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)