SingularValueDecomposition

Value Members

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 apply(P: DenseMatrix[Double]): DenseSVD

   

5. final def asInstanceOf[T0]: T0

   

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6. def attribCorrelation(svD: SVD[DenseMatrix[Double], DenseVector[Double]]): DenseMatrix[Double]

   

   calculate the correlation matrix of attribute to attribute (columns to columns) derived from the SVD decomposition $$ X = U\SigmaV' $$ The correlation matrix is approximated such that $$ X'X = V\Sigma^ V' $$

7. def clone(): AnyRef

   

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8. def contributions(svD: SVD[DenseMatrix[Double], DenseVector[Double]]): DenseVector[Double]

   

   calculate the contribution of each singular vector to the whole entropy for each singular component calculated as

   calculate the contribution of each singular vector to the whole entropy for each singular component calculated as

   $$ f_k = s_k^2/\sum_{i=1}^r s_i^2 $$

   returns

   vector f of values for contribution to entropy.

9. def entropy(svD: SVD[DenseMatrix[Double], DenseVector[Double]]): Double

   

   calculate the entropy for the entire data set.

   calculate the entropy for the entire data set. Based on the singular components this gives a value between 0 and 1 which indicates the spread of variation within the singular components. If the entropy is close to 0 the spread of variation is explained by the first singular component if the entropy is close to 1 the spread of variation is almost uniform between all singular components.

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. final def ne(arg0: AnyRef): Boolean

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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19. def objectAttribCorrelation(svD: SVD[DenseMatrix[Double], DenseVector[Double]]): DenseMatrix[Double]

    

    approximate correlation matrix of object (rows) and attributes (columns) is derived from the SVD decomposition $$ X = U\SigmaV' $$ such that $$ XX' = U\Sigma^2U' $$ Where $\Sigma$ is the diagonal matrix of the singular values.

20. def singularDiagonal(svD: SVD[DenseMatrix[Double], DenseVector[Double]]): DenseMatrix[Double]

    

    convert the singular values into a matrix

21. def singularRootDiagonal(svD: SVD[DenseMatrix[Double], DenseVector[Double]]): DenseMatrix[Double]

    

    convert the singular values into a diagonal matrix where the values are the square root of the diagonal.

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

    

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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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