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wealthwavetransfer_2_0.py

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+# -*- coding: utf-8 -*-
+"""wealthwavetransfer 2.0
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qbxvR0Ivvxx9uEMROqKuwsX7WKI96gb9
+"""
+
+pip install torch torchvision
+
+import numpy as np
+import torch
+
+# Generate synthetic data
+np.random.seed(42)
+num_samples = 1000
+
+# Features: Age, Income, Investments
+age = np.random.randint(18, 70, size=num_samples)
+income = np.random.normal(50000, 15000, size=num_samples)  # Average income
+investments = np.random.normal(10000, 5000, size=num_samples)  # Average investments
+
+# Wealth target: a simple function of the features (you can modify this)
+wealth = 0.4 * age + 0.5 * (income / 1000) + 0.3 * (investments / 1000) + np.random.normal(0, 5, size=num_samples)
+
+# Convert to PyTorch tensors
+X = torch.tensor(np.column_stack((age, income, investments)), dtype=torch.float32)
+y = torch.tensor(wealth, dtype=torch.float32).view(-1, 1)
+
+import torch.nn as nn
+import torch.optim as optim
+
+class WealthModel(nn.Module):
+    def __init__(self):
+        super(WealthModel, self).__init__()
+        self.fc1 = nn.Linear(3, 64)  # 3 input features
+        self.fc2 = nn.Linear(64, 32)
+        self.fc3 = nn.Linear(32, 1)   # Output is a single value (wealth)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)  # No activation function on output layer for regression
+        return x
+
+model = WealthModel()
+
+# Training settings
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+num_epochs = 100
+
+# Training loop
+for epoch in range(num_epochs):
+    model.train()
+
+    # Forward pass
+    outputs = model(X)
+    loss = criterion(outputs, y)
+
+    # Backward pass and optimization
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    if (epoch+1) % 10 == 0:
+        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+model.eval()
+with torch.no_grad():
+    predicted = model(X)
+
+# Optionally, you can visualize or calculate performance metrics
+import matplotlib.pyplot as plt
+
+plt.scatter(y.numpy(), predicted.numpy(), alpha=0.5)
+plt.xlabel('True Wealth')
+plt.ylabel('Predicted Wealth')
+plt.title('True vs Predicted Wealth')
+plt.plot([y.min(), y.max()], [y.min(), y.max()], '--', color='red')
+plt.show()
+
+class ObfuscationLayer(nn.Module):
+    def __init__(self):
+        super(ObfuscationLayer, self).__init__()
+
+    def forward(self, x):
+        # Add noise to simulate obfuscation/encryption
+        noise = torch.normal(0, 0.1, x.size()).to(x.device)  # Adjust the standard deviation for noise level
+        return x + noise
+
+class EnhancedWealthModel(nn.Module):
+    def __init__(self):
+        super(EnhancedWealthModel, self).__init__()
+        self.obfuscation = ObfuscationLayer()
+        self.fc1 = nn.Linear(3, 128)  # More units for complexity
+        self.fc2 = nn.Linear(128, 64)
+        self.fc3 = nn.Linear(64, 32)
+        self.fc4 = nn.Linear(32, 1)    # Output is a single value (wealth)
+
+    def forward(self, x):
+        x = self.obfuscation(x)  # Apply obfuscation
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = torch.relu(self.fc3(x))
+        x = self.fc4(x)  # No activation function on output layer for regression
+        return x
+
+model = EnhancedWealthModel()
+
+# Training settings
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+num_epochs = 100
+
+# Training loop
+for epoch in range(num_epochs):
+    model.train()
+
+    # Forward pass
+    outputs = model(X)
+    loss = criterion(outputs, y)
+
+    # Backward pass and optimization
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+model.eval()
+with torch.no_grad():
+    predicted = model(X)
+
+# Visualizing True vs. Predicted Wealth
+plt.scatter(y.numpy(), predicted.numpy(), alpha=0.5)
+plt.xlabel('True Wealth')
+plt.ylabel('Predicted Wealth')
+plt.title('True vs Predicted Wealth with Obfuscation Layer')
+plt.plot([y.min(), y.max()], [y.min(), y.max()], '--', color='red')
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define grid size
+grid_size = 20
+
+# Generate a sine waveform to represent wealth data
+def generate_wealth_waveform(grid_size):
+    x = np.linspace(0, 2 * np.pi, grid_size)
+    wealth_waveform = np.sin(x)
+    return wealth_waveform
+
+# Create wealth data for the grid
+wealth_waveform = generate_wealth_waveform(grid_size)
+wealth_data = np.tile(wealth_waveform, (grid_size, 1))  # Repeat waveform along one axis
+
+# Convert wealth data to PyTorch tensor
+wealth_data = torch.tensor(wealth_data, dtype=torch.float32)
+
+# Define a simple neural network to "transfer" wealth data to a targeted account
