Boys weight/height ratio data from Gallant 1987 Nonlinear Statistical Models
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]
0.46000000 0.50000000 28
0.47000000 1.5000000 3
0.56000000 2.5000000 63
0.61000000 3.5000000 38
0.61000000 4.5000000 42
0.67000000 5.5000000 5
0.68000000 6.5000000 19
0.78000000 7.5000000 10
0.69000000 8.5000000 45
0.74000000 9.5000000 1
0.77000000 10.500000 17
0.78000000 11.500000 60
0.75000000 12.500000 37
0.80000000 13.500000 22
0.78000000 14.500000 58
0.82000000 15.500000 33
0.77000000 16.500000 57
0.80000000 17.500000 61
0.81000000 18.500000 26
0.78000000 19.500000 8
0.87000000 20.500000 71
0.80000000 21.500000 18
0.83000000 22.500000 31
0.81000000 23.500000 62
0.88000000 24.500000 49
0.81000000 25.500000 54
0.83000000 26.500000 47
0.82000000 27.500000 69
0.82000000 28.500000 48
0.86000000 29.500000 40
0.82000000 30.500000 21
0.85000000 31.500000 70
0.88000000 32.500000 13
0.86000000 33.500000 27
0.91000000 34.500000 67
0.87000000 35.500000 6
0.87000000 36.500000 7
0.87000000 37.500000 16
0.85000000 38.500000 41
0.90000000 39.500000 12
0.87000000 40.500000 35
0.91000000 41.500000 34
0.90000000 42.500000 52
0.93000000 43.500000 51
0.89000000 44.500000 9
0.89000000 45.500000 68
0.92000000 46.500000 36
0.89000000 47.500000 29
0.92000000 48.500000 56
0.96000000 49.500000 11
0.92000000 50.500000 55
0.91000000 51.500000 24
0.95000000 52.500000 66
0.93000000 53.500000 65
0.93000000 54.500000 53
0.98000000 55.500000 44
0.95000000 56.500000 14
0.97000000 57.500000 23
0.97000000 58.500000 4
0.96000000 59.500000 59
0.97000000 60.500000 32
0.94000000 61.500000 46
0.96000000 62.500000 25
1.0300000 63.500000 30
0.99000000 64.500000 72
1.0100000 65.500000 39
0.99000000 66.500000 43
0.99000000 67.500000 64
0.97000000 68.500000 50
1.0100000 69.500000 2
0.99000000 70.500000 15
1.0400000 71.500000 20
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