spnn (version 1.2.1)

cspnn.learn: cspnn.learn

Description

Create or update a Condensed Scale Invariant Probabilistic Neural Network.

Usage

cspnn.learn(set, nn, xr, 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 Condensed 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 CSPNN object is created.

xr

The m by n reference matrix containing optimal parameters for probability estimation. Where m is the number of unique categories and n is the number of input factors used. This matrix must be provided.

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 Condensed Scale Invariant Probabilistic Neural Network (CSPNN)

Details

The function cspnn.learn creates a new Condensed Scale Invariant Probabilistic Neural Network with a given training data set or updates the training data of an existing CSPNN. It sets the parameters: model, set, xr, category.column, categories, sigma, sigmaInverse, k, and n for the CSPNN.

See Also

spnn-package, cspnn.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)],]

# reference matrix must be supplied
# this is not the optimal reference matrix
# this matrix is provided as a simple example
xr <- matrix(c(c(5.00, 3.41, 1.44, 0.24),
               c(5.88, 2.75, 4.23, 1.30),
               c(6.61, 2.97, 5.59, 2.01)),
             nrow = length(unique(trainData$Species)),
             ncol = ncol(trainData) - 1,
             byrow = TRUE)

# fit cspnn
cspnn <- cspnn.learn(set = trainData, xr = xr, category.column = 5)

# estimate probabilities
predictions <- cspnn.predict(nn = cspnn, newData = testData[,1:4])

# }