## Porting Support Vector Machine Models from R to Another Language – Part 2

This second part of “Porting Support Vector Machine Models…” explains the algorithm for prediction of new data by nonlinear support vector machines (SVM) and Gaussian radial basis kernel. Instead of using pseudo-code, the algorithm is low level implemented in R. This low level implementation is easy to translate to C/C++ or any other language.

Remind the following steps already listed in part 1:

Step 1: To port your SVM model, first extract all data and model parameters you need for prediction from the R environment and save it outside R.

Step 2: Implement the SVM prediction algorithm in a programming language of your choice and parameterise it with the data coming from R.

In part 1 you learned how to finish off step 1. You learned which data you need for prediction in another environment and how to extract it from kernlab SVM model.

In part 2 of this article you will learn how to finish off step 2. The SVM prediction algorithm and an implementation example will be presented.

## Step 2

Formal the prediction algorithm is displayed in the following manner:

The nonlinear support vector machine prediction formula

$\hat{y} = \sum_{\iota=1}^{\mu} \alpha_{\iota} K(x_{\iota}, x) + b$

and the kernel function (Gaussian radial basis function)

$K(x_{\iota}, x) = \exp(-\sigma||x_{\iota}-x||_{2}^{2})$

### Import model parameters

First import all model parameters you need (compare part 1). Following is listet if the model information is a single value or an array of one ([]) or two ([][])  dimensions.

• sigma ($\sigma$), single value
• a ($\alpha$), array dim usually: []
• supportV ($x_{\iota}$), Array dim usually: [][]
• bias ($b$), single value
• ymean, single value
• yscale, single value
• xmean, Array dim usually: []
• xscale, Array dim usually: []

### Import new data to predict

Below the new predictors are named as newdata. For example use predictors X from your SVM training (svmdat, part 1) to test your algorithm:

    newdata <- X      #(from R in part 1: svmdat[,colnames(svmdat)!="y"])

### Kernel Function: Gaussian Radial Basis Function

Define the kernel function, in our example the Gaussian radial basis function.

K <- function(xi,x,sigma) {
if(length(xi) != length(x)) stop("vector dimensions not consistent")
p <- length(xi)
#Preallocation
r <-0
#Calculate euklidian norm of differences
for(l in 1:p)  r <- r + (xi[l]-x[l]) * (xi[l]-x[l])
return(exp(-sigma * r ))
}

### Autoscale New Data

n <- ncol(X) # j = 1...n (columns of X)
m <- nrow(X) # i = 1...m (rows of X)

Preallocation with zeros

    newdataAS <- matrix(data = numeric(m*n),nrow = m, ncol = n)

Columnwise mean subtraction and scale division

for(k in 1:n){
for(i in 1:m){
newdataAS[i,j] <- (newdata[i,j]-xmean[j]) / xscale[j]
}
}

### SVM Prediction Algorithm

Dimension (number of rows) of support vector matrix

mu <- nrow(supportV) # iota = 1...mu (rows of support vector matrix)

Preallocation with zeros

H <- matrix(numeric(mu * m), ncol = mu)

Calculate kernel (projecting newdataAS and supportV to the feature space H)

for (i in 1:m) {
for (iota in 1:mu) {
H[i, iota] <- K(supportV[iota, ], newdataAS[i, ], sigma)
}
}

Prediction of new values yp

yp <- numeric(m) # Preallocation with zeros

for(i in 1:m){
for(iv in 1:mu){
yp[i] <- yp[i] + (H[i,iota] * a[iota])
}
yp[i] <- (yp[i] - bias) * yscale + ymean
}

That’s it!

Yours faithfully,

Dennis Vier