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Classify_kerdensom_v3
Purpose: Kernel Density Estimator Self-Organizing Map KerDenSOM stands for Kernel Probability Density Estimator Self-Organizing Map. It maps a set of high dimensional input vectors into a two-dimensional grid. For more information, please see the following reference.
The topology of the network can be hexagonal or rectangular (see below). It is advised to design maps with one of its sides larger than the other (e.g. 10x5).
Xdim is ------>
HEXAGONAL:
O O O O O O O O O
O O O & & & O O O
O O & @ @ & O O O
O O & @ + @ & O O
O O & @ @ & O O O
O O O & & & O O O
O O O O O O O O O
RECTANGULAR:
O O O O O O O O O
0 O O O & O O O O
O O O & @ & O O O
O O & @ + @ & O O
O O O & @ & O O O
O O O O & O O O O
O O O O O O O O O
See also Image_vectorize_v3
Parameters
$: Input data file This file is generated by[[Image_vectorize_v3]] $: rootname_classes.xmd, rootname_images.xmd and rootname_vectors.xmd will be created This file mst be read byImage_vectorize_v3 $--xdim <Hdimension10> $--ydim <Vdimension5> $--topology <topologyRECT> where <topology> can be:
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- $
--deterministic_annealing <steps10> <Initial_reg=1000> <Final_reg=100> $--eps <epsilon1e-7> $--iter <N200> $``: Normalize input data
xmipp_image_vectorize -i images.stk -o vectors.xmd xmipp_classify_kerdensom -i vectors.xmd -o kerdensom.xmd