numpy.random.choice
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-07.)
numpy.random.choice has 7 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(2), has parameter(2), takes argument(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
callsFunctionCalls Function(2)
- Lambda Expression
ex:lambda-expression - Retrieve Documents Function
ex:retrieve-documents-function
usesRandomChoiceUses Random Choice(2)
- Sprint Duration Generation
ex:sprint-duration-generation - Sprint Label Generation
ex:sprint-label-generation
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 |
|---|---|---|
| Rdf:type | Function | [1] |
| Rdf:type | Numpy Function | [3] |
| Has Parameter | size | [3] |
| Has Parameter | choice | [3] |
| Takes Argument | Binary Array | [2] |
| Takes Keyword Arg | Size Parameter | [2] |
Timeline
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References (3)
ctx:claims/beam/18537b2d-1de5-488d-90f1-3d6d6503ecc3- full textbeam-chunktext/plain1 KB
doc:beam/18537b2d-1de5-488d-90f1-3d6d6503ecc3Show excerpt
1. **Generate Documents and Relevant Labels**: Create synthetic documents and labels indicating which documents are relevant. 2. **Implement Retrieval Tools**: Define how each retrieval tool works. For simplicity, let's assume each tool ret…
ctx:claims/beam/059dfa3d-8d94-4bfc-bbe2-1c2228c8c6fe- full textbeam-chunktext/plain1 KB
doc:beam/059dfa3d-8d94-4bfc-bbe2-1c2228c8c6feShow excerpt
total_duration += timer.duration total_throughput += num_queries / timer.duration latencies.append(timer.duration) # Assuming results is a binary array indicating relevance precision = precision_scor…
ctx:claims/beam/16d89879-916d-41b5-b2b5-74925939f0b9- full textbeam-chunktext/plain1 KB
doc:beam/16d89879-916d-41b5-b2b5-74925939f0b9Show excerpt
Here's an example implementation: ```python import pandas as pd import numpy as np # Generate sample data for 50 tasks np.random.seed(0) # For reproducibility task_ids = [f'Task {i+1}' for i in range(50)] sprint_durations = np.random.cho…
See also
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