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package oc45
Module Oc45.FloatOc45Source
Similar to Oc45.Make(struct type t = float let ... end)
Data types
Raised when trying to construct a data set with wrong arguments.
A feature id.
Raised when appending a data vector with a continuous feature instead of a discrete one, or the other way.
DiscreteFeatureOutOfBounds feat class is raised when trying to classify a data vector with its discrete feature feat equal to class when the tree was created assuming that the values for this feature would remain < class. This usually means that the value is very rare and was not encountered in the training set, thus the limit inferred for the maximal value of this feature is not high enough. You then have to set it manually, using setFeatureMax.
A category (ie. classification) id.
Source
type trainVal = {data : data;(*Associates each feature id to its value. If the feature is continuous, it may take any value; if the feature is discrete, it must be an integer in a range 0..N inclusive for a bound N inferred as the maximum of the given data. You can also set this bound manually with
*)setFeatureMax.category : category;(*The category to which this data vector belongs.
*)
}A value used to train the algorithm.
Generated by emptyTrainSet, represents a training set for the algorithm.
Main functions
Generates a decision tree from a training set.
Classifies a data vector, given a decision tree.
Training set manipulation
emptyTrainSet nbFeatures nbCategories featContinuity creates an empty train set with nbFeatures features and nbCategories categories. The array featContinuity must have nbFeatures elements, with a true value if the corresponding feature is continuous (that is, may take any value) or false if the feature is discrete in a restrained set (eg., "Yes"/"No").
Raises InvalidArgument if featContinuity has not a length of nbFeatures
Adds a list of data vectors to the given training set.
setFeatureMax feat maxVal trainSet sets the maximum value the discrete feature feat may take. A discrete value is represented by an integer between 0 and maxVal (inclusive).
In most cases, you won't have to call this function and the bound will be automatically set to the maximum value you gave, but you can still set it in case you need to have more values that are not represented.
Returns the feature bound array, see setFeatureMax.
Returns the feature continuity array, see emptyTrainSet.
Pretty-printing
Source
val toDot :
Format.formatter ->
(Format.formatter -> contData -> unit) ->
decisionTree ->
unitPretty-prints the given decision tree as a Dot file in the given formatter, using the second argument as a pretty-printer for the contData type (ie., the type of a continuous data).
Same as toDot, but prints directly to stdout.
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