| Title: | Supervised and Unsupervised Self-Organising Maps |
|---|---|
| Description: | Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM. |
| Authors: | Ron Wehrens and Johannes Kruisselbrink |
| Maintainer: | Ron Wehrens <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 3.0.12 |
| Built: | 2024-11-21 03:49:32 UTC |
| Source: | https://github.com/cran/kohonen |
Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
The kohonen package implements several forms of self-organising maps
(SOMs). Online and batch training algorithms are available; batch
training can also be done in parallel. Multiple data layers may be
presented to the training algorithm, with potentially different distance
measures for each layer. The overall distance is a weighted average of
the layer distances. Layers may be selected through the whatmap
argument, or by providing a weight of zero. The basic function is
supersom; som is simply a wrapper for SOMs using just one
layer (the classical form).
New data may be mapped to a trained SOM using the map.kohonen
function. Function predict.kohonen will map data to the SOM, and
will return predictions (i.e., average values for winning units) for
those layers that are not in the new data object.
Several visualisation methods are available in function
plot.kohonen.
Index of help topics:
check.whatmap Check the validity of a whatmap argument
classvec2classmat Convert a classification vector into a matrix
or the other way around.
degelder Powder pattern data by Rene de Gelder
expandMap Expand a self-organising map
getCodes Extract codebook vectors from a kohonen object
kohonen-package Supervised and Unsupervised Self-Organising
Maps
layer.distances Assessing distances to winning units
map.kohonen Map data to a supervised or unsupervised SOM
nir Near-infrared data with temperature effects
object.distances Calculate distances between object vectors in a
SOM
peppaPic Synthetic image of a pepper plant with peppers
plot.kohonen Plot kohonen object
predict.kohonen Predict properties using a trained Kohonen map
summary.kohonen Summary and print methods for kohonen objects
supersom Self- and super-organising maps
tricolor Provides smooth unit colors for SOMs
unit.distances SOM-grid related functions
wines Wine data
yeast Yeast cell-cycle data
Ron Wehrens and Johannes Kruisselbrink
Maintainer: Ron Wehrens <[email protected]>
R. Wehrens and J. Kruisselbrink: Flexible Self-Organising Maps in kohonen 3.0. Journal of Statistical Software, 87, 7 (2018).
Not meant to be called directly by the user.
check.whatmap(x, whatmap)check.whatmap(x, whatmap)
x |
A |
whatmap |
An indication of a subset of the data; either by naming
the elements, or giving indices. If |
Returns a numerical vector with the indices of the selected layers. An invalid selection leads to an error.
Ron Wehrens
Functions toggle between a matrix representation, where class membership is indicated with one '1' and for the rest zeros at each row, and a factor. 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).
classvec2classmat(yvec) classmat2classvec(ymat, threshold=0)classvec2classmat(yvec) classmat2classvec(ymat, threshold=0)
yvec |
class vector. Usually a factor; if it is a vector of integer values, it will be converted to a factor. |
ymat |
class matrix: every column corresponds to a class. |
threshold |
only classify into a class if the probability is larger than this threshold. |
classvec2classmat returns the classification matrix, where each
column consists of zeros and ones; classmat2classvec returns a
factor.
Ron Wehrens
classes <- c(rep(1, 5), rep(2, 7), rep(3, 9)) classmat <- classvec2classmat(classes) classmat classmat2classvec(classmat)classes <- c(rep(1, 5), rep(2, 7), rep(3, 9)) classmat <- classvec2classmat(classes) classmat classmat2classvec(classmat)
X-ray powder patterns of 131 crystallographic structures, contributed by Rene de Gelder.
data(degelder)data(degelder)
This yields a list with three components: the first component, '"patterns"', is a matrix of 131 rows and 441 variables, containing the powder patterns; the second component is "thetas", the 2theta values at which intensities have been measured. The final component, '"properties"', gives information on the crystallographic properties of the structures.
Rene de Gelder, Institute of Molecules and Materials, Radboud University Nijmegen.
## Not run: data(degelder) mydata <- list(patterns = degelder$patterns, CellVol = log(degelder$properties[,"cell.vol"])) ## custom distance function require(Rcpp) sourceCpp(system.file("Distances", "wcc.cpp", package = "kohonen")) set.seed(7) powsom <- supersom(data = mydata, grid = somgrid(6, 4, "hexagonal"), dist.fcts = c("WCCd", "sumofsquares"), keep.data = TRUE) summary(powsom) ## End(Not run)## Not run: data(degelder) mydata <- list(patterns = degelder$patterns, CellVol = log(degelder$properties[,"cell.vol"])) ## custom distance function require(Rcpp) sourceCpp(system.file("Distances", "wcc.cpp", package = "kohonen")) set.seed(7) powsom <- supersom(data = mydata, grid = somgrid(6, 4, "hexagonal"), dist.fcts = c("WCCd", "sumofsquares"), keep.data = TRUE) summary(powsom) ## End(Not run)
Double the size of a map, imputing the codebookvectors of the new units by averiging their immediate neighbours.
expandMap(kohobj)expandMap(kohobj)
kohobj |
Object of class |
A new kohonen object, with a double size.
