Incremental Improvements Function
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Incremental Improvements Function has 10 facts recorded in Dontopedia across 1 reference.
Mostly:rdf:type(1), has parameter(1), has loop(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (8)
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.
callsCalls(1)
- Code Test
ex:code-test
callsFunctionCalls Function(1)
- Code Test
ex:code-test
demonstratesDemonstrates(1)
- Code Test
ex:code-test
executesAfterExecutes After(1)
- Code Test
ex:code-test
followsFollows(1)
- Code Test
ex:code-test
invokesInvokes(1)
- Code Test
ex:code-test
reducedByReduced by(1)
- Training Errors
ex:training-errors
verifiesVerifies(1)
- Code Test
ex:code-test
Other facts (10)
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 |
|---|---|---|
| Rdf:type | Function | [1] |
| Has Parameter | Errors Array | [1] |
| Has Loop | For Loop Improvements | [1] |
| Returns | Errors | [1] |
| Purpose | Reduce Training Errors | [1] |
| Defined in | Code Snippet | [1] |
| Has Comment | Code Comment 1 | [1] |
| Has Return Statement | Return Statement | [1] |
| Precedes | Code Test | [1] |
| Implements | Incremental Reduction Pattern | [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.
References (1)
ctx:claims/beam/3c07262c-005c-4dd9-9b36-cade8afcedea- full textbeam-chunktext/plain1 KB
doc:beam/3c07262c-005c-4dd9-9b36-cade8afcedeaShow excerpt
# Apply incremental improvements for improvement in improvements: # Reduce training errors errors = errors - improvement return errors # Test the function errors = np.array([10, 20, 30, 40, 50]) result = in…
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