#PubH 5470-2, S'04
#Example 7.1: Use of neural networks.

library(nnet)

data(iris)
#     This famous (Fisher's or Anderson's) iris data set gives the
#     measurements in centimeters of the variables sepal length and
#     width and petal length and width, respectively, for 50 flowers
#     from each of 3 species of iris.  The species are _Iris setosa_,
#     _versicolor_, and _virginica_.

iris[1:3,]
#  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1          5.1         3.5          1.4         0.2  setosa
#2          4.9         3.0          1.4         0.2  setosa
#3          4.7         3.2          1.3         0.2  setosa

###fit a single-hidden-layer neural network:
#training data: n=100; test data: n=50
set.seed(022104)
tr<-sample(1:150, 100)
ir.nnet <- nnet(Species ~., data=iris[tr,], size=5, decay=0.01, maxit=500)

#########Input: among others,
# weights: (case) weights for each example - if missing defaults to 1. 

#    size: number of units in the hidden layer. Can be zero if there are
#          skip-layer units. 
#   decay: parameter for weight decay.  Default 0. 

#   maxit: maximum number of iterations. Default 100. 

#########Returned Value: 
#     object of class '"nnet"' or '"nnet.formula"'. Mostly internal
#     structure, but has components

#     wts: the best set of weights found 

#   value: value of fitting criterion plus weight decay term. 

#fitted.values: the fitted values for the training data. 

#residuals: the residuals for the training data. 


#########doing prediction:
table(iris$Species[-tr], predict(ir.nnet, iris[-tr,], type="class"))
#             setosa versicolor virginica
#  setosa     11      0          0       
#  versicolor  0     20          0       
#  virginica   0      0         19 

####predicted: matrix values returned by the network:
# predict(ir.nnet, iris[-tr,], type="raw")
#          setosa   versicolor    virginica
#6   0.9991274553 0.0004588820 4.136627e-04
#13  0.9977274973 0.0022070330 6.546974e-05
#15  0.9994675454 0.0003600989 1.723556e-04
#16  0.9993765284 0.0002482534 3.752182e-04
#...