Predict the neuron where a new observation is classified
# S3 method for somRes predict(object, x.new = NULL, ..., radius = 0, tolerance = 10^(-10))
| object | a |
|---|---|
| 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
|
| tolerance | numeric tolerance (to avoid numeric instability during 'cosine' pre-processing). Default value is 10^(-10) |
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.
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).
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