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SnnsRObject-somPredictCurrPatSetWinnersSpanTree

Get the spanning tree of the SOM


Description

SnnsR low-level function to get the spanning tree of the SOM, This function calls directly the corresponding SNNS kernel function (the only one available for SOM). Advantage are faster computation, disadvantage is somewhat limited information in the output.

Usage

## S4 method for signature 'SnnsR'
somPredictCurrPatSetWinnersSpanTree()

Value

the spanning tree, which is the som, showing for each unit a number identifying the last pattern for which this unit won. (We note that, also if there are more than one patterns, only the last one is saved)

See Also


RSNNS

Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

v0.4-12
LGPL (>= 2) | file LICENSE
Authors
Christoph Bergmeir [aut, cre, cph], José M. Benítez [ths], Andreas Zell [ctb] (Part of original SNNS development team), Niels Mache [ctb] (Part of original SNNS development team), Günter Mamier [ctb] (Part of original SNNS development team), Michael Vogt [ctb] (Part of original SNNS development team), Sven Döring [ctb] (Part of original SNNS development team), Ralf Hübner [ctb] (Part of original SNNS development team), Kai-Uwe Herrmann [ctb] (Part of original SNNS development team), Tobias Soyez [ctb] (Part of original SNNS development team), Michael Schmalzl [ctb] (Part of original SNNS development team), Tilman Sommer [ctb] (Part of original SNNS development team), Artemis Hatzigeorgiou [ctb] (Part of original SNNS development team), Dietmar Posselt [ctb] (Part of original SNNS development team), Tobias Schreiner [ctb] (Part of original SNNS development team), Bernward Kett [ctb] (Part of original SNNS development team), Martin Reczko [ctb] (Part of original SNNS external contributors), Martin Riedmiller [ctb] (Part of original SNNS external contributors), Mark Seemann [ctb] (Part of original SNNS external contributors), Marcus Ritt [ctb] (Part of original SNNS external contributors), Jamie DeCoster [ctb] (Part of original SNNS external contributors), Jochen Biedermann [ctb] (Part of original SNNS external contributors), Joachim Danz [ctb] (Part of original SNNS development team), Christian Wehrfritz [ctb] (Part of original SNNS development team), Patrick Kursawe [ctb] (Contributors to SNNS Version 4.3), Andre El-Ama [ctb] (Contributors to SNNS Version 4.3)
Initial release
2019-09-16

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