np.argsort
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
np.argsort has 13 facts recorded in Dontopedia across 5 references, with 3 live disagreements.
Mostly:rdf:type(5), applied to(3), returns order(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (1)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
usedByUsed by(1)
- Hybrid Scores
ex:hybrid-scores
Other facts (11)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Sorting Operation | [1] |
| Rdf:type | Operation | [2] |
| Rdf:type | Numpy Operation | [3] |
| Rdf:type | Sorting Operation | [4] |
| Rdf:type | Numpy Operation | [5] |
| Applied to | predicted_scores | [3] |
| Applied to | Scores | [4] |
| Applied to | Similarity List | [5] |
| Returns Order | ascending | [3] |
| Has Parameter | Descending | [4] |
| Uses Descending Order | true | [4] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (5)
ctx:claims/beam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc- full textbeam-chunktext/plain1 KB
doc:beam/0101eba2-9f85-41c1-ac05-d4c55e85d3fcShow excerpt
if max_score == min_score: return np.zeros_like(scores) return (scores - min_score) / (max_score - min_score) def hybrid_ranking(sparse_scores, dense_scores, alpha=0.6): # Normalize scores to ensure they are on the same…
ctx:claims/beam/f4aef03b-af1f-48d6-9f2c-e041983c87f7ctx:claims/beam/c07ae379-ae89-4db6-8cc7-34e24961d945ctx:claims/beam/fa097ab4-7c54-4d7c-bce6-50883cbc7667ctx:claims/beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7- full textbeam-chunktext/plain1 KB
doc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7Show excerpt
for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.