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2024/Neural Network/Chapter01/Chapter1_NN_Introduction_Restaurant.R

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+########################################################################
+##Chapter 1 - Introduction to Neural Networks - using R ################
+###Simple R program to build, train and test neural networks ###########
+### Classification based on 3 inputs and 1 categorical output ##########
+########################################################################
+
+###Choose the libraries to use
+library(NeuralNetTools)
+library(nnet)
+
+###Set working directory for the training data
+setwd("C:/R")
+getwd()
+
+###Read the input file
+mydata=read.csv('RestaurantTips.csv',sep=",",header=TRUE)
+mydata
+attach(mydata)
+names(mydata)
+
+##Train the model based on output from input
+model=nnet(CustomerWillTip~Service+Ambience+Food, 
+           data=mydata, 
+           size =5, 
+           rang=0.1, 
+           decay=5e-2, 
+           maxit=5000)
+print(model)
+plotnet(model)
+garson(model)
+########################################################################

2024/Neural Network/Chapter01/Chapter1_NN_Introduction_Squares.R

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+#########################################################################
+###Chapter 1 - Introduction to Neural Networks - using R ################
+###Simple R program to build, train and test neural Networks#############
+#########################################################################
+#Choose the libraries to use
+library("neuralnet")
+
+#Set working directory for the training data
+setwd("C:/R")
+getwd()
+
+#Read the input file
+mydata=read.csv('Squares.csv',sep=",",header=TRUE)
+mydata
+attach(mydata)
+names(mydata)
+
+#Train the model based on output from input
+model=neuralnet(formula = Output~Input, 
+                data = mydata, 
+                hidden=10, 
+                threshold=0.01 )
+print(model)
+
+#Lets plot and see the layers
+plot(model)
+
+#Check the data - actual and predicted
+final_output=cbind (Input, Output, 
+                    as.data.frame(model$net.result) )
+colnames(final_output) = c("Input", "Expected Output", 
+                           "Neural Net Output" )
+print(final_output)
+#########################################################################

2024/Neural Network/Chapter01/RestaurantTips.csv

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+no,CustomerWillTip,Service,Ambience,Food,TipOrNo
+1,1,4,4,5,Tip
+2,1,6,4,4,Tip
+3,1,5,2,4,Tip
+4,1,6,5,5,Tip
+5,1,6,3,4,Tip
+6,1,3,4,5,Tip
+7,1,5,5,5,Tip
+8,1,5,4,4,Tip
+9,1,7,6,4,Tip
+10,1,7,6,4,Tip
+11,1,6,7,2,Tip
+12,1,5,6,4,Tip
+13,1,7,3,3,Tip
+14,1,5,1,4,Tip
+15,1,7,5,5,Tip
+16,0,3,1,3,No-tip
+17,0,4,6,2,No-tip
+18,0,2,5,2,No-tip
+19,0,5,2,4,No-tip
+20,0,4,1,3,No-tip
+21,0,3,3,4,No-tip
+22,0,3,4,5,No-tip
+23,0,3,6,3,No-tip
+24,0,4,4,2,No-tip
+25,0,6,3,6,No-tip
+26,0,3,6,3,No-tip
+27,0,4,3,2,No-tip
+28,0,3,5,2,No-tip
+29,0,5,5,3,No-tip
+30,0,1,3,2,No-tip

2024/Neural Network/Chapter01/Squares.csv

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+Input,Output
+0,0
+1,1
+2,4
+3,9
+4,16
+5,25
+6,36
+7,49
+8,64

