kernlab (version 0.9-25)

onlearn-class: Class "onlearn"

Description

The class of objects used by the Kernel-based Online learning algorithms

Objects from the Class

Objects can be created by calls of the form new("onlearn", ...). or by calls to the function inlearn.

Slots

kernelf:

Object of class "function" containing the used kernel function

buffer:

Object of class "numeric" containing the size of the buffer

kpar:

Object of class "list" containing the hyperparameters of the kernel function.

xmatrix:

Object of class "matrix" containing the data points (similar to support vectors)

fit:

Object of class "numeric" containing the decision function value of the last data point

onstart:

Object of class "numeric" used for indexing

onstop:

Object of class "numeric" used for indexing

alpha:

Object of class "ANY" containing the model parameters

rho:

Object of class "numeric" containing model parameter

b:

Object of class "numeric" containing the offset

pattern:

Object of class "factor" used for dealing with factors

type:

Object of class "character" containing the problem type (classification, regression, or novelty

Methods

alpha

signature(object = "onlearn"): returns the model parameters

b

signature(object = "onlearn"): returns the offset

buffer

signature(object = "onlearn"): returns the buffer size

fit

signature(object = "onlearn"): returns the last decision function value

kernelf

signature(object = "onlearn"): return the kernel function used

kpar

signature(object = "onlearn"): returns the hyper-parameters used

onlearn

signature(obj = "onlearn"): the learning function

predict

signature(object = "onlearn"): the predict function

rho

signature(object = "onlearn"): returns model parameter

show

signature(object = "onlearn"): show function

type

signature(object = "onlearn"): returns the type of problem

xmatrix

signature(object = "onlearn"): returns the stored data points

See Also

onlearn, inlearn

Examples

# NOT RUN {
## create toy data set
x <- rbind(matrix(rnorm(100),,2),matrix(rnorm(100)+3,,2))
y <- matrix(c(rep(1,50),rep(-1,50)),,1)

## initialize onlearn object
on <- inlearn(2,kernel="rbfdot",kpar=list(sigma=0.2),
              type="classification")

## learn one data point at the time
for(i in sample(1:100,100))
on <- onlearn(on,x[i,],y[i],nu=0.03,lambda=0.1)

sign(predict(on,x))

# }