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com.iaja.ldavis

LDAvisMath

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object LDAvisMath

Created by mageswarand on 26/4/17.

Some routines are written from ground-up imitating python routines

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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 PCA(data: DenseMatrix[Double], components: Int): DenseMatrix[Double]

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  5. def PCoA(pairDistMatrix: DenseMatrix[Double], numComponents: Int = 2): DenseMatrix[Double]

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  6. final def asInstanceOf[T0]: T0

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  7. def binomialCoefficient(n: Int, r: Int): BigInt

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  8. def clone(): AnyRef

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  9. def entropy(pk: DenseVector[Double], qk: DenseVector[Double]): Double

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    Computes the Kullback-Leibler divergence S = sum(pk * log(pk / qk), axis=0)

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. def jensenShannon(pointP: DenseVector[Double], pointQ: DenseVector[Double]): Double

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  17. def klDivergence(p1: Array[Double], p2: Array[Double]): Array[Double]

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  18. def matVecElementWiseOp(mat: DenseMatrix[Double], vec: DenseVector[Double], op: (Double, Double) ⇒ Double): DenseMatrix[Double]

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  19. def mean(v: Vector[Double]): Double

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

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  21. final def notify(): Unit

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  22. final def notifyAll(): Unit

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  23. def pairNDimDistance(X: DenseMatrix[Double], metric: (DenseVector[Double], DenseVector[Double]) ⇒ Double = jensenShannon): DenseVector[Double]

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  24. def squareForm(X: DenseVector[Double]): DenseMatrix[Double]

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

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  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. def zeroMean(m: DenseMatrix[Double]): DenseMatrix[Double]

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