Ron Wehrens
data(yeast) yeast.supersom <- supersom(yeast, somgrid(4, 4, "hexagonal"), whatmap = 3:6, maxNA.fraction = .5) yeast.supersom2 <- expandMap(yeast.supersom) yeast.supersom3 <- supersom(yeast, yeast.supersom2$grid, whatmap = 3:6, maxNA.fraction = .5, init = yeast.supersom2$codes[3:6])data(yeast) yeast.supersom <- supersom(yeast, somgrid(4, 4, "hexagonal"), whatmap = 3:6, maxNA.fraction = .5) yeast.supersom2 <- expandMap(yeast.supersom) yeast.supersom3 <- supersom(yeast, yeast.supersom2$grid, whatmap = 3:6, maxNA.fraction = .5, init = yeast.supersom2$codes[3:6])
Utility function for extracting codebook vectors. These
are present as a list element in a kohonen object, and
themselves are a list as well, with one entry for each data
layer. This function returns either a list of codebook matrices (if
more layers are selected), or just one matrix (if one layer is
selected).
getCodes(x, idx = 1:length(codes))getCodes(x, idx = 1:length(codes))
x |
An object of class |
idx |
Indices of the layer(s) for which codebook vectors are returned. |
If idx is a single number, a matrix of codebook vectors;
if it is a vector of numbers, a list of codebook matrices.
Ron Wehrens
data(wines) set.seed(7) som.wines <- som(scale(wines), grid = somgrid(5, 5, "hexagonal")) dim(getCodes(som.wines))data(wines) set.seed(7) som.wines <- som(scale(wines), grid = somgrid(5, 5, "hexagonal")) dim(getCodes(som.wines))
Given a trained SOM, distances of individual objects to their closest
units may be calculated with function dist2WU. Aggregation on
the unit level is obtained through the function
layer.distances. The latter function is the workhorse for the
"quality" plots in function plot.kohonen.
layer.distances(kohobj, whatmap, data, classif = NULL) dist2WU(kohobj, whatmap, data, classif = NULL)layer.distances(kohobj, whatmap, data, classif = NULL) dist2WU(kohobj, whatmap, data, classif = NULL)
kohobj |
A trained |
whatmap |
What layers to take into account - default is to consider all layers used in training. Also single layers may be chosen. Note that although the underlying C code can also calculate results for any subset, currently subsets larger than one are forbidden. |
data |
Data to use - default is to use the data from the trained SOM. |
classif |
Classification vector, corresponding to the
|
The results will be weighted using both the user weights and
distance weights. Summing all the results for individual layers
therefore would lead to the unit.classif vector of the
kohonen object.
Function dist2WU returns a vector, representing for each
object the distance to its winning unit. Function
layer.distances returns (as a vector) for each unit the average
distance of objects for which it is the winning unit.
Ron Wehrens
Quality plots from plot.kohonen.
library(kohonen) data(wines) wines.sc <- scale(wines) set.seed(7) xyf.wines <- xyf(wines.sc, vintages, grid = somgrid(5, 5, "hexagonal")) dist2WU(xyf.wines, whatmap = 1) plot(xyf.wines, "quality", whatmap = 1) plot(xyf.wines, "property", property = layer.distances(xyf.wines, whatmap = 1))library(kohonen) data(wines) wines.sc <- scale(wines) set.seed(7) xyf.wines <- xyf(wines.sc, vintages, grid = somgrid(5, 5, "hexagonal")) dist2WU(xyf.wines, whatmap = 1) plot(xyf.wines, "quality", whatmap = 1) plot(xyf.wines, "property", property = layer.distances(xyf.wines, whatmap = 1))
Map a data matrix onto a trained SOM.
## S3 method for class 'kohonen' map(x, newdata, whatmap = NULL, user.weights = NULL, maxNA.fraction = x$maxNA.fraction, ...)## S3 method for class 'kohonen' map(x, newdata, whatmap = NULL, user.weights = NULL, maxNA.fraction = x$maxNA.fraction, ...)
x |
An object of class |
newdata |
list of data matrices (numerical) of factors, equal to
the |
whatmap, user.weights, maxNA.fraction
|
parameters that usually will
be taken from the |
... |
Currently ignored. |
A list with elements
unit.classif |
a vector of units that are closest to the objects in the data matrix. |
distances |
distances of the objects to the closest units. Distance measures are the same ones used in training the map. |
whatmap, user.weights
|
Values used for these arguments. |
Ron Wehrens
data(wines) set.seed(7) training <- sample(nrow(wines), 150) Xtraining <- scale(wines[training, ]) somnet <- som(Xtraining, somgrid(5, 5, "hexagonal")) map(somnet, scale(wines[-training, ], center=attr(Xtraining, "scaled:center"), scale=attr(Xtraining, "scaled:scale")))data(wines) set.seed(7) training <- sample(nrow(wines), 150) Xtraining <- scale(wines[training, ]) somnet <- som(Xtraining, somgrid(5, 5, "hexagonal")) map(somnet, scale(wines[-training, ], center=attr(Xtraining, "scaled:center"), scale=attr(Xtraining, "scaled:scale")))
A data object containing near-infrared spectra of ternary mixtures of ethanol, water and iso-propanol, measured at five different temperatures (30, 40, ..., 70 degrees Centigrade).
