Upload wealthfortress.py

wealthfortress.py

@@ -0,0 +1,1096 @@
+# -*- coding: utf-8 -*-
+"""WealthFortress
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1rOSJ2jfGMkC1yn8yzGd3KcsWH0s8Qz6f
+"""
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics.pairwise import cosine_similarity
+
+num_consumers = 10
+interest_size = 5
+wealth_size = 1
+feature_size = interest_size + wealth_size
+
+consumer_profiles = torch.rand((num_consumers, feature_size))
+
+interests = consumer_profiles[:, :interest_size]
+wealth_data = consumer_profiles[:, interest_size:]
+
+class WealthTransferNet(nn.Module):
+  def __init__(self):
+    super(WealthTransferNet, self).__init__()
+    self.fc1 = nn.Linear(wealth_size, wealth_size)
+
+  # The forward function is now correctly defined as a method of the class
+  def forward(self, x):
+    return self.fc1(x)
+
+net = WealthTransferNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Calculate cosine similarity between consumer interests
+similarity_matrix = cosine_similarity(interests)
+
+# Find pairs of consumers with similarity above a certain threshold
+threshold = 0.8
+similar_pairs = np.argwhere(similarity_matrix > threshold)
+
+# We will only consider upper triangular values to avoid double matching or self-matching
+similar_pairs = similar_pairs[similar_pairs[:, 0] < similar_pairs[:, 1]]
+
+# Simulate wealth transfer between matched pairs
+for pair in similar_pairs:
+    consumer_a, consumer_b = pair
+
+    # Get wealth data for the pair
+    wealth_a = wealth_data[consumer_a]
+    wealth_b = wealth_data[consumer_b]
+
+    # Train the network to transfer wealth between matched consumers
+    for epoch in range(100):
+        optimizer.zero_grad()
+        transferred_wealth_a = net(wealth_a)
+        transferred_wealth_b = net(wealth_b)
+
+        # Simulate bidirectional transfer: A to B and B to A
+        loss_a_to_b = criterion(transferred_wealth_a, wealth_b)
+        loss_b_to_a = criterion(transferred_wealth_b, wealth_a)
+        total_loss = loss_a_to_b + loss_b_to_a
+
+        total_loss.backward()
+        optimizer.step()
+
+# Display the similarity matrix and transfer results
+print("Cosine Similarity Matrix (Interest-based Matching):\n", similarity_matrix)
+
+# Plotting the interest similarity matrix for visualization
+plt.figure(figsize=(8, 6))
+plt.imshow(similarity_matrix, cmap='hot', interpolation='nearest')
+plt.colorbar(label='Cosine Similarity')
+plt.title("Interest Similarity Matrix")
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Define the number of consumers and feature size (interests + wealth)
+num_consumers = 10
+interest_size = 5  # Number of interests
+wealth_size = 1  # Each consumer has one wealth data point
+feature_size = interest_size + wealth_size  # Total feature size
+
+# Generate random consumer profiles (interest + wealth)
+consumer_profiles = torch.rand((num_consumers, feature_size))
+
+# Split into interests and wealth data
+interests = consumer_profiles[:, :interest_size]
+wealth_data = consumer_profiles[:, interest_size:]
+
+# Define a neural network to transfer wealth between consumers
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(wealth_size, wealth_size)
+
+    def forward(self, x):
+        return self.fc1(x)
+
+# Define a VPN-like layer for data encryption and passcode check
+class VPNLayer(nn.Module):
+    def __init__(self, encryption_key):
+        super(VPNLayer, self).__init__()
+        self.encryption_key = encryption_key  # Simulate encryption key
+
+    def encrypt_data(self, data):
+        # Simulate encryption by applying a non-linear transformation
+        encrypted_data = data * torch.sin(self.encryption_key)
+        return encrypted_data
+
+    def decrypt_data(self, encrypted_data, passcode):
+        # Check if passcode matches the encryption key (this is our 'authentication')
+        if passcode == self.encryption_key:
+            decrypted_data = encrypted_data / torch.sin(self.encryption_key)
+            return decrypted_data
+        else:
+            raise ValueError("Invalid Passcode! Access Denied.")
