The library documents itself · NLP & linguistics

stzKnn

13 own methods · 0 forms written with extensions, listed under their method

the source: base/learning/stzKnn.ring(opens in a new tab) · All classes · The area reference: NLP & linguistics

Under each method, its extensions (Q CS XT Z ZZ IB W U): bold and ticked, it exists; struck, it does not. The extensions and what they mean

Examples: 0 come from the library's tests; 5 were composed from each method's signature and run for this page, on a fresh object, for example oDS = new stzTrainingSet([]) oDS.AddExample([ 1, 1 ], "yes") oDS.AddExample([ 1, 0 ], "yes") oDS.AddExample([ 0, 1 ], "no") oDS.AddExample([ 0, 0 ], "no") o1 = new stzKnn(oDS). 8 methods have none yet. Examples run on 2026-10-03 20:23 inside the library at commit 0e72e2e2c.

Method

Explanation, as the source carries it

init

a different thing whenever the vote overrules proximityno extension

TrainingSetQ

THE HELD SET IS LIVE THROUGH HERE. Haro COPIES an object into an attribute, so the set handed to the constructor is a SNAPSHOT: growing the caller's own object afterwards would NOT reach this learner. Grow it through this accessor -- oLearner.TrainingSetQ().AddExample(...) -- which reaches the real held set (accessor + method call is live in Haro).no extension

SetK

(no comment in the source: the name reads as a sentence) set k 1 examples runno extension

SetApproximate

Label agreement measured 1.000 in both rows above -- on separable data the vote absorbed every neighbour the forest missed. Do not read that as a guarantee; read it as the reason AgreementWithExact() takes YOUR queries.no extension

IsApproximate

(no comment in the source: the name reads as a sentence) is approximate

composed example

oDS = new stzTrainingSet([])
oDS.AddExample([ 1, 1 ], "yes")
oDS.AddExample([ 1, 0 ], "yes")
oDS.AddExample([ 0, 1 ], "no")
oDS.AddExample([ 0, 0 ], "no")
o1 = new stzKnn(oDS)
? o1.IsApproximate()
#--> 0

composed from the signature, run for this page

no extension

SetApproximateTrees

More trees, better recall, bigger index and a slower build.no extension

SetApproximateBudget

Candidates examined per query before the true distances decide. 0 lets the engine scale it from k. This is the recall dial.no extension

ApproximateTrees

(no comment in the source: the name reads as a sentence) approximate trees

composed example

oDS = new stzTrainingSet([])
oDS.AddExample([ 1, 1 ], "yes")
oDS.AddExample([ 1, 0 ], "yes")
oDS.AddExample([ 0, 1 ], "no")
oDS.AddExample([ 0, 0 ], "no")
o1 = new stzKnn(oDS)
? o1.ApproximateTrees()
#--> 24

composed from the signature, run for this page

no extension

ApproximateBudget

(no comment in the source: the name reads as a sentence) approximate budget

composed example

oDS = new stzTrainingSet([])
oDS.AddExample([ 1, 1 ], "yes")
oDS.AddExample([ 1, 0 ], "yes")
oDS.AddExample([ 0, 1 ], "no")
oDS.AddExample([ 0, 0 ], "no")
o1 = new stzKnn(oDS)
? o1.ApproximateBudget()
#--> 0

composed from the signature, run for this page

no extension

AgreementWithExact

Returns the fraction of the given queries on which approximate and exact classification agree. Each mode is built once, not once per query.no extension

(no comment in the source: the name reads as a sentence) k

composed example

oDS = new stzTrainingSet([])
oDS.AddExample([ 1, 1 ], "yes")
oDS.AddExample([ 1, 0 ], "yes")
oDS.AddExample([ 0, 1 ], "no")
oDS.AddExample([ 0, 0 ], "no")
o1 = new stzKnn(oDS)
? o1.K()
#--> 3

composed from the signature, run for this page

no extension

Classify

(no comment in the source: the name reads as a sentence) classify 1 examples run

composed example

oDS = new stzTrainingSet([])
oDS.AddExample([ 1, 1 ], "yes")
oDS.AddExample([ 1, 0 ], "yes")
oDS.AddExample([ 0, 1 ], "no")
oDS.AddExample([ 0, 0 ], "no")
o1 = new stzKnn(oDS)
? o1.Classify([ 2, 3 ])
#--> yes

composed from the signature, run for this page

no extension

Why

(no comment in the source: the name reads as a sentence) whyno extension