scalation.analytics

MarkovClusteringTest

object MarkovClusteringTest extends App

The MarkovClusteringTest 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. final def !=(arg0: AnyRef): Boolean

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    AnyRef

  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. def args: Array[String]

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  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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  9. val cluster: Array[Int]

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

    Definition Classes
    App → DelayedInit

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

    Definition Classes
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  12. def equals(arg0: Any): Boolean

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  13. val executionStart: Long

    Definition Classes
    App

  14. def finalize(): Unit

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

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  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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

    Definition Classes
    App

  19. val my: MarkovClustering

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

    Definition Classes
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  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), 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 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, 0.0) x(0) = ListMap ((1, 1.0), (5, 1.0), (6, 1.0), (9, 1.0)) x(1) = ListMap ((0, 1.0), (2, 1.0), (4, 1.0)) x(2) = ListMap ((1, 1.0), (3, 1.0), (4, 1.0)) x(3) = ListMap ((2, 1.0), (7, 1.0), (8, 1.0), (10, 1.0)) x(4) = ListMap ((1, 1.0), (2, 1.0), (6, 1.0), (7, 1.0)) x(5) = ListMap ((0, 1.0), (9, 1.0)) x(6) = ListMap ((0, 1.0), (4, 1.0), (9, 1.0)) x(7) = ListMap ((3, 1.0), (4, 1.0), (8, 1.0), (10, 1.0)) x(8) = ListMap ((3, 1.0), (7, 1.0), (10, 1.0), (11, 1.0)) x(9) = ListMap ((0, 1.0), (5, 1.0), (6, 1.0)) x(10) = ListMap ((3, 1.0), (7, 1.0), (8, 1.0), (11, 1.0)) x(11) = ListMap ((8, 1.0))

    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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  30. val y: MatrixD

Inherited from App

Inherited from DelayedInit

Inherited from AnyRef

Inherited from Any

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