The operation creates a classified data frame and collects the indexes of the data records to classes. The principle has been described in the figure below. As the figure shows, the operation moves a window along a data frame collecting indexes inside the window to the classes. The parameter <length> defines the length of the window. The parameter <step1> defines how many data records are skipped when the window is moved, whereas the parameter <step2> defines steps between data records. Of cource, then the true length of the time window is: lenght + (length -1 ) * step2.
Example (ex3.6): This example demonstrates one way to process data including a code sequence. For instance, data includes codes 1, 9, 20, 2, 2, 3, 9, .... In the example, these codes are represented as binary variables (uniq and bin operations), and binary data is summarized over sequencies (selseq and clsum).
NDA> load seq.dat # # binarize code data # NDA> uniq -d seqdata -cout sequniq NDA> bin -d seqdata -c sequniq -dout binseq # # select sequencies and summarize them # NDA> selseq -d binseq -cout seqcld -len 3 NDA> clsum -d binseq -c seqcld -dout binhst # # the basic SOM analysis # NDA> prepro -d binhst -dout pre -edev -n NDA> somtr -d pre -sout som1 -l 5 NDA> somcl -d pre -s som1 -cout cld NDA> clstat -d binhst -c cld -dout sta -avg -min -max -hits NDA> mkgrp win1 -s som1 NDA> setgdat win1 -d sta NDA> setgcld win1 -c cld NDA> layer win1 -l 4 NDA> bar win1 -f sta.code_0_avg -co red NDA> bar win1 -f sta.code_6_avg -co green NDA> bar win1 -f sta.code_8_avg -co blue NDA> bar win1 -f sta.code_10_avg -co yellow NDA> traj win1 -n tr1 -min 100 -max 110 -co black NDA> show win1
