Dontopedia

Search Lambda

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

Search Lambda has 9 facts recorded in Dontopedia across 2 references, with 3 live disagreements.

9 facts·6 predicates·2 sources·3 in dispute

Mostly:returns(2), rdf:type(2), has parameter(2)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

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hasValueHas Value(1)

Other facts (9)

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.

9 facts
PredicateValueRef
Returnsnp.random.rand(x.shape[0], 128)[1]
ReturnsBinary Array[2]
Rdf:typeFunction[1]
Rdf:typeLambda Function[2]
Has Parameterx[1]
Has ParameterX Parameter[2]
Returns ArrayNumpy Array 128d[1]
CallsNp Random Choice[2]
Returns Array of ShapeX Shape[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.

returnsbeam/af451cc6-36be-49c7-9fbe-3e2034fe77ed
np.random.rand(x.shape[0], 128)
typebeam/af451cc6-36be-49c7-9fbe-3e2034fe77ed
ex:Function
hasParameterbeam/af451cc6-36be-49c7-9fbe-3e2034fe77ed
x
returnsArraybeam/af451cc6-36be-49c7-9fbe-3e2034fe77ed
ex:numpy-array-128d
typebeam/3d2ebcc2-edde-456b-8a3a-1cb1f7bd0026
ex:LambdaFunction
hasParameterbeam/3d2ebcc2-edde-456b-8a3a-1cb1f7bd0026
ex:x-parameter
callsbeam/3d2ebcc2-edde-456b-8a3a-1cb1f7bd0026
ex:np-random-choice
returnsbeam/3d2ebcc2-edde-456b-8a3a-1cb1f7bd0026
ex:binary-array
returnsArrayOfShapebeam/3d2ebcc2-edde-456b-8a3a-1cb1f7bd0026
ex:x-shape

References (2)

2 references
  1. ctx:claims/beam/af451cc6-36be-49c7-9fbe-3e2034fe77ed
    • full textbeam-chunk
      text/plain1 KBdoc:beam/af451cc6-36be-49c7-9fbe-3e2034fe77ed
      Show excerpt
      'search': lambda x: np.random.rand(x.shape[0], 128) } print(test_sparse_retrieval_engine(engine)) ``` ->-> 3,29 [Turn 2397] Assistant: To effectively assess the performance of your sparse retrieval engine, you can enhance the test to i
  2. ctx:claims/beam/3d2ebcc2-edde-456b-8a3a-1cb1f7bd0026
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3d2ebcc2-edde-456b-8a3a-1cb1f7bd0026
      Show excerpt
      # Example usage engine = { 'search': lambda x: np.random.choice([0, 1], size=x.shape[0]) } metrics = test_sparse_retrieval_engine(engine) print(f"Average Duration: {metrics['average_duration']:.4f} seconds") print(f"Average Throughput:

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