spnn.learn {spnn} | R Documentation |
spnn.learn
Description
Create or update a Scale Invariant Probabilistic Neural Network.
Usage
spnn.learn(set, nn, sigma, category.column = 1)
Arguments
set |
data.frame or matrix representing the training set. The first column (default category.column = 1) is used to define the category or class of each observation. |
nn |
(optional) A Scale Invariant Probabilistic Neural Network object. If provided, the training data set input is concatenated to the current training data set of the neural network. If not provided, a new SPNN object is created. |
sigma |
An n by n square matrix of smoothing parameters where n is the number of input factors. Defaults to using the covariance matrix of the training data set excluding the category.column. |
category.column |
The column number of category data. Default is 1. |
Details
The function spnn.learn creates a new Scale Invariant Probabilistic Neural Network with a given training data set or updates the training data of an existing SPNN. It sets the parameters: model, set, category.column, categories, sigma, sigmaInverse, k, and n for the SPNN.
Value
A trained Scale Invariant Probabilistic Neural Network (SPNN)
See Also
spnn-package
, spnn.predict
, iris
Examples
library(spnn)
library(datasets)
data(iris)
# shuffle the iris data set
indexRandom <- sample(1:nrow(iris), size = nrow(iris), replace = FALSE)
# use 100 observations for training set
trainData <- iris[indexRandom[1:100],]
# use remaining observations for testing
testData <- iris[indexRandom[101:length(indexRandom)],]
# fit spnn
spnn <- spnn.learn(set = trainData, category.column = 5)
# estimate probabilities
predictions <- spnn.predict(nn = spnn, newData = testData[,1:4])