Upload fortunepulseypt_wealthstreamline.py

fortunepulseypt_wealthstreamline.py

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+# -*- coding: utf-8 -*-
+"""FortunePulseYPT//wealthstreamline
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Iwap73e65BQ2-bWLdvVV9ZWgU0Q1L4pE
+"""
+
+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()