descriptor_to_table — REPRCONV descriptor op

• Data kinds: descriptor → table

• Call: import fullseye as fs; fs.ledger.descriptor_to_table(descriptor) (to call the implementation directly, import reprconv; reprconv.descriptor_to_table(descriptor); from the registry, opsreprconv.get("descriptor_to_table"))

Usage

Descriptor → a summary table. One-way.

Produces the dimension, norm and energy concentration (the fraction taken by the top 10% of the normalised sum of squares). It is for seeing "how many dimensions a descriptor effectively uses", and makes visible the failure of having many dimensions that are almost all 0.

Args:

descriptor: (n,) or (m, n).

Returns:

dict (shape / n / l2 / mean / std / min /

max / top10pct_energy / nonzero_fraction).

Raises:

ValueError: invalid shape / non-finite.

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• representation_conversion — py -3.11 examples/representation_conversion.py

Ops the type connects to (they accept table as input)

—

Same category (descriptor)

descriptor_to_matrix · matrix_to_descriptor

*Provenance: reprconv.py — REPRCONV operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.