F. Wulfert , W.Th. Kok, A.K. Smilde: Anal. Chem. 1998, 1761-1767
data(nir) set.seed(3) nirnet <- xyf(X = nir$spectra[nir$training,], Y = nir$composition[nir$training,], user.weights = c(3,1), grid = somgrid(6, 6, "hexagonal"), rlen=500) plot(nirnet, "counts", main="Counts") ## Focus on compound 2 (water): par(mfrow = c(1,2)) set.seed(13) nirnet <- xyf(X = nir$spectra[nir$training,], Y = nir$composition[nir$training, 2, drop = FALSE], grid = somgrid(6, 6, "hexagonal"), rlen=500) water.xyf <- predict(nirnet, newdata = nir$spectra[nir$training,], unit.predictions = getCodes(nirnet, 2), whatmap = 1)$prediction plot(nirnet, "property", property = water.xyf[[1]], main="Prediction of water content") ## Plot temperatures as circles symbols(nirnet$grid$pts[nirnet$unit.classif,] + matrix(rnorm(sum(nir$training)*2, sd=.1), ncol=2), circles = (nir$temperature[nir$training] - 20)/250, inches = FALSE, add = TRUE) ## Model temperatures set.seed(13) nirnet2 <- xyf(X = nir$spectra[nir$training,], Y = matrix(nir$temperature[nir$training], ncol = 1), user.weights = c(1,3), grid = somgrid(6, 6, "hexagonal"), rlen=500) temp.xyf <- predict(nirnet2, newdata = nir$spectra[nir$training,], unit.predictions = getCodes(nirnet2, 2), whatmap = 1)$prediction plot(nirnet2, "property", property = temp.xyf[[1]], palette.name = rainbow, main="Prediction of temperatures") ## Plot concentrations of water as circles symbols(nirnet2$grid$pts[nirnet2$unit.classif,] + matrix(rnorm(sum(nir$training)*2, sd=.1), ncol=2), circles = 0.05 + 0.4 * nir$composition[nir$training,2], inches = FALSE, add = TRUE)data(nir) set.seed(3) nirnet <- xyf(X = nir$spectra[nir$training,], Y = nir$composition[nir$training,], user.weights = c(3,1), grid = somgrid(6, 6, "hexagonal"), rlen=500) plot(nirnet, "counts", main="Counts") ## Focus on compound 2 (water): par(mfrow = c(1,2)) set.seed(13) nirnet <- xyf(X = nir$spectra[nir$training,], Y = nir$composition[nir$training, 2, drop = FALSE], grid = somgrid(6, 6, "hexagonal"), rlen=500) water.xyf <- predict(nirnet, newdata = nir$spectra[nir$training,], unit.predictions = getCodes(nirnet, 2), whatmap = 1)$prediction plot(nirnet, "property", property = water.xyf[[1]], main="Prediction of water content") ## Plot temperatures as circles symbols(nirnet$grid$pts[nirnet$unit.classif,] + matrix(rnorm(sum(nir$training)*2, sd=.1), ncol=2), circles = (nir$temperature[nir$training] - 20)/250, inches = FALSE, add = TRUE) ## Model temperatures set.seed(13) nirnet2 <- xyf(X = nir$spectra[nir$training,], Y = matrix(nir$temperature[nir$training], ncol = 1), user.weights = c(1,3), grid = somgrid(6, 6, "hexagonal"), rlen=500) temp.xyf <- predict(nirnet2, newdata = nir$spectra[nir$training,], unit.predictions = getCodes(nirnet2, 2), whatmap = 1)$prediction plot(nirnet2, "property", property = temp.xyf[[1]], palette.name = rainbow, main="Prediction of temperatures") ## Plot concentrations of water as circles symbols(nirnet2$grid$pts[nirnet2$unit.classif,] + matrix(rnorm(sum(nir$training)*2, sd=.1), ncol=2), circles = 0.05 + 0.4 * nir$composition[nir$training,2], inches = FALSE, add = TRUE)
This function calculates the distance between objects using the distance
functions, weights and other attributes of a trained SOM. This function
is used in the calculation of the U matrix in function
plot.kohonen using the type = "dist.neighbours" argument.
object.distances(kohobj, type = c("data", "codes"), whatmap)object.distances(kohobj, type = c("data", "codes"), whatmap)
kohobj |
An object of class |
type |
Whether to calculate distances between the data objects, or the codebook vectors. |
whatmap |
What data layers to use. If unspecified the data layers defined in the kohonen object are used. |
An object of class dist, which can be directly fed into
(e.g.) a hierarchical clustering.
Ron Wehrens
R. Wehrens and J. Kruisselbrink, submitted, 2017.
data(wines) set.seed(7) sommap <- supersom(list(measurements = scale(wines), vintages = vintages), grid = somgrid(6, 4, "hexagonal")) obj.dists <- object.distances(sommap, type = "data") code.dists <- object.distances(sommap, type = "codes")data(wines) set.seed(7) sommap <- supersom(list(measurements = scale(wines), vintages = vintages), grid = somgrid(6, 4, "hexagonal")) obj.dists <- object.distances(sommap, type = "data") code.dists <- object.distances(sommap, type = "codes")
A data matrix with four columns representing a 600 by 800 image of a pepper plant. Each row is a pixel in the image. The first column is the class label; the other columns contain the RGB values.
data("peppaPic")data("peppaPic")
http://dx.doi.org/10.4121/uuid:884958f5-b868-46e1-b3d8-a0b5d91b02c0
This is image 10039 from a set of 10,500 images described in
Barth R, IJsselmuiden J, Hemming J, and van Henten E (2017). "Data Synthesis Methods for Semantic Segmentation in Agriculture. A Capsicum annuum Dataset." Submitted.
data(peppaPic) head(peppaPic) ## show ground truth per pixel image(t(matrix(peppaPic[,1], 600, 800))[,600:1], col = rainbow(10))data(peppaPic) head(peppaPic) ## show ground truth per pixel image(t(matrix(peppaPic[,1], 600, 800))[,600:1], col = rainbow(10))
Plot objects of class kohonen. Several types
of plots are supported.
