spnn.learn: spnn.learn

In spnn: Scale Invariant Probabilistic Neural Networks

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])

spnn documentation built on Jan. 9, 2020, 1:06 a.m.