fortunepulseypt_wealthstreamline.py

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()