Packages

trait Classifier extends Model

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.

Linear Supertypes
Model, Error, AnyRef, Any
Known Subclasses
BayesClassifier, BayesClfML, BayesNetwork, ClassifierInt, ClassifierReal, DecisionTreeC45, DecisionTreeC45wp, DecisionTreeID3, DecisionTreeID3wp, FANBayes, FANBayes0, GMM, HiddenMarkov, KNN_Classifier, LDA, LogisticRegression, NaiveBayes, NaiveBayes0, NaiveBayesR, NullModel, OneBAN, OneBAN0, PGMHD3, PGMHD3cp, PGMHD3fl, RandomForest, RandomForest2, SimpleLDA, SimpleLogisticRegression, SupportVectorMachine, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0, BayesClassifier, NaiveBayes, NaiveBayes0, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0
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  1. Classifier
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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 fits into, returning the best class, its name and its relative probability.

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

    z

    the real vector to classify

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

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

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

    z

    the integer vector to classify

  3. abstract def crossValidate(nx: Int = 10, show: Boolean = false): Array[Statistic]

    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 10x).

    show

    the show flag (show result from each iteration)

  4. abstract def crossValidateRand(nx: Int = 10, show: Boolean = false): Array[Statistic]

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method randomizes the instances/rows selected for the test dataset.

    nx

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

    show

    the show flag (show result from each iteration)

  5. abstract def eval(x_e: MatriD, y_e: VectoD): Model

    Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model).

    Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classess should implement various diagnostics for the test and full (training + test) datasets.

    x_e

    the test/full data/input matrix (impl. classes should default to x)

    y_e

    the test/full response/output vector (impl. classes should default to y)

    Definition Classes
    Model
  6. abstract def hparameter: HyperParameter

    Return the model hyper-parameters (if none, return null).

    Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.

    Definition Classes
    Model
  7. abstract def parameter: VectoD

    Return the vector of model parameter/coefficient values.

    Return the vector of model parameter/coefficient values.

    Definition Classes
    Model
  8. abstract def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    Definition Classes
    Model
    See also

    'summary' method for more details

  9. abstract def reset(): Unit

    Reset variables such as frequency counters.

  10. abstract def size: Int

    Return the number of data vectors/points in the entire dataset (training + testing).

  11. abstract def test(itest: Ints): Double

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

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

    itest

    the indices of the instances considered test data

  12. abstract def train(itest: Ints): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. The indices for the testing dataset are given and the training dataset consists of all the other instances. Must be implemented in any extending class.

    itest

    the indices of the instances considered as testing data

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. val modelConcept: URI

    An optional reference to an ontological concept

    An optional reference to an ontological concept

    Definition Classes
    Model
  13. def modelName: String

    An optional name for the model (or modeling technique)

    An optional name for the model (or modeling technique)

    Definition Classes
    Model
  14. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  16. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  17. def setStream(str: Int = 0): Unit

    Set the random number 'stream' to 'str'.

    Set the random number 'stream' to 'str'. This is useful for testing purposes, since a fixed stream will follow the same sequence each time.

    str

    the new fixed random number stream

  18. val stream: Int

    the random number stream {0, 1, ..., 999} to be used

    the random number stream {0, 1, ..., 999} to be used

    Attributes
    protected
  19. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  20. def test(testStart: Int, testEnd: Int): Double

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

    Test the quality of the training with a test dataset and return the fraction of correct classifications. Can be used when the dataset is randomized so that the testing/training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

  21. def toString(): String
    Definition Classes
    AnyRef → Any
  22. def train(xx: MatriD = null, yy: VectoD = null): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the whole dataset is used for training.

    xx

    the data/input matrix (impl. classes should ignore or default xx to x)

    yy

    the response/classification vector (impl. classes should ignore or default yy to y)

    Definition Classes
    Classifier → Model
  23. def train(testStart: Int, testEnd: Int): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the dataset is randomized so that the training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

    starting index of test region (inclusive) used in cross-validation

    testEnd

    ending index of test region (exclusive) used in cross-validation

  24. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  25. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  26. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from Model

Inherited from Error

Inherited from AnyRef

Inherited from Any

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