Dontopedia

numpy.random.rand

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

numpy.random.rand has 7 facts recorded in Dontopedia across 4 references, with 1 live disagreement.

7 facts·3 predicates·4 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound 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.

generatedByGenerated by(3)

createdWithCreated With(1)

createsRandomVectorsCreates Random Vectors(1)

generated-byGenerated by(1)

rdf:typeRdf:type(1)

Other facts (6)

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.

6 facts
PredicateValueRef
Rdf:typeRandom Function[1]
Rdf:typeRandom Generation Function[2]
Rdf:typeRandom Number Generator[3]
Rdf:typeRandom Number Generator[4]
ReturnsRandom Vector[1]
Called Asnp.random.rand[2]

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.

typebeam/70bbc43a-27da-4ee6-abde-0b83af52d874
ex:RandomFunction
labelbeam/70bbc43a-27da-4ee6-abde-0b83af52d874
numpy.random.rand
returnsbeam/70bbc43a-27da-4ee6-abde-0b83af52d874
ex:random-vector
typebeam/02a7ad2c-cb05-4e89-b0b4-a0cfec772912
ex:RandomGenerationFunction
calledAsbeam/02a7ad2c-cb05-4e89-b0b4-a0cfec772912
np.random.rand
typebeam/03e96dd9-ead9-4715-acb5-53b244eba5f8
ex:random-number-generator
typebeam/f026078e-8f4c-49fe-81e1-c274e43d2156
ex:RandomNumberGenerator

References (4)

4 references
  1. ctx:claims/beam/70bbc43a-27da-4ee6-abde-0b83af52d874
  2. ctx:claims/beam/02a7ad2c-cb05-4e89-b0b4-a0cfec772912
    • full textbeam-chunk
      text/plain1 KBdoc:beam/02a7ad2c-cb05-4e89-b0b4-a0cfec772912
      Show excerpt
      [Turn 4754] User: I'm trying to optimize the search time for my 100K vectors using FAISS 1.7.4, but I'm seeing a search time of 180ms, which seems a bit high. Can you help me improve this? I've heard that indexing tools can make a big diffe
  3. ctx:claims/beam/03e96dd9-ead9-4715-acb5-53b244eba5f8
  4. ctx:claims/beam/f026078e-8f4c-49fe-81e1-c274e43d2156
    • full textbeam-chunk
      text/plain1006 Bdoc:beam/f026078e-8f4c-49fe-81e1-c274e43d2156
      Show excerpt
      By implementing these optimizations, you should be able to achieve a significant improvement in your dense search goals. [Turn 6398] User: I'm trying to map 3 dense search hurdles with Kathryn for future iterations, and I was wondering if

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