scalation.analytics

BayesNetwork

class BayesNetwork extends Classifier with Error

The BayesNetwork class implements a Bayesian Network Classifier. It classifies a data vector 'z' by determining which of 'k' classes has the highest Joint Probability of 'z' and the outcome (i.e., one of the 'k' classes) of occurring. The Joint Probability calculation is factored into multiple calculations of Conditional Probability. Conditional dependencies are specified using a Directed Acyclic Graph (DAG). Nodes are conditionally dependent on their parents only. Conditional probability are recorded in tables. Training is achieved by ...

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Instance Constructors

  1. new BayesNetwork(dag: DAG, table: Array[Map[Int, Double]], k: Int, cn: Array[String])

    dag

    the directed acyclic graph specifying conditional dependencies

    table

    the array of tables recording conditional probabilities

    k

    the number of classes

    cn

    the names for all classes

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def classify(z: VectorD): (Int, String)

    Given a continuous data vector 'z', classify it returning the class number (0, .

    Given a continuous data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. The vector 'z' id first converted to an integer valued vector by rounding.

    z

    the data vector to classify

    Definition Classes
    BayesNetworkClassifier
  8. def classify(z: VectorI): (Int, String)

    Given an integer-valued data vector 'z', classify it returning the class number (0, .

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

    z

    the data vector to classify

    Definition Classes
    BayesNetworkClassifier
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( ... )
  10. def cp(i: Int, key: VectorI): Double

    Compute the Conditional Probability (CP) of 'x_i' given its parents' values.

    Compute the Conditional Probability (CP) of 'x_i' given its parents' values.

    i

    the ith variable (whose conditional probability is sought)

    key

    the values of x_i's parents and x_i

  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  14. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  15. final def getClass(): Class[_]

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  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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  18. def jp(x: VectorI): Double

    Compute the Joint Probability (JP) of vector 'x' ('z' concat outcome).

    Compute the Joint Probability (JP) of vector 'x' ('z' concat outcome). as the product of each of its element's conditional probability.

    x

    the vector of variables

  19. final def ne(arg0: AnyRef): Boolean

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. final def synchronized[T0](arg0: ⇒ T0): T0

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  23. def toString(): String

    Definition Classes
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  24. def train(): Unit

    Train the classifier, i.

    Train the classifier, i.e., ...

    Definition Classes
    BayesNetworkClassifier
  25. final def wait(): Unit

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    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  27. final def wait(arg0: Long): Unit

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