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, 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 = WealthTransferNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Target account: Wealth directed to bottom-right corner of the grid
+target_account = torch.zeros((grid_size, grid_size))
+target_account[-5:, -5:] = 1  # Simulating the transfer to a targeted account
+
+# Convert the grid to a single vector for the neural network
+input_data = wealth_data.view(-1)
+target_data = target_account.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 wealth waveform and transferred wealth
+fig, axes = plt.subplots(1, 3, figsize=(18, 6))
+axes[0].imshow(wealth_data, cmap='viridis')
+axes[0].set_title('Original Wealth Waveform')
+axes[1].imshow(target_account, cmap='viridis')
+axes[1].set_title('Target Account Location')
+axes[2].imshow(output_grid, cmap='viridis')
+axes[2].set_title('Transferred Wealth to Target')
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define the size of the waveform
+waveform_size = 100
+
+# Generate a sine waveform to represent wealth data
+def generate_wealth_waveform(waveform_size):
+    x = np.linspace(0, 2 * np.pi, waveform_size)
+    wealth_waveform = np.sin(x)
+    return wealth_waveform
+
+# Create wealth data as a single waveform
+wealth_waveform = generate_wealth_waveform(waveform_size)
+wealth_data = torch.tensor(wealth_waveform, dtype=torch.float32)
+
+# Define a neural network to transfer wealth data to a targeted point in the waveform
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(waveform_size, 64)
+        self.fc2 = nn.Linear(64, waveform_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 = WealthTransferNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Target account: Wealth directed to the end of the waveform (right side)
+target_account = torch.zeros(waveform_size)
+target_account[-10:] = 1  # Simulating the transfer to the last 10 positions
+
+# Training the network
+epochs = 1000
+for epoch in range(epochs):
+    optimizer.zero_grad()
+    output = net(wealth_data)
+    loss = criterion(output, target_account)
+    loss.backward()
+    optimizer.step()
+
+# Convert output to numpy for plotting
+output_waveform = output.detach().numpy()
+
+# Plot the original and transferred wealth waveform
+fig, ax = plt.subplots(figsize=(10, 5))
+ax.plot(wealth_data.numpy(), label="", linestyle="--")
+ax.plot(target_account.numpy(), label="", linestyle=":")
+ax.plot(output_waveform, label="")
+ax.set_title('')
+ax.legend()
+plt.show()
+
+!pip install torch torchvision
+
+import numpy as np
+import torch
+
+# Generate synthetic data
+np.random.seed(42)
+num_samples = 1000
+
+# Features: Age, Income, Investments
+age = np.random.randint(18, 70, size=num_samples)
+income = np.random.normal(50000, 15000, size=num_samples)  # Average income
+investments = np.random.normal(10000, 5000, size=num_samples)  # Average investments
+
+# Wealth target: a simple function of the features (you can modify this)
+wealth = 0.4 * age + 0.5 * (income / 1000) + 0.3 * (investments / 1000) + np.random.normal(0, 5, size=num_samples)
+
+# Convert to PyTorch tensors
+X = torch.tensor(np.column_stack((age, income, investments)), dtype=torch.float32)
+y = torch.tensor(wealth, dtype=torch.float32).view(-1, 1)
+
+import torch.nn as nn
+import torch.optim as optim
+
+class WealthModel(nn.Module):
+    def __init__(self):
+        super(WealthModel, self).__init__()
+        self.fc1 = nn.Linear(3, 64)  # 3 input features
+        self.fc2 = nn.Linear(64, 32)
+        self.fc3 = nn.Linear(32, 1)   # Output is a single value (wealth)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)  # No activation function on output layer for regression
+        return x
+
+model = WealthModel()
+
+# Training settings
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+num_epochs = 100
+
+# Training loop
+for epoch in range(num_epochs):
+    model.train()
+
+    # Forward pass
+    outputs = model(X)
+    loss = criterion(outputs, y)
+
+    # Backward pass and optimization
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    if (epoch+1) % 10 == 0:
+        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+model.eval()
+with torch.no_grad():
+    predicted = model(X)
+
+# Optionally, you can visualize or calculate performance metrics
+import matplotlib.pyplot as plt
+
+plt.scatter(y.numpy(), predicted.numpy(), alpha=0.5)
+plt.xlabel('True Wealth')
+plt.ylabel('Predicted Wealth')
+plt.title('True vs Predicted Wealth')
+plt.plot([y.min(), y.max()], [y.min(), y.max()], '--', color='red')
+plt.show()
+
+class ObfuscationLayer(nn.Module):
+    def __init__(self):
+        super(ObfuscationLayer, self).__init__()
+
+    def forward(self, x):