2024/Neural Network/Chapter02/Chapter2_Boston.R

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+#####################################################################
+###Chapter 2 - Introduction to Neural Networks - using R ############
+###Simple R program to build, train, test regression neural networks#
+#####################################################################
+library("neuralnet")
+library(MASS)
+
+set.seed(1)
+
+data = Boston
+
+max_data <- apply(data, 2, max) 
+min_data <- apply(data, 2, min)
+data_scaled <- scale(data,center = min_data, scale = max_data - min_data) 
+
+index = sample(1:nrow(data),round(0.70*nrow(data)))
+train_data <- as.data.frame(data_scaled[index,])
+test_data <- as.data.frame(data_scaled[-index,])
+
+n = names(data)
+f = as.formula(paste("medv ~", paste(n[!n %in% "medv"], collapse = " + ")))
+net_data = neuralnet(f,data=train_data,hidden=10,linear.output=T)
+plot(net_data)
+
+predict_net_test <- compute(net_data,test_data[,1:13])
+
+predict_net_test_start <- predict_net_test$net.result*(max(data$medv)-min(data$medv))+min(data$medv)
+test_start <- as.data.frame((test_data$medv)*(max(data$medv)-min(data$medv))+min(data$medv))
+MSE.net_data <- sum((test_start - predict_net_test_start)^2)/nrow(test_start)
+
+Regression_Model <- lm(medv~., data=data)
+summary(Regression_Model)
+test <- data[-index,]
+predict_lm <- predict(Regression_Model,test)
+MSE.lm <- sum((predict_lm - test$medv)^2)/nrow(test)
+
+MSE.net_data
+MSE.lm
+###########################################################################

2024/Neural Network/Chapter02/Chapter2_Kohonen.R

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+######################################################################
+###Chapter 2 - Introduction to Neural Networks - using R    ##########
+###Usuervised ML technique using Kohonen package  ####################
+######################################################################
+
+library("kohonen")
+
+data("wines")
+str(wines)
+head(wines)
+View (wines)
+
+set.seed(1)
+som.wines = som(scale(wines), grid = somgrid(5, 5, "hexagonal"))
+som.wines
+dim(getCodes(som.wines))
+
+plot(som.wines, main = "Wine data Kohonen SOM")
+par(mfrow = c(1, 1))
+plot(som.wines, type = "changes", main = "Wine data: SOM")
+
+training = sample(nrow(wines), 150)
+Xtraining = scale(wines[training, ])
+Xtest = scale(wines[-training, ],
+              center = attr(Xtraining, "scaled:center"),
+              scale = attr(Xtraining, "scaled:scale"))
+trainingdata = list(measurements = Xtraining,
+              vintages = vintages[training])
+testdata = list(measurements = Xtest, vintages = vintages[-training])
+mygrid = somgrid(5, 5, "hexagonal")
+som.wines = supersom(trainingdata, grid = mygrid)
+
+som.prediction = predict(som.wines, newdata = testdata)
+table(vintages[-training], som.prediction$predictions[["vintages"]])
+######################################################################

2024/Neural Network/Chapter03/Chapter3_College.r

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+###########################################################################
+#############Chapter 3 - Deep Learning with neuralnet###################### 
+###########################################################################
+library("neuralnet")
+library(ISLR)
+
+data = College
+View(data)
+
+max_data <- apply(data[,2:18], 2, max) 
+min_data <- apply(data[,2:18], 2, min)
+data_scaled <- scale(data[,2:18],center = min_data, scale = max_data - min_data) 
+
+Private = as.numeric(College$Private)-1
+data_scaled = cbind(Private,data_scaled)
+
+index = sample(1:nrow(data),round(0.70*nrow(data)))
+train_data <- as.data.frame(data_scaled[index,])
+test_data <- as.data.frame(data_scaled[-index,])
+
+n = names(train_data)
+f <- as.formula(paste("Private ~", paste(n[!n %in% "Private"], collapse = " + ")))
+deep_net = neuralnet(f,data=train_data,hidden=c(5,3),linear.output=F)
+plot(deep_net)
+
+predicted_data <- compute(deep_net,test_data[,2:18])
+print(head(predicted_data$net.result))
+predicted_data$net.result <- sapply(predicted_data$net.result,round,digits=0)
+
+table(test_data$Private,predicted_data$net.result)
+
+###########################################################################

