Man page - pkregann(1)
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apt-get install pktools
Manual
pkregann
NAMESYNOPSIS
DESCRIPTION
OPTIONS
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)