Description Usage Arguments Details Value See Also Examples
View source: R/cspnn.predict.R
Estimates the category probabilities of new observations using a fitted CSPNN.
1 | cspnn.predict(nn, newData)
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nn |
A trained Condensed Scaled Invariant Probabilistic Neural Network. |
newData |
A matrix of new observations where each row represents a single observation vector. |
Given a trained Condensed Scale Invariant Probabilistic Neural Network and new data, the function cspnn.predict returns the category with the highest probability and the probability estimates for each category.
A list of the guessed categories and the probability estimates of each category.
spnn-package
, cspnn.learn
, iris
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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])
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