2024/Neural Network/Chapter03/Chapter3_H2O.R

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+##########################################################################
+#################Chapter 3 - Deep Learning with H2O and R################# 
+##########################################################################
+
+library(h2o)
+
+c1=h2o.init(max_mem_size = "2G", 
+      nthreads = 2, 
+      ip = "localhost", 
+      port = 54321)
+
+data(iris)
+summary(iris)
+
+iris_d1 <- h2o.deeplearning(1:4,5,
+              as.h2o(iris),hidden=c(5,5),
+              export_weights_and_biases=T)
+iris_d1
+plot(iris_d1)
+
+h2o.weights(iris_d1, matrix_id=1)
+h2o.weights(iris_d1, matrix_id=2)
+h2o.weights(iris_d1, matrix_id=3)
+h2o.biases(iris_d1, vector_id=1)
+h2o.biases(iris_d1, vector_id=2)
+h2o.biases(iris_d1, vector_id=3)
+
+#plot weights connecting `Sepal.Length` to first hidden neurons
+plot(as.data.frame(h2o.weights(iris_d1, matrix_id=1))[,1])
+
+##########################################################################

2024/Neural Network/Chapter04/Chapter4_Perceptron.R

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+######################################################################
+###Chapter 4 - Introduction to Neural Networks - using R    ##########
+###Simple Perceptron implementation function in R - iris dataset  ####
+######################################################################
+
+data(iris)
+head(iris, n=20)
+
+iris_sub=iris[1:100, c(1, 3, 5)] 
+names(iris_sub)=c("sepal", "petal", "species") 
+head(iris_sub) 
+
+library(ggplot2) 
+
+ggplot(iris_sub, aes(x = sepal, y = petal)) + 
+ geom_point(aes(colour=species, shape=species), size = 3) +
+ xlab("Sepal length") +
+ ylab("Petal length") +
+ ggtitle("Species vs Sepal and Petal lengths")
+
+euclidean.norm = function(x) {sqrt(sum(x * x))}
+
+distance.from.plane = function(z,w,b) {
+ sum(z*w) + b
+}
+
+classify.linear = function(x,w,b) {
+ distances = apply(x, 1, distance.from.plane, w, b)
+ return(ifelse(distances < 0, -1, +1))
+}
+
+perceptron = function(x, y, learning.rate=1) {
+ w = vector(length = ncol(x)) # initialize weights
+ b = 0 # Initialize bias
+ k = 0 # count updates
+ R = max(apply(x, 1, euclidean.norm))
+ mark.complete = TRUE 
+
+ while (mark.complete) {
+    mark.complete=FALSE 
+    yc = classify.linear(x,w,b)
+    for (i in 1:nrow(x)) {
+       if (y[i] != yc[i]) {
+          w = w + learning.rate * y[i]*x[i,]
+          b = b + learning.rate * y[i]*R^2
+          k = k+1
+          mark.complete=TRUE
+       }
+    }
+ }
+ s = euclidean.norm(w)
+ return(list(w=w/s,b=b/s,updates=k))
+}
+
+x = cbind(iris_sub$sepal, iris_sub$petal)
+
+y = ifelse(iris_sub$species == "setosa", +1, -1)
+
+p = perceptron(x,y)
+
+plot(x,cex=0.2)
+
+points(subset(x,Y==1),col="black",pch="+",cex=2)
+points(subset(x,Y==-1),col="red",pch="-",cex=2)
+
+intercept = - p$b / p$w[[2]]
+slope = - p$w[[1]] /p$ w[[2]]
+
+abline(intercept,slope,col="green")
+}
+
+
+# Set the x inputs as Sepal and Petal lengths
+x = cbind(iris_sub$sepal, iris_sub$petal)
+
+# Lets label output as positive for setosa and the rest as negative
+y = ifelse(iris_sub$species == "setosa", +1, -1)
+
+#run the perceptron function
+p = perceptron(x,y)
+
+#Plot the linear separation
+plot(x,cex=0.2)
+
+points(subset(x,Y==1),col="black",pch="+",cex=2)
+points(subset(x,Y==-1),col="red",pch="-",cex=2)
+
+intercept = - p$b / p$w[[2]]
+slope = - p$w[[1]] /p$ w[[2]]
+
+abline(intercept,slope,col="green")
+
+######################################################################