Trait

smile.math

Operators

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trait Operators extends AnyRef

High level feature selection operators.

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

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  2. final def ##(): Int

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  3. def +(other: String): String

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    Implicit information
    This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.
    Definition Classes
    any2stringadd
  4. def ->[B](y: B): (Operators, B)

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    Implicit information
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
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    ArrowAssoc
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    @inline()
  5. final def ==(arg0: Any): Boolean

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  6. implicit def array2VectorExpression(x: Array[Double]): VectorLift

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  7. final def asInstanceOf[T0]: T0

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  8. def beta(x: Double, y: Double): Double

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    The beta function, also called the Euler integral of the first kind.

    The beta function, also called the Euler integral of the first kind.

    B(x, y) = 01 tx-1 (1-t)y-1dt

    for x, y > 0 and the integration is over [0,1].The beta function is symmetric, i.e. B(x,y) = B(y,x).

  9. def chisqtest(table: Array[Array[Int]]): CorTest

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    Given a two-dimensional contingency table in the form of an array of integers, returns Chi-square test for independence.

    Given a two-dimensional contingency table in the form of an array of integers, returns Chi-square test for independence. The rows of contingency table are labels by the values of one nominal variable, the columns are labels by the values of the other nominal variable, and whose entries are non-negative integers giving the number of observed events for each combination of row and column. Continuity correction will be applied when computing the test statistic for 2x2 tables: one half is subtracted from all |O-E| differences. The correlation coefficient is calculated as Cramer's V.

  10. def chisqtest(x: Array[Int], prob: Array[Double], constraints: Int = 1): ChiSqTest

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    One-sample chisq test.

    One-sample chisq test. Given the array x containing the observed numbers of events, and an array prob containing the expected probabilities of events, and given the number of constraints (normally one), a small value of p-value indicates a significant difference between the distributions.

  11. def chisqtest2(x: Array[Int], y: Array[Int], constraints: Int = 1): ChiSqTest

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    Two-sample chisq test.

    Two-sample chisq test. Given the arrays x and y, containing two sets of binned data, and given one constraint, a small value of p-value indicates a significant difference between two distributions.

  12. def cholesky(A: MatrixExpression): CholeskyDecomposition

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    Cholesky decomposition.

  13. def cholesky(A: DenseMatrix): CholeskyDecomposition

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    Cholesky decomposition.

  14. def cholesky(A: Array[Array[Double]]): CholeskyDecomposition

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    Cholesky decomposition.

  15. def clone(): AnyRef

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    @throws( ... )
  16. def det(A: MatrixExpression): Double

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    Returns the determinant of matrix.

  17. def det(A: DenseMatrix): Double

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    Returns the determinant of matrix.

  18. def diag(A: Matrix): Array[Double]

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    Returns the diagonal elements of matrix.

  19. def digamma(x: Double): Double

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    The digamma function is defined as the logarithmic derivative of the gamma function.

  20. def eigen(A: DenseMatrix, k: Int): EigenValueDecomposition

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    Eigen decomposition.

  21. def eigen(A: MatrixExpression): EigenValueDecomposition

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    Eigen decomposition.

  22. def eigen(A: DenseMatrix): EigenValueDecomposition

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    Eigen decomposition.

  23. def eigen(A: Array[Array[Double]]): EigenValueDecomposition

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    Eigen decomposition.

  24. def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators

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    Implicit information
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  25. def ensuring(cond: (Operators) ⇒ Boolean): Operators

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    Implicit information
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  26. def ensuring(cond: Boolean, msg: ⇒ Any): Operators

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    Implicit information
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  27. def ensuring(cond: Boolean): Operators

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    Implicit information
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  28. final def eq(arg0: AnyRef): Boolean

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    AnyRef
  29. def equals(arg0: Any): Boolean

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  30. def erf(x: Double): Double

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    The error function (also called the Gauss error function) is a special function of sigmoid shape which occurs in probability, statistics, materials science, and partial differential equations.

    The error function (also called the Gauss error function) is a special function of sigmoid shape which occurs in probability, statistics, materials science, and partial differential equations. It is defined as:

    erf(x) = 0x e-t2dt

    The complementary error function, denoted erfc, is defined as erfc(x) = 1 - erf(x). The error function and complementary error function are special cases of the incomplete gamma function.

  31. def erfc(x: Double): Double

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    The complementary error function.

  32. def erfcc(x: Double): Double

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    The complementary error function with fractional error everywhere less than 1.2 × 10-7.

    The complementary error function with fractional error everywhere less than 1.2 × 10-7. This concise routine is faster than erfc.

  33. def eye(m: Int, n: Int): ColumnMajorMatrix

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    Returns an m-by-n identity matrix.

  34. def eye(n: Int): ColumnMajorMatrix

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    Returns an n-by-n identity matrix.

  35. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  36. def formatted(fmtstr: String): String

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    Implicit information
    This member is added by an implicit conversion from Operators to StringFormat[Operators] performed by method StringFormat in scala.Predef.
    Definition Classes
    StringFormat
    Annotations
    @inline()
  37. def ftest(x: Array[Double], y: Array[Double]): FTest

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    Test if the arrays x and y have significantly different variances.

    Test if the arrays x and y have significantly different variances. Small values of p-value indicate that the two arrays have significantly different variances.

  38. def gamma(x: Double): Double

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    Gamma function.

    Gamma function. Lanczos approximation (6 terms).

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

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  40. def hashCode(): Int

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  41. def inv(A: MatrixExpression): DenseMatrix

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    Returns the inverse of matrix.

  42. def inv(A: DenseMatrix): DenseMatrix

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    Returns the inverse of matrix.

  43. def inverf(p: Double): Double

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    The inverse error function.

