float
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
float has 7 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
convertsTypeConverts Type(2)
- Complexity Calculation
ex:complexity-calculation - Update Sprint Completion Route
ex:update-sprint-completion-route
appliesApplies(1)
- Inputs
ex:inputs
Other facts (5)
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 | Type Conversion | [1] |
| Rdf:type | Operation | [2] |
| Rdf:type | Type Conversion | [4] |
| Applied to | Example Output | [2] |
| Ensures | Float Dtype | [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.
References (4)
ctx:claims/beam/4f2d86b9-89bd-4a30-9535-87e1824a731f- full textbeam-chunktext/plain1 KB
doc:beam/4f2d86b9-89bd-4a30-9535-87e1824a731fShow excerpt
# Total deliverables and target coverage total_deliverables = 100 target_coverage = 95 # Function to update completion percentage def update_completion_percentage(sprint, percentage): df.loc[df['Sprint'] == sprint, 'Completion Percenta…
ctx:claims/beam/8db83f0d-819a-4f3b-b500-3a38a63092b2ctx:claims/beam/c8102774-0736-45ab-8d51-87fae35d0377- full textbeam-chunktext/plain1 KB
doc:beam/c8102774-0736-45ab-8d51-87fae35d0377Show excerpt
for epoch in range(100): for batch in data_loader: inputs = batch['query'].float().to(device) labels = batch['label'].long().to(device) optimizer.zero_grad() outputs = model(input…
ctx:claims/beam/d722ad53-d442-458e-b561-cab7e12fcbbf- full textbeam-chunktext/plain1 KB
doc:beam/d722ad53-d442-458e-b561-cab7e12fcbbfShow excerpt
optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True) scaler = GradScaler() try: for epoch in range(100): running…
See also
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