+
+# Instantiate the VPN layer
+vpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))
+
+# Encrypt consumer profiles (interest + wealth data) using the VPN layer
+encrypted_consumer_profiles = vpn_layer.encrypt_data(consumer_profiles)
+
+# Passcode required to access data (for simplicity, using the same as the encryption key)
+passcode = torch.tensor(0.5)
+
+# Try to access the encrypted data with the correct passcode
+try:
+    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, passcode)
+    print("Access Granted. Decrypted Consumer Data:")
+    print(decrypted_profiles)
+except ValueError as e:
+    print(e)
+
+# Simulate incorrect passcode
+wrong_passcode = torch.tensor(0.3)
+
+try:
+    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, wrong_passcode)
+except ValueError as e:
+    print(e)
+
+# Instantiate the wealth transfer network
+net = WealthTransferNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Calculate cosine similarity between consumer interests
+similarity_matrix = cosine_similarity(interests)
+
+# Find pairs of consumers with similarity above a certain threshold
+threshold = 0.8
+similar_pairs = np.argwhere(similarity_matrix > threshold)
+
+# We will only consider upper triangular values to avoid double matching or self-matching
+similar_pairs = similar_pairs[similar_pairs[:, 0] < similar_pairs[:, 1]]
+
+# Simulate wealth transfer between matched pairs
+for pair in similar_pairs:
+    consumer_a, consumer_b = pair
+
+    # Get wealth data for the pair
+    wealth_a = wealth_data[consumer_a]
+    wealth_b = wealth_data[consumer_b]
+
+    # Train the network to transfer wealth between matched consumers
+    for epoch in range(100):
+        optimizer.zero_grad()
+        transferred_wealth_a = net(wealth_a)
+        transferred_wealth_b = net(wealth_b)
+
+        # Simulate bidirectional transfer: A to B and B to A
+        loss_a_to_b = criterion(transferred_wealth_a, wealth_b)
+        loss_b_to_a = criterion(transferred_wealth_b, wealth_a)
+        total_loss = loss_a_to_b + loss_b_to_a
+
+        total_loss.backward()
+        optimizer.step()
+
+# Display the similarity matrix and transfer results
+print("Cosine Similarity Matrix (Interest-based Matching):\n", similarity_matrix)
+
+# Plotting the interest similarity matrix for visualization
+plt.figure(figsize=(8, 6))
+plt.imshow(similarity_matrix, cmap='hot', interpolation='nearest')
+plt.colorbar(label='Cosine Similarity')
+plt.title("Interest Similarity Matrix")
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Define the number of consumers and feature size (interests + wealth)
+num_consumers = 10
+interest_size = 5  # Number of interests
+wealth_size = 1  # Each consumer has one wealth data point
+feature_size = interest_size + wealth_size  # Total feature size
+
+# Generate random consumer profiles (interest + wealth)
+consumer_profiles = torch.rand((num_consumers, feature_size))
+
+# Split into interests and wealth data
+interests = consumer_profiles[:, :interest_size]
+wealth_data = consumer_profiles[:, interest_size:]
+
+# Define a neural network to transfer wealth between consumers
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(wealth_size, wealth_size)
+
+    def forward(self, x):
+        return self.fc1(x)
+
+# Define a VPN-like layer for data encryption and passcode check
+class VPNLayer(nn.Module):
+    def __init__(self, encryption_key):
+        super(VPNLayer, self).__init__()
+        self.encryption_key = encryption_key  # Simulate encryption key
+
+    def encrypt_data(self, data):
+        # Simulate encryption by applying a non-linear transformation
+        encrypted_data = data * torch.sin(self.encryption_key)
+        return encrypted_data
+
+    def decrypt_data(self, encrypted_data, passcode):
+        # Check if passcode matches the encryption key (this is our 'authentication')
+        if passcode == self.encryption_key:
+            decrypted_data = encrypted_data / torch.sin(self.encryption_key)
+            return decrypted_data
+        else:
+            raise ValueError("Invalid Passcode! Access Denied.")
