# Unit 6 - Recitation

# Video 2

flower = read.csv("flower.csv", header=FALSE)
str(flower)

# Change the data type to matrix
flowerMatrix = as.matrix(flower)
str(flowerMatrix)

# Turn matrix into a vector
flowerVector = as.vector(flowerMatrix)
str(flowerVector)

flowerVector2 = as.vector(flower)
str(flowerVector2)

# Compute distances
distance = dist(flowerVector, method = "euclidean")



# Video 3

# Hierarchical clustering
clusterIntensity = hclust(distance, method="ward")

# Plot the dendrogram
plot(clusterIntensity)

# Select 3 clusters
rect.hclust(clusterIntensity, k = 3, border = "red")
flowerClusters = cutree(clusterIntensity, k = 3)
flowerClusters

# Find mean intensity values
tapply(flowerVector, flowerClusters, mean)

# Plot the image and the clusters
dim(flowerClusters) = c(50,50)
image(flowerClusters, axes = FALSE)

# Original image
image(flowerMatrix,axes=FALSE,col=grey(seq(0,1,length=256)))



# Video 4

# Let's try this with an MRI image of the brain

healthy = read.csv("healthy.csv", header=FALSE)
healthyMatrix = as.matrix(healthy)
str(healthyMatrix)

# Plot image
image(healthyMatrix,axes=FALSE,col=grey(seq(0,1,length=256)))

# Hierarchial clustering
healthyVector = as.vector(healthyMatrix)
distance = dist(healthyVector, method = "euclidean")

# We have an error - why?
str(healthyVector)



# Video 5

# Specify number of clusters
k = 5

# Run k-means
set.seed(1)
KMC = kmeans(healthyVector, centers = k, iter.max = 1000)
str(KMC)

# Extract clusters
healthyClusters = KMC$cluster
KMC$centers[2]

# Plot the image with the clusters
dim(healthyClusters) = c(nrow(healthyMatrix), ncol(healthyMatrix))

image(healthyClusters, axes = FALSE, col=rainbow(k))



# Video 6

# Apply to a test image
 
tumor = read.csv("tumor.csv", header=FALSE)
tumorMatrix = as.matrix(tumor)
tumorVector = as.vector(tumorMatrix)

# Apply clusters from before to new image, using the flexclust package
install.packages("flexclust")
library(flexclust)

KMC.kcca = as.kcca(KMC, healthyVector)
tumorClusters = predict(KMC.kcca, newdata = tumorVector)

# Visualize the clusters
dim(tumorClusters) = c(nrow(tumorMatrix), ncol(tumorMatrix))

image(tumorClusters, axes = FALSE, col=rainbow(k))