{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30775,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nnum_consumers = 10\ninterest_size = 5\nwealth_size = 1\nfeature_size = interest_size + wealth_size\n\nconsumer_profiles = torch.rand((num_consumers, feature_size))\n\ninterests = consumer_profiles[:, :interest_size]\nwealth_data = consumer_profiles[:, interest_size:]\n\nclass WealthTransferNet(nn.Module):\n  def __init__(self):\n    super(WealthTransferNet, self).__init__()\n    self.fc1 = nn.Linear(wealth_size, wealth_size)\n\n  # The forward function is now correctly defined as a method of the class\n  def forward(self, x):\n    return self.fc1(x)\n\nnet = WealthTransferNet()\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(net.parameters(), lr=0.01)\n\n# Calculate cosine similarity between consumer interests\nsimilarity_matrix = cosine_similarity(interests)\n\n# Find pairs of consumers with similarity above a certain threshold\nthreshold = 0.8\nsimilar_pairs = np.argwhere(similarity_matrix > threshold)\n\n# We will only consider upper triangular values to avoid double matching or self-matching\nsimilar_pairs = similar_pairs[similar_pairs[:, 0] < similar_pairs[:, 1]]\n\n# Simulate wealth transfer between matched pairs\nfor pair in similar_pairs:\n    consumer_a, consumer_b = pair\n    \n    # Get wealth data for the pair\n    wealth_a = wealth_data[consumer_a]\n    wealth_b = wealth_data[consumer_b]\n\n    # Train the network to transfer wealth between matched consumers\n    for epoch in range(100):\n        optimizer.zero_grad()\n        transferred_wealth_a = net(wealth_a)\n        transferred_wealth_b = net(wealth_b)\n        \n        # Simulate bidirectional transfer: A to B and B to A\n        loss_a_to_b = criterion(transferred_wealth_a, wealth_b)\n        loss_b_to_a = criterion(transferred_wealth_b, wealth_a)\n        total_loss = loss_a_to_b + loss_b_to_a\n        \n        total_loss.backward()\n        optimizer.step()\n\n# Display the similarity matrix and transfer results\nprint(\"Cosine Similarity Matrix (Interest-based Matching):\\n\", similarity_matrix)\n\n# Plotting the interest similarity matrix for visualization\nplt.figure(figsize=(8, 6))\nplt.imshow(similarity_matrix, cmap='hot', interpolation='nearest')\nplt.colorbar(label='Cosine Similarity')\nplt.title(\"Interest Similarity Matrix\")\nplt.show()\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# Define the number of consumers and feature size (interests + wealth)\nnum_consumers = 10\ninterest_size = 5  # Number of interests\nwealth_size = 1  # Each consumer has one wealth data point\nfeature_size = interest_size + wealth_size  # Total feature size\n\n# Generate random consumer profiles (interest + wealth)\nconsumer_profiles = torch.rand((num_consumers, feature_size))\n\n# Split into interests and wealth data\ninterests = consumer_profiles[:, :interest_size]\nwealth_data = consumer_profiles[:, interest_size:]\n\n# Define a neural network to transfer wealth between consumers\nclass WealthTransferNet(nn.Module):\n    def __init__(self):\n        super(WealthTransferNet, self).__init__()\n        self.fc1 = nn.Linear(wealth_size, wealth_size)\n    \n    def forward(self, x):\n        return self.fc1(x)\n\n# Define a VPN-like layer for data encryption and passcode check\nclass VPNLayer(nn.Module):\n    def __init__(self, encryption_key):\n        super(VPNLayer, self).__init__()\n        self.encryption_key = encryption_key  # Simulate encryption key\n\n    def encrypt_data(self, data):\n        # Simulate encryption by applying a non-linear transformation\n        encrypted_data = data * torch.sin(self.encryption_key)\n        return encrypted_data\n\n    def decrypt_data(self, encrypted_data, passcode):\n        # Check if passcode matches the encryption key (this is our 'authentication')\n        if passcode == self.encryption_key:\n            decrypted_data = encrypted_data / torch.sin(self.encryption_key)\n            return decrypted_data\n        else:\n            raise ValueError(\"Invalid Passcode! Access Denied.\")\n\n# Instantiate the VPN layer\nvpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))\n\n# Encrypt consumer profiles (interest + wealth data) using the VPN layer\nencrypted_consumer_profiles = vpn_layer.encrypt_data(consumer_profiles)\n\n# Passcode required to access data (for simplicity, using the same as the encryption key)\npasscode = torch.tensor(0.5)\n\n# Try to access the encrypted data with the correct passcode\ntry:\n    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, passcode)\n    print(\"Access Granted. Decrypted Consumer Data:\")\n    print(decrypted_profiles)\nexcept ValueError as e:\n    print(e)\n\n# Simulate incorrect passcode\nwrong_passcode = torch.tensor(0.3)\n\ntry:\n    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, wrong_passcode)\nexcept ValueError as e:\n    print(e)\n\n# Instantiate the wealth transfer network\nnet = WealthTransferNet()\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(net.parameters(), lr=0.01)\n\n# Calculate cosine similarity between consumer interests\nsimilarity_matrix = cosine_similarity(interests)\n\n# Find pairs of consumers with similarity above a certain threshold\nthreshold = 0.8\nsimilar_pairs = np.argwhere(similarity_matrix > threshold)\n\n# We will only consider upper triangular values to avoid double matching or self-matching\nsimilar_pairs = similar_pairs[similar_pairs[:, 0] < similar_pairs[:, 1]]\n\n# Simulate wealth transfer between matched pairs\nfor pair in similar_pairs:\n    consumer_a, consumer_b = pair\n    \n    # Get wealth data for the pair\n    wealth_a = wealth_data[consumer_a]\n    wealth_b = wealth_data[consumer_b]\n\n    # Train the network to transfer wealth between matched consumers\n    for epoch in range(100):\n        optimizer.zero_grad()\n        transferred_wealth_a = net(wealth_a)\n        transferred_wealth_b = net(wealth_b)\n        \n        # Simulate bidirectional transfer: A to B and B to A\n        loss_a_to_b = criterion(transferred_wealth_a, wealth_b)\n        loss_b_to_a = criterion(transferred_wealth_b, wealth_a)\n        total_loss = loss_a_to_b + loss_b_to_a\n        \n        total_loss.backward()\n        optimizer.step()\n\n# Display the similarity matrix and transfer results\nprint(\"Cosine Similarity Matrix (Interest-based Matching):\\n\", similarity_matrix)\n\n# Plotting the interest similarity matrix for visualization\nplt.figure(figsize=(8, 6))\nplt.imshow(similarity_matrix, cmap='hot', interpolation='nearest')\nplt.colorbar(label='Cosine Similarity')\nplt.title(\"Interest Similarity Matrix\")\nplt.show()\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# Define the number of consumers and feature size (interests + wealth)\nnum_consumers = 10\ninterest_size = 5  # Number of interests\nwealth_size = 1  # Each consumer has one wealth data point\nfeature_size = interest_size + wealth_size  # Total feature size\n\n# Generate random consumer profiles (interest + wealth)\nconsumer_profiles = torch.rand((num_consumers, feature_size))\n\n# Split into interests and wealth data\ninterests = consumer_profiles[:, :interest_size]\nwealth_data = consumer_profiles[:, interest_size:]\n\n# Define a neural network to transfer wealth between consumers\nclass WealthTransferNet(nn.Module):\n    def __init__(self):\n        super(WealthTransferNet, self).__init__()\n        self.fc1 = nn.Linear(wealth_size, wealth_size)\n    \n    def forward(self, x):\n        return self.fc1(x)\n\n# Define a VPN-like layer for data encryption and passcode check\nclass VPNLayer(nn.Module):\n    def __init__(self, encryption_key):\n        super(VPNLayer, self).__init__()\n        self.encryption_key = encryption_key  # Simulate encryption key\n\n    def encrypt_data(self, data):\n        # Simulate encryption by applying a non-linear transformation\n        encrypted_data = data * torch.sin(self.encryption_key)\n        return encrypted_data\n\n    def decrypt_data(self, encrypted_data, passcode):\n        # Check if passcode matches the encryption key (this is our 'authentication')\n        if passcode == self.encryption_key:\n            decrypted_data = encrypted_data / torch.sin(self.encryption_key)\n            return decrypted_data\n        else:\n            raise ValueError(\"Invalid Passcode! Access Denied.\")\n\n# Instantiate the VPN layer\nvpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))\n\n# Encrypt consumer profiles (interest + wealth data) using the VPN layer\nencrypted_consumer_profiles = vpn_layer.encrypt_data(consumer_profiles)\n\n# Passcode required to access data (for simplicity, using the same as the encryption key)\npasscode = torch.tensor(0.5)\n\n# Try to access the encrypted data with the correct passcode\ntry:\n    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, passcode)\n    print(\"Access Granted. Decrypted Consumer Data:\")\n    print(decrypted_profiles)\nexcept ValueError as e:\n    print(e)\n\n# Simulate incorrect passcode\nwrong_passcode = torch.tensor(0.3)\n\ntry:\n    decrypted_profiles = vpn_layer.decrypt_data(encrypted_consumer_profiles, wrong_passcode)\nexcept ValueError as e:\n    print(e)\n\n# Instantiate the wealth transfer network\nnet = WealthTransferNet()\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(net.parameters(), lr=0.01)\n\n# Calculate cosine similarity between consumer interests\nsimilarity_matrix = cosine_similarity(interests)\n\n# Find pairs of consumers with similarity above a certain threshold\nthreshold = 0.8\nsimilar_pairs = np.argwhere(similarity_matrix > threshold)\n\n# We will only consider upper triangular values to avoid double matching or self-matching\nsimilar_pairs = similar_pairs[similar_pairs[:, 0] < similar_pairs[:, 1]]\n\n# Simulate wealth transfer between matched pairs\nfor pair in similar_pairs:\n    consumer_a, consumer_b = pair\n    \n    # Get wealth data for the pair\n    wealth_a = wealth_data[consumer_a]\n    wealth_b = wealth_data[consumer_b]\n\n    # Train the network to transfer wealth between matched consumers\n    for epoch in range(100):\n        optimizer.zero_grad()\n        transferred_wealth_a = net(wealth_a)\n        transferred_wealth_b = net(wealth_b)\n        \n        # Simulate bidirectional transfer: A to B and B to A\n        loss_a_to_b = criterion(transferred_wealth_a, wealth_b)\n        loss_b_to_a = criterion(transferred_wealth_b, wealth_a)\n        total_loss = loss_a_to_b + loss_b_to_a\n        \n        total_loss.backward()\n        optimizer.step()\n\n# Display the similarity matrix and transfer results\nprint(\"Cosine Similarity Matrix (Interest-based Matching):\\n\", similarity_matrix)\n\n# Plotting the interest similarity matrix for visualization\nplt.figure(figsize=(8, 6))\nplt.imshow(similarity_matrix, cmap='hot', interpolation='nearest')\nplt.colorbar(label='Cosine Similarity')\nplt.title(\"FortuneArch\")\nplt.show()\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport time\nimport numpy as np\n\n# Define the number of mobile receivers\nnum_receivers = 5\n\n# Define the size of the data packets\ndata_packet_size = 256\n\n# Simulate high-speed data transmission by creating data packets\ndef generate_data_packet(size):\n    return torch.rand(size)\n\n# Simulate a mobile receiver processing the data\nclass MobileReceiver(nn.Module):\n    def __init__(self):\n        super(MobileReceiver, self).