trait Operators extends AnyRef
High level feature selection operators.
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final
def
!=(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

final
def
##(): Int
 Definition Classes
 AnyRef → Any
 def +(other: String): String
 def >[B](y: B): (Operators, B)

final
def
==(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any
 implicit def array2VectorExpression(x: Array[Double]): VectorLift

final
def
asInstanceOf[T0]: T0
 Definition Classes
 Any

def
beta(x: Double, y: Double): Double
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) = ∫_{0}^{1} t^{x1} (1t)^{y1}dt
for x, y > 0 and the integration is over [0,1].The beta function is symmetric, i.e. B(x,y) = B(y,x).

def
chisqtest(table: Array[Array[Int]]): CorTest
Given a twodimensional contingency table in the form of an array of integers, returns Chisquare test for independence.
Given a twodimensional contingency table in the form of an array of integers, returns Chisquare 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 nonnegative 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 OE differences. The correlation coefficient is calculated as Cramer's V.

def
chisqtest(x: Array[Int], prob: Array[Double], constraints: Int = 1): ChiSqTest
Onesample chisq test.
Onesample 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 pvalue indicates a significant difference between the distributions.

def
chisqtest2(x: Array[Int], y: Array[Int], constraints: Int = 1): ChiSqTest
Twosample chisq test.
Twosample chisq test. Given the arrays x and y, containing two sets of binned data, and given one constraint, a small value of pvalue indicates a significant difference between two distributions.

def
cholesky(A: MatrixExpression): Cholesky
Cholesky decomposition.

def
cholesky(A: DenseMatrix): Cholesky
Cholesky decomposition.

def
cholesky(A: Array[Array[Double]]): Cholesky
Cholesky decomposition.

def
clone(): AnyRef
 Attributes
 protected[java.lang]
 Definition Classes
 AnyRef
 Annotations
 @throws( ... )

def
det(A: MatrixExpression): Double
Returns the determinant of matrix.

def
det(A: DenseMatrix): Double
Returns the determinant of matrix.

def
diag(A: Matrix): Array[Double]
Returns the diagonal elements of matrix.

def
digamma(x: Double): Double
The digamma function is defined as the logarithmic derivative of the gamma function.

def
eig(A: MatrixExpression): Array[Double]
Returns eigen values.

def
eig(A: DenseMatrix): Array[Double]
Returns eigen values.

def
eig(A: Array[Array[Double]]): Array[Double]
Returns eigen values.

def
eigen(A: DenseMatrix, k: Int, kappa: Double = 1E8, maxIter: Int = 1): EVD
Eigen decomposition.

def
eigen(A: MatrixExpression): EVD
Eigen decomposition.

def
eigen(A: DenseMatrix): EVD
Eigen decomposition.

def
eigen(A: Array[Array[Double]]): EVD
Eigen decomposition.
 def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators
 def ensuring(cond: (Operators) ⇒ Boolean): Operators
 def ensuring(cond: Boolean, msg: ⇒ Any): Operators
 def ensuring(cond: Boolean): Operators

final
def
eq(arg0: AnyRef): Boolean
 Definition Classes
 AnyRef

def
equals(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

def
erf(x: Double): Double
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) = ∫_{0}^{x} e^{t2}dt
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.

def
erfc(x: Double): Double
The complementary error function.

def
erfcc(x: Double): Double
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.

def
eye(m: Int, n: Int): DenseMatrix
Returns an mbyn identity matrix.

def
eye(n: Int): DenseMatrix
Returns an nbyn identity matrix.

def
finalize(): Unit
 Attributes
 protected[java.lang]
 Definition Classes
 AnyRef
 Annotations
 @throws( classOf[java.lang.Throwable] )
 def formatted(fmtstr: String): String

def
ftest(x: Array[Double], y: Array[Double]): FTest
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 pvalue indicate that the two arrays have significantly different variances.

def
gamma(x: Double): Double
Gamma function.
Gamma function. Lanczos approximation (6 terms).

final
def
getClass(): Class[_]
 Definition Classes
 AnyRef → Any

def
hashCode(): Int
 Definition Classes
 AnyRef → Any

def
inv(A: MatrixExpression): DenseMatrix
Returns the inverse of matrix.

def
inv(A: DenseMatrix): DenseMatrix
Returns the inverse of matrix.

def
inverf(p: Double): Double
The inverse error function.

def
inverfc(p: Double): Double
The inverse complementary error function.

final
def
isInstanceOf[T0]: Boolean
 Definition Classes
 Any

def
kendalltest(x: Array[Double], y: Array[Double]): CorTest
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 nonparametric data. The pvalue is calculated by approximation, which is good for n > 10.

