antitheft159
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- securewealth_transmittor.py +266 -0
Screenshot (940).png
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securewealth_transmittor.py
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# -*- coding: utf-8 -*-
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"""SecureWealth Transmittor
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/1bRloWQX62u7zc3Bzev_Lg_yNfqOYOZ7t
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"""
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import torch
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import torch.nn as nn
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# Define a simple neural network to generate random frequencies
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class FrequencyMaskingNet(nn.Module):
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def __init__(self, input_size=1, hidden_size=64, output_size=1):
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super(FrequencyMaskingNet, self).__init__()
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self.fc1 = nn.Linear(input_size, hidden_size)
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self.fc2 = nn.Linear(hidden_size, hidden_size)
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self.fc3 = nn.Linear(hidden_size, output_size)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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# Function to create random frequencies to mask IP data
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def generate_frequencies(ip, model, iterations=100):
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# Convert the IP address (dummy) into tensor format
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ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
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# Create a list to store frequency signals
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frequencies = []
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# Iterate and generate frequencies using the neural network
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for _ in range(iterations):
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# Generate a masked frequency
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frequency = model(ip_tensor)
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frequencies.append(frequency.item())
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return frequencies
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# Initialize the neural network
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model = FrequencyMaskingNet()
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# Example IP address to be masked (as a float for simplicity, convert if needed)
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ip_address = 192.168 # Example, could use a different encoding for real IPs
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# Generate pseudo-random frequencies to mask the IP
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masked_frequencies = generate_frequencies(ip_address, model)
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print(masked_frequencies)
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import torch
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import torch.nn as nn
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import matplotlib.pyplot as plt
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# Define the neural network for generating pseudo-random frequencies
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class FrequencyMaskingNet(nn.Module):
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def __init__(self, input_size=1, hidden_size=64, output_size=1):
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super(FrequencyMaskingNet, self).__init__()
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self.fc1 = nn.Linear(input_size, hidden_size)
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self.fc2 = nn.Linear(hidden_size, hidden_size)
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self.fc3 = nn.Linear(hidden_size, output_size)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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# Function to create random frequencies to mask IP data
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def generate_frequencies(ip, model, iterations=100):
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# Convert the IP address (dummy) into tensor format
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ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
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# Create a list to store frequency signals
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frequencies = []
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# Iterate and generate frequencies using the neural network
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for _ in range(iterations):
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# Generate a masked frequency
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frequency = model(ip_tensor)
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frequencies.append(frequency.item())
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return frequencies
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# Function to visualize frequencies as a waveform
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def plot_frequencies(frequencies):
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plt.figure(figsize=(10, 4))
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plt.plot(frequencies, color='b', label="Masked Frequencies")
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plt.title("Generated Frequency Waveform for IP Masking")
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plt.xlabel("Iterations")
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plt.ylabel("Frequency Amplitude")
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plt.grid(True)
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plt.legend()
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plt.show()
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# Initialize the neural network
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model = FrequencyMaskingNet()
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# Example IP address to be masked (as a float for simplicity)
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ip_address = 192.168 # Example, you can encode the IP better in practice
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# Generate pseudo-random frequencies to mask the IP
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masked_frequencies = generate_frequencies(ip_address, model)
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# Visualize the generated frequencies as a waveform
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plot_frequencies(masked_frequencies)
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import torch
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import torch.nn as nn
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import matplotlib.pyplot as plt
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import numpy as np
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# Define the neural network for generating pseudo-random frequencies
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class FrequencyMaskingNet(nn.Module):
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def __init__(self, input_size=1, hidden_size=64, output_size=1):
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super(FrequencyMaskingNet, self).__init__()
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self.fc1 = nn.Linear(input_size, hidden_size)
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self.fc2 = nn.Linear(hidden_size, hidden_size)
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self.fc3 = nn.Linear(hidden_size, output_size)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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# Function to create random frequencies to mask IP data
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def generate_frequencies(ip, model, iterations=100):
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ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
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frequencies = []
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for _ in range(iterations):
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frequency = model(ip_tensor)
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frequencies.append(frequency.item())
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return frequencies
