A | |
| addData [Oc45.S] |
Adds the given value to the training set.
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| addDataList [Oc45.S] |
Adds a list of data vectors to the given training set.
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| avg [Oc45.Comparable] |
A function that returns "the average" of its two arguments, or the
closest thing to it.
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C | |
| c45 [Oc45.S] |
Generates a decision tree from a training set.
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| classify [Oc45.S] |
Classifies a data vector, given a decision tree.
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| compare [Oc45.Comparable] |
A function such that
compare a b is zero if a equals b and
is strictly negative (resp.
|
E | |
| emptyTrainSet [Oc45.S] | emptyTrainSet nbFeatures nbCategories featContinuity creates an
empty train set with nbFeatures features and nbCategories
categories.
|
G | |
| getFeatContinuity [Oc45.S] |
Returns the feature continuity array, see
Oc45.S.emptyTrainSet.
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| getFeatureMax [Oc45.S] |
Returns the feature bound array, see
Oc45.S.setFeatureMax.
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| getNbCategories [Oc45.S] |
Returns the number of categories.
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| getNbFeatures [Oc45.S] |
Returns the number of features.
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| getSet [Oc45.S] |
Extracts the data vector list from a training set.
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| getSetSize [Oc45.S] |
Returns the number of training cases in a given training set.
|
S | |
| setFeatureMax [Oc45.S] | setFeatureMax feat maxVal trainSet sets the maximum value the
discrete feature feat may take.
|
T | |
| toDot [Oc45.S] |
Pretty-prints the given decision tree as a Dot file in the given
formatter, using the second argument as a pretty-printer for the
Oc45.S.contData type (ie., the type of a continuous data).
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| toDotStdout [Oc45.S] |
Same as
Oc45.S.toDot, but prints directly to stdout.
|