Dontopedia

numpy.array_split

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

numpy.array_split has 3 facts recorded in Dontopedia across 1 reference.

3 facts·2 predicates·1 sources
Maturity scale raw canonical shape-checked rule-derived certified

Other facts (2)

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.

2 facts
PredicateValueRef
Rdf:typeNumpy Function[1]
Used inBatch Search Function[1]

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/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
ex:NumpyFunction
labelbeam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
numpy.array_split
usedInbeam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
ex:batch-search-function

References (1)

1 references
  1. ctx:claims/beam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
      Show excerpt
      from concurrent.futures import ThreadPoolExecutor # Create a FAISS index d = 128 # dimension index = faiss.IndexFlatL2(d) # Add vectors to the index vectors = np.random.rand(10000, d).astype('float32') index.add(vectors) # Function to p

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.