+
+# Instantiate the VPN layer
+vpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))
+
+# Encrypt consumer profiles (interest + wealth data) using the VPN layer
+encrypted_consumer_profiles = vpn_layer.encrypt_data(consumer_profiles)
+
+# Passcode required to access data (for simplicity, using the same as the encryption key)
+passcode = torch.tensor(0.5)
+
+# Try to access the encrypted data with the correct passcode
+try:
+    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, passcode)
+    print("Access Granted. Decrypted Consumer Data:")
+    print(decrypted_profiles)
+except ValueError as e:
+    print(e)
+
+# Simulate incorrect passcode
+wrong_passcode = torch.tensor(0.3)
+
+try:
+    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, wrong_passcode)
+except ValueError as e:
+    print(e)
+
+# Instantiate the wealth transfer network
+net = WealthTransferNet()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(net.parameters(), lr=0.01)
+
+# Calculate cosine similarity between consumer interests
+similarity_matrix = cosine_similarity(interests)
+
+# Find pairs of consumers with similarity above a certain threshold
+threshold = 0.8
+similar_pairs = np.argwhere(similarity_matrix > threshold)
+
+# We will only consider upper triangular values to avoid double matching or self-matching
+similar_pairs = similar_pairs[similar_pairs[:, 0] < similar_pairs[:, 1]]
+
+# Simulate wealth transfer between matched pairs
+for pair in similar_pairs:
+    consumer_a, consumer_b = pair
+
+    # Get wealth data for the pair
+    wealth_a = wealth_data[consumer_a]
+    wealth_b = wealth_data[consumer_b]
+
+    # Train the network to transfer wealth between matched consumers
+    for epoch in range(100):
+        optimizer.zero_grad()
+        transferred_wealth_a = net(wealth_a)
+        transferred_wealth_b = net(wealth_b)
+
+        # Simulate bidirectional transfer: A to B and B to A
+        loss_a_to_b = criterion(transferred_wealth_a, wealth_b)
+        loss_b_to_a = criterion(transferred_wealth_b, wealth_a)
+        total_loss = loss_a_to_b + loss_b_to_a
+
+        total_loss.backward()
+        optimizer.step()
+
+# Display the similarity matrix and transfer results
+print("Cosine Similarity Matrix (Interest-based Matching):\n", similarity_matrix)
+
+# Plotting the interest similarity matrix for visualization
+plt.figure(figsize=(8, 6))
+plt.imshow(similarity_matrix, cmap='hot', interpolation='nearest')
+plt.colorbar(label='Cosine Similarity')
+plt.title("FortuneArch")
+plt.show()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import time
+import numpy as np
+
+# Define the number of mobile receivers
+num_receivers = 5
+
+# Define the size of the data packets
+data_packet_size = 256
+
+# Simulate high-speed data transmission by creating data packets
+def generate_data_packet(size):
+    return torch.rand(size)
+
+# Simulate a mobile receiver processing the data
+class MobileReceiver(nn.Module):
+    def __init__(self):
+        super(MobileReceiver, self).__init__()
+        self.fc1 = nn.Linear(data_packet_size, data_packet_size)
+
+    def forward(self, data):
+        processed_data = torch.relu(self.fc1(data))
+        return processed_data
+
+# Instantiate the mobile receivers
+receivers = [MobileReceiver() for _ in range(num_receivers)]
+
+# Define a function to simulate instantaneous transmission to all receivers
+def transmit_data_to_receivers(data_packet, receivers):
+    received_data = []
+
+    # Start timing to simulate high-speed transmission
+    start_time = time.time()
+
+    # Transmit the data packet to each receiver
+    for receiver in receivers:
+        received_packet = receiver(data_packet)
+        received_data.append(received_packet)
+
+    # End timing
+    end_time = time.time()
+
+    transmission_time = end_time - start_time
+    print(f"Data transmitted to {num_receivers} receivers in {transmission_time:.10f} seconds")
+
+    return received_data
+
+# Generate a random data packet
+data_packet = generate_data_packet(data_packet_size)
+
+# Simulate data transmission to the receivers
+received_data = transmit_data_to_receivers(data_packet, receivers)
+
+# Display results
+print(f"Original Data Packet (Sample):\n {data_packet[:5]}")
+print(f"Processed Data by Receiver 1 (Sample):\n {received_data[0][:5]}")
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the Bank Account class
+class BankAccount:
+    def __init__(self, account_number, balance=0.0):
+        self.account_number = account_number
+        self.balance = balance
+
+    def deposit(self, amount):
+        self.balance += amount
+
+    def get_balance(self):
+        return self.balance
+
+# Define a VPN layer for data encryption and passcode check
+class VPNLayer:
+    def __init__(self, encryption_key):
+        self.encryption_key = encryption_key  # Simulate encryption key
+        self.data_storage = {}
+
+    def encrypt_data(self, data):
+        # Simulate encryption by applying a non-linear transformation
+        encrypted_data = data * torch.sin(self.encryption_key)
+        return encrypted_data
+
+    def decrypt_data(self, encrypted_data, passcode):
+        # Check if passcode matches the encryption key (authentication)
+        if passcode == self.encryption_key:
+            decrypted_data = encrypted_data / torch.sin(self.encryption_key)
+            return decrypted_data
+        else:
+            raise ValueError("Invalid Passcode! Access Denied.")
