Generating data according to the grid of a TS-SOM

 

somgrid	Generate a data according to the grid of a TS-SOM
-s <som>	name of a TS-SOM
-min <mindef>	definition for minimum values
-max <maxdef>	definition for maximum values
-sca <func>	scaling function
[-dim <dim>]	index of the SOM's dimension (default 0)
[-d <src-data>]	source data for naming
[-dout <data-out>]	output data
[-fout <field-out>]	output field

This operation generates new data points according to the grid of a TS-SOM. It creates a data point for each neuron of the given TS-SOM <som>.

The basic idea is to generate values according to the indexes of the neurons. Actually, only one index can run, and it is defined with dimension <dim>. Thus, new values will be constant related to other dimensions. When values are generated, they are scaled into the given range [<mindef>,<maxdef>].

The scaling function <func> defines, how new values are generated. Also, parameters <mindef> and <maxdef> depend on the function as follows:

<func> = "vec"

The definitions, for instance, statistics for fields, are read from two data fields that are referred to by parameters <mindef> and <maxdef>. As many new fields are generated as fields <mindef> and <maxdef> contain data records. They must have the same length, of course.

<func> = "abs"

The definitions <mindef> and <maxdef> are given as absolute values. If parameter <src-data> has been omitted, then only one new data field is generated. Otherwise, a new field is created for each field within this data frame.

There are three alternatives, how to name the new data fields. If only one data field is created, then its name can be defined with parameter <field-out>. If parameter <src-data> has been given, then new data fields are named according to fields in this data frame. If both of these parameters have been omitted, new fields are named automatically as f0, f1,

The data points can be stored in two alternative ways. If the output data frame <data-out> has been specified, then the generated data points are stored there. Otherwise new data fields are added into the current directory.

Example (ex5.17): In this example, the MLP network is trained with Boston data (zn, indus rate). Then, an empty TS-SOM is created for the basis of visualization. Two variables (zn and indus) are used for dimensions x and y by issuing command somgrid, and the trained network is used to predict the value of variable rate for each node of the grid.

...
# Train MLP network by the rprop
NDA> select src -f boston.zn boston.indus
NDA> prepro -d src -dout src2 -e
NDA> select trg -f boston.rate
NDA> prepro -d trg -dout trg2 -e
NDA> rprop -d src2 -dout trg2 -net 2 6 1 -full -types t t
      -em 300 -bs 0.01 -mup 1.1 -mdm 0.8 -wout wei -ef virhe
# Build TS-SOM and generate values based on statistics
NDA> somtr build -sout s1 -l 6 -D 2
...
NDA> select x -f src2.zn
NDA> select y -f src2.indus
NDA> fldstat -d x -dout xsta -min -max
NDA> fldstat -d y -dout ysta -min -max
NDA> somgrid -s s1 -min xsta.min -max xsta.max -d x
      -dout datain -sca vec -dim 0
NDA> somgrid -s s1 -min ysta.min -max ysta.max -d y
      -dout datain -sca vec -dim 1
# Predict "rate"
NDA> fbp -d datain -win wei -dout trgout
# Create graphics and show it
NDA> mkgrp /win1 -s /s1
NDA> show win1