## S3 method for class 'kohonen' plot(x, type = c("codes", "changes", "counts", "dist.neighbours", "mapping", "property", "quality"), whatmap = NULL, classif = NULL, labels = NULL, pchs = NULL, main = NULL, palette.name = NULL, ncolors, bgcol = NULL, zlim = NULL, heatkey = TRUE, property, codeRendering = NULL, keepMargins = FALSE, heatkeywidth = .2, shape = c("round", "straight"), border = "black", na.color = "gray", ...) ## S3 method for class 'kohonen' identify(x, ...) add.cluster.boundaries(x, clustering, lwd = 5, ...)## S3 method for class 'kohonen' plot(x, type = c("codes", "changes", "counts", "dist.neighbours", "mapping", "property", "quality"), whatmap = NULL, classif = NULL, labels = NULL, pchs = NULL, main = NULL, palette.name = NULL, ncolors, bgcol = NULL, zlim = NULL, heatkey = TRUE, property, codeRendering = NULL, keepMargins = FALSE, heatkeywidth = .2, shape = c("round", "straight"), border = "black", na.color = "gray", ...) ## S3 method for class 'kohonen' identify(x, ...) add.cluster.boundaries(x, clustering, lwd = 5, ...)
x |
kohonen object. |
type |
type of plot. (Wow!) |
whatmap |
For a "codes" plot: what maps to show; for the "dist.neighbours" plot: what maps to take into account when calculating distances to neighbouring units. |
classif |
classification object, as returned by
|
labels |
labels to plot when |
pchs |
symbols to plot when |
main |
title of the plot. |
palette.name |
colors to use as unit background for "codes", "counts", "prediction", "property", and "quality" plotting types. |
ncolors |
number of colors to use for the unit backgrounds. Default is 20 for continuous data, and the number of distinct values (if less than 20) for categorical data. |
bgcol |
optional argument to colour the unit backgrounds for the "mapping" and "codes" plotting type. Defaults to "gray" and "transparent" in both types, respectively. |
zlim |
optional range for color coding of unit backgrounds. |
heatkey |
whether or not to generate a heatkey at the left side of the plot in the "property" and "counts" plotting types. |
property |
values to use with the "property" plotting type. |
codeRendering |
How to show the codes. Possible choices: "segments", "stars" and "lines". |
keepMargins |
if |
heatkeywidth |
width of the colour key; the default of 0.2 should work in most cases but in some cases, e.g. when plotting multiple figures, it may need to be adjusted. |
shape |
kind shape to be drawn: "round" (circle) or "straight". Choosing "straight" produces a map of squares when the grid is "rectangular", and produces a map of hexagons when the grid is "hexagonal". |
border |
color of the shape's border. |
na.color |
background color matching NA - default "gray". |
lwd, ...
|
other graphical parameters. |
clustering |
cluster labels of the map units. |
Several different types of plots are supported:
shows the mean distance to the closest codebook vector during training.
shows the codebook vectors.
shows the number of objects mapped to the individual units. Empty units are depicted in gray.
shows the sum of the distances to all immediate neighbours. This kind of visualisation is also known as a U-matrix plot. Units near a class boundary can be expected to have higher average distances to their neighbours. Only available for the "som" and "supersom" maps, for the moment.
shows where objects are mapped. It needs the "classif" argument, and a "labels" or "pchs" argument.
properties of each unit can be calculated and
shown in colour code. It can be used to visualise the similarity
of one particular object to all units in the map, to show the mean
similarity of all units and the objects mapped to them,
etcetera. The parameter property contains the numerical
values. See examples below.
shows the mean distance of objects mapped to a
unit to the codebook vector of that unit. The smaller the
distances, the better the objects are represented by the codebook
vectors. It is possible to visualize this for the complete set of
layers used in training, or for individual layers only (using the
whatmap argument).
Function identify.kohonen shows the number of a unit that is
clicked on with the mouse. The tolerance is calculated from the ratio
of the plotting region and the user coordinates, so clicking at any
place within a unit should work.
Function add.cluster.boundaries will add to an existing plot of
a map thick lines, visualizing which units would be clustered
together. In toroidal maps, boundaries at the edges will only be shown
on the top and right sides to avoid double boundaries.
Several types of plots return useful values (invisibly): the
"counts", "dist.neighbours", and "quality" return
vectors corresponding to the information visualized in the plot (unit
background colours and heatkey).
Ron Wehrens
som, supersom, xyf,
predict.kohonen
