SpicyMKL: DALMKL

Description Usage Arguments Value Examples

View source: R/DALMKL.R

Description

This function conducts DALMKL for precomputed gramm matrices

Usage

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SpicyMKL(K, y, loss = "hinge", C = 0.5, tolOuter = 0.01,
  tolInner = 1e-06, OuterMaxiter = 500, InnerMaxiter = 500,
  calpha = 10)

Arguments

K

The multiple kernel cube (3-d array)

y

The outome variable, must be -1/1

loss

The loss function to be used, must be either 'hinge' or 'logistic', default to be 'hinge'

C

tuning parameter for block one norm, default to be .5

tolOuter

change between to iterations is smaller than this, algorithms is considered to have converged for outer loop, default to be .01

tolInner

change between to iterations is smaller than this, algorithms is considered to have converged for inner loop, default to be .000001

OuterMaxiter

maximum number of allowed iteratons for outer loop, default to be 500

InnerMaxiter

maximum number of allowed iteratons for inner loop, default to be 500

calpha

Lagrangian parameter, default to be 10

Value

b Estimated Intercept

alpha coeffiencents of the dual of MKL

weight Estimated between kernel weight

rho Estimated within kernel weight

Examples

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data(benchmark.data)
data.mkl=benchmark.data[[1]]
kernels=rep('radial',2)
sigma=c(2,1/20)
train.samples=sample(1:nrow(data.mkl),floor(0.7*nrow(data.mkl)),replace=FALSE)
degree=sapply(1:length(kernels), function(a) ifelse(kernels[a]=='p',2,0))
#Kernels.gen splts the data into a training and test set, and generates the desired kernel matrices.
#Here we generate two gaussisan kernel matrices with sigma hyperparameter 2 and 0.05
K=kernels.gen(data=data.mkl[,1:2],train.samples=train.samples,kernels=kernels,sigma=sigma,
degree=degree,scale=rep(0,length(kernels)))
C=0.05 #Cost parameter for DALMKL
K.train=K$K.train
K.test=K$K.test
# parameters set up
 ytr=data.mkl[train.samples,3]
#Converts list of kernel matrices in to an array with is appropriate for C++ code
 k.train=simplify2array(K.train)  
 k.test=simplify2array(K.test)
#Implement DALMKL with the hinge loss function
 spicy_svmb1n=SpicyMKL(K=k.train,y=ytr, loss='hinge',C=C)
 #Implement DALMKL with the hinge loss function
 spicy_logistic=SpicyMKL(K=k.train,y=ytr, loss='logistic',C=C)#' 

Example output



RMKL documentation built on May 2, 2019, 7:55 a.m.