__init__()\n        self.fc1 = nn.Linear(data_packet_size, data_packet_size)\n    \n    def forward(self, data):\n        processed_data = torch.relu(self.fc1(data))\n        return processed_data\n\n# Instantiate the mobile receivers\nreceivers = [MobileReceiver() for _ in range(num_receivers)]\n\n# Define a function to simulate instantaneous transmission to all receivers\ndef transmit_data_to_receivers(data_packet, receivers):\n    received_data = []\n    \n    # Start timing to simulate high-speed transmission\n    start_time = time.time()\n\n    # Transmit the data packet to each receiver\n    for receiver in receivers:\n        received_packet = receiver(data_packet)\n        received_data.append(received_packet)\n\n    # End timing\n    end_time = time.time()\n    \n    transmission_time = end_time - start_time\n    print(f\"Data transmitted to {num_receivers} receivers in {transmission_time:.10f} seconds\")\n\n    return received_data\n\n# Generate a random data packet\ndata_packet = generate_data_packet(data_packet_size)\n\n# Simulate data transmission to the receivers\nreceived_data = transmit_data_to_receivers(data_packet, receivers)\n\n# Display results\nprint(f\"Original Data Packet (Sample):\\n {data_packet[:5]}\")\nprint(f\"Processed Data by Receiver 1 (Sample):\\n {received_data[0][:5]}\")\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Define the Bank Account class\nclass BankAccount:\n    def __init__(self, account_number, balance=0.0):\n        self.account_number = account_number\n        self.balance = balance\n\n    def deposit(self, amount):\n        self.balance += amount\n\n    def get_balance(self):\n        return self.balance\n\n# Define a VPN layer for data encryption and passcode check\nclass VPNLayer:\n    def __init__(self, encryption_key):\n        self.encryption_key = encryption_key  # Simulate encryption key\n        self.data_storage = {}\n\n    def encrypt_data(self, data):\n        # Simulate encryption by applying a non-linear transformation\n        encrypted_data = data * torch.sin(self.encryption_key)\n        return encrypted_data\n\n    def decrypt_data(self, encrypted_data, passcode):\n        # Check if passcode matches the encryption key (authentication)\n        if passcode == self.encryption_key:\n            decrypted_data = encrypted_data / torch.sin(self.encryption_key)\n            return decrypted_data\n        else:\n            raise ValueError(\"Invalid Passcode! Access Denied.\")\n\n    def store_data(self, data, consumer_id):\n        encrypted_data = self.encrypt_data(data)\n        self.data_storage[consumer_id] = encrypted_data\n\n    def retrieve_data(self, consumer_id, passcode):\n        if consumer_id in self.data_storage:\n            return self.decrypt_data(self.data_storage[consumer_id], passcode)\n        else:\n            raise ValueError(\"Consumer ID not found!\")\n\n# Generate a wealth waveform\ndef generate_wealth_waveform(size, amplitude, frequency, phase):\n    t = torch.linspace(0, 2 * np.pi, size)\n    waveform = amplitude * torch.sin(frequency * t + phase)\n    return waveform\n\n# Define the WealthTransferNet neural network\nclass WealthTransferNet(nn.Module):\n    def __init__(self):\n        super(WealthTransferNet, self).__init__()\n        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer\n    \n    def forward(self, x):\n        return self.fc1(x)\n\n# Function to simulate the wealth transfer process\ndef transfer_wealth(waveform, target_account):\n    # Ensure the waveform represents positive wealth for transfer\n    wealth_amount = torch.sum(waveform[waveform > 0]).item()\n    \n    # Instantiate the wealth transfer network\n    net = WealthTransferNet()\n    \n    # Create a tensor for the wealth amount\n    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n    \n    # Train the network (for demonstration, no real training here)\n    optimizer = optim.SGD(net.parameters(), lr=0.01)\n    criterion = nn.MSELoss()\n    \n    # Dummy target for training (for simulation purpose)\n    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n\n    # Simulate the transfer process\n    for epoch in range(100):  # Simulating a few training epochs\n        optimizer.zero_grad()\n        output = net(input_data)\n        loss = criterion(output, target_data)\n        loss.backward()\n        optimizer.step()\n\n    # Transfer the wealth to the target account\n    target_account.deposit(wealth_amount)\n    \n    return wealth_amount\n\n# Define the InfraredSignal class to simulate signal transmission\nclass InfraredSignal:\n    def __init__(self, waveform):\n        self.waveform = waveform\n\n    def transmit(self):\n        # Simulate transmission through space (in this case, just return the waveform)\n        print(\"Transmitting infrared signal...