data(wines) set.seed(7) kohmap <- xyf(scale(wines), vintages, grid = somgrid(5, 5, "hexagonal"), rlen=100) plot(kohmap, type="changes") counts <- plot(kohmap, type="counts", shape = "straight") ## show both sets of codebook vectors in the map par(mfrow = c(1,2)) plot(kohmap, type="codes", main = c("Codes X", "Codes Y")) par(mfrow = c(1,1)) similarities <- plot(kohmap, type="quality", palette.name = terrain.colors) plot(kohmap, type="mapping", labels = as.integer(vintages), col = as.integer(vintages), main = "mapping plot") ## add background colors to units according to their predicted class labels xyfpredictions <- classmat2classvec(getCodes(kohmap, 2)) bgcols <- c("gray", "pink", "lightgreen") plot(kohmap, type="mapping", col = as.integer(vintages), pchs = as.integer(vintages), bgcol = bgcols[as.integer(xyfpredictions)], main = "another mapping plot", shape = "straight", border = NA) ## Show 'component planes' set.seed(7) sommap <- som(scale(wines), grid = somgrid(6, 4, "hexagonal")) plot(sommap, type = "property", property = getCodes(sommap, 1)[,1], main = colnames(getCodes(sommap, 1))[1]) ## Show the U matrix Umat <- plot(sommap, type="dist.neighbours", main = "SOM neighbour distances") ## use hierarchical clustering to cluster the codebook vectors som.hc <- cutree(hclust(object.distances(sommap, "codes")), 5) add.cluster.boundaries(sommap, som.hc) ## and the same for rectangular maps set.seed(7) sommap <- som(scale(wines),grid = somgrid(6, 4, "rectangular")) plot(sommap, type="dist.neighbours", main = "SOM neighbour distances") ## use hierarchical clustering to cluster the codebook vectors som.hc <- cutree(hclust(object.distances(sommap, "codes")), 5) add.cluster.boundaries(sommap, som.hc)data(wines) set.seed(7) kohmap <- xyf(scale(wines), vintages, grid = somgrid(5, 5, "hexagonal"), rlen=100) plot(kohmap, type="changes") counts <- plot(kohmap, type="counts", shape = "straight") ## show both sets of codebook vectors in the map par(mfrow = c(1,2)) plot(kohmap, type="codes", main = c("Codes X", "Codes Y")) par(mfrow = c(1,1)) similarities <- plot(kohmap, type="quality", palette.name = terrain.colors) plot(kohmap, type="mapping", labels = as.integer(vintages), col = as.integer(vintages), main = "mapping plot") ## add background colors to units according to their predicted class labels xyfpredictions <- classmat2classvec(getCodes(kohmap, 2)) bgcols <- c("gray", "pink", "lightgreen") plot(kohmap, type="mapping", col = as.integer(vintages), pchs = as.integer(vintages), bgcol = bgcols[as.integer(xyfpredictions)], main = "another mapping plot", shape = "straight", border = NA) ## Show 'component planes' set.seed(7) sommap <- som(scale(wines), grid = somgrid(6, 4, "hexagonal")) plot(sommap, type = "property", property = getCodes(sommap, 1)[,1], main = colnames(getCodes(sommap, 1))[1]) ## Show the U matrix Umat <- plot(sommap, type="dist.neighbours", main = "SOM neighbour distances") ## use hierarchical clustering to cluster the codebook vectors som.hc <- cutree(hclust(object.distances(sommap, "codes")), 5) add.cluster.boundaries(sommap, som.hc) ## and the same for rectangular maps set.seed(7) sommap <- som(scale(wines),grid = somgrid(6, 4, "rectangular")) plot(sommap, type="dist.neighbours", main = "SOM neighbour distances") ## use hierarchical clustering to cluster the codebook vectors som.hc <- cutree(hclust(object.distances(sommap, "codes")), 5) add.cluster.boundaries(sommap, som.hc)
Map objects to a trained Kohonen map, and return for each object the
desired property associated with the corresponding winning
unit. These properties may be provided explicitly (argument
unit.predictions) or implicitly (by providing
trainingdata, that will be mapped to the SOM - the averages of
the winning units for the trainingdata then will be used as
unit.predictions). If not given at all, the codebook vectors of the
map will be used.
## S3 method for class 'kohonen' predict(object, newdata = NULL, unit.predictions = NULL, trainingdata = NULL, whatmap = NULL, threshold = 0, maxNA.fraction = object$maxNA.fraction, ...)## S3 method for class 'kohonen' predict(object, newdata = NULL, unit.predictions = NULL, trainingdata = NULL, whatmap = NULL, threshold = 0, maxNA.fraction = object$maxNA.fraction, ...)
object |
Trained network, containing one or more information layers. |
newdata |
List of data matrices, or one single data matrix, for
which predictions are to be made. The data layers should match those
in the trained map. If not presented, the training data in the map
will be used. No |
unit.predictions |
Explicit definition of the predictions for each
unit. Should be a list of matrices, vectors or factors, of the same
length as |
trainingdata |
List of data matrices, or one single data matrix,
determining the mapping of the training data. Normally, data stored
in the |
whatmap, maxNA.fraction
|
parameters that usually will
be taken from the |
threshold |
Used in converting class predictions back into
factors; see |
... |
Further arguments to be passed to |
The new data are mapped to the trained SOM using
the layers indicated by the whatmap argument. The predictions
correspond to the unit.predictions, normally corresponding to
the averages of the training data mapping to individual units. If no
unit.predictions are provided, the trainingdata will be
used to calculate them - if trainingdata is not provided by the
user and the kohonen object contains data, these will be used.
If no objects of the training data are mapping to a particular unit,
the prediction for that unit will be NA.
