SimpleExpRegression

scalation.modeling.SimpleExpRegression
See theSimpleExpRegression companion class

The SimpleExpRegression companion object provides factory methods for creating simple exponential regression models.

Attributes

Companion
class
Graph
Supertypes
class Object
trait Matchable
class Any
Self type

Members list

Value members

Concrete methods

def apply(xy: MatrixD, fname: Array[String], hparam: HyperParameter, nonneg: Boolean)(col: Int): SimpleExpRegression

Create an SimpleExpRegression object from a combined data-response matrix. The last column is assumed to be the response column.

Create an SimpleExpRegression object from a combined data-response matrix. The last column is assumed to be the response column.

Value parameters

col

the designated response column (defaults to the last column)

fname

the feature/variable names (defaults to null)

hparam

the hyper-parameters (currently has none)

nonneg

whether to check that responses are nonnegative (defaults to true)

xy

the combined data-response matrix (predictors and response)

Attributes

def rescale(x: MatrixD, y: VectorD, fname: Array[String], hparam: HyperParameter, nonneg: Boolean): SimpleExpRegression

Create an SimpleExpRegression object from a data matrix and a response vector. This mathod provides data rescaling.

Create an SimpleExpRegression object from a data matrix and a response vector. This mathod provides data rescaling.

Value parameters

fname

the feature/variable names (default to null)

hparam

the hyper-parameters (currently has none)

nonneg

whether to check that responses are nonnegative (defualts to true)

x

the data/input m-by-n matrix (augment with a first column of ones to include intercept in model)

y

the response/output m-vector

Attributes