\")\n        return self.waveform\n\n# Define a receiver to detect infrared signals\nclass SignalReceiver:\n    def __init__(self):\n        self.received_data = None\n\n    def receive(self, signal):\n        print(\"Receiving signal...\")\n        self.received_data = signal\n        print(\"Signal received.\")\n\n    def decode(self):\n        # For simplicity, return the received data directly\n        return self.received_data\n\n# Parameters for the wealth waveform\nwaveform_size = 1000\namplitude = 1000.0\nfrequency = 2.0\nphase = 0.0\n\n# Generate a wealth waveform\nwealth_waveform = generate_wealth_waveform(waveform_size, amplitude, frequency, phase)\n\n# Create a target bank account\ntarget_account = BankAccount(account_number=\"1234567890\")\n\n# Create a VPN layer\nvpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))\n\n# Store consumer data (e.g., wealth waveform) in the VPN layer\nconsumer_id = \"consumer_001\"\nvpn_layer.store_data(wealth_waveform, consumer_id)\n\n# Attempt to retrieve data with the correct passcode\npasscode = torch.tensor(0.5)\n\ntry:\n    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)\n\n    # Create an infrared signal to transmit the wealth waveform\n    infrared_signal = InfraredSignal(retrieved_waveform)\n\n    # Transmit the signal\n    transmitted_signal = infrared_signal.transmit()\n\n    # Create a receiver and receive the signal\n    signal_receiver = SignalReceiver()\n    signal_receiver.receive(transmitted_signal)\n\n    # Decode the received signal\n    decoded_waveform = signal_receiver.decode()\n\n    # Transfer wealth represented by the decoded waveform\n    transferred_amount = transfer_wealth(decoded_waveform, target_account)\n\n    # Display the results\n    print(f\"Transferred Amount: ${transferred_amount:.2f}\")\n    print(f\"New Balance of Target Account: ${target_account.get_balance():.2f}\")\n\n    # Plot the wealth waveform\n    plt.figure(figsize=(10, 5))\n    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform')\n    plt.title(\"Wealth Waveform Representation\")\n    plt.xlabel(\"Time\")\n    plt.ylabel(\"Wealth Amount\")\n    plt.legend()\n    plt.grid()\n    plt.show()\n\nexcept ValueError as e:\n    print(e)\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Define the Bank Account class\nclass BankAccount:\n    def __init__(self, account_number, balance=0.0):\n        self.account_number = account_number\n        self.balance = balance\n\n    def deposit(self, amount):\n        self.balance += amount\n\n    def get_balance(self):\n        return self.balance\n\n# Define a VPN layer for data encryption and passcode check\nclass VPNLayer:\n    def __init__(self, encryption_key):\n        self.encryption_key = encryption_key  # Simulate encryption key\n        self.data_storage = {}\n\n    def encrypt_data(self, data):\n        # Simulate encryption by applying a non-linear transformation\n        encrypted_data = data * torch.sin(self.encryption_key)\n        return encrypted_data\n\n    def decrypt_data(self, encrypted_data, passcode):\n        # Check if passcode matches the encryption key (authentication)\n        if passcode == self.encryption_key:\n            decrypted_data = encrypted_data / torch.sin(self.encryption_key)\n            return decrypted_data\n        else:\n            raise ValueError(\"Invalid Passcode! Access Denied.\")\n\n    def store_data(self, data, consumer_id):\n        encrypted_data = self.encrypt_data(data)\n        self.data_storage[consumer_id] = encrypted_data\n\n    def retrieve_data(self, consumer_id, passcode):\n        if consumer_id in self.data_storage:\n            return self.decrypt_data(self.data_storage[consumer_id], passcode)\n        else:\n            raise ValueError(\"Consumer ID not found!\")\n\n# Generate a wealth waveform\ndef generate_wealth_waveform(size, amplitude, frequency, phase):\n    t = torch.linspace(0, 2 * np.pi, size)\n    waveform = amplitude * torch.sin(frequency * t + phase)\n    return waveform\n\n# Define the WealthTransferNet neural network\nclass WealthTransferNet(nn.Module):\n    def __init__(self):\n        super(WealthTransferNet, self).__init__()\n        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer\n    \n    def forward(self, x):\n        return self.fc1(x)\n\n# Function to simulate the wealth transfer process\ndef transfer_wealth(waveform, target_account):\n    # Ensure the waveform represents positive wealth for transfer\n    wealth_amount = torch.sum(waveform[waveform > 0]).item()\n    \n    # Instantiate the wealth transfer network\n    net = WealthTransferNet()\n    \n    # Create a tensor for the wealth amount\n    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n    \n    # Train the network (for demonstration, no real training here)\n    optimizer = optim.SGD(net.parameters(), lr=0.01)\n    criterion = nn.MSELoss()\n    \n    # Dummy target for training (for simulation purpose)\n    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n\n    # Simulate the transfer process\n    for epoch in range(100):  # Simulating a few training epochs\n        optimizer.zero_grad()\n        output = net(input_data)\n        loss = criterion(output, target_data)\n        loss.backward()\n        optimizer.step()\n\n    # Transfer the wealth to the target account\n    target_account.deposit(wealth_amount)\n    \n    return wealth_amount\n\n# Define the InfraredSignal class to simulate signal transmission\nclass InfraredSignal:\n    def __init__(self, waveform):\n        self.waveform = waveform\n\n    def transmit(self):\n        # Simulate transmission through space (in this case, just return the waveform)\n        print(\"Transmitting infrared signal...\")\n        return self.waveform\n\n# Define a receiver to detect infrared signals\nclass SignalReceiver:\n    def __init__(self):\n        self.received_data = None\n\n    def receive(self, signal):\n        print(\"Receiving signal...\")\n        self.received_data = signal\n        print(\"Signal received.\")\n\n    def decode(self):\n        # For simplicity, return the received data directly\n        return self.received_data\n\n# Parameters for the wealth waveform\nwaveform_size = 1000\namplitude = 1000.0\nfrequency = 2.0\nphase = 0.0\n\n# Generate a wealth waveform\nwealth_waveform = generate_wealth_waveform(waveform_size, amplitude, frequency, phase)\n\n# Create a target bank account\ntarget_account = BankAccount(account_number=\"1234567890\")\n\n# Create a VPN layer\nvpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))\n\n# Store consumer data (e.g., wealth waveform) in the VPN layer\nconsumer_id = \"consumer_001\"\nvpn_layer.store_data(wealth_waveform, consumer_id)\n\n# Attempt to retrieve data with the correct passcode\npasscode = torch.tensor(0.5)\n\ntry:\n    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)\n\n    # Create an infrared signal to transmit the wealth waveform\n    infrared_signal = InfraredSignal(retrieved_waveform)\n\n    # Transmit the signal\n    transmitted_signal = infrared_signal.transmit()\n\n    # Create a receiver and receive the signal\n    signal_receiver = SignalReceiver()\n    signal_receiver.receive(transmitted_signal)\n\n    # Decode the received signal\n    decoded_waveform = signal_receiver.decode()\n\n    # Transfer wealth represented by the decoded waveform\n    transferred_amount = transfer_wealth(decoded_waveform, target_account)\n\n    # Display the results\n    print(f\"Transferred Amount: ${transferred_amount:.2f}\")\n    print(f\"New Balance of Target Account: ${target_account.get_balance():.2f}\")\n\n    # Plot the wealth waveform\n    plt.figure(figsize=(10, 5))\n    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform', color='blue')\n    plt.title(\"Wealth Waveform Representation\")\n    plt.xlabel(\"Sample Number\")\n    plt.ylabel(\"Wealth Amount\")\n    plt.legend()\n    plt.grid()\n    plt.show()\n\nexcept ValueError as e:\n    print(e)\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Define the Bank Account class\nclass BankAccount:\n    def __init__(self, account_number, balance=0.0):\n        self.account_number = account_number\n        self.balance = balance\n\n    def deposit(self, amount):\n        self.balance += amount\n\n    def get_balance(self):\n        return self.balance\n\n# Define a VPN layer for data encryption and passcode check\nclass VPNLayer:\n    def __init__(self, encryption_key):\n        self.encryption_key = encryption_key  # Simulate encryption key\n        self.data_storage = {}\n\n    def encrypt_data(self, data):\n        # Simulate encryption by applying a non-linear transformation\n        encrypted_data = data * torch.sin(self.encryption_key)\n        return encrypted_data\n\n    def decrypt_data(self, encrypted_data, passcode):\n        # Check if passcode matches the encryption key (authentication)\n        if passcode == self.encryption_key:\n            decrypted_data = encrypted_data / torch.sin(self.encryption_key)\n            return decrypted_data\n        else:\n            raise ValueError(\"Invalid Passcode! Access Denied.\")\n\n    def store_data(self, data, consumer_id):\n        encrypted_data = self.encrypt_data(data)\n        self.data_storage[consumer_id] = encrypted_data\n\n    def retrieve_data(self, consumer_id, passcode):\n        if consumer_id in self.data_storage:\n            return self.decrypt_data(self.data_storage[consumer_id], passcode)\n        else:\n            raise ValueError(\"Consumer ID not found!\")\n\n# Generate a wealth waveform with a random amplitude\ndef generate_wealth_waveform(size, frequency, phase):\n    random_amplitude = torch.rand(1).item() * 1000  # Random amplitude between 0 and 1000\n    t = torch.linspace(0, 2 * np.pi, size)\n    waveform = random_amplitude * torch.sin(frequency * t + phase)\n    return waveform, random_amplitude\n\n# Define the WealthTransferNet neural network\nclass WealthTransferNet(nn.Module):\n    def __init__(self):\n        super(WealthTransferNet, self).__init__()\n        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer\n    \n    def forward(self, x):\n        return self.fc1(x)\n\n# Function to simulate the wealth transfer process\ndef transfer_wealth(waveform, target_account):\n    # Ensure the waveform represents positive wealth for transfer\n    wealth_amount = torch.sum(waveform[waveform > 0]).item()\n    \n    # Instantiate the wealth transfer network\n    net = WealthTransferNet()\n    \n    # Create a tensor for the wealth amount\n    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n    \n    # Train the network (for demonstration, no real training here)\n    optimizer = optim.SGD(net.parameters(), lr=0.01)\n    criterion = nn.MSELoss()\n    \n    # Dummy target for training (for simulation purpose)\n    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n\n    # Simulate the transfer process\n    for epoch in range(100):  # Simulating a few training epochs\n        optimizer.zero_grad()\n        output = net(input_data)\n        loss = criterion(output, target_data)\n        loss.backward()\n        optimizer.step()\n\n    # Transfer the wealth to the target account\n    target_account.deposit(wealth_amount)\n    \n    return wealth_amount\n\n# Define the InfraredSignal class to simulate signal transmission\nclass InfraredSignal:\n    def __init__(self, waveform):\n        self.waveform = waveform\n\n    def transmit(self):\n        # Simulate transmission through space (in this case, just return the waveform)\n        print(\"Transmitting infrared signal...