+
+    def store_data(self, data, consumer_id):
+        encrypted_data = self.encrypt_data(data)
+        self.data_storage[consumer_id] = encrypted_data
+
+    def retrieve_data(self, consumer_id, passcode):
+        if consumer_id in self.data_storage:
+            return self.decrypt_data(self.data_storage[consumer_id], passcode)
+        else:
+            raise ValueError("Consumer ID not found!")
+
+# Generate a wealth waveform
+def generate_wealth_waveform(size, amplitude, frequency, phase):
+    t = torch.linspace(0, 2 * np.pi, size)
+    waveform = amplitude * torch.sin(frequency * t + phase)
+    return waveform
+
+# Define the WealthTransferNet neural network
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer
+
+    def forward(self, x):
+        return self.fc1(x)
+
+# Function to simulate the wealth transfer process
+def transfer_wealth(waveform, target_account):
+    # Ensure the waveform represents positive wealth for transfer
+    wealth_amount = torch.sum(waveform[waveform > 0]).item()
+
+    # Instantiate the wealth transfer network
+    net = WealthTransferNet()
+
+    # Create a tensor for the wealth amount
+    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Train the network (for demonstration, no real training here)
+    optimizer = optim.SGD(net.parameters(), lr=0.01)
+    criterion = nn.MSELoss()
+
+    # Dummy target for training (for simulation purpose)
+    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Simulate the transfer process
+    for epoch in range(100):  # Simulating a few training epochs
+        optimizer.zero_grad()
+        output = net(input_data)
+        loss = criterion(output, target_data)
+        loss.backward()
+        optimizer.step()
+
+    # Transfer the wealth to the target account
+    target_account.deposit(wealth_amount)
+
+    return wealth_amount
+
+# Define the InfraredSignal class to simulate signal transmission
+class InfraredSignal:
+    def __init__(self, waveform):
+        self.waveform = waveform
+
+    def transmit(self):
+        # Simulate transmission through space (in this case, just return the waveform)
+        print("Transmitting infrared signal...")
+        return self.waveform
+
+# Define a receiver to detect infrared signals
+class SignalReceiver:
+    def __init__(self):
+        self.received_data = None
+
+    def receive(self, signal):
+        print("Receiving signal...")
+        self.received_data = signal
+        print("Signal received.")
+
+    def decode(self):
+        # For simplicity, return the received data directly
+        return self.received_data
+
+# Parameters for the wealth waveform
+waveform_size = 1000
+amplitude = 1000.0
+frequency = 2.0
+phase = 0.0
+
+# Generate a wealth waveform
+wealth_waveform = generate_wealth_waveform(waveform_size, amplitude, frequency, phase)
+
+# Create a target bank account
+target_account = BankAccount(account_number="1234567890")
+
+# Create a VPN layer
+vpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))
+
+# Store consumer data (e.g., wealth waveform) in the VPN layer
+consumer_id = "consumer_001"
+vpn_layer.store_data(wealth_waveform, consumer_id)
+
+# Attempt to retrieve data with the correct passcode
+passcode = torch.tensor(0.5)
+
+try:
+    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)
+
+    # Create an infrared signal to transmit the wealth waveform
+    infrared_signal = InfraredSignal(retrieved_waveform)
+
+    # Transmit the signal
+    transmitted_signal = infrared_signal.transmit()
+
+    # Create a receiver and receive the signal
+    signal_receiver = SignalReceiver()
+    signal_receiver.receive(transmitted_signal)
+
+    # Decode the received signal
+    decoded_waveform = signal_receiver.decode()
+
+    # Transfer wealth represented by the decoded waveform
+    transferred_amount = transfer_wealth(decoded_waveform, target_account)
+
+    # Display the results
+    print(f"Transferred Amount: ${transferred_amount:.2f}")
