| import numpy as np | |
| from scipy.linalg import eigh | |
| def soft_partition(eigenvectors, eigenvalues, num_partitions): | |
| # Apply a weighted sum of eigenvectors in an eigenvalue-dependent manner | |
| partitions = np.linspace(np.min(eigenvalues), np.max(eigenvalues), num_partitions) | |
| encodings = np.zeros_like(eigenvectors) | |
| for i, val in enumerate(partitions[:-1]): | |
| start, end = partitions[i], partitions[i+1] | |
| mask = (eigenvalues >= start) & (eigenvalues < end) |
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