\")\n        return self.waveform\n\n# Define a receiver to detect infrared signals\nclass SignalReceiver:\n    def __init__(self):\n        self.received_data = None\n\n    def receive(self, signal):\n        print(\"Receiving signal...\")\n        self.received_data = signal\n        print(\"Signal received.\")\n\n    def decode(self):\n        # For simplicity, return the received data directly\n        return self.received_data\n\n# Parameters for the wealth waveform\nwaveform_size = 1000\nfrequency = 2.0\nphase = 0.0\n\n# Generate a wealth waveform with random amplitude\nwealth_waveform, randomized_amplitude = generate_wealth_waveform(waveform_size, frequency, phase)\n\n# Create a target bank account\ntarget_account = BankAccount(account_number=\"1234567890\")\n\n# Create a VPN layer\nvpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))\n\n# Store consumer data (e.g., wealth waveform) in the VPN layer\nconsumer_id = \"consumer_001\"\nvpn_layer.store_data(wealth_waveform, consumer_id)\n\n# Attempt to retrieve data with the correct passcode\npasscode = torch.tensor(0.5)\n\ntry:\n    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)\n\n    # Create an infrared signal to transmit the wealth waveform\n    infrared_signal = InfraredSignal(retrieved_waveform)\n\n    # Transmit the signal\n    transmitted_signal = infrared_signal.transmit()\n\n    # Create a receiver and receive the signal\n    signal_receiver = SignalReceiver()\n    signal_receiver.receive(transmitted_signal)\n\n    # Decode the received signal\n    decoded_waveform = signal_receiver.decode()\n\n    # Transfer wealth represented by the decoded waveform\n    transferred_amount = transfer_wealth(decoded_waveform, target_account)\n\n    # Display the results\n    print(f\"Transferred Amount: ${transferred_amount:.2f}\")\n    print(f\"New Balance of Target Account: ${target_account.get_balance():.2f}\")\n    print(f\"Randomized Amplitude: ${randomized_amplitude:.2f}\")\n\n    # Plot the wealth waveform\n    plt.figure(figsize=(10, 5))\n    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform', color='blue')\n    plt.title(\"Wealth Waveform Representation\")\n    plt.xlabel(\"Number\")\n    plt.ylabel(\"Amount\")\n    plt.legend()\n    plt.grid()\n    plt.show()\n\nexcept ValueError as e:\n    print(e)\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport hashlib\n\n# Define the Bank Account class\nclass BankAccount:\n    def __init__(self, account_number, balance=0.0):\n        self.account_number = account_number\n        self.balance = balance\n\n    def deposit(self, amount):\n        self.balance += amount\n\n    def get_balance(self):\n        return self.balance\n\n# Define a VPN layer for data encryption and passcode check\nclass VPNLayer:\n    def __init__(self, encryption_key):\n        self.encryption_key = encryption_key  # Simulate encryption key\n        self.data_storage = {}\n        self.hash_storage = {}\n\n    def encrypt_data(self, data):\n        # Simulate encryption by applying a non-linear transformation\n        encrypted_data = data * torch.sin(self.encryption_key)\n        return encrypted_data\n\n    def decrypt_data(self, encrypted_data, passcode):\n        # Check if passcode matches the encryption key (authentication)\n        if passcode == self.encryption_key:\n            decrypted_data = encrypted_data / torch.sin(self.encryption_key)\n            return decrypted_data\n        else:\n            raise ValueError(\"Invalid Passcode! Access Denied.\")\n\n    def store_data(self, data, consumer_id):\n        encrypted_data = self.encrypt_data(data)\n        # Store the encrypted data\n        self.data_storage[consumer_id] = encrypted_data\n        \n        # Store a hash of the data for integrity check\n        data_hash = hashlib.sha256(data.numpy()).hexdigest()\n        self.hash_storage[consumer_id] = data_hash\n\n    def retrieve_data(self, consumer_id, passcode):\n        if consumer_id in self.data_storage:\n            encrypted_data = self.data_storage[consumer_id]\n            decrypted_data = self.decrypt_data(encrypted_data, passcode)\n            # Verify data integrity\n            original_hash = self.hash_storage[consumer_id]\n            current_hash = hashlib.sha256(decrypted_data.numpy()).hexdigest()\n            if original_hash == current_hash:\n                return decrypted_data\n            else:\n                raise ValueError(\"Data integrity compromised!\")\n        else:\n            raise ValueError(\"Consumer ID not found!