+    print(f"New Balance of Target Account: ${target_account.get_balance():.2f}")
+
+    # Plot the wealth waveform
+    plt.figure(figsize=(10, 5))
+    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform')
+    plt.title("Wealth Waveform Representation")
+    plt.xlabel("Time")
+    plt.ylabel("Wealth Amount")
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+except ValueError as e:
+    print(e)
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the Bank Account class
+class BankAccount:
+    def __init__(self, account_number, balance=0.0):
+        self.account_number = account_number
+        self.balance = balance
+
+    def deposit(self, amount):
+        self.balance += amount
+
+    def get_balance(self):
+        return self.balance
+
+# Define a VPN layer for data encryption and passcode check
+class VPNLayer:
+    def __init__(self, encryption_key):
+        self.encryption_key = encryption_key  # Simulate encryption key
+        self.data_storage = {}
+
+    def encrypt_data(self, data):
+        # Simulate encryption by applying a non-linear transformation
+        encrypted_data = data * torch.sin(self.encryption_key)
+        return encrypted_data
+
+    def decrypt_data(self, encrypted_data, passcode):
+        # Check if passcode matches the encryption key (authentication)
+        if passcode == self.encryption_key:
+            decrypted_data = encrypted_data / torch.sin(self.encryption_key)
+            return decrypted_data
+        else:
+            raise ValueError("Invalid Passcode! Access Denied.")
+
+    def store_data(self, data, consumer_id):
+        encrypted_data = self.encrypt_data(data)
+        self.data_storage[consumer_id] = encrypted_data
+
+    def retrieve_data(self, consumer_id, passcode):
+        if consumer_id in self.data_storage:
+            return self.decrypt_data(self.data_storage[consumer_id], passcode)
+        else:
+            raise ValueError("Consumer ID not found!")
+
+# Generate a wealth waveform
+def generate_wealth_waveform(size, amplitude, frequency, phase):
+    t = torch.linspace(0, 2 * np.pi, size)
+    waveform = amplitude * torch.sin(frequency * t + phase)
+    return waveform
+
+# Define the WealthTransferNet neural network
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer
+
+    def forward(self, x):
+        return self.fc1(x)
+
+# Function to simulate the wealth transfer process
+def transfer_wealth(waveform, target_account):
+    # Ensure the waveform represents positive wealth for transfer
+    wealth_amount = torch.sum(waveform[waveform > 0]).item()
+
+    # Instantiate the wealth transfer network
+    net = WealthTransferNet()
+
+    # Create a tensor for the wealth amount
+    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Train the network (for demonstration, no real training here)
+    optimizer = optim.SGD(net.parameters(), lr=0.01)
+    criterion = nn.MSELoss()
+
+    # Dummy target for training (for simulation purpose)
+    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Simulate the transfer process
+    for epoch in range(100):  # Simulating a few training epochs
+        optimizer.zero_grad()
+        output = net(input_data)
+        loss = criterion(output, target_data)
+        loss.backward()
+        optimizer.step()
+
+    # Transfer the wealth to the target account
+    target_account.deposit(wealth_amount)
+
+    return wealth_amount
+
+# Define the InfraredSignal class to simulate signal transmission
+class InfraredSignal:
+    def __init__(self, waveform):
+        self.waveform = waveform
+
+    def transmit(self):
+        # Simulate transmission through space (in this case, just return the waveform)
+        print("Transmitting infrared signal...")
+        return self.waveform
+
+# Define a receiver to detect infrared signals
+class SignalReceiver:
+    def __init__(self):
+        self.received_data = None
+
+    def receive(self, signal):
+        print("Receiving signal...")
+        self.received_data = signal
+        print("Signal received.")