Returns a list with components
prediction |
predicted values for the properties of interest. When multiple values are predicted, this element is a list, otherwise a vector or a matrix. |
unit.classif |
vector of unit numbers to which objects in the newdata object are mapped. |
unit.predictions |
prediction values associated with map units. Again, when multiple properties are predicted, this is a list. |
whatmap |
the numbers of the data layers in the kohonen object used in the mapping on which the predictions are based. |
Ron Wehrens
data(wines) training <- sample(nrow(wines), 120) Xtraining <- scale(wines[training, ]) Xtest <- scale(wines[-training, ], center = attr(Xtraining, "scaled:center"), scale = attr(Xtraining, "scaled:scale")) trainingdata <- list(measurements = Xtraining, vintages = vintages[training]) testdata <- list(measurements = Xtest, vintages = vintages[-training]) mygrid = somgrid(5, 5, "hexagonal") som.wines <- supersom(trainingdata, grid = mygrid) ## ################################################################ ## Situation 0: obtain expected values for training data (all layers, ## also if not used in training) on the basis of the position in the map som.prediction <- predict(som.wines) ## ################################################################ ## Situation 1: obtain predictions for all layers used in training som.prediction <- predict(som.wines, newdata = testdata) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## ################################################################ ## Situation 2: obtain predictions for the vintage based on the mapping ## of the sample characteristics only. There are several ways of doing this: som.prediction <- predict(som.wines, newdata = testdata, whatmap = "measurements") table(vintages[-training], som.prediction$predictions[["vintages"]]) ## same, but now indicated implicitly som.prediction <- predict(som.wines, newdata = testdata[1]) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## if no names are present in the list elements whatmap needs to be ## given explicitly; note that the order of the data layers needs to be ## consistent with the kohonen object som.prediction <- predict(som.wines, newdata = list(Xtest), whatmap = 1) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## for xyf: explicitly indicate which layer is to be used for the mapping xyf.wines <- xyf(Xtraining, vintages[training], grid = mygrid) xyf.prediction <- predict(xyf.wines, Xtest, whatmap = 1) table(vintages[-training], xyf.prediction$predictions[[2]]) ## ############################################################### ## Situation 3: predictions for layers not present in the original ## data. Training data need to be provided for those layers. som.wines <- supersom(Xtraining, grid = mygrid) som.prediction <- predict(som.wines, newdata = testdata, trainingdata = trainingdata) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## ################################################################ ## yeast examples, including NA values data(yeast) training.indices <- sample(nrow(yeast$alpha), 300) training <- rep(FALSE, nrow(yeast$alpha)) training[training.indices] <- TRUE ## unsupervised mapping, based on the alpha layer only. Prediction ## for all layers including alpha yeast.som <- supersom(lapply(yeast, function(x) subset(x, training)), somgrid(4, 6, "hexagonal"), whatmap = "alpha", maxNA.fraction = .5) yeast.som.prediction <- predict(yeast.som, newdata = lapply(yeast, function(x) subset(x, !training))) table(yeast$class[!training], yeast.som.prediction$prediction[["class"]]) ## ################################################################ ## supervised mapping - creating the map is now based on both ## alpha and class, prediction for class based on the mapping of alpha. yeast.som2 <- supersom(lapply(yeast, function(x) subset(x, training)), grid = somgrid(4, 6, "hexagonal"), whatmap = c("alpha", "class"), maxNA.fraction = .5) yeast.som2.prediction <- predict(yeast.som2, newdata = lapply(yeast, function(x) subset(x, !training)), whatmap = "alpha") table(yeast$class[!training], yeast.som2.prediction$prediction[["class"]])data(wines) training <- sample(nrow(wines), 120) Xtraining <- scale(wines[training, ]) Xtest <- scale(wines[-training, ], center = attr(Xtraining, "scaled:center"), scale = attr(Xtraining, "scaled:scale")) trainingdata <- list(measurements = Xtraining, vintages = vintages[training]) testdata <- list(measurements = Xtest, vintages = vintages[-training]) mygrid = somgrid(5, 5, "hexagonal") som.wines <- supersom(trainingdata, grid = mygrid) ## ################################################################ ## Situation 0: obtain expected values for training data (all layers, ## also if not used in training) on the basis of the position in the map som.prediction <- predict(som.wines) ## ################################################################ ## Situation 1: obtain predictions for all layers used in training som.prediction <- predict(som.wines, newdata = testdata) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## ################################################################ ## Situation 2: obtain predictions for the vintage based on the mapping ## of the sample characteristics only. There are several ways of doing this: som.prediction <- predict(som.wines, newdata = testdata, whatmap = "measurements") table(vintages[-training], som.prediction$predictions[["vintages"]]) ## same, but now indicated implicitly som.prediction <- predict(som.wines, newdata = testdata[1]) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## if no names are present in the list elements whatmap needs to be ## given explicitly; note that the order of the data layers needs to be ## consistent with the kohonen object som.prediction <- predict(som.wines, newdata = list(Xtest), whatmap = 1) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## for xyf: explicitly indicate which layer is to be used for the mapping xyf.wines <- xyf(Xtraining, vintages[training], grid = mygrid) xyf.prediction <- predict(xyf.wines, Xtest, whatmap = 1) table(vintages[-training], xyf.prediction$predictions[[2]]) ## ############################################################### ## Situation 3: predictions for layers not present in the original ## data. Training data need to be provided for those layers. som.wines <- supersom(Xtraining, grid = mygrid) som.prediction <- predict(som.wines, newdata = testdata, trainingdata = trainingdata) table(vintages[-training], som.prediction$predictions[["vintages"]]) ## ################################################################ ## yeast examples, including NA values data(yeast) training.indices <- sample(nrow(yeast$alpha), 300) training <- rep(FALSE, nrow(yeast$alpha)) training[training.indices] <- TRUE ## unsupervised mapping, based on the alpha layer only. Prediction ## for all layers including alpha yeast.som <- supersom(lapply(yeast, function(x) subset(x, training)), somgrid(4, 6, "hexagonal"), whatmap = "alpha", maxNA.fraction = .5) yeast.som.prediction <- predict(yeast.som, newdata = lapply(yeast, function(x) subset(x, !training))) table(yeast$class[!training], yeast.som.prediction$prediction[["class"]]) ## ################################################################ ## supervised mapping - creating the map is now based on both ## alpha and class, prediction for class based on the mapping of alpha. yeast.som2 <- supersom(lapply(yeast, function(x) subset(x, training)), grid = somgrid(4, 6, "hexagonal"), whatmap = c("alpha", "class"), maxNA.fraction = .5) yeast.som2.prediction <- predict(yeast.som2, newdata = lapply(yeast, function(x) subset(x, !training)), whatmap = "alpha") table(yeast$class[!training], yeast.som2.prediction$prediction[["class"]])
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, the
distance functions used, and the
mean distance of an object to its closest codebookvector, which is an
indication of the quality of the mapping.
