Upload atmosecure_2_0.py

atmosecure_2_0.py

@@ -0,0 +1,396 @@
+# -*- coding: utf-8 -*-
+"""Atmosecure 2.0
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1x0I-yURUQrFwXE8cUwmTB4gLXN5FK2G_
+"""
+
+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()