Delete securewealth_transmittor.py

securewealth_transmittor.py

@@ -1,257 +0,0 @@
-import torch
-import torch.nn as nn
-
-# Define a simple neural network to generate random frequencies
-class FrequencyMaskingNet(nn.Module):
-    def __init__(self, input_size=1, hidden_size=64, output_size=1):
-        super(FrequencyMaskingNet, self).__init__()
-
-        self.fc1 = nn.Linear(input_size, hidden_size)
-        self.fc2 = nn.Linear(hidden_size, hidden_size)
-        self.fc3 = nn.Linear(hidden_size, output_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-
-# Function to create random frequencies to mask IP data
-def generate_frequencies(ip, model, iterations=100):
-    # Convert the IP address (dummy) into tensor format
-    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
-
-    # Create a list to store frequency signals
-    frequencies = []
-
-    # Iterate and generate frequencies using the neural network
-    for _ in range(iterations):
-        # Generate a masked frequency
-        frequency = model(ip_tensor)
-        frequencies.append(frequency.item())
-
-    return frequencies
-
-# Initialize the neural network
-model = FrequencyMaskingNet()
-
-# Example IP address to be masked (as a float for simplicity, convert if needed)
-ip_address = 192.168  # Example, could use a different encoding for real IPs
-
-# Generate pseudo-random frequencies to mask the IP
-masked_frequencies = generate_frequencies(ip_address, model)
-
-print(masked_frequencies)
-
-import torch
-import torch.nn as nn
-import matplotlib.pyplot as plt
-
-# Define the neural network for generating pseudo-random frequencies
-class FrequencyMaskingNet(nn.Module):
-    def __init__(self, input_size=1, hidden_size=64, output_size=1):
-        super(FrequencyMaskingNet, self).__init__()
-
-        self.fc1 = nn.Linear(input_size, hidden_size)
-        self.fc2 = nn.Linear(hidden_size, hidden_size)
-        self.fc3 = nn.Linear(hidden_size, output_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-
-# Function to create random frequencies to mask IP data
-def generate_frequencies(ip, model, iterations=100):
-    # Convert the IP address (dummy) into tensor format
-    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
-
-    # Create a list to store frequency signals
-    frequencies = []
-
-    # Iterate and generate frequencies using the neural network
-    for _ in range(iterations):
-        # Generate a masked frequency
-        frequency = model(ip_tensor)
-        frequencies.append(frequency.item())
-
-    return frequencies
-
-# Function to visualize frequencies as a waveform
-def plot_frequencies(frequencies):
-    plt.figure(figsize=(10, 4))
-    plt.plot(frequencies, color='b', label="Masked Frequencies")
-    plt.title("Generated Frequency Waveform for IP Masking")
-    plt.xlabel("Iterations")
-    plt.ylabel("Frequency Amplitude")
-    plt.grid(True)
-    plt.legend()
-    plt.show()
-
-# Initialize the neural network
-model = FrequencyMaskingNet()
-
-# Example IP address to be masked (as a float for simplicity)
-ip_address = 192.168  # Example, you can encode the IP better in practice
-
-# Generate pseudo-random frequencies to mask the IP
-masked_frequencies = generate_frequencies(ip_address, model)
-
-# Visualize the generated frequencies as a waveform
-plot_frequencies(masked_frequencies)
-
-import torch
-import torch.nn as nn
-import matplotlib.pyplot as plt
-import numpy as np
-
-# Define the neural network for generating pseudo-random frequencies
-class FrequencyMaskingNet(nn.Module):
-    def __init__(self, input_size=1, hidden_size=64, output_size=1):
-        super(FrequencyMaskingNet, self).__init__()
-
-        self.fc1 = nn.Linear(input_size, hidden_size)
-        self.fc2 = nn.Linear(hidden_size, hidden_size)
-        self.fc3 = nn.Linear(hidden_size, output_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-
-# Function to create random frequencies to mask IP data
-def generate_frequencies(ip, model, iterations=100):
-    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
-    frequencies = []
-
-    for _ in range(iterations):
-        frequency = model(ip_tensor)
-        frequencies.append(frequency.item())
-
-    return frequencies
-
-# Function to generate a wealth signal that transmits in the direction of energy (e.g., linear increase)
-def generate_wealth_signal(iterations=100):
