Packages

class NullModel extends Classifier

The NullModel class implements an Integer-Based Null Model Classifier, which is a simple classifier for discrete input data. The classifier is trained just using a classification vector 'y'. Each data instance is classified into one of 'k' classes numbered 0, ..., k-1.

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Instance Constructors

  1. new NullModel(y: VectoI, k: Int, cn: Array[String])

    y

    the class vector, where y(i) = class for instance i

    k

    the number of classes

    cn

    the names for all classes

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. def actualVpredicted(y: VectoI, yp: VectoI): Map[String, Double]

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.

    y

    the actual class labels

    yp

    the precicted class labels

    Definition Classes
    Classifier
    See also

    www.dataschool.io/simple-guide-to-confusion-matrix-terminology

  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def classify(z: VectoI): (Int, String, Double)

    Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.

    Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. Return the best class, its name and its relative probability.

    z

    the data vector to classify

    Definition Classes
    NullModelClassifier
  7. 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. Override in classes that require precise real values for classification.

    z

    the real vector to classify

    Definition Classes
    Classifier
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. def crossValidate(nx: Int = 10, show: Boolean = false): 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'. FIX - should return a StatVector

    nx

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

    show

    the show flag (show result from each iteration)

    Definition Classes
    Classifier
  10. def crossValidateRand(nx: Int = 10, show: Boolean = false): 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. FIX - are the comments correct? FIX - should return a StatVector

    nx

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

    show

    the show flag (show result from each iteration)

    Definition Classes
    Classifier
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  15. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  20. def reset(): Unit

    Reset or re-initialize all the frequency counters.

    Reset or re-initialize all the frequency counters.

    Definition Classes
    NullModelClassifier
  21. def size: Int

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

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

    Definition Classes
    NullModelClassifier
  22. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  23. def test(itest: IndexedSeq[Int]): Double

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

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

    itest

    indices of the instances considered test data

    Definition Classes
    NullModelClassifier
  24. 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).

    Definition Classes
    Classifier
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. def train(itest: IndexedSeq[Int]): NullModel

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    itest

    indices of the instances considered as testing data

    Definition Classes
    NullModelClassifier
  27. def train(): 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.

    Definition Classes
    Classifier
  28. 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

    Definition Classes
    Classifier
  29. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

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