atmosecure_2_0.py

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

# Define wealth wave function
def wealth_wave(t, freq, phase_shift=0):
    return torch.sin(2 * np.pi * freq * t + phase_shift)

# Neural Network class representing brain signals directed to nerves
class WealthBrainModel(nn.Module):
    def __init__(self):
        super(WealthBrainModel, self).__init__()
        # Define layers of the network
        self.fc1 = nn.Linear(1, 64)  # Input layer (brain)
        self.fc2 = nn.Linear(64, 64)  # Hidden layer (signal propagation)
        self.fc3 = nn.Linear(64, 64)  # Storage layer (wealth data stored in nerves)
        self.fc4 = nn.Linear(64, 1)   # Pulse layer (output pulse representing stored data)

    def forward(self, x):
        # Wealth signal propagation through layers
        x = torch.relu(self.fc1(x))  # Brain layer
        x = torch.relu(self.fc2(x))  # Signal propagation layer
        stored_data = torch.relu(self.fc3(x))  # Store data in the nerves

        # Generate pulse signal based on stored data
        pulse_signal = torch.sigmoid(self.fc4(stored_data))
        return pulse_signal, stored_data

# Initialize the model
model = WealthBrainModel()

# Define optimizer and loss function
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# Time steps and frequencies for the wealth waves
time_steps = torch.linspace(0, 10, 1000)
freq_alpha = 10  # Alpha frequency (10 Hz)
freq_beta = 20   # Beta frequency (20 Hz)
freq_gamma = 40  # Gamma frequency (40 Hz)

# Simulate a continuous loop of wealth wave propagation
stored_data_all = []
for epoch in range(100):  # Simulate over 100 epochs (continuous propagation)
    model.train()

    # Generate wealth waves with phase shifts
    wealth_alpha = wealth_wave(time_steps, freq_alpha, phase_shift=epoch)
    wealth_beta = wealth_wave(time_steps, freq_beta, phase_shift=epoch + 0.5)
    wealth_gamma = wealth_wave(time_steps, freq_gamma, phase_shift=epoch + 1)

    # Combine signals (multi-layered wealth wave)
    wealth_input = wealth_alpha + wealth_beta + wealth_gamma
    wealth_input = wealth_input.unsqueeze(1)  # Reshape for model input

    # Forward pass through the network (brain -> nerves -> stored pulse)
    pulse_signal, stored_data = model(wealth_input)

    # Store the data for analysis
    stored_data_all.append(stored_data.detach().numpy())

    # Compute loss (if needed, could be set up to simulate nerve response)
    target = torch.zeros_like(pulse_signal)  # Dummy target
    loss = criterion(pulse_signal, target)

    # Backpropagation
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # Plot the pulse signal at every few steps to visualize pulse storage
    if epoch % 10 == 0:
        plt.plot(time_steps.numpy(), pulse_signal.detach().numpy(), label=f'Epoch {epoch}')

plt.title("Wealth Data Stored as Pulse in Nerves")
plt.xlabel("Time")
plt.ylabel("Pulse Signal")
plt.legend()
plt.show()

# Visualize stored wealth data over time
#plt.imshow(np.array(stored_data_all).squeeze().T, aspect='auto', cmap='viridis') # Transpose the array to get the correct orientation
# The above line caused the error. We need to average across the 1000 data points.
plt.imshow(np.mean(np.array(stored_data_all), axis=1).T, aspect='auto', cmap='viridis') # Average across the first axis
plt.colorbar(label="Stored Wealth Data in Nerves")
plt.xlabel("Epochs")
plt.ylabel("Nerve Data Points")
plt.title("Stored Wealth Data in Nerves Over Time")
plt.show()