+        # Add noise to simulate obfuscation/encryption
+        noise = torch.normal(0, 0.1, x.size()).to(x.device)  # Adjust the standard deviation for noise level
+        return x + noise
+
+class EnhancedWealthModel(nn.Module):
+    def __init__(self):
+        super(EnhancedWealthModel, self).__init__()
+        self.obfuscation = ObfuscationLayer()
+        self.fc1 = nn.Linear(3, 128)  # More units for complexity
+        self.fc2 = nn.Linear(128, 64)
+        self.fc3 = nn.Linear(64, 32)
+        self.fc4 = nn.Linear(32, 1)    # Output is a single value (wealth)
+
+    def forward(self, x):
+        x = self.obfuscation(x)  # Apply obfuscation
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = torch.relu(self.fc3(x))
+        x = self.fc4(x)  # No activation function on output layer for regression
+        return x
+
+model = EnhancedWealthModel()
+
+# Training settings
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+num_epochs = 100
+
+# Training loop
+for epoch in range(num_epochs):
+    model.train()
+
+    # Forward pass
+    outputs = model(X)
+    loss = criterion(outputs, y)
+
+    # Backward pass and optimization
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+model.eval()
+with torch.no_grad():
+    predicted = model(X)
+
+# Visualizing True vs. Predicted Wealth
+plt.scatter(y.numpy(), predicted.numpy(), alpha=0.5)
+plt.xlabel('True Wealth')
+plt.ylabel('Predicted Wealth')
+plt.title('True vs Predicted Wealth with Obfuscation Layer')
+plt.plot([y.min(), y.max()], [y.min(), y.max()], '--', color='red')
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define grid size
+grid_size = 20
+
+# Generate a sine waveform to represent wealth data
+def generate_wealth_waveform(grid_size):
+    x = np.linspace(0, 2 * np.pi, grid_size)
+    wealth_waveform = np.sin(x)
+    return wealth_waveform
+
+# Create wealth data for the grid
+wealth_waveform = generate_wealth_waveform(grid_size)
+wealth_data = np.tile(wealth_waveform, (grid_size, 1))  # Repeat waveform along one axis
+
+# Convert wealth data to PyTorch tensor
+wealth_data = torch.tensor(wealth_data, dtype=torch.float32)
+
+# Define a simple neural network to "transfer" wealth data to a targeted account
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, 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 = WealthTransferNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Target account: Wealth directed to bottom-right corner of the grid
+target_account = torch.zeros((grid_size, grid_size))
+target_account[-5:, -5:] = 1  # Simulating the transfer to a targeted account
+
+# Convert the grid to a single vector for the neural network
+input_data = wealth_data.view(-1)
+target_data = target_account.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 wealth waveform and transferred wealth
+fig, axes = plt.subplots(1, 3, figsize=(18, 6))
+axes[0].imshow(wealth_data, cmap='viridis')
+axes[0].set_title('Original Wealth Waveform')
+axes[1].imshow(target_account, cmap='viridis')
+axes[1].set_title('Target Account Location')
+axes[2].imshow(output_grid, cmap='viridis')
+axes[2].set_title('Transferred Wealth to Target')
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define the size of the waveform
+waveform_size = 100
+
+# Generate a sine waveform to represent wealth data
+def generate_wealth_waveform(waveform_size):
+    x = np.linspace(0, 2 * np.pi, waveform_size)
+    wealth_waveform = np.sin(x)
+    return wealth_waveform
+
+# Create wealth data as a single waveform
+wealth_waveform = generate_wealth_waveform(waveform_size)
+wealth_data = torch.tensor(wealth_waveform, dtype=torch.float32)
+
+# Define a neural network to transfer wealth data to a targeted point in the waveform
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(waveform_size, 64)
+        self.fc2 = nn.Linear(64, waveform_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 = WealthTransferNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Target account: Wealth directed to the end of the waveform (right side)
+target_account = torch.zeros(waveform_size)
+target_account[-10:] = 1  # Simulating the transfer to the last 10 positions
+
+# Training the network
+epochs = 1000
+for epoch in range(epochs):
+    optimizer.zero_grad()
+    output = net(wealth_data)
+    loss = criterion(output, target_account)
+    loss.backward()
+    optimizer.step()
+
+# Convert output to numpy for plotting
+output_waveform = output.detach().numpy()
+
+# Plot the original and transferred wealth waveform
+fig, ax = plt.subplots(figsize=(10, 5))
+ax.plot(wealth_data.numpy(), label="", linestyle="--")
+ax.plot(target_account.numpy(), label="", linestyle=":")
+ax.plot(output_waveform, label="")
+ax.set_title('')
+ax.legend()
+plt.show()