template
1	np = number of predictors 
2	pwr = power used in loss, 2 for mean, 1 for median or quantile
0.5	alpha = quantile probability, 0 < alpha < 1, alpha = 0.5 for median 
0	ctfm = nonnegative transformation parameter, positive to reduce correlations

Initial number of basis functions, smoothing parameter
nk  lambda
1	.0	
0	0    !must finish with row of zeros

Cross-validation options
0	indcv = 0/1 indicator for cross-validation 
10	ncv = number of subsets for cross-validation partition
1	cvopt = option for partition: 1 sequential, 2 input permutation, 3 random varying

Output options
1	iprint = 0/1 indicator to print output (0/1)
1	ipredict = 0/1 indicator to print predictors (0/1)
-1	ngcl = number of gradient cluster projections, if < 0 then min{np,nk-1})
1000	nprint = upper bound on number of cases output

GCV search options (lambda should be 0 if GCV search is used)
1	isearch = 0/1 indicator for initial selection of nk
0	idr = 0/1 indicator for dimension reduction option
3	nfail = number of successive failures before stopping search

Initialization: gain(i) = gain(0)*w/(i + w), i=1,nm
3000	nm1 determines number of iterations nm = nm1*nk**.5
1.0	rpt0 = rpt gain(0)
100	rpt1  determines w = rpt1*nk**.5
1	ctau (tau = ctau*snn, if ctau <= 0 then set default 1.
0	ywt determines weight assigned to (dlta-yy)**2 in vq

Training algorithm tuning parameters:
50000	nm1 determines number of iterations nm = nm1*nk**.5
.25	rpt0 = rpt gain(0)
500	rpt1 determines w = rpt1*nk**.5
10	nrep = number of repetitions of first prop*nm stoch. approx. iterations
.10	prop = proportion of training repeated

training sample - rows yy,xx(1),...,xx(np),[+ indx optional]
1.0D99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
test sample - rows yy,xx(1),...,xx(np)
1.0D99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0