# -*- coding: utf-8 -*-
"""SecureWealth Transmittor

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1bRloWQX62u7zc3Bzev_Lg_yNfqOYOZ7t
"""

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)