-    # Simulate wealth signal as a sine wave with increasing amplitude (simulating directional energy)
-    time = np.linspace(0, 10, iterations)
-    wealth_signal = np.sin(2 * np.pi * time) * np.linspace(0.1, 1, iterations)  # Amplitude increases over time
-    return wealth_signal
-
-# Function to visualize frequencies as a waveform
-def plot_frequencies(frequencies, wealth_signal):
-    plt.figure(figsize=(10, 4))
-    plt.plot(frequencies, color='b', label="Masked Frequencies")
-    plt.plot(wealth_signal, color='g', linestyle='--', label="Wealth Signal")
-    plt.title("Generated Frequency Waveform with Wealth Signal")
-    plt.xlabel("Iterations")
-    plt.ylabel("Amplitude")
-    plt.grid(True)
-    plt.legend()
-    plt.show()
-
-# Initialize the neural network
-model = FrequencyMaskingNet()
-
-# Example IP address to be masked (as a float for simplicity)
-ip_address = 192.168
-
-# Generate pseudo-random frequencies to mask the IP
-masked_frequencies = generate_frequencies(ip_address, model)
-
-# Generate a wealth signal that grows in the direction of energy
-wealth_signal = generate_wealth_signal(len(masked_frequencies))
-
-# Visualize the generated frequencies and wealth signal
-plot_frequencies(masked_frequencies, wealth_signal)
-
-import torch
-import torch.nn as nn
-import matplotlib.pyplot as plt
-import numpy as np
-
-# Define the neural network for generating pseudo-random frequencies
-class FrequencyMaskingNet(nn.Module):
-    def __init__(self, input_size=1, hidden_size=64, output_size=1):
-        super(FrequencyMaskingNet, self).__init__()
-
-        self.fc1 = nn.Linear(input_size, hidden_size)
-        self.fc2 = nn.Linear(hidden_size, hidden_size)
-        self.fc3 = nn.Linear(hidden_size, output_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-
-# Function to create random frequencies to mask IP data
-def generate_frequencies(ip, model, iterations=100):
-    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
-    frequencies = []
-
-    for _ in range(iterations):
-        frequency = model(ip_tensor)
-        frequencies.append(frequency.item())
-
-    return frequencies
-
-# Function to generate a wealth signal that transmits in the direction of energy
-def generate_wealth_signal(iterations=100):
-    time = np.linspace(0, 10, iterations)
-    wealth_signal = np.sin(2 * np.pi * time) * np.linspace(0.1, 1, iterations)  # Amplitude increases over time
-    return wealth_signal
-
-# Function to generate a dense encryption waveform
-def generate_encryption_waveform(iterations=100):
-    time = np.linspace(0, 10, iterations)
-    # Dense waveform with higher frequency and random noise for encryption
-    encryption_signal = np.sin(10 * np.pi * time) + 0.2 * np.random.randn(iterations)
-    return encryption_signal
-
-# Function to visualize frequencies, wealth signal, and encryption
-def plot_frequencies(frequencies, wealth_signal, encryption_signal, target_reached_index):
-    plt.figure(figsize=(10, 4))
-
-    # Plot masked frequencies
-    plt.plot(frequencies, color='b', label="Masked Frequencies")
-
-    # Plot wealth signal
-    plt.plot(wealth_signal, color='g', linestyle='--', label="Wealth Signal")
-
-    # Add encryption signal at target point
-    plt.plot(range(target_reached_index, target_reached_index + len(encryption_signal)),
-             encryption_signal, color='r', linestyle='-', label="Encrypted Wealth Data", linewidth=2)
-
-    plt.title("SecureWealth Transmittor")
-    plt.xlabel("Iterations")
-    plt.ylabel("Amplitude")
-    plt.grid(True)
-    plt.legend()
-    plt.show()
-
-# Initialize the neural network
-model = FrequencyMaskingNet()
-
-# Example IP address to be masked (as a float for simplicity)
-ip_address = 192.168
-
-# Generate pseudo-random frequencies to mask the IP
-masked_frequencies = generate_frequencies(ip_address, model)
-
-# Generate a wealth signal that grows in the direction of energy
-wealth_signal = generate_wealth_signal(len(masked_frequencies))
-
-# Determine where the wealth signal reaches its target (e.g., at its peak)
-target_reached_index = np.argmax(wealth_signal)
-
-# Generate dense encryption waveform once the wealth signal reaches its target
-encryption_signal = generate_encryption_waveform(len(masked_frequencies) - target_reached_index)
-
-# Visualize the generated frequencies, wealth signal, and encryption signal
-plot_frequencies(masked_frequencies, wealth_signal, encryption_signal, target_reached_index)