Numpy Random
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Numpy Random has 3 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
- Function[1]all time · Fc7cf36b Fb78 4d1e 89ff 75395398d5c6
- Numpy Module[2]all time · 3f9cc74a F64d 4f42 9644 00d4f42d4751
Rdfs:labelrdfs:label
- numpy.random.rand[1]all time · Fc7cf36b Fb78 4d1e 89ff 75395398d5c6
Inbound mentions (3)
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(2)
- Query Vector
ex:query_vector - Vectors
ex:vectors
usesUses(1)
- Example Usage
ex:example_usage
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 (2)
- custom
ctx:claims/beam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6- full textbeam-chunktext/plain1 KB
doc:beam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6Show excerpt
"dimension": dimension, "index_file_size": 1024, # Size of each segment file in MB "metric_type": METRIC_TYPE } milvus.create_collection(param) # Create an index def create_index(name, index_type, nlist): …
- custom
ctx:claims/beam/3f9cc74a-f64d-4f42-9644-00d4f42d4751
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.