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package orandforest
Data types
exception BadContinuity of featureRaised when appending a data vector with a continuous feature instead of a discrete one, or the other way.
exception DiscreteFeatOutOfBounds of feature * intDiscreteFeatureOutOfBounds 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.
The type of a continuous data, defined through Oc45.Make
A value of a feature field.
type data = dataVal arrayA feature/data value association table.
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.
Output of c45.
Main functions
val c45 : trainSet -> decisionTreeGenerates a decision tree from a training set.
val classify : decisionTree -> data -> categoryClassifies a data vector, given a decision tree.
Training set manipulation
val emptyTrainSet : int -> int -> bool array -> trainSetemptyTrainSet 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.
val setFeatureMax : int -> int -> trainSet -> unitsetFeatureMax 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.
val getNbFeatures : trainSet -> intReturns the number of features.
val getFeatureMax : trainSet -> int arrayReturns the feature bound array, see setFeatureMax.
val getFeatContinuity : trainSet -> bool arrayReturns the feature continuity array, see emptyTrainSet.
val getNbCategories : trainSet -> intReturns the number of categories.
val getSetSize : trainSet -> intReturns the number of training cases in a given training set.
Pretty-printing
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).
val toDotStdout :
(Format.formatter -> contData -> unit) ->
decisionTree ->
unitSame as toDot, but prints directly to stdout.
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