pkregann - Online in the Cloud

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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