weights
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
weights has 28 facts recorded in Dontopedia across 4 references, with 4 live disagreements.
Mostly:contains value(6), rdf:type(5), contains(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (1)
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.
containsContains(1)
- Example Usage
ex:example-usage
Other facts (27)
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 |
|---|---|---|
| Contains Value | 0.2 | [1] |
| Contains Value | 0.3 | [1] |
| Contains Value | 0.5 | [1] |
| Contains Value | 0.2 | [4] |
| Contains Value | 0.3 | [4] |
| Contains Value | 0.5 | [4] |
| Rdf:type | List | [1] |
| Rdf:type | Weight Array | [2] |
| Rdf:type | List | [2] |
| Rdf:type | Variable | [3] |
| Rdf:type | Python List | [4] |
| Contains | 0.2 | [2] |
| Contains | 0.3 | [2] |
| Contains | 0.5 | [2] |
| Has Value | 0.2 | [3] |
| Has Value | 0.3 | [3] |
| Has Value | 0.5 | [3] |
| Assigned Value | [0.2, 0.3, 0.5] | [2] |
| Has Length | 3 | [2] |
| Initialization | example-usage | [2] |
| Represents | initial-guess | [2] |
| Used As | initialization | [2] |
| Data Structure | List | [3] |
| Sum | 1 | [3] |
| Normalized | true | [3] |
| Element Count | 3 | [3] |
| Variable Name | weights | [4] |
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)
ctx:claims/beam/1a703b63-707c-46bd-a78c-717c0d3777f8ctx:claims/beam/2b82365a-fa1b-4c40-a4d8-b4995b335ba4- full textbeam-chunktext/plain1 KB
doc:beam/2b82365a-fa1b-4c40-a4d8-b4995b335ba4Show excerpt
- Use `minimize` from `scipy.optimize` to find the optimal weights that minimize the MSE. ### Additional Considerations - **Normalization**: Normalize the queries if they are on different scales. - **Constraint**: Add constraints to th…
ctx:claims/beam/3c399a7b-cdb0-4ea1-9eb4-12f84952a5d3- full textbeam-chunktext/plain1 KB
doc:beam/3c399a7b-cdb0-4ea1-9eb4-12f84952a5d3Show 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…
ctx:claims/beam/465f9836-8514-49bd-9fc2-f3db6d101967- full textbeam-chunktext/plain1 KB
doc:beam/465f9836-8514-49bd-9fc2-f3db6d101967Show excerpt
```python import numpy as np from sklearn.model_selection import GridSearchCV from sklearn.metrics import make_scorer, f1_score def hybrid_ranking(weights, features): # Calculate the weighted sum of the features weighted_sum = np.s…
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