Allocates samples to nodes of a self-organizing map (R.W. Payne).
No options
Parameters
DATA = matrices or pointers |
Data values used to allocate the samples to the nodes of the map |
|---|---|
SOM = pointers |
Save the information about each map |
FITNODES = factors |
Save nodes allocated to the data points |
FITROWS = factors |
Save the positions of the rows allocated to the data points |
FITCOLUMNS = factors |
Save the positions of the columns allocated to the data points |
Description
A self-organizing map is a two dimensional grid of nodes, used to classify vectors of observations on p variables. Each node is characterized by a vector of p weights (one for each variable); these can be estimated, from a training dataset, by procedure SOMESTIMATE. This procedure, SOMIDENTIFY, allows you to allocate samples in a new dataset to the nodes of a map.
The new dataset is specified by the DATA parameter, either as a matrix with n rows and p columns (where n is the number of samples) or as a pointer containing p variates each with n units. The SOM parameter supplies the information about the self-organizing map, saved in a pointer using the SOM parameter of SOMESTIMATE. The FITNODES parameter saves a factor containing the number of the node to which each sample has been allocated. The FITROWS and FITCOLUMNS parameters save factors containing the row and column positions of the nodes.
Options: none.
Parameters: DATA, SOM, FITNODES, FITROWS, FITCOLUMNS.
Action with RESTRICT
SOMIDENTIFY takes account of any restrictions defined on the DATA variates.
See also
Procedures: SOM, SOMADJUST, SOMDESCRIBE, SOMESTIMATE, SOMPREDICT.
Commands for: Data mining.
Example
CAPTION 'SOMIDENTIFY example',!t('Fisher''s Iris Data'); STYLE=meta,plain
SOM Som; VARIABLENAMES=!t(Sepal_L,Sepal_W,Petal_L,Petal_W)
MATRIX [ROWS=150; COLUMNS=!t(Sepal_L,Sepal_W,Petal_L,Petal_W)] Measures
READ Measures
5.1 3.5 1.4 0.2
4.9 3.0 1.4 0.2
4.7 3.2 1.3 0.2
4.6 3.1 1.5 0.2
5.0 3.6 1.4 0.2
5.4 3.9 1.7 0.4
4.6 3.4 1.4 0.3
5.0 3.4 1.5 0.2
4.4 2.9 1.4 0.2
4.9 3.1 1.5 0.1
5.4 3.7 1.5 0.2
4.8 3.4 1.6 0.2
4.8 3.0 1.4 0.1
4.3 3.0 1.1 0.1
5.8 4.0 1.2 0.2
5.7 4.4 1.5 0.4
5.4 3.9 1.3 0.4
5.1 3.5 1.4 0.3
5.7 3.8 1.7 0.3
5.1 3.8 1.5 0.3
5.4 3.4 1.7 0.2
5.1 3.7 1.5 0.4
4.6 3.6 1.0 0.2
5.1 3.3 1.7 0.5
4.8 3.4 1.9 0.2
5.0 3.0 1.6 0.2
5.0 3.4 1.6 0.4
5.2 3.5 1.5 0.2
5.2 3.4 1.4 0.2
4.7 3.2 1.6 0.2
4.8 3.1 1.6 0.2
5.4 3.4 1.5 0.4
5.2 4.1 1.5 0.1
5.5 4.2 1.4 0.2
