np.argsort
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
np.argsort has 17 facts recorded in Dontopedia across 6 references, with 2 live disagreements.
Mostly:rdf:type(5), applied to(2), uses order(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
usesUses(1)
- Sorting
ex:sorting
usesNumpyFunctionUses Numpy Function(1)
- Rank Documents
ex:rank_documents
usesOperationUses Operation(1)
- Top K Selection
ex:top-k-selection
Other facts (15)
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 | Mathematical Operation | [1] |
| Rdf:type | Numpy Function | [2] |
| Rdf:type | Numpy Function | [4] |
| Rdf:type | Method | [5] |
| Rdf:type | Numpy Function | [6] |
| Applied to | Negative Similarities | [1] |
| Applied to | Similarities | [6] |
| Uses Order | descending | [3] |
| Sorts by | Scores | [3] |
| Produces | Sorted Indices | [3] |
| Uses | Negative Sign | [4] |
| Member of | Np | [5] |
| Sorts | Similarities | [5] |
| Operation | Numpy Argsort | [5] |
| Library | Numpy | [6] |
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 (6)
ctx:claims/beam/1c92d7b3-5e81-4735-8dba-06ce859d99dcctx:claims/beam/5278119f-c632-4b91-b193-f1e7bddf1e64- full textbeam-chunktext/plain1 KB
doc:beam/5278119f-c632-4b91-b193-f1e7bddf1e64Show excerpt
# Calculate the similarity between the query vector and each vector in the database similarities = [np.dot(query_vector, vector) for vector in self.vectors] # Return the indices of the top 10 most similar vectors …
ctx:claims/beam/b729dc6d-53ff-42db-95a2-0b4b64111a65- full textbeam-chunktext/plain1 KB
doc:beam/b729dc6d-53ff-42db-95a2-0b4b64111a65Show excerpt
self.fc3 = nn.Linear(32, 1) self.dropout = nn.Dropout(0.5) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.dropout(x) x = torch.relu(self.fc2(x)) x = self.dropout(x) x …
ctx:claims/beam/3f9cc74a-f64d-4f42-9644-00d4f42d4751ctx:claims/beam/f0cc860e-7f75-4530-abef-84dc82b5e5ad- full textbeam-chunktext/plain1 KB
doc:beam/f0cc860e-7f75-4530-abef-84dc82b5e5adShow excerpt
term_embedding = get_contextual_embeddings(term) closest_synonyms = [] for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_context…
ctx: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.