penalizedSVM (version 1.1.4)

.plot.EPSGO.parms: Plot Interval Search Plot Visited Points and the Q Values.

Description

For interval search plot visited points and the Q values (=Ytrain) exclude: for D=1 make an additional plot: skip values for empty model, for example: Ytrain.exclude=10^16.

Usage

.plot.EPSGO.parms(Xtrain, Ytrain,bounds, Ytrain.exclude=10^16, plot.name=NULL )

Value

None, only graphs are created.

Arguments

Xtrain

X points to train

Ytrain

Y points to train

bounds

bounds for parameters, see examples

Ytrain.exclude

If exclude for Ytrain exists, skip those points. Defaults to 10^16.

plot.name

Defaults to NULL

Author

Natalia Becker

Details

The code source was adopted from MATLAB originals, special thanks to Holger Froehlich.

References

Froehlich, H. and Zell, A. (2005) "Effcient parameter selection for support vector machines in classification and regression via model-based global optimization" In Proc. Int. Joint Conf. Neural Networks, 1431-1438 .

See Also

svmfs

Examples

	# \donttest{
	seed <- 123
			
	train<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=seed )
	print(str(train)) 
			
	Q.func<- ".calc.scad"
	
	bounds=t(data.frame(log2lambda1=c(-10, 10)))
							colnames(bounds)<-c("lower", "upper")	
			
	print("start interval search")
	# computation intensive; 
	# for demostration reasons only for the first 100 features 
	# and only for 10 iterations maxIter=10, default maxIter=700
	system.time(fit<-EPSGO(Q.func, bounds=bounds, parms.coding="log2", fminlower=0, 
		 show='none', N=21,  maxevals=500, 
		 pdf.name=NULL,  seed=seed,  
		 verbose=FALSE,
		 # Q.func specific parameters:
		 x.svm=t(train$x)[,1:100], y.svm=train$y,
		 inner.val.method="cv",
		 cross.inner=5, maxIter=10 ))
									 
	print(paste("minimal 5-fold cv error:", fit$fmin, "by log2(lambda1)=", fit$xmin))
		
	print(" all lambdas with the same minimum? ")
	print(fit$ points.fmin) 
			
	print(paste(fit$neval, "visited points"))
			
			
	print(" overview: over all visitied points in tuning parameter space 
				with corresponding cv errors")
	print(data.frame(Xtrain=fit$Xtrain, cv.error=fit$Ytrain))

	# create  3 plots om one screen: 
	# 1st plot: distribution of initial points in tuning parameter space
	# 2nd plot: visited lambda points vs. cv errors
	# 3rd plot: the same as the 2nd plot, Ytrain.exclude points are excluded. 
	#    The value cv.error = 10^16 stays for the cv error for an empty model ! 
	.plot.EPSGO.parms (fit$Xtrain, fit$Ytrain,bound=bounds, 
				Ytrain.exclude=10^16, plot.name=NULL )
	# } # end of \donttest