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Update Weights

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

Update Weights has 28 facts recorded in Dontopedia across 4 references, with 4 live disagreements.

28 facts·18 predicates·4 sources·4 in dispute

Mostly:has parameter(5), rdf:type(3), rdfs:label(3)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Purposein disputepurpose

  • Adjust weights based on performance[3]sourceall time · 589987e0 D7a7 43a1 8209 A674b2085e34
  • weight_adjustment[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Has Parameterin disputehasParameter

  • Current Weights[2]sourceall time · 0c1b8dfa Ca03 4575 B85f 46f8c09fe7b5
  • Scores1[2]all time · 0c1b8dfa Ca03 4575 B85f 46f8c09fe7b5
  • True Labels[2]sourceall time · 0c1b8dfa Ca03 4575 B85f 46f8c09fe7b5
  • engine1_accuracy[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e
  • engine2_accuracy[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Descriptionin disputedescription

  • update weights based on performance[2]sourceall time · 0c1b8dfa Ca03 4575 B85f 46f8c09fe7b5
  • update weights based on performance of each engine[2]sourceall time · 0c1b8dfa Ca03 4575 B85f 46f8c09fe7b5

Rdfs:labelrdfs:label

  • update_weights[4]all time · 7c39567a D596 4c72 Aa0d D70287a5c1e4
  • update_weights[2]all time · 0c1b8dfa Ca03 4575 B85f 46f8c09fe7b5
  • update_weights[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Data Flow todataFlowTo

Uses AdditionusesAddition

  • true[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Uses DivisionusesDivision

  • true[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Parameter CountparameterCount

  • 2[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Returns TuplereturnsTuple

  • true[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Part of SystempartOfSystem

Definition OrderdefinitionOrder

  • 2[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e

Inbound mentions (15)

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.

inputToInput to(2)

used-byUsed by(2)

computedByComputed by(1)

consistsOfConsists of(1)

containsContains(1)

containsFunctionContains Function(1)

containsFunctionDefinitionContains Function Definition(1)

demonstratesDemonstrates(1)

isComputedByIs Computed by(1)

results-fromResults From(1)

updated-byUpdated by(1)

updatedByUpdated by(1)

usedInUsed in(1)

Other facts (6)

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.

6 facts
PredicateValueRef
Has ConditionTotal Accuracy Equals Zero[1]
Has Conditional ReturnDefault Equal Weights[1]
Returnsnew_weights[1]
RequiresTrue Labels[2]
UpdatesWeights[2]
UsesTrue Labels[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.

dataFlowTobeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
ex:simulation_loop
definitionOrderbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
2
descriptionbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
update weights based on performance
descriptionbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
update weights based on performance of each engine
hasConditionbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
ex:total_accuracy_equals_zero
hasConditionalReturnbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
ex:default_equal_weights
hasParameterbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
ex:current_weights
hasParameterbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
ex:scores1
hasParameterbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
ex:true_labels
hasParameterbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
engine1_accuracy
hasParameterbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
engine2_accuracy
parameterCountbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
2
partOfSystembeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
ex:ensemble_learning_system
purposebeam/589987e0-d7a7-43a1-8209-a674b2085e34
Adjust weights based on performance
purposebeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
weight_adjustment
labelbeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
update_weights
labelbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
update_weights
labelbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
update_weights
typebeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
ex:Function
typebeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
ex:Function
typebeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:WeightUpdateFunction
requiresbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
ex:true_labels
returnsbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
new_weights
returnsTuplebeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
true
updatesbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
ex:weights
usesbeam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
ex:true_labels
usesAdditionbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
true
usesDivisionbeam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
true

References (4)

4 references
  1. customctx:claims/beam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e
  2. [2]beam-chunk10 facts
    customctx:claims/beam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5
      Show excerpt
      - `apply_threshold`: Filters out scores below a certain threshold. - `threshold=0.5`: Only keeps scores above 0.5. 3. **Post-processing**: - `post_process_results`: Selects the top `n` indices based on the filtered scores. - `
  3. [3]beam-chunk1 fact
    customctx:claims/beam/589987e0-d7a7-43a1-8209-a674b2085e34
    • full textbeam-chunk
      text/plain1 KBdoc:beam/589987e0-d7a7-43a1-8209-a674b2085e34
      Show excerpt
      # Compute ensemble scores ensemble_scores = compute_weighted_ensemble_scores(scores1, scores2, weights=weights) print("Current Ensemble Scores:", ensemble_scores) # Calculate predictions predictions1 = np.argmax(scores1
  4. [4]beam-chunk2 facts
    customctx:claims/beam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
      Show excerpt
      # Calculate accuracy for each engine accuracy1 = np.mean(np.argmax(scores1, axis=1) == true_labels) accuracy2 = np.mean(np.argmax(scores2, axis=1) == true_labels) # Update weights based on accuracy new_weights = (ac

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