# Visualize stored wealth data over time
#plt.imshow(np.array(stored_data_all).squeeze().T, aspect='auto', cmap='viridis') # Transpose the array to get the correct orientation
# The above line caused the error. We need to average across the 1000 data points.
plt.imshow(np.mean(np.array(stored_data_all), axis=1).T, aspect='auto', cmap='viridis') # Average across the first axis
plt.colorbar(label="Stored Wealth Data in Nerves")
plt.xlabel("Epochs")
plt.ylabel("Nerve Data Points")
plt.title("Stored Wealth Data in Nerves Over Time")
plt.show()

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

# Define wealth wave function
def wealth_wave(t, freq, phase_shift=0):
    return torch.sin(2 * np.pi * freq * t + phase_shift)

# Neural Network class representing brain signals directed to nerves
class WealthBrainModel(nn.Module):
    def __init__(self):
        super(WealthBrainModel, self).__init__()
        # Define layers of the network
        self.fc1 = nn.Linear(1, 64)  # Input layer (brain)
        self.fc2 = nn.Linear(64, 64)  # Hidden layer (signal propagation)
        self.fc3 = nn.Linear(64, 64)  # Storage layer (wealth data stored in nerves)
        self.fc4 = nn.Linear(64, 1)   # Pulse layer (output pulse representing stored data)

    def forward(self, x):
        # Wealth signal propagation through layers
        x = torch.relu(self.fc1(x))  # Brain layer
        x = torch.relu(self.fc2(x))  # Signal propagation layer
        stored_data = torch.relu(self.fc3(x))  # Store data in the nerves

        # Generate pulse signal based on stored data
        pulse_signal = torch.sigmoid(self.fc4(stored_data))
        return pulse_signal, stored_data

# Initialize the model
model = WealthBrainModel()

# Define optimizer and loss function
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# Time steps and frequencies for the wealth waves
time_steps = torch.linspace(0, 10, 1000)
freq_alpha = 10  # Alpha frequency (10 Hz)
freq_beta = 20   # Beta frequency (20 Hz)
freq_gamma = 40  # Gamma frequency (40 Hz)

# Simulate a continuous loop of wealth wave propagation
stored_data_all = []
for epoch in range(100):  # Simulate over 100 epochs (continuous propagation)
    model.train()

    # Generate wealth waves with phase shifts
    wealth_alpha = wealth_wave(time_steps, freq_alpha, phase_shift=epoch)
    wealth_beta = wealth_wave(time_steps, freq_beta, phase_shift=epoch + 0.5)
    wealth_gamma = wealth_wave(time_steps, freq_gamma, phase_shift=epoch + 1)

    # Combine signals (multi-layered wealth wave)
    wealth_input = wealth_alpha + wealth_beta + wealth_gamma
    wealth_input = wealth_input.unsqueeze(1)  # Reshape for model input

    # Forward pass through the network (brain -> nerves -> stored pulse)
    pulse_signal, stored_data = model(wealth_input)

    # Store the data for analysis
    stored_data_all.append(stored_data.detach().numpy())

    # Compute loss (if needed, could be set up to simulate nerve response)
    target = torch.zeros_like(pulse_signal)  # Dummy target
    loss = criterion(pulse_signal, target)

    # Backpropagation
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # Plot the pulse signal at every few steps to visualize pulse storage
    if epoch % 10 == 0:
        plt.plot(time_steps.numpy(), pulse_signal.detach().numpy(), label=f'Epoch {epoch}')

plt.title("Wealth Data Stored as Pulse in Nerves")
plt.xlabel("Time")
plt.ylabel("Pulse Signal")
plt.legend()
plt.show()

# Visualize stored wealth data over time
#plt.imshow(np.array(stored_data_all).squeeze().T, aspect='auto', cmap='viridis') # Transpose the array to get the correct orientation
# The above line caused the error. We need to average across the 1000 data points.
plt.imshow(np.mean(np.array(stored_data_all), axis=1).T, aspect='auto', cmap='viridis') # Average across the first axis
plt.colorbar(label="Stored Wealth Data in Nerves")
plt.xlabel("Epochs")
plt.ylabel("Nerve Data Points")
plt.title("Stored Wealth Data in Nerves Over Time")
plt.show()

