lasvmTrain: lasvmTrain

In aydindemircioglu/simanle: A simple manifold learning package

Description

Use lasvm to train a given problem.

Usage

lasvmTrain(x, y, gamma = 1, cost = 1, degree = 3, coef0 = 0,
  optimizer = 1, kernel = 2, selection = 0, termination = 0,
  sample = 1e+08, cachesize = 256, bias = 1, epochs = 1,
  epsilon = 0.001, verbose = FALSE)

Arguments

x	data matrix
y	labels
gamma	RBF kernel parameter
cost	regularization parameter
degree	degree for poly kernel
coef0	coefficient for poly kernel
optimizer	type of optimizer
kernel	kernel type
selection	selection strategy
termination	criterion for stopping
sample	time for stopping/number of iterations tec
cachesize	size of kernel cache
bias	use bias?
epochs	number of epochs
epsilon	stopping criterion parameter
verbose	verbose output?

Value

a list consisting of alpha alpha for SVs as vector SV support vectors as matrix

Examples

model = simanle::lasvmTrain (x = as.matrix(iris[seq(1,150,2),1:4]),
	y = (as.numeric(iris[seq(1,150,2),5]) %% 2)*2-1,
	gamma = 1,
	cost = 1,
	kernel = 2)
ytrue = (as.numeric(iris[seq(2,150,2),5]) %% 2)*2-1
result = lasvmPredict (x = as.matrix(iris[seq(2,150,2),1:4]), model)
ypred = result$predictions
error = sum(abs(ypred - ytrue))/length(ytrue)
cat ("Error rate =", error*100)

aydindemircioglu/simanle documentation built on May 11, 2019, 4:14 p.m.