4.9 3.1 1.5 0.2
5.0 3.2 1.2 0.2
5.5 3.5 1.3 0.2
4.9 3.6 1.4 0.1
4.4 3.0 1.3 0.2
5.1 3.4 1.5 0.2
5.0 3.5 1.3 0.3
4.5 2.3 1.3 0.3
4.4 3.2 1.3 0.2
5.0 3.5 1.6 0.6
5.1 3.8 1.9 0.4
4.8 3.0 1.4 0.3
5.1 3.8 1.6 0.2
4.6 3.2 1.4 0.2
5.3 3.7 1.5 0.2
5.0 3.3 1.4 0.2
7.0 3.2 4.7 1.4
6.4 3.2 4.5 1.5
6.9 3.1 4.9 1.5
5.5 2.3 4.0 1.3
6.5 2.8 4.6 1.5
5.7 2.8 4.5 1.3
6.3 3.3 4.7 1.6
4.9 2.4 3.3 1.0
6.6 2.9 4.6 1.3
5.2 2.7 3.9 1.4
5.0 2.0 3.5 1.0
5.9 3.0 4.2 1.5
6.0 2.2 4.0 1.0
6.1 2.9 4.7 1.4
5.6 2.9 3.6 1.3
6.7 3.1 4.4 1.4
5.6 3.0 4.5 1.5
5.8 2.7 4.1 1.0
6.2 2.2 4.5 1.5
5.6 2.5 3.9 1.1
5.9 3.2 4.8 1.8
6.1 2.8 4.0 1.3
6.3 2.5 4.9 1.5
6.1 2.8 4.7 1.2
6.4 2.9 4.3 1.3
6.6 3.0 4.4 1.4
6.8 2.8 4.8 1.4
6.7 3.0 5.0 1.7
6.0 2.9 4.5 1.5
5.7 2.6 3.5 1.0
5.5 2.4 3.8 1.1
5.5 2.4 3.7 1.0
5.8 2.7 3.9 1.2
6.0 2.7 5.1 1.6
5.4 3.0 4.5 1.5
6.0 3.4 4.5 1.6
6.7 3.1 4.7 1.5
6.3 2.3 4.4 1.3
5.6 3.0 4.1 1.3
5.5 2.5 4.0 1.3
5.5 2.6 4.4 1.2
6.1 3.0 4.6 1.4
5.8 2.6 4.0 1.2
5.0 2.3 3.3 1.0
5.6 2.7 4.2 1.3
5.7 3.0 4.2 1.2
5.7 2.9 4.2 1.3
6.2 2.9 4.3 1.3
5.1 2.5 3.0 1.1
5.7 2.8 4.1 1.3
6.3 3.3 6.0 2.5
5.8 2.7 5.1 1.9
7.1 3.0 5.9 2.1
6.3 2.9 5.6 1.8
6.5 3.0 5.8 2.2
7.6 3.0 6.6 2.1
4.9 2.5 4.5 1.7
7.3 2.9 6.3 1.8
6.7 2.5 5.8 1.8
7.2 3.6 6.1 2.5
6.5 3.2 5.1 2.0
6.4 2.7 5.3 1.9
6.8 3.0 5.5 2.1
5.7 2.5 5.0 2.0
5.8 2.8 5.1 2.4
6.4 3.2 5.3 2.3
6.5 3.0 5.5 1.8
7.7 3.8 6.7 2.2
7.7 2.6 6.9 2.3
6.0 2.2 5.0 1.5
6.9 3.2 5.7 2.3
5.6 2.8 4.9 2.0
7.7 2.8 6.7 2.0
6.3 2.7 4.9 1.8
6.7 3.3 5.7 2.1
7.2 3.2 6.0 1.8
6.2 2.8 4.8 1.8
6.1 3.0 4.9 1.8
6.4 2.8 5.6 2.1
7.2 3.0 5.8 1.6
7.4 2.8 6.1 1.9
7.9 3.8 6.4 2.0
6.4 2.8 5.6 2.2
6.3 2.8 5.1 1.5
6.1 2.6 5.6 1.4
7.7 3.0 6.1 2.3
6.3 3.4 5.6 2.4
6.4 3.1 5.5 1.8
6.0 3.0 4.8 1.8
6.9 3.1 5.4 2.1
6.7 3.1 5.6 2.4
6.9 3.1 5.1 2.3
5.8 2.7 5.1 1.9
6.8 3.2 5.9 2.3
6.7 3.3 5.7 2.5
6.7 3.0 5.2 2.3
6.3 2.5 5.0 1.9
6.5 3.0 5.2 2.0
6.2 3.4 5.4 2.3
5.9 3.0 5.1 1.8 :
FACTOR [NVALUES=150; LABELS=!t(Setosa,Versicolor,Virginica);\
VALUES=50(1,2,3)] Species
SOMESTIMATE [PRINT=weights,report; PLOT=*; NCYCLE=!(100,200);\
SIGMA=!(5,1)] Som; DATA=Measures; SEED=419749
VARIATE [NVALUES=6] Sepal_L,Sepal_W,Petal_L,Petal_W
READ Sepal_L,Sepal_W,Petal_L,Petal_W
5.1 3.5 1.4 0.2
4.9 3.0 1.4 0.2
7.0 3.2 4.7 1.4
6.4 3.2 4.5 1.5
6.3 3.3 6.0 2.5
5.8 2.7 5.1 1.9 :
SOMIDENTIFY !p(Sepal_L,Sepal_W,Petal_L,Petal_W); SOM=Som;\
FITNODES=inode; FITROWS=irow; FITCOLUMNS=icol
PRINT Sepal_L,Sepal_W,Petal_L,Petal_W,inode,irow,icol;\
FIELD=9; DECIMALS=4(*),3(0)