Dontopedia

np.sum

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

np.sum has 9 facts recorded in Dontopedia across 3 references, with 2 live disagreements.

9 facts·5 predicates·3 sources·2 in dispute

Mostly:rdf:type(3), called in(1), has parameter(1)

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.

usesFunctionUses Function(2)

aggregatesAggregates(1)

appliesApplies(1)

computedAsComputed As(1)

returnStatementReturn Statement(1)

usesLibraryFunctionUses Library Function(1)

Other facts (7)

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.

7 facts
PredicateValueRef
Rdf:typeNumpy Function[1]
Rdf:typeNumpy Function[2]
Rdf:typeNumpy Function[3]
Called inTrue Positive Definition[1]
Has ParameterAxis Parameter[2]
Specifies Axis0[2]
InputBoolean Array[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/eb7f55ff-6715-4dd8-81f8-023b5f9693f2
ex:NumpyFunction
labelbeam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2
np.sum
calledInbeam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2
ex:true-positive-definition
typebeam/3c399a7b-cdb0-4ea1-9eb4-12f84952a5d3
ex:NumpyFunction
hasParameterbeam/3c399a7b-cdb0-4ea1-9eb4-12f84952a5d3
ex:axis-parameter
specifiesAxisbeam/3c399a7b-cdb0-4ea1-9eb4-12f84952a5d3
0
typebeam/f5a5540b-3c9d-4103-85d7-7db7b8ea25d3
ex:NumpyFunction
labelbeam/f5a5540b-3c9d-4103-85d7-7db7b8ea25d3
np.sum()
inputbeam/f5a5540b-3c9d-4103-85d7-7db7b8ea25d3
ex:boolean-array

References (3)

3 references
  1. ctx:claims/beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2
    • full textbeam-chunk
      text/plain1 KBdoc:beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2
      Show excerpt
      retrieved_labels = relevant_labels[retrieved_indices] true_positives = np.sum(retrieved_labels) recall = true_positives / num_relevant return recall # Initialize the recall scores recall_scores = [] for tool in tools:
  2. ctx:claims/beam/3c399a7b-cdb0-4ea1-9eb4-12f84952a5d3
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3c399a7b-cdb0-4ea1-9eb4-12f84952a5d3
      Show excerpt
      # Calculate the weighted sum of the queries weighted_sum = np.sum([weight * query for weight, query in zip(weights, queries)], axis=0) return weighted_sum def loss_function(weights, queries, true_values): # Calculate the we
  3. ctx:claims/beam/f5a5540b-3c9d-4103-85d7-7db7b8ea25d3

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