Predict the neuron where a new observation is classified

# S3 method for somRes
predict(object, x.new = NULL, ..., radius = 0, tolerance = 10^(-10))

Arguments

object

a somRes object.

x.new

a new observation (optional). Default values is NULL which corresponds to performing prediction on the training dataset.

...

not used.

radius

current radius used to perform soft affectation (when affectation="heskes", see initSOM for further details about Heskes's soft affectation). Default value is 0, which corresponds to a hard affectation.

tolerance

numeric tolerance (to avoid numeric instability during 'cosine' pre-processing). Default value is 10^(-10)

Value

predict.somRes returns the number of the neuron to which the new observation is assigned (i.e., neuron with the closest prototype).

When the algorithm's type is "korresp", x.new must be the original contingency table passed to the algorithm.

Details

The number of columns of the new observations (or its length if only one observation is provided) must match the number of colums of the data set given to the SOM algorithm (see trainSOM).

See also

Examples

set.seed(2343) my.som <- trainSOM(x.data=iris[-100,1:4], dimension=c(5,5)) predict(my.som, iris[100,1:4])
#> 2 #> 2