scalation.analytics

MarkovClusteringTest

object MarkovClusteringTest extends App

This object is used to test the MarkovClustering class.

See also

www.cs.ucsb.edu/~xyan/classes/CS595D-2009winter/MCL_Presentation2.pdf

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

    Definition Classes
    AnyRef

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

    Definition Classes
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  3. final def ##(): Int

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

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

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  6. def args: Array[String]

    Attributes
    protected
    Definition Classes
    App

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

    Attributes
    protected[lang]
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    @throws()

  9. val cluster: Array[Int]

  10. def delayedInit(body: ⇒ Unit): Unit

    Definition Classes
    App → DelayedInit

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

    Definition Classes
    AnyRef

  12. def equals(arg0: Any): Boolean

    Definition Classes
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  13. val executionStart: Long

    Definition Classes
    App

  14. def finalize(): Unit

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  15. final def getClass(): java.lang.Class[_]

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  16. def hashCode(): Int

    Definition Classes
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  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  18. def main(args: Array[String]): Unit

    Definition Classes
    App

  19. val my: MarkovClustering

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

    Definition Classes
    AnyRef

  21. final def notify(): Unit

    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  23. val rg: RandomGraph

    * val g = new MatrixD ((12, 12), 0.

    * val g = new MatrixD ((12, 12), 0., 1., 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.)

    println ("-----------------------------------------------------------") println ("g = " + g) val mg = new MarkovClustering (g) mg.addSelfLoops () mg.normalize () println ("result = " + mg.processMatrix ()) println ("cluster = " + mg.cluster ())

    // Test the MCL Algorithm on a Markov transition matrix.

    val t = new MatrixD ((12, 12), 0.2, 0.25, 0.0, 0.0, 0.0, 0.333, 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.2, 0.25, 0.25, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.2, 0.0, 0.0, 0.0, 0.2, 0.2, 0.0, 0.2, 0.0, 0.0, 0.25, 0.25, 0.0, 0.2, 0.0, 0.25, 0.2, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.333, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.2, 0.0, 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.0, 0.0, 0.2, 0.2, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.2, 0.2, 0.0, 0.2, 0.333, 0.2, 0.0, 0.0, 0.0, 0.0, 0.333, 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.2, 0.2, 0.0, 0.2, 0.333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.2, 0.333)

    println ("-----------------------------------------------------------") println ("t = " + t) val mt = new MarkovClustering (t) println ("result = " + mt.processMatrix ()) println ("cluster = " + mt.cluster ())

    // Test the MCL Algorithm on a graph represented as a sparse adjacency matrix.

    val x = new SparseMatrixD (12, 0.) x(0) = ListMap ((1, 1.), (5, 1.), (6, 1.), (9, 1.)) x(1) = ListMap ((0, 1.), (2, 1.), (4, 1.)) x(2) = ListMap ((1, 1.), (3, 1.), (4, 1.)) x(3) = ListMap ((2, 1.), (7, 1.), (8, 1.), (10, 1.)) x(4) = ListMap ((1, 1.), (2, 1.), (6, 1.), (7, 1.)) x(5) = ListMap ((0, 1.), (9, 1.)) x(6) = ListMap ((0, 1.), (4, 1.), (9, 1.)) x(7) = ListMap ((3, 1.), (4, 1.), (8, 1.), (10, 1.)) x(8) = ListMap ((3, 1.), (7, 1.), (10, 1.), (11, 1.)) x(9) = ListMap ((0, 1.), (5, 1.), (6, 1.)) x(10) = ListMap ((3, 1.), (7, 1.), (8, 1.), (11, 1.)) x(11) = ListMap ((8, 1.))

    println ("-----------------------------------------------------------") println ("x = " + x) val mx = new MarkovClustering (x) mx.addSelfLoops () mx.normalize () println ("result = " + mx.processMatrix ()) println ("cluster = " + mx.cluster ()) *

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

    Definition Classes
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  25. val t0: Long

  26. def toString(): String

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  27. final def wait(): Unit

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  28. final def wait(arg0: Long, arg1: Int): Unit

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  29. final def wait(arg0: Long): Unit

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    @throws()

  30. val y: MatrixD

Inherited from App

Inherited from DelayedInit

Inherited from AnyRef

Inherited from Any