\")\n\n# Generate a wealth waveform with a random amplitude\ndef generate_wealth_waveform(size, frequency, phase):\n    random_amplitude = torch.rand(1).item() * 1000  # Random amplitude between 0 and 1000\n    t = torch.linspace(0, 2 * np.pi, size)\n    waveform = random_amplitude * torch.sin(frequency * t + phase)\n    return waveform, random_amplitude\n\n# Define the WealthTransferNet neural network\nclass WealthTransferNet(nn.Module):\n    def __init__(self):\n        super(WealthTransferNet, self).__init__()\n        self.fc1 = nn.Linear(1, 1)  # Simple linear layer for wealth transfer\n    \n    def forward(self, x):\n        return self.fc1(x)\n\n# Function to simulate the wealth transfer process\ndef transfer_wealth(waveform, target_account):\n    # Ensure the waveform represents positive wealth for transfer\n    wealth_amount = torch.sum(waveform[waveform > 0]).item()\n    \n    # Instantiate the wealth transfer network\n    net = WealthTransferNet()\n    \n    # Create a tensor for the wealth amount\n    input_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n    \n    # Train the network (for demonstration, no real training here)\n    optimizer = optim.SGD(net.parameters(), lr=0.01)\n    criterion = nn.MSELoss()\n    \n    # Dummy target for training (for simulation purpose)\n    target_data = torch.tensor([[wealth_amount]], dtype=torch.float32)\n\n    # Simulate the transfer process\n    for epoch in range(100):  # Simulating a few training epochs\n        optimizer.zero_grad()\n        output = net(input_data)\n        loss = criterion(output, target_data)\n        loss.backward()\n        optimizer.step()\n\n    # Transfer the wealth to the target account\n    target_account.deposit(wealth_amount)\n    \n    return wealth_amount\n\n# Define the InfraredSignal class to simulate signal transmission\nclass InfraredSignal:\n    def __init__(self, waveform):\n        self.waveform = waveform\n\n    def transmit(self):\n        # Simulate transmission through space (in this case, just return the waveform)\n        print(\"Transmitting infrared signal...\")\n        return self.waveform\n\n# Define a receiver to detect infrared signals\nclass SignalReceiver:\n    def __init__(self):\n        self.received_data = None\n\n    def receive(self, signal):\n        print(\"Receiving signal...\")\n        self.received_data = signal\n        print(\"Signal received.\")\n\n    def decode(self):\n        # For simplicity, return the received data directly\n        return self.received_data\n\n# Parameters for the wealth waveform\nwaveform_size = 1000\nfrequency = 2.0\nphase = 0.0\n\n# Generate a wealth waveform with random amplitude\nwealth_waveform, randomized_amplitude = generate_wealth_waveform(waveform_size, frequency, phase)\n\n# Create a target bank account\ntarget_account = BankAccount(account_number=\"1234567890\")\n\n# Create a VPN layer\nvpn_layer = VPNLayer(encryption_key=torch.tensor(0.5))\n\n# Store consumer data (e.g., wealth waveform) in the VPN layer\nconsumer_id = \"consumer_001\"\nvpn_layer.store_data(wealth_waveform, consumer_id)\n\n# Attempt to retrieve data with the correct passcode\npasscode = torch.tensor(0.5)\n\ntry:\n    retrieved_waveform = vpn_layer.retrieve_data(consumer_id, passcode)\n\n    # Create an infrared signal to transmit the wealth waveform\n    infrared_signal = InfraredSignal(retrieved_waveform)\n\n    # Transmit the signal\n    transmitted_signal = infrared_signal.transmit()\n\n    # Create a receiver and receive the signal\n    signal_receiver = SignalReceiver()\n    signal_receiver.receive(transmitted_signal)\n\n    # Decode the received signal\n    decoded_waveform = signal_receiver.decode()\n\n    # Transfer wealth represented by the decoded waveform\n    transferred_amount = transfer_wealth(decoded_waveform, target_account)\n\n    # Display the results\n    print(f\"Transferred Amount: ${transferred_amount:.2f}\")\n    print(f\"New Balance of Target Account: ${target_account.get_balance():.2f}\")\n    print(f\"Randomized Amplitude: ${randomized_amplitude:.2f}\")\n\n    # Plot the wealth waveform\n    plt.figure(figsize=(10, 5))\n    plt.plot(decoded_waveform.numpy(), label='Wealth Waveform', color='blue')\n    plt.title(\"Wealth Waveform Representation\")\n    plt.xlabel(\"Sample Number\")\n    plt.ylabel(\"Wealth Amount\")\n    plt.legend()\n    plt.grid()\n    plt.show()\n\nexcept ValueError as e:\n    print(e)","metadata":{"execution":{"iopub.status.busy":"2024-10-03T09:27:20.201816Z","iopub.execute_input":"2024-10-03T09:27:20.202217Z","iopub.status.idle":"2024-10-03T09:27:33.003006Z","shell.execute_reply.started":"2024-10-03T09:27:20.202177Z","shell.execute_reply":"2024-10-03T09:27:33.001917Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Cosine Similarity Matrix (Interest-based Matching):\n [[1.         0.6205558  0.8994259  0.74716455 0.68222123 0.7541172\n  0.8844081  0.8707739  0.827073   0.6520639 ]\n [0.6205558  1.0000002  0.65353334 0.8640435  0.80969363 0.8451819\n  0.78864473 0.8525326  0.60188663 0.9252756 ]\n [0.8994259  0.65353334 1.0000001  0.8531215  0.5652321  0.7349403\n  0.78990984 0.9342856  0.6269557  0.61648655]\n [0.74716455 0.8640435  0.8531215  1.         0.60754615 0.9375373\n  0.68103075 0.9684841  0.5745309  0.89999753]\n [0.68222123 0.80969363 0.5652321  0.60754615 1.         