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# Function to generate a wealth signal that transmits in the direction of energy (e.g., linear increase)
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def generate_wealth_signal(iterations=100):
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# Simulate wealth signal as a sine wave with increasing amplitude (simulating directional energy)
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time = np.linspace(0, 10, iterations)
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wealth_signal = np.sin(2 * np.pi * time) * np.linspace(0.1, 1, iterations) # Amplitude increases over time
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return wealth_signal
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# Function to visualize frequencies as a waveform
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def plot_frequencies(frequencies, wealth_signal):
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plt.figure(figsize=(10, 4))
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plt.plot(frequencies, color='b', label="Masked Frequencies")
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plt.plot(wealth_signal, color='g', linestyle='--', label="Wealth Signal")
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plt.title("Generated Frequency Waveform with Wealth Signal")
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plt.xlabel("Iterations")
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plt.ylabel("Amplitude")
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plt.grid(True)
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plt.legend()
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plt.show()
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# Initialize the neural network
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model = FrequencyMaskingNet()
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# Example IP address to be masked (as a float for simplicity)
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ip_address = 192.168
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# Generate pseudo-random frequencies to mask the IP
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masked_frequencies = generate_frequencies(ip_address, model)
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# Generate a wealth signal that grows in the direction of energy
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wealth_signal = generate_wealth_signal(len(masked_frequencies))
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# Visualize the generated frequencies and wealth signal
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plot_frequencies(masked_frequencies, wealth_signal)
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import torch
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import torch.nn as nn
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import matplotlib.pyplot as plt
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import numpy as np
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# Define the neural network for generating pseudo-random frequencies
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class FrequencyMaskingNet(nn.Module):
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def __init__(self, input_size=1, hidden_size=64, output_size=1):
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super(FrequencyMaskingNet, self).__init__()
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self.fc1 = nn.Linear(input_size, hidden_size)
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self.fc2 = nn.Linear(hidden_size, hidden_size)
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self.fc3 = nn.Linear(hidden_size, output_size)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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# Function to create random frequencies to mask IP data
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def generate_frequencies(ip, model, iterations=100):
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ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
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frequencies = []
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for _ in range(iterations):
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frequency = model(ip_tensor)
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frequencies.append(frequency.item())
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return frequencies
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# Function to generate a wealth signal that transmits in the direction of energy
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def generate_wealth_signal(iterations=100):
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time = np.linspace(0, 10, iterations)
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wealth_signal = np.sin(2 * np.pi * time) * np.linspace(0.1, 1, iterations) # Amplitude increases over time
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return wealth_signal
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# Function to generate a dense encryption waveform
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def generate_encryption_waveform(iterations=100):
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time = np.linspace(0, 10, iterations)
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# Dense waveform with higher frequency and random noise for encryption
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encryption_signal = np.sin(10 * np.pi * time) + 0.2 * np.random.randn(iterations)
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return encryption_signal
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# Function to visualize frequencies, wealth signal, and encryption
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def plot_frequencies(frequencies, wealth_signal, encryption_signal, target_reached_index):
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plt.figure(figsize=(10, 4))
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# Plot masked frequencies
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plt.plot(frequencies, color='b', label="Masked Frequencies")
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# Plot wealth signal
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plt.plot(wealth_signal, color='g', linestyle='--', label="Wealth Signal")
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# Add encryption signal at target point
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plt.plot(range(target_reached_index, target_reached_index + len(encryption_signal)),
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encryption_signal, color='r', linestyle='-', label="Encrypted Wealth Data", linewidth=2)
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plt.title("SecureWealth Transmittor")
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plt.xlabel("Iterations")
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plt.ylabel("Amplitude")
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plt.grid(True)
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plt.legend()
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plt.show()
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# Initialize the neural network
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model = FrequencyMaskingNet()
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# Example IP address to be masked (as a float for simplicity)
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ip_address = 192.168
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# Generate pseudo-random frequencies to mask the IP
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masked_frequencies = generate_frequencies(ip_address, model)
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# Generate a wealth signal that grows in the direction of energy
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wealth_signal = generate_wealth_signal(len(masked_frequencies))
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# Determine where the wealth signal reaches its target (e.g., at its peak)
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target_reached_index = np.argmax(wealth_signal)
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# Generate dense encryption waveform once the wealth signal reaches its target
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encryption_signal = generate_encryption_waveform(len(masked_frequencies) - target_reached_index)
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# Visualize the generated frequencies, wealth signal, and encryption signal
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plot_frequencies(masked_frequencies, wealth_signal, encryption_signal, target_reached_index)
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