spectralbrain.sgw_descriptor#
- spectralbrain.sgw_descriptor(decomp, scales=None, *, n_scales=5, kernel=<function mexican_hat_kernel>, aggregate='energy')[source]#
Spectral Graph Wavelet descriptor from precomputed eigenpairs.
Faster than Chebyshev-based SGW when the eigenpairs are already available (from HKS/WKS computation).
\[\psi_{t}(x) = \sum_{i=0}^{k} g(t \cdot \lambda_i)\, \varphi_i(x)\]The per-vertex wavelet energy at scale t is:
\[W(x, t) = \psi_t^2(x) = \left( \sum_i g(t \lambda_i) \varphi_i(x) \right)^2\]- Parameters:
decomp (SpectralDecomposition)
scales (ndarray, shape (S,), optional) – Wavelet scales.
None= auto log-spaced.n_scales (int) – Number of auto scales.
kernel (callable) – Wavelet kernel g(x).
aggregate (str) –
"energy"— ψ²(x, t), wavelet energy per vertex per scale."raw"— ψ(x, t), raw wavelet coefficients (signed)."abs_mean"— |ψ(x, t)|, absolute coefficients.
- Returns:
ndarray, shape (N, S) – Multi-scale wavelet descriptor.
- Return type: