Showing posts with label Training of Neural Networks. Show all posts
Showing posts with label Training of Neural Networks. Show all posts

Friday, November 30, 2018

Training of Neural Networks

Training of Neural Networks

library(MASS)
library(nnet)
set.seed(123)
DataFrame <- Boston
help("Boston")

View(DataFrame)
str(DataFrame)

> View(DataFrame)
> str(DataFrame)
'data.frame': 506 obs. of  14 variables:
 $ crim   : num  0.00632 0.02731 0.02729 0.03237 0.06905 ...
 $ zn     : num  18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...
 $ indus  : num  2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87 ...
 $ chas   : int  0 0 0 0 0 0 0 0 0 0 ...
 $ nox    : num  0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524 ...
 $ rm     : num  6.58 6.42 7.18 7 7.15 ...
 $ age    : num  65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...
 $ dis    : num  4.09 4.97 4.97 6.06 6.06 ...
 $ rad    : int  1 2 2 3 3 3 5 5 5 5 ...
 $ tax    : num  296 242 242 222 222 222 311 311 311 311 ...
 $ ptratio: num  15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2 ...
 $ black  : num  397 397 393 395 397 ...
 $ lstat  : num  4.98 9.14 4.03 2.94 5.33 ...
 $ medv   : num  24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9
hist(DataFrame$medv)
Neural Networks library

apply(DataFram, 2,range)
appply(DataFrame,2,range)
 maxValue<- apply(DataFrame, 2, max)
minValue <- apply(DataFrame, 2,min)
DataFrame <- as.data.frame(scale(DataFrame,center = minValue,scale = maxValue))
ind<-sample(1:nrow(DataFrame),400)
trainDF <- DataFrame [ind ,]
testDF <-DataFrame[-ind,]
allVars <- colnames(DataFrame)
predictorVars <- paste(predictorVars,collapse = "+")
form=as.formula(paste("medv~",predictorVars,collapse = "+"))
neuralModel<- nnet(formula =form, hidden = c(4,2.linear.output = T,data =trainDF))
plot(neuralModel)

Black-Scholes formula-R

 Black-Scholes formula-R > BlackScholes <- function(TypeFlag = c("c", "p"), S, X, Time, r, b, sigma) { TypeFla...