Class Iadem2

    • Constructor Detail

      • Iadem2

        public Iadem2()
    • Method Detail

      • isRandomizable

        public boolean isRandomizable()
        Description copied from interface: Learner
        Gets whether this learner needs a random seed. Examples of methods that needs a random seed are bagging and boosting.
        Specified by:
        isRandomizable in interface Learner<Example<Instance>>
        Returns:
        true if the learner needs a random seed.
      • resetLearningImpl

        public void resetLearningImpl()
        Description copied from class: AbstractClassifier
        Resets this classifier. It must be similar to starting a new classifier from scratch.

        The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.
        Specified by:
        resetLearningImpl in class AbstractClassifier
      • trainOnInstanceImpl

        public void trainOnInstanceImpl​(Instance inst)
        Description copied from class: AbstractClassifier
        Trains this classifier incrementally using the given instance.

        The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.
        Specified by:
        trainOnInstanceImpl in class AbstractClassifier
        Parameters:
        inst - the instance to be used for training
      • getModelMeasurementsImpl

        protected Measurement[] getModelMeasurementsImpl()
        Description copied from class: AbstractClassifier
        Gets the current measurements of this classifier.

        The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.
        Specified by:
        getModelMeasurementsImpl in class AbstractClassifier
        Returns:
        an array of measurements to be used in evaluation tasks
      • getModelDescription

        public void getModelDescription​(StringBuilder out,
                                        int indent)
        Description copied from class: AbstractClassifier
        Returns a string representation of the model.
        Specified by:
        getModelDescription in class AbstractClassifier
        Parameters:
        out - the stringbuilder to add the description
        indent - the number of characters to indent
      • getVotesForInstance

        public double[] getVotesForInstance​(Instance inst)
        Description copied from interface: Classifier
        Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero.
        Specified by:
        getVotesForInstance in interface Classifier
        Specified by:
        getVotesForInstance in class AbstractClassifier
        Parameters:
        inst - the instance to be classified
        Returns:
        an array containing the estimated membership probabilities of the test instance in each class
      • createRoot

        public void createRoot​(Instance instance)
      • getMaxNumberOfBins

        public int getMaxNumberOfBins()
      • getNumberOfInstancesProcessed

        public long getNumberOfInstancesProcessed()
      • newLeafNode

        public Iadem2.LeafNode newLeafNode​(Iadem2.Node parent,
                                           long instTreeCountSinceVirtual,
                                           long instNodeCountSinceVirtual,
                                           double[] classDist,
                                           Instance instance)
      • getAttributeDifferentiation

        public double getAttributeDifferentiation()
      • setTreeRoot

        public void setTreeRoot​(Iadem2.Node newRoot)
      • getClassVotes

        public double[] getClassVotes​(Instance instance)
      • getPercentInCommon

        public double getPercentInCommon()
      • getValuesOfNominalAttributes

        public int getValuesOfNominalAttributes​(int attIndex,
                                                Instance instance)
      • getNaiveBayesLimit

        public int getNaiveBayesLimit()
      • isOnlyMultiwayTest

        public boolean isOnlyMultiwayTest()
      • isOnlyBinaryTest

        public boolean isOnlyBinaryTest()
      • incrNumberOfInstancesProcessed

        public void incrNumberOfInstancesProcessed()
      • getNumberOfNodes

        public void getNumberOfNodes​(int[] count)
      • newSplit

        public void newSplit​(int numOfLeaves)
      • getNumberOfNodes

        public int getNumberOfNodes()
      • setNumberOfNodes

        public void setNumberOfNodes​(int numberOfNodes)
      • getNumberOfLeaves

        public int getNumberOfLeaves()
      • setNumberOfLeaves

        public void setNumberOfLeaves​(int numberOfLeaves)