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

# Define wealth wave function
def wealth_wave(t, freq, phase_shift=0):
    return torch.sin(2 * np.pi * freq * t + phase_shift)

# Neural Network class representing brain signals directed to nerves with VPN protection
class WealthBrainModel(nn.Module):
    def __init__(self):
        super(WealthBrainModel, self).__init__()
        # Define layers of the network
        self.fc1 = nn.Linear(1, 64)  # Input layer (brain)
        self.fc2 = nn.Linear(64, 64)  # Hidden layer (signal propagation)
        self.fc3 = nn.Linear(64, 64)  # Storage layer (wealth data stored in nerves)
        self.fc_vpn = nn.Linear(64, 64)  # VPN protection layer
        self.fc4 = nn.Linear(64, 1)   # Pulse layer (output pulse representing stored data)

    def forward(self, x):
        # Wealth signal propagation through layers
        x = torch.relu(self.fc1(x))  # Brain layer
        x = torch.relu(self.fc2(x))  # Signal propagation layer
        stored_data = torch.relu(self.fc3(x))  # Store data in the nerves

        # VPN protection layer: Protect the stored wealth data
        protected_data = torch.relu(self.fc_vpn(stored_data))  # Data is encrypted and protected here

        # Generate pulse signal based on protected data
        pulse_signal = torch.sigmoid(self.fc4(protected_data))
        return pulse_signal, protected_data

# Initialize the model
model = WealthBrainModel()

# Define optimizer and loss function
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# Time steps and frequencies for the wealth waves
time_steps = torch.linspace(0, 10, 1000)
freq_alpha = 10  # Alpha frequency (10 Hz)
freq_beta = 20   # Beta frequency (20 Hz)
freq_gamma = 40  # Gamma frequency (40 Hz)

# Simulate a continuous loop of wealth wave propagation
stored_data_all = []
for epoch in range(100):  # Simulate over 100 epochs (continuous propagation)
    model.train()

    # Generate wealth waves with phase shifts
    wealth_alpha = wealth_wave(time_steps, freq_alpha, phase_shift=epoch)
    wealth_beta = wealth_wave(time_steps, freq_beta, phase_shift=epoch + 0.5)
    wealth_gamma = wealth_wave(time_steps, freq_gamma, phase_shift=epoch + 1)

    # Combine signals (multi-layered wealth wave)
    wealth_input = wealth_alpha + wealth_beta + wealth_gamma
    wealth_input = wealth_input.unsqueeze(1)  # Reshape for model input

    # Forward pass through the network (brain -> nerves -> VPN -> stored pulse)
    pulse_signal, protected_data = model(wealth_input)

    # Store the protected data for analysis
    stored_data_all.append(protected_data.detach().numpy())

    # Simulate intruders (random noise) trying to tamper with the data
    intruder_noise = torch.randn_like(pulse_signal) * 0.1  # Small noise signal
    corrupted_pulse = pulse_signal + intruder_noise  # Intruder tries to corrupt the pulse

    # Compute loss based on how well the VPN layer protects from noise
    loss = criterion(corrupted_pulse, pulse_signal)  # Aim to protect pulse from noise

    # Backpropagation
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # Plot the pulse signal at every few steps to visualize protection
    if epoch % 10 == 0:
        plt.plot(time_steps.numpy(), pulse_signal.detach().numpy(), label=f'Epoch {epoch}')

plt.title("Wealth Data Protected by VPN Layer")
plt.xlabel("Time")
plt.ylabel("Pulse Signal")
plt.legend()
plt.show()

# Visualize protected wealth data over time
plt.imshow(np.mean(np.array(stored_data_all), axis=0), aspect='auto', cmap='viridis') # Average across the first axis to get a 2D array
plt.colorbar(label="Protected Wealth Data in Nerves")
plt.xlabel("Epochs")
plt.ylabel("Nerve Data Points")
plt.title("Protected Wealth Data in Nerves Over Time")
plt.show()

