| """EEGFaceSem - Minimal Usage Examples""" | |
| import EEGFaceSem | |
| import numpy as np | |
| from sklearn.discriminant_analysis import LinearDiscriminantAnalysis | |
| from sklearn.metrics import accuracy_score | |
| # ============================================================================= | |
| # Example 1: Quick test with single subject (downloads ~300MB) | |
| # ============================================================================= | |
| X, Y, ids = EEGFaceSem.load_data(task='female', subjects=[1]) | |
| X_flat = X.reshape(X.shape[0], -1) | |
| (X_train, Y_train), (X_test, Y_test) = EEGFaceSem.split_random(X_flat, Y) | |
| model = LinearDiscriminantAnalysis() | |
| model.fit(X_train, Y_train) | |
| acc = accuracy_score(Y_test, model.predict(X_test)) | |
| print(f"Accuracy: {acc:.2f}") | |
| # ============================================================================= | |
| # Example 2: Full dataset with all subjects (downloads ~9GB) | |
| # ============================================================================= | |
| # X, Y, ids = EEGFaceSem.load_data(task='female') # All 30 subjects | |
| # | |
| # # Leave-one-subject-out cross-validation | |
| # for test_subj in range(30): | |
| # (X_train, Y_train), (X_test, Y_test) = EEGFaceSem.split_by_subject(X, Y, ids, test_subj) | |
| # # ... train and evaluate | |
| # ============================================================================= | |
| # Example 3: Run full benchmark | |
| # ============================================================================= | |
| # EEGFaceSem.benchmark(model='LDA', task_id=0) | |
| # ============================================================================= | |
| # Example 4: Generate face images from latent vectors | |
| # ============================================================================= | |
| # EEGFaceSem.download_models() # Download PGAN model (~277MB) | |
| # images = EEGFaceSem.generate(np.random.randn(1, 512)) | |
| # images[0].save("generated_face.png") | |