## S3 method for class 'kohonen' summary(object, ...) ## S3 method for class 'kohonen' print(x, ...)## S3 method for class 'kohonen' summary(object, ...) ## S3 method for class 'kohonen' print(x, ...)
x, object
|
a |
... |
Not used. |
Ron Wehrens
data(wines) xyf.wines <- xyf(scale(wines), classvec2classmat(vintages), grid = somgrid(5, 5, "hexagonal")) xyf.wines summary(xyf.wines)data(wines) xyf.wines <- xyf(scale(wines), classvec2classmat(vintages), grid = somgrid(5, 5, "hexagonal")) xyf.wines summary(xyf.wines)
A supersom is an extension of self-organising maps (SOMs) to multiple
data layers, possibly with different numbers and different types of
variables (though equal numbers of objects). NAs are allowed. A
weighted distance over all layers is calculated to determine the
winning units during training.
Functions som and xyf are simply wrappers for supersoms
with one and two layers, respectively. Function nunits is a
utility function returning the number of units in the map.
som(X, ...) xyf(X, Y, ...) supersom(data, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01), radius = quantile(nhbrdist, 2/3), whatmap = NULL, user.weights = 1, maxNA.fraction = 0L, keep.data = TRUE, dist.fcts = NULL, mode = c("online", "batch", "pbatch"), cores = -1, init, normalizeDataLayers = TRUE) nunits(kohobj)som(X, ...) xyf(X, Y, ...) supersom(data, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01), radius = quantile(nhbrdist, 2/3), whatmap = NULL, user.weights = 1, maxNA.fraction = 0L, keep.data = TRUE, dist.fcts = NULL, mode = c("online", "batch", "pbatch"), cores = -1, init, normalizeDataLayers = TRUE) nunits(kohobj)
X, Y
|
numerical data matrices, or factors. No |
data |
list of data matrices (numerical) of factors. If a vector
is entered, it will be converted to a one-column matrix. No
|
grid |
a grid for the codebook vectors:
see |
rlen |
the number of times the complete data set will be presented to the network. |
alpha |
learning rate, a vector of two numbers indicating the
amount of change. Default is to decline linearly from 0.05 to 0.01
over |
radius |
the radius of the neighbourhood, either given as a
single number or a vector (start, stop). If it is given as a single
number the radius will change linearly from |
whatmap |
What data layers to use. If unspecified all layers are used. |
user.weights |
the weights given to individual layers. This can
be a single number (all layers have the same weight, the default), a
vector of the same length as the |
maxNA.fraction |
the maximal fraction of values that may be NA to prevent the row to be removed. |
keep.data |
if TRUE, return original data and mapping information. If FALSE, only return the trained map (in essence the codebook vectors). |
dist.fcts |
vector of distance functions to be used for the
individual data layers, of the same length as the |
mode |
type of learning algorithm. |
cores |
number of cores to use in the "pbatch" learning mode. The default, -1, corresponds to using all available cores. |
init |
list of matrices, initial values for the codebook vectors. The list should have the same length as the data list, and corresponding numbers of variables (columns). Each list element should have a number of rows corresponding to the number of units in the map. |
normalizeDataLayers |
boolean, indicating whether
|
kohobj |
an object of class |
... |
Further arguments for the |
In order to avoid some layers to overwhelm others, simply
because of the scale of the data points, the supersom function
by default applies internal weights to balance this. The user.weights
argument is applied on top of that: the result is that when a user
specifies equal weights for all layers (the default), all layers
contribute equally to the global distance measure. For large data
sets (defined as containing more than 500 records), a sample of size
500 is used to calculate the mean distances in each data layer. If
normalizeDataLayers == FALSE the user weights are applied
directly to the data (distance.weights are set to 1).
Various definitions of the Tanimoto distance exist in the literature. The implementation here returns (for two binary vectors of length n) the fraction of cases in which the two vectors disagree. This is basically the Hamming distance divided by n - the incorrect naming is retained (for the moment) to guarantee backwards compatibility. If the vectors are not binary, they will be converted to binary strings (with 0.5 as the class boundary). This measure should not be used when variables are outside the range [0-1]; a check is done to make sure this is the case.
An object of class "kohonen" with components
data |
data matrix, only returned if |
unit.classif |
winning units for all data objects,
only returned if |
distances |
distances of objects to their corresponding winning
unit, only returned if |
grid |
the grid, an object of class |
codes |
a list of matrices containing codebook vectors. |
changes |
matrix of mean average deviations from code vectors; every map corresponds with one column. |
na.rows |
vector of row numbers with too many NA values
(according to argument |
alpha, radius, user.weights, whatmap, maxNA.fraction
|
input arguments presented to the function. |
distance.weights |
if |
dist.fcts |
distance functions corresponding to all layers of the data, not just the ones indicated by the whatmap argument. |
Ron Wehrens and Johannes Kruisselbrink
R. Wehrens and L.M.C. Buydens, J. Stat. Softw. 21 (5), 2007; R. Wehrens and J. Kruisselbrink, submitted, 2017.
somgrid, plot.kohonen,
predict.kohonen, map.kohonen
data(wines) ## som som.wines <- som(scale(wines), grid = somgrid(5, 5, "hexagonal")) summary(som.wines) nunits(som.wines) ## xyf xyf.wines <- xyf(scale(wines), vintages, grid = somgrid(5, 5, "hexagonal")) summary(xyf.wines) ## supersom example data(yeast) yeast.supersom <- supersom(yeast, somgrid(6, 6, "hexagonal"), whatmap = c("alpha", "cdc15", "cdc28", "elu"), maxNA.fraction = .5) plot(yeast.supersom, "changes") obj.classes <- as.integer(yeast$class) colors <- c("yellow", "green", "blue", "red", "orange") plot(yeast.supersom, type = "mapping", col = colors[obj.classes], pch = obj.classes, main = "yeast data")data(wines) ## som som.wines <- som(scale(wines), grid = somgrid(5, 5, "hexagonal")) summary(som.wines) nunits(som.wines) ## xyf xyf.wines <- xyf(scale(wines), vintages, grid = somgrid(5, 5, "hexagonal")) summary(xyf.wines) ## supersom example data(yeast) yeast.supersom <- supersom(yeast, somgrid(6, 6, "hexagonal"), whatmap = c("alpha", "cdc15", "cdc28", "elu"), maxNA.fraction = .5) plot(yeast.supersom, "changes") obj.classes <- as.integer(yeast$class) colors <- c("yellow", "green", "blue", "red", "orange") plot(yeast.supersom, type = "mapping", col = colors[obj.classes], pch = obj.classes, main = "yeast data")
Function provides colour values for SOM units in such a way that the colour changes smoothly in every direction.
