write.svm: Write SVM Object to File

In e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien

Write SVM Object to File

Description

This function exports an SVM object (trained by svm) to two specified files. One is in the format that the function 'svm_load_model()' of libsvm can read. The other is for scaling data, containing a data with centers and scales for all variables.

Usage

write.svm(object, svm.file = "Rdata.svm",
          scale.file = "Rdata.scale", yscale.file = "Rdata.yscale")

Arguments

object	Object of class "svm", created by svm.
svm.file	filename to export the svm object to.
scale.file	filename to export the scaling data of the explanatory variables to.
yscale.file	filename to export the scaling data of the dependent variable to, if any.

Details

This function is useful when SVM models trained in R shall be used in other environments. The SVM model is saved in the standard format of libsvm. The scaling data are written to separate files because scaling data are not included in the standard format of libsvm. The format of the scaling data file is a n times 2 matrix: the n-th row corresponds to the n-th dimension of the data, the columns being formed of the corresponding mean and scale. If scaling information for the dependent variable exists (in case of regression models), it is stored in yet another file (1 times 2 matrix).

Author(s)

Tomomi TAKASHINA (based on 'predict.svm' by David Meyer) [e-mail]

See Also

svm

Examples

data(iris)
attach(iris)

## classification mode
# default with factor response:
model <- svm (Species~., data=iris)

# export SVM object to (temporary) files
svm_file <- tempfile()
scale_file <- tempfile()

write.svm(model, svm.file = svm_file, scale.file = scale_file)

# read scale file
# the n-th row is corresponding to n-th dimension. The 1st column contains the
# center value, the 2nd column is the scale value.
read.table(scale_file)

# clean up
unlink(svm_file)
unlink(scale_file)

e1071 documentation built on Sept. 17, 2024, 1:06 a.m.