import torch import torch.nn as nn import torch.optim as optim import numpy as np class WealthFrequencyPredictor(nn.Module): def __init__(self): super(WealthFrequencyPredictor, self).__init__() self.fc1 = nn.Linear(10, 64) # Example input features (10) self.fc2 = nn.Linear(64, 64) # Hidden layer self.fc3 = nn.Linear(64, 1) # Output: 1 frequency (wealth signal) def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = self.fc3(x) return x # Initialize the model, loss function, and optimizer model = WealthFrequencyPredictor() criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # Example training data (features and target frequencies) # Simulating brainwave input data (100 samples with 10 features each) inputs = torch.randn(100, 10) # Simulating target brainwave frequencies between 8 and 100 Hz targets = torch.rand(100, 1) * (100 - 8) + 8 # Frequencies between 8 and 100 Hz dataset = torch.utils.data.TensorDataset(inputs, targets) data_loader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True) # Train the model def train_model(model, data_loader, epochs=100): for epoch in range(epochs): for batch_inputs, batch_targets in data_loader: optimizer.zero_grad() outputs = model(batch_inputs) loss = criterion(outputs, batch_targets) loss.backward() optimizer.step() if (epoch + 1) % 10 == 0: print(f'Epoch {epoch + 1}/{epochs}, Loss: {loss.item()}') train_model(model, data_loader) import torch import torch.nn as nn import torch.optim as optim import numpy as np import matplotlib.pyplot as plt # 1. Define the Neural Network Model class WealthFrequencyPredictor(nn.Module): def __init__(self): super(WealthFrequencyPredictor, self).__init__() self.fc1 = nn.Linear(10, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, 1) def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = self.fc3(x) return x # Initialize the model model = WealthFrequencyPredictor() criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 2. Training Data inputs = torch.randn(100, 10) # Random features targets = torch.rand(100, 1) * (100 - 8) + 8 # Frequencies between 8 and 100 Hz dataset = torch.utils.data.TensorDataset(inputs, targets) data_loader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True) # Train the model def train_model(model, data_loader, epochs=100): for epoch in range(epochs): for batch_inputs, batch_targets in data_loader: optimizer.zero_grad() outputs = model(batch_inputs) loss = criterion(outputs, batch_targets) loss.backward() optimizer.step() if (epoch + 1) % 10 == 0: print(f'Epoch {epoch + 1}/{epochs}, Loss: {loss.item()}') train_model(model, data_loader) # 3. Generate and Visualize the Predicted Wealth Frequency # Generate a sine wave based on the predicted frequency def generate_sine_wave(frequency, duration, amplitude=0.5, sample_rate=44100): t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) wave = amplitude * np.sin(2 * np.pi * frequency * t) return t, wave # Predict frequency and visualize it def predict_and_visualize_wave(model, input_data): model.eval() with torch.no_grad(): input_tensor = torch.tensor(input_data, dtype=torch.float32) predicted_frequency = model(input_tensor).item() print(f'Predicted Wealth Frequency: {predicted_frequency} Hz') # Generate sine wave duration = 5 # 5 seconds t, wave = generate_sine_wave(predicted_frequency, duration) # Plot the sine wave plt.figure(figsize=(10, 6)) plt.plot(t, wave) plt.title(f'Sine Wave for Predicted Wealth Frequency: {predicted_frequency} Hz') plt.xlabel('Time [s]') plt.ylabel('Amplitude') plt.grid(True) plt.show() # Example usage: Visualize the wealth frequency input_data = np.random.rand(10) # Example input data predict_and_visualize_wave(model, input_data) import numpy as np import torch # Generate a sine wave (the data to be encrypted) def