Convert the grid to a single vector for the neural network
+input_data = wealth_data.view(-1)
+target_data = target_wealth.view(-1)
+
+# Training the network
+epochs = 500
+for epoch in range(epochs):
+    optimizer.zero_grad()
+    output = net(input_data)
+    loss = criterion(output, target_data)
+    loss.backward()
+    optimizer.step()
+
+# Reshape the output to the grid size
+output_grid = output.detach().view(grid_size, grid_size)
+
+# Plot the original and adjusted wealth distribution
+fig, axes = plt.subplots(1, 2, figsize=(12, 6))
+axes[0].imshow(wealth_data, cmap='viridis')
+axes[0].set_title('Original Wealth Distribution')
+axes[1].imshow(output_grid, cmap='viridis')
+axes[1].set_title('Directed Wealth Distribution')
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+# Define grid size
+grid_size = 20
+
+# Create a grid with random initial wealth data
+wealth_data = torch.rand((grid_size, grid_size))
+
+# Define a neural network with an additional layer for infrared conversion
+class WealthNet(nn.Module):
+    def __init__(self):
+        super(WealthNet, self).__init__()
+        self.fc1 = nn.Linear(grid_size * grid_size, 128)
+        self.fc2 = nn.Linear(128, 128)
+        self.fc3 = nn.Linear(128, grid_size * grid_size)
+        self.infrared_layer = nn.Sigmoid()  # Simulating the conversion to infrared energy
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        stored_wealth = torch.relu(self.fc2(x))  # Store wealth data here
+        infrared_energy = self.infrared_layer(stored_wealth)  # Convert to infrared energy
+        x = self.fc3(infrared_energy)
+        return x, stored_wealth, infrared_energy
+
+# Instantiate the network, loss function, and optimizer
+net = WealthNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Target direction to direct wealth (e.g., bottom right corner)
+target_wealth = torch.zeros((grid_size, grid_size))
+target_wealth[-5:, -5:] = 1  # Direct wealth towards the bottom right corner
+
+# Convert the grid to a single vector for the neural network
+input_data = wealth_data.view(-1)
+target_data = target_wealth.view(-1)
+
+# Training the network
+epochs = 500
+for epoch in range(epochs):
+    optimizer.zero_grad()
+    output, stored_wealth, infrared_energy = net(input_data)
+    loss = criterion(output, target_data)
+    loss.backward()
+    optimizer.step()
+
+# Reshape the outputs to the grid size
+output_grid = output.detach().view(grid_size, grid_size)
+stored_wealth_grid = stored_wealth.detach().view(128)  # Displayed as a 1D representation
+infrared_energy_grid = infrared_energy.detach().view(128)  # Displayed as a 1D representation
+
+# Plot the original and adjusted wealth distribution
+fig, axes = plt.subplots(1, 4, figsize=(20, 6))
+axes[0].imshow(wealth_data, cmap='viridis')
+axes[0].set_title('Original Wealth Distribution')
+axes[1].imshow(output_grid, cmap='viridis')
+axes[1].set_title('Directed Wealth Distribution')
+axes[2].plot(stored_wealth_grid.numpy())
+axes[2].set_title('Stored Wealth Data (1D)')
+axes[3].plot(infrared_energy_grid.numpy())
+axes[3].set_title('Infrared Energy (1D)')
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+# Define grid size
+grid_size = 20
+
+# Create a grid with random initial wealth data
+wealth_data = torch.rand((grid_size, grid_size))
+
+# Define a neural network with an additional layer for data protection
+class WealthNet(nn.Module):
+    def __init__(self):
+        super(WealthNet, self).__init__()
+        self.fc1 = nn.Linear(grid_size * grid_size, 128)
+        self.fc2 = nn.Linear(128, 128)
+        self.fc3 = nn.Linear(128, grid_size * grid_size)
+        self.infrared_layer = nn.Sigmoid()  # Simulating the conversion to infrared energy
+        # Removed the incorrect instantiation of GaussianNoise here
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        stored_wealth = torch.relu(self.fc2(x))  # Store wealth data here
+        protected_wealth = self.protection_layer(stored_wealth)  # Protect the stored data
+        infrared_energy = self.infrared_layer(protected_wealth)  # Convert to infrared energy
+        x = self.fc3(infrared_energy)
+        return x, stored_wealth, protected_wealth, infrared_energy
+
+# Custom layer to add Gaussian noise (PyTorch does not have this built-in)
+class GaussianNoise(nn.Module):
+    def __init__(self, stddev):
+        super(GaussianNoise, self).__init__()
+        self.stddev = stddev
+
+    def forward(self, x):
+        if self.training:
+            noise = torch.randn_like(x) * self.stddev
+            return x + noise
+        return x
+
+# Instantiate the network, loss function, and optimizer
+net = WealthNet()
+# Add the GaussianNoise layer to the network instance
+net.protection_layer = GaussianNoise(0.1)
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Target direction to direct wealth (e.g., bottom right corner)
+target_wealth = torch.zeros((grid_size, grid_size))
+target_wealth[-5:, -5:] = 1  # Direct wealth towards the bottom right corner
+
+# Convert the grid to a single vector for the neural network
+input_data = wealth_data.view(-1)
+target_data = target_wealth.view(-1)
+
+# Training the network
+epochs = 500
+for epoch in range(epochs):
+    optimizer.zero_grad()
+    output, stored_wealth, protected_wealth, infrared_energy = net(input_data)
+    loss = criterion(output, target_data)
+    loss.backward()
+    optimizer.step()
+
+# Reshape the outputs to the grid size
+output_grid = output.detach().view(grid_size, grid_size)
+stored_wealth_grid = stored_wealth.detach().view(128)  # Displayed as a 1D representation
+protected_wealth_grid = protected_wealth.detach().view(128)  # Displayed as a 1D representation
+infrared_energy_grid = infrared_energy.detach().view(128)  # Displayed as a 1D representation
+
+# Plot the original and adjusted wealth distribution
+fig, axes = plt.subplots(1, 5, figsize=(25, 6))
+axes[0].imshow(wealth_data, cmap='viridis')
+axes[0].set_title('Original')
+axes[1].imshow(output_grid, cmap='viridis')
+axes[1].set_title('Directed')
+axes[2].plot(stored_wealth_grid.numpy())
+axes[2].set_title('Stored')
+axes[3].plot(protected_wealth_grid.numpy())
+axes[3].set_title('Protected')
+axes[4].plot(infrared_energy_grid)