spnn (version 1.2.1)

spnn.learn: 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.

Value

A trained Scale Invariant Probabilistic Neural Network (SPNN)

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.

See Also

spnn-package, spnn.predict, iris

Examples

# NOT RUN {
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])

# }