Dontopedia

calculate_term_frequencies

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

calculate_term_frequencies has 37 facts recorded in Dontopedia across 2 references, with 7 live disagreements.

37 facts·26 predicates·2 sources·7 in dispute

Mostly:contains(4), rdf:type(2), has parameter(2)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (9)

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.

authorOfAuthor of(1)

callsFunctionCalls Function(1)

containsContains(1)

demonstratesDemonstrates(1)

executesExecutes(1)

importedByImported by(1)

invokesInvokes(1)

usesUses(1)

validatesValidates(1)

Other facts (36)

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.

36 facts
PredicateValueRef
ContainsConditional Check[1]
ContainsDictionary Initialization[1]
ContainsAssignment Statement[1]
ContainsIncrement Statement[1]
Rdf:typeFunction[1]
Rdf:typeFunction[2]
Has ParameterDocuments[1]
Has ParameterAll Terms[2]
ReturnsTerm Frequencies[1]
ReturnsTerm Frequencies[2]
Has LoopDocument Loop[1]
Has LoopTerm Loop[1]
PerformsDictionary Check[1]
PerformsDictionary Increment[1]
Algorithmincremental-counting[1]
AlgorithmCounter-based frequency counting[2]
Called byUser[1]
Called byCprofile Run[2]
ImportsNumpy[1]
InitializesTerm Frequencies Dictionary[1]
Has PurposeTerm Frequency Calculation[1]
Used byUser[1]
Has ComplexityO N Squared[1]
ImplementsBag of Words Model[1]
Returns toTerm Frequencies Variable[1]
Has Return StatementReturn Term Frequencies[1]
Has SignatureFunction Signature[1]
ScopeGlobal Scope[1]
Iteration OrderDocument Then Term[1]
Languagepython[1]
Algorithm Typenaive-counting[1]
Optimization TargetReduce Dictionary Operations[1]
Import DependencyNumpy[1]
Optimized byCounter data structure[2]
Optimizationavoids dictionary operation overhead[2]
Addressesterm frequency calculation problem[2]

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.

typebeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:Function
hasParameterbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:documents
returnsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:term-frequencies
importsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:numpy
initializesbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:term-frequencies-dictionary
hasLoopbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:document-loop
hasLoopbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:term-loop
performsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:dictionary-check
performsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:dictionary-increment
hasPurposebeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:term-frequency-calculation
usedBybeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:user
hasComplexitybeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:O-n-squared
containsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:conditional-check
implementsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:bag-of-words-model
algorithmbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
incremental-counting
containsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:dictionary-initialization
calledBybeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:user
returnsTobeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:term-frequencies-variable
hasReturnStatementbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:return-term-frequencies
hasSignaturebeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:function-signature
scopebeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:global-scope
iterationOrderbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:document-then-term
languagebeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
python
algorithmTypebeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
naive-counting
optimizationTargetbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:reduce-dictionary-operations
containsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:assignment-statement
containsbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:increment-statement
importDependencybeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
ex:numpy
typebeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
ex:Function
labelbeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
calculate_term_frequencies
hasParameterbeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
ex:all-terms
returnsbeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
ex:term-frequencies
calledBybeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
ex:cprofile-run
algorithmbeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
Counter-based frequency counting
optimizedBybeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
Counter data structure
optimizationbeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
avoids dictionary operation overhead
addressesbeam/eabb3e09-011d-40ed-912d-4eb9d1d27f37
term frequency calculation problem

References (2)

2 references
  1. ctx:claims/beam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
      Show excerpt
      [Turn 8666] User: I've been digging into the bottlenecks of my sparse training code, and I've found that term frequency miscalculations are delaying 14% of the 6,000 training cycles by 350ms, I'm using the following code to calculate the te
  2. ctx:claims/beam/eabb3e09-011d-40ed-912d-4eb9d1d27f37

See also

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.