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

# Define wealth wave function
def wealth_wave(t, freq, phase_shift=0):
    return torch.sin(2 * np.pi * freq * t + phase_shift)

# Neural Network class representing brain signals directed to nerves with VPN protection
class WealthBrainModel(nn.Module):
    def __init__(self):
        super(WealthBrainModel, self).__init__()
        # Define layers of the network
        self.fc1 = nn.Linear(1, 64)  # Input layer (brain)
        self.fc2 = nn.Linear(64, 64)  # Hidden layer (signal propagation)
        self.fc3 = nn.Linear(64, 64)  # Storage layer (wealth data stored in nerves)
        self.fc_vpn = nn.Linear(64, 64)  # VPN protection layer
        self.fc4 = nn.Linear(64, 1)   # Pulse layer (output pulse representing stored data)

    def forward(self, x):
        # Wealth signal propagation through layers
        x = torch.relu(self.fc1(x))  # Brain layer
        x = torch.relu(self.fc2(x))  # Signal propagation layer
        stored_data = torch.relu(self.fc3(x))  # Store data in the nerves

        # VPN protection layer: Protect the stored wealth data
        protected_data = torch.relu(self.fc_vpn(stored_data))  # Data is encrypted and protected here

        # Generate pulse signal based on protected data
        pulse_signal = torch.sigmoid(self.fc4(protected_data))
        return pulse_signal, protected_data

# Initialize the model
model = WealthBrainModel()

# Define optimizer and loss function
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# Time steps and frequencies for the wealth waves
time_steps = torch.linspace(0, 10, 1000)
freq_alpha = 10  # Alpha frequency (10 Hz)
freq_beta = 20   # Beta frequency (20 Hz)
freq_gamma = 40  # Gamma frequency (40 Hz)

# Simulate a continuous loop of wealth wave propagation
stored_data_all = []
for epoch in range(100):  # Simulate over 100 epochs (continuous propagation)
    model.train()

    # Generate wealth waves with phase shifts
    wealth_alpha = wealth_wave(time_steps, freq_alpha, phase_shift=epoch)
    wealth_beta = wealth_wave(time_steps, freq_beta, phase_shift=epoch + 0.5)
    wealth_gamma = wealth_wave(time_steps, freq_gamma, phase_shift=epoch + 1)

    # Combine signals (multi-layered wealth wave)
    wealth_input = wealth_alpha + wealth_beta + wealth_gamma
    wealth_input = wealth_input.unsqueeze(1)  # Reshape for model input

    # Forward pass through the network (brain -> nerves -> VPN -> stored pulse)
    pulse_signal, protected_data = model(wealth_input)

    # Store the protected data for analysis
    stored_data_all.append(protected_data.detach().numpy())

    # Simulate intruders (random noise) trying to tamper with the data
    intruder_noise = torch.randn_like(pulse_signal) * 0.1  # Small noise signal
    corrupted_pulse = pulse_signal + intruder_noise  # Intruder tries to corrupt the pulse

    # Compute loss based on how well the VPN layer protects from noise
    loss = criterion(corrupted_pulse, pulse_signal)  # Aim to protect pulse from noise

    # Backpropagation
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # Plot the pulse signal at every few steps to visualize protection
    if epoch % 10 == 0:
        plt.plot(time_steps.numpy(), pulse_signal.detach().numpy(), label=f'Epoch {epoch}')

plt.title("Wealth Data Protected by VPN Layer")
plt.xlabel("Time")
plt.ylabel("Pulse Signal")
plt.legend()
plt.show()

# Visualize protected wealth data over time
plt.imshow(np.mean(np.array(stored_data_all), axis=0), aspect='auto', cmap='viridis') # Average across the first axis to get a 2D array
plt.colorbar(label="Protected Wealth Data in Nerves")
plt.xlabel("Epochs")
plt.ylabel("Nerve Data Points")
plt.title("Protected Wealth Data in Nerves Over Time")
plt.show()