Packages

class DynBayesNetwork extends Classifier

The DynBayesNetwork class provides Dynamic Bayesian Network (DBN) models.

Linear Supertypes
Classifier, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. DynBayesNetwork
  2. Classifier
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new DynBayesNetwork()

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 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

    Definition Classes
    DynBayesNetwork → Classifier
  6. 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

    Definition Classes
    DynBayesNetwork → Classifier
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. 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).

    Definition Classes
    Classifier
  9. 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).

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

    Reset the frequency and probability tables.

    Reset the frequency and probability tables.

    Definition Classes
    DynBayesNetwork → Classifier
  20. def size: Int

    Return the size of the feature set.

    Return the size of the feature set.

    Definition Classes
    DynBayesNetwork → Classifier
  21. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  22. 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).

    Definition Classes
    DynBayesNetwork → Classifier
  23. 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

    Definition Classes
    Classifier
  24. def toString(): String
    Definition Classes
    AnyRef → Any
  25. 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).

    Definition Classes
    DynBayesNetwork → Classifier
  26. def train(): 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.

    Definition Classes
    Classifier
  27. 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

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

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped