Below is R script snippets that I put into R studio.

**Assignments**

Rabbit <- c(10, 7, 1, 2, NA, 1)

Cow <- c( 7, 10, NA, NA, NA, NA)

Dog <- c(NA, 1, 10, 10, NA, NA)

Pig <- c( 5, 6, 4, NA, 7, 3)

Chicken <- c( 7, 6, 2, NA, 10, NA)

Pinguin <- c( 2, 2, NA, 2, 2, 10)

Bear <- c( 2, NA, 8, 8, 2, 7)

Lion <- c(NA, NA, 9, 10, 2, NA)

Tiger <- c(NA, NA, 8, NA, NA, 5)

Antilope <- c( 6, 10, 1, 1, NA, NA)

Wolf <- c( 1, NA, NA, 8, NA, 3)

Sheep <- c(NA, 8, NA, NA, NA, 2)

**Create Array**

animals <- c(“Rabbit”,”Cow”,”Dog”,”Pig”,”Chicken”,”Pinguin”,”Bear”,”Lion”,”Tiger”,”Antilope”,”Wolf”,”Sheep”)

foods <- c(“Carrots”,”Grass”,”Pork”, “Beef”, “Corn”, “Fish”)

matrixRowAndColNames <- list(animals, foods)

**Create Matrix**

animal2foodRatings <-matrix(data=c(Rabbit,Cow,Dog,Pig,Chicken,Pinguin,Bear,Lion,Tiger,Antilope,Wolf,Sheep),nrow=12,ncol=6,byrow=TRUE,matrixRowAndColNames)

animal2foodRatingsWithMean <- animal2foodRatingsanimal2foodRatingsWithMean[is.na(animal2foodRatingsWithMean)] <- mean(rowMeans(animal2foodRatingsRecMatrix))

FactorStructure <- svd(animal2foodRatingsWithMean)

#

D <- diag(FactorStructure$d)

PredictedRatings <- FactorStructure$u %*% D %*% t(FactorStructure$v)

dimnames(PredictedRatings) <- matrixRowAndColNames

PredictiveMatrix <- matrix(nrow=length(animals), ncol=length(foods))

dimnames(PredictiveMatrix) <- matrixRowAndColNames

# Sheep Carrots prediction

k <- 2

for(animal in 1:length(animals)) {

for(food in 1:length(foods)) {

PredictiveMatrix[animal,food] <- (((FactorStructure$u[animal,1:k]*sqrt(FactorStructure$d[1:k]))%*%(sqrt(FactorStructure$d[1:k])*t(FactorStructure$v)[1:k,food]))[1,1])

}

}

PredictiveMatrix

library(recommenderlab)

animal2foodRatingsRecMatrix <- as(animal2foodRatings, “realRatingMatrix”)

animal2foodRatingsRecMatrix_n <- normalize(animal2foodRatingsRecMatrix)

animal2foodRatingsRecMatrix_n2 <- normalize(animal2foodRatingsRecMatrix, method=”Z-score”)

# Average user rating

mean(rowMeans(animal2foodRatingsRecMatrix))

# Average number of ratings per User

mean(rowCounts(animal2foodRatingsRecMatrix))

# Average number of ratings per Item

mean(colCounts(animal2foodRatingsRecMatrix))

# Amount of all ratings

length(getRatings(animal2foodRatingsRecMatrix))

# Histogram of ratings

hist(getRatings(animal2foodRatingsRecMatrix), breaks=10, main=paste(“Distribution of Ratings”))

image(animal2foodRatingsRecMatrix, main=”Raw Data”)

image(animal2foodRatingsRecMatrix_n, main=”Centered”)

image(animal2foodRatingsRecMatrix_n2, main=”Z-Score Normalization”)

rec <- Recommender(animal2foodRatingsRecMatrix[1:10,], method = “IBCF”)

recommenderRegistry$get_entry_names()

https://github.com/ManuelB/facebook-recommender-demo/blob/master/docs/BedConExamples.R

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