##### Chapter 7: Neural Networks and Support Vector Machines -------------------

##### Part 1: Neural Networks -------------------
## Example: Modeling the Strength of Concrete  ----

## Step 2: Exploring and preparing the data ----
# read in data and examine structure
concrete <- read.csv("concrete.csv")
str(concrete)

# custom normalization function
normalize <- function(x) { 
  return((x - min(x)) / (max(x) - min(x)))
}

# apply normalization to entire data frame
concrete_norm <- as.data.frame(lapply(concrete, normalize))

# confirm that the range is now between zero and one
summary(concrete_norm$strength)

# compared to the original minimum and maximum
summary(concrete$strength)

# create training and test data
concrete_train <- concrete_norm[1:773, ]
concrete_test <- concrete_norm[774:1030, ]

## Step 3: Training a model on the data ----
# train the neuralnet model
library(neuralnet)

# simple ANN with only a single hidden neuron
set.seed(12345) # to guarantee repeatable results
concrete_model <- neuralnet(formula = strength ~ cement + slag +
                              ash + water + superplastic + 
                              coarseagg + fineagg + age,
                              data = concrete_train)

# visualize the network topology
plot(concrete_model)

# Reference: http://www.r-bloggers.com/neuralnettools-1-0-0-now-on-cran/
# alternative plot
library(NeuralNetTools)

# plotnet
par(mar = numeric(4), family = 'serif')
plotnet(concrete_model, alpha = 0.6)

## Step 4: Evaluating model performance ----
# obtain model results
model_results <- compute(concrete_model, concrete_test[1:8])
# obtain predicted strength values
predicted_strength <- model_results$net.result
# examine the correlation between predicted and actual values
cor(predicted_strength, concrete_test$strength)   # higher than stated in book 0.7170368646

# produce actual predictions by 

head(predicted_strength)

concrete_train_original_strength <- concrete[1:773,"strength"]

strength_min <- min(concrete_train_original_strength)
strength_max <- max(concrete_train_original_strength)

head(concrete_train_original_strength)

# custom normalization function
unnormalize <- function(x, min, max) { 
  return( (max - min)*x + min )
}

strength_pred <- unnormalize(predicted_strength, strength_min, strength_max)
strength_pred

## Step 5: Improving model performance ----
# a more complex neural network topology with 5 hidden neurons
set.seed(12345) # to guarantee repeatable results
concrete_model2 <- neuralnet(strength ~ cement + slag +
                               ash + water + superplastic + 
                               coarseagg + fineagg + age,
                               data = concrete_train, hidden = 5, act.fct = "logistic")

# plot the network
plot(concrete_model2)

# plotnet
par(mar = numeric(4), family = 'serif')
plotnet(concrete_model2, alpha = 0.6)

# evaluate the results as we did before
model_results2 <- compute(concrete_model2, concrete_test[1:8])
predicted_strength2 <- model_results2$net.result
cor(predicted_strength2, concrete_test$strength)  # higher than stated in book 0.801444583

# try different activation function
# a more complex neural network topology with 5 hidden neurons
set.seed(12345) # to guarantee repeatable results
concrete_model2 <- neuralnet(strength ~ cement + slag +
                               ash + water + superplastic + 
                               coarseagg + fineagg + age,
                             data = concrete_train, hidden = 5, act.fct = "tanh")

# evaluate the results as we did before
model_results2 <- compute(concrete_model2, concrete_test[1:8])
predicted_strength2 <- model_results2$net.result
cor(predicted_strength2, concrete_test$strength)  

##### Part 2: Support Vector Machines -------------------
## Example: Optical Character Recognition ----

## Step 2: Exploring and preparing the data ----
# read in data and examine structure
letters <- read.csv("letterdata.csv")
str(letters)

# divide into training and test data
letters_train <- letters[1:16000, ]
letters_test  <- letters[16001:20000, ]

## Step 3: Training a model on the data ----
# begin by training a simple linear SVM
library(kernlab)
letter_classifier <- ksvm(letter ~ ., data = letters_train,
                          kernel = "vanilladot")

# look at basic information about the model
letter_classifier

## Step 4: Evaluating model performance ----
# predictions on testing dataset
letter_predictions <- predict(letter_classifier, letters_test)

head(letter_predictions)

table(letters_test$letter, letter_predictions)

# look only at agreement vs. non-agreement
# construct a vector of TRUE/FALSE indicating correct/incorrect predictions
agreement <- letter_predictions == letters_test$letter
table(agreement)
prop.table(table(agreement))

## Step 5: Improving model performance ----
set.seed(12345)
letter_classifier_rbf <- ksvm(letter ~ ., data = letters_train, kernel = "rbfdot")
letter_predictions_rbf <- predict(letter_classifier_rbf, letters_test)

table(letters_test$letter, letter_predictions_rbf)

agreement_rbf <- letter_predictions_rbf == letters_test$letter
table(agreement_rbf)
prop.table(table(agreement_rbf))