generate_sine_wave(frequency, duration=5, amplitude=0.5, sample_rate=44100): t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) wave = amplitude * np.sin(2 * np.pi * frequency * t) return wave # Example: Generate a frequency predicted_frequency = 40.0 # Example predicted frequency wave_data = generate_sine_wave(predicted_frequency) # XOR encryption function def xor_encrypt_decrypt(data, key): return bytearray(a ^ key for a in data) # Convert wave data to bytes for encryption wave_data_bytes = bytearray(np.float32(wave_data).tobytes()) # Choose a key for encryption (VPN-like key) encryption_key = 55 # Example key # Encrypt the wave data encrypted_wave = xor_encrypt_decrypt(wave_data_bytes, encryption_key) print(f'Encrypted Wave (first 100 bytes): {encrypted_wave[:100]}') # Decrypt the wave data using the same XOR key decrypted_wave_bytes = xor_encrypt_decrypt(encrypted_wave, encryption_key) # Convert bytes back to float array (the original wave data) decrypted_wave_data = np.frombuffer(decrypted_wave_bytes, dtype=np.float32) print(f'Decrypted Wave (first 10 samples): {decrypted_wave_data[:10]}') import numpy as np import torch # Generate a sine wave (the data to be encrypted) def generate_sine_wave(frequency, duration=5, amplitude=0.5, sample_rate=44100): t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) wave = amplitude * np.sin(2 * np.pi * frequency * t) return wave # XOR encryption function def xor_encrypt_decrypt(data, key): return bytearray(a ^ key for a in data) # Generate a frequency predicted_frequency = 40.0 # Example predicted frequency wave_data = generate_sine_wave(predicted_frequency) # Convert wave data to bytes for encryption wave_data_bytes = bytearray(np.float32(wave_data).tobytes()) # Encrypt the wave data encryption_key = 55 # Example key for XOR encryption encrypted_wave = xor_encrypt_decrypt(wave_data_bytes, encryption_key) print(f'Encrypted Wave (first 100 bytes): {encrypted_wave[:100]}') # Decrypt the wave data decrypted_wave_bytes = xor_encrypt_decrypt(encrypted_wave, encryption_key) decrypted_wave_data = np.frombuffer(decrypted_wave_bytes, dtype=np.float32) # Print the decrypted wave data print(f'Decrypted Wave (first 10 samples): {decrypted_wave_data[:10]}') # Optionally: You can now store `encrypted_wave` securely in a file or database. import numpy as np import matplotlib.pyplot as plt # 1. Generate a sine wave (the data to be encrypted) def generate_sine_wave(frequency, duration=5, amplitude=0.5, sample_rate=44100): t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) wave = amplitude * np.sin(2 * np.pi * frequency * t) return t, wave # 2. XOR encryption function def xor_encrypt_decrypt(data, key): return bytearray(a ^ key for a in data) # 3. Generate a frequency wave predicted_frequency = 40.0 # Example predicted frequency (wealth signal) t, wave_data = generate_sine_wave(predicted_frequency) # Convert wave data to bytes for encryption wave_data_bytes = bytearray(np.float32(wave_data).tobytes()) # 4. Encrypt the wave data using XOR encryption_key = 55 # Example encryption key encrypted_wave = xor_encrypt_decrypt(wave_data_bytes, encryption_key) # 5. Decrypt the wave data decrypted_wave_bytes = xor_encrypt_decrypt(encrypted_wave, encryption_key) # Convert bytes back to float array (the original wave data) decrypted_wave_data = np.frombuffer(decrypted_wave_bytes, dtype=np.float32) # 6. Plotting the Original and Decrypted Waves plt.figure(figsize=(12, 6)) # Original Wave plt.subplot(2, 1, 1) plt.plot(t[:1000], wave_data[:1000], label='Original Wave') plt.title('Original Bytes') plt.xlabel('Time [s]') plt.ylabel('Amplitude') plt.grid(True) # Decrypted Wave plt.subplot(2, 1, 2) plt.plot(t[:1000], decrypted_wave_data[:1000], label='Decrypted Wave', color='orange') plt.title('Decrypted Bytes') plt.xlabel('Time [s]') plt.ylabel('Amplitude') plt.grid(True) plt.tight_layout() plt.show()