This is the command pkregann that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator
PROGRAM:
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)
24 January 2016 pkregann(1)
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