NormForm

scalation.mathstat.NormForm
class NormForm(x_: VecMat, robust: Boolean = ...) extends Transform

The NormForm class applies the Z-transformation/normalization/standardization (subtract mean b(0) and divide by standard deviation b(1)). Like ''StandardScalar'' in sk-learn.

Value parameters

robust

whether to add a small value (ε) to the standard deviation to avoid DBZ when a whole column has zero stdev (σ) it should be removed in pre-processing

x_

the input vector or matrix to be transformed (needed to get w)

Attributes

See also

www.geeksforgeeks.org/machine-learning/standardscaler-minmaxscaler-and-robustscaler-techniques-ml/ x -> (x - μ)/σ robust version: (x - μ)/(σ + ε) or (x - μ)/√(σ² + ε)

Graph
Supertypes
trait Transform
class Object
trait Matchable
class Any

Members list

Value members

Concrete methods

def f(x: MatrixD): MatrixD
def fi(y: MatrixD): MatrixD

Inherited methods

inline def b_: VectorD

Attributes

Inherited from:
Transform
def df(x: MatrixD, i: Int): MatrixD

Attributes

Inherited from:
Transform
def df(x: MatrixD): MatrixD

Attributes

Inherited from:
Transform
def df(x: VectorD): MatrixD

Attributes

Inherited from:
Transform
def setLU(_lu: VectorD): Unit

Attributes

Inherited from:
Transform
def testM(x: MatrixD): Unit

Attributes

Inherited from:
Transform
def testV(x: VectorD): Unit

Attributes

Inherited from:
Transform

Concrete fields

val ε: Double

Inherited fields

val f: FunctionV2V

Attributes

Inherited from:
Transform

Attributes

Inherited from:
Transform

Attributes

Inherited from:
Transform

Attributes

Inherited from:
Transform