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196bee3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | """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")
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