tricolor(grid, phis = c(0, 2 * pi/3, 4 * pi/3), offset = 0)tricolor(grid, phis = c(0, 2 * pi/3, 4 * pi/3), offset = 0)
grid |
An object of class |
phis |
A vector of three rotation angles. Values for red, green and blue are given by the y-coordinate of the units after rotation with these three angles, respectively. The default corresponds to (approximate) red colour of the middle unit in the top row, and pure green and blue colours in the bottom left and right units, respectively. In case of a triangular map, the top unit is pure red. |
offset |
Defines the minimal value in the RGB colour definition (default is 0). By supplying a value in the range [0, .9], pastel-like colours are provided. |
Returns a matrix with three columns corresponding to red, green and
blue. This can be used in the rgb function to provide colours
for the units.
Ron Wehrens
data(wines) som.wines <- som(wines, grid = somgrid(5, 5, "hexagonal")) colour1 <- tricolor(som.wines$grid) plot(som.wines, "mapping", bg = rgb(colour1)) colour2 <- tricolor(som.wines$grid, phi = c(pi/6, 0, -pi/6)) plot(som.wines, "mapping", bg = rgb(colour2)) colour3 <- tricolor(som.wines$grid, phi = c(pi/6, 0, -pi/6), offset = .5) plot(som.wines, "mapping", bg = rgb(colour3))data(wines) som.wines <- som(wines, grid = somgrid(5, 5, "hexagonal")) colour1 <- tricolor(som.wines$grid) plot(som.wines, "mapping", bg = rgb(colour1)) colour2 <- tricolor(som.wines$grid, phi = c(pi/6, 0, -pi/6)) plot(som.wines, "mapping", bg = rgb(colour2)) colour3 <- tricolor(som.wines$grid, phi = c(pi/6, 0, -pi/6), offset = .5) plot(som.wines, "mapping", bg = rgb(colour3))
Function somgrid (modified from the version in the class
package) sets up a grid of units, of a specified size
and topology. Distances between grid units are calculated by function
unit.distances.
somgrid(xdim = 8, ydim = 6, topo = c("rectangular", "hexagonal"), neighbourhood.fct = c("bubble", "gaussian"), toroidal = FALSE) unit.distances(grid, toroidal)somgrid(xdim = 8, ydim = 6, topo = c("rectangular", "hexagonal"), neighbourhood.fct = c("bubble", "gaussian"), toroidal = FALSE) unit.distances(grid, toroidal)
xdim, ydim
|
dimensions of the grid. |
topo |
choose between a hexagonal or rectangular topology. |
neighbourhood.fct |
choose between bubble and gaussian neighbourhoods when training a SOM. |
toroidal |
logical, whether the grid is toroidal or not. If not
provided to the |
grid |
an object of class |
Function somgrid returns an object of class "somgrid", with
elements pts, and the input arguments to the function.
Function unit.distances returns a (symmetrical) matrix
containing distances. When grid$n.hood equals "circular",
Euclidean distances are used; for grid$n.hood is "square"
maximum distances. For toroidal maps (joined at the edges) distances
are calculated for the shortest path.
Ron Wehrens
mygrid <- somgrid(5, 5, "hexagonal") fakesom <- list(grid = mygrid) class(fakesom) <- "kohonen" par(mfrow = c(2,1)) dists <- unit.distances(mygrid) plot(fakesom, type="property", property = dists[1,], main="Distances to unit 1", zlim=c(0,6), palette = rainbow, ncolors = 7) dists <- unit.distances(mygrid, toroidal=TRUE) plot(fakesom, type="property", property = dists[1,], main="Distances to unit 1 (toroidal)", zlim=c(0,6), palette = rainbow, ncolors = 7)mygrid <- somgrid(5, 5, "hexagonal") fakesom <- list(grid = mygrid) class(fakesom) <- "kohonen" par(mfrow = c(2,1)) dists <- unit.distances(mygrid) plot(fakesom, type="property", property = dists[1,], main="Distances to unit 1", zlim=c(0,6), palette = rainbow, ncolors = 7) dists <- unit.distances(mygrid, toroidal=TRUE) plot(fakesom, type="property", property = dists[1,], main="Distances to unit 1 (toroidal)", zlim=c(0,6), palette = rainbow, ncolors = 7)
A data frame containing 177 rows and thirteen columns; object
vintages contains the class labels.
These data are the results of chemical analyses of wines grown in the same region in Italy (Piedmont) but derived from three different cultivars: Nebbiolo, Barberas and Grignolino grapes. The wine from the Nebbiolo grape is called Barolo. The data contain the quantities of several constituents found in each of the three types of wines, as well as some spectroscopic variables.
data(wines)data(wines)
M. Forina, C. Armanino, M. Castino and M. Ubigli. Vitis, 25:189-201 (1986)
Microarray cell-cycle data for 800 yeast genes, arrested with six different methods, arranged in a list. Additional class information is present as well.
data(yeast)data(yeast)
P. Spellman et al., Mol. Biol. Cell 9, 3273-3297 (1998)