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.
Mostly:has parameter(5), rdf:type(3), rdfs:label(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
Purposein disputepurpose
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
Rdfs:labelrdfs:label
Data Flow todataFlowTo
- Simulation Loop[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e
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
- Ensemble Learning System[1]all time · Cd4eee06 62c7 4b95 B0dc 16ff32dffa4e
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)
- Engine1 Accuracy
ex:engine1_accuracy - Engine2 Accuracy
ex:engine2_accuracy
used-byUsed by(2)
- Engine1 Accuracy
ex:engine1-accuracy - Engine2 Accuracy
ex:engine2-accuracy
computedByComputed by(1)
- New Weights
ex:new-weights
consistsOfConsists of(1)
- Ensemble Learning System
ex:ensemble_learning_system
containsContains(1)
- Source Code
ex:source_code
containsFunctionContains Function(1)
- Source Code
ex:source_code
containsFunctionDefinitionContains Function Definition(1)
- Source Code
ex:source_code
demonstratesDemonstrates(1)
- Python Code Block
ex:python_code_block
isComputedByIs Computed by(1)
- New Weights Update
ex:new_weights_update
results-fromResults From(1)
- New Weights
ex:new-weights
updated-byUpdated by(1)
- Weights
ex:weights
updatedByUpdated by(1)
- Weights
ex:weights
usedInUsed in(1)
- Python
ex:python
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.
| Predicate | Value | Ref |
|---|---|---|
| Has Condition | Total Accuracy Equals Zero | [1] |
| Has Conditional Return | Default Equal Weights | [1] |
| Returns | new_weights | [1] |
| Requires | True Labels | [2] |
| Updates | Weights | [2] |
| Uses | True 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.
References (4)
- custom
ctx:claims/beam/cd4eee06-62c7-4b95-b0dc-16ff32dffa4e - custom
ctx:claims/beam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5- full textbeam-chunktext/plain1 KB
doc:beam/0c1b8dfa-ca03-4575-b85f-46f8c09fe7b5Show 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. - `…
- custom
ctx:claims/beam/589987e0-d7a7-43a1-8209-a674b2085e34- full textbeam-chunktext/plain1 KB
doc:beam/589987e0-d7a7-43a1-8209-a674b2085e34Show 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…
- custom
ctx:claims/beam/7c39567a-d596-4c72-aa0d-d70287a5c1e4- full textbeam-chunktext/plain1 KB
doc:beam/7c39567a-d596-4c72-aa0d-d70287a5c1e4Show 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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