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import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from sklearn.preprocessing import MinMaxScaler
class Autoencoder(nn.Module):
def __init__(self, input_size):
super(Autoencoder, self).__init__()
self.encoder = nn.Sequential(
nn.Linear(input_size, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 32)
)
self.decoder = nn.Sequential(
nn.Linear(32, 64),
nn.ReLU(),
nn.Linear(64, 128),
nn.ReLU(),
nn.Linear(128, 256),
nn.ReLU(),
nn.Linear(256, input_size)
)
def forward(self, x):
batch_size, seq_len, _ = x.size()
x = x.view(batch_size * seq_len, -1)
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded.view(batch_size, seq_len, -1)
def anomaly_detection(X_embeddings, X_posture, epochs=200, patience=5):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Normalize posture
scaler_posture = MinMaxScaler()
X_posture_scaled = scaler_posture.fit_transform(X_posture.reshape(-1, 1))
# Process facial embeddings
X_embeddings = torch.FloatTensor(X_embeddings).to(device)
if X_embeddings.dim() == 2:
X_embeddings = X_embeddings.unsqueeze(0)
# Process posture
X_posture_scaled = torch.FloatTensor(X_posture_scaled).to(device)
if X_posture_scaled.dim() == 2:
X_posture_scaled = X_posture_scaled.unsqueeze(0)
model_embeddings = Autoencoder(input_size=X_embeddings.shape[2]).to(device)
model_posture = Autoencoder(input_size=X_posture_scaled.shape[2]).to(device)
criterion = nn.MSELoss()
optimizer_embeddings = optim.Adam(model_embeddings.parameters())
optimizer_posture = optim.Adam(model_posture.parameters())
# Train models
for epoch in range(epochs):
for model, optimizer, X in [(model_embeddings, optimizer_embeddings, X_embeddings),
(model_posture, optimizer_posture, X_posture_scaled)]:
model.train()
optimizer.zero_grad()
output = model(X)
loss = criterion(output, X)
loss.backward()
optimizer.step()
# Compute MSE for embeddings and posture
model_embeddings.eval()
model_posture.eval()
with torch.no_grad():
reconstructed_embeddings = model_embeddings(X_embeddings).cpu().numpy()
reconstructed_posture = model_posture(X_posture_scaled).cpu().numpy()
mse_embeddings = np.mean(np.power(X_embeddings.cpu().numpy() - reconstructed_embeddings, 2), axis=2).squeeze()
mse_posture = np.mean(np.power(X_posture_scaled.cpu().numpy() - reconstructed_posture, 2), axis=2).squeeze()
return mse_embeddings, mse_posture
def determine_anomalies(mse_values, threshold):
mean = np.mean(mse_values)
std = np.std(mse_values)
anomalies = mse_values > (mean + threshold * std)
return anomalies |