+
+    def decode(self):
+        # For simplicity, return the received data directly
+        return self.received_data
+
+# Parameters for the wealth waveform
+waveform_size = 1000
+amplitude = 1000.0
+frequency = 2.0
+phase = 0.0
+
+# Generate a wealth waveform
+wealth_waveform = generate_wealth_waveform(waveform_size, amplitude, frequency, phase)
+
+# Create a target bank account
+target_account = BankAccount(account_number="1234567890")
+
+# Create a VPN layer
+vpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))
+
+# Store consumer data (e.g., wealth waveform) in the VPN layer
+consumer_id = "consumer_001"
+vpn_layer.store_data(wealth_waveform, consumer_id)
+
+# Attempt to retrieve data with the correct passcode
+passcode = torch.tensor(0.5)
+
+try:
+    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)
+
+    # Create an infrared signal to transmit the wealth waveform
+    infrared_signal = InfraredSignal(retrieved_waveform)
+
+    # Transmit the signal
+    transmitted_signal = infrared_signal.transmit()
+
+    # Create a receiver and receive the signal
+    signal_receiver = SignalReceiver()
+    signal_receiver.receive(transmitted_signal)
+
+    # Decode the received signal
+    decoded_waveform = signal_receiver.decode()
+
+    # Transfer wealth represented by the decoded waveform
+    transferred_amount = transfer_wealth(decoded_waveform, target_account)
+
+    # Display the results
+    print(f"Transferred Amount: ${transferred_amount:.2f}")
+    print(f"New Balance of Target Account: ${target_account.get_balance():.2f}")
+
+    # Plot the wealth waveform
+    plt.figure(figsize=(10, 5))
+    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform', color='blue')
+    plt.title("Wealth Waveform Representation")
+    plt.xlabel("Sample Number")
+    plt.ylabel("Wealth Amount")
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+except ValueError as e:
+    print(e)
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the Bank Account class
+class BankAccount:
+    def __init__(self, account_number, balance=0.0):
+        self.account_number = account_number
+        self.balance = balance
+
+    def deposit(self, amount):
+        self.balance += amount
+
+    def get_balance(self):
+        return self.balance
+
+# Define a VPN layer for data encryption and passcode check
+class VPNLayer:
+    def __init__(self, encryption_key):
+        self.encryption_key = encryption_key  # Simulate encryption key
+        self.data_storage = {}
+
+    def encrypt_data(self, data):
+        # Simulate encryption by applying a non-linear transformation
+        encrypted_data = data * torch.sin(self.encryption_key)
+        return encrypted_data
+
+    def decrypt_data(self, encrypted_data, passcode):
+        # Check if passcode matches the encryption key (authentication)
+        if passcode == self.encryption_key:
+            decrypted_data = encrypted_data / torch.sin(self.encryption_key)
+            return decrypted_data
+        else:
+            raise ValueError("Invalid Passcode! Access Denied.")
+
+    def store_data(self, data, consumer_id):
+        encrypted_data = self.encrypt_data(data)
+        self.data_storage[consumer_id] = encrypted_data
+
+    def retrieve_data(self, consumer_id, passcode):
+        if consumer_id in self.data_storage:
+            return self.decrypt_data(self.data_storage[consumer_id], passcode)
+        else:
+            raise ValueError("Consumer ID not found!")
+
+# Generate a wealth waveform with a random amplitude
+def generate_wealth_waveform(size, frequency, phase):
+    random_amplitude = torch.rand(1).item() * 1000  # Random amplitude between 0 and 1000
+    t = torch.linspace(0, 2 * np.pi, size)
+    waveform = random_amplitude * torch.sin(frequency * t + phase)
+    return waveform, random_amplitude
+
+# Define the WealthTransferNet neural network
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer
+
+    def forward(self, x):
+        return self.fc1(x)
+
+# Function to simulate the wealth transfer process
+def transfer_wealth(waveform, target_account):
+    # Ensure the waveform represents positive wealth for transfer
+    wealth_amount = torch.sum(waveform[waveform > 0]).item()
+
+    # Instantiate the wealth transfer network
+    net = WealthTransferNet()
+
+    # Create a tensor for the wealth amount
+    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Train the network (for demonstration, no real training here)
+    optimizer = optim.SGD(net.parameters(), lr=0.01)
+    criterion = nn.MSELoss()
+
+    # Dummy target for training (for simulation purpose)
+    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Simulate the transfer process
+    for epoch in range(100):  # Simulating a few training epochs
+        optimizer.zero_grad()
+        output = net(input_data)
+        loss = criterion(output, target_data)
+        loss.backward()
+        optimizer.step()
+
+    # Transfer the wealth to the target account
+    target_account.deposit(wealth_amount)
+
+    return wealth_amount
+
+# Define the InfraredSignal class to simulate signal transmission
+class InfraredSignal:
+    def __init__(self, waveform):
+        self.waveform = waveform
+
+    def transmit(self):
+        # Simulate transmission through space (in this case, just return the waveform)
+        print("Transmitting infrared signal...")
