complexity -d <prob-matr> -fout <compl-value> -acc <accuracy>
- Calculate the amount of bits needed to represent some data using a
specified accuracy
condent -d <prob-matr> -fout <ent-value>
- Calculate the entropy of a conditional probability matrix
condprob -f <intfld> -dout <prob-matr> [-c <cldata> -aout<avg-fld>]
- Calculate conditional probability matrix indicating how integers follow
each other within a field. The order of the integers can also be specified
as a classification, in which case the beginning and end of each class is
noted
difference -p <prob-matr1> -q <prob-matr2> -fout <diff-value>
[-acc <accuracy>]
- Calculate the Kullback-Leibler difference of two probability distributions
evalkern -f <fldin> -k <kernels> -fout <kern-est>
- Evaluate kernel estimates for the points in <fldin>
kernel -d <datain> -dout <kernels> -nkern <num-kernels>
- Fit <num-kernels> gaussian kernels into input data
modelcomp -s <som> -d <datain> -c <som-cld> -dout <results> [-acc <accuracy> -pinf <pin-fld> -cbook -int]
- Evaluate the model complexities of a TS-SOM representation of some original
data using a specified accuracy or histogramming with specified number of pins.
The results can be calculated as true bits (-int) or fractions of bits
optacc -d <datain> -fout <pin-fld> [-s <som> -c <som-cld> -acc <accuracy> -files -cbook]
- Optimize representation accuracy for data and, if a TS-SOM and
SOM classification are specified, SOM + residuals for each layer of TS-SOM
probent -d <prob-matr> -fout <ent-value>
- Calculate the entropy of a simple probability matrix
viewpd -s <som> -d <datain> -c <som-cld> [-dhisto <datahisto> -shisto <somhisto> -rhisto <residuals>]
- Obtain histograms from data, SOM and residuals (data - SOM neuron)