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# -*- coding: utf-8 -*-
"""MagNet 2.0
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1n4ADxn-u0nAkYm6mKMzzhiH1vl97qImr
"""
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
wealth_distribution = torch.randn(32, 24, 1)
target_direction = torch.randn(32, 24, 1)
class WealthTransferModelWithVPN(nn.Module):
def __init__(self, input_size, hidden_size, lstm_hidden_size, output_size, vpn_size):
super(WealthTransferModelWithVPN, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.lstm = nn.LSTM(hidden_size, lstm_hidden_size, batch_first=True)
self.fc2 = nn.Linear(lstm_hidden_size, output_size)
self.vpn_layer = nn.Linear(output_size, vpn_size)
self.decrypt_layer = nn.Linear(vpn_size, output_size)
def forward(self, x, target):
x = torch.cat((x, target), dim=1)
x = self.relu(self.fc1(x))
x, _ = self.lstm(x)
x = self.fc2(x)
encrypted_output = torch.sigmoid(self.vpn_layer(x))
decrypted_output = self.decrypt_layer(encrypted_output)
return decrypted_output
input_size = wealth_distribution[-1] + target_direction.shape[-1]
hidden_size = 64
lstm_hidden_size = 32
output_size = wealth_distribution.shape[-1]
vpn_size = 128
model = WealthTransferWithVPN(input_size, hidden_sizse, lstm_hidden_size, vpn_size)
with torch.no_grad():
output_signal = model(wealth_distribution, target_direction)
wealth_waveform = output_signal[0].squeeze().numpy()
hours = list(range(24))
plt.figure(figsize=(10, 5))
plt.plot(hours, wealth_waveform, label='Wealth Transfer Signal over 24 hours', marker='o')
plt.title('Wealth Transfer Signal in 24-Hour Intervals')
plt.xlabel('Hour of the Day')
plt.ylabel('Wealth Signal Intensity')
plt.xticks(hours)
plt.grid(True)
plt.legend()
plt.show()
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