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docs(third-door): clarify sur_norm is the definite Euclidean threshold magnitude (#20 review)
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Shay 2026-07-12 15:20:28 -07:00
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7
core/physics/surprise.py
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@ -135,8 +135,11 @@ def surprise_residual(
Both branches solve the metric normal equations ``G c = r`` (``G_ij =
<b_i,b_j>``, ``r_i = <b_i,x>``) via ``lstsq``, fail-closed on a
metric-degenerate span. Returns ``(residual_vector, residual_norm)`` where the
norm is the DEFINITE (Euclidean) magnitude of the residual — 0 iff nothing is
unexplained (see the module docstring on why this is not the metric norm).
norm is the DEFINITE Euclidean magnitude of the projection residual — NOT the
CGA metric — used as the stable threshold for productivity and discovery
routing; the projection itself is metric-orthogonal under ``cga_inner``. It is
0 iff nothing is unexplained, whereas the indefinite reversion norm can vanish
on a nonzero null residual (see the module docstring).
"""
x_arr = np.asarray(x, dtype=np.float64)
B = np.asarray(basis, dtype=np.float64)