map.kohonen
(Package: kohonen) :
Map data to a supervised or unsupervised SOM
Map a data matrix onto a trained SOM.
● Data Source:
CranContrib
● Keywords: classif
● Alias: map, map.kohonen
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classvec2classmat
(Package: kohonen) :
Convert a classification vector into a matrix or the other way around.
Functions toggle between a matrix representation, where class membership is indicated with one '1' and for the rest zeros at each row, and an class vector (maybe integers or class names). The classification matrix contains one column per class. Conversion from a class matrix to a class vector assigns each row to the column with the highest value. An optional argument can be used to assign only those objects that have a probability higher than a certain threshold (default is 0).
● Data Source:
CranContrib
● Keywords: classif
● Alias: classmat2classvec, classvec2classmat
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xyf
(Package: kohonen) :
Supervised version of Kohonen's self-organising maps
Supervised version of self-organising maps for mapping high-dimensional spectra or patterns to 2D. The name stands for X-Y fused SOMs. One vector for each object is created by concatenating X and Y, and a SOM is trained in the usual way, with one exception: the distance of an object to a unit is the sum of separate distances for X and Y spaces. Prediction is done only using the X-space. For continuous Y, the Euclidean distance is used; for categorical Y the Tanimoto distance.
● Data Source:
CranContrib
● Keywords: classif
● Alias: xyf
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som
(Package: kohonen) :
Kohonen's self-organising maps
Self-organising maps for mapping high-dimensional spectra or patterns to 2D; Euclidean distance is used. Modelled after the SOM function in package class.
● Data Source:
CranContrib
● Keywords: classif
● Alias: som
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tricolor
(Package: kohonen) :
Provides smooth unit colors for SOMs
Function provides colour values for SOM units in such a way that the colour changes smoothly in every direction.
● Data Source:
CranContrib
● Keywords: classif
● Alias: tricolor
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Plot self-organising map, obtained from function kohonen. Several types of plots are supported.
● Data Source:
CranContrib
● Keywords: classif
● Alias: add.cluster.boundaries, identify.kohonen, plot.kohonen
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unit.distances
(Package: kohonen) :
Calculate distances between units in a SOM
Calculate distances between units in a SOM.
● Data Source:
CranContrib
● Keywords: classif
● Alias: unit.distances
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check.whatmap
(Package: kohonen) :
Check the validity of a whatmap argument
Not meant to be called directly by the user.
● Data Source:
CranContrib
● Keywords: classif
● Alias: check.whatmap
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summary.kohonen
(Package: kohonen) :
Summary and print methods for kohonen objects
Summary and print methods for kohonen objects. The print method shows the dimensions and the topology of the map; if information on the training data is included, the summary method additionally prints information on the size of the data and the mean distance of an object to its closest codebookvector, which is an indication of the quality of the mapping.
● Data Source:
CranContrib
● Keywords: classif
● Alias: print.kohonen, summary.kohonen
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bdk
(Package: kohonen) :
Supervised version of Kohonen's self-organising maps
Supervised version of self-organising maps for mapping high-dimensional spectra or patterns to 2D: the Bi-Directional Kohonen map. This is an alternating training of the X-space and the Y-space of the map, where for updating the X-space more weight is given to the features in Y, and vice versa. Weights start by default with values of (0.75, 0.25) and during training go to (0.5, 0.5). Prediction is done only using the X-space. For continuous Y, the Euclidean distance is used; for categorical Y the Tanimoto distance.
● Data Source:
CranContrib
● Keywords: classif
● Alias: bdk
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