Dontopedia

Type Casting to float32

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

Type Casting to float32 has 7 facts recorded in Dontopedia across 3 references, with 2 live disagreements.

7 facts·4 predicates·3 sources·2 in dispute

Mostly:rdf:type(2), applied to(1), converts to(1)

Maturity scale raw canonical shape-checked rule-derived certified

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.

appliesAfterConversionApplies After Conversion(2)

performsPerforms(1)

Other facts (5)

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.

5 facts
PredicateValueRef
Rdf:typeOperation[1]
Rdf:typeProcess[2]
Applied toDataset[2]
Converts tofloat32[2]
Converts toFloat32[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.

typebeam/0bca54e2-f808-47ad-b21b-1dfd747efe98
ex:Operation
labelbeam/0bca54e2-f808-47ad-b21b-1dfd747efe98
Type Conversion to float32
typebeam/9aef4a43-c110-4730-bed6-18e6312b77ad
ex:Process
labelbeam/9aef4a43-c110-4730-bed6-18e6312b77ad
Type Casting to float32
applied-tobeam/9aef4a43-c110-4730-bed6-18e6312b77ad
ex:dataset
converts-tobeam/9aef4a43-c110-4730-bed6-18e6312b77ad
float32
convertsTobeam/9170f193-72c4-43d3-9c09-87f869d91b8b
ex:float32

References (3)

3 references
  1. ctx:claims/beam/0bca54e2-f808-47ad-b21b-1dfd747efe98
  2. ctx:claims/beam/9aef4a43-c110-4730-bed6-18e6312b77ad
  3. ctx:claims/beam/9170f193-72c4-43d3-9c09-87f869d91b8b
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9170f193-72c4-43d3-9c09-87f869d91b8b
      Show excerpt
      index.nprobe = nprobe return index # Example usage: vectors = np.random.rand(10000, 128).astype(np.float32) index = create_ivfpq_index(vectors, nlist=200, m=8, nprobe=15) print(index.ntotal) # Test the index query_vectors = np.ran

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