sorted indices
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
sorted indices has 13 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Mostly:rdf:type(2), generated by(1), uses(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
containsContains(1)
- Example Usage
ex:example-usage
derivedFromDerived From(1)
- Predicted Labels
ex:predicted-labels
outputOutput(1)
- Sorting Operation
ex:sorting-operation
producesProduces(1)
- Argsort
ex:argsort
reorderedByReordered by(1)
- Results
ex:results
returnsReturns(1)
- Rerank Results
ex:rerank-results
usedOnUsed on(1)
- Inverse Indexing
ex:inverse-indexing
Other facts (12)
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 | Numpy Array | [1] |
| Rdf:type | Index Tensor | [4] |
| Generated by | Numpy Argsort | [1] |
| Uses | Hybrid Scores Large | [1] |
| Order | descending | [1] |
| Orders | Hybrid Scores Large | [1] |
| Ordering | descending | [1] |
| Is Variable | Code Variable | [2] |
| Assigned From | Numpy Argsort | [2] |
| Uses Operation | Reverse Sort | [2] |
| Used to Index | Hybrid Scores | [2] |
| Used for | Reranking | [3] |
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 (4)
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/cc7e2701-5558-4a53-b31f-07382bf903bd- full textbeam-chunktext/plain1 KB
doc:beam/cc7e2701-5558-4a53-b31f-07382bf903bdShow excerpt
dense_scores = np.array([0.7, 0.3, 0.1]) # Normalize and compute hybrid scores hybrid_scores = hybrid_ranking(sparse_scores, dense_scores) print(hybrid_scores) # Optionally, sort documents based on hybrid scores sorted_indices = np.argsor…
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/fa097ab4-7c54-4d7c-bce6-50883cbc7667
See also
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