  44. def inverfc(p: Double): Double

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    The inverse complementary error function.

  45. final def isInstanceOf[T0]: Boolean

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  46. def kendalltest(x: Array[Double], y: Array[Double]): CorTest

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    Kendall rank correlation test.

    Kendall rank correlation test. The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant. It is used with non-parametric data. The p-value is calculated by approximation, which is good for n > 10.

  47. def kstest(x: Array[Double], y: Array[Double]): KSTest

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    The two-sample KS test for the null hypothesis that the data sets are drawn from the same distribution.

    The two-sample KS test for the null hypothesis that the data sets are drawn from the same distribution. Small values of p-value show that the cumulative distribution function of x is significantly different from that of y. The arrays x and y are modified by being sorted into ascending order.

  48. def kstest(x: Array[Double], y: Distribution): KSTest

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    The one-sample KS test for the null hypothesis that the data set x is drawn from the given distribution.

    The one-sample KS test for the null hypothesis that the data set x is drawn from the given distribution. Small values of p-value show that the cumulative distribution function of x is significantly different from the given distribution. The array x is modified by being sorted into ascending order.

  49. def lgamma(x: Double): Double

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    log of the Gamma function.

    log of the Gamma function. Lanczos approximation (6 terms)

  50. def lu(A: MatrixExpression): LUDecomposition

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    LU decomposition.

  51. def lu(A: DenseMatrix): LUDecomposition

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    LU decomposition.

  52. def lu(A: Array[Array[Double]]): LUDecomposition

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    LU decomposition.

  53. implicit def matrix2MatrixExpression(x: DenseMatrix): MatrixLift

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  54. implicit def matrixExpression2Array(exp: MatrixExpression): DenseMatrix

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

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  56. final def notify(): Unit

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  57. final def notifyAll(): Unit

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  58. def ones(m: Int, n: Int): ColumnMajorMatrix

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    Returns an m-by-n matrix of all ones.

  59. def ones(n: Int): ColumnMajorMatrix

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    Returns an n-by-n matrix of all ones.

  60. def pearsontest(x: Array[Double], y: Array[Double]): CorTest

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    Pearson correlation coefficient test.

  61. implicit def pimpArray2D(data: Array[Array[Double]]): PimpedArray2D

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  62. implicit def pimpDouble(x: Double): PimpedDouble

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  63. implicit def pimpDoubleArray(data: Array[Double]): PimpedDoubleArray

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  64. implicit def pimpIntArray(data: Array[Int]): PimpedArray[Int]

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  65. implicit def pimpMatrix(matrix: DenseMatrix): PimpedMatrix

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  66. def qr(A: MatrixExpression): QRDecomposition

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    QR decomposition.

  67. def qr(A: DenseMatrix): QRDecomposition

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    QR decomposition.

  68. def qr(A: Array[Array[Double]]): QRDecomposition

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    QR decomposition.

  69. def rank(A: MatrixExpression): Int

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    Returns the rank of matrix.

  70. def rank(A: DenseMatrix): Int

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    Returns the rank of matrix.

  71. def spearmantest(x: Array[Double], y: Array[Double]): CorTest

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    Spearman rank correlation coefficient test.

    Spearman rank correlation coefficient test. The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie. when variables are ordinal). It can be used when there is non-parametric data and hence Pearson cannot be used.

    The raw scores are converted to ranks and the differences between the ranks of each observation on the two variables are calculated.

    The p-value is calculated by approximation, which is good for n > 10.

  72. def svd(A: DenseMatrix, k: Int): SingularValueDecomposition

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    SVD decomposition.

  73. def svd(A: MatrixExpression): SingularValueDecomposition

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    SVD decomposition.

  74. def svd(A: DenseMatrix): SingularValueDecomposition

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    SVD decomposition.

  75. def svd(A: Array[Array[Double]]): SingularValueDecomposition

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    SVD decomposition.

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

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  77. def toString(): String

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    AnyRef → Any
  78. def trace(A: Matrix): Double

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    Returns the trace of matrix.

  79. def ttest(x: Array[Double], y: Array[Double]): TTest

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    Given the paired arrays x and y, test if they have significantly different means.

    Given the paired arrays x and y, test if they have significantly different means. Small values of p-value indicate that the two arrays have significantly different means.

  80. def ttest(x: Array[Double], mean: Double): TTest

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    Independent one-sample t-test whether the mean of a normally distributed population has a value specified in a null hypothesis.

    Independent one-sample t-test whether the mean of a normally distributed population has a value specified in a null hypothesis. Small values of p-value indicate that the array has significantly different mean.

  81. def ttest2(x: Array[Double], y: Array[Double], equalVariance: Boolean = false): TTest

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    Test if the arrays x and y have significantly different means.

    Test if the arrays x and y have significantly different means. Small values of p-value indicate that the two arrays have significantly different means.

    equalVariance

    true if the data arrays are assumed to be drawn from populations with the same true variance. Otherwise, The data arrays are allowed to be drawn from populations with unequal variances.

  82. implicit def vectorExpression2Array(exp: VectorExpression): Array[Double]

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  83. final def wait(): Unit

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

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

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  86. def zeros(m: Int, n: Int): ColumnMajorMatrix

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    Returns an m-by-n zero matrix.

  87. def zeros(n: Int): ColumnMajorMatrix

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    Returns an n-by-n zero matrix.

  88. def [B](y: B): (Operators, B)

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    Implicit information
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion any2stringadd from Operators to any2stringadd[Operators]

Inherited by implicit conversion StringFormat from Operators to StringFormat[Operators]

Inherited by implicit conversion Ensuring from Operators to Ensuring[Operators]

Inherited by implicit conversion ArrowAssoc from Operators to ArrowAssoc[Operators]

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