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trait Classifier extends AnyRef

The Classifier trait provides a common framework for several classifiers. A classifier is for bounded responses. When the number of distinct responses cannot be bounded by some integer 'k', a predictor should be used.

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AnyRef, Any
Known Subclasses
BayesClassifier, BayesClfML, BayesNetwork, ClassifierInt, ClassifierReal, DecisionTreeC45, DecisionTreeID3, DynBayesNetwork, GMM, HiddenMarkov, KNN_Classifier, LDA, LogisticRegression, NaiveBayes, NaiveBayes0, NaiveBayesR, OneBAN, OneBAN0, PGMHD3, PGMHD3cp, PGMHD3fl, SupportVectorMachine, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0, BayesClassifier, NaiveBayes, NaiveBayes0, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0
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Abstract Value Members

  1. abstract def classify(z: VectoD): (Int, String, Double)

    Given a new continuous data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.

    Given a new continuous data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.

    z

    the vector to classify

  2. abstract def classify(z: VectoI): (Int, String, Double)

    Given a new discrete data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.

    Given a new discrete data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.

    z

    the vector to classify

  3. abstract def reset(): Unit

    Reset the frequency and probability tables.

  4. abstract def size: Int

    Return the size of the feature set.

  5. abstract def test(testStart: Int, testEnd: Int): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

  6. abstract def train(testStart: Int, testEnd: Int): Unit

    Given a set of data vectors and their classifications, build a classifier.

    Given a set of data vectors and their classifications, build a classifier.

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  6. def crossValidate(nx: Int = 10): Double

    Test the accuracy of the classified results by cross-validation, returning the accuracy.

    Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'.

    nx

    the number of crosses and cross-validations (defaults to 5x).

  7. def crossValidateRand(nx: Int = 10): Double

    Test the accuracy of the classified results by cross-validation, returning the accuracy.

    Test the accuracy of the classified results by cross-validation, returning the accuracy. This version of cross-validation relies on "subtracting" frequencies from the previously stored global data to achieve efficiency.

    nx

    number of crosses and cross-validations (defaults to 10x).

  8. final def eq(arg0: AnyRef): Boolean
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  10. def finalize(): Unit
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  11. final def getClass(): Class[_]
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  15. final def notify(): Unit
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  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. def test(itest: VectorI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    itest

    the indices of the instances considered test data

  19. def toString(): String
    Definition Classes
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  20. def train(): Unit

    Given a set of data vectors and their classifications, build a classifier.

  21. def train(itest: IndexedSeq[Int]): Unit

    Given a set of data vectors and their classifications, build a classifier.

    Given a set of data vectors and their classifications, build a classifier.

    itest

    the indices of the instances considered as testing data

  22. final def wait(): Unit
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  23. final def wait(arg0: Long, arg1: Int): Unit
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  24. final def wait(arg0: Long): Unit
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