NAME

       pkregann - regression with artificial neural network (multi-layer perceptron)

SYNOPSIS

       pkregann -i input -t training [-ic col] [-oc col] -o output [options] [advanced options]

DESCRIPTION

       pkregann performs a regression based on an artificial neural network.  The regression is trained from the
       input  (-ic)  and output (-oc) columns in a training text file.  Each row in the training file represents
       one sampling unit.  Multi-dimensional input features can be defined with multiple  input  options  (e.g.,
       -ic 0 -ic 1 -ic 2 for three dimensional features).

OPTIONS

       -i filename, --input filename
              input ASCII file

       -t filename, --training filename
              training  ASCII file (each row represents one sampling unit.  Input features should be provided as
              columns, followed by output)

       -o filename, --output filename
              output ASCII file for result

       -ic col, --inputCols col
              input columns (e.g., for three dimensional input data in first three columns use: -ic 0 -ic 1  -ic
              2

       -oc col, --outputCols col
              output  columns  (e.g., for two dimensional output in columns 3 and 4 (starting from 0) use: -oc 3
              -oc 4

       -from row, --from row
              start from this row in training file (start from 0)

       -to row, --to row
              read until this row in training file (start from 0 or set leave 0 as default to read until end  of
              file)

       -cv size, --cv size
              n-fold cross validation mode

       -nn number, --nneuron number
              number  of  neurons in hidden layers in neural network (multiple hidden layers are set by defining
              multiple number of neurons: -n 15 -n 1, default is one hidden layer with 5 neurons)

       -v level, --verbose level
              set to: 0 (results only), 1 (confusion matrix), 2 (debug)

       Advanced options

       --offset value
              offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]

       --scale value
              scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0
              if scale min and max in each band to -1.0 and 1.0)

       --connection rate
              connection rate (default: 1.0 for a fully connected network)

       -l rate, --learning rate
              learning rate (default: 0.7)

       --maxit number
              number of maximum iterations (epoch) (default: 500)

                                                 01 January 2025                                     pkregann(1)