Dontopedia

Random Integer

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Random Integer has 3 facts recorded in Dontopedia across 2 references.

3 facts·3 predicates·2 sources
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

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isGeneratedByIs Generated by(1)

Other facts (3)

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3 facts
PredicateValueRef
Uses Functionnp.random.randint[1]
Source Functionnp.random.randint[1]
Uses DistributionBernoulli Distribution[2]

Timeline

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usesFunctionbeam/f971d9d3-7050-4d32-844b-58db9f4972d7
np.random.randint
sourceFunctionbeam/f971d9d3-7050-4d32-844b-58db9f4972d7
np.random.randint
usesDistributionbeam/c12a5314-5117-4beb-a829-e08beb503951
ex:bernoulli-distribution

References (2)

2 references
  1. ctx:claims/beam/f971d9d3-7050-4d32-844b-58db9f4972d7
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f971d9d3-7050-4d32-844b-58db9f4972d7
      Show excerpt
      Manually clean the dataset to create a reference for comparison. This step involves fixing the inconsistencies introduced in the previous step. ```python # Manually clean the dataset df_cleaned = df.copy() # Replace 'Unknown' names with o
  2. ctx:claims/beam/c12a5314-5117-4beb-a829-e08beb503951
    • full textbeam-chunk
      text/plain1 KBdoc:beam/c12a5314-5117-4beb-a829-e08beb503951
      Show excerpt
      dense_scores = np.random.rand(num_queries, num_documents) # Test queries test_queries = np.random.rand(num_queries, num_documents) predictions = [] for i in range(num_queries): query = test_queries[i] sparse_scores_i = sparse_scor

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