#Enter number of classes 
#Hey I am works or no ?!
2
#Enter BUTSTRAP replicas
1
#TRAINING SAMPLES NAMES:
gamf protf 
#CONTROL(EXPERIMENTAL) SAMPLE NAME
gamf
#DUMP(ARCHIVE) SAMPLE NAME .
nothing
#NTUPLE SAMPLE NAME to WRITE
b.hbook
#BANK TOTAL DIMENSION=?
10
#Relational shifts
0 0 
#Number of events
2000 2000 
#FirsT EVENT COORDINATE SIZE OF CONTROL
0 100
#STATUS (DENCURVE - PAW plot of nonparametric density estimates )
DENCURVE
#OPERATION MODE:
EXP
#ACCESS? Direct, Nomad,sequental
SEQUENTAL
#DENSITY ESTIMATION MODE (PARZ,RFIX)
PARZ
#WEIGHTS IN REGRO MODE (LINEAR, SQUARE) PROPORTIONAL TO DISTANCE
SQUARE
#FORMAT OF SEQUENTAL INPUT
(f13.10,10f15.10)
#PREFERENCE( A PRIORY PROBABILITY OR "COST" FUNCTION) VALUES
#IN NEURAL MODE INTERVALS FOR CLASSIFICATION RATES CALCILATION
3
0.5 0.5 0.3 0.7 0.1 0.9 
#RECONSTRUCT FIRST TYPE EVENTS PORTION ?
noRECONSTRUCT
#NUMBER AND VALUE OF NUCLEI WIDTHS,
#IN NEURAL MODE TRUE VALUES FOR EACH CLASS
5
0.1 0.3 0.5 0.7 0.9 
#maximal exponent in parzen density estimation
40000.
#Strangness criterium
0.000000001
#NUMBER OF NEAREST NEIHBOURS
3
#VARIABLES TO BE PROCESSED (AMOUNT AND RELATIVE NUMBERS)
2
1 2 
#INT. DIMENSION AND BHATACHARYA DISTANCE WEGHTS
4 1. 1.
#LOWER BOUND
0 0 
#UPPER BOUND
9999 9999 
#DEBUGGING (0 MINIMAL PRINT, 1,2 - INTERMIDIATE, 3 - ALL
0
#Random generator used (pseudo, or lp-tau - a uniform sieve in N-dimensions
lp-tau
#Data normalisation to 0-1 - renorm
norenorm
#Writing NTUPLE, code (if =0, no Ntuple generation)
1
#Writing Ntuple id
15
#Writing NTuple memory
10000
#Writing NTuple HBOOK unit
11
#Reading NTUPLE, code 
0
#Reading Ntuple id
20
#Reading NTuple memory
100000
#Reading NTuple HBOOK unit
22
#NEURAL NET CONFIGURATION:N OF LAYERS, N OF NODES IN EACH LAYER
3
2 3 1 
#N OF ITERATIONS
100
# Iterations COEFF.
1.
# Iterations SYMMETRY CONSTRAINT
0.2
# Iterations INITIAL SPREED
0.01
# Iterations SHIFT
11
#SPEED and WSPEED
1. 0.
#QUALITY FUNCTION SYMMETRIZATION WEIGHTS
0.5 0.5 
#Search mode (single  one dimensional search, mullti - all net parm. modificated
#simultaneously, neuron - all couplings and threshold of a selected neuron
neuron
#Qualiti function type (montec - training with M.C., sigmaa - with ON/OFF pairs
estima
#Qualiti type (msd and kolm)
msd
#Memory type (simple - no memory, the best changes not to accumulate, 
memory
#Begin from (random point, or - better point, find in previoue search
random
#Stop iterations if Quality function is less than:
0.0000001
#Decision point (if > - signal event, if > - background
0.5
#number of variants for intervals & intervals
1
0.4 1 
#dsfgdsgfsddfssdf
0.1 0.9 
#scale partitioning for feature space scanning
8
# Regr
1
Lngt_0 