def
kstest(x: Array[Double], y: Array[Double]): KSTest
The twosample KS test for the null hypothesis that the data sets are drawn from the same distribution.
The twosample KS test for the null hypothesis that the data sets are drawn from the same distribution. Small values of pvalue 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.

def
kstest(x: Array[Double], y: Distribution): KSTest
The onesample KS test for the null hypothesis that the data set x is drawn from the given distribution.
The onesample KS test for the null hypothesis that the data set x is drawn from the given distribution. Small values of pvalue 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.

def
lgamma(x: Double): Double
log of the Gamma function.
log of the Gamma function. Lanczos approximation (6 terms)

def
lu(A: MatrixExpression): LU
LU decomposition.

def
lu(A: DenseMatrix): LU
LU decomposition.

def
lu(A: Array[Array[Double]]): LU
LU decomposition.
 implicit def matrix2MatrixExpression(x: DenseMatrix): MatrixLift
 implicit def matrixExpression2Array(exp: MatrixExpression): DenseMatrix

final
def
ne(arg0: AnyRef): Boolean
 Definition Classes
 AnyRef

final
def
notify(): Unit
 Definition Classes
 AnyRef

final
def
notifyAll(): Unit
 Definition Classes
 AnyRef

def
ones(m: Int, n: Int): DenseMatrix
Returns an mbyn matrix of all ones.

def
ones(n: Int): DenseMatrix
Returns an nbyn matrix of all ones.

def
pearsontest(x: Array[Double], y: Array[Double]): CorTest
Pearson correlation coefficient test.
 implicit def pimpArray2D(data: Array[Array[Double]]): PimpedArray2D
 implicit def pimpDouble(x: Double): PimpedDouble
 implicit def pimpDoubleArray(data: Array[Double]): PimpedDoubleArray
 implicit def pimpIntArray(data: Array[Int]): PimpedArray[Int]
 implicit def pimpMatrix(matrix: DenseMatrix): PimpedMatrix

def
qr(A: MatrixExpression): QR
QR decomposition.

def
qr(A: DenseMatrix): QR
QR decomposition.

def
qr(A: Array[Array[Double]]): QR
QR decomposition.

def
randn(m: Int, n: Int, mu: Double = 0.0, sigma: Double = 1.0): DenseMatrix
Returns an mbyn matrix of normally distributed random numbers.

def
randn(n: Int, mu: Double = 0.0, sigma: Double = 1.0): DenseMatrix
Returns an nbyn matrix of normally distributed random numbers.

def
rank(A: MatrixExpression): Int
Returns the rank of matrix.

def
rank(A: DenseMatrix): Int
Returns the rank of matrix.

def
spearmantest(x: Array[Double], y: Array[Double]): CorTest
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 nonparametric 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 pvalue is calculated by approximation, which is good for n > 10.

def
svd(A: DenseMatrix, k: Int, kappa: Double = 1E8, maxIter: Int = 1): SVD
SVD decomposition.

def
svd(A: MatrixExpression): SVD
SVD decomposition.

def
svd(A: DenseMatrix): SVD
SVD decomposition.

def
svd(A: Array[Array[Double]]): SVD
SVD decomposition.

final
def
synchronized[T0](arg0: ⇒ T0): T0
 Definition Classes
 AnyRef

def
toString(): String
 Definition Classes
 AnyRef → Any

def
trace(A: Matrix): Double
Returns the trace of matrix.

def
ttest(x: Array[Double], y: Array[Double]): TTest
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 pvalue indicate that the two arrays have significantly different means.

def
ttest(x: Array[Double], mean: Double): TTest
Independent onesample ttest whether the mean of a normally distributed population has a value specified in a null hypothesis.
Independent onesample ttest whether the mean of a normally distributed population has a value specified in a null hypothesis. Small values of pvalue indicate that the array has significantly different mean.

def
ttest2(x: Array[Double], y: Array[Double], equalVariance: Boolean = false): TTest
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 pvalue 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.
 implicit def vectorExpression2Array(exp: VectorExpression): Array[Double]

final
def
wait(): Unit
 Definition Classes
 AnyRef
 Annotations
 @throws( ... )

final
def
wait(arg0: Long, arg1: Int): Unit
 Definition Classes
 AnyRef
 Annotations
 @throws( ... )

final
def
wait(arg0: Long): Unit
 Definition Classes
 AnyRef
 Annotations
 @throws( ... )

def
zeros(m: Int, n: Int): DenseMatrix
Returns an mbyn zero matrix.

def
zeros(n: Int): DenseMatrix
Returns an nbyn zero matrix.
 def →[B](y: B): (Operators, B)
High level Smile operators in Scala.