+        return self.waveform
+
+# Define a receiver to detect infrared signals
+class SignalReceiver:
+    def __init__(self):
+        self.received_data = None
+
+    def receive(self, signal):
+        print("Receiving signal...")
+        self.received_data = signal
+        print("Signal received.")
+
+    def decode(self):
+        # For simplicity, return the received data directly
+        return self.received_data
+
+# Parameters for the wealth waveform
+waveform_size = 1000
+frequency = 2.0
+phase = 0.0
+
+# Generate a wealth waveform with random amplitude
+wealth_waveform, randomized_amplitude = generate_wealth_waveform(waveform_size, frequency, phase)
+
+# Create a target bank account
+target_account = BankAccount(account_number="1234567890")
+
+# Create a VPN layer
+vpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))
+
+# Store consumer data (e.g., wealth waveform) in the VPN layer
+consumer_id = "consumer_001"
+vpn_layer.store_data(wealth_waveform, consumer_id)
+
+# Attempt to retrieve data with the correct passcode
+passcode = torch.tensor(0.5)
+
+try:
+    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)
+
+    # Create an infrared signal to transmit the wealth waveform
+    infrared_signal = InfraredSignal(retrieved_waveform)
+
+    # Transmit the signal
+    transmitted_signal = infrared_signal.transmit()
+
+    # Create a receiver and receive the signal
+    signal_receiver = SignalReceiver()
+    signal_receiver.receive(transmitted_signal)
+
+    # Decode the received signal
+    decoded_waveform = signal_receiver.decode()
+
+    # Transfer wealth represented by the decoded waveform
+    transferred_amount = transfer_wealth(decoded_waveform, target_account)
+
+    # Display the results
+    print(f"Transferred Amount: ${transferred_amount:.2f}")
+    print(f"New Balance of Target Account: ${target_account.get_balance():.2f}")
+    print(f"Randomized Amplitude: ${randomized_amplitude:.2f}")
+
+    # Plot the wealth waveform
+    plt.figure(figsize=(10, 5))
+    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform', color='blue')
+    plt.title("Wealth Waveform Representation")
+    plt.xlabel("Number")
+    plt.ylabel("Amount")
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+except ValueError as e:
+    print(e)
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+import hashlib
+
+# Define the Bank Account class
+class BankAccount:
+    def __init__(self, account_number, balance=0.0):
+        self.account_number = account_number
+        self.balance = balance
+
+    def deposit(self, amount):
+        self.balance += amount
+
+    def get_balance(self):
+        return self.balance
+
+# Define a VPN layer for data encryption and passcode check
+class VPNLayer:
+    def __init__(self, encryption_key):
+        self.encryption_key = encryption_key  # Simulate encryption key
+        self.data_storage = {}
+        self.hash_storage = {}
+
+    def encrypt_data(self, data):
+        # Simulate encryption by applying a non-linear transformation
+        encrypted_data = data * torch.sin(self.encryption_key)
+        return encrypted_data
+
+    def decrypt_data(self, encrypted_data, passcode):
+        # Check if passcode matches the encryption key (authentication)
+        if passcode == self.encryption_key:
+            decrypted_data = encrypted_data / torch.sin(self.encryption_key)
+            return decrypted_data
+        else:
+            raise ValueError("Invalid Passcode! Access Denied.")
+
+    def store_data(self, data, consumer_id):
+        encrypted_data = self.encrypt_data(data)
+        # Store the encrypted data
+        self.data_storage[consumer_id] = encrypted_data
+
+        # Store a hash of the data for integrity check
+        data_hash = hashlib.sha256(data.numpy()).hexdigest()
+        self.hash_storage[consumer_id] = data_hash
+
+    def retrieve_data(self, consumer_id, passcode):
+        if consumer_id in self.data_storage:
+            encrypted_data = self.data_storage[consumer_id]
+            decrypted_data = self.decrypt_data(encrypted_data, passcode)
+            # Verify data integrity
+            original_hash = self.hash_storage[consumer_id]
+            current_hash = hashlib.sha256(decrypted_data.numpy()).hexdigest()
+            if original_hash == current_hash:
+                return decrypted_data
+            else:
+                raise ValueError("Data integrity compromised!")