0.57616335\n  0.87017    0.66852206 0.89194554 0.6932644 ]\n [0.7541172  0.8451819  0.7349403  0.9375373  0.57616335 0.9999999\n  0.67472327 0.9155117  0.58092636 0.9614153 ]\n [0.8844081  0.78864473 0.78990984 0.68103075 0.87017    0.67472327\n  0.9999999  0.8182335  0.81615865 0.6765592 ]\n [0.8707739  0.8525326  0.9342856  0.9684841  0.66852206 0.9155117\n  0.8182335  1.0000001  0.6580807  0.8543922 ]\n [0.827073   0.60188663 0.6269557  0.5745309  0.89194554 0.58092636\n  0.81615865 0.6580807  0.99999994 0.611774  ]\n [0.6520639  0.9252756  0.61648655 0.89999753 0.6932644  0.9614153\n  0.6765592  0.8543922  0.611774   1.0000001 ]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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"},"metadata":{}},{"name":"stdout","text":"Access Granted. Decrypted Consumer Data:\ntensor([[0.5621, 0.1219, 0.6116, 0.2410, 0.8745, 0.1966],\n        [0.5947, 0.9864, 0.7350, 0.6629, 0.9500, 0.2544],\n        [0.9592, 0.7431, 0.7246, 0.2686, 0.9140, 0.4565],\n        [0.6745, 0.0859, 0.8632, 0.7762, 0.7239, 0.7175],\n        [0.0541, 0.7121, 0.1545, 0.2491, 0.5404, 0.7472],\n        [0.0768, 0.7720, 0.8069, 0.2406, 0.3324, 0.0346],\n        [0.7259, 0.5053, 0.1277, 0.4705, 0.1808, 0.2620],\n        [0.0162, 0.9962, 0.5323, 0.2355, 0.7705, 0.8299],\n        [0.5753, 0.0048, 0.8152, 0.5565, 0.7434, 0.4068],\n        [0.1242, 0.7778, 0.4166, 0.2506, 0.9118, 0.9682]])\nInvalid Passcode! Access Denied.\nCosine Similarity Matrix (Interest-based Matching):\n [[0.9999999  0.855707   0.9194736  0.92023635 0.6390269  0.66465586\n  0.64604336 0.6919088  0.9537222  0.79530317]\n [0.855707   1.         0.9440816  0.8486754  0.90439725 0.87821066\n  0.8203251  0.91395754 0.82781297 0.93648833]\n [0.9194736  0.9440816  1.0000001  0.8473756  0.7806485  0.7876425\n  0.83260983 0.8072245  0.8501757  0.8578365 ]\n [0.92023635 0.8486754  0.8473756  1.         0.5664542  0.6818789\n  0.7268797  0.61161345 0.9903417  0.6875418 ]\n [0.6390269  0.90439725 0.7806485  0.5664542  0.99999994 0.81724346\n  0.6554679  0.9694748  0.54563177 0.96386516]\n [0.66465586 0.87821066 0.7876425  0.6818789  0.81724346 1.\n  0.59043825 0.914415   0.66870886 0.8453994 ]\n [0.64604336 0.8203251  0.83260983 0.7268797  0.6554679  0.59043825\n  0.99999994 0.5853203  0.65815955 0.61447316]\n [0.6919088  0.91395754 0.8072245  0.61161345 0.9694748  0.914415\n  0.5853203  1.         0.6090576  0.9763702 ]\n [0.9537222  0.82781297 0.8501757  0.9903417  0.54563177 0.66870886\n  0.65815955 0.6090576  0.99999994 0.6957364 ]\n [0.79530317 0.93648833 0.8578365  0.6875418  0.96386516 0.8453994\n  0.61447316 0.9763702  0.6957364  1.        ]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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"},"metadata":{}},{"name":"stdout","text":"Access Granted. Decrypted Consumer Data:\ntensor([[0.1793, 0.8024, 0.8964, 0.2751, 0.4143, 0.5736],\n        [0.3372, 0.5584, 0.3502, 0.9559, 0.4632, 0.4637],\n        [0.8490, 0.6142, 0.8915, 0.7836, 0.4638, 0.8608],\n        [0.4060, 0.5249, 0.4519, 0.5912, 0.1336, 0.0874],\n        [0.8028, 0.7905, 0.0532, 0.4222, 0.2095, 0.5249],\n        [0.0705, 0.7526, 0.2055, 0.4310, 0.1714, 0.3402],\n        [0.2774, 0.8385, 0.8978, 0.4910, 0.4360, 0.7196],\n        [0.6825, 0.2341, 0.5410, 0.3613, 0.0941, 0.7770],\n        [0.9382, 0.2210, 0.5042, 0.3502, 0.2483, 0.5664],\n        [0.1701, 0.7038, 0.2171, 0.9920, 0.2236, 0.2985]])\nInvalid Passcode! Access Denied.\nCosine Similarity Matrix (Interest-based Matching):\n [[1.         0.75073576 0.8541983  0.8450328  0.6404959  0.8278138\n  0.9874873  0.7278442  0.6486307  0.6938778 ]\n [0.75073576 0.99999994 0.8919422  0.9330407  0.7779784  0.85464865\n  0.84147525 0.743212   0.70411843 0.96197355]\n [0.8541983  0.8919422  1.         0.96116275 0.81443566 0.74739915\n  0.9123443  0.95153    0.91810924 0.789905  ]\n [0.8450328  0.9330407  0.96116275 1.0000001  0.8477841  0.8672974\n  0.91477776 0.88670987 0.82031137 0.90453625]\n [0.6404959  0.7779784  0.81443566 0.8477841  0.9999999  0.7910577\n  0.7095412  0.7828142  0.80767745 0.75546753]\n [0.8278138  0.85464865 0.74739915 0.8672974  0.7910577  1.0000001\n  0.8673327  0.57096636 0.49747586 0.9121699 ]\n [0.9874873  0.84147525 0.9123443  0.91477776 0.7095412  0.8673327\n  1.         0.7860911  0.70952547 0.7872345 ]\n [0.7278442  0.743212   0.95153    0.88670987 0.7828142  0.57096636\n  0.7860911  1.         0.9766034  0.62887484]\n [0.6486307  0.70411843 0.91810924 0.82031137 0.80767745 0.49747586\n  0.70952547 0.9766034  0.9999999  0.5584969 ]\n [0.6938778  0.96197355 0.789905   0.90453625 0.75546753 0.9121699\n  0.7872345  0.62887484 0.5584969  1.0000001 ]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"Data transmitted to 5 receivers in 0.0039198399 seconds\nOriginal Data Packet (Sample):\n tensor([0.1759, 0.9934, 0.5185, 0.8647, 0.2988])\nProcessed Data by Receiver 1 (Sample):\n tensor([0.0000, 0.0000, 0.0000, 0.2217, 0.7803], grad_fn=<SliceBackward0>)\nTransmitting infrared signal...\nReceiving signal...\nSignal received.\nTransferred Amount: $317991.31\nNew Balance of Target Account: $317991.31\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"Transmitting infrared signal...\nReceiving signal...\nSignal received.\nTransferred Amount: $317991.31\nNew Balance of Target Account: $317991.31\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"Transmitting infrared signal...\nReceiving signal...\nSignal received.\nTransferred Amount: $75707.64\nNew Balance of Target Account: $75707.64\nRandomized Amplitude: $238.08\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"Data integrity compromised!\n","output_type":"stream"}]}]}