pkregann

regression with artificial neural network (multi-layer perceptron)

SYNOPSIS

Usage: pkregann -i input -t training [-ic col]* [-oc col]* -o output

Options: [-from row] [-to row] [-cv size] [-nn number]

Advanced options: [–offset value] [–scale value] [–connection rate] [–learning rate] [–maxit number]

Description

The utility 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

• use either -short or --long options (both --long=value and --long value are supported)
• short option -h shows basic options only, long option --help shows all options

  short	long	type	default	description
  i	input	std::string		input ASCII file
  t	training	std::string		training ASCII file (each row represents one sampling unit. Input features should be provided as columns, followed by output)
  ic	inputCols	int		input columns (e.g., for three dimensional input data in first three columns use: -ic 0 -ic 1 -ic 2
  oc	outputCols	int		output columns (e.g., for two dimensional output in columns 3 and 4 (starting from 0) use: -oc 3 -oc 4
  o	output	std::string		output ASCII file for result
  from	from	double	0	start from this row in training file (start from 0)
  to	to	double	0	read until this row in training file (start from 0 or set leave 0 as default to read until end of file)
  cv	cv	unsigned short	0	n-fold cross validation mode
  nn	nneuron	unsigned int	5	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)
  	offset	double	0	offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]
  	scale	double	0	scale value for each spectral band input features: refl=(DN[band]-offset[band])/scaleband
  	connection	float	1	connection reate (default: 1.0 for a fully connected network)
  l	learning	float	0.7	learning rate (default: 0.7)
  	maxit	unsigned int	500	number of maximum iterations (epoch) (default: 500)

  Usage: pkregann -i input -t training [-ic col]* [-oc col]* -o output