+        else:
+            raise ValueError("Consumer ID not found!")
+
+# Generate a wealth waveform with a random amplitude
+def generate_wealth_waveform(size, frequency, phase):
+    random_amplitude = torch.rand(1).item() * 1000  # Random amplitude between 0 and 1000
+    t = torch.linspace(0, 2 * np.pi, size)
+    waveform = random_amplitude * torch.sin(frequency * t + phase)
+    return waveform, random_amplitude
+
+# Define the WealthTransferNet neural network
+class WealthTransferNet(nn.Module):
+    def __init__(self):
+        super(WealthTransferNet, self).__init__()
+        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer
+
+    def forward(self, x):
+        return self.fc1(x)
+
+# Function to simulate the wealth transfer process
+def transfer_wealth(waveform, target_account):
+    # Ensure the waveform represents positive wealth for transfer
+    wealth_amount = torch.sum(waveform[waveform > 0]).item()
+
+    # Instantiate the wealth transfer network
+    net = WealthTransferNet()
+
+    # Create a tensor for the wealth amount
+    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Train the network (for demonstration, no real training here)
+    optimizer = optim.SGD(net.parameters(), lr=0.01)
+    criterion = nn.MSELoss()
+
+    # Dummy target for training (for simulation purpose)
+    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)
+
+    # Simulate the transfer process
+    for epoch in range(100):  # Simulating a few training epochs
+        optimizer.zero_grad()
+        output = net(input_data)
+        loss = criterion(output, target_data)
+        loss.backward()
+        optimizer.step()
+
+    # Transfer the wealth to the target account
+    target_account.deposit(wealth_amount)
+
+    return wealth_amount
+
+# Define the InfraredSignal class to simulate signal transmission
+class InfraredSignal:
+    def __init__(self, waveform):
+        self.waveform = waveform
+
+    def transmit(self):
+        # Simulate transmission through space (in this case, just return the waveform)
+        print("Transmitting infrared signal...")
+        return self.waveform
+
+# Define a receiver to detect infrared signals
+class SignalReceiver:
+    def __init__(self):
+        self.received_data = None
+
+    def receive(self, signal):
+        print("Receiving signal...")
+        self.received_data = signal
+        print("Signal received.")
+
+    def decode(self):
+        # For simplicity, return the received data directly
+        return self.received_data
+
+# Parameters for the wealth waveform
+waveform_size = 1000
+frequency = 2.0
+phase = 0.0
+
+# Generate a wealth waveform with random amplitude
+wealth_waveform, randomized_amplitude = generate_wealth_waveform(waveform_size, frequency, phase)
+
+# Create a target bank account
+target_account = BankAccount(account_number="1234567890")
+
+# Create a VPN layer
+vpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))
+
+# Store consumer data (e.g., wealth waveform) in the VPN layer
+consumer_id = "consumer_001"
+vpn_layer.store_data(wealth_waveform, consumer_id)
+
+# Attempt to retrieve data with the correct passcode
+passcode = torch.tensor(0.5)
+
+try:
+    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)
+
+    # Create an infrared signal to transmit the wealth waveform
+    infrared_signal = InfraredSignal(retrieved_waveform)
+
+    # Transmit the signal
+    transmitted_signal = infrared_signal.transmit()
+
+    # Create a receiver and receive the signal
+    signal_receiver = SignalReceiver()
+    signal_receiver.receive(transmitted_signal)
+
+    # Decode the received signal
+    decoded_waveform = signal_receiver.decode()
+
+    # Transfer wealth represented by the decoded waveform
+    transferred_amount = transfer_wealth(decoded_waveform, target_account)
+
+    # Display the results
+    print(f"Transferred Amount: ${transferred_amount:.2f}")
+    print(f"New Balance of Target Account: ${target_account.get_balance():.2f}")
+    print(f"Randomized Amplitude: ${randomized_amplitude:.2f}")
+
+    # Plot the wealth waveform
+    plt.figure(figsize=(10, 5))
+    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform', color='blue')
+    plt.title("Wealth Waveform Representation")
+    plt.xlabel("Sample Number")
+    plt.ylabel("Wealth Amount")
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+except ValueError as e:
+    print(e)