scalation.analytics

MarkovClusteringTest

Related Doc: package analytics

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: Any): Boolean

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  2. final def ##(): Int

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

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  4. def args: Array[String]

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    @deprecatedOverriding( "args should not be overridden" , "2.11.0" )

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  7. val cluster: Array[Int]

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

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  9. def equals(arg0: Any): Boolean

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  10. val executionStart: Long

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    App

  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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    @deprecatedOverriding( "main should not be overridden" , "2.11.0" )

  16. val my: MarkovClustering

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. val rg: RandomGraph

    * 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.)

    * 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 ()) *

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

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  22. val t0: Long

  23. def toString(): String

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  24. final def wait(): Unit

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

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

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  27. val y: MatrixD

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

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