Probabilistic¶
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class
matchup.models.algorithms.probabilistic.
Probabilistic
¶ Bases:
matchup.models.model.IterModel
Attributes Summary
RANGE
Methods Summary
calculate
(doc, term_scores)Sum the scores of the mapped representation. iter_rank
(vocabulary)One iteration of Probabilistic model execute. number_docs_with_key
(occurrences)Return the vi_value : number of documents with key probabilistic_iterative_perform
(vocabulary)Perform the algorithm iterations. process_terms
(vocabulary)Generate scores for all mapped terms. run
(query, vocabulary)Principal method that represents IR probabilistic model. score
(key, vocabulary)Apply probabilistic concepts to calculate the score of one keyword in vocabulary. Attributes Documentation
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RANGE
= 5¶
Methods Documentation
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calculate
(doc: str, term_scores) → float¶ - Sum the scores of the mapped representation.
Parameters: - doc –
- term_scores –
Returns:
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iter_rank
(vocabulary) → List[matchup.structure.solution.Result]¶ - One iteration of Probabilistic model execute.
Returns: ranked list of documents, scores
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number_docs_with_key
(occurrences) → int¶ - Return the vi_value : number of documents with key
Parameters: occurrences – Occurrences Returns:
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probabilistic_iterative_perform
(vocabulary) → List[matchup.structure.solution.Result]¶ - Perform the algorithm iterations.
Parameters: vocabulary – Returns:
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process_terms
(vocabulary)¶ - Generate scores for all mapped terms.
Parameters: vocabulary – Returns:
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run
(query: List[matchup.presentation.text.Term], vocabulary: matchup.structure.vocabulary.Vocabulary)¶ - Principal method that represents IR probabilistic model.
Parameters: - query – list of all query terms
- vocabulary – data structure that represents the vocabulary
Returns:
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score
(key: str, vocabulary: matchup.structure.vocabulary.Vocabulary) → float¶ - Apply probabilistic concepts to calculate the score of one keyword in vocabulary.
Parameters: - key – keyword to generate score
- vocabulary – base collection
Returns: float score
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